nursery delineation, habitat utilization, movements, and
TRANSCRIPT
W&M ScholarWorks W&M ScholarWorks
Dissertations, Theses, and Masters Projects Theses, Dissertations, & Master Projects
2001
Nursery delineation, habitat utilization, movements, and migration Nursery delineation, habitat utilization, movements, and migration
of juvenile Carcharhinus plumbeus in Chesapeake Bay, Virginia, of juvenile Carcharhinus plumbeus in Chesapeake Bay, Virginia,
United States of America United States of America
R. Dean. Grubbs College of William and Mary - Virginia Institute of Marine Science
Follow this and additional works at: https://scholarworks.wm.edu/etd
Part of the Ecology and Evolutionary Biology Commons, Fresh Water Studies Commons,
Oceanography Commons, and the Zoology Commons
Recommended Citation Recommended Citation Grubbs, R. Dean., "Nursery delineation, habitat utilization, movements, and migration of juvenile Carcharhinus plumbeus in Chesapeake Bay, Virginia, United States of America" (2001). Dissertations, Theses, and Masters Projects. Paper 1539616675. https://dx.doi.org/doi:10.25773/v5-vdqp-sc21
This Dissertation is brought to you for free and open access by the Theses, Dissertations, & Master Projects at W&M ScholarWorks. It has been accepted for inclusion in Dissertations, Theses, and Masters Projects by an authorized administrator of W&M ScholarWorks. For more information, please contact [email protected].
INFORMATION TO USERS
This manuscript has been reproduced from the microfilm master. UMI films
the text directly from the original or copy submitted. Thus, some thesis and
dissertation copies are in typewriter face, while others may be from any type of
computer printer.
The quality of this reproduction is dependent upon the quality of the
copy submitted. Broken or indistinct print, colored or poor quality illustrations
and photographs, print bleedthrough, substandard margins, and improper
alignment can adversely affect reproduction.
In the unlikely event that the author did not send UMI a complete manuscript
and there are missing pages, these will be noted. Also, if unauthorized
copyright material had to be removed, a note will indicate the deletion.
Oversize materials (e.g., maps, drawings, charts) are reproduced by
sectioning the original, beginning at the upper left-hand comer and continuing
from left to right in equal sections with small overlaps.
Photographs included in the original manuscript have been reproduced
xerographically in this copy. Higher quality 6” x 9” black and white
photographic prints are available for any photographs or illustrations appearing
in this copy for an additional charge. Contact UMI directly to order.
Bell & Howell Information and Learning 300 North Zeeb Road, Ann Arbor, Ml 48106-1346 USA
800-521-0600
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
NURSERY DELINEATION, HABITAT UTILIZATION, MOVEMENTS, AND
MIGRATION OF JUVENILE CARCHARHINUS PLUMBEUS IN CHESAPEAKE
BAY, VIRGINIA, USA
A Dissertation
Presented to
The Faculty of the School of Marine Science
The College of William and Mary in Virginia
In Partial Fulfillment
Of the Requirements for the Degree of
Doctor of Philosophy
by
R. Dean Grubbs
2001
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
UMI Number: 3003491
__ ®
UMIUMI Microform 3003491
Copyright 2001 by Bell & Howell Information and Learning Company. All rights reserved. This microform edition is protected against
unauthorized copying under Title 17, United States Code.
Bell & Howell Information and Learning Company 300 North Zeeb Road
P.O. Box 1346 Ann Arbor, Ml 48106-1346
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
APPROVAL SHEET
This dissertation is submitted in partial fulfillment of
the requirements for the degree of
Doctor of Philosophy
R. Dean Grubbs II
Approved April 9, 2001
V jf lb ir A ' Musick, Fm.D.Committee Chairman/ Advisor
Herbert M. Au^in, Ph.D.
D3vrtfA \tvans, PI
Thomas B. Hoff, Ph.D. / /Mid-Atlantic Fisheries Management Council Dover, Delaware
JofyfF. Morrissey, P h , E L / Hofstra University Hempstead, New York
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
DEDICATION
I dedicate the completion of my doctoral dissertation to the memory of Captain Julian Anthony (Tony) Penello and the FA/ Anthony Anne. Captain Tony commercially fished the waters of the western North Atlantic for more than half a century. In an effort to give something back to the sea that had provided him so much, he began providing his vessel, a solid-built wooden trawler of ninety-eight feet, to researchers from the Virginia Institute of Marine Science in the mid- 1980’s. For the use of the Anthony Anne, he charged only the cost of operation and the lifetime of sea-going experiences and knowledge he offered free of charge. Captain Tony lost his cherished vessel, the Anthony Anne, in the winter of 1995. Despite this tragedy, he continued to provide his time, insight, and knowledge to a few students and their projects. This dissertation represents the completion of one of those projects. For me, Captain Tony became an irreplaceable mentor, advisor, confidante, and friend. Unfortunately, Captain Tony lost a battle with cancer in June 2000. For his efforts and undying loyalty I dedicate this body of research to his memory. To have met Captain Tony Penello was a privilege and to have known him was an honor.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
TABLE OF CONTENTS
ACKNOWLEDGEMENTS......................................................................................................vi
FUNDING............................................................................................................................... viii
LIST OF MAPS....................................................................................................................... ix
LIST OF FIGURES...............................................................................................................xiii
LIST OF TABLES...............................................................................................................xviii
ABSTRACT..........................................................................................................................xxii
CHAPTER 1: SPATIAL DELINEATION OF SUMMER NURSERY AREAS TO
DEFINE ESSENTIAL FISH HABITAT FOR JUVENILE CARCHARHINUS
PLUMBEUS IN CHESAPEAKE BAY, VIRGINIA................................................................2
ABSTRACT.......................................................................................................................... 3
INTRODUCTION................................................................................................................. 5
MATERIALS AND METHODS..........................................................................................10Sampling Gear..................................................................................................................10Sampling Design............................................................................................................. 10Data Processing.............................................................................................................. 12Data Analysis....................................................................................................................18
RESULTS...........................................................................................................................27/; Spearman’s Rank Correlation................................................................................... 29II: Classification and Regression Tree (CART) Modeling ......................................36III: Testing the Tree Models......................................................................................... 49
DISCUSSION.....................................................................................................................68
LITERATURE C ITED........................................................................................................75
CHAPTER 2: MIGRATORY MOVEMENTS, PHILOPATRY, AND TEMPORAL
DELINEATION OF SUMMER NURSERIES FOR JUVENILE CARCHARHINUS
PLUMBEUS IN THE CHESAPEAKE BAY REGION.........................................................80
ABSTRACT........................................................................................................................81
INTRODUCTION............................................................................................................... 83
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
TABLE OF CONTENTS (continued)
MATERIALS AND METHODS......................................................................................... 89Sampling Gear.................................................................................................................89Sampling Design.............................................................................................................90Data Processing..............................................................................................................90Survival Factors and Tagging...................................................................................... 92Data Analysis................................................................................................................... 93
RESULTS.......................................................................................................................... 95I: Temporal Nursery Delineation.................................................................................95II: Migration Patterns.................................................................................................. 112III: Philopatry................................................................................................................ 113
DISCUSSION.................................................................................................................. 125
LITERATURE CITED..................................................................................................... 129
CHAPTER 3: SHORT-TERM MOVEMENTS AND SWIMMING DEPTH OF
JUVENILE CARCHARHINUS PLUMBEUS IN CHESAPEAKE BAY ..........................134
ABSTRACT......................................................................................................................135
INTRODUCTION............................................................................................................. 137
MATERIALS AND METHODS....................................................................................... 141Study Area ......................................................................................................................141Telemetry Gear.............................................................................................................. 143Transmitter Attachment............................................................................................... 144Tracking Methodology and Experimental Design..................................................147Data Analysis................................................................................................................. 148
RESULTS.........................................................................................................................157
I Activity Space.............................................................................................................. 159II Depth Telemetry.........................................................................................................171III Tidal Current Correlation........................................................................................ 196
DISCUSSION.................................................................................................................. 206
LITERATURE CITED..................................................................................................... 219
APPENDICESAPPENDIX 1 Nursery Spatial Delineation Station Data............................................ A-6APPENDIX 2-A Nursery Temporal Delineation D ata...............................................A-12APPENDIX 2-B Tag Return Data...............................................................................A-16
VITA
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
ACKNOWLEDGEMENTS
I am indebted to many people who aided in the completion of my doctoral degree and this dissertation. I thank my committee members for their support, patience, and advice, especially Drs. John Morrissey and Thomas Hoff for their critical reviews of this dissertation. Three academics have been especially influential in my professional development. My major advisor, Dr. Jack Musick, heightened my enthusiasm for the natural sciences and taught me that acquired knowledge need not be arbitrarily restricted to microcosmic subjects as has become the norm in biology. He taught me as much about subjects that were far outside my dissertation topic as those within. Dr. John Morrissey broadened my intellect through his highly critical and skeptical approach to science. His deep knowledge and dynamic teaching methods were also very influential in the development of my own style of instruction. Dr. Sonny Gruber hired me as a college freshman to work as a laboratory assistant, and has provided many professional opportunities over the years since that time. I am particularly grateful for the opportunities to develop my true professional love, that of teaching, through the many undergraduate and graduate courses I taught at his laboratory in the Bahamas.
Two men also served as personal mentors and confidantes during my degree tenure. They were the extremely competent captains of the large vessels used for the longline survey. Their importance to the success of this research can not be understated, however, I am more grateful to Captain Durand Ward and the late Captain Tony Penello for their friendship, tolerance, and advice.
I thank VIMS Vessel Operations for their logistical support and professionalism. They are an under-appreciated yet invaluable resource to the institute. I am especially grateful to George Pongonis for providing a dedicated vessel for the telemetry work, Mike Spangler for equipment fabrication, Robert Hudgins for emergency mechanical assistance, Raymond Forrest for vessel preparation, and Susan Rollins for scheduling logistics.
I thank the following volunteers and interns that participated in the monotonous yet grueling tracking cruises: S. Nichols, M. Hardt, K. MacDonald,R. Pemberton, J. Romine, S. Payne, C. Jones, J. Gelslecihter, K. Goldman, B. Watkins, E. Flores, and D. Ha. I am especially indebted to Christina Conrath who participated in the majority of the tracking cruises. These are the unsung heroes of projects such as this. I also thank the VIMS longline crew (Jim Gelsleichter, Rich Kraus, Ken Goldman, Jason Romine, and Christina Conrath) as well as the many volunteers that endured long hours and harsh conditions during the delineation and tagging cruises. I also thank Pat Geer for GIS advice and for providing environmental data from the trawl survey, and Rich Kraus for GIS and statistical advice.
vi
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
I am grateful for the many friendships made while at VIMS. I especially thank Rich Kraus for the fishing and herping adventures, Tripp MacDonald for enduring as my roommate for five years yet remaining a friend, Wolf Lange for helping maintain my sanity through snowboarding, and Roy Pemberton and Brett Falterman just for being themselves.
I owe tremendous gratitude to my family for their love and encouragement. I especially thank my mother, Audrey, for her undying support, my father, Ralph, for introducing me to the outdoors, my brothers, Derek, Austin, and Garrett, for enduring the trials of having me as an older brother, and my stepmother, Teresa, for her patience. I am also indebted to my in-laws, Dwight and Debbie Bullock, for their love, friendship, and support throughout this degree. Finally, I thank my wonderful wife, Audra. Her love and kindness were often neglected, yet her generosity, unselfishness and confidence never wavered in calm or angry seas.
vii
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
FUNDING
Financial support for this research was provided by the Virginia Marine Resources Commission - Salt Water License Fund, Wallop-Breaux Funds through the U.S. Fish & Wildlife Service, the National Marine Fisheries Service’s Apex Predator Investigations, and the Environmental Protection Agency’s STAR Fellowship Program.
viii
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
LIST OF MAPS
Map 1-1: Locations of standard stations sampled by the VIMS longline survey from 1973-1999 including stations K (Kiptopeke) and M (Middleground) in the lower Chesapeake Bay........................................................................................... 14
Map 1-2: Distribution of longline stations used to spatially delineate nursery for Carcharhinus plumbeus in Chesapeake Bay. Darker circles represent higher CPUE (sharks per 100 hooks).......................................................................... 15
Map 1-3: VIMS Trawl Survey stations sampled in August 1999 and interpolated monthly grids of variables used to assign values to associated longline stations. a) VIMS Trawl Survey Stations, b-d) August 1999 longline stations with b) bottom salinity grid (practical salinity units), c) bottom temperature grid (°C), d) bottom dissolved oxygen grid (parts per million) 16
Map 1-4: Interpolated grid of Distance to Bay Mouth (kilometers) variable. This grid was merged with longline stations (also shown) to estimate distance of each station to the mouth of the estuary......................................................................17
Map 1-5: Distribution of VIMS Trawl Survey stations sampled during the monthsof June through September for the years 1995 - 1999..............................................22
Map 1-6: Examples of interpolated variable grids used to display response surfaces for nursery delineation models, a) Distance to Bay Mouth, b)Depth, c) bottom salinity, d) bottom dissolved oxygen. Depth data are from EPA, Chesapeake Bay Program. Salinity and dissolved oxygen grids are interpreted from VIMS Trawl Survey data combined over the period June - September, 1995-1999.................................................................................................23
Map 1-7: Interpolated grid based on CPUE from longline stations....................................28
Map 1-8: Response surface regression tree (CART) modeling for Ecological Model I. Shaded area is portion of Chesapeake Bay with average summer salinity greater than 20.5 psu, depth greater than 5.5 meters, and dissolved oxygen concentration greater than 5.35 ppm. This area is interpreted to represent suitable nursery habitat according to the model......................................... 41
Map 1-9: Response surface regression tree (CART) modeling for Ecological Model II. Shaded area is portion of Chesapeake Bay with average summer salinity greater than 20.5 psu and depth greater than 5.5 meters,. This area is interpreted to represent suitable nursery habitat according to the reduced Ecological Model............................................................................................................42
ix
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Map 1-10: Response surface regression tree (CART) modeling for theManagement Model. Shaded area is portion of Chesapeake Bay less than 34.5 km from the mouth of the Bay and depth greater than 5.5 meters,. This area is interpreted to represent suitable nursery habitat according to the reduced Management Model........................................................................................47
Map 1-11: Tag recaptures for juvenile C. plumbeus recaptured in Chesapeake Bay during the same year and subsequent years compared to CART suitable nursery habitat models, a) Recaptures compared with Ecological Model II b) Recapture compared with the Management Model................................ 51
Map 1-12: Telemetry fixes for nine juvenile C. plumbeus manually tracked for 11 to 64 hours (cumulative 350 hours) between 1996 and 1999 compared to CART suitable nursery habitat models. The minimum interval between fixes was six hours to avoid autocorrelation, a) Telemetry fixes compared with Ecological Model II b) Telemetry fixes compared with the Management Model..............................................................................................................................52
Map 1-13: Maps comparing suitable nursery habitat defined by CART modelsand logistic regression models a) Ecological Model b) Management Model.............67
Map 1-14: Comparison of suitable nursery habitat defined by a) CART Ecological Model I and b) CART Ecological Model II for a wet year (low salinity) and a drought year (high salinity)........................................................................................... 74
Map 2-1: Locations of standard stations sampled by the VIMS longline survey from 1973-1999 including stations K (Kiptopeke) and M (Middleground) in the lower Chesapeake Bay........................................................................................... 91
Map 2-2: Distribution of juvenile C. plumbeus tagging by VIMS during summersof 1995 to 2000........................................................................................................... 110
Map 2-3: Long distance tag returns. All recaptures made >200 kilometers fromtagging location, seasons combined.......................................................................... 118
Map 2-4: Short-term tag recaptures. All sharks recaptured the same summer inwhich they were tagged...............................................................................................120
Map 2-5: Evidence of natal homing. Tag recaptures made < 50 kilometers fromtagging location in summers following at least one winter migration...................... 122
Map 3-1: Movements of shark 9631 tracked for 50 consecutive hours (August 26- 28, 1997). Light circles are location fixes recorded during the day and dark circle are location fixes recorded at night. The track is outlined by the Minimum Convex Polygon of activity space.............................................................. 161
x
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Map 3-2: Movements of shark 9632 tracked for 49 consecutive hours (August 6- 8,1997). Light circles are location fixes recorded during the day and dark circle are location fixes recorded at night. The shark was tracked for 15 additional hours three weeks later (August 27-28). Day and night courses of this second track are shown as light and dark lines respectively. The track is outlined by the Minimum Convex Polygon of activity space.................................... 162
Map 3-3: Movements of shark 9635 tracked for 43 consecutive hours (July 7-9,1998). Light circles are location fixes recorded during the day and darkcircle are location fixes recorded at night. The track is outlined by theMinimum Convex Polygon of activity space............................................................. 163
Map 3-4: Movements of shark 9637 tracked for 50 consecutive hours (July 13-15,1998). Light circles are location fixes recorded during the day and dark circle are location fixes recorded at night. The track is outlined by theMinimum Convex Polygon of activity space.............................................................. 164
Map 3-5: Movements of shark 9639 tracked for 44 consecutive hours (August 12- 14, 1998). Light circles are location fixes recorded during the day and dark circle are location fixes recorded at night. The track is outlined by the Minimum Convex Polygon of activity space...............................................................165
Map 3-6: Movements of shark 5285 tracked for 50 consecutive hours (July 26-28,1999). Light circles are location fixes recorded during the day and dark circle are location fixes recorded at night. The track is outlined by theMinimum Convex Polygon of activity space...............................................................166
Map 3-7: Movements of sharks tracked for less than 40 consecutive hours.Shark 9633 tracked for 13 hours (June 20-21, 1997); Shark 9630 tracked for 25 hours (July 8-9, 1997); Shark 9634 tracked for 10 hours (June 9-10,1998); and Shark 5375 tracked for 11 hours (September 2, 1999). Light circles are location fixes recorded during the day and dark circle are location fixes recorded at night. The track is outlined by the Minimum Convex Polygon of activity space............................................................................................ 167
Map 3-8: Minimum Convex Polygons for all ten juvenile Carcharhinus plumbeustracked. See Table 3-2 for area calculations associated with polygons................168
Map 3-9: Kernal Activity Space of Shark 9632. 50%, 75%, and 95% kernals areshown. See Table 3-2 for area calculations of kernals for all sharks tracked 169
XI
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Map 3-10: Combined tracks for sharks 9631, 9632, 9635, and 5285 showing utilization of deep channel in eastern Chesapeake Bay and increased dispersal during night tracks, a) Daytime fixes only - Minimum Convex Polygon for these fixes combined contained an area of 160.08 km2, b)Nighttime fixes only - Minimum Convex Polygon for these fixes combined contained an area of 181.20 km2............................................................................... 217
Map 3-11: Perimeters of 95% Kernals for eight (9637 excluded) Chesapeake Bay tracks combined over bathymetry grid. Kernals are centered over deep channels throughout the lower estua........................................................................ 218
xii
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
LIST OF FIGURES
Figure 1-1: Matrix plot of CPUE plus five independent variables (distance tomouth, salinity, minimum set depth, dissolved oxygen, temperature)..................... 31
Figure 1-2: Matrix plot of CPUE plus four independent variables (year, latitude,longitude, maximum set depth).................................................................................... 32
Figure 1-3: Matrix plot of seven independent variables significantly correlatedwith CPUE according Spearman’s Rank Correlation statistics................................. 34
Figure 1-4: Full Ecological Model with CPUE as response and salinity, minimum depth, dissolved oxygen, and temperature as predictors, a) Full tree with predicted CPUE at each terminal node (11 nodes) b) Plot of residual mean deviance versus number of terminal nodes generated by cost-complexity pruning (value of cost-complexity parameter along top of graph).............................38
Figure 1-5: a) Ecological Model regression tree pruned to six terminal nodes with predicted CPUE below each node, b) Results of ten-fold cross-validation of each pruned subtree. Plot of cross-validated residual mean deviance versus number of terminal nodes (value of cost-complexity parameter along top of graph)..............................................................................................................................39
Figure 1-6: Ecological Model final tree pruned to three terminal nodes. Predicted CPUE and histogram shown below each terminal node. Histograms show distribution of CPUE values among the observations in each node.........................40
Figure 1-7: Full Management Model with CPUE as response and distance to mouth of estuary, minimum depth, dissolved oxygen, and temperature as predictors, a) Full tree with predicted CPUE at each terminal node (10 nodes) b) Plot of residual mean deviance versus number of terminal nodes generated by cost-complexity pruning (value of cost-complexity parameter along top of graph)........................................................................................................ 44
Figure 1-8: a) Management Model regression tree pruned to five terminal nodes with predicted CPUE below each node, b) Results of ten-fold cross- validation of each pruned subtree. Plot of cross-validated residual mean deviance versus number of terminal nodes (value of cost-complexity parameter along top of graph)......................................................................................45
Figure 1-9: Management Model final tree pruned to three terminal nodes.Predicted CPUE and histogram shown below each terminal node.Histograms show distribution of CPUE values among the observations ineach node.......................................................................................................................46
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Figure 1-10: Plot of univariate selection functions ( w'(z) ). Parameter estimates from univariate logistic regressions for salinity, distance from mouth, depth, depth adjusted for distance, and depth adjusted for salinity (Table 1.12).................66
Figure 2-1: Monthly CPUE (sharks per 100 hooks) for the period 1996-1999 at a) Kiptopeke and b) Middleground stations using both standard hooks and circle hooks.....................................................................................................................98
Figure 2-2: Mean semimonthly C. plumbeus CPUE (sharks per 100 hooks) for Middleground and Kiptopeke stations combined. a)Standard 9/0 hooks - 1990 to 1999; b) 5/0 circle hooks - 1996 to 1999...................99
Figure 2-3: Mean semimonthly C. plumbeus CPUE (sharks per 100 hooks) andsurface temperature (°C) for Middleground and Kiptopeke stations combined for the years 1990-1999 (standard 9/0 hooks only)................................................. 101
Figure 2-4: Fitted line plots from linear regression of mean C. plumbeus CPUE(sharks per 100 hooks) vs. mean surface temperature. Means are for semimonthly intervals, (error bars = SEM) a) immigration period: May 1 - July 31; b) emigration period: July 15 - October 15..................................................103
Figure 2-5: Mean semimonthly C. plumbeus CPUE (sharks per 100 hooks) and day length (hours) for Middleground and Kiptopeke stations combined for the years 1990-1999 (standard 9/0 hooks only).............................................................. 105
Figure 2-6: Fitted line plot from linear regression of mean C. plumbeus CPUE (sharks per 100 hooks) vs. day length (hours) for emigration period (July 15 -October 15) only. Means are for semimonthly intervals, (error bars = SEM ) 107
Figure 2-7: Temporal delineation of C. plumbeus summer nursery using tagrecapture data. Distance between tag and recapture location vs. day of year of recapture...................................................................................................................116
Figure 2-8: Location and temporal delineation of C. plumbeus winter nursery using tag recapture data. Latitude of recapture location vs. day of year of recapture....................................................................................................................... 117
Figure 2-9: Evidence of natal homing from tag recaptures. Distance from tagging location versus days at liberty for all sharks recaptured less than 200 kilometers from the tagging location...........................................................................124
Figure 3-1: Swimming depth and bottom depth recordings for Shark 9630 tracked for 25 consecutive hours. (Bottom depths were interpolated from a bathymetry grid ArcView GIS using corrected fix locations - see text for correction methodology.)............................................................................................177
xiv
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Figure 3-2: Swimming depth and bottom depth recordings for Shark 9631 tracked for 50 consecutive hours. (Bottom depths were interpolated from a bathymetry grid ArcView GIS using corrected fix locations - see text for correction methodology.)............................................................................................ 178
Figure 3-3: Swimming depth and bottom depth recordings for Shark 9632 tracked for 49 consecutive hours. (Bottom depths were interpolated from a bathymetry grid ArcView GIS using corrected fix locations - see text for correction methodology.)............................................................................................ 179
Figure 3-4: Swimming depth and bottom depth recordings for Shark 9635 tracked for 43 consecutive hours. (Bottom depths were interpolated from a bathymetry grid ArcView GIS using corrected fix locations - see text for correction methodology.)............................................................................................ 180
Figure 3-5: Swimming depth and bottom depth recordings for Shark 9637 tracked for 50 consecutive hours. (Bottom depths were interpolated from a bathymetry grid ArcView GIS using corrected fix locations - see text for correction methodology.)............................................................................................ 181
Figure 3-6: Swimming depth and bottom depth recordings for Shark 9639 tracked for 44 consecutive hours. (Bottom depths were interpolated from a bathymetry grid ArcView GIS using corrected fix locations - see text for correction methodology.)............................................................................................ 182
Figure 3-7: Shark 9630 swimming depth dynamics, a) Comparison of the distributions of swimming depth during day and night, b) Distribution of the distance from swimming depth to bottom of estuary during day and night 183
Figure 3-8: Shark 9631 swimming depth dynamics, a) Comparison of the distributions of swimming depth during day and night, b) Distribution of the distance from swimming depth to bottom of estuary during day and night 184
Figure 3-9: Shark 9632 swimming depth dynamics, a) Comparison of the distributions of swimming depth during day and night, b) Distribution of the distance from swimming depth to bottom of estuary during day and night 185
Figure 3-10: Shark 9635 swimming depth dynamics, a) Comparison of the distributions of swimming depth during day and night, b) Distribution of the distance from swimming depth to bottom of estuary during day and night............ 186
Figure 3-11: Shark 9637 swimming depth dynamics, a) Comparison of the distributions of swimming depth during day and night, b) Distribution of the distance from swimming depth to bottom of estuary during day and night............ 187
xv
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Figure 3-12: Shark 9639 swimming depth dynamics, a) Comparison of the distributions of swimming depth during day and night, b) Distribution of the distance from swimming depth to bottom of estuary during day and night 188
Figure 3-13: Mean swimming depth during Day and Night based on sunrise and sunset for sharks 9630, 9631, 9632, 9635, 9637, and 9639. An asterisk (*) indicates a significant difference according to individual t-tests. Overali mean daytime swimming depth was significantly deeper than nighttime swimming depth (t-test, T = 2.95, p=0.032). Error bars are standard error of the mean....................................................................................................................... 189
Figure 3-14: Mean swimming depth during Light and Dark based on timing of nautical twilight for sharks 9630, 9631, 9632, 9635, 9637, and 9639. An asterisk (*) indicates a significant difference according to individual t-tests.Overall mean swimming depth during light was significantly deeper than mean swimming depth during dark (t-test, T = 2.85, p=0.036). Error bars are standard error of the mean..........................................................................................191
Figure 3-15: Mean swimming depth during Lunar High (moon risen) and Lunar Low (moon set) for sharks 9630, 9631, 9632, 9635, 9637, and 9639. An asterisk (*) indicates a significant difference according to individual t-tests.Overall mean swimming depth was significantly deeper during lunar high than during lunar low (t-test, T = 2.71, p=0.042). Error bars are standard error of the mean..........................................................................................................193
Figure 3-16: Shark 9631 Directional Swimming Data, a) Frequency histogram of deviation of swimming direction from tidal current direction, b) Shark/Currentcorrelation index plotted with tidal current amplitude................................................199
Figure 3-17: Shark 9632 Directional Swimming Data, a) Frequency histogram of deviation of swimming direction from tidal current direction, b) Shark/Currentcorrelation index plotted with tidal current amplitude............................................... 200
Figure 3-18: Shark 9635 Directional Swimming Data, a) Frequency histogram of deviation of swimming direction from tidal current direction, b) Shark/Currentcorrelation index plotted with tidal current amplitude............................................... 201
Figure 3-19: Shark 9637 Directional Swimming Data, a) Frequency histogram of deviation of swimming direction from tidal current direction, b) Shark/Currentcorrelation index plotted with tidal current amplitude................................................202
Figure 3-20: Shark 9639 Directional Swimming Data, a) Frequency histogram of deviation of swimming direction from tidal current direction, b) Shark/Currentcorrelation index plotted with tidal current amplitude................................................203
xvi
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Figure 3-21: Shark 5285 Directional Swimming Data, a) Frequency histogram of deiation of swimming direction from tidal current direction, b) Shark/Current correlation index plotted with tidal current amplitude...............................................204
xvii
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
LIST OF TABLES
Table 1-1: Spearman’s Rank Correlation (rho) for CPUE (sharks per 100 hooks)versus potential predictor variables..............................................................................33
Table 1-2: Spearman’s Rank Correlation (rho) for predictor variables. Onlysignificantly correlated predictor variables are shown................................................35
Table 1-3: Classification and regression tree (CART) pruning for Ecological and Management models. RSM is the residual mean deviance for the overall tree. RSMCV is the residual mean deviance of the ten-fold cross-validated model.............................................................................................................................. 48
Table 1-4: Classification ability of reduced ecological and management tree models. Each model was pruned to three terminal nodes. Classification is measured by the proportion of each estimation parameter is included in the terminal node possessing the highest shark CPUE................................................... 50
Table 1-5: Results of univariate logistic regressions, a) Model significance,classification, Somer’s D measure of model predictive ability; b) Goodness of fit tests for each univariate model (significance indicates lack of f it) ........................58
Table 1-6: Summary of overall logistic regression statistics for full and reducedEcological and Management Models a) overall model significance (-2 log likelihood = G), classification, Somer’s D measure of model predictive ability b) goodness-of-fit tests (p<0.05 indicates lack of fit)..................................................59
Table 1-7: Full Ecological Model logistic regression a) parameter estimates, significance, and odds ratios; b) model goodness-of-fit statistics; c) model classification. Overall model significance (-2 log likelihood, G = 27.925, df=4, p<0.0001)..............................................................................................................60
Table 1-8: Reduced Ecological Model (full model - temperature) logisticregression a) parameter estimates, significance, and odds ratios; b) model goodness-of-fit statistics; c) model classification. Overall model significance (-2 log likelihood, G = 23.102, df=3, p<0.0001)................................................ 61
Table 1-9: Reduced Ecological Model (salinity and depth ONLY) logisticregression a) parameter estimates, significance, and odds ratios; b) model goodness-of-fit statistics; c) model classification. Overall model significance (-2 log likelihood, G = 19.329, df=2, p=0.0001)................................................ 62
Table 1-10: Full Management Model logistic regression a) parameter estimates, significance, and odds ratios; b) model goodness-of-fit statistics; c) model classification. Overall model significance (-2 log likelihood, G = 38.876, df=4, p<0.0001)..............................................................................................................63
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Table 1-11: Reduced Management Model (distance and depth ONLY) logistic regression a) parameter estimates, significance, and odds ratios; b) model goodness-of-fit statistics; c) model classification. Overall model significance(-2 log likelihood, G = 38.300, df=2, p<0.0001).......................................................... 64
Table 1-12: Parameter estimates, significance, and odds ratios from univariate logistic regressions for salinity, distance, depth, depth adjusted for distance, and depth adjusted for salinity. These parameter estimates were used to plot selection functions in Figure 1.9............................................................................. 65
Table 2-1: Linear regression summary for mean CPUE vs. mean surface temperature a) for the immigration period May 1 - July 31 and b) for the emigration period July 15 - October 15. Means are for semimonthly intervals......................................................................................................................... 102
Table 2-2: Linear regression summary for mean CPUE vs. mean day length a) for the immigration period May 1 - July 31 and b) for the emigration period July 15 - October 15. Means are for semimonthly intervals...................................... 106
Table 2-3: Summary of VIMS juvenile Carcharhinus plumbeus tagging activity1995-2000.................................................................................................................. 111
Table 2-4: Summary of WINTER tag recapture data. Distance was measured as the most direct route from tagging location to the recapture location using land as an impenetrable boundary. State refers to the coast nearest the recapture location labeled as NC (North Carolina), SC (South Carolina), NJ (New Jersey).................................................................................................................119
Table 2-5: Summary of SUMMER tag recapture data for returns occurring the same summer as tagging. Distance was measured as the most direct route from tagging location to the recapture location using land as an impenetrable boundary. All recaptures occurred in Virginia waters. Area of recapture summarized as Eastern Chesapeake Bay (CB-E), Western Chesapeake Bay (CB-W), and Virginia Eastern Shore (ES)..................................................................121
Table 2-6: Summary of SUMMER tag recapture data for returns occurring following at least one winter season. Distance was measured as the most direct route from tagging location to the recapture location using land as an impenetrable boundary. Area of Virginia recapture summarized as Eastern Chesapeake Bay (CB-E), Western Chesapeake Bay (CB-W), and Virginia Eastern Shore (ES), Virginia Beach (VB). Recapture from other states labeled as NC (North Carolina), SC (South Carolina), NJ (New Jersey)................123
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Table 3-1: Summary of juvenile Carcharhinus plumbeus tracks.Regions: SECB - Southeastern Chesapeake Bay; SWCB - Southwestern Chesapeake Bay; SCCB - South Central Chesapeake Bay; VAES - Atlantic Side of Virginia’s Eastern Shore.................................................................................158
Table 3-2: Minimum Convex Polygon (MCP) and Kernal activity space, mean swimming speed, and mean swimming rate for all ten juvenile C. plumbeus tracked. Grand means only include tracks of greater than 24 hours in duration. Italicized activity space estimates were not included in calculation of mean activity spaces. (S.D. = standard deviation)............................................. 170
Table 3-3: Results of t-tests for difference between mean swimming depth (meters) during day and night based on sunrise and sunset. Means are given with standard deviation in parentheses. Results of t-tests using raw depth data for each track are followed by t-test results using overall mean for each track as a replicate. (* indicates T statistic is significant at a= 0.05.)........... 190
Table 3-4: Results of t-tests for difference between mean swimming depth(meters) during day and night based on time of nautical twilight. Means are given with standard deviation in parentheses. Results of t-tests using raw depth data for each track are followed by t-test results using overall mean for each track as a replicate. (* indicates T statistic is significant at a= 0.05.)...........192
Table 3-5: Results of t-tests for difference between mean swimming depth (meters) while moon was risen versus while moon was set. Means are given with standard deviation in parentheses. Results of t-tests using raw depth data for each track are followed by t-test results using overall mean for each track as a replicate. (* indicates T statistic is significant at a= 0.05.)...........194
Table 3-6: Results of one-way Analysis of Variance for difference between mean swimming depth (meters) during ebb, slack, and flood tidal current phases.Means are given with standard deviation in parentheses. Results of analyses using raw depth data for each track are followed by ANOVA results using overall mean for each track as a replicate. (* indicates T statistic is significant at a= 0.05.).................................................................................................195
Table 3-7: Calculation of non-statistical correlation index between swimmingdirection and tidal current direction............................................................................ 197
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Table 3-8: Carcharhinus plumbeus directional swimming (current correlation) data summary. Concentration index (r), angular dispersion (s), and mean angle are in relation to mean tidal current direction, not the overall distribution of the raw swimming direction data. Significant Rayleigh’s Z indicates the data are not distributed randomly around a circle, but have a mean directionality. Significant V-test results (as determined by the u statistic) indicate that the mean swimming direction is not statistically different from the mean tidal current direction.......................................................... 205
Table 3-9: Activity space (MCP) estimate for C. plumbeus compared with thepublished findings for three species of carcharhiniform sharks.............................. 216
xxi
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
ABSTRACT
Chesapeake Bay is possibly the largest summer nursery for Carcharhinus plumbeus in the western Atlantic. Longline sampling conducted from 1990-1999 was used to delineate this nursery spatially and temporally. Catch data from 83 longline stations sampled throughout the Virginia Chesapeake Bay were analyzed as a function of nine physical and environmental variables to delineate this nursery spatially. Tree-based models determined which variables best discriminated between stations with high and low catches and indicated that complex distribution patterns could be adequately modeled with few variables. The highest abundance of juvenile sharks was predicted where salinity was greater than 20.5 and depth was greater than 5.5 meters. Longline data from 100 sets made at two standard stations in the lower Bay indicated that immigration occurred in late May and early June and was highly correlated with increasing water temperature. Emigration from the estuary occurred in late September and early October and was highly correlated with decreasing day length. Between 1995 and 2000, 1846 juvenile C. plumbeus were tagged. With two exceptions, recaptures made in summer months were within 50 kilometers of the tagging location. Those recaptured in winter months were caught between 200 and 830 kilometers from the tagging location and indicated that the coastal waters of North Carolina and South Carolina serve as important winter nurseries from late October until May. Tag recaptures made in subsequent summers suggest that most juvenile sandbar sharks return to the same summer nurseries annually. Ultrasonic telemetry was used in investigate the diel activity patterns of juvenile C. plumbeus in Chesapeake Bay. Ten sharks were tracked for 10 to 50 consecutive hours. Swimming direction was correlated with mean direction of tidal currents. Mean activity space was conservatively estimated to be 110 km2, which is two orders of magnitude greater than that reported for other carcharhiniform species. Swimming depth ranged from surface to 40 meters and was significantly deeper during the day (12.8 meters) than during the night (8.5 meters). This diel activity pattern and large activity space is hypothesized to be an adaptation for foraging on patchy prey in a productive, yet dynamic, temperate estuary.
xxii
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
NURSERY DELINEATION, HABITAT UTILIZATION, MOVEMENTS, AND MIGRATION OF JUVENILE CARCHARHINUS PLUMBEUS IN CHESAPEAKE BAY, VIRGINIA, USA
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
CHAPTER 1Spatial delineation of summer nursery areas to define essential fish habitat
for juvenile Carcharhinus plumbeus in Chesapeake Bay, Virginia
2
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
3
ABSTRACT
The 1996 amendment to the Magnuson-Stevens Fishery Conservation
and Management Act mandated the delineation of essential fish habitat (EFH) for
all species regulated under federal management plans highlighting the
importance of nursery areas. Carcharhinus plumbeus has been federally
regulated since 1993, though much of its habitat lies in state-controlled waters.
The lower Chesapeake Bay is possibly the largest summer nursery for sandbar
sharks in the western Atlantic. The VIMS Longline Survey was expanded from
1990 to 1999 to include ancillary stations throughout the Virginia Chesapeake
Bay to delineate this nursery spatially. Catch per unit effort (CPUE) data from 83
stations were analyzed as a function of nine physical and environmental
variables. Spearman’s rank correlation was used to determine which variables
would be included in the models. Tree-based regression models determined
threshold values of the variables that were most influential and best discriminated
between stations with high and low CPUE. Minimum cost-complexity pruning
and cross-validation of the pruned tree models were used to develop optimal
trees. The tree models indicated that complex distribution patterns could be
adequately modeled with few variables. The highest abundance of juvenile C.
plumbeus was predicted where salinity was greater than 20.5 and depth was
greater than 5.5 meters. To increase applicability of the models to actual
management practices, distance to the mouth of the estuary was introduced as a
surrogate variable for salinity. The models determined that the highest
abundance of sharks was in areas less than 34.5 km from the mouth of the
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
4
estuary and in depths greater than 5.5 meters. Several dependent and
independent measures were used to estimate the classification and predictive
ability of these models. Both models performed very well using all measures.
Logistic regression using presence/absence data was used to validate the tree
models. The logistic models agreed very well with the tree models, selecting the
same variable combinations in predicting shark presence and absence. The
threshold variable values were spatially mapped in a Geographic Information
System (GIS). The resulting response surfaces were interpreted to represent
essential nursery habitat for C. plumbeus in Chesapeake Bay.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
5
INTRODUCTION
Meek (1916) proposed that the available evidence indicated that females
of viviparous sharks resorted to specific areas of their range for the liberation of
young. He also stated that the carcharhiniform sharks Galeorhinus canis (=
galeus) and Mustelus canis (actually M. mustelus) had been observed to pup in
shallow water with the young remaining after the larger sharks migrated into
deeper water alluding to the use of shallows as protective nursery areas.
Springer (1967) stated that females of many coastal species, with special
reference to carcharhinids, migrate to specific areas in the shallower portions of
their range to pup and the young of many of these species remain in restricted
nurseries that are void of larger sharks that prey on the juveniles. Nurseries for
most species in the western North Atlantic are very discrete geographically and
are usually located in highly productive bays and estuaries (Castro 1987). It is
thought that such areas provide protection from predation as well as abundant
food to promote rapid growth in juveniles (Springer 1967, Branstetter 1990). The
use of such areas is an adaptation that probably evolved with the evolution of the
K-selected reproductive strategy characterized by large parental investment into
few young (Simpfendorfer and Milward 1993). Indeed, Lund (1990) has found
evidence of elasmobranch nursery areas in the fossil record from 320 million
years ago.
Though locations of nurseries are known for many species of coastal
sharks (Clarke 1971, Bass 1978, van der Elst 1979, Medved and Marshall 1981,
Gruber et al. 1988), data characterizing these areas geographically, physically,
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
6
chemically, and biotically are scant. Castro (1993) described length frequencies
and temporal migrations of nine species of carcharhiniform sharks utilizing Bull’s
Bay, South Carolina, as a nursery, however, environmental characterization of
this bay in relation to shark utilization was not included. Simpfendorfer and
Milward (1993) described temporal utilization and the distribution patterns
(aggregate versus random) for eight species of carcharhinid and sphyrnid sharks
utilizing Australia’s Cleveland Bay as a nursery, though no environmental data
were used to describe the patterns. Morrissey and Gruber (1993a,b) described
spatial utilization and habitat preferences based on depth, bottom type,
temperature, and salinity for juvenile Negaprion brevirostris in the North Sound of
Bimini, Bahamas. This study provided the most comprehensive physical
description of a shark nursery to date.
In 1996, the reauthorization of the Magnuson-Stevens Fishery
Conservation and Management Act through the Sustainable Fisheries Act (SFA)
established a mandate for the National Marine Fisheries Service (NMFS) and
Federal Fishery Management Councils requiring the identification of essential
fish habitat (EFH) for all species regulated by federal fishery management plans.
In short, 39 federal management plans had to be amended to include plans for
identification of EFH for more than 700 fishery stocks (Schmitten 1999). The
SFA defines EFH as “ ...those waters and substrate necessary to fish for
spawning, breeding, feeding, or growth to maturity” (USDOC 1996). Musick
(1999) pointed out, however, that in past EFH investigations, virtually all habitats
where a species is known to occur have been deemed EFH. This is particularly
problematic for highly migratory species such as large coastal sharks. Musick
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
7
(1999) recommended an EFH designation system based on utilization,
availability, and vulnerability. Using these criteria, estuarine nursery areas in
regions heavily impacted by humans are of particular concern. Packer and Hoff
(1999) proposed identifying 100% of estuarine areas where juvenile Paralichthys
dentatus (summer flounder) were found as EFH because the life stage is
estuarine dependent and the resource is overfished.
Carcharhinus plumbeus, the sandbar shark, is the dominant species in the
directed commercial shark fishery along the east coast of the United States,
constituting more than 2/3 of landings (Anonymous 1996, Castro 1993). It has
been federally regulated since 1993 by the Fishery Management Plan for Sharks
of the Atlantic Ocean (NMFS 1993). The plan covers 39 species of sharks
divided into large coastal, small coastal, and pelagic species groups.
Carcharhinus plumbeus is managed as part of the large coastal species group. It
reaches at least 239 cm in total length (Compagno 1984) and requires at least 15
years to reach sexual maturity (Sminkey and Musick 1995). In addition, this
species has been listed in the World Conservation Union (IUCN) Red List of
Threatened Species as a lower-risk, conservation-dependent species of concern
(IUCN 2000). This species inhabits insular regions to at least 250 m depth
(Garrick 1982) and is highly migratory with several hundred kilometers separating
summer and winter habitats (Bigelow and Schroeder 1948, Springer 1960), a
pattern that begins as neonates (Grubbs and Musick in prep a). These habits
make delineation of EFH problematic at best. Among the most critical EFH data
needs, however, is the delineation of summer pupping and nursery areas (Hoff
and Musick 1990, NMFS 1996).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
8
The historical nursery grounds for Carcharhinus plumbeus in the western
North Atlantic are distributed in shallow water habitats on the east coast of the
United States from Long Island, New York (possibly north to Cape Cod,
Massachusetts) to Cape Canaveral, Florida (Springer 1960). Merson (1998)
found that this nursery range has contracted and now only extends from New
Jersey south to South Carolina. A secondary nursery exists in the northwestern
Gulf of Mexico (Springer 1960). The Chesapeake Bay is thought to be the
largest estuarine nursery area for C. plumbeus in the western Atlantic. Mature
females enter the lower Bay as well as the saline lagoons along the Eastern
Shore of Virginia to pup in May and June (Musick and Colvocoresses 1986).
These mature individuals then migrate offshore while the neonates remain in the
estuary until September or October. The young then migrate offshore and south
below Cape Hatteras (Grubbs and Musick in prep. a). The following spring, the
juveniles return to their natal nurseries to take advantage of the high productivity
and the protection provided by the estuary (Grubbs and Musick in prep a). The
young sandbar sharks continue to use the nursery during the warmer months for
the first four to ten years of life (Sminkey 1994, Grubbs and Musick in prep. b).
They then remain coastal year round, presumably only entering Mid-Atlantic
estuaries after reaching maturity to bear young.
The primary objective of this study was to delineate the summer nursery
for juvenile C. plumbeus in Chesapeake Bay spatially to define EFH and provide
a framework for delineation of EFH for other species in a format that can be
applied to management problems. This was accomplished by using the catch-
per-unit-effort (CPUE) from stations sampled by the VIMS longline survey in
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
9
Chesapeake Bay. These data were analyzed as a function of physical and
environmental variables using classification and regression tree (CART)
modeling and logistic regression to determine which variables were important in
defining suitable nursery habitat. These results were combined in a Geographic
Information System (GIS) framework using ArcView 3.1 and spatial grid
coverages of potential EFH were developed.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
10
MATERIALS AND METHODS
Sampling GearSampling was conducted during longline cruises conducted by the Virginia
Institute of Marine Science (VIMS). These cruises are part of a long-term survey
to monitor the abundance and diversity of coastal sharks. Collections were made
with a commercial-style longline consisting of 3/8-inch tarred, hard-laid nylon
main line, which was anchored at each end and marked by a hi-flier marker buoy.
Three-meter gangions were spaced approximately 18 meters apart along the
main line and a large inflatable float was attached to the main line following every
20th gangion. Each gangion was composed of a stainless-steel tuna clip
attached to a 2-meter section of 1/8-inch tarred nylon trawl line, the end of which
was attached to a large barrel swivel. A 1-meter section of 1/16 inch galvanized
aircraft cable was crimped to the swivel and the other end was crimped to a
Mustad 9/0 stainless-steel shark hook. Each longline set consisted of 80-120
gangions. Bait consisted mostly of Brevoortia tyrannus, Atlantic menhaden, and
Scomber scombrus, Atlantic mackerel. Soak times were between three and four
hours. A standard 100-hook set covered about two kilometers. The statistical
unit was catch per unit effort (CPUE) defined as the number of sharks per 100
hooks.
Sampling Design
Since its inception in 1974, the longline survey conducted by VIMS has
routinely included stations in Chesapeake Bay. Two locations in the lower
eastern Bay, Kiptopeke (37°10' N, 76° 00' W) and Middleground (37° 06' N, 76°
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
11
03’ W), have been standard stations since 1980 (Map 1-1). The historical data
indicated the abundance of juvenile C. plumbeus in this region was very high
from June to September suggesting it is part of a primary summer nursery. The
survey was expanded in 1990 and 1993 to include additional ancillary sampling
in the Bay in an effort to delineate the summer nursery. These preliminary data
indicated the primary nursery was indeed concentrated in the lower eastern part
of the Bay where salinity is highest. Based on these preliminary sets, continued
sampling to delineate the nursery was limited to the Virginia portion of the Bay
south of 37° 50' N, an area of approximately 4000 km2. Logistical constraints
made random sampling of this area impractical, therefore, selection of sampling
sites were chosen haphazardly to maximize coverage. In the ten-year span from
1990 to 1999, 174 longline sets were made in Chesapeake Bay. Grubbs and
Musick (in prep a) indicated that utilization of the primary nursery occurs from
early June through September. Therefore, only sets made in these months were
used for spatial delineation. This reduced the number of sets to 147. Of these,
73 were standard sets made at Kiptopeke and Middleground, and 74 were
ancillary sets sampled to delineate the nursery. Twenty of these ancillary sets
were made between during the years 1990-1994, whereas 54 were made during
the period 1995-1999. To minimize sampling bias toward stations K and M,
mean CPUE was calculated for each of these stations over two-year periods.
This provided four CPUE estimates for station M and five for station K. These
were combined with the ancillary stations giving a total of 83 stations used for the
spatial delineation of the nursery (Map 1-2).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
12
Data Processing
Catch-per-unit-effort was recorded for each station. Ail sharks were
measured and a condition factor from “Excellent" to “Poor” was assigned. All
healthy sharks were tagged with Hallprint® nylon dart tags. In addition, surface
temperature, minimum and maximum depth, and time of day were recorded. A
Hydrolab® multiprobe was used to record temperature in degrees Celsius, salinity
in practical salinity units (Lewis and Perkin 1978, JPOTS 1980), and dissolved
oxygen in parts per million at two-meter intervals from surface to bottom for
stations sampled from 1996 to 1999. The Virginia Institute of Marine Science
has conducted a trawl survey for juvenile finfish and blue crabs since 1955. This
survey consists of monthly, random stations sampled throughout Chesapeake
Bay. From 1990 to present these stations included surface and bottom records
of water temperature, dissolved oxygen concentration, and salinity. The number
of stations sampled per month ranged from a mean of 62 in the early 90’s to 119
in 1999. These data were used in ArcView 3.1 with the Spatial Analyst extension
to interpolate grids mapping each of these variables. Maps were made for each
variable for the months June through September 1990-1999. All longline stations
were layered over the appropriate monthly maps. By merging tables,
interpolated environmental variables could be assigned to each station. For
example, Map 1-3a shows the distribution of stations sampled by the trawl survey
for August 1999. From these stations, grids were interpolated for bottom salinity
(Map 1 -3b), temperature (Map 1-3c), and dissolved oxygen (Map 1 -3d) using
ArcView 3.1. The longline stations sampled were merged with these grids and a
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
13
value for each variable was assigned for the station. This method was used to
assign environmental variables to all stations for which these were not measured
in situ. In addition, a line was drawn from Cape Henry to Fisherman’s Island to
delimit the mouth of Chesapeake Bay. A grid coverage of distance to this line
was interpolated in ArcView 3.1 (Map 1-4).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
14
Map 1-1: Locations of standard stations sampled by the VIMS longline survey from 1973-1999 including stations K (Kiptopeke) and M (Middleground) in the lower Chesapeake Bay.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
76°30'76°p0'76°30’
76°00’ 75°30'76°30‘
76°00* 74°30'
<8*..A
C i)
$
{ ' i
<8>
20/(
40 Kilometers C*0>
75°00‘ 74°30‘
15
Map 1-2: Distribution of longline stations used to delineate the Carcharhinus plumbeus nursery spatially in Chesapeake Bay. Darker circles represent higher CPUE (sharks per 100 hooks).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
36°6
0’ 37
°00‘
37°1
0'
37‘?
0‘ 37
^30'
37“4
0*
37^0
*
76°30‘ 76°20' 76°10' 76°00*
Nursery Stations Stations K & M condensed
CPUE (sharks per 100 hooks)O oO 1.0-3.0 • 3.0-6.0
>6.0
10 0 . 1C 20 K i l o m e t e r s
76°30‘ 76°20‘ 76°10‘ 76°00‘
75°50'
75°60'
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
16
Map 1-3: Stations sampled by the trawl survey in August 1999 and monthly grids of variables, interpolated in ArcView, used to assign values to associated longline stations, a) Stations sampled by the trawl survey, b-d) longline stations sampled in August 1999 with grids of b) bottom salinity (practical salinity units), c) bottom temperature (°C), and d) bottom dissolved-oxygen (parts per million).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
37^0*_________ 37*30*_________ 37 TO*_________3710-_________ 3 7 W 37^0*_______ 37*30* 37TO* 37*10*
c£> ►_ OD ’Ti O •- <~1 -7 ir> to--r-^Nc<rufNr<isoj•-iniSf'CDO'Q'-Mrl’Ti/lN » - -r-i-r-NfirtNNN A
r*> n in id ^o» • • . ■ • r*.V N fl ^ tf) (fl A
6661 isnBnw joj!nsd) ou0 Dc*n.*!0 djs3:ui
5 6 6 1 i^ n B n y (oidd) p u s u a B ^ o paAjossiQ
E g
■07. IS OCOC AZ*tC J3U1C A k t t JtKtt Mat atut jouts oaur
■OKif O t . i t A i t O U lt JM Li£
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
17
Map 1-4: Interpolated grid of Distance to Bay Mouth (kilometers) variable. This grid was merged with longline stations (also shown) to estimate distance of each station to the mouth of the estuary.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
37°10‘
.OCoZC ,0Zol£ .0 UXC .OOblS
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
18
Data Analysis
The null hypothesis states:
Ho: The distribution of juvenile Carcharhinus plumbeus in Chesapeake
Bay is completely random.
The working alternative hypothesis states:
Hi: Juvenile Carcharhinus plumbeus distribution in Chesapeake Bay is
not random, but is a function of some combination of nine measured
environmental and physical variables.
All stations were mapped in a Geographic Information System (GIS) using
ArcView 3.1 software. Using the ArcView extension, Spatial Analyst, a grid was
interpolated using the Inverse Distance Weighted method using the three nearest
neighbors and a power of three. This grid interpolated areas where high
(CPUE>6.0), moderate (CPUE between 3.0 and 6.0), and low (CPUE between
1.0 and 3.0) shark abundance would be expected based on the data. These
areas were interpreted to represent the spatial delineation of primary, secondary,
and tertiary nursery areas.
Spearman’s rank correlation (rho) was calculated between CPUE and nine
measured variables using SPSS statistical software. The non-parametric
Spearman’s Rho was chosen due to the non-normality of the CPUE data, even
after multiple transformation attempts and due to its low sensitivity to outliers.
These results were used to determine which variables best explained the
observed abundance distribution. Catch per unit of effort was modeled as a
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
19
function of these habitat parameters with Classification and Regression Trees
(CART) using S-Plus statistical software. CART models use the method of
binary-recursive partitioning to split data according to the predictor variables in a
way that minimizes response variance within resulting nodes. The best splits are
those that separate high and low values of the response variable (Breiman et al.
1984). Partitioning continues until nodes are homogeneous or there are too few
observations to continue (S-Plus 2000). In this study, the minimum node size
was set at one and five observations to grow initial trees. The initial regression
trees tend to overfit the data and are more complex and difficult to interpret than
needed (S-Plus 2000, Norcross et al. 1997). A three-step process was used to
simplify these trees. In the first step minimal cost-complexity pruning was
applied to the full tree using S-Plus statistical software. Pruning reduces the
number of terminal nodes by successively clipping the least important nodes
from the full tree (S-Plus 2000). For each subtree constructed the cost-
complexity measure, Ra(T) , is calculated wherein:
Ra(T) = R(T) + a \f
The variable a is a cost complexity parameter, R(T) is the total deviance of
subtree T, and f is the tree complexity defined as the number of terminal nodes
(Breiman et al. 1984). For each value of or, a subtree is constructed that
minimizes the cost-complexity, thereby producing a nested sequence of subtrees
of progressively decreasing complexity and increasing deviance. Steps two and
three involved selecting the best subtree. Ten-fold cross-validation was
performed on the pruned tree sequence using S-Plus. This procedure uses 90%
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
20
of the data to construct a tree, then tests this tree using the 10% of the data held
out. This is repeated ten times for the initial tree and each pruned subtree. The
deviance, R"(T) , is calculated for each subtree. This deviance is typically
lowest at intermediate tree sizes (Breiman et al. 1984). The optimum tree size
was chosen as the one wherein RCV(T) is at a minimum.
The ability of a given tree to predict areas of high abundance of C.
plumbeus was assessed by three post-hoc estimates of classification. The first
was the proportion of total sharks caught in the survey that resided in the
terminal node predicting highest shark abundance. The second was the
proportion of sets with CPUE>3.0 that resided in the terminal node predicting
highest shark abundance. The threshold of 3.0 was selected arbitrarily to
represent primary nursery areas. The third was the proportion of sets with
CPUE>1.0 that resided in the terminal node predicting highest shark abundance.
The threshold of 1.0 was selected arbitrarily to represent primary and secondary
nursery areas combined.
Spatial grid coverages were developed for all predictor variables using
ArcView 3.1 GIS. A grid coverage of distance to Bay mouth was also interpolated
as described in the previous section (Map 1-6 a). Greater than 2000 trawl-survey
stations were sampled during the months June through September in the years
1995-1999. The distribution of these stations is shown in Map 1-5. These
stations were used to interpolate spatial grids of average summer temperature,
dissolved oxygen, and salinity (Map 1-6, c, d). A bathymetry grid was
interpolated from TIN (triangular irregular network) data from the EPA,
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
21
Chesapeake Bay Program (Map 1-6 b). Crucial values of those variables that
were most influential in explaining increased C. plumbeus CPUE distribution
according to the CART models were combined spatially and mapped. The
resulting spatial-grid coverages were interpreted to represent suitable nursery
habitat and thus define EFH according to the model.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
22
Map 1-5: Distribution of stations sampled by the trawl survey during the months of June through September for the years 1995 -1999.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
37°40' 37°30‘ 37°20* 37°10‘ 37aQ0'
!0 I
I
i
SI*bl
S i
o2
.ofc^e .0 SoLt .0 Zolt .Oiolt .oobie
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
76°3
0’ 76
°201
76°1
0‘ 76
°00‘
76°6
0‘ 76
°40*
76
°30*
23
Map 1-6: Examples of interpolated variable grids used to display response surfaces for nursery-delineation models, a) Distance to Bay Mouth, b) Depth, c) Bottom salinity, d) Bottom dissolved-oxygen. Depth data are from EPA, Chesapeake Bay Program. Salinity and dissolved-oxygen grids are interpreted from data collected by the trawl survey combined over the period June • September, 1995-1999
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
37*®*________J 7 *3 0 *_________ 37*20* 37-10*
233~*siasssM M H(MBui)mdaajiaM
37-i®* 37*30* J7T0* 37*10*
V CM rt *t /I ID A
6 6 5 1 *5 6 6 L J^> q u j3 idvS -o un r irudd) p u s u o fi/< o paAjos^iQ
J9WS MUt OLtf 4Xk£C 4€jc mu c ou u otkzr
OUiX
37-40*_________ 37*30* 37*20* 37-10*
666 1*9 6 6 : io q u jo id o s - o u n r 'n*»d) piiQ /iiu in s oc*u*iodjo;ui
AK4X J ttU t J X U t O U tt
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
24
Testing the Model
Logistic regression was used to validate the CART nursery models.
CPUE was coded as a binary-response variable for all stations used in the CART
models. Stations with CPUE equal to zero were coded as 0 and those with
CPUE greater than zero were coded as 1. The logistic regression models used
the same predictor variables used in the CART models. All continuous predictors
were coded as categorical variables to improve model stability and goodness-of -
fit (Hosmer and Lemeshowe 1989). Highly correlated variables were not
analyzed in the same model to avoid multicollinearity. Logistic regression is a
predictive analysis that employs the principle of maximum likelihood rather than
least squares for parameter estimation. Therefore, it is free of many of the
restrictive assumptions of ordinary least squares regression. Logistic regression
does not assume the dependent variable is normally distributed and
homoscedastic, that error terms are normally distributed, or that a linear
relationship exists between dependent and independent variables (Menard,
1995). Multiple logistic regression has been used in similar habitat-selection
studies (Manly et al. 1993, Norcross et al. 1999). The resulting probability
function, \v(z), which predicts resource selection (Manly et al. 1993), takes the
form:
a+fix.X\+...+Ppxp
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
25
wherein a represents a constant, and Pp is the coefficient of the independent
variable xp. For this study, this was the probability of catching one juvenile C.
plumbeus given the model variables. Values of w'{z) <0-5 predicted shark
absence and those >0.5 predicted shark presence.
Univariate logistic regressions were conducted on each of the
independent variables. Multivariate regressions were performed using the same
variable combinations used in the CART models. All variables with coefficient
estimates not significantly different from zero were removed from the models.
The overall significance of each model was determined by the -2 log-likelihood
statistic, G, which approximates a chi-square distribution (Agresti 1990). Model
fit was assessed using three goodness-of-fit tests: Hosmer-Lemeshow, Deviance
chi-square, and Pearson chi-square. Predictor variables from significant
univariate and multivariate models were compared to the variables selected by
the CART models. The probability function estimating resource selection was
plotted for the most significant univariate models. The value of the predictor
variable wherein w’ (^)=0.5 was taken as a reference and compared to the
critical values dividing stations of high and low shark abundance in the CART
models. Areas of Chesapeake Bay that the predictor variables predicted the
probability of catching a shark to be greater than 50% were mapped spatially and
the resulting grid coverages were added to the maps from the CART modeling
for comparison.
The CART models were further validated by two independent data
sources. During the summers of 1997, 1998, and 1999 a total of nine juvenile C.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
26
plumbeus were tracked manually in Chesapeake Bay using ultrasonic telemetry
for periods ranging from 10 to 64 hours to examine habitat utilization and diel
activity patterns (Grubbs and Musick in prep. c.). Position fixes were recorded at
10-minute intervals. The position fixes were overlaid on the model nursery grid
as a post-hoc means of validating the model. To avoid autocorrelation of
samples, only fixes separated by at least six hours were used. The percentage
of fixes within the model nursery grid was used as an estimate of model
validation.
From 1995 to 1999, 1615 juvenile C. plumbeus were tagged with Hallprint
nylon dart tags to examine population structure and long-term movements
(Grubbs and Musick, in prep. a,c). To date, 45 recaptures have been reported.
Information is complete for 40 of these. Of the 31 recaptures occurring in
summer months all except one were recaptured inside the Bay, just outside the
mouth of the Bay, or in seaside lagoons along the Virginia Eastern Shore. Of the
22 that were recaptured inside the Bay, 13 were recaptured the same summer
they were tagged and 9 were recaptured in subsequent years. These recapture
locations were overlaid to examine the fit of the nursery model. The percentage
of these long-term and short-term recaptures that occurred within the model
nursery was used as an additional estimate of model validation.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
27
RESULTS
The highest abundance of juvenile C. plumbeus was found in the southern
portion of the Bay. The distribution of longline stations sampled in Chesapeake
Bay between 1990 and 1999 and associated CPUE, after accounting for clumped
sampling at Kiptopeke and Middleground, are shown in Map 1-2. A grid
interpolated from these data showing areas of high (CPUE > 3.0) and low (CPUE
>1.0, <3.0) estimated abundance is shown in Map 1-7. These highlighted areas
represent the primary and secondary nursery areas according to the data. This
nursery grid is relatively patchy, which is not surprising given the relatively low
sample size and the extremely high variance associated with CPUE data
collected using longline sampling. The grid does, however, suggest that the
primary nursery is concentrated south of 37°30' N latitude and is relatively evenly
distributed longitudinally. Interestingly, the primary and secondary nursery areas
include the outer mouth of the York River but very little of the James River
located closer to the mouth. This may be explained by the higher volume of
freshwater discharge and increased industrial and agricultural runoff typical of the
lower James. The standard longline stations, Kiptopeke and Middleground, are
well within the primary nursery area according to these data. Those areas
outside the grid coverage are not in the described nursery and are interpreted to
have very low densities of juvenile C. plumbeus.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
28
Map 1-7: Interpolated grid based on CPUE from longline stations.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
op.00TO
<o|
76°30' 76°15' 76°00'
Nursery GridInverse Distance Weighted Interpolation Neighbors = 3, Power = 3 H 1.0 - 3 . 0
3 . 0 - 6 . 0 > 6.0
oS?h-TO
•Or .h-TO
r* A \/-v
OPKTO
14 K i l o m e t e r s
76°30' 76°15' 76°00'
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
75°45‘
75°45‘
.Stto9
C io
SIe
.91-
olt
.O&L
t .9f
c, It
.00.
89
29
/: Spearman’s Rank Correlation
Nine independent variables were used in the analysis and all were
significantly correlated to CPUE except maximum set-depth and year, therefore
these two were not included in the nursery models. The results of the
Spearman’s Rank Correlation are shown in Table 1-1. Matrix plots of the
independent variables and the dependent variable, CPUE, are shown in Figures
1-1 and 1-2. Distance to bay mouth, bottom temperature, and latitude had highly
significant, negative correlation coefficients with CPUE (p<0.001, 2-tailed).
Bottom salinity had a highly significant, positive correlation coefficient with CPUE
(p<0.001, 2-tailed). Significant positive correlation coefficients (p<0.05, 2-tailed)
were found between CPUE and bottom dissolved-oxygen, minimum set-depth,
and longitude also.
Figure 1-3 is a matrix plot of the seven predictor variables that had
significant rank-correlation coefficients. Many of these variables were correlated
with each other Table 1-2. Latitude was highly correlated with all six other
variables, and was therefore eliminated from consideration for the CART models.
Longitude was highly correlated to three of the other variables. In addition, the
significant correlation of longitude to CPUE was largely driven by one point (Fig.
1-3), therefore it was also eliminated from model consideration. The remaining
five variables (distance to Bay mouth, bottom salinity, bottom dissolved-oxygen
concentration, bottom temperature, and minimum set depth) were retained for
the CART models though several of them were correlated (Fig. 1-3). The highest
correlation was between distance to Bay mouth and salinity (Table 1-2). This is
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
30
intuitive as distance to Bay mouth was initially selected as a potential predictor
variable only to act as a surrogate to salinity to increase applicability of the
models to management problems. Due to this high correlation these two
variables were not included together in any model.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
31
Figure 1-1: Matrix plot of CPUE plus five independent variables (distance to mouth, salinity, minimum set-depth, dissolved oxygen, temperature)
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
80 “60
40120 ~
o l
2015_
1 (f5o-
2826242220
20T51050
3025201510
86420
i i i i i G) 70 40 60 80 i i i i (a 5 10 1fi 20 1 i i i 20 22 24 26 28
CPUE
0 -PO - oo o § % °
°oo0
° v K Lo a a jP * '
o-*<^8oQOt]8O0BB o
% o °
® °<9&°‘b
0 8 O
° °%% 8
Distance to Bay Mouth
0 D
%
c
c
5 Q O jxg^gcr r> ^r>
3
O
BottomSalinity
—
° 00 oo
oo o uo
o
° O i
co
" F - 88 o 8
— a---------
o
3
O
O
O
QJ ^ h° 8 o
MinimumDepth
0
o
a 3
0
c
8 o
>
1 f t° §
o
. _°
8 °
O o °
° ° o
°o °
o ° o°°
BottomDissolvedOxygen
o o o ooooo
n oo
a> o c<8 < # > {r °00 O 000
o1 \ \ \ \
03D O O O O
ooooo ocgotaert) (D m x d ra o o o o
oo o o
1 1 1 1 I
o oo o o o
^«QOOOOO 00 oSosqqoo oo
o o o
1 T 1 1
coo o oo
0 oooooo OOQQOQQOOOQ O
oo o o
1 I 1 I
O COO ODD
0 OOOGDO O OOTnii» iB in |iiiiii
o ooupJJ iw —o o o q ^ o oo o
000o
\ T I I
BottomTemperature
—1-------1-------1-------1-------1—
0 5 10 15 20 10 15 20 25 30 0 2 4 6 8
32
Figure 1-2: Matrix plot of CPUE plus four independent variables (year, latitude, longitude, maximum set-depth).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
OOGD
me %
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission
33
Table 1-1: Spearman’s Rank Correlation (rho) for CPUE (sharks per 100 hooks) versus potential predictor variables.
SPEARMAN’S NONPARAMETRIC CORRELATION (rho)VARIABLE Correlation Coefficient Significance NDistance to Bay Mouth -0.535** <0.001 83Bottom Salinity 0.447** <0.001 83Bottom Temperature -0.351** <0.001 83Bottom Dissolved Oxygen 0.232* 0.034 83Minimum Set-Depth 0.265* 0.016 83Maximum Set-Depth 0.177 0.109 83Latitude -0.401** <0.001 83Longitude 0.248* 0.024 83Year -0.047 0.676 83•‘ Correlation is significant at the .01 level (2-tailed)
•Correlation is significant at the .05 level (2-tailed)
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
34
Figure 1-3: Matrix plot of seven independent variables significantly correlated with CPUE according Spearman’s Rank Correlation.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright ow
ner. Further
reproduction prohibited
without
permission.
J I I I L -766 l -76.2 l i i i i i 5 1iO 1i5 20 i i i I UlQ li5 20 25 20,I I I I I
Latitude
.37.8
.37.637.437.2370
-76.0-762-764-76.6
f io'OP
Longitude COf t C? %Oo
Distance to Bay Mouth
cP
»
80604020!o
2015’10‘5'0 -
00
MinimumDepth
8 O
O GO (DODO
QD O
O OOO O_o mmm o000 o 00ODOOOOt
CD mtM I®000
S «CDo 00o
000 o 00O 000000
00W &00 o o
BottomTemperature
o 00 00 o 1000C po 00
0000 CDD
ooaso o 00
0(o o o v o cxy” o
aoD o
28262422
’2030252015
10H
°8o o
BottomSalinity O V&OQOO ^
o9> o
Oo00
< r—i— r
e o
o00
cP—]— i— r
BottomDissolvedOxygen
“ i— i— i— r 0 2 4 6 8
i i i i r370 374 37.8
i i i i i r0 20 40 60 80
1 I I I 20 22 24 26 28
35
Table 1-2: Spearman’s Rank Correlation (rho) for predictor variables. Only significantly correlated predictor variables are shown.
SPEARMAN’S NONPARAMETRIC CORRELATION (rho)___________________FACTOR COMBINATION Correlation
Coefficient(rho)
Sign.(2-tailed)
N
Latitude vs. Bottom Temperature 0.483“ <0.001 83Latitude vs. Bottom Salinity -0.613“ <0.001 83Latitude vs. Bottom Dissolved-Oxygen -0.685“ <0.001 83Latitude vs. Distance to Bay Mouth 0.900“ <0.001 83Latitude vs. Minimum Set-Depth 0.314“ 0.004 83Latitude vs. Maximum Set-Depth 0.296“ 0.007 83Longitude vs. Bottom Salinity 0.347“ 0.001 83Longitude vs. Minimum Set-Depth 0.389“ <0.001 83Longitude vs. Maximum Set-Depth 0.512“ <0.001 83Bottom Salinity vs. Bottom Temperature -0550“ <0.001 83Bottom Salinity vs. Bottom Dissolved-Oxygen 0.302“ 0.006 83Distance to Bay Mouth vs. Bottom Temperature 0.563“ <0.001 83Distance to Bay Mouth vs. Bottom Salinity -0.737“ <0.001 83Distance to Bay Mouth vs. Bottom D. Oxygen -0.652“ <0.001 83Bottom Dissolved-Oxygen vs. Minimum Set-Depth -0.223* 0.043 83Minimum Set-Depth vs. Maximum Set-Depth 0.721“ <0.001 83‘ ‘ Correlation is significant at the .01 level (2-tailed)
‘ Correlation is significant at the .05 level (2-tailed)
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
36
II: Classification and Regression Trees (CART)
Two primary models were used in this analysis. Both used CPUE
as the dependent variable. The first model included bottom salinity, bottom
dissolved oxygen, bottom temperature, and minimum set depth as the predictor
(independent) variables and is referred to as the Ecological Model. In the second
model, distance to Bay mouth replaced bottom salinity whereas all other
variables remained unchanged. The introduction of the distance variable was
intended to increase applicability of the associated models to resource-
management problems. This second model is, therefore, referred to as the
Management Model. The initial trees for both models were allowed to grow to
completion with a minimum node size of one observation. The resulting trees
overfit the data in both models. Each full tree had 26 terminal nodes. A rule
limiting the minimum node size to five observations was applied. Given 83 total
observations in the analysis, this limited the number of terminal nodes to a
maximum of 16 for each full tree.
A. The Ecological Model
The full ecological tree model (min. node = 5) again was overly complex,
consisting of 11 terminal nodes (Fig. 1-4a) and had a residual mean deviance of
13.51 (Table 1-3). All four predictor variables were used in constructing this tree.
Figure 1-4b was generated using minimal cost-complexity pruning, plotting
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
37
deviance versus number of terminal nodes. The numbers along the top of the
graph are the values of the cost-complexity parameter. Most of the reduction in
deviance was explained by the first six terminal nodes, therefore the initial tree
was pruned accordingly (Figure 1-5a). The resulting tree was much easier to
interpret but came at the cost of increased residual mean deviance of 15.78.
Only salinity, depth, and dissolved oxygen were used to build the tree. It
suggests that the primary nursery areas (CPUE>3.0) are located where bottom
salinity is greater than 20.5, depth is greater than 5.5 meters, and dissolved
oxygen is greater than 5.35 parts per million (ppm). The response surface of
areas in the Bay that meet these criteria is shown in Map 1-8 termed Ecological
Model I.
Cross-validation suggested that the tree model pruned to six nodes also
overfit the data. A plot of cross-validation deviance versus tree size (Figure 1 -5b)
indicated that deviance was minimized at only three terminal nodes (Table 1-3),
therefore, the tree was pruned again (Figure 1-6). The residual mean deviance
increased to 21.62, but the resulting tree was simple and easily interpreted. It
suggested the primary nursery is located in areas where bottom salinity is greater
than 20.5 and depth is greater than 5.5 meters. The geographic response
surface for these criteria is shown in Map 1-9 termed Ecological Model II.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
38
Figure 1-4: Ecological Model with CPUE as response and salinity, minimum depth, dissolved oxygen, and temperature as predictors, a) Full tree with predicted CPUE at each terminal node (11 nodes) b) Plot of residual mean deviance versus number of terminal nodes generated by cost-complexity pruning (value of cost-complexity parameter along top of graph).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
B.SAUN|7Y<20.!
B.SAUN TY<19.5 MIN.IX5.5
0.2632 2.7610 0.6364B.SAUN TY<22.5
MIN.IX7.5
5.1530 14.0700
B.DQ<5.35
B.TEMP<24 B.SAUNirY<24.75
5.1040 1.4480B.TEMP<23.3 B.TEMP<23.1
12.6800 8.1620 5.0400 6.9450
400 340 170 110 51 41 32 11 -Inf
4 6 8
Number of Teririnal Nodes10
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
39
Figure 1-5: Ecological Model, a) Tree pruned to six terminal nodes with predicted CPUE below each node, b) Results of ten-fold cross-validation of each pruned subtree. Plot of cross-validated residual mean deviance versus number of terminal nodes (value of cost-complexity parameter along top of graph).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
aBottom Salipity<20.5psu
Minimum Depth<5.5m0.9358
Bottom Salinity<22.5psu0.6364
Minimum 0epth<7.5m B. Dias. Oxygen<5.35ppm
5.1530 14.0700 Z8540 8.0050
400 340
oos
I sc CN(B>
4 6 8
Number of Terminal Nodes
10
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
40
Figure 1-6: Final tree of the Ecological Model pruned to three terminal nodes. Predicted CPUE and histogram shown below each terminal node. Histograms show distribution of CPUE values among the observations in each node.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
<0n
to
IOin a
inin
Qin
tototoCM
CM
00IOtO
o
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
41
Map 1-8: Response surface for Ecological Model I. Shaded area is portion of Chesapeake Bay with average summer salinity greater than 20.5, depth greater than 5.5 meters, and dissolved oxygen concentration greater than 5.35 ppm. This area is interpreted to represent suitable nursery habitat according to the model.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
76°30‘ 76°15' 76°00' 75045,
oP .COco
ECOLOGICAL M O D E L I Salinity > 20.5 psu Depth > 5.5 mDissolved Oxygen > 5.35 ppm
uC D
1
102*h-n
u
£
os?.n
<*>
IOr.h-cn
u-.2<71
OP-CO 3
IO2* .<oCO
<dO)*
14 K i l o m e te t S '
76°30‘ 76°151 76°00' 75°45'
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
42
Map 1-9: Response surface for Ecological Model II. Shaded area is portion of Chesapeake Bay with average summer salinity greater than 20.5 and depth greater than 5.5 meters. This area is interpreted to represent suitable nursery habitat according to the reduced Ecological Model.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
76°30‘ 76°15’ 76°00' 75°45'
oP .GOm
ECOLOGICAL M O D E L II Salinity > 20.5 psu Depth > 5 . 5 m
NWl
oP.h-n
i0r.h-n
oP-kn
inr .ton
14 K i l o m e t e r s Ki l o m e t e r s ’
76°30' 76°15* 76°001 75°45’
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
,9to9
£ .OO
oZE
.Sio
l£ ,0£
oL2
.9fc>
Z£
,0Q,
8E
43
B. The Management Model
The full management tree (min. node = 5) again was overly complex,
consisting of 10 terminal nodes (Fig. 1-7a) and had a residual mean deviance of
15.33 (Table 1-3). Note that even in this full tree, only distance to Bay mouth and
minimum depth were used to build the model. Figure 1 -7b was generated using
minimal cost-complexity pruning, plotting deviance versus number of terminal
nodes. The first five terminal nodes explained most of the reduction in deviance.
The initial tree was therefore pruned accordingly (Fig. 1-8a), resulting in a tree
that was much easier to interpret but came at the cost of a slight increase in
residual mean deviance to 16.18 (Table 1-3). All of the high CPUE stations were
actually grouped by the first two splits in this tree, indicating that the third and
fourth splits, which divided the one terminal node into three, was unnecessary.
Cross-validation agreed that the tree model pruned to five nodes overfit the
data. A plot of cross-validation deviance versus tree size (Fig. 1-8b, Table 1-3)
indicated that deviance was minimized at only three terminal nodes, therefore,
the tree was again pruned accordingly (Fig. 1-9). The residual mean deviance
increased to 18.97, but the resulting tree was simple and easily interpreted. It
suggested the primary nursery is located in areas less than 34.6 kilometers from
the Bay mouth where depth is greater than 5.5 meters. The geographic
response surface for these criteria is shown in Map 1-10 termed the
Management Model.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
44
Figure 1-7: Management Model with CPUE as response and distance to mouth of estuary, minimum depth, dissolved oxygen, and temperature as predictors, a) Full tree with predicted CPUE at each terminal node (10 nodes), b) Plot of residual mean deviance versus number of terminal nodes generated by cost-complexity pruning (value of cost-complexity parameter along top of graph).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
DISTf:34.6
MIN.IX5.5
0.7692DIST =31.4
DIST<6.4
10.9000
DIST< 39.55
3.1120 0.4304
12.6700
DIST429.95
DIST< 14.05
MINI <7.15 DIST:20.3
8.2980
5.7820 8.4180 3.0250 5.9290
480 470 140 110 35 31 29 17 -Inf
eo
4 6 8
Number of Terrrinal Nodes10
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
45
Figure 1-8: Management Model, a) Regression tree pruned to five terminal nodes with predicted CPUE below each node, b) Results of ten-fold cross- validation of each pruned subtree. Plot of cross-validated residual mean deviance versus number of terminal nodes (value of cost-complexity parameter along top of graph).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Distanccj<34.6km
Minimum DepttK5.5m0.9667
DistancB<31.4km0.7692
Distance<6.4km12.6700
10.9000 S.1400
480 470
8'5
o8
4 6
Number of Terminal Nodes
10
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
46
Figure 1-9: Final tree of the Management Model pruned to three terminal nodes. Predicted CPUE and histogram shown below each terminal node. Histograms show distribution of CPUE values among the observations in each node.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
CO
oo>.to
<0
1010
CO JC
1010
C40>U>
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
47
Map 1-10: Response surface for the Management Model. Shaded area is portion of Chesapeake Bay less than 34.5 km from the mouth of the Bay and depth greater than 5.5 meters. This area is interpreted to represent suitable nursery habitat according to the reduced Management Model.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
76°30‘ 76°15’ 76°00’ 75°45‘
o
PGOr>
M A N A G EM EN T M O DEL Distance to M outh < 34.5 Depth > 5.5 m
IO
CO
o9MCO
■or .<o
o9-h-co
IO
IOl*»
76°30* 76°15' 76°00* 75°45'
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
.9fc>9e
.O
Ooi£
.Sio
L£
,0£o
i£
,9Po
L£
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Table 1-3: Pruning of Classification and Regression Trees (CART) for Ecological and Management models. RSM is the residual mean deviance for the overall tree. RSMCV is the residual mean deviance of the ten-fold crossvalidated model.
MODELc = > Ecological (Salinity, Depth, Temp, DO) Management (Distance, Depth, Temp, DO)
# Nodes VariablesUsed
RSM RSMCV # Nodes VariablesUsed
RSM RSMCV
FULL TREE 11 ALL 13.51(973/72)
29.32(2111/72)
10 Distance,Depth
15.33(1119/73)
31.41(2293/73)
1st PRUNED TREE 6 Salinity,Depth
15.78(1215/77)
28.03(2158/77)
5 Distance,Depth
16.18(1262/78)
29.24(2281/78)
2nd PRUNED TREE 3 Salinity,Depth
21.62(1729/80)
23.63(1890/80)
3 Distance,Depth
18.97(1517/80)
24.32(1946/80)
400
49
III: Testing the Tree Models
A. Tree Model Classification and Validation
Both of these very simple spatial models performed very well in all post-hoc
measures of classification (Table 1-4). The terminal node predicting highest
shark abundance in Ecological Model II contained 74.1% of all sets with CPUE
>1.0 and 91.2% of all sets with CPUE >3.0. That of the Management Model
performed even better, with 81.5% of all sets with CPUE >1.0 and 97.1% of all
sets with CPUE >3.0. Of the total number of sharks caught, 90.1% were in the
high abundance terminal node of Ecological Model II and 88.7% were in that of
the Management Model.
The ability of these models to classify and delineate the nursery correctly
was assessed by two independent data sources, tag-recapture data and
telemetry data. Nineteen of 22 (86.4%) tag recaptures from the Bay were within
the response surface for Ecological Model II whereas 20 of these 22 (81.8%)
were within the response surface of the Management Model (Maps 1-11 a, b). A
total of nine sharks manually tracked for a cumulative 350 hours generated 67
location fixes temporally separated by at least six hours. All 67 (100%) of these
location fixes were within the response surfaces for both models (Maps 1-12 a,b).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Table 1-4: Classification ability of reduced ecological and management tree models. Each model was pruned to three terminal nodes. Classification is measured by the proportion of each estimation parameter is included in the terminal node possessing the highest shark CPUE.
Estimation Parameter (% encompassed by model)
Ecological (S.Z.T.DO) Salinity/Min.Depth (3)
Management (D.Z.T.DO) Distance/Min.Depth (3)
Sets where CPUE>1.0 74.1% (40/54) 81.5% (44/54)
Sets where CPUE>3.0 91.2% (31/34) 97.1% (33/34)
Total Sharks Caught 90.1% 88.7%
Tag Recaptures 86.4% (19/22) 81.8% (18/22)
Telemetry Fixes 100% (67/67) 100% (67/67)
U1o
51
Map 1-11: Tag recaptures for juvenile C. plumbeus recaptured in Chesapeake Bay during the same year and subsequent years compared to CART models of nursery habitat, a) Recaptures compared with Ecological Model II, b) Recapture compared with the Management Model
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
a.76*?0' 76“ 10'
a_ Recapture Location w (>100 days at liberty „ Recapture Locations
(<100 days at liberty■ Ecological Model II
76*00'
76*20' 76*10' 76*00 '
b.76*?0' 76*10'
★ Recapture Locatio (>100 days at liberty]
_ Recapture Locatio ™ (<100 days at liberty]
I I Management Model
76*00'
K
76*20 76*10' 76*00'
oobic
.o
u/e
.ozU
e.Q
CoiE
!»»
Ie
52
Map 1-12: Telemetry fixes for nine juvenile C. plumbeus manually tracked for 11 to 64 hours (cumulative 350 hours) from 1996 through 1999 compared to CART models of nursery habitat. The minimum interval between fixes was six hours to avoid autocorrelation, a) Telemetry fixes compared with Ecological Model II, b) Telemetry fixes compared with the Management Model.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
a.76*20' 76° 10' 76°00‘
Telemetry Fixes w 6-hour minimum interval
(# denotes individual sharks tracked) |■ I Ecological Model II
b. 76*20' 76°1 O' 76*oo'
^Telemetry Fixes w 6-hour minimum interval
(# denotes individual sharks tracked)|I I Management Model
76-20' 76-10' 76-00' 76“20 76“10 76“00'
lOOk/E
.o
U/e
.o
t>LZ
.o
eU
e,
or
„/
E
53
B. Logistic Regression
Univariate logistic regressions were performed using each independent
variable and CPUE as the dependent or response variable. All five continuous
predictor variables used in the CART models were interval coded to increase
model stability (Table 1-5a). The -2 log-likelihood statistic (G) was significant
(p<0.05) for all univariate models except that for minimum set depth (p=0.059).
The percent correct classification for the univariate regressions ranged from 69
and 77% (Table 1-5a). In all cases, the univariate models were more successful
in predicting presence than absence. Overall model significance was greatest for
distance to Bay mouth (G=27.7, p<0.0001) and bottom salinity (G=16.4,
p<0.0001), comparable to the results of the tree models. Pearson, Deviance,
and Hosmer-Lemeshow goodness-of-fit tests were insignificant for all univariate
models except bottom temperature (Table 1-5b). The tests were all highly
significant (p<0.001) for temperature indicating this univariate model fit the data
very poorly. Insignificant test statistics for all other variables indicated these
models adequately fit the data.
Multivariate regressions were performed using the same combinations of
variables as in the Ecological and Management CART models. The most
significant multivariate logistic models are summarized in Table 1-6. The logistic
regression of Ecological Model I including salinity, depth, and dissolved oxygen
(dropping temperature) predicted presence of juvenile C. plumbeus best (Table
1-8), as in the tree models. The overall model was highly significant (G=23.102,
df=3, p<0.0001) and all goodness-of-fit tests suggested the model adequately fit
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
54
the data. The model had 78.31% correct classification, but predicted presence
(88.89%) much better than absence (58.62%). This model had a Somer’s D
estimate of model predictive ability of 0.61. The scale of Somer’s D ranges from
zero for a model with no predictive ability to one for a perfect predictive model.
The model including temperature was also highly significant (G=27.925, df=4,
p<0.0001) but the Pearson goodness-of-fit chi-square (p=0.08) suggested this
model did not fit the data as well as the model without temperature (Table 1-7).
The model including only salinity and depth was also highly significant
(G=19.329, df=2, p=0.0001) and the goodness-of-fit statistics suggested this
model adequately fit the data as well (Table 1-9). Model classification (74.70%)
and Somer’s D (0.56) were slightly lower than the model that included dissolved
oxygen. Significance of individual variable coefficients was determined by the
Wald statistic evaluated at 0.05 (Tables 1-7,1-8, 1-9). Salinity was the most
significant factor in all three of these models and the odds ratio ranged from1.32
to 1.35 indicating an increased likelihood of shark presence with increased
salinity. Minimum set depth was also a significant factor in the models that
included dissolved oxygen and/or temperature but was insignificant in the
reduced model that included only salinity and depth. Its odds ratio ranged from
1.19 to 1.28 indicating an increased likelihood of shark presence with increased
depth.
The logistic regression of the Management Model using all four variables
used in the CART model was highly significant (G=38.876, df=4, p<0.0001) and
all goodness-of-fit tests suggested the model adequately fit the data (Table 1-10).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
55
The model had 84.34% correct classification (88.89% for presence; 75.86% for
absence) and a Somer’s D of 0.76. The coefficients for dissolved oxygen and
temperature were insignificant, therefore they were dropped from the model. The
reduced Management Model including only distance to mouth and minimum set
depth was also highly significant (G=38.300, df=2, p<0.0001), and the
percentage of correct classification and Somer’s D were virtually unchanged from
the full model (Table 1-11). Distance to mouth was the most significant variable
(p<0.001) and its odds ratio was 0.91 in both models indicating a decrease in
likelihood of shark presence with increased distance from the mouth of the Bay.
Minimum set depth was also highly significant in both models (p<0.01) and its
odds ratio was 1.48 in the full model and 1.50 in the reduced model indicating
and increased likelihood of shark presence with increased depth.
These results were in close agreement with the CART models. In the
Ecological Models, salinity and depth were the most important variables
influencing the distribution of juvenile C. plumbeus using CART and logistic
regression. Both methods suggested dissolved oxygen might also be influential.
For the Management Models, distance to Bay mouth and depth were the most
important variables influencing distribution using both techniques.
Using both regression tree modeling and multivariate logistic regression, it
was found that complex habitat selection patterns could be adequately modeled
with only two variables. To examine potential differences in the response
surfaces delineated using each technique, the selection functions, w '( j) , were
plotted for each influential variables using the coefficients from their univariate
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
56
models (Table 1-12, Fig. 1-9). Values of the selection function, w'{x), greater
than 0.5 predict shark presence and values less than 0.5 predict shark absence.
The value of the independent variable as it crosses this threshold can be
compared with the splitting-rule value of the variable in the CART models.
Differences in actual variable reference points were probably simply a function of
the use of a continuous response variable, CPUE, in the CART models and the
use of a binary response variable, presence/absence, in the logistic models. The
selection function for distance to bay mouth started at a value greater than 0.9 at
zero kilometers and gradually declines. The curve predicted shark presence
when distance to mouth is less than 44.5 km compared to a value of 34.5 km
selected by the CART models (Fig. 1-9a). The selection function for salinity
indicated that shark presence was predicted when salinity is greater than 19.7
(Fig. 1 -9b). This corresponded very closely with the value 20.5 from the CART
models. Three selection functions were plotted for the minimum set depth
variable. The univariate regression model (G=3.551, p=0.59) for this variable
and the corresponding coefficient (Wald=3.22, p=0.0729) were not significant.
This model, however, included all stations whereas the CART models first
divided the stations based on salinity or distance to bay mouth. It then used only
those stations with salinity greater than 20.5 in the Ecological Model and those
stations less than 34.5 km from the mouth in the Management Model to make a
second split based on the depth parameter. Based on this fact, two additional
univariate logistic regressions were performed. The first used only stations where
distance to mouth was less than 44.5 km, the reference from the univariate
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
57
selection function using distance to mouth as the predictor. This model was
highly significant as was the model coefficient for depth (Wald chi-square = 8.35,
p=0.0039). The second used only stations where salinity was greater than 19.7,
the reference from the univariate selection function using salinity as the predictor.
This model was also highly significant as was the model coefficient for depth
(Wald chi-square = 6.63, p = 0.01). When plotted, the selection functions for
these two models predicted presence of C. plumbeus ( w’( j ) > 0.5) in water
deeper than 3.9 and 3.65 meters, respectively (Figure 1-9c), compared with 5.5
meters selected by the CART models.
Though the models appeared to be in close agreement, small differences
in salinity, distance to mouth, and depth correspond to large differences in area
defined (Map 1-13). The area delineated by the CART Ecological Model (salinity
>20.5, depth >5.5 m) was layered over that delineated by the logistic regression
(salinity >19.7, depth >3.65 m) in Map 1-13a. In this case, the area defined by
the logistic model was 39% greater than that defined by the CART model. The
area delineated by the CART Management Model (distance to mouth <34.5 km,
depth >5.5 m) was layered over that delineated by the logistic regression
(distance to mouth <44.5 km, depth >3.9 m) in Map 1-13b. The area defined by
the logistic model was 51% greater than that defined by the CART model.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Table 1-5: Results of univariate logistic regressions, a) Model significance, classification, Somer’s D measure of model predictive ability; b) Goodness-of-Fit tests for each univariate model (significance indicates lack of fit)
a.Variable G df Sign. Classification
% CorrectSomer’s D
Pres Abs OverallBottom Salinity(<16;17-19;20-22;23-25; >26psu)
16.398 1 <0.0001 90.74 48.28 75.90 0.49
Distance from Bay Mouth(<15km; 15-30km; 30-45km; 45-60km; >60km)
27.721 1 <0.0001 90.74 51.72 77.11 0.64
Minimum Set-Depth (1m intervals; 2-20m)
3.551 1 0.059 98.15 13.79 68.67 0.20
Bottom Dissolved-Oxygen (<2;3;4;5;>6 ppm)
4.823 1 0.028 98.15 13.79 68.67 0.26
Bottom Temperature 10.540 1 0.001 87.04 55.17 75.90 0.48(1°C intervals; 20-28°C)
b.Variable Goodness-of-Fit Tests (probabilities)
Bottom SalinityPearson
0.722Deviance
0.716Hosmer-Lemeshow
0.545
Distance from Bay Mouth 0.593 0.536 0.593
Minimum Set-Depth 0.395 0.285 0.110
Bottom Dissolved-Oxygen 0.658 0.637 0.779
Bottom Temperature 0.001 0.005 0.009
cnoo
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Table 1-6: Summary of overall logistic-regression statistics for full and reduced Ecological and Management Models a) overall model significance (-2 log likelihood = G), classification, Somer’s D measure of model predictive ability b) Goodness-of-Fit tests (p<0.05 indicates lack of fit)
a.Model G df Sign. Classification
% CorrectSomer’s D
Pres Abs Overall
Ecological Model 1 (S, Z, DO, T) 27.925 4 <0.0001 83.33 62.07 75.90 0.66Ecological Model 2 (S, Z, DO) 23.102 3 <0.0001 88.89 58.62 78.31 0.61Ecological Model 3 (S, Z) 19.329 2 0.0001 85.19 55.17 74.70 0.56Management Model 1 (Dist, Z, DO, T) 38.876 4 <0.0001 88.89 75.86 84.34 0.76Management Model 2 (Dist, Z) 38.300 2 <0.0001 90.74 72.41 84.34 0.75
b.Model____________________________Goodness-of-Fit Tests (probabilities)
Pearson Deviance Hosmer-LemeshowEcological Model 1 (S, Z, DO, T) 0.088 0.244 0.345Ecological Model 2 (S, Z, DO) 0.291 0.116 0.158Ecological Model 3 (S, Z) 0.143 0.102 0.305Management Model 1 (Dist, Z, DO, T) 0.046 0.683 0.767Management Model 2 (Dist, Z) 0.962 0.937 0.476
OlCD
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Table 1-7: Logistic regression for the full Ecological Model a) parameter estimates, significance, and odds ratios; b) model Goodness-of-Fit statistics; c) model classification. Overall model significance (-2 log likelihood, G = 27.925, df=4, p<0.0001)
a.Variable Coefficient Standard
ErrorWald
Chi-squaredDF Probability Odds Ratio
Constant 2.2494 5.5338 0.1652 1 0.6844Bottom Salinity 0.2262 0.0911 6.1648 1 0.0130* 1.35Minimum Set-Depth 0.2973 0.1308 5.1648 1 0.0231* 0.65Bottom Temperature -0.4338 0.2047 4.4913 1 0.0341* 1.25Bottom Dissolved-Oxygen 0.4694 0.2405 3.8081 1 0.051 1.60
b.Goodness-of-Fit Tests
Method Chi-squared DF ProbabilityPearson 85.343 69 0.088Deviance 76.743 69 0.244
Hosmer-Lemeshow 8.973 8 0.345
c.Classification Table
Predicted Percent CorrectObserved Present AbsentPresent 45 9 83.33%Absent 11 18 62.07%
Overall 75.90%
05O
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Table 1*8: Logistic regression for the Ecological Model excluding temperature a) parameter estimates, significance, and odds ratios; b) model Goodness-of-Fit statistics; c) model classification. Overall model significance (-2 log likelihood, G = 23.102, df=3, p<0.0001)
a.Variable Coefficient Standard
ErrorWald
Chi-squaredDF Probability Odds Ratio
Constant -9.0980 2.4716 13.5493 1 0.0002Bottom Salinity 0.2766 0.0877 9.9535 1 0.0016** 1.32Minimum Set-Depth 0.2462 0.1178 4.3661 1 0.0367* 1.28Bottom Dissolved-Oxygen 0.4267 0.2270 3.5341 1 0.0601 1.53
b.Goodness-of-Fit Tests
Method Chi-squared DF ProbabilityPearson 64.486 59 0.291Deviance 72.199 59 0.116Hosmer-Lemeshow 11.855 8 0.158
c.Classification Table
Predicted Percent CorrectObserved Present AbsentPresent 48 6 88.89%Absent 12 17 58.62%
Overall 78.31%
CD
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Table 1-9: Logistic regression for the Ecological Model using only salinity and depth a) parameter estimates, significance, and odds ratios; b) model Goodness-of-Fit statistics; c) model classification. Overall model significance (-2 log likelihood, G = 19.329, df=2, p=0.0001)
a.Variable Coefficient Standard
ErrorWald
Chi-squaredDF Probability Odds Ratio
Constant -7.2477 2.0868 12.0622 1 0.0004Bottom Salinity 0.3037 0.0856 12.5821 1 0.0005** 1.35Minimum Set-Depth 0.1726 0.1047 2.7205 1 0.0991 1.19
b.Goodness-of-Fit TestsMethod Chi-squared DF ProbabilityPearson 46.172 37 0.143Deviance 48.267 37 0.102Hosmer-Lemeshow 9.459 8 0.305
c.Classification Table
Predicted Percent CorrectObserved Present AbsentPresent 46 8 85.19%Absent 13 16 55.17%
Overall 74.70%05ro
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Table 1-10: Logistic regression for the full Management Model a) parameter estimates, significance, and odds ratios; b) model Goodness-of-Fit statistics; c) model classification. Overall model significance (-2 log likelihood, G = 38.876, df=4, p<0.0001)
a.Variable Coefficient Standard
ErrorWald
Chi-squaredDF Probability Odds Ratio
Constant 5.6059 5.7920 0.9368 1 0.3331Distance from Bay Mouth -0.0896 0.0264 11.4793 1 0.0007** 0.91Minimum Set-Depth 0.3916 0.1508 6.7440 1 0.0094** 1.48Bottom Temperature -0.1820 0.2491 0.5339 1 0.4650 0.83Bottom Dissolved-Oxygen 0.0095 0.2975 0.0010 1 0.9745 0.56
b.Goodness-of-Fit TestsMethod Chi-squared DF ProbabilityPearson 88.849 68 0.046Deviance 61.961 68 0.683Hosmer-Lemeshow 4.909 8 0.767
c.Classification Table
Predicted Percent CorrectObserved Present AbsentPresent 48 6 88.89%Absent 7 22 75.86%
Overall 84.34%
o>CO
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Table 1-11: Logistic regression for the Management Model using only distance and depth a) parameter estimates, significance, and odds ratios; b) model Goodness-of-Fit statistics; c) model classification. Overall model significance (-2 log likelihood, G = 38.300, df=2, p<0.0001)
a.Variable Coefficient Standard
ErrorWald
Chi-squaredDF Probability Odds Ratio
Constant 1.3305 0.9317 2.0394 1 0.1533Distance from Bay Mouth -0.0984 0.0230 18.2326 1 <0.0001** 0.91Minimum Set-Depth 0.4046 0.1423 8.0848 1 0.0045** 1.50
b.Goodness-of-Fit TestsMethod Chi-squared DF ProbabilityPearson 23.248 37 0.962Deviance 24.841 37 0.937Hosmer-Lemeshow 6.565 7 0.476
c.Classification Table
Predicted Percent CorrectObserved Present AbsentPresent 49 5 90.74%Absent 8 21 72.41%
Overall 84.34%o>-p.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Table 1-12: Parameter estimates, significance, and odds ratios from univariate logistic regressions for salinity, distance, depth, depth adjusted for distance, and depth adjusted for salinity. These parameter estimates were used to plot selection functions in Figure 1.9.
Variable Coefficient StandardError
WaldChi-square
df Sign. OddsRatio
Bottom Salinity 0.3013 0.0833 13.0714 1 0.0003 1.35Constant -5.9388 1.8079 10.7911 1 0.0010
Distance from Bay Mouth -0.0728 0.0170 18.4001 1 <0.0001 0.93Constant 3.2405 0.6998 21.4444 1 <0.0001
Minimum Set Depth 0.1586 0.0892 3.2159 1 0.0729 1.17Constant -0.5493 0.6822 0.6720 1 0.4124
Minimum Set Depth 0.5067 0.1753 8.3498 1 0.0039 1.66(stations < 44km from Mouth of Bay)Constant -1.9755 1.0817 3.3348 1 0.0678
Minimum Set Depth 0.3673 0.1427 6.6276 1 0.0100 1.44(Stations with Salinity >20) Constant -1.3420 0.9633 1.9411 1 0.1635
ocn
66
Figure 1-10: Plot of univariate selection functions ( w’* ( /) ) . Parameter estimates from univariate logistic regressions for salinity, distance from mouth, depth, depth adjusted for distance, and depth adjusted for salinity (Table 1.12).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Sele
ctio
n Fu
nctio
n Se
lect
ion
Func
tion
Sele
ctio
n Fu
nctio
nSelection Function - Distance From Bay Mouth
— 0(3 2405 - 0 0728(Dtslance)
■j + 0(3.2405 - 0 0728(D*stance)
0.5
00 10 20 30 40 50 60 70 80 90
Distance From Bay Mouth (KM)
Selection Function - Salinity
W(X) = e('5 94 * 0 30135<Salinjty)1 + p(-5 94 <■ 0.30135(Salioitv)
0.5
0 5 10 15 20 25 30 35 40Salinity (psu)
Selection Function - Minimum Depth
W (x) = e<a + b (Salinity)1 + g(a + b (Salinity)
All Stations (a = -0.5493; b = 0.15857)<44km from Bay Mouth (a = -1.976; b = 0.5067) Salinity >19.5psu (a = -1.3421; b = 0.3673)
0 5 10 15 20 25 30 35 40 45Bottom Depth (m)
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Map 1-13: Maps comparing suitable nursery habitat defined by CART models and logistic regression models: a) Ecological Model, b) Management Model.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
37*16*
•00.8C .9U/C
38*50' 37*46' 37*16* 37*00* 36*46*
mCD£ = CO^ in aO i/) £ cn a —£ 5 i< E QQ. c, . . S Q ~o - =L^ ir, l-~ myj id —
9 ° AO CNs A_ j |E E
3§»<=■ J l - rO cc =/<-K< Cm l ) j
CO .00,80 .9fcie .O C g /S .Sl*/B AUt .97,90
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
.97.44 .0
04
/ .9
14
/ .0
04
/ .97.9/
40
4/
.91
4/
.06
4/
68
DISCUSSION
Delineation of important shark nurseries and defining essential fish habitat
(EFH) have been identified as crucial data needs for proper management of
stocks (Hoff and Musick 1990, Musick eta l. 1993, NMFS 1996, Benaka 1999).
Shark nurseries have been superficially identified for several carcharhinid
species (Castro 1993, Simpfendorfer and Milward 1993), including Carcharhinus
plumbeus (Medved and Marshall 1981, Musick and Colvocoresses 1986, Merson
1998), but none of these were quantified or delineated. Morrissey and Gruber
(1993 b) were the first to investigate and quantify habitat selection for an
elasmobranch, concluding that juvenile Negaprion brevirostris selected
shallower, warmer areas with sandy or rocky substrate. Merson (1998)
investigated habitat selection in C. plumbeus in Delaware Bay and found no
selection based on temperature, salinity, bottom depth, and tidal cycle, however
these variables were analyzed using only t-tests. This study represents the first
attempt to quantify habitat selection spatially and delineate the corresponding
nursery to define essential fish habitat for an elasmobranch. The statistical
procedures used in this study have been used on a number of teleost species.
Regression tree modeling was used to determine important habitat variables in
nursery areas for several species of flatfishes in Alaskan waters (Norcross et al.
1995, 1997). Multivariate logistic regression has been used to investigate habitat
preference by juvenile flatfishes around Kodiak Island, Alaska (Norcross et al.
1999) and by juvenile Paralichthys dentatus in Chesapeake Bay (Kraus and
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
69
Musick in prep). This is the first time these techniques have been used for
elasmobranchs.
Although estuarine habitats are extremely complex and dynamic, this
study suggests that much of the variability in distribution of juvenile sandbar
sharks in Chesapeake Bay may be explained by very few variables. Abundance
of juvenile C. plumbeus, based on CPUE data from longline sampling, was
positively correlated with bottom salinity, depth, and dissolved oxygen, and
negatively correlated with distance to the mouth of the Bay and bottom
temperature. Regression tree modeling indicated salinity was the most important
environmental variable influencing the distribution of juvenile sharks in the
estuary and suggested a preference for areas where salinity is greater than 20.5.
The model selected depths greater than 5.5 meters as the second most
important variable defining nursery habitat and also suggested a preference,
perhaps tolerance-based, for areas with dissolved oxygen concentrations greater
than 5.35 ppm. Cross-validation of the regression tree model indicated
truncation of the model to three terminal nodes, which corresponded to station
splits based on salinity and depth only, was sufficient to explain the data. This
model performed very well in all measures of classification. The corresponding
response surface map (Map 1-9) indicated that suitable nursery habitat
encompasses most of the lower Bay south of 37° 20’ N latitude and extends as
far as 37° 40’ N on the eastern side of the Bay. This reflects the haloclinal tilting
typical of the estuary due to freshwater riverine influx from the western side of the
Bay coupled with tidal influx of high-salinity oceanic water from the south. One
major criticism with this model, however, is that whereas depth is a relatively
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
70
stable variable, salinity is extremely dynamic seasonally and annually. This
model attempted to model this variable as fixed in space and time. To illustrate
the effect of annual variability on the response surface, salinity and dissolved
oxygen grid coverages were interpolated for data from July 1996, representing a
very wet year, and July 1999 representing a drought year. Map 1-14 shows the
response surfaces for Ecological Models I and II applied to these two months.
According to Ecological Model I, the amount of suitable habitat in 1999 (794 km2)
was 50% greater than that of 1996 (524 km2). According to Ecological Model II,
only using salinity and depth, the discrepancy was even greater. The amount of
suitable habitat in July 1999 (2134 km2) was more than 180% greater than that in
July 1996 (741 km2). This is supported by anecdotal evidence of sharks being
caught by recreational fishers as far up the York River as VIMS in 1999.
Schwartz (1960) reported juvenile C. plumbeus as far north as Flag Pond in
Calvert County (four specimens, 1958) and the West River in Anne Arundel
County (one specimen, 1959) in the Maryland portion of the Bay. Perhaps these
were rare forays into marginal habitats or perhaps salinity was anomalously high
during this period. This was prior to the development of any directed shark
fisheries along the East Coast, however. Therefore abundance may have been
much higher and competition may have forced the utilization of these areas.
The first model delineated nursery EFH for juvenile C. plumbeus in
Chesapeake Bay according to the environmental parameters sampled. The
model was very simple to understand ecologically and was based primarily on
selection for high salinity regions. It was called the Ecological Model due to its
insight into the ecology of the organism. A second goal of this study, however,
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
71
was to develop an EFH model that would be of use for management of the
population through the establishment of regulations limiting exploitation within the
nursery. Protecting or regulating geographic areas based on dynamic variables
such as salinity are difficult at best, even during periods of stability. Annual and
seasonal variability render management impossible based on salinity Salinity is
highly correlated with distance to the mouth in Chesapeake Bay; therefore
distance was introduced as a surrogate variable for salinity in a second model.
The regression trees indicated distance to mouth was the most important
variable influencing shark distribution in the Bay, and predicted presence at
stations less than 34.5 km from the Bay mouth. As in the Ecological Model, this
model indicated higher abundance of sharks at depths greater than 5.5 meters.
This model also performed extremely well in all measures of prediction and
classification. Because both variables used in this model are stagnant, the
response area from this model (Map 1-10) is stable. Though distance to mouth
may have no direct ecological significance or influence, the resulting response
surface encompasses most of the suitable habitat from the Ecological EFH
Model but does not fluctuate due to dynamic influential variables. This model
provides a much more functional management tool for regulating the nursery and
was therefore called the Management Model.
As a means of validating these models, logistic regression modeling was
performed on the data. Presence/absence was substituted for CPUE as the
response variable. The results from univariate and multivariate models agreed
closely with the CART models. Both methods determined that distribution of
juvenile sandbar sharks in Chesapeake Bay was influenced by salinity and water
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
72
depth and that distance to Bay mouth serves well as a surrogate to salinity for
management applications. Both methods also suggested that bottom
temperature and dissolved oxygen concentration may also influence this
distribution, though less than salinity and depth. Dissolved oxygen concentration
in Chesapeake Bay is extremely dynamic. Large hypoxic areas are established
in late summer. Most of these areas are in deeper water north of the EFH area
delineated in this study. It is hypothesized, however, that in certain summers this
hypoxic zone may indeed cause constriction of the summer nursery just as low
salinity (wet years) did in 1996.
The results of this study provide a framework for delineating EFH for shark
nursery grounds. Nursery EFH for most shark species occurs in state-controlled
waters. The spatial models resulting from this methodology can be used by state
and regional regulatory agencies to limit harvest in areas determined to
constitute essential nursery habitat. In the summer of 1996, a directed
commercial fishery developed in Chesapeake Bay, targeting juvenile
Carcharhinus plumbeus. About 20,000 kg of juvenile sharks were landed, mostly
in three-week period from mid-June to the beginning of July. All of the sharks
landed came from the area delineated as EFH in this study. As discussed, the
amount of suitable habitat according to these models was severely constricted in
1996 due to low overall estuarine salinity. This may have concentrated the
juvenile sharks making them particularly vulnerable to the gillnet fishery. VIMS
CPUE data indicated this may have equated to the harvest of as much as 75% of
the Chesapeake Bay nursery population and resulted in severe juvenescence of
that portion of the Atlantic stock (Grubbs and Musick in prep b). Minimum size
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
73
limit regulations have recently been established, however the development of a
temporary no-take zones during the summer months, as determined by EFH
modeling, may be a better method for preventing such destructive fishing
practices from resurfacing.
Future research directions with these EFH models will involve including
water quality and other potentially influential anthropogenic variables.
Interestingly, all of the models in this study identified the lower western portion of
Chesapeake Bay as suitable nursery habitat, yet the CPUE data indicated very
low abundance of juvenile C. plumbeus in this region. This is the most urbanized
region of the lower estuary and is subject to intense urban and agricultural run-off
through the James River. It is hypothesized that these factors have severely
degraded otherwise suitable nursery habitat in the region.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
74
Map 1-14: Comparison of suitable nursery habitat defined by a) CART Ecological Model I and b) CART Ecological Model II for a wet year (low salinity) and a drought year (high salinity).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
37^16' 36*45'
■OObSC .OfcZS .9UIC
37*16' 37*00'
.OObSC ,9 U C
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
76=3
0' 76
*16'
76*0
0' 76
*46'
76*3
0' 76
*16'
76*0
0' 76
*46'
75
LITERATURE CITED
Agresti, A. 1990. Categorical data analysis. John Wiley & Sons, Inc. New York. 558pp.
Anonymous. 1996. Final Report. MARFIN Award NA47FF0008. Gulf and South Atlantic Fisheries Development Foundation. March 1996.
Bass, A. J. 1978. Problems in studies of sharks in the southwest Indian Ocean, pp. 545-594 In: Sensory biology of sharks, skates, and rays (E. S.Hodgson and R. F. Mathewson, eds.). U.S. Gov't. Printing Office, Washington, DC.
Benaka, L. R. 1999. Summary of panel discussions and steps toward an agenda for habitat policy and science, pp. 455-459 In: Fish habitat: Essential Fish Habitat and Rehabilitation. American Fisheries Society, Symposium 22, (L. R. Benaka, editor). Bethesda, Maryland. 459 pp.
Bigelow, H. B. and W. C. Schroeder. 1948. Sharks, pp. 59-546 In: Fishes of the western north Atlantic. Part 1. Vol. 1. (J. Tee-Van, C. M. Breder, S. F. Hildebrand, A. E. Parr, and W. C. Schroeder, eds.). Mem. Sears Foundation for Marine Research, Yale Univ. New Haven, CT.
Branstetter, S. 1990. Early life-history strategies of carcharhinoid and lamnoid sharks of the northwest Atlantic, pp. 17-28 In: Elasmobranchs as living resources: advances in the biology, ecology, systematics and the status of the fisheries (H. L. Pratt, Jr., S. H. Gruber, and T. Taniuchi, eds.),. U.S. Dep. Commer., NOAA Tech. Rep. NMFS 90.
Breiman. L., J. H. Friedman, R. A. Olshen, and C. J. Stone. 1984. Classification and regression trees. Wadsworth International Group. Belmont, CA.358 pp.
Castro, J. I. 1987. The position of sharks in marine biological communities, pp. 11-17 In: Sharks, an inquiry into biology, behavior, fisheries, and use. Oregon State University Extension Service, Corvallis. (S. Cook, ed.).
Castro, J. I. 1993. The shark nursery of Bulls Bay, South Carolina, with a review of the shark nurseries of the southeastern coast of the United States. Environ. Biol. Fishes 38: 37-48.
Clarke, T. A. 1971. The ecology of the scalloped hammerhead, Sphyrna lewini, Hawaii. Pac. Sci. 25: 133-144.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
76
Compagno, L. J. V. 1984. FAO species catalogue. Vol. 4 Sharks of the world. Part 2 - Carcharhiniformes. FAO Fish. Synop. 4(2): 250-655.
Garrick, J. A. F. 1982. Sharks of the genus Carcharhinus. NOAA Tech. Rep. NMFS Circ. 445.
Grubbs, R. D. and J. A. Musick. Long-term movements, migration, and temporal delineation of summer nurseries for juvenile Carcharhinus plumbeus in the Mid-Atlantic Bight, in prep a.
Grubbs, R. D. and J. A. Musick. Population Trends, Juvenescence, and mortality estimation for juvenile Carcharhinus plumbeus in Chesapeake Bay and Virginia coastal waters, in prep b.
Grubbs, R. D. and J. A. Musick. Movements of juvenile Carcharhinus plumbeus in Chesapeake Bay. in prep c.
Gruber, S. H., D. R. Nelson, and J. F. Morrissey. 1988. Patterns of activity and space utilization of lemon sharks, Negaprion brevirostris, in a shallow Bahamian lagoon. Bull. Mar. Sci. 43 (1): 61-76.
Hoff, T. B., and J. A. Musick. 1990. Western North Atlantic shark-fisherymanagement problems and informational requirements, pp. 455-472 In Elasmobranchs as living resources: advances in the biology, ecology, systematics and the status of the fisheries (H. L. Pratt, Jr., S. H. Gruber, and T. Taniuchi, eds.), U.S. Dep. Commer., NOAA Tech. Rep. NMFS 90.
Hosmer, D. W. and S. Lemeshow. 1989. Applied logistic regression. John Wiley & Sons, Inc. New York. 307pp.
IUCN (International Union for Conservation of Nature and Natural Resources). 2000. 2000 IUCN Red List of Threatened Animals. IUCN, Gland, Switzerland.
JPOTS. 1980. The Practical Salinity Scale 1978 and the International Equation of State of Seawater 1980. Tenth report of the Joint Panel on Oceanographic Tables and Standards (JPOTS), Sidney, B.C. Canada, 1-5 September 1980, sponsored by UNESCO, ICES, SCOR, IAPSO. UNESCO technical papers in marine science 36.
Kraus. R. T. and J. A. Musick. Modeling habitat occurrence of young-of-year summer flounder, Paralichthys dentatus, in the Chesapeake Bay. in prep.
Lewis, E. L. and R. G. Perkin. 1978. Salinity: its definition and calculation. J. Geophys. Res. 83: 466-478.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
77
Lund, R. 1990. Chondrichthyan life history styles as revealed by the 320 million years old Mississippian of Montana. Environ. Biol. Fish. 27: 1-19.
Manly, F. F. J., L. L. McDonald, and D. L. Thomas. 1993. Resource selection by animals: Statistical design and analysis of field studies. New York: Chapman and Hall, 177 pp.
Medved, R. J., and J. A. Marshall. 1981. Feeding behavior and biology of young sandbar sharks, Carcharhinus plumbeus (Pisces, Carcharhinidae), in Chincoteague Bay, Virginia. Fish. Bull. 79(3): 441-448.
Meek, A. 1916. The migrations offish. Edward Arnold, London. 427 pp.
Menard, S. W. 1995. Applied logistic regression analysis. Sage Publications, Thousand Oaks, CA. 98pp.
Merson, R. R. 1998. Nurseries and maturation of the sandbar shark. Ph.D. Dissertation. University of Rhode Island. 150 pp.
Morrissey, J. F., and S. H. Gruber. 1993a. Home range of juvenile lemon sharks, Negaprion brevirostris. Copeia 1993 (2): 425-434.
Morrissey, J. F., and S. H. Gruber. 1993b. Habitat selection of juvenile lemon sharks, Negaprion brevirostris. Environ. Biol. Fishes 38: 311-319.
Musick, J. A. 1999. Introduction to Part 2: Essential fish habitat identification, p. 41 In: Fish habitat: Essential Fish Habitat and Rehabilitation. American Fisheries Society, Symposium 22, (L. R. Benaka, editor). Bethesda, Maryland. 459 pp.
Musick, J. A., and J. A. Colvocoresses. 1986. Seasonal recruitment ofsubtropical sharks in Chesapeake Bight, U.S.A. pp. 301-311 In: Workshop on recruitment in tropical coastal demersal communities (A. Yanez y Arancibia and D. Pauley, eds.), FAO/UNESCO, Campeche, Mexico, 21-25 April 1986. I.O.C. Workshop Rep. 44.
Musick, J. A., S. Branstetter, and J. A. Colvocoresses. 1993. Trends in shark abundance from 1974 to 1991 for the Chesapeake Bight region of the U.S. Mid-Atlantic Coast, pp. 1-18 In: Conservation Biology of Elasmobranchs (S. Branstetter, ed.), U.S. Dep. Commer., NOAATech. Rep. NMFS 115.
NMFS. 1993. Fishery management plan for sharks of the Atlantic Ocean. National Oceanic and Atmospheric Administration, National Marine Fisheries Sen/ice, U.S. Department of Commerce.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
78
NMFS. 1996. 1996 Report of the shark evaluation workshop. June, 1996. NOAA, National Marine Fisheries Service, Southeast Fisheries Science Center, Miami, Florida.
Norcross, B. L., A. Blanchard, and B. A. Holladay. 1999. Comparison of models for defining nearshore flatfish nursery areas in Alaskan waters. Fish. Oceanogr. 8(1): 50-67.
Norcross, B. L., B. A. Holladay, F. J. Muter. 1995. Nursery area characteristics of Pleuronectids in coastal Alaska, USA. Neth. J. Sea Res. 34:161-175.
Norcross, B. L., F. J. Muter, B. A. Holladay. 1997. Habitat models for juvenile Pleuronectids around Kodiak Island, Alaska, USA. Fish. Bull. 95: 504- 520.
Packer, D. B. and T. Hoff. 1999. Life history, habitat parameters, and essential habitat of mid-Atlantic summer flounder, pp. 76-92 In: Fish habitat: Essential Fish Habitat and Rehabilitation. American Fisheries Society, Symposium 22, (L. R. Benaka, editor). Bethesda, Maryland. 459 pp.
S-Plus. 2000. Guide to Statistics, Volume 1. Data Analysis Products Division, MathSoft, Seattle, WA.
Schmitten, R. A. 1999. Essential fish habitat: opportunities and challenges for the next millennium, pp. 3-10 In: Fish habitat: Essential Fish Habitat and Rehabilitation. American Fisheries Society, Symposium 22, (L. R. Benaka, editor). Bethesda, Maryland. 459 pp.
Schwartz, F. J. 1960. Measurements and the occurrence of young sandbar shark, Carcharhinus milberti, in Chesapeake Bay, Maryland. Ches. Sci. 1 (3): 204-206.
Simpfendorfer, C. A., and N. E. Milward. 1993. Utilization of a tropical bay as a nursery area by sharks of the families Carcharhinidae and Sphyrnidae. Environ. Biol. Fishes 37: 337-345.
Sminkey, T. R. 1994. Age, growth, and population dynamics of the sandbar shark, Carcharhinus plumbeus, at different population levels. Ph.D. Dissertation, Va. Inst. Mar. Sci., College of William and Mary. 99 pp.
Sminkey, T. R. and J. A. Musick. 1995. Age and growth of the sandbar shark, Carcharhinus plumbeus, before and after population depletion. Copeia 1995(4):871-883.
Springer, S. 1960. Natural history of the sandbar shark, Eulamia milberti. U.S. Fish. Wildl. Serv., Fish. Bull. 61: 1-38.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
79
Springer, S. 1967. Social organization of shark populations, pp. 149-174. In Sharks, Skates, and Rays (Gilbert, P. W., R. F. Mathewson, and D. P. Rail, eds.) pp. 111-140.
Van der Elst. 1979. A proliferation of small sharks in the shore-based Natal spots fishery. Environ. Biol. Fish. 4: 349-362.
USDOC (U.S. Department of Commerce). 1996. Magnuson-Stevens Fishery Conservation and Management Act as amended through October 11, 1996. National Oceanic and Atmospheric Administration Technical Memorandum NMFS-F/SPO-23.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
CHAPTER 2Migratory movements, philopatry, and temporal delineation of summer
nurseries for juvenile Carcharhinus plumbeus in the Chesapeake Bay region.
80
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
81
ABSTRACT
The purpose of this study was to delineate temporally the migration
patterns of juvenile Carcharhinus plumbeus, sandbar sharks, in Chesapeake
Bay, to determine the location of wintering areas, and to determine if philopatry
or homing to natal summer nurseries in subsequent years occurs. Between 1990
and 1999, 100 longline sets were made at standard stations in the lower
Chesapeake Bay to delineate the migration patterns temporally. These data
indicated that immigration to the Bay occurred in late May and early June and
was highly correlated with increasing water temperature. Emigration from the
estuary occurred in late September and early October and was highly correlated
with decreasing day length. It is hypothesized that day length is the
environmental trigger to begin fall and spring migrations, whereas temperature
may elicit the response to move into the estuaries that serve as summer
nurseries. Between 1995 and 2000, 1846 juvenile C. plumbeus were tagged.
Only 2.4% were recaptured and reported. This low recapture rate is believed to
be due to under-reporting by the commercial-fishing sector in both summer and
winter nurseries. With two exceptions, recaptures made in summer months were
within 50 kilometers of the tagging location. Those recaptured in winter months
were caught between 200 and 830 kilometers from the tagging location. These
recaptures indicate that wintering areas are concentrated off the coast of North
Carolina between 33°30’N and 34°30’N latitude in water less than 20 meters
deep. However, recaptures were made as far south as Hilton Head, South
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
82
Carolina and as far as 200 kilometers from shore. Return data indicated that
these winter nurseries were utilized from late October until late May. Ninety-
three percent of tag recaptures made in subsequent summers were in the same
summer nursery area in which they were tagged. These data suggest that most
juvenile sandbar sharks return to the same summer nurseries every summer.
Additional data are needed to examine the strength and longevity of this homing
response and to determine if females actually return to their natal nursery to
deliver their own young years later.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
83
INTRODUCTION
A majority of species of marine fishes is migratory to some degree (Meek
1916). Migration enables animals to utilize resources or habitats that are only
temporarily, usually seasonally, suitable or tolerable (Aidley 1981). Migratory
movements may be broadly divided, based on primary purpose, into alimentary,
climatic, and gametic migrations (Heape 1931). The majority of studies of fish
migration have been concerned with the timing of the gametic migrations and
philopatry (natal homing) of diadromous species of commercial importance.
These primarily have involved salmonids (Hallock 1970, Smith 1973, Mundy
1984, Tarbox 1988), clupeids (Talbot and Sykes 1958, Melvin et al. 1986,
Friedland and Haas 1988), anguilliformes, and a few perciforms such as Morone
saxatilis (Chapoton and Sykes 1961, Boreman and Lewis 1987). A large body of
research also has been dedicated to the oceanic migrations of scombrids and
other commercially important pelagic teleost species (Mather 1962, Seckel
1972). Research involving the migratory movements of sharks has been sparse
due to their historically low economic value.
In 1996, the reauthorization of the Magnuson-Stevens Fishery
Conservation and Management Act through the Sustainable Fisheries Act (SFA)
established a mandate for the National Marine Fisheries Service (NMFS) and
Federal Fishery Management Councils requiring the identification of essential
fish habitat (EFH) for all species regulated by federal fishery management plans.
In short, 39 federal management plans had to be amended to include plans for
identification of EFH for more than 700 fishery stocks (Schmitten 1999). The
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
84
SFA defines EFH as “...those waters and substrate necessary to fish for
spawning, breeding, feeding, or growth to maturity" (USDOC 1996). Musick
(1999) pointed out, however, that in past EFH investigations, virtually all habitats
where a species is known to occur have been deemed EFH. This is particularly
problematic for highly migratory species. Musick (1999) recommended an EFH
designation system based on utilization, availability, and vulnerability. Using
these criteria, estuarine nursery areas in regions heavily impacted by humans
are of particular concern.
Most estuaries are seasonally dynamic, therefore estuarine species
assemblages tend to be highly seasonal as well. The diversity and abundance of
fishes in estuaries tend to be highest in spring and summer seasons and lowest
in winter (McErlean 1973, Merriner et al. 1976, Cowan and Birdsong 1985). Most
species migrate to the highly productive estuaries to utilize abundant food
sources (alimentary migration) or to bear young (gametic migration) which in turn
benefit from increased food availability and potentially higher growth rates
(Harden Jones 1968). Physiological limitations later force migrants into climatic
emigration from the estuary to avoid intolerable conditions. Chesapeake Bay
represents one of the most seasonally dynamic marine environments in the world
with temperature extremes ranging as much as 30°C between summer and
winter. The demersal fish fauna of the region is dominated by a few boreal
species in winter and many highly migratory sub-tropical and temperate species
in summer (Musick et al. 1986). Many of these species rely on the Bay as crucial
seasonal nursery habitat. These patterns of species diversity also include the
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
85
elasmobranchs. The winter batoid fauna consists of only two species of rajids
whereas the summer fauna includes one rajid, four dasyatids, two gymnurids,
and two myliobatids, all of which are known to undergo parturition in the Bay
(Grubbs, unpublished). The shark fauna is dominated by Squalus acanthias in
winter, but consists of several species of carcharhiniform and lamniform sharks in
summer (Musick and Colvocoresses 1986). Musick and Colvocoresses (1986)
stated that these migratory movements are probably driven largely by seasonal
temperature changes.
The sandbar shark, Carcharhinus plumbeus, is the most common shark in
Chesapeake Bay and is the dominant species in the directed commercial shark
fishery along the east coast of the United States, constituting more than 65% of
landings (Anonymous 1996). It has been federally regulated since 1993 (NMFS
1993) and is currently regulated by the Fishery Management Plan for Atlantic
Tunas, Swordfish, and Sharks (NMFS 1999). In addition, this species has been
listed in the World Conservation Union (IUCN) Red List of Threatened Species
as a lower-risk, conservation-dependent species of concern (IUCN 2000).
Carcharhinus plumbeus is a large, coastal species reaching at least 239 cm in
total length (Compagno 1984) and requiring at least 15 years to reach sexual
maturity (Sminkey and Musick 1995). It inhabits insular regions to at least 250 m
depth (Garrick 1982) in the western Atlantic from Massachusetts, USA, to Brazil
and is highly migratory with several hundred kilometers separating summer and
winter habitats (Bigelow and Schroeder 1948, Springer 1960). These habits
make them particularly vulnerable to fishery exploitation and render delineation of
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
86
EFH problematic at best. The spatial and temporal delineation of summer
pupping and nursery areas was defined as a crucial data need in defining EFH
for this species (Hoff and Musick 1990, NMFS 1996). The historical nursery
grounds for Carcharhinus plumbeus in the western North Atlantic are distributed
in shallow, principally estuarine habitats on the east coast of the United States
from Long Island, New York (possibly north to Cape Cod, Massachusetts) to
Cape Canaveral, Florida (Springer 1960). Merson (1998) found that this nursery
range has contracted and now only extends from New Jersey south to South
Carolina. A secondary nursery exists in the northwestern Gulf of Mexico
(Carlson 1999). Chesapeake Bay may be the largest nursery area for C.
plumbeus. Mature females enter the lower Bay as well as the saline lagoons
along the Eastern Shore of Virginia in May and June to pup (Musick and
Colvocoresses 1986). These mature individuals then migrate offshore while the
neonates remain in the highly productive estuarine waters until fall (Musick and
Colvocoresses 1986) feeding on the abundant blue crabs (Callinectes sapidus),
Atlantic menhaden (Brevoortia tyrannus), summer flounder (Paralichthys
dentatus), and miscellaneous sciaenid fishes (Medved and Marshall 1981,
Cowan and Birdsong 1985). Young sandbar sharks continue to use Chesapeake
Bay as a nursery during the warmer months for the first four to ten years of life
(Sminkey 1994, Grubbs and Musick in prep. d). They then remain coastal year
round, presumably only entering Mid-Atlantic estuaries after reaching maturity to
bear young.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
87
Grubbs and Musick (in prep a) found that the primary nursery in
Chesapeake Bay is limited to southern portion of the Bay, particularly the
southeastern portion, where salinity is greater than 20.5 and depth is greater
than 5.5 meters. The temporal pattern of utilization and timing of migratory
movements have not been formally investigated, which is one of the objectives of
this study. Young sandbar sharks are particularly susceptible to local overfishing
when aggregated in the estuary. In addition, it has often been hypothesized that
many carcharhinid sharks are philopatric to their natal nurseries. Though this
has not been tested rigorously, if true this would render populations even more at
risk due to local overharvesting and habitat degradation. Grubbs and Musick (in
prep a) reported that more than 20,000 kilograms of juvenile sandbar sharks
were harvested in the principal nursery area of Chesapeake Bay in 1996. The
constriction of suitable nursery habitat due to overall reduced salinity and
increased hypoxia in Chesapeake Bay in 1996 may have concentrated the
sharks making them especially vulnerable. State regulations have been
implemented to protect juvenile sharks in these crucial habitats (Pruitt 1997).
However, as Camhi (1998) pointed out, protection of juveniles in summer
nurseries will have little effect if protection is not also afforded in wintering areas.
Commercial longline and drop-net fishers have exploited aggregations of juvenile
sandbar sharks heavily on near-shore wintering grounds off North Carolina
(Anonymous 1996). Before protection is possible, these winter nurseries must
first be delineated spatially and temporally.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
88
The primary objective of this study was to delineate temporal utilization of
Chesapeake Bay by juvenile C. plumbeus and to determine what environmental
factors act as catalysts to begin the migratory movements. This was
accomplished using CPUE data collected by a longline survey conducted by the
Virginia Institute of Marine Science to monitor shark abundance and from
recapture data obtained as a result of the implementation of a multi-year tagging
program targeting juvenile sandbar sharks. A second objective was to determine
the location of the winter nursery areas and investigate the temporal utilization of
these regions using the tag-recapture data. A final objective was to test the
hypothesis of natal nursery philopatry, or homing, by juvenile sharks in
subsequent summers.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
89
MATERIALS AND METHODS
Sampling Gear
Sampling was conducted during VIMS coastal shark monitoring longline
cruises. Collections were made with commercial-style longlines consisting of
3/8-inch tarred, hard-laid nylon main line, which was anchored at each end and
marked by a hi-flier marker buoy. Three-meter gangions were spaced
approximately 18 meters apart along the main line and a large inflatable buoy
was attached to the main line following every 20th gangion. Each gangion was
composed of a stainless-steel tuna clip attached to a 2-meter section of 1/8-inch
tarred nylon trawl line, the end of which was attached to a large barrel swivel. A
1-meter section of 1/16-inch galvanized aircraft cable was crimped to the swivel
and the other end was crimped to a Mustad-9/0, stainless-steel shark hook.
Each longline set consisted of 80 - 120 gangions. Bait consisted mostly of
Brevoortia tyrannus, Atlantic menhaden, and Scomber scombrus, Atlantic
mackerel. Soak time for each set was between three and four hours. A standard
100-hook set covered about two kilometers. Beginning in 1996, sampling was
expanded to include sets using 5/0 circle hooks in addition to the standard hook
sets. The circle hooks are smaller, therefore they are more efficient at capturing
juvenile, especially neonate, sandbar sharks. In addition, due to the tendency of
circle hooks to hook the sharks in the corner of the mouth, it was believed that
mortality would be lowered using these hooks. The statistical unit was catch per
unit effort (CPUE) defined as the number of sharks per 100 hooks for each set.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
90
Sampling Design
Since its inception in 1974, the VIMS longline survey has routinely
included stations in Chesapeake Bay. Two locations in the lower eastern Bay,
Kiptopeke (37°10' N, 76° 00' W) and Middleground (37° 06' N, 76° 03* W), have
been standard stations since 1980 (Map 2-1). Monthly data collected from May
to October at these two stations using the standard gear during the ten-year
period from 1990 to 1999 were used to delineate migration patterns and nursery
usage temporally in Chesapeake Bay. Data collected using the alternate gear
with circle hooks during the four-year period from 1996 to 1999 were analyzed
separately.
Data Processing
CPUE, defined as the number of juvenile Carcharhinus plumbeus per 100
hooks fished, was recorded for each station. Pre-caudal length (PCL) and
stretched total length (TL) were measured for each shark. PCL was defined as
the distance from the tip of the snout to the caudal peduncle, and TL was defined
as the distance from the tip of the snout to the tip of the caudal fin when
stretched in line with the body axis. In addition, surface temperature, minimum
and maximum depth, and time of day were recorded. A Hydrolab® multiprobe
was introduced to the survey in 1996. It was used to record temperature, salinity,
and dissolved oxygen at two-meter intervals from surface to bottom for stations
sampled from 1996 to 1999.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
91
Map 2-1: Locations of standard stations sampled by the VIMS longline survey from 1973-1999 including stations K (Kiptopeke) and M (Middieground) in the lower Chesapeake Bay.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
76°,3 O'
Standard Longline Stations fished by the VIMS Shark Ecology Program 1973-2000
m il k t78o30• 76°00 ' 75<>3 0 ’
CO
CD
40 Kilometers
75“0 0 ‘ 7 4°30 ‘
92
Survival Factors and Tagging
A survival factor was assigned to all juvenile C. plumbeus captured on
each set. The factor consisted of five rather subjective levels: (1) Excellent, (2)
Good, (3) Fair, (4) Poor, and (5) Dead. Sharks that struggled vigorously while
being measured and swam away rapidly following release were assigned survival
factor 1. Those that struggled moderately but swam strongly upon release were
assigned survival factor 2. Sharks that struggled very little while being measured
and swam slowly upon release were assigned survival factor 3. Those that did
not struggle at all but showed nictitating membrane and jaw response and
attempted to swim when released but with apparent equilibrium disruption were
assigned survival factor 4. Sharks that showed no nictitating membrane or jaw
responses were assumed dead and assigned survival factor 5.
All sharks determined to be in excellent, good, or fair condition based on
pre-release criteria were tagged using Hallprint ® nylon-tipped dart tags. A
stainless-steel applicator was used to apply the tags. The tags were inserted into
the musculature just below the first dorsal fin with an angle of attack of 30-40°
relative to the sagittal plane of the shark’s body. The tag was pushed through
the basal cartilages of the first dorsal fin with the barb directed posteriorly
ensuring the tag was locked behind this plate. Data from tag returns were used
to investigate long-term movements and migration patterns. They also provided
an additional means to validate the temporal nursery-delineation patterns
determined using the CPUE data.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
93
Data Analysis
The sampling period was May 1 to October 15 of each year. This period
was divided into eleven semimonthly intervals. Mean CPUE was calculated for
each interval using the ten-year, standard-hook data set and the four-year, circle-
hook data set. The results of the two data sets were plotted separately. These
plots were used to determine the timing of the summer immigration to
Chesapeake Bay and the fall emigration from the Bay. Temporal trends in CPUE
were compared to surface temperature, surface salinity, day length, and lunar
phase to investigate potential stimuli for migration. The influence of these
environmental factors (independent variables) on CPUE (dependent variable)
was investigated using linear regression over immigration and emigration periods
independently.
Tag-return data were used also to investigate the timing of summer and
fall migrations for juvenile sandbar sharks. All recaptures were mapped using
ArcView 3.1 GIS. Distance from tagging location and recapture location was
measured as the shortest distance between the two points without crossing land.
Data from tag recaptures made in Chesapeake Bay were used to estimate when
sharks first arrive to the estuary in the summer and when they leave the estuary
in the fall. Recaptures made during the winter and spring were used to
determine the general location of the primary wintering grounds for the juvenile
sharks. In addition, these data were used to determine the timing of their arrival
to and departure from the wintering grounds. Finally, recaptures made in
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
94
subsequent summers, those having gone through at least one winter prior to
recapture, were used to determine whether or not these juvenile sharks return to
their natal estuary as a summer nursery (i.e. evidence of philopatry) or move to
new areas in subsequent years.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
95
RESULTS
I: Temporal Nursery Delineation
Chesapeake Bay is utilized only as a summer nursery for juvenile C.
plumbeus, therefore abundance is strongly seasonal. The monthly CPUE (catch
per unit effort) values over the period from 1996-1999 using both standard 9/0
hooks and the smaller 5/0 alternative hooks are shown in Figure 2-1. The
Kiptopeke and Middle ground stations are plotted separately. Though this
seasonal trend is apparent in Figure 2-1, these data also illuminate the high
degree of inter-annual and intra-annual variability typical of the system. To
delineate the overall temporal-utilization pattern for the nursery, standard hook
data for the ten-year period 1990-1999 were combined. In addition, circle-hook
data for 1996-1999 were combined and the results compared to the standard
survey data.
A total of 100 standard longline sets (9/0 hooks) were made at the
Kiptopeke (59 sets) and Middleground (41 sets) stations from 1990 to 1999. No
C. plumbeus were caught during the early May time interval. The first sandbar
sharks appeared in the survey during the late May interval but the CPUE was
very low. CPUE continued to increase through June, peaking in July. These
data indicate that immigration to Chesapeake Bay occurs from late May through
June (Fig. 2-2a). Mean CPUE began to decline in August, particularly later in the
month, and continued to decline through October. The most dramatic decrease
occurred from early September through early October. The mean CPUE for the
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
96
early-October time interval was driven by two anomalously high sets in 1990 and
1991. The data indicate that in most years nearly all sharks have vacated the
Bay by mid-October.
In addition, 59 sets were made between 1996 and 1999 using the
alternative gear with the smaller 5/0 circle hooks. Variance was much higher for
this data set, especially for the early July period due to low sample size.
Nevertheless, these data show the same seasonal trend in CPUE as the
standard data (Fig. 2-2b). No sharks were caught during the May 1-15 time
period and very few were caught from May 16-31. Most immigration occurred
from early June to early July and emigration occurred from early September to
early October. Interestingly, CPUE was lower in the late July and early August
time periods than in early July or late August. This dip may represent the
dispersal of smaller juveniles to more suitable regions of the nursery farther from
the mouth of the estuary. Perhaps this is an adaptation to avoid predators such
as larger juvenile C. plumbeus and large Carcharias taurus (sandtiger sharks)
that are found in the lower Bay and are known to feed on neonate and small
juvenile sandbar sharks.
Sea-surface temperature, day length (photoperiod), salinity, and lunar
phase were investigated as potential environmental catalysts for these migratory
movements. Only the ten-year data set using standard gear was used for this
analysis due to lower variance and larger sample sizes. The immigration period
was defined as the six sampling intervals from May 1 to July 31 and the
emigration phase was defined as the six sampling units from Juiy 16 to October
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
97
15. Highest CPUE was observed during the July 16-31 interval, therefore it was
included in the immigration and emigration periods as an end point. Surface
salinity at the two standard stations was typically lowest in May and highest in
August or September. Salinity was highly variable between years sampled,
although it was not significantly correlated with CPUE. Lunar phase also was not
significantly correlated with CPUE. Salinity and lunar phase, therefore, will not
be discussed. Alternatively, regression analyses indicated that surface
temperature and day length are good predictors of shark migration. The results
for these two variables will be discussed individually.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
98
Figure 2-1: Monthly CPUE (C. plumbeus per 100 hooks) for the period 1996- 1999 at a) Kiptopeke and b) Middleground stations using both standard hooks and circle hooks.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Monthly Longline CPUE 1996-1999 a) Kiptopeke Station
50 j ♦ ■■Circle Hooks4 Q Standard Hooks
30Jg 20 8 10 A A
J M M J S N J M M J S N J M M J S N J M M J S N fc 1996 | 1997 | 1998 | 1999(/)£ b) Middleground Station
— 40UJ? 30Q-° 20
10
J M M J S N J M M J S N J M M J S N J M M J S N 1996 I 1997 I 1998 I 1999
= No sampling
99
Figure 2-2: Mean semi-monthly CPUE (C. plumbeus per 100 hooks) for Middleground and Kiptopeke stations combined, a) Standard 9/0 hooks 1990 to 1999; b) 5/0 circle hooks -1996 to 1999.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
CPUE
(s
hark
s pe
r 10
0 ho
oks)
CP
UE
(sha
rks
per
100
hook
s)a) Temporal CPUE of C. plumbeus in Chesapeake Bay
Standard Hooks (1990-1999)
V v<0 V . « r V .< o V
^ ^ ^ ^
) Temporal CPUE of C. plumbeus in Chesapeake Bay Circle Hooks (1996-1999)
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
100
Sea-Surface Temperature
Mean surface temperature from data collected in situ mirrored the
temporal trend in CPUE very closely, particularly during the immigration period
(Fig. 2-3). No sharks were caught when temperatures were below 18°C and sets
with CPUE>2.0 were observed only when temperature was greater than 21°C.
Peak shark CPUE was observed when temperature was approximately 26°C.
Linear regression using mean surface temperature as the independent variable
and mean CPUE as the dependent variable for the immigration period only was
highly significant (p<0.001, ^=0.98) indicating temperature may act as a factor
triggering immigration to Chesapeake Bay (Fig. 2-4a, Table 2-1 a). During the
emigration period, mean surface temperature also mirrored CPUE, though there
appeared to be a temperature lag (Fig. 2-3). CPUE began to decline a full month
prior to significant declines in temperature, which never cooled below 21 °C
during the emigration phase. Linear regression indicated that CPUE is
significantly correlated with surface temperature during the emigration period
(p=0.02, ^=0.77), and the intercept of the fitted line suggested all sharks leave
the Bay prior to the water cooling to 20°C (Fig. 2-4b, Table 2-1 b). The
relationship, however, was not as strong as during the immigration period and
the observed time lag suggested that temperature may not be the environmental
trigger for these sharks to leave the estuary and begin the fall migration.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
101
Figure 2-3: Mean semi-monthly CPUE (C. plumbeus per 100 hooks) and surface temperature (°C) for Middleground and Kiptopeke stations combined for the years 1990-1999 (standard 9/0 hooks only).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Temporal CPUE of C. plumbeus and Surface TemperatureChesapeake Bay (1990-1999)
18
to 1 5
oo£o 12 o
<D9
(0jc
(06
LU3Q.O 3
/ / ' / ' / /' / *I CPUE —♦ —Temp.
Tem
pera
ture
(d
egre
es
C)
102
Table 2-1a: Linear-regression summary for mean CPUE vs. mean surface temperature for the immigration period May 1 - July 31. Means are for semimonthly intervals.
Dependent Variable: Mean CPUE Independent Variable: Mean Surface Temperature
df SS MS F PRegression 1 199.07 199.07 195.61 0.0002Residual 4 4.07 1.02Total 5 203.14
Multiple R2 0.9899Adjusted R2 0.9800Standard Error 1.0088Observations 6
Table 2-1 b: Linear-regression summary for mean CPUE vs. mean surface temperature for the emigration period July 15 - October 15. Means are for semimonthly intervals.
Dependent Variable: Mean CPUE Independent Variable: Mean Surface Temperature
df SS MS F PRegression 1 58.65 58.65 13.40 0.0216Residual 4 17.51 4.38Total 5 76.16
Multiple R2 0.8776Adjusted R2 0.7701Standard Error 2.0922Observations 6
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
103
Figure 2-4: Fitted line plots from linear regression of mean CPUE (C. plumbeus per 100 hooks) vs. mean surface temperature. Means are for semimonthly intervals, (error bars = SEM) a) Immigration period: May 1 July 31; b) Emigration period: July 15 - October 15.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
CPUE
CP
UE
C. plumbeus CPUE vs. Temperature (Means) May 1 - July 31
18 -rR2 = 0.980015 -
12 -
9 --
6 -
3 -
15 17 19 21 23 25 27Temperature (degrees C)
C. plumbeus CPUE vs. Temperature (Means) July 15 - October 15
20 TR = 0.7701
16 -
8 -
4 -
27 26 25 24 23 22 21 20 19Temperature (degrees C)
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
104
Day Length (Photoperiod)
Day length was defined as the time between sunrise and sunset. These
data were calculated for each set using data supplied by the United States Naval
Observatory Astronomical Applications Department. This variable is annually
conservative, therefore means for the time intervals were calculated for one year
only. Day length changed little during May 1 to July 31 period, varying by only 42
minutes (Fig. 2-5). Linear regression analysis of CPUE as a function of day
length for the immigration period was insignificant (Fig. 2-6a, Table 2-2a). Day
length declined continuously during the emigration period, dropping by
approximately 2.6 hours, and mirrored CPUE remarkably well during this period
(Fig. 2-5). The linear-regression analysis was highly significant (p<0.001,
(^=0.96), suggesting day length may be a significant cue triggering emigration
from the estuary (Fig. 2-6b, Table 2-2b).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
105
Figure 2-5: Mean semi-monthly CPUE (C. plumbeus per 100 hooks) and day length (hours) for Middleground and Kiptopeke stations combined for the years 1990-1999 (standard 9/0 hooks only).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Temporal CPUE of C. plumbeus and Day Length Chesapeake Bay (1990-1999)
15
14
13
12
11
10
l CPUE • Day Length
Day
Leng
th
(hou
rs)
106
Table 2-2a: Linear-regression summary for mean CPUE vs. mean day length for the immigration period May 1 - July 31. Means are for semimonthly intervals.
Dependent Variable: Mean CPUE Independent Variable: Mean Day Length
df SS MS F pRegression 1 30.56 30.56 0.71 0.4474Residual 4 172.59 43.15Total 5 203.14
Multiple R2 0.3878Adjusted R2 0.1504Standard Error 6.5686Observations 6
Table 2-2b: Linear-regression summary for mean CPUE vs. mean day length for the emigration period July 15 - October 15. Means are for semimonthly intervals.
Dependent Variable: Mean CPUE Independent Variable: Mean Day Length
df SS MS F pRegression 1 73.22 73.22 99.45 0.0006Residual 4 2.95 0.74Total 5 76.16
Multiple R2 0.9805Adjusted R2 0.9613Standard Error 0.8581Observations 6
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
107
Figure 2-6: Fitted line plot from linear regression of mean CPUE (C. plumbeus per 100 hooks) vs. day length (hours) for emigration period (July 15 - October 15) only. Means are for semimonthly intervals, (error bars = SEM)
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
a)
1113Q_
C. plumbeus CPUE vs. Day Length (Means) July 15 - October 15
20
16
12
8
4
0
15 14.8 14.6 14.4 14.2 14Day Length (hours)
b) C. plumbeus CPUE vs. Day Length (Means) July 15 - October 15
16 --
4 --
15 14 13 12 11 10Day Length (hours)
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
108
Mark and Recapture
A total of 1,846 juvenile C. plumbeus were tagged in Virginia waters from
1995 through 2000. The distribution of this tagging effort is shown in Map 2-2
and a summary is given in Table 2-3. More sharks were tagged in 1999 than any
other year (n=439, 23.4% of total), followed closely by 1998 (n=416, 22.5% of
total). Sampling and tagging only occurred during the months of May through
October (Table 2-3a). The fewest sharks were tagged in May (n=51, 2.8%) and
the most were tagged in July (n=550, 29.8%). Approximately 61.3% (n=1131) of
the sharks were tagged inside Chesapeake Bay whereas 14.3% (n=264) were
tagged in seaside lagoons and tidal creeks along Virginia's Eastern Shore, and
24.4% (n=451) were tagged in Virginia coastal waters (Table 2-3b). To date, 45
shark recaptures have been reported giving an overall recapture rate of only
2.4%, which is less than half of that reported for juvenile C. plumbeus in
Delaware Bay (Merson 1998). It is believed that the low recovery rate is due to
severe under-reporting by commercial gillnet fishers in the summer nursery and
winter longline and drop-net fishers in North Carolina waters. Four of the
reported recaptures were discarded due to incomplete data, leaving 41 that were
used in the analysis.
Recreational fishers returned more tags than any other group (n=25).
Commercial vessels accounted for ten of the reported recaptures. Six of the
commercial recaptures were reported by independent fisheries observers on
commercial vessels whereas only four were reported by the commercial fishers
themselves. These results coupled with the overall low coverage of commercial
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
vessels by observers indicate that under-reporting in the commercial sector was
extreme. In fact, four of six winter recaptures by commercial vessels in North
Carolina waters were reported by a single observer, Mr. Chris Jensen, formerly
of the Gulf & South Atlantic Fisheries Development Foundation. In addition to
fishery recaptures, five of the tag returns were recaptured by the VIMS longline
survey and researchers from the North Carolina Aquarium returned one tag.
Recapture data also were obtained for two sharks tagged by the VIMS shark-
ecology program using tags supplied by the National Marine Fisheries Service
prior to the inception of the VIMS tagging program bringing the total returns used
in the analysis to 43.
Sharks tagged with VIMS dart tags were recaptured after a mean of 267
days and ranged between 4 and 1,109 days at liberty. Distance between tag and
recapture locations was calculated as the shortest distance between the points
using land as a bounding graphic in ArcView 3.2 GIS. In other words, sharks
were not allowed to cross over land to reach the recapture location giving a
conservative but realistic point-to-point distance measure. The mean distance
between tag and recapture locations was 104 kilometers and ranged from 0 to
830 kilometers. In addition, the two recaptured sharks tagged by VIMS using
NMFS tags were recaptured after 2,049 and 561 days at liberty and were 300
and 560 kilometers from the tagging location, respectively.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
110
Map 2-2: Distribution of juvenile C. plumbeus tagging by VIMS during summers of 1995 to 2000.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
77“2
0' 77
°10'
77*0
0*
76*5
0' 78
*40'
76*3
0*
76*2
0' 76
*10'
76*0
0' 75
*50'
75*4
0' 75
*30'
75°2
0' 75
*10'
75°0
0' 74
*50'
74*4
0' 74
*30’
74*2
0’ 74
*10'
74*00' 37*50' 37*40' 37*30' 37*20' 37*10* 37*00' 36*50' 36*40' 36*30' 36*20'
■ooO)a
JC
CD%
in «
in1A
• •
*fs,s
«or«*
U)rv
s
' 5 ,,r - ^______________ L ^ A iW
y«jr I" > /
r
oo<0
(0r*.s
♦ /
.OSoZC X»U.Z .QColZ JCZaLZ .Ot^C .OOo C .0So9C .0* <3Z J0ZJ9Z OZ,9Z
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Table 2-3: Tagging summary for juvenile Carcharhinus plumbeus tagged by VIMS (1995 - 2000).
a lYear Total
# (% total)May
# (% year)June
# (% year)July
# (% year)August
# (% year)September # (% year)
October # (% year)
1995 169 (9.2%) 0 0 36 34 99 01996 219(11.9%) 10 37 54 61 30 271997 357 (19.3%) 2 31 132 55 96 411998 416 (22.5%) 9 44 149 137 62 151999 439 (23.8%) 30 107 138 121 21 222000 246 (13.3%) 0 52 41 49 64 40T o t# (%) 1846 51 (2.8%) 271 (14.7%) 550 (29.8%) 457 (24.8%) 372 (20.2%) 143 (7.7%)
b lYear Total # Chesapeake Bay
# (%)Eastern Shore Lagoons
# (%)Virginia Coastal
#(%)1995 169 125 (74.0%) 8 (4.7%) 36 (21.3%)1996 219 117(53.4%) 33(15.1%) 69 (31.5%)1997 357 233 (65.3%) 38(10.6%) 86 (24.1%)1998 416 272 (65.4%) 53 (12.7%) 91 (21.9%)1999 439 301 (68.6%) 69(15.7%) 69(15.7%)2000 246 83 (33.7%) 63 (25.6%) 100 (40.7%)Total 1846 1131 (61.3%) 264 (14.3%) 451 (24.4%)
112
II: Migration PatternsOf the 43 tag returns, 31 (72%) were recaptured less than 50 km from the
tagging location (Fig. 2-7). Twenty-five of these were recaptured in the
Chesapeake Bay nursery and six were in nurseries on Virginia’s Eastern Shore.
The earliest of these recaptures occurred on May 28 and the latest return was on
October 15 (Fig. 2-7) suggesting that these summer nursery areas are utilized
from late May to mid-October. These results are in perfect agreement with the
temporal delineation pattern of the summer nursery interpreted using the longline
CPUE data (Fig. 2-2).
All of the remaining 12 recaptures were more than 200 kilometers
(mean=384 km) from the tagging location following a mean of 550 days at liberty.
Eleven of these were recaptured south of the tagging location whereas only one
was recaptured north of its tagging origin (Fig. 2-8, Map 2-3). With only one
exception, all southern recaptures occurred during the winter and spring when
they are suspected to be in winter nursery areas (Table 2-4). These data
indicate that the primary winter nurseries are located in near shore areas along
the Outer Banks of North Carolina between 34° 30' N and 35° 30’ N latitude (Fig.
2-8). The shark tagged with NMFS tag R9670 was approximately age two when
tagged and was recaptured in this region more than 5.5 years later. This
suggests that this region is used as a wintering area for at least the first seven
years of life. These wintering areas may extend much farther south, however.
One shark tagged as a neonate was recaptured the following May in the Inter
coastal Waterway in Hilton Head, South Carolina (32° 9’ N, 80° 50' W), 830 km
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
from the tagging location. A shark tagged with NMFS tag 211102 was
recaptured 1.5 years later in of January and was nearly 200 km off the coast of
Charleston, South Carolina (32° 50’ N, 77° 50’ W). This shark was at least seven
years old when tagged, suggesting older juveniles may utilize deeper, offshore
southern regions as wintering areas.
These data also provide information concerning the timing of these
migratory movements. Sharks were recaptured in the wintering areas as early in
the fall as October 25 and as late in the spring as May 23 (Fig. 2-8). This
corresponds remarkably well with the timing of the immigration and emigration
from the summer nursery in Chesapeake Bay. These data indicate that
Chesapeake Bay and lagoons along Virginia’s Eastern Shore act as summer
nursery and refuge habitat from late May to mid-October and coastal areas of
North Carolina and South Carolina provide important winter habitat from late
October to late May.
Ill: PhilopatryThirty-three of the 43 recaptured juvenile sharks were caught between
May 28 and October 15. Of these, 17 were recaptured the same year they were
tagged (Map 2-4, Table 2-5) after a mean of 30 days at liberty (range = 4-82
days). The mean distance between tag and recapture locations was 15 km
(range = 0-37 km). These recaptures indicate that the sharks do not leave the
protective nursery during this period, but actively move throughout the estuary.
For instance, one shark was recaptured approximately 32 kilometers from its
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
114
tagging location only four days later whereas another was recaptured within one
kilometer of the tagging location after 44 days. These findings agree with those
obtained using ultrasonic telemetry (Grubbs and Musick, in prep. b). Ten juvenile
sandbar sharks were continuously tracked for periods of 10 to 50 hours.
Although none of the tracked sharks moved out of the estuary, their daily
straight-line movements averaged 34 kilometers per day and activity spaces
ranged from 35 to 250 square kilometers.
Fourteen recaptured sharks were caught in the Chesapeake region but in
subsequent summers (Map 2-5, Table 2-6) after a mean of 461 days at liberty
(range = 225-1,109). The mean tag-recapture distance for this group was 17
kilometers (range 0-48 km). Ten were recaptured after approximately one year
at liberty, three after two years at liberty, and one after three years at liberty (Fig.
2-9). The age at recapture based on age at tagging estimated from length-at-age
data from Sminkey (1994) ranged from one to four years.
Two sharks recaptured during the summer months were not recaptured in
Virginia waters, shown as yellow circles in Figures 2-7and 2-8. One of these was
tagged in Virginia coastal waters along Virginia Beach in October 1999. These
near-shore waters are part of the migration route for sharks from nurseries north
of Chesapeake Bay. This shark was recaptured the following August in Little
Egg Harbor, part of Barnegat Bay, in New Jersey (Map 2-3, Figures 2-7,8). This
shark was probably tagged during its fall migration to southern wintering grounds,
then returned the following summer to its natal summer nursery in New Jersey.
Therefore, this does not indicate a departure from natal homing. The same
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
115
cannot be said for the second shark, which was recaptured in the Cape Fear
River in North Carolina in July 1998, one year after being tagged in Chesapeake
Bay (Map 2-3, Fig. 2-7,8). This animal may have migrated to this region the
previous fall as a wintering area but migrated inshore to the river the following
summer rather than returning to its natal nursery to the north. Therefore, in this
data set, 14 of 15 recaptures (93%) returned to their natal summer nursery in
subsequent years. These data provide the first strong evidence for philopatry or
natal homing in this species.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
116
Figure 2-7: Temporal delineation of summer nursery of C. plumbeus using tag recapture data. Distance between tag and recapture location vs. day of year of recapture.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Mark/Recapture Distance vs. Day of Year
<DOCJS(/>b
1000
800
600
400
200
J F M A M Month
May 28
O
O
s o
Summer Nursery
N D
October 15
117
Figure 2-8: Location and temporal delineation of winter nursery of C. plumbeus using tag recapture data. Latitude of recapture location vs. day of year of recapture.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Latit
ude
of
Rec
aptu
re
vs.
Dat
e
Q
Z
o( 0
<
(Uc/}
oc
CO
E
□<DCO13zh_0)*«■*C
CD
E
O A O O S i O I O ^ f O N r^ c o « r t M « r t M n n
(saajBap) apn)pe~|
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
118
Map 2-3: Long-distance tag returns. All recaptures made >200 kilometers from tagging location, seasons combined.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Long-Distance Tag Returns - >200 km (shortest distance with land boundary)
• Tagging Locations (Summer)
▲ Recapture Locations’ (Summer) MDRecapture Locations (Winter)
Virginia
North Carolina
South Carolina
hoo
cohoo
COLooo
CO—<1o
COhoo
cohoo
CO-Ao
COhcoo
oT"co
100 100 200 KilometersT
N+coisoo
coo
81' 80° 79e 78° 77° 76° 75° 74°
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Table 2-4: Summary of tag recapture data from WINTER months. Distance was measured as the most direct route from tagging location to the recapture location using land as an impenetrable boundary. State refers to the coast nearest the recapture location labeled as NC (North Carolina), SC (South Carolina).
Tag# Date Date Tag Tag Recap Recap Distance Days at State GroupTagged Recap. Latitude Longitude Latitude Longitude km large
NMFS-R9670 8/18/92 3/29/98 37.2000 -76.0333 34.7533 -76.0217 300 2049 NC CommercialNMFS-211102 7/4/95 1/15/97 36.9167 -75.7000 32.8333 -77.8333 560 561 SC CommercialVIMS-0146 9/18/95 3/24/98 36.7500 -75.8667 34.5267 -75.9617 270 918 NC CommercialVIMS-0517 7/17/97 11/15/97 37.2667 -76.1000 35.2000 -75.6833 260 121 NC CommercialVIMS-0978 8/17/98 11/17/99 37.2596 -76.8989 35.5167 -75.4633 200 457 NC RecreationalVIMS-1026 8/18/98 5/23/00 37.2000 -76.0400 32.1500 -80.8333 830 644 SC RecreationalVIMS-1073 8/19/98 10/25/00 37.1333 -75.9833 35.1917 -75.6000 330 787 NC CommercialVIMS-1043 8/19/98 2/10/99 37.1450 -75.9900 34.6333 -76.6333 390 176 NC CommercialVIMS-1162 9/30/98 2/10/99 37.2883 -75.7833 35.0333 -75.9667 300 133 NC CommercialVIMS-1968 8/21/00 10/30/00 37.1000 -76.0667 35.0833 -75.8500 280 70 NC Commercial
120
Map 2-4: Short-term tag recaptures. All sharks recaptured the same summer in which they were tagged.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
76°5
0’ 76
°40’
76°3
0’ 76
°20'
76°1
0*
76°0
0’ 75
°50’
75°4
0' 75
°30'
37° 20 ' ________________37° 10' 37° 00 ' Z B °5 0 '
L r , - m £ r
i
.ozoze .OkZC .OOoZC .0So9€
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
76°5
0*
76°4
0' 76
°30’
76°2
0’ 76
°10*
76
°00'
75°5
0' 75
°40’
75°3
0’
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Table 2-5: Summary of tag recapture data from SUMMER months for returns occurring the same summer as tagging. Distance was measured as the most direct route from tagging location to the recapture location using land as an impenetrable boundary. All recaptures occurred in Virginia waters. Area of recapture summarized as Eastern Chesapeake Bay (CB-E), Western Chesapeake Bay (CB-W), and Virginia Eastern Shore (ES).
Tag# DateTagged
DateRecap.
TagLatitude
TagLongitude
RecapLatitude
RecapLongitude
Distance Days at Area km large
Group
VIMS-0186 5/29/96 8/19/96 36.7500 -75.8667 37.0833 -76.0167 37 82 CB-E RecreationalVIMS-0196 6/24/96 7/30/96 37.1833 -76.0333 37.4000 -76.0450 24 30 CB-E RecreationalVI MS-0252 7/24/96 8/24/96 37.1833 -76.0333 37.0917 -75.9917 11 31 CB-E RecreationalVI MS-0297 8/21/96 8/31/96 37.1667 -76.0833 37.1383 -76.2333 14 10 CB-W RecreationalVIMS-0489 7/17/97 8/2/97 37.1000 -76.0667 37.2000 -76.2500 20 16 CB-W RecreationalVIMS-0533 7/17/97 8/9/97 37.4167 -76.1667 37.4000 -76.2000 3.5 23 CB-W RecreationalVIMS-0480 7/17/97 9/14/97 37.1000 -76.0667 37.3583 -76.2717 34 59 CB-W RecreationalVIMS-0578 8/12/97 8/21/97 37.2667 -75.8967 37.2667 -75.8967 0 9 ES RecreationalVIMS-0652 9/17/97 10/15/97 37.1000 -76.0667 37.1550 -76.2500 17.5 28 CB-W CommercialVIMS-0927 7/29/98 8/30/98 37.1833 -76.0333 37.1667 -76.9967 4 32 CB-E RecreationalVIMS-0948 7/30/98 8/15/98 37.2067 -76.1200 37.2167 -76.2667 13 15 CB-W RecreationalVIMS-0988 8/17/98 9/30/98 37.2517 -75.8987 37.2600 -75.8983 0.5 44 ES VIMSVIMS-1210 5/25/99 5/29/99 37.2600 -75.8983 37.0450 -76.0667 32 4 CB-E RecreationalVIMS-1409 7/8/99 8/15/99 37.1820 -76.2130 37.2833 -76.3067 14 38 CB-W RecreationalVIMS-1389 7/8/99 9/10/99 37.1830 -76.0190 37.1500 -76.9800 5 64 CB-E RecreationalVIMS-1576 8/18/99 8/29/99 37.2200 -76.3200 37.1383 -76.0833 23 11 CB-E CommercialVIMS-1962 8/21/00 8/25/00 37.2000 -76.0333 37.2000 -76.0283 0.5 4 CB-E Recreational
122
Map 2-5: Evidence of natal homing. Tag recaptures made < 50 kilometers from tagging location in summers following at least one winter migration.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
76°40' 76°30' 76°20* 76°10’ 76°00' 75 50
aVirginia Beach
10 0 10 20 Kilometers
75°40‘ 75°30'
N
+
76°40' 76°30' 76°20' 76°1O' 76°00'
Long-term Tag Returns# Recapture Location O Tagging Location #= DAL (days at large)
75°50' 75°40* 75°30’
•05o9
C ,00
oZC
.OUZ
G M
olt
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Table 2-6: Summary of tag recapture data from SUMMER months for returns occurring following at least one winter season. Distance was measured as the most direct route from tagging location to the recapture location using land as an impenetrable boundary. Area of Virginia recapture summarized as Eastern Chesapeake Bay (CB-E), Western Chesapeake Bay (CB-W), and Virginia Eastern Shore (ES), Virginia Beach (VB). Recapture from other states labeled as NC (North Carolina), SC (South Carolina), NJ (New Jersey).
Tag# Date Date Tag Tag Recap Recap Distance Days at Area or GroupTagged Recap. Latitude Longitude Latitude Longitude km large State
VIMS-0064 8/19/95 7/11/96 37.1833 -76.0333 37.2667 -75.8967 30 326 ES VIMSVIMS-0135 9/14/95 5/28/96 37.1167 -76.1000 37.1833 -76.0333 9.5 225 CB-E VIMSVIMS-0353 8/21/96 9/12/98 37.1667 -76.0833 36.7500 -75.9333 48 752 VB RecreationalVIMS-0439 7/15/97 7/6/99 37.2611 -75.8980 37.2583 -75.8983 0.75 721 ES VIMSVIMS-0509 7/17/97 7/31/98 37.2000 -76.0333 33.9500 -77.9667 540 379 NC NC AquariumVIMS-0507 7/17/97 8/1/00 37.1833 -76.0167 37.3000 -75.8000 45 1109 ES CommercialVIMS-0650 9/17/97 7/15/98 37.1000 -76.0667 37.0333 -76.0667 7 301 CB-E RecreationalVIMS-0648 9/17/97 8/28/99 37.0930 -76.0670 37.0450 -76.0667 4.5 710 CB-E RecreationalVIMS-0678 9/18/97 8/15/98 37.2167 -76.0500 37.2017 -76.0317 2.5 331 CB-E RecreationalVIMS-0718 10/2/97 6/30/98 37.1833 -76.0167 37.0917 -75.9917 10.75 271 CB-E RecreationalVIMS-0877 7/8/98 7/8/99 37.2105 -76.0620 37.2000 -76.2183 14 365 CB-W VIMSVIMS-0921 7/29/98 6/11/99 37.1867 -76.0317 37.1383 -76.2333 19 317 CB-W CommercialVIMS-0908 7/29/98 6/28/99 37.0667 -76.0967 37.1667 -76.4000 32 334 CB-W RecreationalVIMS-0973 8/12/98 6/25/99 37.1345 -76.0878 37.0770 -76.2230 13.75 317 CB-W RecreationalVIMS-0998 8/17/98 8/24/99 37.2925 -75.7900 37.3217 -75.8417 5.5 372 ES RecreationalVIMS-1618 10/6/99 8/5/00 36.7500 -75.8700 39.5900 -74.2857 350 304 NJ Recreational
N>CO
124
Figure 2-9: Evidence of natal homing from tag recaptures. Distance from tagging location versus days at liberty for all sharks recaptured less than 200 kilometers from the tagging location.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
60o>cEb ^O) r£ I 40E c 2 .2§ § 20S• MMa
Days at Liberty vs. Tag/Recapture Distance Distance < 200 km Only
3651
730 1,095
• Age 0 ■ Age 1 ♦Age 2 A age 4
Days At Liberty
125
DISCUSSION
The results of this study indicated that Chesapeake Bay is utilized as a
summer nursery for C. plumbeus primarily from mid-May to mid-October. The
CPUE data indicated that juvenile sharks began immigrating to the Bay after May
15 with the majority of sharks entering the estuary after June 15. The CPUE
peaked in late July indicating full recruitment to the estuary by this time.
Immigration to the Bay was not significantly correlated with salinity, day length, or
lunar phase and was highly correlated with increasing water temperature as had
been reported previously by Musick and Colvocoresses (1986). Sharks were
absent from longline catches when surface temperature was below 18°C and
CPUE greater than 2.0 sharks per 100 hooks was observed only when
temperature was greater than 21 °C. Peak CPUE occurred when temperature
was approximately 26°C. Merson (1998) reported sharks in Delaware Bay when
water temperature was as low as 15.4°C.
Mean CPUE began to decline in August, particularly later in the month. I
hypothesize that this CPUE decline in August represents dispersal throughout
the nursery rather than the beginning of the emigration period. Similar dispersal
trends have been observed in the movement patterns of other fishes such as
Morone saxatilis in Chesapeake Bay (Moore and Burton 1975). The CPUE data
suggested emigration from the estuary in preparation for the fall migration to
wintering grounds occurred in late September and early October. The timing of
emigration also was correlated significantly with surface temperature, though the
relationship was not as strong as during the immigration period as emigration
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
126
began prior to significant declines in temperature. Interpolation of the regression
line suggested all sharks vacated the estuary prior to temperature falling to 20°C.
The timing of the emigration movement was highly correlated with day length.
Day length has been shown to initiate migration in birds (Berthold 1975) and
Aidley (1981) suggested it might be a possible trigger for fishes. The data
presented in this study indicate that temperature serves as a migratory catalyst
for juvenile C. plumbeus to enter Chesapeake Bay and day length serves as the
stimulus to emigrate from the Bay in fall. It is possible that day length also
serves as the stimulus to begin spring migrations from wintering grounds. In
other words, day length may signal juvenile sharks to begin migrating north, yet
they remain in coastal waters until temperature stimulates a movement into the
estuarine nursery. Additional data from the wintering grounds are needed to test
this hypothesis.
Tag-recapture data indicate that the migrations of juvenile C. plumbeus
are spatially extensive. The wintering grounds are concentrated south of the
summer nurseries as expected. They appear to be concentrated in near shore
waters off the coast of North Carolina and extend to southern South Carolina and
to at least 200 kilometers from shore. The earliest recapture in Chesapeake Bay
occurred on May 28 whereas the latest recapture in the Bay occurred on October
15, supporting the conclusion that the estuary serves as a nursery ground from
late May to the middle of October. The earliest fall recapture on the southern
wintering grounds occurred on October 25 and the latest spring recapture in this
area was on May 23, indicating these areas serve as important wintering areas
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
127
from late October to the middle of May. These data support the timing of the
migratory movements based on CPUE data.
Gerking (1959) defined homing as “going to a place formerly occupied
instead of equally probable places." Tag-recapture data indicate that most
juvenile C. plumbeus return to their natal summer nurseries for at least their first
four years of life. Ninety-three percent of sharks recaptured in subsequent
summers (n = 15) were recaptured within 50 kilometers (mean = 17 km) of the
tagging location. This is considered strong evidence of philopatry, considering
that these sharks have been shown to travel extensively within the nursery with
activity spaces averaging 100 km2 (Grubbs and Musick in prep b) and that
wintering areas are more than 200 km from the summer nurseries. Additional
data are needed to examine the duration of this philopatric behavior and
determine if females maintain this bond to adulthood, returning when mature at
about 15 years old (Sminkey 1995) to deliver their own pups. In addition, future
research directions should focus on determining what environmental cues the
juvenile sharks use to discern their natal nursery. Olfaction has been well
documented as the principal stimulus for homing in diadromous fishes (Hasler
and Scholz 1980). Harden Jones (1968) suggested marine fishes also might
use olfactory cues from groundwater seepage to locate home habitats.
These data elucidate the importance of defining seasonal essential fish
habitat for the various life stages of highly migratory species. Grubbs and Musick
(in prep a) defined EFH for juvenile C. plumbeus in Chesapeake Bay. These
estuarine areas are highly susceptible to anthropogenic degradation and must be
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
128
afforded some level of protection. Fishing regulations have been implemented to
protect these juvenile sharks in these summer nursery habitats. No regulations
exist, however, on the wintering grounds, which are just beginning to be
investigated. Sharks appear to be densely aggregated in these regions and are
therefore highly susceptible to commercial fishing gear and can easily be over-
exploited. Protection of juvenile sandbar sharks while in crucial summer habitats
may prove fruitless unless protection in winter and migratory habitats is
implemented as well.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
129
LITERATURE CITED
Aidley, D. J. 1981. Animal Migration. Society of Experimental Biology - Seminar Series 13. 264 p. Cambridge University Press.
Anonymous. 1996. Final Report. MARFIN Award NA47FF0008. Gulf and South Atlantic Fisheries Development Foundation. March 1996.
Bigelow, H. B. and W. C. Schroeder. 1948. Sharks, pp. 59-546 In: Fishes of the western north Atlantic. Part 1. Vol. 1. (J. Tee-Van, C. M. Breder, S. F. Hildebrand, A. E. Parr, and W. C. Schroeder, eds.). Mem. Sears Foundation for Marine Research, Yale Univ. New Haven, CT.
Berthold, P. 1975. Migration: control and metabolic physiology, pp. 77-128 In: Avian Biology: Volume 5 (D. S. Farner and J. R. King eds.). Academic Press. New York.
Boreman, J. and R. R. Lewis. 1987. Atlantic coastal migration of striped bass, pp. 331-339 In: Common strategies of anadromous and catadromous fishes (M. J. Dadswell, R. J. Klauda, C. M. Moffitt, R. L. Saunders,R. A. Rulifson, and J. E. Cooper, eds.), American Fisheries Society Symposium 1.
Camhi, M. 1998. Sharks on the line: A state-by-state analysis of sharks and their fisheries. National Audubon Society. New York.
Carlson, J. K. 1999. Occurrence of neonate and juvenile sandbar sharks,Carcharhinus plumbeus, from the northeastern Gulf of Mexico. Fishery Bulletin 97(2): 387-391.
Chopoton R. B. and J. E. Sykes. 1961. Atlantic coast migrations of large striped bass as evidenced by fisheries and tagging. Trans. Am. Fish. Soc. 90 (1): 13-20.
Compagno, L. J. V. 1984. FAO species catalogue. Vol. 4. Sharks of the world. Part 2 - Carcharhiniformes. FAO Fish. Synop. 4(2): 250-655.
Cowan, J. H. Jr., and R. S. Birdsong. 1985. Seasonal occurrence of larval and juvenile fishes in a Virginia Atlantic coast estuary with emphasis on drums (family Sciaenidae). Estuaries 8(1): 48-59.
Friedland, K. D. and L. W. Haas. 1988. Emigrations of juvenile Atlanticmenhaden Brevoortia tyrannus from York River estuary. Estuaries 11 (1): 45-50.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
130
Garrick, J. A. F. 1982. Sharks of the genus Carcharhinus. NOAATech. Rep. NMFS Circ. 445.
Gerking, S. D. 1959. The restricted movements of fish populations. Biological Reviews 34: 221-242.
Grubbs, R. D. and J. A. Musick. Spatial delineation of summer nursery areas to define essential fish habitat for juvenile Carcharhinus plumbeus in Chesapeake Bay, Virginia, in prep a.
Grubbs, R. D. and J. A. Musick. Movements of juvenile Carcharhinus plumbeus in Chesapeake Bay. in prep b.
Grubbs, R. D. and J. A. Musick. Population trends, juvenescence, and mortality estimation for juvenile Carcharhinus plumbeus in Chesapeake Bay and Virginia coastal waters, in prep c.
Hallock, R. J., R. F. Elwell. and D. H. Fry, Jr. 1970. Migrations of adult king salmon, Oncorhynchus tshawytscha, in the San Joaquin Delta, as demonstrated by the use of sonic tags. California Department of Fish and Game, Fish Bulletin: 151. 92 p.
Harden Jones, F. R. 1968. Fish Migration. St. Martin’s Press. New York.
Hasler, A. D. and A. T. Scholz. 1980. Artificial imprinting: a procedure forconserving salmon stocks, pp. 179-199 In: Fish behavior and its use in the capture and culture of fishes (J. E. Bardack, J. J. Magnuson, R. C. May, and J. M. Reinhart, eds.). Manila: International Center for Living Aquatic Resources Management.
Heape, W. 1931. Emigration, migration and nomadism. Heffer, Cambridge.369 pp.
Hoff, T. B., and J. A. Musick. 1990. Western North Atlantic shark-fisherymanagement problems and informational requirements, pp. 455-472 In: Elasmobranchs as living resources: advances in the biology, ecology, systematics and the status of the fisheries (H. L. Pratt, Jr., S. H. Gruber, and T. Taniuchi, eds.), U.S. Dep. Commer., NOAATech. Rep. NMFS 90.
IUCN (International Union for Conservation of Nature and Natural Resources). 2000. 2000 IUCN Red List of Threatened Animals. IUCN, Gland, Switzerland.
Mather, F. J. 1962. Transatlantic migration of two large bluefin tuna. J. du Conseil 27: 325-327.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
131
McErlean, A. J. 1973. Abundance, diversity and seasonal patterns of estuarine fish populations. Estuarine and Coastal Marine Science 1(1): 19-36.
Medved, R. J., and J. A. Marshall. 1981. Feeding behavior and biology of young sandbar sharks, Carcharhinus plumbeus (Pisces, Carcharhinidae), in Chincoteague Bay, Virginia. Fish. Bull. 79(3): 441-448.
Meek, A. 1916. The migrations offish. Edward Arnold, London. 427 pp.
Melvin, G. D., M. J. Dadswell, and J. D. Martin. 1986. Fidelity of American shad, Alosa sapidissima (Clupeidae), to its river of previous spawning. Can. J. Fish. Aq. Sci. 43(3): 640-646.
Merriner, J. V., W. H. Kriete and G. C. Grant. 1976. Seasonality, abundance, and diversity of fishes in the Piankatank River, Virginia (1970-1971).Ches. Sci. Vol. 17, No.4:238-245
Merson, R. R. 1998. Nurseries and maturation of the sandbar shark. Ph.D. Dissertation. University of Rhode Island. 150 pp.
Moore, C. J. and D. T. Burton. 1975. Movements of striped bass, Moronesaxatilis, tagged in Maryland waters of Chesapeake Bay. Transactions of the American Fisheries Society 104 (4): 703-709.
Mundy, P. R. 1984. Migratory timing of salmon in Alaska: with an annotated bibliography on migratory behavior and relevance to fisheries research. Alaska Department of Fish and Game: Informational leaflet No. 234.
Musick, J. A. 1999. Introduction to Part 2: Essential fish habitat identification, p. 41 In: Fish habitat: Essential Fish Habitat and Rehabilitation. American Fisheries Society, Symposium 22, (L. R. Benaka, editor). Bethesda, Maryland. 459 pp.
Musick, J. A., and J. A. Colvocoresses. 1986. Seasonal recruitment ofsubtropical sharks in Chesapeake Bight, U.S.A. pp. 301-311 In: Workshop on recruitment in tropical coastal demersal communities (A. Yanez y Arancibia and D. Pauley, eds.), FAO/UNESCO, Campeche, Mexico, 21-25 April 1986. I.O.C. Workshop Rep. 44.
Musick, J. A., J. A. Colvocoresses and E. J. Foell. 1986. Seasonality and the distribution, availability and composition offish assemblages in Chesapeake Bight, pp. 451-474 In: A. Yanez-Arancibia (ed.) Fish Community Ecology in Estuaries and Coastal Lagoons: Towards and Ecosystem Integration.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
132
NMFS. 1999. Final fishery management plan for Atlantic tunas, swordfish, and sharks. April 1999. National Oceanic and Atmospheric Administration, National Marine Fisheries Service, U.S. Department of Commerce.
NMFS. 1996. 1996 Report of the shark evaluation workshop. June, 1996. NOAA, National Marine Fisheries Service, Southeast Fisheries Science Center, Miami, Florida.
NMFS. 1993. Fishery management plan for sharks of the Atlantic Ocean. National Oceanic and Atmospheric Administration, National Marine Fisheries Service, U.S. Department of Commerce.
Pruitt, W. A. 1997. Pertaining to sharks. Virginia Marine Resources Commission. 4 VAC 20-490-10 et seq.
Schmitten, R. A. 1999. Essential fish habitat: opportunities and challenges for the next millennium, pp. 3-10 In: Fish habitat: Essential Fish Habitat and Rehabilitation. American Fisheries Society, Symposium 22, (L. R. Benaka, editor). Bethesda, Maryland. 459 pp.
Seckel, G. R. 1972. Hawaiian-caught skipjack tuna and their physical environment. Fishery Bulletin 70:763-787.
Sminkey, T. R. 1994. Age growth, and population dynamics of the sandbar shark, Carcharhinus plumbeus, at different population levels. Ph.D. Dissertation, Va. Inst. Mar. Sci., College of William and Mary. 99 pp.
Sminkey, T. R. and J. A. Musick. 1995. Age and growth of the sandbar shark, Carcharhinus plumbeus, before and after population depletion. Copeia 1995(4):871-883.
Smith, H. D. 1973. Timing of Babine Lake sockeye salmon stocks in the north- coast commercial fishery as shown by several taggings at the Babine counting fence and rates of travel through the Skeena and Babine Rivers. Fisheries Research Board of Canada. Technical report; no. 418. 31 p.
Springer, S. 1960. Natural history of the sandbar shark, Eulamia milberti. U.S. Fish. Wildl. Serv., Fish. Bull. 61:1-38.
Talbot, G. B. and J. E. Sykes. 1958. Atlantic coastal migrations of American shad. Bull. U.S. Bureau Fish. 58: 473-490.
Tarbox, K. E. 1988. Migratory rate and behavior of salmon in upper Cook Inlet, Alaska, 1983-1984. Alaska Department of Fish and Game, Fishery Research Bulletin 88-05.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
133
USDOC (U.S. Department of Commerce). 1996. Magnuson-Stevens Fishery Conservation and Management Act as amended through October 11,1996. National Oceanic and Atmospheric Administration Technical Memorandum NMFS-F/SPO-23.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Chapter 3Short-term Movements and Swimming Depth of Juvenile Carcharhinus
plumbeus in Chesapeake Bay
134
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
ABSTRACT
135
Manual telemetry was used in investigate the diel activity patterns of
juvenile Carcharhinus plumbeus (sandbar sharks) in Chesapeake Bay.
Ultrasonic transmitters equipped with depth sensors were attached to ten sharks
externally and manually tracked for 10 to 50 consecutive hours. Mean activity
space was conservatively estimated to be 110 km2. This estimate is two orders
of magnitude greater than that reported for other carcharhiniform species.
Swimming depth ranged from surface to 40 meters. Depths of more than 40
meters were common during daylight hours whereas depths greater than 20
meters were rare during the night. Mean daytime swimming depth was 12.8
meters whereas mean nighttime swimming depth was 8.5 meters. This
difference was statistically significant. The activity spaces of nearly every shark
were centered over one of the three deep channels in the lower Chesapeake
Bay. Most sharks tracked remained in the deep channels during the day but
ventured out of the channels to shallower waters during the night. This diel
activity pattern and large activity space is hypothesized to be an adaptation for
foraging on patchy prey in a highly productive, but highly seasonal, temperate
estuary. I challenge the demersal classification of the species because the
sharks were at least three meters from the bottom more than 50% of the tracking
duration and at least six meters from the bottom more than 35% of the duration.
Swimming direction was correlated with mean direction of tidal current. A mean
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
136of 75.8% of all fixes was in the general direction of the mean current. Mean
swimming direction and mean current direction were statistically similar. Angular
dispersion was estimated at 56.8° relative to the tidal-current direction.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
137
INTRODUCTION
TelemetryBiotelemetry methods are valuable tools that enable biologists to monitor
the movements, behavior, and physiological parameters of free-ranging animals.
They enable the collection of data from animals that are not readily visible with
little interference of their natural behaviors and provide more data than are
normally collected with techniques such as mark and recapture (Winter 1992).
The development of telemetry equipment with underwater applications began in
the 1950s (Trefethen 1956). A publication by Johnson (1960) marked the first
actual application of these underwater biotelemetry techniques. He fitted adult
Oncorhynchus tshawytscha with external ultrasonic transmitters and tracked
them on their migration up the Columbia River in Washington. Following this
pioneering work, many researchers recognized the tremendous advantages
underwater telemetry could provide for understanding behavioral patterns of
subjects that lived in such a concealing medium. This is reflected in the increase
to 147 publications and reports utilizing these techniques to study 40 species of
fishes, 10 species of mammals, 4 species of reptiles, and 5 species of
invertebrates by 1975 (Stasko and Pincock 1977).
Many of the early studies involving fishes concentrated on location-only
tracking of diadromous fishes such as Oncorhynchus gorbuscha (Stasko et al.
1973), Anguilla anguilla (Tesch 1972), and Alosa sapadissima (Dodson et al.
1972) during their annual migrations and near obstructions such as dams. Other
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
138
authors used location-only telemetry to study the daily activity patterns of pelagic
oceanic fishes such as Euthynnus pelamis (Yuen 1970) and freshwater fishes
such as Esoxlucius in lakes and impoundments (Malinin 1971). Other early
applications of underwater biotelemetry involved sensored transmitters to detect
parameters such as body temperature (Carey and Lawson 1973), ambient
temperature (Scarriota 1974, Coutant 1975), swimming depth (Standora et al.
1972, Stasko and Rommel 1974), swimming speed (Standora et al. 1972), and
heart rate (Nomura and Ibaraki 1969).
Elasmobranch TelemetryBass and Rascovich (1965) developed the first ultrasonic transmitters for
tracking large, fast fishes. They field tested the equipment on one adult Sphyrna
lewini off Palm Beach, Florida, and one adult Carcharhinus plumbeus off Jupiter
Inlet, Florida, in 1963. The animals were tracked for 2 hours and 4 hours,
respectively. Since this time telemetric techniques have been used with at least
35 species of elasmobranchs. Many of these were tracked for very short periods
or as preliminary reports. In fact, as of 1990, only six species (Prionace glauca,
Carcharhinus amblyrhynchos, Negaprion brevirostris, Sphyrna lewini, Squatina
californica, and Heterodontus francisci) were represented by more than 10
trackings (Nelson 1990). This number has increased dramatically over the past
decade.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
139
Activity SpaceMany volumes of literature have been published on the subjects of activity
spaces and home ranges. An understanding of spatial and temporal activity
patterns is essential to studying the ecology of a species. Most of the pioneering
studies in this area as well as the analytical development were from studies of
mammals and birds. Very few studies have been conducted on the activity
patterns of juvenile elasmobranchs, especially in nursery areas. The North
Sound of Bimini, Bahamas, represents a semi-protected nursery for juvenile
Negaprion brevirostris that has been studied extensively. Morrissey and Gruber
(1993) used ultrasonic telemetry techniques to track 38 juvenile N. brevirostris in
this nursery for periods ranging between 1 and 153 days. They found that these
sharks had very well-defined activity spaces that ranged between 0.23 and
1.26 km2 with a high degree of site fidelity. Size of the activity space was
positively correlated with size of the shark. This relationship has been thoroughly
demonstrated in mammals (McNab 1963), but rarely applied to fishes.
Holland et al. (1993) tracked six juvenile Sphyrna lewini in Kaneohe Bay,
Oahu, Hawaii, a known nursery for this species. Sharks were tracked for up to
12 days and had activity spaces between 0.46 and 3.52 km2. The activity space
was greatly expanded during evening hours, presumably due to foraging, and
restricted during the day, presumably due to refuging.
Two published studies have detailed attempts to track the movements of
juvenile C. plumbeus while they were in their nurseries. Huish and Benedict
(1977) tracked 10 animals reported to be Carcharhinus obscurus in Cape Fear,
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
140
North Carolina. Due to the small size of these animals (65 - 95.5 cm TL) it is
believed that they were actually C. plumbeus (C. obscurus are 90-110 cm TL at
birth). This potential misidentification is common with juveniles of these two
species. Transmitters were implanted into the peritoneal cavity in five subjects
and attached externally to the other five. The sharks were tracked for only 0.7 -
13.0 hours and traveled with the tidal currents at rates of 0.1 to 1.3 kilometers per
hour. Medved and Marshall (1983) tracked three C. plumbeus using ultrasonic
telemetry and 20 C. plumbeus using tethered balloons in the area of
Chincoteague, VA. Animals were tracked for periods ranging from 1.0 to 10.7
hours and traveled between 1.87 and 14.68 kilometers. Twenty of the twenty-
three subjects spent the majority of the time traveling with the tidal currents.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
141
MATERIALS AND METHODS
Study Area
The Chesapeake Bight is characterized by one of the most seasonally
dynamic environmental regimes in the world. Sea-surface temperature may vary
seasonally by as much as 30°C. The demersal fish fauna in the region is largely
migratory, dominated by boreal species in winter and sub-tropical species in
summer (Musick and Colvocoresses 1986). Chesapeake Bay and the seaside
lagoons of Virginia's Eastern Shore are heavily utilized as pupping and nursery
areas for Carcharhinus plumbeus. Pregnant females enter the lower Bay and
seaside lagoons in late May through June to liberate young and then retreat to
deeper offshore waters. The young remain in the nursery through the summer,
emigrating in fall to over-wintering areas south of Cape Hatteras, North Carolina
(Grubbs and Musick, in prep b).
Chesapeake Bay is a coastal-plain estuary characterized by extremely
variable environmental parameters. The hydrographic regime is dominated by
two-layer estuarine circulation in which low-salinity water from numerous rivers
flows over the dense, high-salinity ocean water. The freshwater flow is greatest
in the spring and leads to a stratified water column. This stratification is re
enforced by the warming of surface waters during this period and is maintained
throughout the summer. This stratification causes the development of a
thermocline, pycnocline, and obvious halocline, as weli as local oxyclines as
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
142
waters below the mixing layers get depleted of oxygen and are not replenished.
The hydrography of the Bay is also influenced by the effect of Coriolis, which
deflects incoming oceanic water masses to the right as they enter the lower
estuary. Combined with the fact that most low salinity riverine input is from the
western side of the Bay, these factors lead to a halal tilting of higher salinities
along the eastern portion of the estuary. This hydrography is hypothesized to
have profound effects on the distribution of primary and secondary nursery areas
for juvenile C. plumbeus in this estuary. The stratification in Chesapeake Bay
breaks down in the fall as surface waters cool and sink causing mixing of surface
and bottom layers. The timing of this destratification and its effect on salinity and
temperature may affect the temporal utilization of the nursery.
The hydrographic regime of the seaside creeks and lagoons along the
eastern shore are much less variable than the main estuary in terms of salinity
and dissolved oxygen. The constriction of tidal creeks, however, produces much
higher current velocities on average. Environmental clines rarely form because
the water column remains mixed and very saline as the dynamic tidal currents
constantly flush it. In addition, these creeks and lagoons have little riverine
influence. These hydrographic differences may affect the habitat utilization
patterns of young C. plumbeus in the two habitats.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
143
Telemetry Gear
The telemetry system consisted of ultrasonic transmitters, a directional
hydrophone, an ultrasonic receiver, and a pulse-interval decoder or stopwatch.
The ultrasonic transmitters used were model DT-96 manufactured by
Sonotronics in Tucson, Arizona. These transmitters produced pulses between
32 and 40 kHz, which is well above the auditory range of elasmobranchs.
Transmitters of the same frequency were differentiated by unique aural codes.
For example, a transmitter with code 258 produced 2 pulses followed by a pause,
5 pulses followed by a pause, then 8 pulses followed by a pause, and repeated.
Each transmitter was equipped with a pressure sensor that increased the pulse
interval with increasing pressure enabling swimming depth to be calculated. The
transmitters were 18 mm in diameter and 95 mm long with an average battery life
of 15 months. The weight of the transmitters was 14 grams in water, which
corresponded to approximately 1.4% of the body weight of the smallest C.
plumbeus in the nursery. This is well below the 2.0% maximum limit that is the
standard for telemetric studies with fishes (Mellas and Haynes 1985).
Transmissions were received by a Dukane Model N30A5B Underwater
Acoustic Locator System that consisted of a directional hydrophone and an
ultrasonic receiver that was sensitive to acoustic signals between 25 and 40 kHz.
Pulse interval was measured by a Pulse Interval Docoder developed by
Ultrasonic Telemetry Systems or simply by using a stopwatch to time 30 pulses.
Dividing this time by 30 gave the pulse interval. Due to interference from intense
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
144
background noise the stopwatch method was most often used. A simple
regression equation provided the conversion of pulse-interval to depth.
Transmitter Attachment
Four considerations must be made when determining the proper method
of transmitter attachment for a given study; handling time and stress, post
attachment trauma on the subject, desired duration of transmitter attachment and
acoustic contact with subject, and likelihood of recovery of the transmitter at the
conclusion of tracking.
Ingestion of the transmitter offers the least handling time and stress as
well as the shortest post-release trauma. This method was successfully used on
juvenile Sphyrna lewini in Hawaii (Holland et al. 1993). It also offers the
possibility of recovery if the transmitter is made positively buoyant and the
subject is tracked until the transmitter is voided from the body. There are several
negative aspects of this method. The transmitter may be voided through
regurgitation and therefore provide only brief tracking durations. It may block the
digestive track of subjects causing long-term trauma or death. The presence of
the transmitter in the stomach may also alter natural behaviors such as foraging
if the transmitter is large enough to occupy a significant proportion of the
stomach volume.
Intraperitoneal surgical implantation offers tracking durations limited only
by the life of the transmitter and is assumed to cause no alteration of natural
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
145
behavior following an appropriate post-surgery recovery period. This method
requires longer handling times, more severe stress, and the recovery period due
to post-surgical trauma may be long. This method was successfully used with
juvenile Negaprion brevirostris in Bimini, Bahamas (Morrissey and Gruber 1993).
In addition, surgically implanted transmitters were found to have no significant
effects on growth, hematocrit analysis, leukocyte counts, and condition of
juvenile C. plumbeus held in captivity on the Eastern Shore of Virginia (Leigh
1997). This is probably the most desirable method for long-term tracking
experiments.
External attachment methods are characterized by relatively short
handling times and recovery times. The amount of attachment trauma depends
on the type of external attachment but also may be very low. The duration of the
attachment varies between methods from a few days to a few months. This may
be maximized by carefully selecting the proper attachment method. External
attachment methods also offer an increased probability of transmitter recovery
through the use of positively buoyant transmitters and degradable attachment
links with known degradation rates. In choosing an external method of
attachment, researchers must also consider long-term physical damage such as
tissue necrosis that may occur due to the attachment as well as possible
inhibitory effects the external attachments may have on normal behaviors such
as swimming if the appropriate method is not chosen. One last consideration is
the degree to which an externally attached transmitter may foul with epibiota in a
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
146
given environment or become entangled with macroalgae. Huish and Benedict
(1977) compared external attachment to intraperitoneal implantation in the C.
plumbeus (published as C. obscurus) they tracked briefly in Cape Fear, North
Carolina. They found no differences in behavior or rates of movement between
the two methods. Blaylock (1990) found that externally attached transmitters did
not affect swimming speed significantly in juvenile Rhinoptera bonasus if the
transmitter weighed less than 3% of the body weight of the ray. The transmitter
significantly slowed swimming speed if it weighed more than 7% of the ray’s body
weight.
External attachment was selected in this study because the primary
considerations were short handling times, low trauma due to attachment, short
recovery periods, and unaltered natural behaviors. A small 3-mm hole was
punched in the lower central portion of the first dorsal fin and a small piece of
surgical tubing was inserted through the hole. A section of 50-pound
monofilament was threaded through the tubing down both sides of the dorsal fin
and attached to the transmitter through two perpendicular holes through the
transmitter end-cap. The attachment resulted in the transmitter trailing just
behind the dorsal fin, slightly nose-down, while the subject was swimming. This
method required relatively short handling time (approximately two minutes) and
relatively little stress. It was assumed that subjects would resume normal
behavior within one or two hours after release. There should have been little
inhibitory drag caused by these small-diameter transmitters with such an
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
147
attachment and fouling was minimal over the short duration of the study.
Tracking commenced immediately following release. Due to the physically
dynamic environment in this study and the relatively small size of the subjects, it
was assumed that transmitters would not be recoverable though this attachment
method allowed transmitters to be readily identified and returned should the
subject be recaptured by VIMS, recreational anglers, or commercial fishermen.
Tracking Methodology and Experimental Design
Tracking was conducted primarily from a 25-foot Wellcraft Nova V-bottom
research vessel. Other vessels also were used. Sharks were caught by hook
and line. The first healthy C. plumbeus that was less than 65 centimeters in pre-
caudal length was immediately equipped with a transmitter and released.
Manual tracking began upon release to ensure contact was maintained. Fixes
were recorded at ten-minute intervals. Location of the tracking vessel was
determined using a Northstar differential global positioning system (GPS).
Water depth was recorded using a boat-mounted depth sounder. Position of the
shark was estimated from a compass heading and estimate of the distance to
transmitter based on signal strength. A series of range tests were conducted
prior to each tracking cruise to enhance the crew’s experience and accuracy of
estimation of distance from signal strength. Ideally, sharks were tracked from a
distance of 100 to 200 meters. Pulse interval also was measured during each
location fix and decoded into swimming depth. Salinity, dissolved oxygen, and
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
148
temperature data for the entire water column were recorded in two-meter
intervals with a Hydrolab® Reporter 2 Multiprobe and Surveyor 3 Data Logger
once per hour of tracking. Other environmental information such as tidal cycle,
cloud cover, wind speed and direction, and sea state also were recorded at one-
hour intervals. Sharks were tracked as long as conditions allowed for a
maximum of 50 cumulative hours.
Data Analysis
I Activity Space
Latitude and longitude data were converted to decimal degrees format
using Microsoft Excel 97. The tracks were mapped spatially using ArcView GIS
3.1. These data were used to test the null hypothesis that juvenile C. plumbeus
in Chesapeake Bay are nomadic, showing no site fidelity or home range.
Numerous statistical methods exist for the analysis of activity space or
home-range data. The concepts of “home range" and “activity space" have been
abused in the telemetry literature (White and Garrott 1990). This appears
particularly true in studies utilizing underwater telemetry. Many models exist for
estimating home range and activity space and estimates of core areas of activity
may vary significantly for the same data set depending on the method of analysis
(Wray et al. 1992b). Many of the estimators also make assumptions that are
rarely met in practice. For example, Kemal (Worten 1989) and Jennrich-Tumer
(Jennrich and Turner 1969) estimators are dependent on a bivariate normal
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
149
distribution of locations. Implicit in the parametric models is the assumption of
independence of observations (Cresswell and Smith 1992). Temporally
autocorrelated data are often unavoidable in underwater telemetry studies due to
logistical constraints on tracking. This is true of the data collected in this study.
The degree of autocorrelation has profound effects on activity-space estimates
(Cresswell and Smith 1992). Time to independence of location fixes can be
calculated using several methods such as those of Swihart and Slade (1985a)
and independence then can be obtained by eliminating fixes until all are
separated by sufficient time. This often leads to a data set that is so reduced and
fragmented that it is no longer ecologically meaningful. Non-parametric home-
range estimators such as the Harmonic Mean Method (Dixon and Chapman
1980) and Dirichlet Tesselation (Wray et al. 1992a) are less affected by
autocorrelated data, yet they are grid-based models and quite cumbersome in
practice. Many of these grid-based non-parametric methods also suffer from
severe calculation problems due to dependence on grid origin and spacing
(White and Garrott 1990). The Minimum Convex Polygon (Mohr 1943, Mohr
1947, Winter 1977) is the oldest, most widely used method of activity-space
estimation due to its simplicity and flexibility (White and Garrott 1990). It is
created by connecting all outer location fixes to create a single convex polygon.
The area within the polygon is calculated as an estimate of activity space. This
method is non-statistical and is not affected by temporally autocorrelated data
(Swihart and Slade 1985b), yet it has a tendency to over-estimate activity space
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
150
due to the assumption of concavity (no allowance for convexity) and it has no
associated confidence interval (White and Garrott 1990).
Despite their shortcomings, Minimum Convex Polygon (MCP) and Kernal
activity spaces were calculated for all tracks in this study to enable comparison
with other published studies. These activity spaces were calculated and spatially
modeled using the Animal Movement Analysis Extension for Arc-View GIS 3.1
(Hooge and Eichenlaub 1997). Site attachment was evaluated for each track
using the Site Fidelity Test in the ArcView Animal Movements Extension.
Swimming speed and swimming rates also were calculated for the six
tracks of longest duration. Cumulative distances between fix locations were
calculated using the ArcView GIS Spatial Analyst Extension. These cumulative
distances were divided by time to give a swimming speed, which was converted
to kilometers per hour. This speed was converted to a swimming rate based on
the total length of the shark and was given in body lengths per second.
II Depth Telemetry
Swimming-depth data collected during the tracks were used to test the null
hypotheses that swimming-depth patterns are temporally and spatially random
and that these juvenile sharks utilize the entire water column. Only tracks of 24
hours or more in duration were used in the depth-telemetry analysis. The
pressure sensor on transmitter 5285 failed, therefore this fifty-hour track was also
eliminated from this analysis. Swimming depth and bottom depth were plotted
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
151
over cumulative time for each of the six remaining tracks to investigate temporal
trends in these two factors as well as the relationship between the two factors.
Initial plots using raw water-depth data, which was measured at the tracking
vessel, revealed a problem inherent with working in an estuary that is so dynamic
in three-dimensional space. The shark’s swimming-depth estimate was deeper
than the water depth in as many as 10% of all fixes. This is obviously
impossible; the depth of the estuary in the areas tracked is highly variable and
the distance between the tracking vessel and the shark was enough to cause the
discrepancy. Fixes were, therefore, corrected to estimate the location of the
shark rather than that of the tracking vessel. This was accomplished by
converting all latitude and longitude values to universal transverse mercator
(UTM) coordinates. Then these coordinates were corrected using the bearings
(converted to radians) and distance to shark estimates recorded during tracking
using the following equations:
UTMxb = UTMxa + (Distance x (SIN(bearing)))
UTMyb = UTMya + (Distance x (COS(bearing)))
The new UTM coordinates were plotted and water depths for these locations
were estimated by merging these data with a bathymetry grid interpolated from
TIN (triangular irregular network) data from the EPA, Chesapeake Bay Program.
This assigned a water-depth value to each new fix location. This correction
reduced the number of nonsensical data points to less than one percent of all
fixes. The mean difference between raw and corrected water-depth estimates
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
152
ranged from 1.1 to 4.0 meters. These differences were greatest for those tracks
made during the first summer of tracking, 1996 (range 3.1 - 4.0 m), and
decreased dramatically in 1997 and 1998 (range 1.1 -1 .7 m). This can probably
be attributed to a learning curve for tracking efficiency and an increase in return-
volunteer experience.
Mean swimming depth was calculated for each shark relative the
following potentially influential variables: day/night as defined by sunrise and
sunset, day/night as defined by nautical twilight, moon position (risen/set), and
tidal current (flood/slack/ebb). The difference between mean depth for each
factor was evaluated using a t-test (two-tailed, a=0.05) for the day/night and
moon phase variables and a one-way ANOVA (a=0.05) to examine the influence
of tidal-current phase. These tests were conducted for each track of 24 hours or
more, then on the overall means of all of these tracks combined. In an effort to
visualize the results, histograms of the depth distribution were plotted as a
function of significant factors. The difference between swimming depth and
corresponding water depth also was calculated for all fixes. The distribution of
these differences was used as a preliminary investigation of the degree of
association between the sharks and the bottom, and the proportion of the water
column actually utilized.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
153
III Correlation with Tidal Current
Movement direction and direction of tidal current were used to test the null
hypothesis that the directional movements of juvenile C. plumbeus are random
and are not influenced by or correlated with tidal currents. Data manipulation
was required to estimate the mean direction of travel for a shark at a given time.
Five-point moving averages of fix locations were calculated for the six longest
tracks to filter out the effects of tidal and wind-driven currents on the tracking
vessel between fixes,. These averaged tracks were converted to UTM
coordinates and then spatially plotted in ArcView GIS 3.1. The Animal
Movements Extension for ArcView (Hooge and Eichenlaub 1998) has a function
that automatically calculates successive distance between geographic points.
The distance between successive fixes was calculated for each track. Given the
distance (D) between location A and location B and the UTM coordinates of the
two locations, the bearing {0) required to reach B from A could be calculated
using the following equation:
0= ArcSIN ((UTMxa - UTM xJ/D) X (180P/J]) (Eq. 3.1)
Current was not measure in situ, therefore tidal-current bearing and
magnitude was estimated using data downloaded from the software program
Tides and Currents 2.0 (Nautical Software Inc.). This program has tidal current
calculations for more than 150 location in Chesapeake Bay for any date needed.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
154
Current data were downloaded for each fix using the closest station and depth
possible. The current regime of the Bay is very complex and dynamic due to
many hydrographic features such as riverine input from the north and west,
oceanic tidal flux from the southeast, and the two-layer estuarine circulation that
develops as a function of the varying salinity between these sources. Due to
these factors, the accuracy of the current data from this software program may
be questioned. Nevertheless, this hypothetical inaccuracy only made the test of
the null hypothesis more conservative, and therefore was accepted.
Correlation of movements with tidal currents was evaluated using several
indices. The estimated tidal current bearing was subtracted from the shark
movement bearing for each fix. Negative values were corrected by adding
360 degrees to them. These values were converted from degrees to radians. If
the null hypothesis was true, then the data would be highly dispersed. If the null
hypothesis was incorrect suggesting that shark movements were correlated with
tidal directions, then the resulting data set would have a unimodal distribution
about zero degrees. This was tested in several ways. An angular index of
concentration (r) was calculated for each track using the following equations,
where is the corrected difference between shark and current bearings at each
of n fixes (Zar 1996):
n£ cos3
* = ^ -------- (Eq. 3.2)n
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
155
I sin3Y = -i=LY = (Eq. 3.3)
n
r = V F 7 7 r (Eq. 3.4)
This index of concentration has no units and ranges from zero for data that is
very dispersed to one for data that are all concentrated in exactly the same
direction as the tidal currents. The angular deviation (Zar 1996) was then
calculated as an index of dispersion (s) using the following equation:
This index is analogous to standard deviation in linear analyses and the units are
in degrees.
Two simple tests were utilized to examine the degree of dispersal in the
data statistically. Both tests examine the null hypothesis that the data are
distributed randomly or uniformly around a circle. Rayleigh’s test for circular
uniformity was applied initially. The statistic (z) was calculated from the index of
concentration (r, Eq. 3.3) and the sample size (n) using the following equation:
If z was significant (a=0.05), the null hypothesis was rejected, indicating the
existence of a mean direction of travel. This test, however, gives no indication
that the mean direction is distributed around zero degrees and correlated with
tidal currents. The V-test (Zar 1996), a modification of Rayleigh’s z, was used to
(Eq. 3.5)
Rn
R = n r z = n r '
(Eq. 3.5)
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
156
examine this potential correlation. This test also examines the alternative
hypothesis that the data are not distributed randomly or uniformly about a circle,
but more specifically states that the distribution has a specific mean angle
specified by the researcher. If the sharks’ movements were correlated with tidal
currents, the data would be distributed about a mean of zero degrees as stated
above. The mean angle ( a ) was first calculated from the values of X(Eq. 3.2), Y
(Eq. 3.3), and r(Eq. 3.4) using the following relationships:
- ysina = — (Eq. 3.6)
r
cosa = — (Eq. 3.7)r
The statistic V then was calculated using the following equation wherein // is the
predicted mean angle:
V = R cos (a - / j ) (Eq, 3.8)
The significance of V is evaluated using critical values of u wherein:
u = V (Eq. 3.9)
Rejection of the null hypothesis suggests the data are indeed distributed with a
mean angle statistically similar to the predicted angle.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
157
RESULTS
Tracking of sandbar sharks began in June of 1997. Four sharks were
tracked during the first summer. Five additional sharks were tracked in the
summer of 1998, and one shark was tracked in 1999. In all, ten juvenile C.
plumbeus, six males and four females, were tracked for 10 to 50 consecutive
hours (Table 3-1). One shark was relocated two weeks after the initial track and
monitored for 15 additional hours, giving a total of 64 hours for that animal. The
size of the sharks, measured as pre-caudal length (PCL), ranged from 44 to 62
centimeters. The target duration was 50 consecutive hours for each track. This
goal was achieved for only four of the subjects, and two others were tracked for
more than 40 hours. Weather and sea conditions are extremely unpredictable in
Chesapeake Bay, especially for small vessels at night. This was the usual cause
for early termination tracks.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Table 3-1: Juvenile Carcharhinus plumbeus tracking summary.Regions: SECB - Southeastern Chesapeake Bay; SWCB - Southwestern Chesapeake Bay; SCCB - South Central Chesapeake Bay; VAES - Atlantic Side of Virginia’s Eastern Shore
Transmitter (# / kHz)
Date Sex PCL(cm)
Duration(hours)
Vessel Reason for Termination StartRegion
9633 / 37.0 June 20-21, 1997 9 56 13 RA/ Kingfisher Contact Lost / Weather SECB
9630 / 38.4 July 8-9, 1997 9 44 25 R/V Shearwater Weather SECB
9632 / 38.4 August 6-8, 1997 d 56 4 9 + 1 5 RA/ Shearwater Engine Failure SECB
9631 / 37.0 August 26-28, 1997 9 49 50 R/V Shearwater Goa! Reached SECB
9634 / 38.0 June 9-10, 1998 d 47 10 R/V Shearwater Weather / Water too Shallow VAES
9635 / 38.0 July 7-9. 1998 d 44 43 R/V Shearwater Weather SECB
9637 / 39.0 July 13-15, 1998 d 45 50 R/V Shearwater Goal Reached SECB
9639 / 40.0 August 12-14, 1998 d 52 44 R/V Shearwater Weather / Vessel Interference SWCB
5375 / 38.0 September 2, 1998 d 50 11 R/V Shearwater Weather SCCB
5285 / 38.0 July 26-28, 1999 9 62 50 R/V Tern Goal Reached SECB
CJl00
159
I Activity Space
Maps 3-1 to 3-7 spatially display the movement patterns for each of the
ten tracks. Each track is outlined by its minimum convex polygon (MCP)
representing the activity space for that individual. All sharks tracked were highly
active, covering large portions of the lower Bay, yet none of them exited the Bay
at any time during a track. Estimated MCP and Kernal activity spaces for all ten
sharks are shown in Table 3-2. MCP activity spaces for all ten sharks are shown
in Map 3-8. MCP estimates ranged from 4.20 to 275.84 km2. Only sharks
tracked more than 24 hours (n=7) were used in calculating mean activity spaces.
The mean MCP activity space for these sharks was 110.26 km2 (S.D. = 77.60
km2). A single example of Kernal activity spaces is shown in Map 3-9. The 50%,
75%, and 95% kernals are shown for shark 9632. Mean kernal activity spaces
were calculated using estimates from the seven longest tracks. The mean 50-
percent kernal activity space (core area) was 18.76 km2 (range 7.95 - 64.01 km2).
The mean 75-percent kernal was 75.53 km2 (range 25.79 - 290.63 km2) and the
mean 95-precent kernal was 140.10 km2 (range 62.28 - 382.66 km2). Site-fidelity
tests, conducted using the Animal Movement Analysis ArcView Extension
(Hooge and Eichenlaub 1998), were insignificant. This suggests these animals
were highly nomadic while in the summer nursery and did not establish home
ranges or, alternatively, home ranges were established but they were very large
and the data were insufficient (duration too brief) to elucidate them. In either
case, the estimates presented here underestimate the true activity space. There
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
160
was no correlation between shark size and activity space in this study.
Mean swim speed (km/hr) and swim rate (body lengths/sec) were
calculated for the six sharks tracked for the longest duration. The mean swim
speed was 1.44 km/hr (S.D., 0.24). This is comparable to the estimate of 1.21
km/hr reported by Medved and Marshall (1983), but more than twice the estimate
of 0.66 km/hr reported by Huish and Benedict (1977). The mean swimming rate
was estimated to 0.594 (S.D., 0.161) body lengths per second. Gruber et al.
(1988) demonstrated, that such straight-line estimations of swimming speed or
rate may underestimate the true value by as much as a factor of two.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
161
Map 3-1: Movements of shark 9631 tracked for 50 consecutive hours (August 26-28,1997). Light circles are location fixes recorded during the day and dark circle are location fixes recorded at night. The track is outlined by the Minimum Convex Polygon of activity space.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
75"48' 3r15' 3r10' 37"'5'
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
162
Map 3-2: Movements of shark 9632 tracked for 49 consecutive hours (August 6-8,1997). Light circles are location fixes recorded during the day and dark circle are location fixes recorded at night. The shark was tracked for 15 additional hours three weeks later (August 27-28). Day and night courses of this second track are shown as light and dark lines respectively. The track is outlined by the Minimum Convex Polygon of activity space.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
78*46' 3ri5 ' 37"10'
f t
w 0110 4>< ® * i XS 2S 3 v X O .35 ° jo a o i £ 2 z s u at
«
/+
f t
.0ZolZ ,9 UiC .0U£
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
163
Map 3-3: Movements of shark 9635 tracked for 43 consecutive hours (July 7-9,1998). Light circles are location fixes recorded during the day and dark circle are location fixes recorded at night. The track is outlined by the Minimum Convex Polygon of activity space.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
75”46' 37°15' 37”10l
.SU IE .OUiC
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
164
Map 3-4: Movements of shark 9637 tracked for 50 consecutive hours (July 13-15,1998). Light circles are location fixes recorded during the day and dark circle are location fixes recorded at night. The track is outlined by the Minimum Convex Polygon of activity space.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
75*46’ 37*15' 37*^0*
n •
S ^ H" “
s s * s &f i s ° 2 s «2 S n « s► - ^ o • L j
IVV
9
f
/4 -
tr o -r co r-j cr oC-J ^ r j r-j r j o r > ^> • " r j iri o t co r j iri o
o t co — cj r j cj o r> *t
.OZoIC .suie .Ok, iz
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
165
Map 3-5: Movements of shark 9639 tracked for 44 consecutive hours (August 12-14,1998). Light circles are location fixes recorded during the day and dark circle are location fixes recorded at night. The track is outlined by the Minimum Convex Polygon of activity space.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
37°15* 3 n o '
n ®« s «
“ ^ 2 a. > o -c a 2 s s23VV e i - . n *
2*»<fc
+suie Ok£C ft>Z£
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
166
Map 3-6: Movements of shark 5285 tracked for 50 consecutive hours (July 26-28,1999). Light circles are location fixes recorded during the day and dark circle are location fixes recorded at night. The track is outlined by the Minimum Convex Polygon of activity space.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
75*46' 37*15' 37*10'
,9UiC .ouze
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
167
Map 3-7: Movements of sharks tracked for less than 40 consecutive hours. Shark 9633 tracked for 13 hours (June 20-21,1997); Shark 9630 tracked for 25 hours (July 8-9,1997); Shark 9634 tracked for 10 hours (June 9-10, 1998); and Shark 5375 tracked for 11 hours (September 2,1999). Light circles are location fixes recorded during the day and dark circle are location fixes recorded at night. The track is outlined by the Minimum Convex Polygon of activity space.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
7 6 *2 5 ’ 76 *20 ’ 76 *15 ’
A Track 9633 1 (13 hours)^ t> Day Fixes
» Night Fixes I 1MCP 9633
Track 9630 (25 hours)
b DayFixes ■ Night Fixes
I 1 MCP 9630
Track 9634 (10 hours)
a DayFixes a Night Fixes
I 1 MCP 9634
Track 5375 (11 hours)
o DayFixes • Night Fixes
MCP 5375I I
*
.6»4C
.OV
oie
,Sl.2
£
168
Map 3-8: Minimum Convex Polygons for all ten juvenile Carcharhinus plumbeus tracked. The area calculations associated with polygons can be seen in Table 3-2.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
76°10'
Mnknum Convex Polygons
76°1076°20 76°15
76°00' 75°66‘ 75 50* 75 45 “
Track 9630
Track 9633 Track 9632 Track 9631 Track 9634
Track 9639 Track 9635 Track 9637 Track 5375 Track 5285
13 hours
25 hours49 hours50 hours10 hours
44 hours 43 hours 50 hours11 hours 50 hours
76°5‘I " T"...
76°00''" T " "
75°50'i
75°45‘
V&Ie
.ou
>z
e,9
^e
.v
Llz
169
Map 3-9: Kernai Activity Space of Shark 9632. 50-percent, 75-percent, and 95-percent kernals are shown. The area calculations associated with polygons can be seen in Table 3-2.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
76°15' 76°10‘
76°16' 76°10‘ 76°6' 76°00'
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Table 3*2: Minimum Convex Polygon (MCP) and Kernal activity space, mean swimming speed, and mean swimming rate for all ten juvenile C. plumbeus tracked. Grand means only include tracks of greater than 24 hours in duration. Italicized activity-space estimates were not included in calculation of mean activity spaces. (S.D. = standard deviation)
Shark # Duration(hours)
MCP(km2)
50% Kernal (km2)
75% Kernal (km2)
95% Kernal (km2)
Mean Speed km / hr
Swimming Rate body lengths / sec
9634 10 4.20 1.58 3.66 7.23 not calculated not calculated
5375 11 23.73 4.77 19.02 42.80 not calculated not calculated
9633 13 24.49 15.63 34.68 69.43 not calculated not calculated
9630 25 74.45 10.59 30.91 103.56 not calculated not calculated
9635 43 96.38 10.12 47.76 116.10 1.39 0.553
9639 44 97.94 16.60 47.64 102.03 1.53 0.719
9631 50 65.96 10.39 25.79 78.35 1.48 0.535
9637 50 275.84 64.01 290.63 382.66 1.71 0.779
5285 50 39.59 12.36 33.77 62.28 1.00 0.327
9632 4 9 + 1 5 121.64 7.95 52.21 135.74 1.55 0.652
MEAN(tracks>24hrs)
46.57 110.26 18.86 75.53 140.10 1.44 0.594
S.D. 11.71 77.60 7.59 95.37 109.61 0.24 0.161
~n IO
171
II Depth Telemetry
Only tracks with at least 24 cumulative contact hours (n=7) were used in
this analysis. The pressure sensor failed on one of these transmitters (#5285)
leaving six depth tracks for the analysis. Swimming depth and “corrected”
bottom depth are plotted versus cumulative track time in Figures 3-1 to 3-6. Five
of the six sharks tracked utilized depth ranges from less than five meters to more
than 30 meters. The depth plots for these five sharks suggested that these deep
waters were used primarily during daylight hours whereas the shallowest portions
of the tracks were used during the night. Shark 9631 demonstrated this pattern
most conclusively (Fig. 3-2). This pattern was also obvious, though not as clean,
for sharks 9530, 9632, 9635, and 9637. Shark 9639 showed no obvious diel
pattern in depth use. The bottom depth during this track only varied between
seven and fifteen meters (Fig. 3-6).
Swimming-depth data were separated into day and night fixes based on
the time of sunset and sunrise for that day. These data sets were binned
separately into three-meter depth intervals and plotted on vertical histograms
arranged such that the proportion of daytime fixes within a given depth stratum
was plotted on a vertical axis opposite the proportion of nighttime fixes for that
same stratum (Fig. 3-7a to 3-12a). This species generally has been thought of
as highly demersal. The difference between the swimming depth and the
corrected bottom depth was calculated to investigate this claim. These data also
were separated into day and night fixes plotted on histograms similar to the raw
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
172
swimming depth data to investigate potential diel patterns in deviation from the
bottom of the estuary (Fig. 3-7b to 3-12b).
Three general patterns emerged from the histograms. Swimming depth
was obviously deeper during the day for sharks 9630, 9631, and 9632. The
majority of depth fixes recorded during the night were less than 12 meters deep,
whereas the majority of fixes taken during the day were greater than 12 meters
deep. This pattern was most obvious for shark 9630, which spent greater than
50% of night fixes in less than 9 meters of water whereas more than 50% of day
fixes were greater than 21 meters deep (Fig. 3-7a). Sharks 9631 and 9632 had
very few depth fixes below 21 meters during the night whereas many daytime
fixes were greater than 30 meters (Fig. 3-8a and 3-9a). Sharks 9635 and 9637
also utilized deeper water during the day but the pattern was somewhat different.
The shallower mean nighttime depth was due to the absence of occasional deep
excursions that were made during the day. The majority of day and night fixes
were less than 9 meters deep, yet there were no night fixes deeper than 15
meters whereas day fixes extended to more than 21 meters for shark 9635 (Fig.
3-10a) and more than 30 meters for shark 9637 (Fig. 3-11a). The histogram for
shark 9639 was nearly symmetrical (Fig. 3-12a) as all day and night fixes for this
shark were less than 15 meters deep.
The histograms plotting the difference between swimming depth and
bottom depth (Fig. 3-7b to 3-12b) were nearly symmetrical in all cases indicating
that day and night did not influence the distance from the bottom the sharks
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
173
swam. The overall pattern of these histograms does suggest that the demersal
characterization of juvenile C. plumbeus may be incorrect. In all six cases, more
than 50% of the swimming-depth fixes were more than three meters from the
bottom (range 51.8 - 80.6%). A mean of 37.6% of fixes were more than six
meters from the bottom (range 21.6 - 53.9%) and a mean of 19.1% of fixes were
more than nine meters from bottom (range 6.3 - 33.6%). If C. plumbeus were
truly demersal, the majority of fixes would be less than three meters from the
bottom. These data also indicate that these juvenile sharks make occasional
vertical movements to the surface even in waters deeper than 30 meters.
Anecdotal evidence supports these findings, as free-swimming juvenile C.
plumbeus were observed swimming on the surface on many occasions during
the tracking periods. These observations occurred in water depths greater than
40 meters on three separate occasions.
The statistical significance of the diel pattern in swimming depth was
evaluated using a series of t-tests. The swimming-depth data departed
significantly from the normal distribution for all individual shark tracks. The t-test
was utilized despite the violation of this assumption, as it is robust to such
departures for large sample sizes (Brown and Rothery 1993). T-tests also were
used to investigate significant differences in the overall mean (averaged
individual means) swimming depth for each factor. These data did meet the
assumption of normality.
Two-sample t-tests (a=0.05), assuming unequal variances, initially were
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
174
conducted to compare mean swimming depth during night and day, based on
sunrise and sunset using all fixes. Each shark was analyzed individually. Mean
daytime swimming depths ranged from 7.55 to 18.08 meters and mean nighttime
swimming depths ranged from 5.60 to 12.23 meters. Swimming depths were
significantly deeper during the day for five of the six sharks analyzed (Table 3-3).
Swimming depth was not significantly different between day and night for shark
9639 only. Mean swimming depths for all six sharks during day and night are
shown in Figure 3-13. The overall mean (averaging individual means) daytime
swimming depth was 12.80 (S.D. 5.01) meters and the grand mean swimming
depth during nighttime was 8.46 (S.D. 2.30) meters. To test the significance of
this difference, a paired t-test was conducted using only the day and night means
for each shark. This overall t-test was significant (p=0.032) indicating that
daytime swimming depths were significantly deeper than nighttime swimming
depths.
Swimming-depth differences also were analyzed using alternative
definitions of day and night. Rather than defining day as the period between
sunrise and sunset and night as the period between sunset and sunrise, they
were defined based on nautical twilight. According to this definition, day begins
when the sun rises to 12° below the horizon and ends when the sun sets to 12°
below the horizon. Day and night are commonly equated with light and dark, yet
day and night defined by sunrise and sunset relegate most of the crepuscular
periods, which contain significant light, to part of night. Nautical twilight relegates
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
175
this period to day, and is therefore a better measure of light and dark. If the diel
pattern in depth utilization observed above was a function of light level, then the
difference should be even more significant based on this definition. The mean
depths for each shark based on nautical twilight are shown in Figure 3-14. The
results of the t-tests using this definition are shown in Table 3-4. Whereas the
mean difference between day and night swimming-depth was greater using
nautical twilight, 4.99 meters versus 4.34 meters using sunrise/sunset, the results
of the t-tests were nearly identical to those obtained using definitions based on
sunrise and sunset. Daytime swimming depth was significantly deeper for all
sharks except 9639, and the overall t-test was also significant (p=0.036).
Lunar position, based on moonrise and moonset, also was evaluated as a
potential influential variable on swimming depth. For lack of a better term, in this
discussion the period between moonrise and moonset will be called lunar high
and the period between moonset and moonrise will be referred to as lunar low.
The mean depths for each track during lunar low (set) and lunar high (risen) are
shown in Figure 3-15. Swimming depth was significantly deeper during lunar
high for four of the six sharks analyzed (Table 3-5). The overall mean swimming
depth was 12.47 (S.D. 3.86) meters during lunar high and 9.73 (S.D. 3.95)
meters during lunar low. This difference was significant (p=0.042) indicating that
lunar high swimming depths were significantly deeper than those during lunar low
were.
Tidal currents, estimated from the software program Tides and Currents
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
176
2.0 (Nautical Software Inc.), were used to investigate the potential influence of
tidal phase on shark swimming depth. Tidal currents were divided into three
categories, Ebb, Slack, and Flood. Slack was defined as any period when the
current was less than 0.4 knots. This value was chosen primarily to achieve near
equal sample sizes. A one-way Analysis of Variance was conducted using
swimming depth as the dependent variable and current phase as an independent
variable with three levels. The results, shown in Table 3-6, were significant
(p<0.01) for four of the six sharks but no true pattern existed between the sharks.
Swimming depth was deepest during Ebb tide for two of the sharks, during Slack
tide for one shark, and during Flood tide for one shark. An overall ANOVA was
conducted on the means for these three phases, and the results were
insignificant (p=0.815), suggesting that tidal-current phase had little influence on
swimming depth.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
177
Figure 3-1: Swimming depth and bottom depth recordings for Shark 9630 tracked for 25 consecutive hours. (Bottom depths were interpolated from a bathymetry grid using corrected fix locations in ArcView GIS - see text for correction methodology.)
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Shark #9630 Cumulative Time (hours)
0:00:00 12:00:00 24:00:00 36:00:00 48:00:00
0
5
10
1 15 £| 20
25
30
3 5 d a y n ig h t d a y n ig h t d a y
-^S w im m ing Depth — Bottom Depth (corrected)
178
Figure 3-2: Swimming depth and bottom depth recordings for Shark 9631 tracked for 50 consecutive hours. (Bottom depths were interpolated from a bathymetry grid using corrected fix locations in ArcView GIS - see text for correction methodology.)
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Shark #9631 Cumulative Time (hours)
0:00:00 12:00:00 24:00:00 36:00:00 48:00:00
0
5
10
15
£ 20
& 25 O
30
35
40
45
-•-Swimming Depth — Bottom Depth (corrected)
NIGHTNIGHT
179
Figure 3*3: Swimming depth and bottom depth recordings for Shark 9632 tracked for 49 consecutive hours. (Bottom depths were interpolated from a bathymetry grid using corrected fix locations in ArcView GIS • see text for correction methodology.)
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Shark #9632
00:00
Cumulative Time (hours)
12:00:00 24:00:00 36:00:00 48:00:00
j
y M jk ’J& fi
DAY NIGHT DAY NIGHT DAY
Swimming Depth — Bottom Depth (corrected)
180
Figure 3-4: Swimming depth and bottom depth recordings for Shark 9635 tracked for 43 consecutive hours. (Bottom depths were interpolated from a bathymetry grid using corrected fix locations in ArcView GIS - see text for correction methodology.)
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
12:00:00
Shark #9635 Cumulative Time (hours)
24:00:00 36:00:00 48:00:000:00:00
DAY NIGHT DAY NIGHT
Swimming Depth — Bottom Depth (corrected)
DAY
181
Figure 3-5: Swimming depth and bottom depth recordings for Shark 9637 tracked for 50 consecutive hours. (Bottom depths were interpolated from a bathymetry grid using corrected fix locations in ArcView GIS - see text for correction methodology.)
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
12:00:00
Shark #9637 Cumulative Time (hours)
24:00:00 36:00:00 48:00:000:00:00
D 25
DAY NIGHT DAY NIGHT
Swimming Depth — Bottom Depth (corrected)
DAY
182
Figure 3-6: Swimming depth and bottom depth recordings for Shark 9639 tracked for 44 consecutive hours. (Bottom depths were interpolated from a bathymetry grid using corrected fix locations in ArcView GIS - see text for correction methodology.)
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
0:00:00 12:00:00
Shark #9639 Cumulative Time (hours)
24:00:00 36:00:00 48:00:00
O 12
DAY NIGHT DAY NIGHT
Swimming Depth — Bottom Depth (corrected)
DAY
183
Figure 3-7: Shark 9630 swimming-depth dynamics, a) Comparison of the distributions of swimming depth during day and night, b) Distribution of the distance from swimming depth to bottom of estuary during day and night.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
a) 50
0-3
3-6
_ 6-912Q>| 9-12
£ 12-15 &&CT)C
15-18
I 18-21
I 21-24
24-27
27-30
>30
Swimming Depth Distribution - Shark #9630% of Day Fixes % of Night Fixes
30 20 10 0 10 20 30 40 50
□ Day ■ Night
b)
Day Fixes: N=83 Night Fixes: N=35
Distance from Swimming Depth to Bottom - Shark #9630
27-30 □ Day ■ Night
- 24-27
21-24
« 18-21
5 15-18
Q 12-15
« 9-12
30 20 10 0% o f Day Fixes
10 20 30 40% of Night Fixes
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
184
Figure 3-8: Shark 9631 swimming-depth dynamics, a) Comparison of the distributions of swimming depth during day and night, b) Distribution of the distance from swimming depth to bottom of estuary during day and night.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
a)Swimming Depth Distribution - Shark #9631
50 40 30% o f Day Fixes
20 10
0-3
3-6
— 6-9(0w4>"3 9-12E£ 12-15CL0™ 15-18O)c1 18-21
SCO 21-24
24-27
27-30
>30
% of Night Fixes10 20 30 40 50
□ Day ■ Night
b)>30
27-30
24-27
| 21-24I
18-21
| 15-18
12-15
9-12
6-9
3-6
0-3
Day Fixes: N=164 Night Fixes: N=113
Distance from Swimming Depth to Bottom - Shark #9631
D"□ Day ■ Night
60 50 40 30 20 10% of Day Fixes
10 20 30% of Night Fixes
40 50 60
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
185
Figure 3-9: Shark 9632 swimming-depth dynamics, a) Comparison of the distributions of swimming depth during day and night, b) Distribution of the distance from swimming depth to bottom of estuary during day and night.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
a)Swimming Depth Distribution - Shark #9632
% o f D ay F ixes 50 40 30 20 10
0-3
3-6
— 6-9(Ak.0)% 9 -1 2E5 12 -15Q.0)° 15 -18mc| 18-21
ito 2 1 -2 4
2 4 -2 7
2 7 -3 0
> 30
% o f N ig h t F ixes 10 20 30
n
50
□ D ay ■ N ig ht
b)>30
2 7 -3 0
2 4 -2 7"3TI 2 1 -2 4
8 18‘21 I 15 -18
12 -15
9 -1 2
6 -9
3-6
0 -3
Day Fixes: N=152 Night Fixes: N=104
Distance from Swimming Depth to Bottom - Shark #9632
□ D ay ■ N ight
Da
60 50 40 30 20 10
% of Day Fixes
10 20 30
% of Night Fixes
40 50 60
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
186
Figure 3-10: Shark 9635 swimming-depth dynamics, a) Comparison of the distributions of swimming depth during day and night, b) Distribution of the distance from swimming depth to bottom of estuary during day and night.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Swimming Depth Distribution - Shark #96353 ) % of Day Fixes % of Night Fixes
' 50 40 30 20 W _____ 0 10 20 30
0-3
3-6
_ 6-9#r . . .1 9-12E,£ 12-15 a a>™ 15-18O)cI 18-215OT 21-24
24-27
27-30
>30
50
□
□ Day ■ Night
b)>30
27-30
24-27«T$ 21-24
S18-21
15-18
Day Fixes: N=148 Night Fixes: N=99
■e 12-15
9-12
6-9
3-6
0-3
Distance from Swimming Depth to Bottom - Shark #9635.
□ Day ■ Night
D□
r w
60 50 30 20 10% of Day Fixes
10 20 30% of Night Fixes
50 60
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
187
Figure 3-11: Shark 9637 swimming-depth dynamics, a) Comparison of the distributions of swimming depth during day and night, b) Distribution of the distance from swimming depth to bottom of estuary during day and night.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
a)Swimming Depth Distribution - Shark #9637
50
0-3
3-6
6-9
0)3 9-12££ 12-15 a.«° 15-18O)cI 18-21 1M 21-24
24-27
27-30
>30
b)>30
27-30
24-27
I 21-24
18-21
15-18
12-15
9-12
6-9
3-6
0-3
40% of Day Fixes
30 20 10% of Night Fixes
50
□□
Q
n
□□
□ Day ■ Night
Day Fixes: N=173 Night Fixes: N=102
Distance from Swtnming Depth to Bottom - Shark #9637
□ Day ■ Night
0
□tm
60 50 40 30 20 10% of Day Fixes
10 20 30% of Night Fixes
40 50 60
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
188
Figure 3-12: Shark 9639 swimming-depth dynamics, a) Comparison of the distributions of swimming depth during day and night, b) Distribution of the distance from swimming depth to bottom of estuary during day and night.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Swimming Depth Distribution - Shark #9639
a) 50
0-3
3-6
_ 6-9m4>■S 9-12
£ 12-15 a<u» 15-18 c| 18-21
ico 21-24
24-27
27-30
>30
40% of Day Fixes
30 20 10 0% of Night Fixes 10 20 30 40 50
r " i
□ Day ■ Night
b)>30
27-30
24-27
Day Fixes: N=118 Night Fixes: N=104
Distance from Swimming Depth to Bottom - Shark #9639
a Day ■ Night
* 21-24
18-21
a 15-18
12-15
30 20 10% of Day Fixes
10 20 30% o f Night Fixes
60
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
189
Figure 3-13: Mean swimming depth during Day and Night based on sunrise and sunset for sharks 9630, 9631, 9632, 9635, 9637, and 9639. An asterisk (*) indicates a significant difference according to individual t-tests. Overall mean daytime swimming depth was significantly deeper than nighttime swimming depth (t-test, T = 2.95, p=0.032). Error bars are standard error of the mean.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
(iu) iftdap ueeiu
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
□ Su
n Ri
sen
■ Su
n S
et
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Table 3-3: Results of t-tests for difference between mean swimming depth (meters) during day and night based on sunrise and sunset. Means are given with standard deviation in parentheses. Results of t-tests using raw depth data for each track are followed by t-test results using overall mean for each track as a replicate.(* indicates T statistic is significant at a= 0.05.)
Shark # Sun Risen N Mean (S.D.)
Sun Set N Mean (S.D.)
T statisticAi * M i
P (sign.)
9630 82 16.88 (7.18) m 35 9.72 (6.29) m 5.40* <0.00019631 156 18.08 (8.26) m 121 12.23 (4.86) m 7.37* <0.00019632 152 17.09 (9.50) m 105 8.10 (6.66) m 8.92* <0.00019635 148 8.57 (6.85) m 99 7.00 (2.48) m 2.55* 0.0129637 172 8.65 (8.12) m 103 5.60 (2.81 )m 4.51* <0.00019639 118 7.55 (2.62) m 104 8.11 (2.88) m -1.29 0.13
ALL means 6 12.80 (5.01) m 6 8.46 (2.30) m 2.95* 0.032
COo
191
Figure 3-14: Mean swimming depth during Light and Dark based on timing of nautical twilight for sharks 9630, 9631, 9632, 9635, 9637, and 9639. An asterisk (*) indicates a significant difference according to individual t-tests. Overall mean swimming depth during light was significantly deeper than
mean swimming depth during dark (t-test, T = 2.85, p=0.036). Error bars are standard error of the mean.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Mean
De
pth
with
Naut
ical
C N ^ - C O C O O C N ^ - C O O O O T" r" r* r" r* M
(ui) iftdap ueeui
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
□ Li
ght
■ Da
rk
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Table 3-4: Results of t-tests for difference between mean swimming depth (meters) during day and night based on time of nautical twilight. Means are given with standard deviation in parentheses. Results of t-tests using raw depth data for each track are followed by t-test results using overall mean for each track as a replicate.(* indicates T statistic is significant at a= 0.05.)
Shark # Twilight Day N Mean (S.D.)
Twilight Night N Mean (S.D.)
T statisticM \ * / * 2
P (sign.)
9630 89 16.93 (7.08) m 28 7.74 (4.58) m 8.03* <0.00019631 180 17.84 (7.89) m 97 11.23 (4.39) m 8.97* <0.00019632 171 16.73 (9.38) m 86 6.84 (5.67) m 10.49* <0.00019635 168 8.31 (6.53) m 79 7.16 (2.39) m 2.03* 0.0449637 198 8.55 (7.65) m 77 4.83 (2.26) m 6.18* <0.00019639 136 7.57 (2.60) m 86 8.18 (2.95) m -1.57 0.12
ALL means 6 12.66 (4.97) m 6 7.66 (2.10)m 2.85* 0.036
CDro
193
Figure 3-15: Mean swimming depth during Lunar High (moon risen) and Lunar Low (moon set) for sharks 9630, 9631, 9632, 9635,9637, and 9639. An asterisk (*) indicates a significant difference according to individual t-tests. Overall mean swimming depth was significantly deeper during lunar high than during lunar low (t-test, T = 2.71, p=0.042). Error bars are standard error of the mean.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Mea
n De
pth
with
M
oon
Rise
n vs
Set
(ui) iftdap ueaui
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
! □ Ri
sen
■ S
et
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Table 3-5: Results of t-tests for difference between mean swimming depth (meters) while moon was risen versus while moon was set. Means are given with standard deviation in parentheses. Results of t-tests using raw depth data for each track are followed by t-test results using overall mean for each track as a replicate.(* indicates T statistic is significant at a= 0.05.)
Shark # Lunar High (risen) N Mean (S.D.)
Lunar Low (set)N Mean (S.D.)
T statisticA ^
P (sign.)
9630 60 14.73 (7.51) m 57 14.74 (7.85) m -0.01 0.999631 163 17.19 (7.80) m 114 13.15 (6.51) m 4.67* <0.00019632 120 15.83 (8.74) m 137 11.31 (9.73) m 3.92* <0.00019635 138 9.17 (6.73) m 109 6.39 (2.39) m 4.33* <0.00019637 137 10.29 (8.36) m 138 4.75 (2.81) m 7.37* <0.00019639 115 7.58 (2.54) m 107 8.05 (2.95) m -1.26 0.21
ALL means 6 12.47 (3.96) m 6 9.73 (3.95) m 2.71* 0.042
CD
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Table 3-6: Results of one-way Analysis of Variance for difference between mean swimming depth (meters) during ebb, slack, and flood tidal current phases. Means are given with standard deviation in parentheses. Results of analyses using raw depth data for each track are followed by ANOVA results using overall mean for each track as a replicate. (* indicates T statistic is significant at a= 0.05.)
Shark # EBB Current N Mean (S.D.)
SLACK Current N Mean (S.D.)
FLOOD Current N Mean (S.D.)
F statistic T statisticM x * M i
9630 34 14.55 (9.49) m 39 16.76 (5.84) m 44 13.08 (7.20) m 2.47 0.0899631 80 17.76 (8.64) m 97 14.42 (7.08) m 10 14.81 (6.71) m 5.14* 0.0069632 76 16.91 (9.88) m 94 13.55 (9.74) m 87 10.23 (7.87) m 10.73* <0.0019635 77 7.74 (4.53) m 81 10.14 (6.63) m 89 6.12 (4.62) m 12.11* <0.0019637 106 5.00 (2.99) m 74 8.44 (5.81) m 95 9.59 (9.31 )m 13.48* <0.0019639 65 7.84 (3.27) m 63 7.22 (2.64) m 94 8.19 (2.36) m 2.38 0.095
ALL means 6 11.63 (5.43) m 6 11.76 (3.73) m 6 10.34 (3.18) m 0.21 0.815
COcn
196
III Tidal Current Correlation
Only the six sharks tracked for more than 40 consecutive hours were used
in this analysis. The degree of correlation between swimming direction and tidal-
current direction was first assessed by subtracting the current bearing from the
swimming direction bearing for each fix. Histograms were created for each shark
using 30-degree bins. In the broadest of terms, the proportion of fixes from 0-90°
departure were interpreted as traveling with (not against) the tidal current
whereas those from 90-180° were interpreted as traveling against (not with) the
tidal current. These histograms are shown in Figures 3-16a to 3-21 a. The
proportion of fixes “with” the tidal current ranged from 63.8 %to 85.2% (mean
75.8%). A non-statistical correlation index was developed to aid in visualizing the
relationship between current direction and shark swimming direction. The
bearing calculated for each shark fix was subtracted from the mean current
bearing. The correlation index was assigned to each fix based on this departure
using Table 3-7. The mean correlation index was calculated for each hour of
tracking. This mean correlation index was plotted on a y-axis versus cumulative
track time on the x-axis. Tidal current speed then was plotted on a second y-axis
using positive values for flood currents and negative values for ebb currents.
These plots indicate a high degree of correlation between tidal currents and
swimming direction (Fig. 3-16b to 3-21 b). This was especially true of sharks
9635 and 5285. More than 70% of ail fixes deviated less than 60° from the
current bearing for these two tracks (Fig. 3-18a and 3-21 a).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
197
Table 3-7: Calculation of non-statistical correlation index between swimming direction and tidal-current direction.
Swimming direction - Current direction Tidal-Current Phase CorrelationIndex
0-30 or 330-360 FLOOD 3
30-60 or 300-330 FLOOD 2
60-90 or 270-300 FLOOD 1
90-120 or 240-270 FLOOD -1
120-150 or 210-240 FLOOD -2
150-210 FLOOD -3
0-30 or 330-360 EBB -3
30-60 or 300-330 EBB -2
60-90 or 270-300 EBB -1
90-120 or 240-270 EBB 1
120-150 or 210-240 EBB 2
150-210 EBB 3
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
198
Summary data for the current correlation analyses are compiled in Table
3-8. The Concentration Index (r) ranged from 0.25 to 0.61 (mean = 0.50) and the
mean Angular Dispersion (s) was 56.8° (S.D. 7.1°). These indices were
calculated relative to the tidal-current direction indicating a relatively high degree
of correlation between current and swimming directions. The mean angle of
deviation from the tidal-current direction was 32.7° (range 13°-51°). Rayleigh’s Z
tests were highly significant (p<0.001) for all six sharks indicating that swimming-
direction fixes were not randomly dispersed around a circle, but had a significant
mean directionality. The V-test examines the difference between this mean
direction and that of the tidal current. It tests the same null hypothesis of random
dispersal as Rayleigh’s Z but with the more stringent alternative hypothesis of a
mean directionality that does not differ significantly from that of the tidal current.
These results were also highly significant (p<0.001) for all sharks indicating that
the mean swimming direction did not differ significantly from the mean tidal-
current direction.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
199
Figure 3-16: Shark 9631 Directional-Swimming Data, a) Frequency histogram of deviation of swimming direction from tidal-current direction, b) Shark/Current correlation index plotted with tidal-current amplitude.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
b)
co•4=«£Oo
0-30
Shark #9631 (Current Bearing - Track Bearing)
18.T%"with current against current
x 20
30-60 60-90 90-120
Directional Difference (degrees)
120-150
Shark #9631 - Correlation with Current
150-180
1.503
1.002
0.501
0.000
-0.501
1.002
-1.50312 15 18 21 24 27 30 33 36 39 42 45 486 90 3
•Shark Correlation •CurrentCumulative Hours
Curr
ent
Spee
d (k
nots
)
200
Figure 3-17: Shark 9632 Directional-Swimming Data, a) Frequency histogram of deviation of swimming direction from tidal-current direction, b) Shark/Current correlation index plotted with tidal-current amplitude.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Shark #9632 (Current Bearing - Track Bearing)
33.1%66.9%with current against current
x 20
b)
co•■Cro
eoO
0-30 30-60 60-90 90-120
Directional Difference (degrees)
120-150
Shark #9632 - Correlation with Current
150-180
3
2
1
0
1
2
-312 15 18 21 24 27 30 33 36 39 42 45 4890 3 6
1.5
1
0.5
0
-0.5
-1
-1.5
•Shark Correlation •CurrentCumulative Hours
Curr
ent
Spee
d (k
nots
)
201
Figure 3-18: Shark 9635 Directional-Swimming Data, a) Frequency histogram of deviation of swimming direction from tidal-current direction, b) Shark/Current correlation index plotted with tidal-current amplitude.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
a) Shark #9635 (Current Bearing - Track Bearing)50
40
</> __ a> 30 x
20
10
b)
co5£oo
14.8%85.2%against currentwith current
0-30 30-60 60-90 90-120
Directional Difference (degrees)
120-150
Shark #9635 - Correlation with Current3
2
10
1
2
-39 12 15 18 21 24 27 30 33 36 39 4230 6
150-180
1.5
1
0.5
0
-0.5
-1
-1.545 48
•Shark Correlation •CurrentCumulative Hours
Curr
ent
Spee
d (k
nots
)
202
Figure 3-19: Shark 9637 Directional-Swimming Data, a) Frequency histogram of deviation of swimming direction from tidal-current direction, b) Shark/Current correlation index plotted with tidal-current amplitude.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
a)
M0)X
b)
c01£oO
Shark #9637 (Current Bearing - Track Bearing)
27.0%73.0%with current igainst current
0-30 30-00 60-90 90-120
Directional Difference (degrees)
120-150 150-180
Shark #9637 - Correlation with Current3
2
1
o
1
2
312 15 18 21 24 27 30 33 36 39 42 45 483 6 90
1.5
1
0.5
0
-0.5
-1
-1.5
•Shark Correlation ■CurrentCumulative Hours
Curr
ent
Spee
d (k
nots
)
203
Figure 3-20: Shark 9639 Directional-Swimming Data, a) Frequency histogram of deviation of swimming direction from tidal-current direction, b) Shark/Current correlation index plotted with tidal-current amplitude.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
0-30
Shark #9639 (Current Bearing - Track Bearing)
36.2%63.8%against currentwith current
30-60 60-90 90-120
Directional Difference (degrees)
120-150 150-180
b)
coy=J2£oo
Shark #9639 - Correlation with Current3
2
1
0
1
-2
-39 12 15 18 21 24 27 30 33 36 39 42 45630
1.50
1.00
0.50
0.00
-0.50
- 1.00
-1.5048
■Shark Correlation ■CurrentCumulative Hours
Curr
ent
Spee
d (k
nots
)
204
Figure 3-21: Shark 5285 Directional-Swimming Data, a) Frequency histogram of deviation of swimming direction from tidal-current direction, b) Shark/Current correlation index plotted with tidal-current amplitude.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Shark #L5285 (Current Bearing - Track Bearing)
84.3% 15.7%with current against current
° 20
b)
c0*3101oo
0-30 30-60 60-90 90-120 120-150
Directional Difference (degrees)
Shark #L5285 - Correlation with Current
150-180
3 1.5
2
1 0.5
0
1 -0.5
2
3 -1.50 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48
■Shark Correlation ■Current Cumulative Hours
Curr
ent
Spee
d (k
nots
)
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Table 3-8: Summary of Carcharhinus plumbeus directional swimming analysis. Concentration index (r), angular dispersion (s), and mean angle are in relation to mean tidal-current direction, not the overall distribution of the raw data. Significant Rayleigh’s Z indicates the data are not distributed randomly around a circle, but have a mean directionality. Significant V-test results (as determined by the u statistic) indicate that the mean swimming direction is not statistically different from the mean tidal current direction.
Shark # 9631 9632 9635 9637 9639 5285 MEAN (SD)
Duration (hours) 50 49 43 50 44 50 47.67 (3.27)
n (samples size) 277 257 242 282 229 287 262.33 (23.51)
r (concentration index) 0.59 0.47 0.61 0.46 0.25 0.60 0.497 (0.138)
s (angular dispersion) 52° 59° 51° 59° 69° 51° 56.8° (7.1°)
Mean Angle 37° 51° 16° 41° 38° 13° 32.7° (14.9°)
Rayleigh’s Z 96.53* 56.59* 89.86* 60.90* 14.77* 102.22* 70.15* (33.01)
p (signif.) <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
V statistic 130.93 75.71 141.96 98.12 46.21 167.06 110.00 (44.95)
u (from V-test) 11.13* 6.68* 12.91* 8.26* 4.08* 13.95* 9.50* (3.82)
p (signif.) <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
% fixes with current 81.6% 66.9% 85.2% 73.0% 63.8% 84.3% 75.8% (9.2%)
toocn
206
DISCUSSION
I Activity Space
The determination of three-dimensional activity space and examination of
short-term movement patterns are essential for defining crucial habitats for a
species or life stage of a species. These data are particularly crucial for
exploited species and those that utilize near-shore habitats, which are prone to
degradation, as nurseries or breeding grounds. Chesapeake Bay is utilized as
an important summer nursery area for juvenile C. plumbeus (Grubbs and Musick
in prep a,b). This temperate estuary is particularly susceptible to habitat
degradation due to agricultural pollution, industrial pollution, and habitat
destruction for industrial and residential development.
Tracking experiments reported in this study indicated that juvenile C.
plumbeus are extremely active. Mean activity space was 110.26 km2 over
tracking periods from 25 to 50 hours. Site fidelity tests suggested these sharks
are nomadic and do not establish true activity spaces or they establish activity
spaces much too large to be surveyed during the time frame of these
experiments. In fact, on three occasions, trips were made to search for previously
telemetered sharks. The range of the transmitters was approximately one
kilometer, therefore the entire lower Bay (south of 37°20’ N) was gridded at an
interval of 750 meters. This grid was completely searched systematically on
each occasion. No sharks were ever relocated in this fashion, supporting this
hypothesis. One shark, #9632, was tracked for 49 hours, then relocated during a
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
207
subsequent track three weeks later and tracked for 15 additional hours. The
initial MCP for this shark was 121 km2. The additional data did not increase the
MCP for this animal (Map 3-2).
Although tracking experiments have been published previously for juvenile
C. plumbeus in western Atlantic estuaries (Huish and Benedict 1977 - published
as C. obscurus, Medved and Marshall 1983), the duration of tracks was very
short ( 1 -13 hours), so activity-space was not reported. Several authors have
published activity-space estimates for other carcharhiniform species inhabiting
protective nurseries (Morrissey and Gruber 1993, Holland et al. 1993), but these
studies were conducted in tropical lagoons. These studies showed that these
species establish well-defined core areas of activity that were more confined
during daylight hours. This is the first such report for a highly migratory
elasmobranch in a temperate nursery. The mean MCP activity space from this
study is compared with the results of three studies conducted in tropical
ecosystems in Table 3-9. The subjects of these studies were juvenile Negaprion
brevirostris in the Bahamas, juvenile Sphyma lewini in Hawaii, and adult
Carcharhinus amblyrhynchos in the Marshall Islands. The mean activity space
reported here for juvenile C. plumbeus in Chesapeake Bay (110.26 km2) was 162
times greater than that reported for juvenile N. brevirostris (0.68 km2, Morrissey
and Gruber 1993), 87 times greater than that for juvenile S. lewini (1.26 km2,
Holland et al 1993), and 26 times greater than that reported for adult C.
amblyrhynchos (4.20 km2, McKibben and Nelson 1986). In fact, the
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
208
underestimated mean activity space reported here for juvenile C. plumbeus was
22 times greater than that reported for adult Carcharodon carcharias, white
sharks in the Farallon Islands off the California coast (4.94 km2, Goldman and
Anderson 1999). This mean activity space was comparable, however, to the
results of one large (230 cm TL) N. brevirostris tracked for 113 hours in the
Bahamas by Gruber et al. (1988) that exhibited an activity space of 93 km2.
The data presented herein were insufficient to examine differences in the
size of activity spaces during day and night statistically, yet examination of Map
3-10 suggests a similar pattern as described for the tropical species. The
daytime tracks of four sharks tracked in the same area are layered on one map
and the nighttime tracks are layered on the other. The nighttime tracks are
slightly more dispersed than the daytime tracks. The minimum convex polygon
for the combined day tracks was 160.1 km2 whereas the MCP for the night tracks
was 181.2 km2 supporting this hypothesized pattern.
This tremendous difference in activity space between this study and
similar studies on other species may simply be due to behavioral differences
between the species. It is more likely, however, that these differences are due to
the ecosystem differences rather than the species. Productivity in tropical
ecosystems is generally low, compared to those in temperate regions. Increased
productivity in certain tropical communities, such as the mangrove-fringed lagoon
in the Bahamas where Morrissey and Gruber (1993) tracked the juvenile N.
brevirostris gain most production through the detrital pathway from production of
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
209
leaf matter rather than from phytoplankton. This productivity is fairly uniform
throughout the edge of the lagoon and most of the inhabitants are year-round
residents. Prey organisms also are distributed uniformly along the mangrove
fringe. Morrissey and Gruber (1993) found that juvenile lemon sharks in this area
establish small, very stable home ranges that stretch along this mangrove fringe.
The home ranges of adjacent sharks overlapped considerably. This is an
efficient strategy for utilizing the uniformly distributed abundance of prey while
minimizing interspecific competition, a potential evolutionary advantage because
many are siblings and most are related.
Production in temperate estuaries such as Chesapeake Bay is very high.
This production is driven largely by spring and fall phytoplankton blooms. This
productivity supports a tremendous biomass of consumers. This seasonal
production combined with a climate that is only seasonally habitable, drives most
of the estuaries inhabitants to be highly migratory and highly seasonal as well.
Medved et al. (1985) found that the primary food for juvenile C. plumbeus in
Chincoteague Bay, Virginia, primarily consisted of Callinectes sapidus (blue
crabs) followed by Brevoortia tyrranus (Atlantic menhaden) and Paralichthys
dentatus (summer flounder). All three of these species are extremely abundant
in Chesapeake Bay during the warmer months. Nevertheless, they are all
migratory, however, and extremely patchy in distribution. The aggregations of
Callinectes migrate throughout the Bay and Brevoortia travel in immense, highly
mobile schools. The young sharks utilize this area as a summer nursery to
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
210
maximize food intake for growth while being afforded some degree of protection
from larger predators. It would be highly inefficient to establish small, stable
home ranges and wait for passing schools of fishes or aggregations of
crustaceans. Therefore, they are more nomadic which enables them to seek
patchy prey actively.
Shark 9632 provides speculative evidence of this hypothesis. This shark
initially was tracked for 49 consecutive hours (August 6-8,1997). During the first
day, the shark remained in deep waters (20-40 meters) off Cape Charles. The
first evening the shark moved inshore toward the East to shallow water (1-3
meters) near the mouth of a tidal creek. The following day this pattern repeated
in its entirety, though a different creek was utilized. Large schools of Brevoortia
were observed in the shallow areas on both evenings. Shark 9632 was tracked
for 15 additional hours three weeks later (August 27). Again the shark spent
daylight hours in the deeper waters of the eastern channel; yet at night, it moved
westward only to the edge of the channel in 15-20-meter depth. Large
aggregations of Micropogonias undulatus (Atlantic croaker), another prey
species, were observed in the area during this time. Perhaps C. plumbeus
movements change daily due to changes in abundance of and proximity to
dynamic prey aggregations. Their true activity space would therefore be defined
by the combined movement patterns of these prey species.
The validity of this hypothesis can be examined in two different ways.
The most obvious test would be to track juvenile Carcharhinus plumbeus in a
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
211
tropical nursery. Hawai’i supports a resident tropical population of C. plumbeus.
Little is known about the ecology of this population, but It is known that juveniles
occupy areas of deep reef around 80 meters deep. This is a completely different
habitat than the Chesapeake Bay nursery. A tracking and tagging study of this
population could be compared to the Chesapeake Bay data. If the pattern
observed in the current study were a species characteristic, then Hawaiian C.
plumbeus also would have very large activity spaces. If it were a function of the
temperate ecosystem, then the Hawaiian sharks would exhibit the tropical pattern
of a small, highly stable home range. Secondly, the activity spaces of additional
temperate carcharhinids need to be examined to see if the pattern is similar to
that presented here. Juvenile Sphyrna lewini utilize seaside lagoons throughout
the Mid-Atlantic Bight, from Maryland to South Carolina. The activity spaces of
these animals should be examined. If the activity spaces are similar to those
presented in the current study, this would be interpreted as evidence that the
pattern is ecosystem specific. If it is species specific, however, the young S.
lewini should have small, well-defined activity spaces similar to those exhibited in
Kaneohe Bay, Hawaii (Holland etal. 1993).
II Swimming Depth
The distribution of swimming-depth data ranged from zero to more than
forty meters, which corresponds to the deepest portions of Chesapeake Bay.
Swimming depth was significantly deeper during daylight hours. The overall
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
212
mean swimming depth during the day, as defined by sunrise and sunset, was
12.8 meters whereas the mean depth at night was only 8.5 meters. Most of the
sharks tracked stayed in deep channels during the day, then ventured into
shallower water during the night. This pattern is hypothesized to be a nocturnal
foraging strategy. Other tracking studies have reported similar results, though
these were performed on highly pelagic adult elasmobranchs. Scarriota and
Nelson (1977) reported that adult Prionace glauca were epipelagic during the day
and moved inshore to shallower areas to feed on squid at night. Carey and
Scharold (1990) reported an increase in deep vertical movements during daylight
hours by adult P. glauca. They suggested this pattern had foraging and
thermoregulatory functions. The deepest waters in the lower Chesapeake Bay
(-45 meters) are found in a slough that begins just offshore of Kiptopeke State
Park (37°10’ N, 76°00’ W) and extends northwest for nearly twenty kilometers.
Seven of the ten sharks tracked in this study utilized this deep channel. Four of
the ten spent the majority of daylight hours in this channel. Those sharks that did
not utilize this area used other similar channels such as the Chesapeake
Channel or the York River Entrance Channel. Outlines of the 95-percent Kernal
activity spaces for eight of the nine tracks conducted in Chesapeake Bay are
shown in Map 3-11. Shark 9637 was not included in this map as it had the
largest activity area of any shark tracked and never crossed its own path.
Kernals of activity are therefore meaningless. The kernals for the remaining eight
tracks are layered over the bathymetry of the estuary. At least one deep channel
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
213
is incorporated in each of these Kernals. In fact many of the Kernals are actually
centered by a deep channel. This indicates that these deep sloughs are
important nursery habitat for these juvenile sharks.
Swimming depth was also significantly deeper during lunar high (period
between moonrise and moonset) than lunar low (period between moonset and
moonrise). Additional data are needed to investigate this influence. It is
hypothesized that the significance of this factor during this study may have been
due to a significant interaction effect with sunrise and moonrise being highly
correlated during four of the tracks. This interaction has not been quantified, and
it is possible that this lunar influence on swimming depth is valid and may have a
function in nocturnal foraging strategies.
This study also provides evidence contrary to the belief that Carcharhinus
plumbeus is a demersal species. The majority of all swimming depth fixes were
estimated to be more than three meters from the bottom. In fact, more than one-
third of the fixes were more than six meters from the bottom. This indicates that
this species spends a significant portion of its time in mid-water or at the surface.
Interestingly, the first and third most abundant prey items identified by Medved
et al. (1985) were highly demersal species (Callinectes sapidus, Paralichthys
dentatus) whereas the second most abundant prey item was a mid-water
planktivore (Brevoortia tyrranus). Stillwell and Kohler (1993) found that the
majority of prey items consumed by larger C. plumbeus were demersal
elasmobranchs (Raja sp.) and demersal teleosts (Lophius americanus, Gadidae,
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
214
Bothidae, etc.). Yet they also reported that as much as 10% of the prey items
were mid-water teleosts (Pomatomus saltatrix and Scomber scombrus) and
various pelagic cephalopods.
Ill Correlation Tidal Currents
Swimming direction was significantly correlated with mean tidal direction.
A mean of 75.8% of all fixes deviated less than 90° from the current direction,
meaning they were not against the current. The mean angular dispersion of the
swimming direction was 56.8° relative to the tidal current direction. These data
suggest juvenile C. plumbeus utilize the momentum of tidal currents to conserve
energy. Chesapeake Bay is characterized by strong two-layer estuarine
circulation driven by a stiff halocline. Currents above and below the halocline
may travel in very different directions. Perhaps the vertical movements observed
within the water column function to exploit this two-layer circulation to facilitate
movement.
Future Directions
This study has provided interesting insights into the behavioral ecology
and distribution of crucial habitats of juvenile Carcharhinus plumbeus in
Chesapeake Bay. The proper determination of activity space for these animals
would require much more data than collected here, however. The daunting task
of collecting these data could be accomplished much more easily using newer
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
215
“chat” tags, archival transmitters from which stored data can be downloaded
during tracking. In addition to the data presented here, environmental data
(salinity, dissolved oxygen, and temperature) were collected at one to two hour
intervals during all tracking periods. These data will be analyzed in the future
and may provide important information concerning crucial nursery habitats and
the environmental factors that influence activity space.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Table 3-9: Activity-space (MCP) estimate for C. plumbeus compared with the published findings for three species of carcharhiniform sharks.
Species Negaprionbrevirostris
Sphyrna lewini Carcharhinusambiyrhynchos
Carcharhinusplumbeus
ReferenceMorrissey and Gruber 1993
Holland et al. 1993 McKibben and Nelson 1986
Current Study
N 17 6 26 7
Duration (range) 1-135 days 9-72 hrs 2-6 days 25-64 hrs
Size Range (PCL) 47-65 cm 35-37 cm 137-167 cm 44-62 cm
Mature? NO NO YES NO
Ecosystemtropicalmangrove lagoon
tropical sheltered bay
tropicalcoral atoll lagoon
temperateestuary
Mean MCP 0.68 km2 1.26 km2 4.20 km2 110.26 km2
MCP Range 0.23-1.26 km2 0.46-3.52 km2 0.19-53.00 km2 39.59-275.84 km2
to
Os
217
Map 3-10: Combined tracks for sharks 9631, 9632, 9635, and 5285 showing utilization of a deep channel in eastern Chesapeake Bay and increased dispersal during night tracks, a) Daytime fixes only - Minimum Convex Polygon for these fixes combined contained an area of 160.08 km2, b) Nighttime fixes only - Minimum Convex Polygon for these fixes combined contained an area of 181.20 km2.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
218
Map 3-11: Perimeters of 95-percent Kernals for eight (9637 excluded) Chesapeake Bay tracks combined over bathymetry grid. Kernals are centered over deep channels throughout the lower estuary.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
37*10'
.0Z.Z6 .SUZC ,0U>£C .s .ze
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
219
LITERATURE CITED
Bass, G. A., and M. Rascovich. 1965. A device for the sonic tracking of large fishes. Zoologica 50 (8): 75-83.
Blaylock, R. A. 1990. Effects of external biotelemetry transmitters on thebehavior of the cownose ray Rhinoptera bonasus. J. Exp. Mar. Biol. Ecol. 141: 213-220.
Brown, D. and P. Rothery. 1993. Models in Biology: Mathematics, Statistics, and Computing. Wiley & Sons, West Sussex, England.
Carey, F. G., and K. D. Lawson. 1973. Temperature regulation in free- swimming bluefin tuna. Comp. Biochem. Physiol. 44A: 375-392.
Carey, F. G., and J. V. Scharold. 1990. Movements of blue sharks (Prionace glauca) in depth and course. Marine Bio. 106: 329-342.
Coutant, C. C. 1975. Temperature selection by fish - a factor in power-plant impact assessments, pp. 575-595. In: Environmental effects of cooling systems at nuclear power plants. Int. Atomic Energy, Vienna.
Cresswell, W. J. and G. C. Smith. 1992. The effects of temporallyautocorrelated data on methods of home range analysis, pp. 272-284 In: (Priede, G. and S. M. Swift, eds). Wildlife Telemetry: remote monitoring and tracking of animals. Ellis Horwood Limited, West Sussex, England.
Dixon, K. R. and J. A. Chapman. 1980. Harmonic mean measure of animal activity areas. Ecology 61:1040-1044.
Dodson, J. J., W. C. Leggett, and R. A. Jones. 1972. The behavior of adult American shad (Alosa sapidissima) during migration from salt to fresh water as observed by ultrasonic tracking techniques. J. Fish. Res. Board Can. 29: 1445-1449.
Goldman, K. J. and S. D. Anderson. 1999. Space utilization and swimming depth of white sharks, Carcharodon carchanas, at the South Farallon Islands, central California. Environ. Biol. Fishes 56: 351-364.
Grubbs, R. D. and J. A. Musick. Spatial delineation of summer nursery areas to define essential fish habitat for juvenile Carcharhinus plumbeus in Chesapeake Bay, Virginia, in prep a.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
220
Grubbs, R. D. and J. A. Musick. Migratory movements, philopatry, and temporal delineation of summer nurseries for juvenile Carcharhinus plumbeus in the Chesapeake Bay region, in prep b.
Gruber, S. H., D. R. Nelson, and J. F. Morrissey. 1988. Patterns of activity and space utilization of lemon sharks, Negaprion brevirostris, in a shallow Bahamian lagoon. Bull. Mar. Sci. 43 (1): 61-76.
Holland, K. N., B. M. Wetherbee, J. D. Peterson, and C. G. Lowe. 1993.Movements and distribution of hammerhead shark pups on their natal grounds. Copeia 1993 (2): 495-502.
Hooge, P. N. and W. M. Eichenlaub. 1998. Animal movement extension toArcView. ver. 1.1. Alaska Biological Science Center, USGS, Anchorage, AK.
Huish, M. T., and C. Benedict. 1977. Sonic tracking of dusky sharks in the Cape River, North Carolina. J. Elisha Mitchell Scien. Soc. 93 (1): 21-26.
Jennrich, R. I. and F. B. Turner. 1969. Measurement of non-circular home range. J. Theor. Biol. 22: 227-237.
Johnson, J. H. 1960. Sonic tracking of adult salmon at Bonneville Dam, 1957. U.S. Fish Wildl. Serv. Fish. Bull. 60: 471-485.
Leigh, T. H. 1997. Effects of surgically implanted, dummy transmitters on captive, juvenile sandbar sharks, Carcharhinus plumbeus. Master’s thesis, Hofstra University, Hempstead, NY. 57 pp.
Malinin, L. K. 1971. Home range and actual paths offish in the river pool of the Rybinsk reservoir. Biol. Vnutr. Vod. Inform. Byull. 22 (25): 158-166. (Transl. from Russian by Fish. Res. Board Can. Transl. Ser. 2282, 26p.)
McKibben, J. M., and D. R. Nelson. 1986. Patterns of movement and grouping of gray reef sharks, Carcharhinus amblyrhynchos, at Enewetak, Marshall Islands. Bull. Mar. Sci. 38 (1): 89-110.
McNab, B. K. 1963. Bioenergetics and the determination of home range size. Amer. Nat. 97: 133-140.
Medved, R. J., and J. A. Marshall. 1983. Short-term movements of young sandbar sharks, Carcharhinus plumbeus. Bull. Mar. Sci. 33 (1): 87-93.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
221
Medved, R. J., C. E. Stillwell, and J. J. Casey. 1985. Stomach contents of young sandbar sharks, Carcharhinus plumbeus, in Chincoteague Bay, Virginia. Fish. Bull. 83 (3): 395-402.
Mellas, E. J., and J. M. Haynes. 1985. Swimming performance and behavior of rainbow trout (Salmo gairdneri) and white perch (Morone americana): effects of attaching telemetry transmitters. Can. J. Fish. Aquat. Sci. 42: 488-493.
Mohr, C. 0 . 1947. Table of equivalent populations of North American small mammals. Am. Midi. Nat. 37: 223-249.
Mohr, C. 0 . 1943. A summary of North American small mammal censuses.Am. Midi. Nat. 29: 545-587.
Morrissey, J. F., and S. H. Gruber. 1993. Home range of juvenile lemon sharks, Negaprion brevirostris. Copeia 1993 (2): 425-434.
Musick, J. A., and J. A. Colvocoresses. 1986. Seasonal recruitment of subtropical sharks in Chesapeake Bight, U.S.A. pp. 301-311. In:Workshop on recruitment in tropical coastal demersal communities (A. Yanez y Arancibia and D. Pauley, eds.), FAO/UNESCO, Campeche,
Mexico, 21-25 April 1986. I.O.C. Workshop Rep. 44.
Nelson, D. R. 1990. Telemetry studies of sharks: a review, with applications in resource management, pp. 239-256. In: Elasmobranchs as living resources: advances in the biology, ecology, systematics and the status of the fisheries (H. L. Pratt, Jr., S. H. Gruber, and T. Taniuchi, eds.), U.S. Dep. Commer., NOAATech. Rep. NMFS 90.
Nomura, S., and T. Ibaraki. 1969. Electrocardiogram of the rainbow trout and its radio transmission. Jpn. J. Vet. Sci. 31: 135-147.
Sciarrotta, T. C. 1974. A telemetric study of the behavior blue shark, Prionace glauca, near Santa Catalina, California. Master’s Thesis. California State Univ., Long Beach, Calif.
Sciarrota, T. C. and D. R. Nelson. 1977. Die! behavior of the blue shark(Prionace glauca) near Santa Catalina Island, California. Fish. Bull. 75 (3): 519-528.
Standora, E. A. Jr., T. C. Sciarrota, D. W. Ferrel, H. C. Carter, and D. R. Nelson.1972. Development of a multichannel, ultrasonic telemetry system for the
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
222
study of shark behavior at sea. Calif. State Univ. Long Beach Found. Tech. Rep. 5: 69 pp.
Stasko, A. B., R. M. Horral, A. D. Hasler, and D. Stasko. 1973. Coastal movements of mature Fraser River pink salmon (Oncorhynchus gorbuscha) as revealed by ultrasonic tracking. J. Fish. Res. Board Canada. 30: 1309-1316.
Stasko, A. B., and D. G. Pincock. 1977. Review of underwater biotelemetry, with emphasis on ultrasonic techniques. J. Fish. Res. Board Can. 34 (9): 1261-1285.
Stasko, A. B., and S. A. Rommel Jr. 1974. Swimming depth of adult American eels (Anguilla rostrata) in a saltwater bay as determined by ultrasonic tracking. J. Fish. Res. Board Can. 31:1148-1150.
Stillwell, C. E. and N. E. Kohler. Food habits of the sandbar shark Carcharhinus plumbeus off the U.S. northeast coast, with estimates of daily ration. Fish. Bull. 91 (1): 138-150.
Swihart, R. K. and N. A. Slade. 1985a. Testing the independence of observations in animal movements. Ecology 66:1176-1184.
Swihart, R. K. and N. A. Slade. 1985b. Influence of sampling interval on estimates of home range size. J. Wildl. Manage. 49:1019-1025.
Tesch, F. W. 1972. Experiments on telemetric tracking of spawning migrations of eels, Anguilla anguilla, in the North Sea Helgol. Wiss. Meeresunters. 23:165-183. (Transl. from German by Fish Res. Board Can. Transl. Ser. 2724, 29 p.)
Trefethen, P. S. 1956. Sonic equipment for tracking individual fish. U.S. Fish Wildl. Serv., Spec. Sci. Rep. Fish. 179:11 p.
White, G. C. and R. A. Garrott. 1990. Analysis of Wildlife Radio-tracking Data. Academic Press, New York.
Winter, J. D. 1992. Underwater Biotelemetry, pp. 371-395 In: Fisheries Techniques (Nielsen, L. A. and D. L. Johnson, eds.) American Fisheries Society, Bethesda, Maryland.
Winter, J. D. 1977. Summer home range movements and habitat used by four largemouth bass in Mary Lake, Minnesota. Trans. Am. Fish. Soc. 106: 323-330.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
223
Worton, B. J. 1989. Kernal methods for estimating the utilisation distribution in home range studies. Ecology 70:164-168.
Wray, S., W. J. Cresswell, and D. Rogers. 1992a. Dirichlet tesselations: a new, non-parametric approach to home range analysis, pp. 247-255 In: Wildlife Telemetry: remote monitoring and tracking of animals. (Priede, G. and S. M. Swift, eds.). Ellis Horwood Limited, West Sussex, England.
Wray, S., W. J. Cresswell, P. C. L. White, and S. Harris. 1992b. What,if anything, is a core area? An analysis of the problems of describing internal range configurations, pp. 256-251 In: Wildlife Telemetry: remote monitoring and tracking of animals. (Priede, G. and S. M. Swift. Ellis, eds.) Horwood Limited, West Sussex, England.
Yuen, H. S. H. 1970. Behavior of skipjack tuna, Katsuwonus pelamis, asdetermined by tracking with ultrasonic devices. J. Fish. Res. Board Can. 27: 2071-2079.
Zar, J. H. 1996. Biostatistical Analysis. Third Edition. Prentice Hall. New Jersey.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
APPENDIX 1 Station Data for Spatial Delineation
Set Yr date Stat’n Latitude Longitude Reg- TempCPUE Surf.
1 90 6/5 K 37.1667 -76.0000 2.002 90 7/6 Anc 37.2830 -76.2830 0.003 90 7/13 Anc 37.2670 -76.3670 0.004 90 7/16 K 37.1667 -76.0000 3.005 90 7/20 K 37.1667 -76.0000 24.006 90 7/20 M 37.0830 -76.1330 13.987 90 8/20 M 37.0830 -76.1330 12.008 90 8/20 K 37.1667 -76.0000 8.579 90 8/21 K 37.1667 -76.0000 1.00
10 90 8/24 Anc 37.2000 -76.3000 14.0011 90 9/4 K 37.1667 -76.0000 19.0012 90 9/28 M 37.0830 -76.1330 4.001391 6/4 K 37.1667 -76.0000 9.0014 91 7/8 Anc 37.2330 -76.2830 1.001591 7/19 K 37.1667 -76.0000 29.0016 91 8/12 M 37.0830 -76.1330 15.0017 91 8/12 K 37.1667 -76.0000 37.0018 91 9/9 Anc 37.2330 -76.2830 11.2519 91 9/20 K 37.1667 -76.0000 6.0020 91 9/20 K 37.1667 -76.0000 10.0021 92 6/15 Anc 37.4000 -76.6500 0.0022 92 7/2 K 37.1667 -76.0000 30.0023 92 7/20 K 37.1667 -76.0000 21.0024 92 8/11 Anc 37.2330 -76.2830 3.0025 92 8/17 Anc 37.3830 -76.1670 20.0026 92 8/18 Anc 37.4000 -76.0170 18.33
Salinity Salinity D.O. D.O. Dist. to min- max- setSurface Bottom Surf. Bottom mouth Depth Depth time
25 7.9 6.3 4 8 320 6.3 34.0 3 8 319 6.5 40.0 5 7 324 6.9 6.3 7 24 524 6.9 6.3 12 21 224 5.7 13.8 6 8 225 5.3 13.8 6 7 426 6.3 6.3 7 24 526 6.3 6.3 18 24 420 6.3 31.7 6 8 324 6.1 6.3 18 21 424 5.1 13.8 11 14 225 7.9 6.3 7 24 420 5.1 31.1 5 7 324 6.9 6.3 13 26 225 5.3 13.8 6 9 526 6.3 6.3 7 24 521 5.0 31.1 6 11 324 6.1 6.3 15 22 324 6.1 6.3 7 7 412 5.2 62.9 3 4 524 6.9 6.3 10 20 224 6.9 6.3 7 14 521 6.3 31.1 5 10 321 4.0 34.5 9 16 522 4.1 31.8 9 14 2
TempBottom
1725252424242323232524241725242323252424212424252525
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Set Yr date Stat’n Latitude Longitude Reg- TempCPUE Surf.
27 92 8/18 K 37.1667 -76.0000 16.0028 92 9/14 K 37.1667 -76.0000 23.0029 93 7/21 K 37.1667 -76.0000 26.0030 93 7/21 Anc 37.4500 -76.1000 12.0031 93 7/21 Anc 37.4830 -76.0330 1.0032 93 7/22 Anc 37.0830 -76.1830 1.0033 93 7/22 Anc 37.4000 -76.0500 10.0034 93 8/16 M 37.0830 -76.1330 8.0035 93 8/16 K 37.1667 -76.0000 14.0036 93 8/16 Anc 37.3330 -76.0500 1.2537 93 8/26 Anc 37.2330 -76.2830 0.0038 93 8/17 Anc 37.6830 -76.0000 1.0039 93 8/17 Anc 37.6670 -76.1170 0.0040 93 8/17 Anc 37.6670 -76.2330 0.0041 93 8/17 Anc 37.1000 -76.1670 1.0042 93 9/21 K 37.1667 -76.0000 18.0043 94 6/16 K 37.1667 -76.0000 23.7544 94 6/16 M 37.0830 -76.1330 13.7545 94 7/6 K 37.1667 -76.0000 7.5046 94 8/11 K 37.1667 -76.0000 2.0047 94 8/11 Anc 37.2330 -76.2830 0.0048 95 7/5 M 37.0830 -76.1330 17.0049 95 7/5 K 37.1667 -76.0000 14.0050 95 7/5 Anc 37.8330 -76.2330 0.0051 95 7/5 Anc 37.7670 -76.0170 1.0052 95 7/6 Anc 37.6330 -76.1000 0.0053 95 7/6 Anc 37.4580 -76.1140 2.0054 95 7/26 M 37.0830 -76.1330 24.0055 95 7/27 K 37.1667 -76.0000 12.94
Salinity Salinity D.O. D.O. Dist. to min- max- setSurface Bottom Surf. Bottom mouth Depth Depth time
26 6.3 6.3 7 24 524 6.1 6.3 11 13 524 6.9 6.3 9 15 221 3.3 39.1 9 13 521 3.9 41.6 7 13 423 5.4 18.4 7 15 322 4.8 32.3 7 7 225 5.3 13.8 11 11 426 6.3 6.3 20 22 423 4.1 25.4 9 9 521 6.3 31.1 7 11 319 3.0 62.9 8 11 219 3.0 62.4 11 13 318 1.0 65.8 9 11 425 4.9 17.0 6 11 524 6.1 6.3 18 22 525 7.9 6.3 12 20 324 6.8 13.8 8 8 424 6.9 6.3 10 17 326 6.3 6.3 9 15 321 6.3 31.1 5 7 425 5.7 13.8 9 12 127 6.0 6.3 6 8 315 3.0 83.2 11 15 517 5.4 72.6 9 15 119 3.0 58.8 11 13 222 4.2 40.5 7 11 325 5.7 13.8 9 9 327 6.0 6.3 15 19 3
>iro
TempBottom
2324242424242423232525262626232417172423252526262526262526
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Set Yr date Stat’n Latitude Longitude Reg- TempCPUE Surf.
56 95 7/26 Anc 37.4070 -76.1000 0.0057 95 7/26 Anc 37.3800 -76.0690 0.0058 95 7/26 Anc 37.5020 -76.1950 0.0059 95 7/27 Anc 37.2340 -76.2380 5.0060 95 8/15 Anc 36.9410 -76.0890 0.0061 95 8/19 M 37.1170 -76.0740 5.0062 95 8/19 K 37.1920 -76.0350 10.0063 95 9/12 M 37.1350 -76.0725 7.0064 95 9/12 K 37.1770 -76.0210 2.3865 95 9/19 Anc 37.2010 -76.0570 3.0066 95 9/20 Anc 37.1030 -76.0860 8.0867 96 6/24 M 37.0950 -76.0820 0.00 2668 96 6/24 K 37.1830 -76.0340 2.5069 96 6/25 K 37.1830 -76.0340 9.00 2570 96 6/28 Anc 36.9500 -76.1950 1.00 2671 96 6/28 Anc 37.0770 -76.2230 1.00 2672 96 7/4 Anc 37.3420 -76.2100 1.00 2473 96 7/24 M 37.0940 -76.0590 1.25 2274 96 7/24 Anc 37.2290 -76.0400 0.00 2575 96 7/24 K 37.1880 -76.0350 6.2576 96 7/24 Anc 37.8330 -75.9330 0.00 2677 96 7/24 Anc 37.7300 -75.9470 0.00 2678 96 7/25 Anc 37.1670 -76.0000 1.00 2679 96 7/25 Anc 37.1330 -76.0000 1.00 2380 96 7/25 Anc 37.2670 -76.0670 1.67 2781 96 7/25 Anc 37.1670 -76.0830 11.67 2682 96 8/13 M 37.0940 -76.0660 5.00 2583 96 8/13 Anc 37.1900 -76.2150 5.00 2584 96 8/20 K 37.1830 -76.0340 11.25 25
Salinity Salinity D.O. D.O. Dist. to min- max- setSurface Bottom Surf. Bottom mouth Depth Depth time
24 4.8 34.7 9 16 524 4.8 30.9 9 11 518 4.1 47.8 9 9 123 3.0 27.9 8 8 329 5.3 6.5 7 9 528 5.4 9.5 8 8 424 5.7 10.6 19 38 428 6.0 10.0 9 19 228 6.0 8.6 7 11 527 6.0 12.6 15 27 528 6.0 10.1 8 8 2
19 25 9 6.3 9.9 6 9 427 6.2 9.9 5 27 5
19 27 7 6.2 9.9 5 27 317 20 8 5.4 15.7 5 8 615 16 8 7.8 21.4 5 8 617 19 7 4.4 33.2 6 11 324 27 7 6.5 7.5 8 11 320 26 7 5.8 13.8 2 16 4
21 6.6 10.3 13 19 413 15 8 4.6 79.8 9 17 614 18 9 2.8 68.2 4 13 119 26 7 6.0 6.3 2 13 425 25 7 6.6 3.5 4 7 516 26 8 5.5 19.5 8 13 518 23 8 6.3 12.3 7 9 320 26 7 6.2 8.4 6 9 317 25 7 5.9 23.9 6 10 421 24 9 6.8 9.9 13 19 1
>IGO
TempBottom
2626262522242725252525222525242624212124252523232120252326
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Set Yr date Stat’n Latitude Longitude Reg- Temp Temp Salinity Salinity D.O. D.O. Dist. to min- max- setCPUE Surf. Bottom Surface Bottom Surf. Bottom mouth Depth Depth time
85 96 8/21 Anc 37.1670 -6.0830 10.00 26 25 15 23 9 5.5 12.3 7 9 186 96 8/21 Anc 37.5580 -76.2420 0.00 26 26 13 15 8 8.2 54.9 8 10 687 96 8/21 Anc 37.5000 -76.2570 0.00 26 25 14 17 9 3.7 50.3 4 6 688 96 9/3 Anc 37.2330 -76.2830 1.00 26 25 17 24 8 4.9 31.1 4 689 96 9/17 Anc 37.1670 -76.0830 1.00 24 24 18 19 7 6.1 12.3 7 9 390 96 9/17 K 37.1830 -76.0320 3.75 24 24 19 20 7 6.5 9.6 6 19 491 97 6/9 M 37.1210 -76.0710 1.00 18 16 19 27 6 5.2 9.6 6 9 492 97 6/9 K 37.1860 -76.0240 0.00 18 16 19 29 5 5.0 9.7 7 24 493 97 7/16 M 37.0950 -76.0700 11.25 26 25 22 25 5 5.0 8.3 6 9 294 97 7/16 K 37.1700 -76.0180 4.35 26 24 22 25 5 4.1 7.9 7 24 295 97 7/16 Anc 37.2130 -76.3450 0.00 28 26 19 21 5 4.6 35.8 5 6 696 97 7/16 Anc 37.3370 -76.3660 0.00 28 28 18 18 5 4.9 43.3 4 7 197 97 7/17 M 37.0970 -76.0700 16.67 25 22 24 26 6 5.4 8.4 6 9 298 97 7/17 K 37.1970 -76.0270 13.64 26 23 22 26 5 4.0 10.5 7 24 299 97 7/17 Anc 37.2790 -76.1010 4.00 27 22 20 27 6 4.4 21.6 20 27 4
100 97 7/17 Anc 37.4220 -76.1710 6.67 27 25 16 21 6 3.6 39.1 7 10 5101 97 7/17 Anc 37.3100 -76.1950 11.67 28 25 17 22 6 3.2 30.1 8 11 5102 97 8/13 M 37.0920 -76.0660 9.00 26 25 21 24 7 6.4 8.3 6 9 1103 97 8/13 K 37.2010 -76.0330 4.00 26 25 22 25 7 4.7 11.1 7 24 1104 97 8/13 Anc 37.5660 -76.0300 1.00 27 25 16 24 6 3.8 50.6 13 15 4105 97 8/13 Anc 37.6360 -76.0420 1.00 27 25 16 24 6 3.1 58.4 12 23 5106 97 8/13 Anc 37.7350 -75.9180 0.00 27 26 17 19 6 3.7 68.8 12 20 6107 97 8/13 Anc 37.7440 -76.0240 0.00 27 26 15 18 7 5.4 69.8 10 17 6108 97 8/14 Anc 37.7620 -76.1650 0.00 27 26 15 20 6 3.1 74.1 8 26 2109 97 8/14 Anc 37.6380 -76.1980 0.00 27 25 16 23 6 1.5 61.6 10 11 3110 97 8/14 Anc 37.5630 -76.1410 3.33 27 25 15 24 6 2.1 52.3 11 12 4111 97 8/14 Anc 37.4910 -76.1920 0.00 27 25 16 22 6 2.7 46.3 5 9 4112 97 8/14 Anc 37.1950 -76.2990 0.00 27 27 20 20 6 6.3 30.9 2 6 511397 9/17 M 37.0930 -76.0660 6.00 24 24 27 26 7 7.1 8.3 7 12 5
h - 00 00 CM N- CO CO in M" O 03 00 CO CO 03 03 CO CO 03 CO f CM ■M" 03 00 CO COT— t—
T— T—
00 CO CO 03 O O M" CO in ■t CO 00 co Is- CO M" o O CO Is- CO CO q o T—co COCO 03 ■t- 03 03 o CO 00 •r- r f 03 CO ■*—' rL ''f r 00 yL o 00 03 00 00 oo 03 d r- oT- T— T— T— T—CM T— CM T— CM CM T— r— CM T“
o c o c o c o c o i ' - f ' - i o c o i o i ' - ' f r c o c o r ' - C M o r - . c o i n i n i n c o i n o o i ' - c o c o c oOOCMCMCMCMCMCMCMCMCMCMCMCMCMCNCMCMCMCMCMCMCMCMCMCMCNCMCMCM
( j j f O n - l f i T r T f C O C O r f l f i C D r - T - C N j T - T - C O
03 E1/3
r ^ O T - f v j T t C O C S K O N Q O C O O O ^ - C O W l N £ ~_CM t— t— t — CN CM r r r r M r rCD mE Q
c CL .b 0)E O2 S*-• 3CO OQ E
g c n c n o o M - o o i c o i n o N c o c o o M - i - c D c o . o d d d d d d d d d d d d d d t ^ d d
q * N□ CD
N N C 0 S ( 0 0 ) C 0 C M C N O 0 ) 0 ) f l 0 C 0 N S S
Q %Q CO
£ E c o = *sCD Oco m
£ 8 = -cCD 3 CO CO
E c 0 o
H CDQ.E t0 3 I - CO
LU0 3 ?0 CLa: o 0■O 303 Co
O O C O C O T f ' t C O C O ' t ' t C O C M C O A-5
CMCOeOCOWOS'tCOCOOasCMi - CM CM t- CM CM t- CM t- CM
O 3 C O a ) O 3 N N 0 O O T j - 0 Md d i n r - . ' i ^ d d i ^ d ' t d d
h- h - C30 CO 03 0 S S S N
0 N M - 0 S O O 0 O M S 0 M , 0 0 C M t- M - 0 0 0CN C M C M i - i - C N C M t- i - t- i - t-CM CM CM CM CM CN CM CM CM
i* - co cm co coCM CM CM CM CM
M " O - M - C O C O ^ T t c O C M C O C M C 3 S S N S 0 l O U 3 O T - T - O O i n 0 C O N NCMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCMCM
■M- ' t i n •M" i n Is- Is- 03 00 00 00 03 r - r - Is- c o c o i n i n CM CM CO CO fo- Is- |o- OOCM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM
O O CM r - CO i n Is- CO o O CO Is- o i n i n CO CO o o i n m m o m m m O oO O 00 c o CO i - CO CO o O CO CO o CM Is- CO CO o i n o CM lo o CM h - 00 O inCM CO CM c d 00 c d d cd T - o d d d r — d d c d i n CM d c d q co d d d d o i ^
T_ T“ T_
O O o o o o o o o o o o Is- m CO 03 CO CO CO 00 CO 00 CM T f lo- r - COM 1CM 03 o o CM CO i n i n 00 o o o i n o CO CO 00 CO 03 00 CM CO CO 03 CO 03 00 ti n r* - T— 00 CM CO CO o CM f CM CO 03 CO 00 CO CO CO CM CO o CO CO i n COO o o o O o CM t - CM T - T - 03 o o o o o o d o o O o CM o od c d c d c d c d cd d d d d CO CO d d d d d d d d d c - d d d d d d d
r - 1'- . r* - Is- Is- r - . h - r-. h - r - N - r - Is- f - Co to- c - Is- lo- i (o- lo- fo- |o- Is- lo- lo-
0■o3
CD
CS o o o o o o o o O o o CO r - — oo CO i n O r— r - . i n N- CO 03 O ■M" CO i nh - CM i n |o- |o- i n o T—co o f CO CO h - N- m r-- N- T— o h - CO i n 00 COT“ CM CO T— 00 00 03 T—CM CO o CO 03 03 h - s 03 03 00 03 00 03 oo 03 00 o 00 COCM f- T— o o O T~ CM CO T— O t— T— CM O T—T— o T— O T— O T— CM ofol 1^ r-^ r-J 1^ r-^ 1^ is: 1^ r-J 1 1^ 1^ r - i 1^ 1^ 1^ 1^ r-‘ d 1^ 1^ 1^ i ^ N-CO CO CO CO CO CO CO CO CO CO CO CO CO CO CO CO CO CO CO CO CO CO CO CO CO CO CO co co
m o O a o o o o o o avw c c c c c c c c c cV) X S < < < < < < < < < 2 * < X.
CO 00 00 03 03 03 03 03 03 03 o o 03 oo 00 03 03 CM CM ' t CO CO CO CO CO 00 CO0) T~ T— ■*— CM CM CM CM CM CM CM CO CO T— T— T— T— T— CM CM CO CM CM r-.ro"O 03 03 03 d d n : r*- h - n : 00 CO 00 00 00 03 03 CO CO d CO CO cok_ h - r - r-. 00 00 oo oo 00 00 00 00 00 00 CO 00 oo 00 00 00 03 03 03 03 03 03 03 03 03 03> - 03 03 03 03 03 03 03 03 03 03 03 03 03 03 03 03 03 03 03 03 03 03 03 03 03 03 03 03 03
t in CO h - 00 03 O T— CM CO t in CO r - 00 03 O r— CM CO ■t- in CO r-. CO 03 O T— CM0 T— T— T~ T- T - T~ CM CM CM CM CM CM CM CM CM CM CO CO CO CO CO CO CO co CO CO f ■ f M"CO ■*“ t” '— T“ T“ T— T- T— T~ T- ■»” T- T—T—
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Set Yr date Stat’n Latitude Longitude Reg- Temp Temp Salinity Salinity D.O. D.O. Dist. to min- max- setCPUE Surf. Bottom Surface Bottom Surf. Bottom mouth Depth Depth time
143 99 8/18 Anc 37.2535 -76.2596 18.57 28 28 23 24 9 5.6 30.6 6 11 5144 99 8/18 Anc 37.2856 -76.316 0.00 29 28 23 24 8 6.0 36.5 6 8 5145 99 8/18 Anc 37.2226 -76.3325 0.00 28 28 22 23 7 6.4 34.7 5 9 6146 99 9/14 M 37.0925 -76.0698 2.50 24 24 30 30 8 7.5 8.3 6 9 4147 99 9/14 K 37.1820 -76.0166 0.00 24 24 26 29 8 7.2 8.7 6 22 4
>iO)
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
APPENDIX 2-A Temporal Delineation Data
Date Sunrise Sunset DL Stat standard Standardhrs CPUE hooks
05/29/90 5:48 20:20 14.53 K 2.00 10006/05/90 5:46 20:24 14.63 K 2.00 10007/16/90 5:58 20:27 14.48 K 3.00 10007/20/90 6:01 20:25 14.40 K 24.00 10007/20/90 6:01 20:25 14.40 M 13.98 9308/20/90 6:26 19:49 13.38 M 12.00 10008/20/90 6:26 19:49 13.38 K 8.57 7008/21/90 6:27 19:48 13.35 K 1.00 10009/04/90 6:39 19:28 12.82 K 19.00 10009/28/90 6:59 18:52 11.88 M 4.00 10010/10/90 7:09 18:34 11.42 K 15.00 10010/11/90 7:10 18:33 11.38 M 9.00 10006/04/91 5:46 20:24 14.63 K 9.00 10007/19/91 6:00 20:25 14.42 K 29.00 10008/12/91 6:20 20:03 13.72 M 15.00 10008/12/91 6:20 20:03 13.72 K 37.00 10009/20/91 6:52 19:04 12.20 K 6.00 10009/20/91 6:52 19:04 12.20 K 10.00 6007/02/92 5:50 20:31 14.68 K 30.00 10007/20/92 6:02 20:24 14.37 K 21.00 10008/18/92 6:25 19:51 13.43 K 17.00 10009/14/92 6:48 19:12 12.40 K 23.00 10007/21/93 6:02 20:24 14.37 K 26.00 10008/16/93 6:24 19:54 13.50 M 8.00 10008/16/93 6:24 19:54 13.50 K 14.00 10009/21/93 6:53 19:02 12.15 K 18.00 100
circle Circle C.plum. Temp- Temp- Sal- Sal-CPUE hooks Circle surf, bottom surf, bottom
18 1820 2026 25
25 2527 2727 272726 2621 2223 2320 20
27 2726 26
25 2525 2523 2326 23
24 2425 2527 2523 2324 23
C.plum.Stan.
223
24131261
194
1599
291537
66
3021172326
81418
>i-vi
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Date Sunrise Sunset DL Stat standard Standard C.plum. circle Circle C.plum. Temp- Temp- Sal- Sal-hrs CPUE hooks Stan. CPUE hooks Circle surf. bottom surf. bottom
06/16/94 5:45 20:30 14.75 K 23.75 80 1906/16/94 5:45 20:30 14.75 M 13.75 80 11 20 2007/06/94 5:52 20:31 14.65 K 7.50 80 6 28 2308/11/94 6:19 20:05 13.77 K 2.00 100 2 26 2207/05/95 5:52 20:31 14.65 M 17.00 100 17 26.4 2607/05/95 5:52 20:31 14.65 K 14.00 100 14 26.1 26.107/26/95 6:06 20:21 14.25 M 24.00 100 24 28.607/26/95 6:06 20:21 14.25 K 12.94 85 11 29.1 28.608/19/95 6:26 19:51 13.42 K 5.00 100 508/19/95 6:26 19:51 13.42 M 10.00 100 1009/12/95 6:45 19:17 12.53 M 7.00 100 709/12/95 6:45 19:17 12.53 K 2.38 84 205/09/96 6:02 20:05 14.05 M 0.00 50 0 0.00 50 0 15.6 13.7 18.3 25.305/09/96 6:02 20:05 14.05 K 0.00 50 0 0.00 50 005/28/96 5:49 20:20 14.52 K 0.00 80 0 7.50 40 3 17.95 15.3 18.9 27.205/28/96 5:49 20:20 14.52 M 1.25 80 1 18.3 17.1 17 25.806/24/96 5:47 20:32 14.75 M 3.75 80 3 0.00 40 0 25.9 22.3 18.7 24.806/24/96 5:47 20:32 14.75 K 2.50 80 2 12.50 40 506/25/96 5:48 20:32 14.73 K 9.00 100 9 25.4 24.8 19.3 26.207/24/96 6:05 20:22 14.28 M 0.00 80 0 2.50 40 1 22.3 20.5 24.4 27.307/24/96 6:05 20:22 14.28 K 6.25 80 5 17.50 40 707/25/96 6:06 20:21 14.25 M 11.67 60 7 27.50 40 11 25.8 20.4 17.6 27.908/13/96 6:21 20:02 13.68 M 5.00 80 4 12.50 40 5 24.8 24.7 20 26.108/20/96 6:27 19:49 13.37 K 22.50 80 18 22.50 40 9 24.95 25.9 16.5 23.508/21/96 6:27 19:49 13.37 M' 10.00 60 6 20.00 40 8 25.95 24.69 15.4 2309/17/96 6:50 19:08 12.30 M’ 0.00 80 0 2.50 40 1 24.1 24 18.1 19.309/17/96 6:50 19:08 12.30 K 3.75 80 3 2.50 40 1 24.4 23.9 19.1 20.310/02/96 7:03 18:45 11.70 M 0.00 60 0 0.00 40 0 21.6 21.1 20.2 2410/02/96 7:03 18:45 11.70 K 0.00 60 0 0.00 40 0 21.6 20.9 22.3 26.3
>I00
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Date Sunrise Sunset DL Stat standard Standard C.plum. circle Circle C.plum. Temp- Temp- Sal- Sal-hrs CPUE hooks Stan. CPUE hooks Circle surf. bottom surf. bottom
05/12/97 6:00 20:07 14.12 M 0.00 80 0 0.00 30 0 15.49 14.77 20.3 23.405/12/97 6:00 20:07 14.12 K 0.00 80 0 0.00 30 0 15.44 14.62 19.4 22.705/27/97 5:49 20:19 14.50 K 1.00 100 1 18 1606/09/97 5:46 20:27 14.68 M 1.00 100 1 0.00 40 0 17.64 15.9 19.2 27.406/09/97 5:46 20:27 14.68 K 0.00 100 0 0.00 40 0 17.61 15.89 18.5 29.207/16/97 5:59 20:27 14.47 M 11.25 80 9 12.50 40 5 25.82 24.84 22.4 24.907/16/97 5:59 20:27 14.47 K 4.35 69 3 15.00 40 6 26.2 23.48 21.7 25.407/17/97 6:00 20:27 14.45 M 16.67 60 10 28.00 50 14 24.63 22.38 23.9 26.407/17/97 6:00 20:27 14.45 K 13.64 88 12 10.26 39 4 25.6 23.39 22.1 25.508/13/97 6:21 20:02 13.68 M 9.00 100 9 16.00 50 8 25.71 24.84 21.3 24.108/13/97 6:21 20:02 13.68 K 4.00 100 4 12.00 50 6 26.07 24.82 21.8 25.109/17/97 6:50 19:08 12.30 M 6.00 100 6 19.00 100 19 24.41 24.27 26.7 26.809/18/97 6:51 19:07 12.27 K 2.00 100 2 21.28 94 20 24.03 24.19 24.6 29.609/18/97 6:51 19:07 12.27 M 6.00 50 3 11.58 95 11 24.06 23.85 27.1 28.210/02/97 7:03 18:46 11.72 M 2.00 100 2 4.00 25 1 20.65 20.58 25.9 27.610/02/97 7:03 18:46 11.72 K 1.00 100 1 4.00 25 1 20.63 21.04 23.9 25.305/07/98 6:05 20:03 13.97 M 0.00 80 0 0.00 40 0 18.2 16.35 18.2 23.205/07/98 6:05 20:03 13.97 K 0.00 80 0 0.00 40 0 18.03 15.46 19.4 22.605/27/98 5:50 20:19 14.48 M 0.00 80 0 0.00 40 0 19.91 18.85 18.8 23.205/27/98 5:50 20:19 14.48 K 0.00 80 0 0.00 40 0 19.46 19.23 19.3 24.706/29/98 5:49 20:32 14.72 M 16.67 60 10 22.50 40 9 23.96 23.24 19.4 22.806/29/98 5:49 20:32 14.72 K 8.33 60 5 20.00 40 8 25.22 22.46 16.9 2607/29/98 6:09 20:18 14.15 M 12.50 80 10 27.50 40 11 26.58 23.59 19.5 26.807/29/98 6:09 20:18 14.15 K 13.75 80 11 22.50 40 9 26.79 23.82 20.2 26.708/18/98 6:25 19:52 13.45 M 11.25 80 9 22.50 40 9 27.15 27.28 25.4 2608/18/98 6:25 19:52 13.45 K 13.75 80 11 37.50 40 15 27.34 27.42 25 2709/22/98 6:54 19:01 12.12 M 5.00 80 4 10.00 40 4 24.96 25.02 23.5 27.309/22/98 6:54 19:01 12.12 K 2.50 80 2 17.50 40 7 24.93 25.37 24.8 28.110/01/98 7:02 18:48 11.77 M 6.25 80 5 12.50 40 5 24.08 24.04 25.7 26.7
>ICO
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
Date Sunrise Sunset DL Stat standard Standard C.plum. circle Circle C.plum. Temp- Temp- Sal- Sal-hrs CPUE hooks Stan. CPUE hooks Circle surf. bottom surf. bottom
10/01/98 7:02 18:48 11.77 K 5.00 80 4 5.00 40 2 24.32 24.32 24.7 26.605/19/99 5:55 20:13 14.30 M 0.00 80 0 0.00 40 0 18.73 17.57 24 26.505/19/99 5:55 20:13 14.30 K 0.00 80 0 0.00 40 0 17.26 16.87 25.5 28.706/04/99 5:47 20:24 14.62 K 6.43 140 9 29.00 100 29 21.79 20.3 22.8 24.5
6/14/99 5:46 20:29 14.72 M 3.75 80 3 17.50 40 7 22.28 20.93 24.76/14/99 5:46 20:29 14.72 K 0.00 80 0 10.00 40 4 22.84 20.96/23/99 5:47 20:32 14.75 M 3.75 80 3 22.50 40 96/23/99 5:47 20:32 14.75 K 5.00 80 4 12.50 40 5
7/8/99 5:53 20:31 14.63 M 6.25 80 5 2.50 40 1 26.27 24.72 27.2 27.57/8/99 5:53 20:31 14.63 K 3.75 80 3 50.00 40 20 27.29 25.57 26.1 27.3
8/18/99 6:25 19:53 13.47 M 0.00 80 0 5.00 40 2 27.31 27.31 25.9 25.98/18/99 6:25 19:53 13.47 K 17.50 80 14 32.50 40 13 27.7 27.06 25.9 27.99/14/99 6:47 19:14 12.45 M 3.75 80 3 32.50 40 13 23.76 23.76 30.4 30.29/14/99 6:47 19:14 12.45 K 0.00 80 0 15.00 40 6 23.96 24.03 26 29.210/6/99 7:06 18:41 11.58 M 1.25 80 1 20.00 40 8 20.53 20.6 28.7 29.510/6/99 7:06 18:41 11.58 K 0.00 80 0 5.00 40 2 20.41 20.5 28.9 29.7
>Io
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
APPENDIX 2-B Tag-Return Data
Tag# Date Date Tag Tag Recp Recp Dist days T TT L R R GroupTagged Recap. Latitude Longitude Latitude Longitude km PCL PCL TL
R9670 8/18/92 3/29/98 37.2000 -76.0333 34.7533 -76.0217 300 2049 66 91 73 100 Commercial211102 7/4/95 1/15/97 36.9167 -75.7000 32.8333 -77.8333 560 561 120 167 Commercial
64 8/19/95 7/11/96 37.1833 -76.0333 37.2667 -75.8967 30 326 75 102 79 108 VIMS135 9/14/95 5/28/96 37.1167 -76.1000 37.1833 -76.0333 9.5 225 49 66 51 70 VIMS146 9/18/95 3/24/98 36.7500 -75.8667 34.5267 -75.9617 270 918 78 78 69 94 Commercial186 5/29/96 8/19/96 36.7500 -75.8667 37.0833 -76.0167 37 82 58 78 Recreational196 6/24/96 7/30/96 37.1833 -76.0333 37.4000 -76.0450 24 30 42 57 Recreational252 7/24/96 8/24/96 37.1833 -76.0333 37.0917 -75.9917 11 31 47 64 Recreational297 8/21/96 8/31/96 37.1667 -76.0833 37.1383 -76.2333 14 10 46 64 Recreational353 8/21/96 9/12/98 37.1667 -76.0833 36.7500 -75.9333 48 752 47 64 67 91 Recreational439 7/15/97 7/6/99 37.2611 -75.8980 37.2583 -75.8983 0.75 721 69 94 82 112 VIMS489 7/17/97 8/2/97 37.1000 -76.0667 37.2000 -76.2500 20 16 47 65 Recreational533 7/17/97 8/9/97 37.4167 -76.1667 37.4000 -76.2000 3.5 23 42 57 Recreational480 7/17/97 9/14/97 37.1000 -76.0667 37.3583 -76.2717 34 59 42 57 Recreational517 7/17/97 11/15/97 37.2667 -76.1000 35.2000 -75.6833 260 121 66 66 70 95 Commercial509 7/17/97 7/31/98 37.2000 -76.0333 33.9500 -77.9667 540 379 45 61 63 86 NC Aquarium507 7/17/97 8/1/00 37.1833 -76.0167 37.3000 -75.8000 45 1109 63 87 Commercial578 8/12/97 8/21/97 37.2667 -75.8967 37.2667 -75.8967 0 9 47 65 Recreational648 9/17/97 8/28/99 37.0930 -76.0670 37.0450 -76.0667 4.5 710 49 66 69 94 Recreational652 9/17/97 10/15/97 37.1000 -76.0667 37.1550 -76.2500 17.5 28 49 66 Commercial650 9/17/97 7/15/98 37.1000 -76.0667 37.0333 -76.0667 7 301 46 64 Recreational678 9/18/97 8/15/98 37.2167 -76.0500 37.2017 -76.0317 2.5 331 48 65 Recreational718 10/2/97 6/30/98 37.1833 -76.0167 37.0917 -75.9917 10.75 271 53 72 Recreational877 7/8/98 7/8/99 37.2105 -76.0620 37.2000 -76.2183 14 365 57 78 65 88 VIMS927 7/29/98 8/30/98 37.1833 -76.0333 37.1667 -76.9967 4 32 49 65 Recreational921 7/29/98 6/11/99 37.1867 -76.0317 37.1383 -76.2333 19 317 41 56 61 84 Commercial
>I
Reproduced
with perm
ission of the
copyright owner.
Further reproduction prohibited
without perm
ission.
908 7/29/98 6/28/99 37.0667 -76.0967 37.1667948 7/30/98 8/15/98 37.2067 -76.1200 37.2167973 8/12/98 6/25/99 37.1345 -76.0878 37.0770998 8/17/98 8/24/99 37.2925 -75.7900 37.3217988 8/17/98 9/30/98 37.2517 -75.8987 37.2600978 8/17/98 11/17/99 37.2596 -76.8989 35.5167
1026 8/18/98 5/23/00 37.2000 -76.0400 32.15001043 8/19/98 2/10/99 37.1450 -75.9900 34.63331073 8/19/98 10/25/00 37.1333 -75.9833 35.19171162 9/30/98 2/10/99 37.2883 -75.7833 35.03331210 5/25/99 5/29/99 37.2600 -75.8983 37.04501409 7/8/99 8/15/99 37.1820 -76.2130 37.28331389 7/8/99 9/10/99 37.1830 -76.0190 37.15001576 8/18/99 8/29/99 37.2200 -76.3200 37.13831618 10/6/99 8/5/00 36.7500 -75.8700 39.59001962 8/21/00 8/25/00 37.2000 -76.0333 37.20001968 8/21/00 10/30/00 37.1000 -76.0667 35.0833
-76.4000 32 334 41 56 59 81 Recreational-76.2667 13 15 58 79 Recreational-76.2230 13.75 317 48 65 Recreational-75.8417 5.5 372 77 103 78 107 Recreational-75.8983 0.5 44 49 67 51 70 VIMS-75.4633 200 457 46 63 63 86 Recreational-80.8333 830 644 46 64 78 107 Recreational-76.6333 390 176 63 85 Commercial-75.6000 330 787 44 59 67 91 Commercial-75.9667 300 133 55 75 Commercial-76.0667 32 4 55 74 Recreational-76.3067 14 38 43 59 Recreational-76.9800 5 64 47 63 Recreational-76.0833 23 11 47 64 Commercial-74.2857 350 304 69 92 Recreational-76.0283 0.5 4 46 64 Recreational-75.8500 280 70 48 67 Commercial