assessing the ecological implications of the altered flow
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All Graduate Theses and Dissertations Graduate Studies
12-2018
Assessing the Ecological Implications of the Altered Flow and Assessing the Ecological Implications of the Altered Flow and
Sediment Regimes of the Rio Grande Along the West Texas-Sediment Regimes of the Rio Grande Along the West Texas-
Mexico Border Mexico Border
Demitra E. Blythe Utah State University
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ASSESSING THE ECOLOGICAL IMPLICATIONS OF THE ALTERED FLOW AND
SEDIMENT REGIMES OF THE RIO GRANDE ALONG THE WEST TEXAS-
MEXICO BORDER
by
Demitra E. Blythe
A thesis submitted in partial fulfillment
of the requirements for the degree
of
MASTER OF SCIENCE
in
Aquatic Ecology
Approved:
______________________ ____________________
Phaedra Budy, Ph.D. John C. Schmidt, Ph.D.
Major Professor Committee Member
______________________ ____________________
Janice Brahney, Ph.D. Laurens H. Smith, Ph.D.
Committee Member Interim Vice President for Research and
Dean of the School of Graduate Studies
UTAH STATE UNIVERSITY
Logan, Utah
2018
ii
Copyright © Demitra Blythe 2018
All Rights Reserved
iii
ABSTRACT
Assessing the ecological implications of the altered flow and sediment regimes of the Rio
Grande along the West Texas-Mexico border
by
Demitra E. Blythe, Master of Science
Utah State University, 2018
Major Professor: Dr. Phaedra Budy
Department: Watershed Sciences
Development of water resources alters flow and sediment regimes, often at the
expense of aquatic ecological integrity and diversity. Many riverine species within the
Rio Grande have been reduced or eradicated from the loss, or fragmentation, of viable
habitat, poor water quality, and spread of invasive species. The primary goal of our study
was to determine how the modified hydrologic and sediment regimes of the Rio Grande
have impacted the aquatic ecological integrity, with an emphasis on native fish diversity.
We used a multi-faceted approach to 1) examine the fish community between two
segments of the river with varying levels of degradation (e.g., Forgotten Reach and Big
Bend region) using a novel modeling approach, 2) quantify and describe the native fish
community and aquatic food web structure in Big Bend National Park, and 3) determine
if and how invasive vegetation impacts aquatic food resources. We found native fish
richness to be more influenced by channel narrowing and low flows in the Big Bend
region than in the Forgotten Reach. In contrast, water quality and channel narrowing
were the most influential variables describing native fish richness in the Forgotten Reach.
iv
We found fish abundance (based on catch per unit effort estimates) was greatest and
lowest in reaches we a priori classified as complex and simple, respectively, at the
microhabitat scale. At the macrohabitat scale, where we compared abundance between
alluvial valleys and canyons, fish abundance was greater in canyon reaches than in
alluvial reaches. Food web structure followed a similar pattern as fish abundance,
appearing to be more complex within complex and canyon sites, than simple and alluvial
sites. Contrary to what we expected, native vegetation and periphyton appear to be
contributing to the base of the aquatic food web more than nonnative vegetation.
Collectively, our results suggest the modern flow and sediment regimes may limit the
available habitat and potential food resources suitable for native fishes within the Rio
Grande. Native fish recovery and maintenance may depend largely on the effective
management of stream flow in the Rio Grande, and concordant changes in lower level
productivity and food availability.
(144 pages)
v
PUBLIC ABSTRACT
Assessing the ecological implications of the altered flow and sediment regimes of the Rio
Grande along the West Texas-Mexico border
Demitra E. Blythe
Large, exotic (those whose headwaters are in distant places) rivers are some of the
most unique and diverse ecosystems on earth. Because they often flow through a
multitude of biomes and climates, their waters are a vital resource not only for the
organisms that inhabit these rivers, but for human societies as well. Thus, large rivers,
like the Rio Grande, that flow through arid and agricultural regions are highly regulated
and diverted. Regulation and dewatering upset a river’s natural flow regime (e.g.,
magnitude, duration, timing of large flood events), subsequently impacting the river’s
ability to transport its sediment supply, and eventually perturbing a river into either
sediment surplus or deficit. The combination of altered flow and sediment regimes
influence the availability of habitat essential for the survival and viability of aquatic
organisms, such as fish and invertebrates. In addition, increased deposition of sediment
creates areas suitable for invasive riparian vegetation to establish, likely affecting habitat
complexity and increasing the abundance of leaf litter deposited into the river. The
altered flow and sediment regimes, in combination with invasive riparian vegetation,
culminate and eventually affect the food resources and aquatic communities present in a
river ecosystem. Most often, the links between the physical perturbations to a system
with the biological factors are poorly understood. In this study, we use distinct segments
of the Rio Grande along the US-Mexico border to compare areas with greater and lower
habitat heterogeneity, water quality, and invasive riparian species abundance to better
vi
understand what physical factors can influence aquatic species such as fish and
invertebrate communities. We identify critical limiting factors for the native fish
community present, and link the altered flow and sediment regimes with the aquatic
ecological template of the Rio Grande.
vii
ACKNOWLEDGMENTS
This work was funded by the US Geological Survey South Central Climate
Science Center, the National Park Service, and the Utah State University Ecology Center,
thank you for making this thesis possible. I want to give my husband, Todd Blythe, the
biggest thank you. His levelheadedness and his deep well of patience and encouragement,
gave me what I needed to pursue and persevere my Master’s degree. I am incredibly
thankful to both my advisor, Phaedra Budy, and Jack Schmidt (Todd’s advisor) for
allowing us the opportunity to work on a beautiful, unique river together. I also give my
gratitude to my parents, Gerald and Stephanie, they have always been incredibly
supportive of whatever I do in my life, and they gave me my passion for exploring the
outdoors and pushing my limits. To Phaedra, you have my sincerest respect and gratitude.
You have been a wonderful mentor, role model, friend, and absolute QUEEN of this
fisheries world! You gave me the extra boost of confidence to succeed in the fisheries
realm and to become more than I even expected of myself. To Jack, I am so thankful you
were a part of my project. You helped me think outside the box and pushed me to always
see the bigger picture, you have made me a stronger person because of it. A HUGE thank
you to Jeff Bennett, Dave Larson, Lydia Smith, and the rest of the Science Resource and
Management and Rio Cache staff. Your help and organization with our field trips was
absolutely necessary and would not have been possible without your generosity! To Brian
Laub, Dr. Laub-ster, thank you so much for your guidance, wisdom, and personality
throughout my project and on our field trips, you are awesome! A BIG thank you to my
fellow FEL and GLEL lab mates, your willingness to help with my field work in a remote
place and your wisdom and guidance throughout my project was so beneficial, I feel
viii
more confident in myself because of you guys! Finally, thank you to all the friends,
colleagues, and professors at USU who made being a part of the Natural Resources and
Watershed Department so much fun!
Demitra E. Blythe
ix
CONTENTS
Page
ABSTRACT ...................................................................................................................... iii
PUBLIC ABSTRACT ........................................................................................................v
ACKNOWLEDGMENTS ............................................................................................... vii
LIST OF TABLES ........................................................................................................... xii
LIST OF FIGURES ......................................................................................................... xiii
CHAPTER
1. INTRODUCTION .................................................................................................1
Literature Cited .................................................................................................5
2. REMEBERING A ‘FORGOTTEN’ RIVER: IDENTIFYING
CRITICAL LIMITING FACTORS FOR THE NATIVE FISH
DIVERSITY OF THE RIO GRANDE IN FAR WEST TEXAS ..........................7
Abstract ..............................................................................................................7
Introduction .......................................................................................................8
Contemporary Ecosystem of the Rio Grande ...........................................11
Hydrology ......................................................................................11
Geomorphology .............................................................................14
Associated Changes to the Ecosystem Structure and Function .................15
Methods ...........................................................................................................20
Study Area .................................................................................................20
Data Collection ..........................................................................................21
Fish community and water quality .................................................21
Hydrologic and geomorphic data ...................................................22
Structural Equation Modeling ....................................................................22
Determination of model fit.............................................................23
SEM Conceptual Model .............................................................................24
Defining direct hydrologic and geomorphic variables ...................24
Defining water quality as a latent variable ....................................24
Nonnative fish abundance ..............................................................25
Data Preparation.........................................................................................25
Results ..............................................................................................................26
Comparison of Big Bend Region and Forgotten Reach Models................26
Examining SEM Results Using Standardized Coefficients .......................27
Big Bend SEM model ....................................................................27
Forgotten Reach SEM results ........................................................28
x
Discussion ........................................................................................................29
Mechanisms Influencing Ecosystem Structure and Function ....................29
Identifying Critical Limiting Factors for Each Study Region ...................31
Comparing the Forgotten Reach and Big Bend Regions ...........................34
Model Limitations ......................................................................................35
Implications................................................................................................36
Acknowledgments............................................................................................37
Literature Cited ................................................................................................37
Tables and Figures ...........................................................................................46
3. TOO MUCH SEDIMENT, TOO LITTLE WATER: HOW THE
MODIFIED HYDROLOGIC AND SEDIMENT REGIMES OF THE
RIO GRANDE AFFECT THE NATIVE FISH COMMUNITY AND
FOOD WEB STRUCTURE IN BIG BEND NATIONAL PARK, TEXAS,
UNITED STATES ...............................................................................................53
Abstract ............................................................................................................53
Introduction ......................................................................................................55
Study Area .......................................................................................................60
Methods............................................................................................................63
Field Sampling ...........................................................................................63
Habitat complexity and water quality ............................................63
Fish community .............................................................................64
Primary productivity ......................................................................65
Terrestrial and aquatic invertebrates ..............................................66
Laboratory Methods ...................................................................................68
Chlorophyll a abundance and community analysis .......................68
Terrestrial and aquatic invertebrate community classification ......68
Stable isotope preparation ..............................................................70
Statistical Analyses ....................................................................................71
Random forest classification ..........................................................71
Fish community diversity ..............................................................73
Stable isotope analyses ..................................................................73
Results ..............................................................................................................77
Habitat Complexity ...........................................................................77
Fish Abundance and Diversity ..........................................................77
Abundance of Primary Productivity ..................................................78
Terrestrial and Aquatic Invertebrate Relative Abundance ................79
Stable isotope analyses ......................................................................80
Discussion ........................................................................................................82
Implications................................................................................................92
Acknowledgments............................................................................................93
Literature Cited ................................................................................................94
Tables and Figures .........................................................................................102
4. CONCLUSIONS ................................................................................................116
xi
APPENDIX...............................................................................................................120
xii
LIST OF TABLES
Table Page
2.1 Structural equation model fit statistics calculated
for the Big Bend region and Forgotten Reach ..............................................46
2.2 Structural equation model path coefficients
calculated for the Big Bend region and Forgotten Reach ............................47
3.1 Summary of the measured discharges occurring over
the entire sampling period in the Big Bend region .....................................102
3.2 Catch per unit effort summary statistics .....................................................102
3.3 Layman community fish community metrics .............................................103
3.4 Three-source mixing model summary results
for fish as consumers ..................................................................................103
3.5 Two-source mixing model summary results using
the Hydrogen isotope .................................................................................103
3.6 Three-source mixing model summary results
for aquatic invertebrates as consumers .......................................................103
A1 Average water quality parameters during all
sampling events ..........................................................................................121
A2 Average length and weight results for all fish
captured in complex and simple sites .........................................................123
A3 Average length and weight results for all fish
captured in alluvial and canyon sites ..........................................................124
A4 Total summed abundance of terrestrial invertebrates
captured during the sampling periods ........................................................125
A5 Average relative abundance for aquatic invertebrate
functional feeding groups ...........................................................................126
A6 Shannon’s Diversity Index calculated for terrestrial
invertebrates ...............................................................................................127
xiii
LIST OF FIGURES
Figure Page
2.1 Study area map for the Forgotten Reach
and Big Bend region .....................................................................................48
2.2 Conceptual diagram for the hypothesized
structural equation model .............................................................................49
2.3 Big Bend region structural equation model result ........................................50
2.4 Forgotten Reach structural equation model
using the Big Bend model construct ............................................................51
2.5 Final structural equation model structure for
the Forgotten Reach .....................................................................................52
3.1 Study area map for the Big Bend region sampling
sites .............................................................................................................104
3.2a Conceptual diagram of the mapped mesohabitats ......................................105
3.2b Relative proportion of mapped mesohabitats
across varying levels of habitat heterogeneity ...........................................105
3.3 Average depths and velocities calculated during
each sampling discharge period .................................................................106
3.4 Random forest variable importance plot ....................................................106
3.5a Average catch per unit effort of fish caught
udring sampling periods .............................................................................107
3.5b Shannon’s Diversity Index calculated for the
fish community across varying degrees of habitat heterogeneity ..............107
3.6 Average chlorophyll a abundance across varying
degrees of habitat heterogeneity .................................................................108
3.7 Average relative abundance of aquatic invertebrate
functional feeding groups ..........................................................................109
3.8 Calculated food chain lengths for fish occurring
in complex and simple sampling sites ........................................................110
xiv
3.9 Whole food web structure for varying levels
of habitat heterogeneity ..............................................................................111
3.10 Stable isotope ellipses calculated for the fish community
across varying levels of habitat heterogeneity ...........................................112
3.11 Three-source mixing model summary results
for fish as consumers ..................................................................................113
3.12 Two-source mixing model summary results using
the Hydrogen isotope .................................................................................114
3.13 Three-source mixing model summary results
for aquatic invertebrates as consumers .......................................................115
A1 Total area calculated for each the varying levels of
habitat heterogeneity ..................................................................................127
A2 Average catch per unit effort of fish for each sampling
month ..........................................................................................................128
A3 Fish species evenness calculated across the varying
levels of habitat heterogeneity ....................................................................129
A4 Chlorophyll a abundance calculated by season ..........................................130
CHAPTER 1
INTRODUCTION
Rivers are some of the most ecologically-dynamic and complex systems globally.
However, river ecosystem structure and function are increasingly threatened by the
anthropogenic demand for water (Hauer and Lorang 2004). Thus, the natural flow and
sediment regimes of many rivers have been dramatically altered (e.g., Johnson and others
1995; Poff and others 1997; Allan 2004; Lytle and Poff 2004; Nilsson and others. 2005,
Schmidt and Wilcock 2008, Wohl and others 2015). Water diversion and impoundment
affects the timing and delivery of natural stream flows, ultimately changing
characteristics of the flow regime including magnitude, frequency, and duration of
stream-flow events (Magilligan and Nislow 2005). Diminishing any aspect of a river’s
flood regime can have important geomorphic and ecological consequences.
The natural stream flow variability of a river has been established as the ‘master’
variable for maintaining river ecosystem health and biodiversity (e.g., Walker and others
1995; Poff and others 1997; Hart and Finelli 1999; Poff and others 2010). Large flood
events are important for channel development and maintain habitat complexity (Junk and
others 1989; Ward and Stanford 1989; Ligon and others 1995). Reductions and
simplifications to channel morphology, such as narrowing, aggradation, or incision can
limit instream habitat complexity and create losses in aquatic taxa abundance and
diversity (Ligon and others 1995; Bunn and Arthington 2002). In addition, flows that
maintain longitudinal and lateral connectivity with the active floodplain drive food web
structure and function (Wootton and others 1996; Power and others 1996). Floodplains
that are regularly (or seasonally) inundated increase the area available for aquatic
2
biological activity, and organic material stored in the active floodplain becomes available
for associated consumers. Also, many aquatic species depend on spring flood pulses for
spawning cues (Resh and others 1988; Brouder 2001; King and others 2003; Archdeacon
2016). Finally, the combination of stream flow and channel geometry determines water
level and resource availability, which effectively help or hinder invasive species and the
efficiency of energy dynamics within a river (Bunn and Arthington 2002, Stromberg and
others 2007). A river’s ecosystem health is therefore dependent on stream flows that can
sustain habitat complexity, life history traits, and the endemic taxa. None of this
literature, however, considers that the sediment mass balance is perturbed into sediment
surplus, as is the case in the Big Bend region.
Adequate stream flow and sediment supply are necessary for maintaining
freshwater biodiversity, because each are important drivers of ecosystem processes, drive
species communities and abundances, and maintain habitat heterogeneity and water
quality (e.g., Poff and others 1997; Hart and Finelli 1999; Poff and others 2010). Habitat
heterogeneity can provide adequate areas for growth, safety from predators, food
resources, and reproduction. Varying types of habitat often facilitate the coexistence of
multiple species, and aquatic diversity is often associated with complex habitat
heterogeneity (Nagayama and Nakamura 2018). In addition, water quality, specifically
salinization, is an increasing threat to freshwater ecosystems and water resources globally
(Williams 1999). The ecological effects often include losses in biodiversity, shifts in
community structure, or even trophic cascades. Finally, modified flow and sediment
supply can facilitate nonnative species establishment, potentially displacing native
species and homogenizing both aquatic and terrestrial riparian communities (Thomaz and
3
others 2015). Determining how the hydrological, geomorphic, and ecological variables
interact in large, exotic rivers (e.g., rivers whose flow regime is derived from the climate
of distant headwaters), as is the case for the exotic Rio Grande, is an important question
for effective environmental flow management and conservation efforts.
Linking the physical and ecological components of a river ecosystem has become
a critical aspect of understanding how the aquatic ecosystem interacts with its
hydrological and geomorphic environments (Vaughan and others 2009; Elosegi and
others 2010; Poole 2010). However, there is an added complexity when incorporating
both the physical and ecological environment. Often the questions of interest are better
explained by using multiple direct and indirect variables (Arhonditsis and others 2006).
For the first chapter of my thesis, we used structural equation modeling (SEM) as a
method to address the above ecological complexity, because SEM allows the user to test
both direct and indirect, or intermediary, effects on an overall response variable, or
variables. The overall goals of this study were to provide a synthesis of the state of
knowledge on the existing ecological condition between two hydrologically and
geomorphologically distinct parts of the Rio Grande, the Forgotten Reach and the Big
Bend region. Second, we used that body of knowledge to identify testable hypotheses of
the most important limiting factors for ecosystem integrity within two regions. We also
used SEMs to test those hypotheses, and we compared SEMs between the two river
segments, each with varying degrees of contemporary ecological integrity.
For the second chapter of my thesis, the overarching goal for this study was to use
a habitat availability and food web-based approach to examine how changes to the
physical environment alter the aquatic ecological structure and function of a desert river
4
ecosystem. Within this context, I had two objectives to better understand the altered
physical and ecological environment and associated effects on the native fish community
of the highly regulated Rio Grande along the US-Mexico border. My first objective was
to identify the effects of the altered hydrologic and sediment regimes of the Rio Grande
on habitat heterogeneity and the associated native fish community. Based on prior
geomorphic assessments (Dean and Schmidt and others 2011, 2013; J. Bennett, personal
communication), I chose a suite of sampling sites exhibiting varying levels of channel
morphology and simplification throughout the river. I predicted sampling sites with
greater habitat heterogeneity would be more complex and contain greater fish diversity
than sites with low habitat heterogeneity. My second objective was to determine if and
how these altered hydrologic and sediment regimes have potentially affected energy flow
pathways (e.g., basal resource production) and structure of the aquatic food web. I
predicted food web structure (e.g., food chain length, trophic niche space) would be more
complex within sites with greater habitat heterogeneity than sites with low habitat
heterogeneity because I assumed complex sites to better partition resource-use and
facilitate multiple species. I also predicted nonnative vegetation alters the food web
function by contributing differentially to dietary items of upper trophic-level consumers
than does native vegetation. Overall, I hope to contribute to the ecological knowledge
surrounding desert food web structure and function and provide insight to more effective
and innovative stream flow management solutions for the Rio Grande both in the
Forgotten Reach, and in the Big Bend region, arguably the most ecologically-intact reach
of this river that remains.
5
Literature Cited
Allan JD. 2004. Landscapes and riverscapes: the influence of land use on stream
ecosystems. Annual Review of Ecology, Evolution, and Systematics 35: 257-284.
Archdeacon TP. 2016. Reduction in spring flow threatens Rio Grande Silvery Minnow:
trends in abundance during river intermittency. Transactions of the American
Fisheries Society 145: 754-765.
Brouder MJ. 2001. Effects of flooding on recruitment of roundtail chub, Gila robusta, in
a southwestern river. The Southwester Naturalist 46: 302-310.
Bunn SE, Arthington AH. 2002. Basic principles and ecological consequences of altered
flow regimes for aquatic biodiversity. Environmental Management 30: 492-507.
Hart DD, Finelli CM. 1999. Physical-biological coupling in streams: the pervasive effects
of flow on benthic organisms. Annual Review of Ecology and Systematics 30:
363-395.
Hauer FR, Lorang MS. 2004. River regulation, decline of ecological resources, and
potential for restoration in a semi-arid lands river in the western USA. Aquatic
Sciences 66: 388-401.
Johnson BL, Richardson WB, Naimo TJ. 1995. Past, present, and future concepts in large
river ecology. BioScience 45: 134-141.
Junk WJ, Bayley PB, Sparks RE. 1989. The flood pulse concept in river-floodplain
systems. Canadian Special Publication of Fisheries and Aquatic Sciences 106:
110-127.
King AJ, Humphries P, Lake PS. 2003. Fish recruitment on floodplains: the roles of
patterns of flooding and life history characteristics. Canadian Journal of Fisheries
and Aquatic Sciences 60: 773-786.
Ligon FK, Dietrich WE, Trush WJ. 1995. Downstream ecological effects of dams.
Bioscience 45: 183-192.
Lytle, DA, Poff NL. 2004. Adaptation to natural flow regimes. Trends in Ecology and
Evolution 19: 94-100.
Magilligan FJ, Nislow KH. 2005. Changes in hydrologic regime by dams.
Geomorphology 71: 61-78.
Nilsson, C, Reidy CA, Dynesius M, Revenga C. 2005. Fragmentation and flow regulation
of the world’s large river systems. Science 308: 405-408.
6
Poff NL, Allan JD, Bain MB, Karr JR, Prestegaard KL, Richter BD, Sparks RE,
Stromberg JC. 1997. The natural flow regime: a paradigm for river conservation
and restoration. Bioscience 47: 769-784.
Poff NL, Zimmerman JKH. 2010. Ecological responses to altered flow regimes: a
literature review to inform the science and management of environmental flows.
Freshwater Biology 55: 194-205.
Power ME, Dietrich WE, Finlay JC. 1996. Dams and downstream aquatic biodiversity:
potential food web consequences of hydrologic and geomorphic change.
Environmental Management 20: 887-895.
Resh VH, Brown AV, Covich AP, Gurtz ME, Li HW, Minshall GW, Reice SR, Sheldon
AL, Wallace JB, Wissmar RC. 1988. The role of disturbance in stream ecology.
Journal of the North American Benthological Society 7: 433-455.
Schmidt JC, Wilcock PR. 2008. Metrics for assessing the downstream effects of dams.
Water Resources Research 44: 1-19.
Stromberg JC, Beauchamp VB, Dixon MD, Lite SJ, Paradzick C. 2007. Importance of
low-flow and high-flow characteristics to restoration of riparian vegetation along
rivers in arid south-western United States. Freshwater Biology 52: 651-679.
Ward JV, Stanford JA. 1983. The serial discontinuity concept of lotic ecosystems. Pages
29-42 in T.D. Fontaine and S.M. Bartell, editors. Dynamics of Lotic Ecosystems.
Ann Arbor Sciences, Ann Arbor, MI.
Wohl E, Bledsoe BP, Jacobson RB, Poff NL, Rathburn SL, Walters DM, Wilcox AC.
2015. The natural sediment regime in rivers: Broadening the foundation for
ecosystem management. BioScience 65(4): 358-71.
Wootton JT, Parker MS, Power ME. 1996. Effects of disturbance on river food webs.
Science 273: 1558-1561.
7
CHAPTER 2
REMEMBERING A ‘FORGOTTEN’ RIVER: IDENTIFYING LIMITING FACTORS
FOR THE NATIVE FISH DIVERSITY OF THE RIO GRANDE IN FAR WEST
TEXAS
Abstract
Desert river ecosystems are some of the most threatened aquatic systems because
river ecosystems compete with human societies for the most precious resource on earth,
water. Anthropogenic impacts such as stream-flow modification and depletion have
negatively impacted biodiversity and ecosystem integrity at a global scale. Our overall
approach was to identify the most critical limiting factors to the native aquatic diversity
and richness of the Rio Grande within two different segments along the US-Mexico
border: the degraded Forgotten Reach and the more ecologically-intact Big Bend region.
We synthesized available sampling data conducted within the Rio Grande from El Paso-
Ciudad Juarez to Big Bend National Park (sampling n ~100 events) that describe the fish
community, water quality, and the hydrologic and geomorphic characteristics for each
segment. We then used structural equation modeling (SEM) to explore the hydrologic,
geomorphic, and ecologic components of the Rio Grande in both segments. Both the
Forgotten Reach and the Big Bend region SEM results fit the observed data relatively
well, with both segments having high chi-squared values (χ2; p > 0.05 = 0.400 for the Big
Bend region, p > 0.05 = 0.066 for the Forgotten Reach), where non-significant p-values
indicate better fit. We found native fish richness to be more influenced by channel
narrowing and low flows in the Big Bend region than in the Forgotten Reach. In contrast,
in the Forgotten Reach, water quality and channel narrowing were the most influential
8
variables describing native fish richness. Each region appeared to be unaffected by the
nonnative fish abundance; however, there were higher abundances of generalist, saline-
tolerant native fish species compared to more sensitive species within the Forgotten
Reach. Thus, the community may be shifting more towards generalist native fishes
relative to specialist native fishes or nonnative fishes overall. Our comparative approach
demonstrates how two long river segments can be affected differentially by dewatering
and low flows, and result in different factors that are most limiting to ecological integrity.
In a contemporary world of extreme competition for water and associated physical
impairment to rivers, it is important to understand which factors are most limiting to
ecological health at a local scale, such that management decisions regarding flow can be
effectively prioritized.
Introduction
Reliable water supply is a vital and limited resource for life in arid regions.
Human demand for water has increased due to population growth and expansion of
irrigated agriculture, leading to construction of dams, increased surface-water diversions,
and increased groundwater pumping. The collective increase in river regulation and the
depletion of stream flow threatens aquatic riverine ecosystem structure and function
(Hauer and Lorang 2004; Richter and others 2011). Globally, there have been large
changes to the frequency, magnitude, and duration of flow and sediment inputs associated
with altered water quantity and timing (Poff and others 1997; Brandt 2000; Bunn and
Arthington 2002; Nilsson and others 2005; Schmidt and Wilcock 2008). River ecosystem
integrity is dependent on stream flow and sediment supply that can sustain habitat
complexity, complex life history traits, and diverse endemic taxa (Bunn and Arthington
9
2002; Stromberg and others 2007; Archdeacon 2016).
Freshwater ecosystems are some of the most imperiled in the world and more than
81 percent of freshwater populations have declined since 1970 (WWF 2016). Flow
modification, habitat degradation, water pollution, and species invasions are the primary
threats to freshwater biodiversity worldwide (Dudgeon and others 2006). Losses in
biodiversity can affect ecosystem functions such as carbon and nutrient cycling, and
primary productivity (Hooper and others 2005). For example, disturbances or losses of
benthic functional group diversity influences primary and secondary productivity by
depleting sugars or nutrients necessary for productivity processes such as photosynthesis
(Palmer and others 1997). Thus, upper trophic levels such as secondary and tertiary
consumers may lose critical food resources and subsequently decline in abundance and
diversity, shortening the food chain length and depleting the flow of energy through an
aquatic ecosystem.
Adequate stream flow and sediment supply are necessary for maintaining
freshwater biodiversity, because each are important drivers of ecosystem processes, drive
species communities and abundances, and maintain habitat heterogeneity and water
quality (e.g., Poff and others 1997; Hart and Finelli 1999; Poff and others 2010). Habitat
heterogeneity can provide adequate areas for growth, safety from predators, food
resources, and reproduction. Varying types of habitat often facilitate the coexistence of
multiple species, and aquatic diversity is often associated with complex habitat
heterogeneity (Nagayama and Nakamura 2018). In addition, water quality, specifically
salinization, is an increasing threat to freshwater ecosystems and water resources globally
(Williams 1999). The ecological effects often include losses in biodiversity, shifts in
10
community structure, or even trophic cascades. Finally, modified flow and sediment
supply can facilitate nonnative species establishment, potentially displacing native
species and homogenizing both aquatic and terrestrial riparian communities (Thomaz and
others 2015). Determining how the hydrological, geomorphic, and ecological variables
interact in large, exotic rivers (e.g., rivers whose flow regime is derived from the climate
of distant headwaters), as is the case for the exotic Rio Grande, is an important question
for effective environmental flow management and conservation efforts.
Desert river biodiversity is particularly at risk due to climate-induced drought and
increased regulation and diversion in these water-scarce regions. Receiving less than 500
mm of rainfall a year, desert rivers and their ecosystem communities and processes are
most often limited by water quantity (Kingston and others 2006). Water quantity is
coupled with physical processes such as sediment transport in arid regions. Losses of
stream flow facilitate channel change such as narrowing or incision, which can have
deleterious impacts to habitat heterogeneity and associated biodiversity. Combined with
low water quantity, high evaporation rates in arid systems can degrade water quality
conditions by causing increases in water temperatures, salinity, or nutrients and decreases
in dissolved oxygen, all of which can negatively impact aquatic fauna. Desert rivers pose
a unique challenge to effective management because these ecosystems are partially
dependent on stream flow that is also needed by human society. Therefore, a full
understanding of how the physical and ecological processes are impacted by flow
depletion contributes to innovative solutions for managing these unique and imperiled
ecosystems.
Linking the physical and ecological components of a river ecosystem has become
11
a critical aspect of understanding how the aquatic ecosystem interacts with its
hydrological and geomorphic environments (Vaughan and others 2009; Elosegi and
others 2010; Poole 2010). However, there is an added complexity when incorporating
both the physical and ecological environment. Often the questions of interest are better
explained by using multiple direct and indirect variables (Arhonditsis and others 2006).
We use structural equation modeling (SEM) in this study as a method to address the
above ecological complexity, because SEM allows the user to test both direct and
indirect, or intermediary, effects on an overall response variable, or variables. The overall
goals of this study were to provide a synthesis of the state of knowledge on the existing
ecological condition between two hydrologically and geomorphologically distinct parts
of the Rio Grande, the Forgotten Reach and the Big Bend region. Second, we used that
body of knowledge to identify testable hypotheses of the most important limiting factors
for ecosystem integrity within two regions. We also used SEMs to test those hypotheses,
and we compared SEMs between the two river segments, each with varying degrees of
contemporary ecological integrity.
Contemporary Ecosystem of the Rio Grande
Hydrology
The Rio Grande is the fifth longest river in North America and has had a long
history of human use and modification beginning at the time of Spanish settlement
(Scurlock 1999; Fullerton and Batts 2003). To meet the needs of irrigated agriculture,
municipal, and industrial use, much of the water contributed to the Rio Grande by
snowmelt and monsoon floods is diverted or stored in many reservoirs (Collier and others
1996; Schmidt and others 2003; Porter and others 2009; Sandoval-Solis and others 2010).
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The contemporary Rio Grande functions hydrologically as two separate rivers, each with
differing headwaters: the northern branch (headwaters to Rio Conchos) and the lower Rio
Grande (Rio Conchos to Gulf of Mexico). The hydrology of the northern branch
headwaters is driven by annual spring snowmelt flows primarily from the San Juan and
Sangre de Cristo mountains in southern Colorado, which peak in early April to June
(Schmidt and others 2003; Blythe and Schmidt 2018). In contrast, the lower Rio Grande
(e.g., the Big Bend region, Rio Conchos to Gulf of Mexico) is predominately sustained
by flows from the Rio Conchos, a large tributary originating in the Sierra Madre
Occidental in northern Mexico, summer thunderstorms over desert tributaries, and
groundwater spring input (Fullerton and Batts 2003; Raines and others 2012). Overall,
the river has declined in total annual volume by as much as 95% (Blythe and Schmidt
2018) and has lost much of its hydrologic variability.
The Forgotten Reach is one of the most impacted segments due to the nearly
complete diversion of upstream flows. This segment is between Fort Quitman, Texas and
Presidio, Texas – Ojinaga, Chihuahua and is approximately 320 river kilometers, its
hydrology is that of the northern branch of the Rio Grande (Figure 1; Collier and others
1996). Prior to extensive irrigation and upstream dam construction, the Forgotten Reach
would have had a snowmelt flood that occurred between April and June, dominating the
annual hydrograph (Blythe and Schmidt 2018). Summer monsoon flash floods also
occurred between July and October, but contributed little runoff compared to the spring
snowmelt. Since closure of Elephant Butte Dam in 1915, snowmelt flows have been
eliminated. All stream flow inputs come from irrigation water return and the remaining
summer monsoon flash floods (Johnson and others 1977; Collier and others 1996; Landis
13
2001; USACE 2008; Schmidt and others 2003). The annual flow regime of the
contemporary Forgotten Reach is largely stable, experiencing relatively minor changes in
flow magnitudes annually. Loss of variability in the flow regime has had compounding
effects on the geomorphology, water quality, and native aquatic communities.
Downstream from the Forgotten Reach, total annual stream flow in the Big Bend
region significantly increases due to inflow from the Rio Conchos (Kelly 2001). Floods
in the Rio Conchos occur during the North American monsoon season and are the
primary contribution to the water quantity of the Rio Grande as it flows through the
Chihuahuan Desert and Big Bend region for approximately 480 km (to Amistad
Reservoir near Del Rio, Texas-Ciudad Acuña, Coahuila; Dahm and others 2005;
Woodhouse and others 2012). Like the northern branch in New Mexico, the Rio Conchos
basin is highly regulated by multiple dams for hydroelectricity, and the river is
extensively diverted for irrigation and municipal uses, and extended periods of low flows
occur (Dean and others 2011; Dean and Schmidt 2011; Dean and others 2016). A
distinctive attribute of the flow regime in the Big Bend region is the occurrence of ‘reset’
floods (Dean and Schmidt 2013). These large (>1000 m3/s), relatively rare flood events
(recurrence of ~10 – 15 years) are ecologically important, because they restore channel
complexity by evacuating fine sediment that narrows the channel during the periods
between the reset floods; abundant exotic vegetation also invades the surface of the fine
sediment deposits that accumulate, and reset floods remove some of the exotic vegetation
(discussed further below; Dean and Schmidt 2011, 2013; Dean and others 2011; Dean
and others 2016).
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Geomorphology
The Forgotten Reach is significantly perturbed into sediment surplus and has
undergone a series of successive geomorphic changes since the Elephant Butte Dam
closure in 1915 (Everitt 1993). The Forgotten Reach was once a wide, braided, and
shallow river with extensive cottonwood bosques and willow galleries, but has since
narrowed greatly (Everitt 1977, 1993, 1998). Initially, natural tributary flows entering the
study reach deposited substantial amounts of sediment within the channel and on the
floodplain (Everitt 1977, 1993; Collier and others 1996), and the channel shrank and
formed inset floodplains, or sediment berms (Everitt 1993). Shrinkage caused the river to
temporarily disconnect from its former floodplain, and lateral migration of the channel
ceased. As sediment input and low flows continued, the channel began to aggrade high
enough that stream flows once again flowed overbank. The overbank flows cut new
channels to detour around the sediment-laden tributary junctions, causing the river to
migrate away from its tributaries (Everitt 1993). The channel continues to adjust today
with increasing anthropogenic water demand and hydroclimatic variability, but it remains
narrowed and filled with sediment. Restoration of this segment to its historic geomorphic
state would require restoration of the large magnitude duration snowmelt floods last
observed in the 1800s.
Like the Forgotten Reach, reduction in the total annual flow in the Big Bend
region has contributed to channel narrowing and allowed subsequent nonnative
vegetation establishment (Dean and Schmidt 2011). The channel width is estimated to be
50% narrower today than it was more than a century ago, and nonnative saltcedar
(Tamarix spp.) and giant cane (Arundo donax) act as a positive feedback to accelerate
15
channel narrowing between reset floods (Dean and Schmidt 2011, 2013; Dean and others
2011; Dean and others 2016). Historically, the Rio Grande was once a wide, sandy, and
dynamic channel with a low-elevation floodplain in the wide alluvial valleys. The
intervening canyons were historically more confined and may not have had an extensive
floodplain. The present Rio Grande in the Big Bend region has narrowed considerably
and formed high banks in both the alluvial valleys and the canyon-bound reaches.
Subsequently, saltcedar and giant cane have established on parts of the floodplain and
contribute to bank stability and sediment accumulation. In contrast to the Forgotten
Reach, the narrow channel is occasionally rewidened by large, rare flood events (Dean
and Schmidt 2011). These floods are high in magnitude (>1000 m3/s) and duration (>7
days) and erode substantial portions of the inset floodplains that form during low flow
periods (Dean and Schmidt 2011, 2013; Dean and others 2011).
Associated Changes to the Ecosystem Structure and Function
Ecosystem structure is composed of the biota and their interactions with their
physical environment, and ecosystem function is defined as is the physical and chemical
processes influencing structure. For example, the complexity of a food web can drive the
amount of energy transferred/lost within a system (Allan and Castillo, 2007). Similarly, a
floodplain ‘structure’ will influence nutrient deposition and cycling throughout the length
of a river, in turn stimulating aquatic primary and secondary productivity. In fluvial
ecosystems, these feedbacks between structure and function are important for
maintaining ecosystem health. River ecosystem processes are largely driven by the
quantity and timing of stream flow, water quality, and the biological community present.
Alterations to these aspects of flow can affect either structure or function by degrading
16
water quality, changing critical hydrologic cues, reshaping river channel form, or altering
riparian community composition.
The Forgotten Reach is critically altered in terms of its ecological integrity.
Continuous low flows and sediment surplus in the region have contributed to narrowing
the channel, thereby decreasing in-channel and floodplain habitat complexity (USACE
2008). Abundance and diversity of habitat often has a strong correlation with abundance
and diversity of native species (Ricklefs and Schluter 1993). Monitoring surveys on the
lower Rio Grande in west Texas have demonstrated decreasing trends in native species
richness associated with habitat availability (Blythe Chapter 2; Bestgen and Platania
1988; Platania 1991; Edwards and Contreras-Balderas 1991; Heard and others 2012).
Species adapted to particular habitat types have a higher risk of extirpation and are more
sensitive to degraded flow conditions (Laub and Budy 2015). For example, loss of
floodplain habitat associated with high flow events has contributed to the decline in
native fish species, such as the federally endangered Rio Grande silvery minnow
(Hybognathus amarus). These minnows rely on spring flood pulses to cue spawning and
create nutrient-rich floodplain ‘nurseries’ for the larval fish to grow and survive
(Archdeacon 2016), and their endangered-status is directly related to flow and available
habitat for these life strategies.
In addition to decreased habitat heterogeneity, water quality has been a growing
concern in the Forgotten Reach (IBWC 1994, 2008; Miyazono and others 2015). Total
dissolved solids increased substantially after the completion of Elephant Butte Dam, and
increased agricultural diversions in the early 1900s; water quality has continued to
deteriorate in response to recent increased population growth in northern Mexico after
17
signing of the North American Free Trade Agreement. Concentrations of total dissolved
solids (mg/L) are up to five times higher within the Forgotten Reach compared to other
parts of the river (Moyer and others 2013). Consequently, the fish assemblage for the
Forgotten Reach is made up of generalists more tolerant of poor water quality conditions
(Hubbs and others 1977; Bestgen and Platania 1988; USACE 2008; Miyazono and others
2015), reducing food web complexity and subsequent energy transfer for the system by
creating a more homogenous fish community structure. Mollusk and crustacean richness
is also relatively low throughout the region (Metcalf 1978) and is composed primarily of
opportunistic or invasive species, such as the exotic Asian clam (Corbicula fluminea).
Asian clams have high filtration rates and produce substantial amounts of phosphorus,
potentially stimulating primary productivity and increasing the occurrences of
eutrophication in the study region (Sousa and others 2008).
Finally, the riparian community has shifted from native species (e.g., cottonwood
(Populus spp.), willow (Salix spp.), and mesquite (Prosopis spp.)), to almost exclusively
nonnative saltcedar in the Forgotten Reach (Everitt 1998). Compared with native riparian
species, saltcedar decreases the amount of light reaching a narrow river, deposits
substantially more leaf litter and detritus, and creates a system dependent on seasonal
allochthonous sources of energy (Kennedy and others 2004). In addition, two riparian
bird species have become endangered by the loss of native willows, which provide
important nesting habitat for the Southwestern willow flycatcher (Empidonax traillii
extimus) and Western yellow-billed cuckoo (Coccyzus americanus occidentalis; US Fish
and Wildlife 2013). The riparian composition change has likely impacted the Forgotten
Reach ecosystem by altering the sources of energy (e.g., shifting from autochthonous to
18
allochthonous) and altering carbon cycling by changing the types of carbon available for
uptake by secondary consumers. Thus, the removal of nonnative vegetation may increase
ecosystem production and create more available food and habitat resources for threatened
or endangered aquatic native species (Kennedy and others 2005).
Like the Forgotten Reach, the long-term trend of sediment accumulation and
channel narrowing in the Big Bend region has decreased in-channel habitat complexity.
Even after a reset flood, habitat features are only partially recovered, and narrowing and
associated habitat simplification generally resumes relatively quickly (Dean and Schmidt
2011). Because any given species has its own unique habitat requirements, habitat
simplification restricts the amount and types available for a species, often leading to
increased predation and competition (Laub and Budy 2015). Many species of aquatic
organisms in the Big Bend region (e.g., fish, macroinvertebrates, mussels) have become
threatened, endangered, or extirpated due to decreased habitat heterogeneity (Davis 1980;
Heard and others 2012).
Prolonged low flow periods have contributed to the establishment of nonnative
giant cane in the Big Bend region, with multiple ecological consequences. Giant cane
actively displaces native riparian vegetation and uses considerable amounts of water
compared to native species (Yang and others 2011). Giant cane also acts as a positive
feedback to channel narrowing by stabilizing channel banks and trapping sediment,
ultimately contributing to habitat simplification (Dean and Schmidt 2011, 2013).
Terrestrial arthropods have less diverse assemblages in giant cane compared to native
riparian species like willows and cottonwoods (Herrera and Dudley 2003), potentially
shifting the types and abundances of terrestrial arthropod input into the river. Similarly,
19
giant cane produces up to 20 t/ha of leaf litter, likely altering the river’s energy sources
by becoming more reliant on allochthonous inputs (Yang and others 2011) and
influencing the water chemistry through increasing the abundance of organic materials
within the river.
Salinity, nutrients, and organic matter concentrations decrease downstream
because base flows increase downstream from Big Bend National Park where there is
significant groundwater input (URGBBEST 2012). This downstream trend of improving
water quality is reflected within the algal communities as they shift from hypereutrophic-
and eutrophic-affiliated species to mesotrophic and nitrogen-fixing species (Porter and
Longley 2011). Similarly, there have been increased abundances of threatened or
endangered mussel species, such as Texas hornshell (Popenaias popeii), occurring near
higher-quality groundwater in the Lower Canyons part of the Big Bend (Winemiller and
others 2010; Porter and Longley 2011; Burlakova and others 2011a,b). Mussels also have
important roles in river nutrient cycling, often forming the link between benthic and
pelagic energy transfer because they excrete nutrients into the water column and deposit
organic material to river sediments (Vaughn 2008). In addition, diversity and abundance
of the native fish community is tied with the quantity and quality of water, increasing as
base flows increase (Hubbs and others 1977; Bestgen and Platania 1988; Edwards and
Contreras-Balderas 1991; Miyazono and others 2015).
Our goal was to build an SEM for both the Forgotten Reach and Big Bend region
to explore and compare the relationships among the hydrologic and geomorphic
properties, water quality, and nonnative fish species on the native fish richness for each
region. Based on the observed changes to the ecosystem structure and function for the
20
Forgotten Reach and Big Bend region, we hypothesized poor water quality conditions
and degraded habitat heterogeneity have contributed to degraded ecosystem structure and
function for the Forgotten Reach, specifically native fish richness. We also hypothesized
reset floods, elevated base flows, and groundwater spring input likely contribute to higher
ecological integrity in the Big Bend region than within the Forgotten Reach. By
comparing each region, our goal is to describe the mechanisms linking stream flows and
the physical environment with the aquatic ecosystem structure and function. Identifying
these potential mechanisms will contribute to our knowledge of which components of the
flow and sediment regimes are the most important for ecosystem rehabilitation for the
Forgotten Reach relative to the Big Bend region. Our comparative approach allows us to
identify how the two very different regions operate similarly or differently.
Methods
Study Area
The Rio Grande is approximately 550,000 km2 and flows through eight states –
three in the United States and five in Mexico. In addition, the Rio Grande forms the U.S.-
Mexico international border for approximately 1900 km. The Forgotten Reach and Big
Bend region are located within the Basin and Range physiographic province and
Chihuahuan Desert ecosystem (Figure 2.1; USACE 2008). Within each region, the Rio
Grande alternates flowing between wide, alluvial valleys and steep, narrow canyons
(USACE 2008; Dean and Schmidt 2011, 2013). The climate for each region is arid and
dry. Sources of precipitation for the region are primarily from heavy local thunderstorms
associated with the North American monsoon. Total annual precipitation ranges from 10
to 82 cm, with most of the annual precipitation occurring between the months of July and
21
September (Johnson and others 1977; USACE 2008). The average annual temperatures in
the region vary between 5 and 27 C, with the hottest temperatures recorded in the
months of June and July, and the lowest in January (USACE 2008; PRISM Climate
Group 2017).
Data Collection
Fish community and water quality
We compiled fish data for the Forgotten Reach using data collected by Hubbs and
others (1977), Bestgen and Platania (1988), and Miyazono and others (2015) that
extended from El Paso-Ciudad Juarez to Big Bend National Park. For the Big Bend
region, Laub and Budy (2016) compiled fish data from Heard and others (2012), Garrett
and Edwards (2014), Moring and others (2014), and Ken Saunders of the Texas Parks
and Wildlife Department. The fish data included date collected, recorded sampling
locations (given in decimal degrees), total number of fish caught for each species
collected while sampling, and water quality for each sampling location (including water
temperature (°C), dissolved oxygen (mg L-1), specific conductance (µg cm-1), pH). If
water quality was not recorded, we used both the Environmental Protection Agency
(EPA) Water Quality portal tool (https://www.epa.gov/waterdata/water-quality-data-wqx)
and Texas Commission on Environmental Quality database
(https://www.tceq.texas.gov/agency/data) to extract point measurements taken near the
fish sampling locations.
22
Hydrologic and geomorphic data
For our hydrologic parameters, we collected historic mean daily discharge data
from gaging stations operated either by the International Boundary and Water
Commission (IBWC) or U.S. Geological Survey (USGS). Depending on the location of
the sampling site in the Forgotten Reach, we used the following IBWC data from gaging
stations: Rio Grande at Fort Quitman, Texas near Colonia Luis Leon, Chihuahua (08-
3705.00), Rio Grande near Candelaria, Texas and San Antonio Del Bravo, Chihuahua
(08-3712.00), and Rio Grande above Rio Conchos near Presidio, Texas and Ojinaga,
Chihuahua (08-3715.00). For the Big Bend region, Laub and Budy (2016) compiled
instantaneous flow data using both the IBWC Johnson Ranch gaging station (08-3750.00)
and USGS Castolon (08374550) and Rio Grande Village gaging stations (08375300).
For geomorphic data, we remotely measured active channel width (in meters) at
each sampling location using a combination of Google Earth historical aerial imagery and
National Agriculture Imagery Program (NAIP) 1-m aerial photography downloaded from
the Texas Natural Resources Information System (TNRIS). In addition, we used Everitt
(1993) active channel width measurements in the Forgotten Reach to extrapolate
potential channel widths during the Hubbs and others (1977) sampling events. Laub and
Budy (2016) used published average channel width data from Dean and Schmidt (2013)
and Dean and others (2016).
Structural Equation Modeling
Structural equation modeling (SEM) is a useful tool for describing the likely
relationships between the ecology and physical aspects of a river because of its ability to
test multiple direct and indirect effects, and the interactions between each, on a response
23
variable (Grace 2008; Grace and others 2010; Hermoso and others 2011). SEM is a
multivariate statistical technique that uses path analysis and regression to test an a priori
defined pattern of relationships among explanatory and response variables (Arhonditsis
and others 2006). Based on existing knowledge about an ecosystem, users define and
create an initial framework or hypothesis and test their hypothesis against a covariance
matrix of the observed data within an SEM (Sutton-Grier and others 2010). Thus, the
user-defined model can be confirmed or rejected if the data do or do not fit the
hypothetical framework. For example, an SEM was used to understand phytoplankton
dynamics between two different lakes with varying levels of eutrophication - one lake
was eutrophic and the other mesotrophic (Arhonditsis and others 2006). By using two
hypothetical SEMs, phytoplankton dynamics were shown to be dependent on several
ecological processes naturally occurring in these lakes, with more variability explained in
the eutrophic lake.
Determination of model fit
Multiple indices can be used to assess whether the actual data support the
hypothesized model. A chi-square (χ2) test is most often used, and a non-significant p-
value at the α = 0.05 significance level suggests the proposed model structure and the
actual data do not differ significantly (Beaujean 2014). In addition to the chi-square test,
we assessed model fit using a Comparative Fit Index (CFI), Tucker-Lewis Index (TLI),
and Root Mean Square Error of Approximation (RMSEA). For the CFI and TLI, values
closer to 1.0 tend to indicate good fit. For RMSEA, values that are significant (p-value >
0.05) and closer to 0.0 indicate better fit (Beaujean 2014).
24
SEM Conceptual Model
Defining direct hydrologic and geomorphic variables
Given the knowledge of the hydrologic and geomorphic properties of the Rio
Grande for both the Forgotten Reach and Big Bend region, we chose variables to capture
the effects of stream flow variability on fish habitat and communities. Thus, our
hydrologic variables included mean daily discharge on the day of fish sampling, the
median and minimum mean daily discharge during the previous 30 days, and the number
of flow spikes (defined as a flow rise 1.5 times the previous day’s flow based on mean
daily discharge) during the previous 90 days. Base flows were represented by median
flow, extreme low flow or drying events were captured within minimum flows, and
tributary-derived flash flood events were associated with flow spikes. In addition, we
chose active channel width (m) to represent the geomorphic and physical habitat
conditions for each region. While we understand there are other physical/geomorphic
variables that also affect habitat conditions (e.g., depth, velocity, area of in-channel
habitat features, sediment loads), active channel width was the most consistent variable
reported and we assumed it was an adequate measure of habitat complexity because
wider channels tend to support multiple, varying types of in-channel habitat.
Defining water quality as a latent variable
SEMs often include the use of latent variables, or variables not directly measured
or observed. Latent variables are thus defined using indicator variables, or variables
directly measured. There are two types of latent variables: reflective and formative. For
reflective latent variables, the latent variables cause the indicator variables (e.g.,
intelligence level (latent variable, not directly measured) predicts how well one does on a
25
test (test score, indicator variable)). In contrast, formative latent variables are caused by
the indicator variables (e.g., socioeconomic status (latent variable) is determined by
income or occupation (indicator variables; Beaujean 2012). For our model, salinity and
sulfates are some of the greatest threats to primary productivity and fish health for the
Rio Grande (Porter and Longley 2011). Therefore, we defined water quality as a
reflective latent variable, because we assumed water quality causes the chosen indicator
variables to covary. Our indicator variables for water quality were specific conductance
(µS cm-1), sulfate (mg L-1), and water temperature (°C). We chose specific conductance
as a measurement of salinity, because it was the most consistent parameter measured, and
we considered water temperature an important variable to consider because it controls
multiple physiological functions for aquatic species.
Nonnative fish abundance
Finally, invasive species tend to be higher in abundance and can influence and
restructure aquatic communities. Therefore, we included the summed nonnative fish
relative abundance in the model, because there were at least five different nonnative
species present in the fish sampling data for each study region, including: common carp
(Cyprinus carpio), inland silverside (Menidia beryllina), plains killifish (Fundulus
zebrinus), white bass (Morone chrysops), and blue tilapia (Oreochromis aurea).
Data Preparation
Before we tested the conceptualized SEM model for the Forgotten Reach, we
scaled all variables using z-scores, because some parameters had much higher values than
others (e.g., measurements of specific conductance, channel width, and sulfates were at
26
times almost 1000 times greater than native fish richness estimates). We completed all
data exploration using R 3.4.3 (R Development Core Team, 2017), and modeled all
SEMs using the lavaan package in R (Yves Rosseel 2012).
Results
Comparison of Big Bend Region and Forgotten Reach SEM Models
Because the Big Bend region and the Forgotten Reach models were developed
from independent datasets, we began by testing the Big Bend region against our
hypothesized conceptual model and considered the Big Bend SEM model the baseline
model (Figure 2.2). We then compared the Forgotten Reach to the baseline Big Bend
model using the original hypothesized model structure. Following the baseline
comparison, we adjusted our Forgotten Reach model accordingly, using a combination of
correlation and parameter estimates from the baseline model structure. Finally, we
compared the final, better-fit Forgotten Reach model with the baseline Big Bend model.
The Big Bend data supported the hypothesized model, and the SEM fit measures
indicated a good fit (χ2 p-value = 0.400, CFI= 0.994, TLI = 0.985, RMSEA = 0.03; Table
2.1). The hypothesized model included the direct effects of active channel width, all the
hydrologic variables (e.g., flow median and minimum during the previous 30 days, and
spikes during the previous 90 days), the water quality latent variable (defined using
specific conductance and sulfate), and nonnative fish abundance on native fish richness
(Figure 2.3). In addition, the hypothesized model included the indirect effects of the flow
variables on the water quality latent variable and nonnative fish abundance. While the
Big Bend model performed relatively well overall, the native fish richness variance was
only partially explained by the model structure (R2 = 0.269).
27
Next, the Forgotten Reach dataset was compared using the baseline Big Bend
SEM structure (hypothesized model, Figure 2.4). The Forgotten Reach data did not fit the
baseline structure well, and the overall fit measures were relatively low (χ2 p-value =
0.006, CFI = 0.905, TLI = 0.752, RMSEA = 0.178; Table 2.1). However, native fish
richness was explained by the model relatively well (R2 = 0.664). Because model fit was
relatively low for the Forgotten Reach using the baseline structure, we altered the
Forgotten Reach structure based on observing the significant parameter estimates within
the Forgotten Reach baseline model results. The final alternate model structure included
the direct effects of flow spikes, active channel width, nonnative fish abundance, and the
water quality latent variable on native fish richness (Figure 2.5). we also included the
indirect effects of flow spikes on active channel width and water quality, and water
quality on nonnative fish abundance. In addition to specific conductance and sulfate, we
added water temperature to the water quality latent variable. The data fit the altered
Forgotten Reach model structure relatively well (χ2 p-value = 0.066, CFI = 0.946, TLI =
0.915, RMSEA = 0.104; Table 2.1). Native fish richness continued to be explained well
even with the altered model structure (R2 = 0.630).
Examining SEM Results Using Standardized Coefficients
Big Bend SEM model
We present the standardized coefficients (unitless parameter estimates) to further
understand the similarities and differences between each of the above presented models.
By comparing the standardized coefficients, we are essentially comparing the strengths of
each pathway on overall native fish richness (Grace 2006). Thus, within the Big Bend
SEM structure, the most influential, or statistically significant, paths for native fish
28
richness were the direct effects of median and minimum flow during the past 30 days and
active channel width (Table 2.2). Native fish richness tended to increase with increased
active channel width and median flows and decrease with increases in minimum flow
events. Indirect effects of the hydrologic variables on water quality and nonnative
abundance were relatively negligible.
Forgotten Reach SEM results
For the Forgotten Reach under the baseline structure (hypothesized), native fish
richness was most strongly influenced by the direct effects of active channel width, water
quality, flow on the day of sampling, and flow spikes during the previous 90 days (Table
2.2). In contrast to the Big Bend model, the indirect effect of the median flows during the
previous 30 days had a statistically significant influence on water quality. For the altered
Forgotten Reach model, the direct effects were similar to the baseline model results
where water quality, active channel width, flow on the day of sampling, and flow spikes
during the previous 90 days had the most influence on overall native fish richness (Table
2.2). For each Forgotten Reach model structure, native fish richness tended to increase
with increases in flow spikes and active channel width and decrease with decreases in
water quality and flow on day of sampling. Overall, both the Big Bend and Forgotten
Reach model had significant direct paths between active channel width and native fish
richness. However, each model was influenced by different hydrologic variables (e.g.,
median and minimum mean daily discharge for the Big Bend region, median mean daily
discharge and flow spikes for the Forgotten Reach), and the Forgotten Reach was most
affected by water quality.
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Discussion
The goal of this study was to understand and identify the linkages between the
physical and ecological components of a regulated riverine ecosystem and to identify the
most important limiting factors to native fish richness for the Rio Grande in the
Chihuahuan Desert. Using a novel modeling approach to accomplish this goal, we were
able to provide a better understanding of how ecosystem structure and function is
affected by both the modified hydrologic and sediment regimes of an extensively diverted
arid river. Comparing two regions similar in physical structure (e.g., geology, sediment
input, gradient and elevation) but differing in the magnitude of change of the natural flow
regime, allowed us to identify the most critical limiting factors for ecosystem integrity
and biodiversity in the Rio Grande in these two regions. In addition, comparing the Big
Bend region, a segment of greater ecological integrity, with the degraded Forgotten
Reach allowed us to explore the ways in which each region functioned similarly or
differently, and affected the native fish richness for the Rio Grande within each segment.
Mechanisms Influencing Ecosystem Structure and Function
Water quality and habitat heterogeneity appear to be the most critical limiting
factors for native fish richness in the Forgotten Reach. Across the entire Rio Grande,
salinity has been increasing since the 1950s; however, the highest concentrations occur
between Fort Quitman and Presidio-Ojinaga (2000 to 5000 mg L-1; Miyamoto and others
1995). An estimated 1.84 million MT of salt flow is input annually into the Forgotten
Reach and salt concentration is exacerbated by high evaporation rates in the region
(Miyamoto and others 1995). Trace elements in the Forgotten Reach often exceed the
U.S. Fish and Wildlife Service standards for aquatic species health. Fish samples near
30
Presidio contained elevated levels of mercury, lead, and copper in their tissues which
were attributed to mining sediments input from surrounding tributaries (Miyamoto and
others 1995; Gray and others 2006; Miyazono and others 2015). Fish and mussel species
are particularly sensitive to changes in water quality, and populations are often affected
by limited growth and abundance associated with poor water quality (e.g., Lorenz and
others 2016; Morcillo and others 2016; Hintz and Relyea 2017; Hintz and others 2017).
In addition, the channel in the Forgotten Reach has shrunk dramatically, with an almost
90% decrease in width (approximately 300 m wide pre-Elephant Butte Dam in 1902 to
30-50 m wide by 1970; Everitt 1993; Dean and Schmidt 2011). Habitat heterogeneity has
likely decreased as a result, having gone from a broad, shallow channel with multiple
oxbows to a narrow, aggraded and sediment-filled river (Everitt 1993).
In contrast, the Big Bend region water quality improves with increased stream
flows supplied by the Rio Conchos and groundwater aquifer complexes within the region
(Hubbs and others 1977; Miyamoto and others 1995; Porter and Longley 2011; Raines
and others 2012; Miyazono and others 2015). Thus, while still a key factor influencing
ecosystem integrity in the Big Bend region, water quality may not be a critical limiting
factor. Rather, sources of ecosystem degradation associated with low flows appear to be
more strongly impacted by losses in available in-channel habitat due to channel
narrowing and extreme low flow events in the region (Laub and Budy 2016).
Historically, the Rio Grande in the Big Bend region likely maintained multiple
types of shallow, slow-velocity habitats including floodplain pools, backwaters, and
embayments (Mueller 1975; Stotz 2000; Dean and Schmidt 2011). Presently, the river
becomes more complex immediately following a reset flood but narrows within a decade
31
after the reset event and has a small width-to-depth ratio (Everitt 1993; Dean and Schmidt
2011). Thus, there are many sections of the Big Bend region consisting almost entirely of
large, high-velocity runs and/or deep pools, and are considered ‘simple’ or ‘low’ in terms
of habitat heterogeneity (Moring and others 2014; Blythe, Chapter 2). In addition, the
alluvial valleys are narrowing at a faster rate compared to canyon-bound regions
following a recent 2008 reset flood (Dean et al. 2016; T. Blythe, in progress). The Big
Bend region is just one example of a desert river that has become simplified. For
example, the Green River in Utah, the longest tributary to the Colorado, experienced
decreases in average channel width by nearly 20% within the twentieth century (Allred
and Schmidt 1999). China’s desert Yellow River has also experienced channel change
and aggradation associated with regulated stream flow (Ta and others 2008), illustrating
that patterns of channel narrowing and habitat simplification are ubiquitous in desert
rivers.
Identifying Critical Limiting Factors for Each Study Region
As we hypothesized, our model results suggest low habitat heterogeneity may
have important consequences for the native fish richness currently present in the Big
Bend region. Channel width and low flows had the strongest influence on native fish
richness for the Big Bend region, likely because the channel is still dynamic and rewidens
after a reset flood event. The influence of channel width and low flows are unsurprising,
given that wider channels tend to support multiple types of habitat, and low flows, or
drying events, decrease the availability of habitat. For example, saltcedar removal on the
San Rafael River, Utah, a smaller but similar desert river, caused the channel to become
wider after a large flood event and increased the abundance of complex habitat areas
32
(e.g., riffles, backwaters, pools; Keller and others 2014). Subsequently, state-sensitive
(e.g., flannelmouth sucker (Catostomus latipinnis), bluehead sucker (Catostomus
discobolus)), and roundtail chub (Gila robusta)) native fish species were higher in
abundance associated with increased abundance of their preferred habitats within this
upper reach. In addition, the relatively small impact of water quality on the native fish
richness was expected with the increased groundwater input in the downstream direction
through the Big Bend region (Raines and others 2012).
Nonnative fish species often deleteriously affect the native fish communities in
many aquatic ecosystems. However, our results indicate the nonnative fish abundance did
not significantly affect the native fish richness. These non-significant results are contrary
to what we predicted, given nonnative fish species generally negatively impact the native
fish community by either predation or outcompeting for food and habitat resources.
Similarly, in the lower San Rafael River, native fish species were affected by a
synergistic combination of degraded habitat and inter- and intra-specific competition and
had to compete for habitat and food resources among native and non-native species (e.g.,
common carp (Cyprinus carpio), red shiner (Cyprinella lutrensis), and fathead minnow
(Pimephales promelas); Walsworth and others 2013). Overall our SEM for the Big Bend
region suggests a wider channel facilitates the maintenance of native fish diversity, and it
appears the current habitat simplifications have likely contributed to the decline and local
extirpations of native species such as the endangered Rio Grande silvery minnow.
Consistent with our observations in the available data, our model results for the
Forgotten Reach suggested water quality and channel width as the primary influences on
native fish richness. In addition, our results are consistent with previous monitoring
33
surveys in the Forgotten Reach (e.g., Hubbs and others 1977; Bestgen and Platania 1988;
Miyazono and others 2015), where higher abundances of generalist, saline-tolerant fish
species (e.g., red shiner, common carp, gizzard shad (Dorosoma cepedianum), bullhead
minnow (Pimephales vigilax)) occurred more frequently at sampling sites than other,
more sensitive species (e.g., Mexican tetra (Astyanax mexicanus), Chihuahua shiner
(Notropis chihuahua), Rio Grande shiner (Notropis jemezanus)). Low flows in the
Forgotten Reach, combined with increased maquiladores (manufacturing plants) in the El
Paso valley and irrigation return, are the primary sources of high salinization in this
region (Miyamoto et al. 1995).
Poor water quality has important ecological effects which can be felt at multiple
levels of biological organization. For example, fish predator response in a high salinity
environment via mesocosm experiments significantly reduced the zooplankton richness
and biomass, which induced a phytoplankton bloom due to the absence of zooplankton
grazing pressure (Hintz and others 2017). Similarly, salinity can influence fish growth by
increasing osmoregulation, subsequently increasing energy costs and limiting growth of
an individual fish (Bœuf and Payan 2001). Limited growth impacts reproduction
capabilities, occurrences of predation, competitive ability, and associated recruitment into
a population (Fogarty and others 1991; Cargnelli and Gross 1996). In addition, flow
spikes during the previous 90 days had a more direct impact on native fish richness,
which suggests any increases in quantity of stream flows may benefit native fish species.
Similarly, flow spikes indirectly affected native fish richness through negatively
influencing water quality and positively influencing channel width, suggesting flow
spikes may increase water quality (e.g., flow spikes may decrease the concentrations of
34
specific conductance and sulfates) and channel width.
Comparing the Forgotten Reach and Big Bend Regions
When we compared model results for the Forgotten Reach to the Big Bend region,
the native fish richness for each region appeared to be influenced by active channel
width. Compared with the Forgotten Reach, the Big Bend region experiences a history of
reset and rewidening of channel width over a decade and Forgotten Reach does not. As
wider channels are associated with increased habitat heterogeneity, native fish richness
may be subsequently increased in areas containing greater channel width due to greater
potential for multiple habitat types. In contrast, each region is affected directly by
different aspects of the flow regime. For the Big Bend region, minimum flows during the
previous 30 days (or low flow/drying events) had a more significant negative impact on
the native fish richness, where native fish richness tended to decrease with increases in
low flow/drying events. The only direct hydrologic effect observed with our SEM
modeling for the native fish richness within the Forgotten Reach was flow spikes or
assumed tributary-derived flash flood events, where native fish richness tended to
increase with increases in flow spikes. Poor water quality appeared to be a more
influential factor in determining the integrity of the native fish community for the
Forgotten Reach. Each region also appeared to be unaffected by the nonnative fish
abundance; however, there were higher abundances of generalist, saline-tolerant native
species compared with more sensitive species in the Forgotten Reach. Thus, generalist
native fishes may be affecting other native fishes more than nonnative species within the
region. Overall, we were able to observe how two regions, similar in their historical
physical template but different in ecological integrity, are affected similarly (e.g., habitat
35
simplification) and differently (e.g., poor water quality) by the modern flow and sediment
regimes of the Rio Grande.
Model Limitations
SEMs have become increasingly popular in ecological studies due to their
multivariate nature and ability to include latent variables (Grace 2008; Grace and others
2010; Tomarken and Waller 2004). They are especially intriguing for use in ecology
because of their ability to test multiple different structures for a given system. However,
as with all statistical models, they are only an estimation of reality, and SEMs routinely
omit important variables to increase model fit (Tomarken and Waller 2004). These
omitted variables can result in biased model constructs and final inferences for a given
system. For our models, we had to work largely with sporadic monitoring data and thus
had limited sample sizes for both the Forgotten Reach and the Big Bend region. We were
limited to using mean daily data that may completely miss most flows spikes as some can
occur over a matter of hours. Furthermore, many of the sampling sites within the
Forgotten Reach are in anthropogenically altered reaches of Forgotten Reach segment
(e.g., levees, agriculture), which may not be representative of the entire Forgotten Reach
segment as there are reaches not directly affected by human impact. Discussed
previously, there are multiple (potentially infinite) ways to specify an SEM, and for our
models, we assumed geomorphic, hydrologic, water quality, and nonnative fish species to
be the most critical limiting factors impacting the native fish diversity for the Rio Grande
in the Forgotten Reach and Big Bend region. Nonetheless, our models presented here
illustrate their potential use for identifying limiting factors on large, regulated riverine
ecosystems. Perhaps more importantly, the SEM results presented here also provide the
36
impetus for future studies to directly measure and test both the physical and ecological
components of an ecosystem.
Implications
Using SEMs have allowed us to better understand changes to ecosystem structure
and function for a degraded desert river where few comprehensive studies have
previously been conducted. Habitat heterogeneity and adequate stream flows may be
critical mechanisms driving alterations to the aquatic ecosystem and associated
biodiversity for the Big Bend region. In combination with adequate stream flows, water
quality and habitat heterogeneity may drive the ecosystem structure and associated
biodiversity for the Forgotten Reach. Having identified the most critical limiting factors
affecting the Rio Grande’s ecosystem integrity may contribute to the identification of
management policies aimed at innovative solutions to increase the amount, magnitude, or
timing of flows. Specifically, the Big Bend region will benefit most from the restoration
of flows that can reestablish and maintain critical habitat. The Forgotten Reach is most
affected by water quality and habitat degradation associated with the substantial channel
narrowing and simplification in this segment. Thus, there would need to be a substantial
change in the quantity and timing of flows to re-widen and reestablish complex habitat;
however, flows that can maintain better water quality conditions should be the primary
consideration in terms of restoration. Prescribing adequate stream flows for maintaining
sufficient habitat complexity and water quality conditions may be key for restoring the
Rio Grande back to a suitable level of ecosystem integrity and resilience that allows the
persistence of imperiled native fishes. We now have a better understanding of how the
physical template of a river can structure the ecological factors, and in turn, how the
37
ecological factors are used to inform the importance of the physical structure.
Acknowledgments
Funding was provided by the U.S. Geological Survey, South Central Climate
Science Center, the National Park Service, USU award by the Ecology Center at Utah
State University, and by the U.S. Geological Survey, Utah Cooperative Fish and Wildlife
Research Unit (in-kind). Data were generously shared by S. Miyazono, R. Patiño, C.M.
Taylor, and K. Saunders for both the Forgotten Reach and the Big Bend region. I would
like to thank B. Laub for his generous help with both the modeling process and reviews
of earlier drafts. In addition, I would like to thank J. Brahney for their guidance and
reviews throughout my project. Any use of trade, firm, or product names is for
descriptive purposes only and does not imply endorsement by the United States
Government.
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Tables and Figures
Table 2.1. Structural equation model fit statistics for the explained variation in the native
fish richness in the Big Bend and Forgotten Reach regions. The alternate models for the
Forgotten Reach are compared with the Big Bend model (Chi-squared value (χ2 ),
Comparative Fit Index (CFI), Tucker Lewis Index (TLI), Root Mean Square Error of
Approximation (RMSEA), proportion of explained variance in native fish richness (R2)).
Model
χ2 (p-value)
CFI
TLI
RMSEA
R2
Big Bend Region
0.400 0.994 0.985 0.030* 0.269
Forgotten Reach (compared to baseline Big Bend)
0.006
0.905
0.752
0.178*
0.664
Forgotten Reach (altered model structure)
0.066
0.946
0.915
0.104
0.630
47
Table 2.2. Standardized path coefficients for each SEM structure (=~ symbolizes a latent
variable, → symbolizes a regression; estimates in bold were statistically significant at p-
value < 0.05).
Path coefficients
Big Bend
Estimates
Forgotten
Reach
(baseline)
Estimates
Forgotten
Reach
(altered)
Estimates
Water quality =~ specific conductance
0.954
1.00
1.00
Water quality =~ sulfate 0.721 0.966 0.954
Water quality =~ water temperature -0.762
Water quality → flow median -0.054 -0.238 -0.356
Water quality → flow minimum 0.222 -0.343
Water quality → flow spikes -0.687 -0.214
Water quality → flow day of sampling -0.423
Nonnative → water quality 0.244 0.147 0.258
Nonnative → channel width
Nonnative → flow median 0.452 -0.009
Nonnative → flow minimum 0.006 -0.105
Nonnative → flow spikes 0.037 -0.204
Nonnative → flow day of sampling
Native richness → water quality 0.027 -0.437 -0.535
Native richness → channel width 0.483 0.378 0.376
Native richness → nonnative 0.147 0.009
Native richness → flow median 0.396 0.012
Native richness → flow minimum -0.544 0.209
Native richness → flow spikes -0.078 0.408 0.374
Native richness → flow day of sampling -0.137 -0.219 -0.213
Channel width → flow spikes 0.375
48
Figure 2.1. Map of the study areas: Forgotten Reach through Big Bend region along the
Rio Grande in West Texas, United States. Outline represents park boundary for Big Bend
National Park.
49
Figure 2.2. Hypothesized model for the overall native fish diversity of the Rio Grande
for both the Forgotten Reach and the Big Bend region.
50
Figure 2.3. SEM result for the Big Bend region using the hypothesized model structure
(Figure 2). Width of lines are scaled to the strength of the path coefficient, which is also
given over the path line.
51
Figure 2.4. SEM result for Forgotten Reach using the baseline Big Bend model structure.
Width of lines are scaled to the strength of the path coefficient, which is also given over
the path line.
52
Figure 2.5. SEM result for Forgotten Reach using an altered model structure where the
direct effects of minimum flows, median flows and nonnative relative abundance have
been removed from native richness. In addition, water temperature has been added to the
water quality latent variable and flow spikes now influence channel width. Width of lines
are scaled to the strength of the path coefficient, which is also given over the path line.
53
CHAPTER 3
TOO MUCH SEDIMENT, TOO LITTLE WATER: HOW THE MODIFIED
HYDROLOGIC AND SEDIMENT REGIMES OF THE RIO GRANDE
AFFECT THE NATIVE FISH COMMUNITY AND FOOD WEB
STRUCTURE IN BIG BEND NATIONAL PARK, TEXAS,
UNITED STATES
Abstract
Desert rivers are complex and dynamic and are also some of the most degraded
ecosystems globally, in part because they are competing with man for water resources.
Many native riverine species endemic to the arid and highly regulated Rio Grande have
been reduced or eradicated due to the loss of viable and connected habitat, poor water
quality, and nonnative plants and animals, all often associated with modified flow and
sediment regimes. The overall goal of my study was to determine if and how the
modified hydrologic and sediment regimes of the Rio Grande in the Big Bend region
have impacted the aquatic ecosystem structure and function with an emphasis on the
native fish community and food web structure. I used a multi-faceted approach involving
habitat availability analysis, fish community and abundance comparisons, and stable
isotope food web techniques to compare 12 sampling sites of differing habitat
heterogeneity, a priori classified as either complex or simple, and/or canyon and alluvial.
I found complex and canyon sites to contain a greater diversity of habitat types than
simple or alluvial sites. In addition, using random forest classification, the categorization
of complex or simple was best predicted based on the proportion of backwater
mesohabitats. Though fish diversity was not significantly different between sites of
54
varying habitat heterogeneity, I observed significantly greater catch per unit effort
(CPUE) in complex and canyon sites (~19% greater) relative to simple or alluvial sites,
indicating higher abundance. Chlorophyll a concentrations were relatively low and not
variable between sites of varying habitat complexity; however, the ‘scraper’
macroinvertebrate functional feeding group was significantly greater in all sites of greater
habitat complexity suggesting there is an abundance of attached algae, or periphyton in
those areas. Food web structure also appeared to be influenced by habitat heterogeneity,
and complex and canyons sites tended to be more trophically-diverse (nitrogen range
(NR) = 10.6 and 9.25, respectively) and contained more types of basal resources (carbon
range (CR) = 1.83 for both complex and canyon) than simple or alluvial sites (NR = 5.49
and 2.57; CR = 0.586 and 0.710, respectively). In addition, the niche breadth of fish and
aquatic and terrestrial invertebrates was wider and more complex in complex and canyon
sites. There was very little variability in isotopic niche space for fish and invertebrates
across vegetation types, and nonnative vegetation did not appear to trophically influence
the fish community. Finally, my mixing models demonstrated that aquatic
macroinvertebrates provided a greater contribution to the proportion of dietary items
consumed by fish across most sites except for alluvial sites. Collectively, my results
contribute to a greater understanding of desert river ecosystems by linking the physical
components (e.g., hydrology and sediment supply) to the habitat for biota and the
ecological components (e.g., fish community and food web structure). Ultimately these
results have important implications for native fish conservation by providing an
ecological framework to inform effective water management solutions.
55
Introduction
Rivers are some of the most ecologically-dynamic and complex ecosystems on
Earth. Stream ecologists have long studied the multi-dimensional interactions of a river
ecosystem between both the terrestrial and aquatic environment, such that multiple
theories have been proposed to explain the structure and function of these aquatic
systems (e.g., Vannote et al. 1980; Junk et al. 1989; Thorp and Delong 1994).
Understanding how biotic assemblages and nutrient pathways (e.g., aquatic and
terrestrial) interact to influence food web structure in large riverine ecosystems remains a
relevant question in stream ecology. Vannote et al. (1980) suggested a longitudinal
gradient where downstream biotic assemblages of a river are increasingly dependent on
upstream sources of nutrients. As a result, the biotic assemblages are structured
accordingly as the abundance of upstream basal resources and local processes change
along the river gradient. Junk et al. (1989) stressed the importance of flooding and
floodplain interactions to the biotic community. Thorp and Delong (1994) described
autochthonous primary production and direct, local sources of riparian organic matter as
important basal resources of food for upper aquatic trophic levels (e.g., primary
consumers). While all these theories have proven fundamental to our growing
understanding of large river ecosystem dynamics, a question remaining is how and if
these theories can be applied to large, dewatered desert rivers with extremely altered
hydrologic and sediment regimes, a now common phenomenon.
Desert river ecosystem structure and function are increasingly threatened by both
human society’s demand for water and climate-induced drought (Hauer and Lorang
2004). Long-term water regulation and diversions on many North American arid land
56
rivers have altered the natural variability and magnitude of stream flows and sediment
delivery (Ward and Stanford 1983; Poff et al. 1997; Schmidt and Wilcock 2008). For
example, reductions in stream flow that occur while the local sediment supply remains
unchanged, have led to channel simplification of many desert rivers in the southwest US,
including the Green River in Utah, the Rio Grande in far west Texas, and the Little
Colorado River in Arizona (e.g., Hereford 1984; Allred and Schmidt 1997; Dean and
Schmidt 2011). Dewatering often reduces flood magnitudes to the point where the river
can no longer maintain the former floodplain, disconnecting it from the main channel. As
a result, the channel banks often become more stable and can facilitate native and
nonnative riparian vegetation encroachment (Brandt 2000; Graf 2006).
Ecosystems can be fundamentally changed in cases where their physical structure
is altered by changes to the flow and sediment regimes. Habitat for biota can become
simplified and reduced in availability, and biotic assemblages typically change in
response. Endemic taxa are often lost or forced into sub-optimal habitat due to decreased
habitat availability or suitability (Townsend and Winfield 1985; McDonald et al. 1992;
Kalb et al. 2018). For example, a simulated spring flood pulse within the northern branch
of the Rio Grande in New Mexico created access to adequate floodplain spawning
grounds for the Rio Grande silvery minnow, a federally-listed endangered species, and
cued spawning behaviors (Archdeacon 2016). Access to these floodplain-spawning
grounds created habitat and productivity necessary for the development of larval
minnows, which appeared to increase larval survival (Dudley and Platania 1997, 2007;
Medley and Shirey 2013). However, the timing of this simulated flood pulse may have
also resulted in the early evacuation of developing fry (D. Strohm, personal
57
communication), with potentially negative consequences at the next life stage.
Nonetheless, this example of flood management demonstrated how hydrologic timing
and magnitude of a river are inextricably coupled with the life history of organisms, and
even small alterations to flood regimes can have important consequences for a sensitive
species.
Changing the hydrologic and sediment regimes can also impact riparian
communities and their distributions. Associated modifications to riparian composition
influences the trophic structure and associated food web energy pathways within a river
(Mineau et al. 2011, 2012). For example, nonnative saltcedar (Tamarix spp) altered a
small desert river once seasonally-dependent on autochthonous sources of energy to an
ecosystem dependent on continuously-available allochthonous sources of energy,
resulting in nonnative fish species out-competing native fish species (Kennedy et al.
2004). Consequently, the removal of saltcedar in this system contributed to the increased
abundance of native fish species (Kennedy et al. 2005). Anthropogenic changes or
impacts to either the physical (e.g., hydrology or sediment supply) or ecological integrity
(e.g., shifted riparian composition) of desert rivers compounds the already sensitive
nature of these harsh, drought-prone environments (Ward and Stanford 1983; Cross et al.
2011).
Examining fish communities in modified river systems can be a useful tool to
assess the ecological integrity of these aquatic systems. The structure of communities and
how they change through time and space is important for determining both the physical
components of these environments and investigating the potential timing and magnitude
of human-induced impacts (Sá-Oliveira et al. 2015). In the Murray-Darling river basin,
58
there was an observed trend of reduced species diversity in highly regulated catchments
of the basin presumably due to modified stream flows (Gehrke et al. 1995). Fish
community assessment is also beneficial for determining the effectiveness of restoration.
For example, flow restoration to reaches with previous modified flow changes increased
the abundance of fish guilds present in the Rhone River, France (Daufresne et al. 2015).
In general, understanding the response of fish communities to modified hydrologic and
sediment regimes will increase our knowledge of degraded desert river systems and help
prioritize management actions.
Riverine food web structure is also influenced by degraded habitat conditions
associated with anthropogenic water use and drought. Habitat size and structure
determine productivity, food types and abundances, resource partitioning, and
interactions among organisms (Finlay 2011; Takimoto and Post 2013). Thus, changes to
habitat availability can influence the types and abundances of food resources for aquatic
consumers, impacting the food web structure by changing the overall communities
present (Bain et al. 1988; McHugh et al. 2015). Low complexity habitat may lead to
increased competition because of the limited access and availability of resources. Those
that outcompete other organisms within these simplified habitats may homogenize the
native community if they are dominant competitors (Menge and Sutherland 1987;
Jackson et al. 2001; Hayden et al. 2015). Food web structure is subsequently affected by
these changes to the communities, and food chain length and lower trophic diversity (e.g.,
increased omnivory) are often the result of increased community homogenization (Post
2002a).
Given the well documented impacts and associated changes of desert river
59
ecosystems based on altered flow and sediment regimes, it is sufficient to say the
fundamental theories of river ecology described previously all likely apply at some level.
There are important hydrologic and sediment dynamics that propagate downstream,
critical flood stages/pulses allow access to essential floodplain habitats and nutrients, and
direct riparian input alters energy pathways (Kennedy et al. 2004, 2005; Schmidt and
Wilcock 2008; Archdeacon 2016). Thus, the overarching goal for this study was to use a
habitat availability and food web-based approach to examine how changes to the physical
environment alter the aquatic ecological structure and function of a desert river
ecosystem.
Within this context, I had two objectives to better understand the altered physical
and ecological environment and associated effects on the native fish community of the
highly regulated Rio Grande along the US-Mexico border. My first objective was to
identify the effects of the altered hydrologic and sediment regimes of the Rio Grande on
habitat heterogeneity and the associated native fish community. Based on prior
geomorphic assessments (Dean and Schmidt et al. 2011, 2013; J. Bennett, personal
communication), I chose a suite of sampling sites exhibiting varying levels of channel
morphology and simplification throughout the river. I predicted sampling sites with
greater habitat heterogeneity would be more complex and contain greater fish diversity
than sites with low habitat heterogeneity. My second objective was to determine if and
how these altered hydrologic and sediment regimes have potentially affected energy flow
pathways (e.g., basal resource production) and structure of the aquatic food web. I
predicted food web structure (e.g., food chain length, trophic niche space) would be more
complex within sites with greater habitat heterogeneity than sites with low habitat
60
heterogeneity because I assumed complex sites to better partition resource-use and
facilitate multiple species. I also predicted nonnative vegetation alters the food web
function by contributing differentially to dietary items of upper trophic-level consumers
than does native vegetation. Overall, I hope to contribute to the ecological knowledge
surrounding desert food web structure and function, and provide insight to more effective
and innovative stream flow management solutions for the Rio Grande in the Big Bend
region, arguably the most ecologically-intact reach of this river that remains.
Study area
The Rio Grande, known as the Río Bravo in Mexico, is the fifth longest river in
North America, extending approximately 3,000 km-long across eight states and two
countries and draining a basin of approximately 558,000 km2 (Patiño-Gomez et al. 2007).
My study was conducted in the Big Bend region of the Lower Rio Grande Basin,
designated as the segment extending from the confluence of the Rio Grande and Rio
Conchos, a large tributary originating in the Sierra Madre Occidental of northern Mexico,
to Amistad Reservoir for approximately 490 km (Figure 3.1; Dean and Schmidt 2011).
The Big Bend region is located in the Chihuahuan Desert, receiving as little as 200 mm
annually in total precipitation and having maximum air temperatures reaching 33°C
(PRISM Climate Group, 2017).
Historically, floods within the Big Bend region were supplied by both spring
snowmelt in the southern Rocky Mountains and rains associated with the North American
monsoon or dissipating tropical cyclones. Thus, the high flow period in the Big Bend
region of the Rio Grande likely extended from early April to early fall (Blythe and
Schmidt 2018, T. Blythe 2018 thesis). Construction of Elephant Butte Dam in New
61
Mexico in 1915 and irrigation diversions eliminated the spring snowmelt stream flows
such that approximately 95% of the total annual flow upstream of the Rio Conchos is
consumed before it reaches the confluence. Contemporary stream flows in the Big Bend
region are now reliant upon flows provided by natural floods of the Rio Conchos,
occasional dam releases from Luis L. Leon Dam on the Rio Conchos in Mexico, and
floods on Chihuahuan Desert tributaries (Dean and others 2016). Consumptive water use
in the Rio Conchos watershed is not as complete as it is upstream of the Rio Conchos-Rio
Grande confluence, and the Rio Conchos continues to supply most of the flow to the Big
Bend region. In other words, most of the Rio Grande’s stream flow in the Big Bend
region has come from Mexico. Today, the Rio Grande in the Big Bend is one of the most
ecologically-intact portions of the river (CEC 2014).
Though stream flows are higher in magnitude through the Big Bend region, the
loss of spring snowmelt flows is likely to have impacted the river’s ability to effectively
transport the continued sediment supply from tributary-sourced flash flood events (T.
Blythe 2018 thesis). Thus, sediment is not evacuated and contributes to channel
narrowing and vertical accretion in the Big Bend region (Dean and Schmidt 2011; Dean
and other 2011). In addition, nonnative riparian plant species, primarily giant reed
(Arundo donax), have become well established on the inset floodplains associated with
sediment accumulation. These nonnative species, in combination with the modified
timing and magnitude of large flood events, likely prevent the re-establishment of native
riparian vegetation such as cottonwood (Populus spp.) and willow (Salix spp.), and act as
a positive feedback to channel narrowing as they trap sediment and actively stabilize the
channel banks (Dean and Schmidt 2011, 2013). Occasionally the channel is re-widened
62
by large (>1000 m3 s-1), rare (10- to 15-year frequency) flood events, termed ‘reset’
floods (Dean and Schmidt 2013). These reset events are ecologically-important, because
they have the capacity to restore channel complexity by actively rewidening the channel
and reducing nonnative giant cane (Dean and Schmidt 2011, 2013; Dean et al. 2011;
Dean et al. 2016).
The native fish community of the Rio Grande in the Big Bend region has
responded in kind to these hydrologic, geomorphic, and riparian community composition
changes. Many native species have become threatened (e.g., state-threatened Mexican
stoneroller (Campostoma ornatum), Chihuahua shiner (Notropis chihuahua), blue sucker
(Cycleptus elongatus)) or listed as federally endangered (e.g., Rio Grande silvery
minnow) and extirpated from the Big Bend region (Miyazono et al. 2013, 2015). The
relative abundances of many sensitive native species within this region have decreased in
the last 20th century associated with altered hydrologic conditions, poor water quality,
and loss of necessary habitat associated with increased sedimentation. In contrast, native
generalist, saline-tolerant species such as red shiner (Cyprinella lutrensis), Tamaulipas
shiner (Notropis braytoni), and western mosquitofish (Gambusia affinis) have increased
in their relative abundances in recent years (Miyazono et al. 2015). Nonnative fish
species such as plains killifish (Fundulus zebrinus) and common carp (Cyprinus carpio)
are also more abundant in the present-day Big Bend region of the Rio Grande.
Restoration of stream flows that would increase water quality and habitat heterogeneity
will be necessary to prevent species homogenization and rehabilitate and restore the
native fish community for the Big Bend region.
63
Methods
Field Sampling Habitat complexity and water quality
Sufficient habitat complexity, to the same extent that was the case in an
unregulated river, is often associated with diverse species and aquatic communities, and
thus the degree of habitat complexity can provide one measure of the ecological integrity
of an aquatic ecosystem. I sampled 12 1-km reaches with different degrees of habitat
complexity, a priori classified as either simple or complex based on previous geomorphic
surveys (Dean and Schmidt 2011, 2013; J. Bennett and D. Dean, personal
communication), and classified as either in canyon-bound portions of the river or in wide
alluvial valleys (hereafter called alluvial). I sampled these sites five times in 2016 and
2017. I collected samples in different seasons: late winter/early spring 2016, spring 2016,
fall 2016, winter 2017, and summer 2017 (Table 3.1). I measured discharge during each
sampling event to determine if and how the magnitude of stream flow influences
available in-channel habitat complexity and thus, native aquatic diversity (e.g., do higher
magnitude flows provide more or less diversity for in-channel habitat types and perhaps
increase/decrease native fish diversity?). Discharge differed during each sampling trip by
approximately one order of magnitude: 1.9 m3/s in May 2016 and 30 m3/s in October
2016 (Table 3.1). I considered the May 2016 discharge measurement to be considered
low stream flow and the October 2016 discharge is considered a moderate stream flow
magnitude.
I sampled eight sites within Big Bend National Park, two of which were in the
alluvial valleys and six in Boquillas Canyon. I also selected four sample sites located
within the Lower Canyons of the Rio Grande Wild and Scenic River. Stream flow
64
increases in the Lower Canyons due to numerous springs from the Edwards-Trinity
aquifer (Raines et al. 2012). As a result, baseflow progressively increases downstream
and water quality improves.
Using methods similar to Moring et al. (2014), I mapped complexity at the
mesohabitat scale for each 1-km site at baseflow conditions. and delineated runs, pools,
riffles, backwaters, forewaters, embayments, boulder bars, and side pools (Figure 3.2a). I
measured depth (m), velocity (m/s), and substrate composition of each mesohabitat type
in proportion to their overall distribution in each sampling site (Figure 3.2b, 3.3). Within
a geographic information system (GIS), I digitized habitat maps using aerial imagery
flown at similar discharges in ESRI ArcMap software (version 10.3.1) to quantify total
area and calculate the proportions of mesohabitat area for each sampling site. I used a
one-way analysis of variance to identify differences in total area between simple and
complex sampling sites and between alluvial and canyon reaches. I recorded water
quality measurements using a YSI 556 Multiprobe meter for each mesohabitat type to
identify potential patterns in water quality associated with each mesohabitat and overall
habitat complexity. I measured water temperature (°C), specific conductance (µS/cm),
total dissolved solids (mg/L), dissolved oxygen concentration (mg/L) and saturation (%),
and pH. I calculated the mean of each water quality parameter for each mesohabitat type
and between seasons (Appendix, Table A1).
Fish community
Freshwater fish community assemblages are determined in large part by their
interactions with their surrounding physical environment (Jackson et al. 2001). Thus,
modifications to fishes preferred and required habitat can impact and alter the fish
65
community present (Fausch et al. 1990). I compared fish diversity and abundance
between simple and complex sampling sites and between canyon and alluvial reaches to
assess how the fish community has responded to the modified flow and sediment regimes
of the Rio Grande. I used a combination of active and passive field sampling techniques
and multiple types of gear to quantify abundance of all size classes of fishes present and
minimize potential gear bias within my results for describing the native fish community.
I actively seined (seine size: 6 m x 1 m, 6 mm mesh) and used angling techniques
in all mesohabitat types for each sampling site and recorded length of the seine haul (m)
and time (hrs) spent angling to calculate catch per unit effort (CPUE; fish m-2, fish hr-1).
In addition, I set passive fyke nets, hoop nets, and minnow traps in side pool, pool, and
boulder bar mesohabitats that are deep and have slow velocity, and recorded the time set
for each sampling site. For all catch, I identified, counted, and measured total length and
weight for each individual fish. In addition, I fin-clipped at least five individual fish for
each species per length class and air-dried fin-clips in a 1.5 mL micro-centrifuge tube for
stable isotope analysis.
Primary productivity
Primary producers are important basal food resources for both fish that directly
consume primary producers and fish that feed on secondary consumers feeding on
primary productivity. To better understand the contemporary patterns in primary
productivity abundance and identify the importance of primary producers supporting
secondary production, I measured the concentrations of chlorophyll a (µg/L) for seston,
soft sediment algae, and periphyton and analyzed each using stable isotopes. Within each
sampling site, I sampled periphyton by randomly selecting five different stones within a
66
riffle mesohabitat and scraping a known area for each stone into a sampling pan. I then
measured total volume scraped (mL) and filtered a known volume through a 2.4 cm
Whatman glass fiber filter (GF/C), collecting multiple filters for both chlorophyll a and
stable isotope analysis. For my soft sediment algae samples, I targeted slow, shallow
mesohabitat types (e.g., backwaters, forewaters, and embayments) with either a silt or
sand substrate at the time of sampling. I then gently placed a petri-dish with a known area
of 31.7 cm2 within the thin, flocculent layer of algae on top of the silt/sand substrate,
placed a flattened piece of plastic between the flocculent layer and the substrate, and
deposited sample into a sample pan. I used the same method of filtering for the soft
sediment samples as the periphyton. I collected seston samples by directly sampling from
the mid-point of the river’s depth within a run mesohabitat and filtering the known
volume through a glass fiber filter. For all algal samples collected, I placed filters in
aluminum foil, labeled by type and volume filtered, and kept frozen until further analysis.
Terrestrial and aquatic invertebrates
In many riverine ecosystems throughout the world, nonnative vegetation has
changed river ecosystem efficiency by increasing the amount of leaf litter deposited in
the river and decreasing autochthonous primary productivity (Mineau et al. 2011, 2012).
Along much of the Rio Grande, nonnative giant cane has become well established and
altered the contemporary riparian community (Everitt 1998; Dean et al. 2011). As giant
cane can produce up to 20 t ha-1 of leaf litter, a substantial portion of organic matter from
giant cane is likely input into the river (Yang et al. 2011). It is important to note that Big
Bend National Park has undergone substantial effort to eradicate giant cane throughout
the park, particularly within Boquillas Canyon. Thus, the sites I sampled were a
67
combination of treated and untreated sites in terms of nonnative vegetation eradication
efforts. To quantify the impacts of nonnative vegetation to the food web structure of the
Rio Grande and identify if the system has shifted in terms of domination by allochthony
or autochthony, I collected terrestrial invertebrates and leaf litter from native and
nonnative vegetation, and open sites, or sites where there were exposed gravel and sand
bars near water’s edge. I used small plastic tubs equipped with UV blacklights and filled
each with 250 mL of distilled water and one to two drops of dish detergent (to break
surface tension) in each of the three sites. This method was pioneered by Kennedy et al.
(2016) in a Citizen Science protocol used along the Colorado River in Grand Canyon. I
turned lights on for one hour after approximate nautical sunset and preserved the entire
contents collected during that hour in a 250-mL Nalgene bottle filled with a 95% ethanol
solution in the field. Upon return to the laboratory, I changed the ethanol solution to 70%
to prevent invertebrate breakdown.
Aquatic invertebrates are important primary consumers and food resources for
native fishes, as well as excellent indicators of aquatic ecosystem health (Hawkins et al.
2000). Thus, aquatic invertebrates are an important aspect for understanding the structure
of the Rio Grande’s aquatic food web and ecological integrity. I sampled invertebrates in
both slow velocity (e.g., pools, backwaters, forewaters, embayments) and high velocity
(e.g., riffles, runs) areas. I used a combination of 3-min kicks and 1-m sweeps in all
mesohabitat types using a 0.30-m diameter, heavy duty D-frame dip net with 500-µm
mesh. Because I was interested in community differences between complex and simple
sites, and between alluvial and canyon sites, I combined all samples collected in 18.9-L
plastic bucket and picked all invertebrates in the field. I preserved invertebrates in a 250-
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mL Nalgene bottle filled with a 95% ethanol solution. Like my terrestrial invertebrate
samples, I changed the ethanol solution to 70% upon return to the laboratory.
Laboratory Methods
Chlorophyll a abundance and community analysis
In order to estimate primary productivity abundance, I analyzed my algal samples
using fluorometric analysis. Fluorometric analysis is a highly sensitive technique to
extract chlorophyll a (Welschmeyer 1994). I began by extracting all algal filter samples
in a 10-mL 95% ethanol solution for 24 hours. After extraction, I thoroughly mixed the
chlorophyll a from the filter into the solution and measured 6-mL of solution into a glass
cuvette. I then measured the extracted chlorophyll a concentrations with a Turner 10 AU
Fluorometer. To calculate the final concentrations from the original samples, I used two
different equations based on the types of algae being analyzed:
For seston samples:
𝜇𝑔 𝐿−1 =𝑓𝑙𝑢𝑜𝑟𝑜𝑚𝑒𝑡𝑒𝑟 𝑟𝑒𝑎𝑑𝑖𝑛𝑔 (𝜇𝑔 𝐿−1)∗𝑒𝑥𝑡𝑟𝑎𝑐𝑡𝑖𝑜𝑛 𝑣𝑜𝑙𝑢𝑚𝑒 (𝑚𝐿)
𝑣𝑜𝑙𝑢𝑚𝑒 𝑓𝑖𝑙𝑡𝑒𝑟𝑒𝑑 (𝑚𝐿)
For periphyton and soft sediment samples:
𝜇𝑔 𝑐𝑚−2 =𝑓𝑙𝑢𝑜𝑟𝑜𝑚𝑒𝑡𝑒𝑟 𝑟𝑒𝑎𝑑𝑖𝑛𝑔 (𝜇𝑔 𝐿−1)∗𝑒𝑥𝑡𝑟𝑎𝑐𝑡𝑖𝑜𝑛 𝑣𝑜𝑙𝑢𝑚𝑒 (𝑚𝐿)∗(𝑖𝑓 𝑢𝑠𝑒𝑑) 𝑑𝑖𝑙𝑢𝑡𝑖𝑜𝑛 𝑓𝑎𝑐𝑡𝑜𝑟
𝑣𝑜𝑙𝑢𝑚𝑒 𝑓𝑖𝑙𝑡𝑒𝑟𝑒𝑑 (𝑚𝐿)∗𝑎𝑟𝑒𝑎 𝑠𝑐𝑟𝑎𝑝𝑒𝑑 (𝑐𝑚2)
Terrestrial and aquatic invertebrate community classification
Though allochthonous input can be a key basal resource for many rivers (Vannote
et al. 1980), shifts in the types of allochthonous input can change the types and quality of
carbon input into upper levels of the aquatic food web (Kennedy et al. 2004, 2005). For
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example, nonnative riparian vegetation encroachment may alter the communities and
abundances of terrestrial invertebrates input into river ecosystems compared to native
riparian vegetation. I compared terrestrial invertebrate communities between native and
nonnative vegetation, and open sites. For all sample types, I counted and identified the
entire contents of each native, nonnative, and open site within each sampling site. I
identified aquatic invertebrate emergent adults to family level and all other invertebrates
to order. My goal was to identify the functional importance of aquatic emergent adults
within the terrestrial environment (e.g., riparian vegetation) and link that importance with
the potential impacts of nonnative riparian vegetation to the aquatic food web structure
for the Rio Grande.
Functional groups of aquatic invertebrates can serve as important indicators of
ecological integrity, primarily because of the multiple ways they can consume food
resources (Wallace and Webster 1996). In addition, aquatic invertebrates can influence
primary productivity, decomposition, and pathways of nutrient cycles. I sorted, counted,
and identified aquatic invertebrate samples to functional feeding group for each sampling
site. I classified aquatic invertebrate samples into five different categories including:
scrapers, shredders, filtering collectors, gathering collectors, and predators. These
functional feeding groups follow the classifications set forth by Cummins et al. (1979)
and provide an important linkage with the physical template of the river and the
ecological integrity. For example, by comparing functional groups between different
types of habitat complexity (e.g., simple versus complex), I identified mesohabitats
critical for maintaining diversity at the secondary productivity level, and subsequently
food resources important for the native fish community.
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Stable isotope preparation
Stable isotopes have the power to infer trophic position, trace the sources of
carbon and nitrogen, and provide insight on the integrity of an ecosystem (Vander
Zanden et al. 1997, Grey 2006). Carbon and nitrogen isotopes are useful indictors of
trophic position and the source of carbon throughout the system (Vander Zanden et al.
1996, Post 2002, Grey 2006). The amount of the heavy isotope of nitrogen in an
organism’s tissue is related to the diet items each organism consumes, and thus, their
trophic level. Carbon isotopes stay constant as they are transferred from an ultimate
source to the aquatic food web and can be used to identify sources of allochthonous of
energy to the river. In addition, hydrogen isotopes are useful for both deriving
allochthonous sources of energy and providing distinctions among local food webs.
I used a subset of all the different samples taken as described previously (fish fin-
clips n = 311, algae n = 47, terrestrial and aquatic invertebrate n = 140, and leaf litter n =
13) and analyzed each for carbon (δ13C) and nitrogen (δ15N) content. In addition, I
analyzed both fish fin-clips (n = 92) and invertebrates (n = 140) for hydrogen (δ2H)
content. To analyze sufficient mass of fish fin-clips, I consolidated fish samples of the
same species and similar length class; however, this reduced my sample size of total fish
analyzed. I dried all samples for 48-hours at 70°C and ground entire sample (e.g., fin-
clips, terrestrial and aquatic invertebrates, or leaf litter) into a homogenous powder using
a mortar and pestle. I placed each sample into either an Ultra-pure tin capsule for δ13C
and δ15N isotopes, or a silver capsule for the δ2H isotope (Costech Analytical, Valencia,
CA, USA), and weighed samples to the nearest 0.0008 mg.
I sent samples to both Washington State University Stable Isotope Laboratory and
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the Dennis Newell Stable Isotope Laboratory at Utah State University. Methods at each
laboratory were similar, and technicians converted samples for δ13C and δ15N analysis to
N2 and CO2 with an elemental analyzer (ECS 4010 Costech Analytical, Valencia, CA,
USA). N2 and CO2 were separated with a 3-meter Gas Chromatography (GC) column and
analyzed with a continuous flow isotope ratio mass spectrometer (Delta PlusXP, Thermo
Finnigan, Bremen). The standards for the δ13C and δ15N samples were PeeDee belemnite
(δ13C) and atmospheric nitrogen (δ15N). For δ2H, samples were converted to CO and H2
with a pyrolysis elemental analyzer (TC/EA, Thermo Finnigan, Bremen), then each gas
was separated with a GC column and analyzed using a continuous flow isotope ratio mass
spectrometer (Delta PlusXP, Thermo Finnigan, Bremen). The standards for δ2H were
potassium nitrate, caribou hoof, and kudu horn and were normalized relative to the
Vienna standard Mean Ocean Water content. Isotopic signatures are reported in -
notation:
δ13C, δ15N, or δ2H = [(Rsample
Rstandard) -1] x1000
Where R is the ratio of 13C/12C, 15N/14N, or 2H/1H.
Statistical Analyses
Random forest classification
Classification regression methods are widely used in ecology, because they are
simple to interpret, accurate, and characterize complex interactions (Cutler et al. 2007).
Classification trees can predict complex patterns using either numerical or categorical
response data. These types of models use an optimization pattern to select a ‘split’ in the
data, a predictor variable, and a ‘cut-off’ that homogenizes the data into subgroups. The
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‘splitting’ of a classification tree continues until the homogenized groups of data no
longer decrease the accuracy of the model (Cutler et al. 2007). Random forests are a
particular class of classification tree that fit many trees to a given data set and combine
each prediction from every tree, essentially bootstrapping the data. Those data that are
not randomly selected during the bootstrapping process are used for later prediction once
a tree is ‘fully grown’, and are known as ‘out-of-bag’ (OOB) estimates. The OOB data
are then used to assess the accuracy and error estimates of each predictor analyzed within
the model. Validation of the model fit is assessed using five different metrics: overall
percentage of models correctly classified (PCC), sensitivity, specificity, kappa, which is a
measure of agreement between predicted presences and absences, and area under the
curve (AUC) estimates. Model fit is ‘good’ when all metrics are high in value, and
specifically, if the AUC estimates are closer to one.
To predict the level of habitat complexity for each of my sampling sites, and to
verify my a priori classifications of simple or complex sites, I used random forest (RF)
regression analysis to identify which types of mesohabitat are important for predicting
overall habitat complexity for each sampling reach. As described previously, I used my
digitized habitat maps to calculate area (m2) of each mesohabitat type for every sampling
reach. I then calculated the relative proportions of each mesohabitat for every simple (n =
3) and complex (n = 9) sampling site. Thus, my predictors for the RF model were the
relative proportions of each mesohabitat including: runs, pools, riffles, backwaters,
forewaters, embayments, boulder bars, and side pools. Within the model, I ran 5000
bootstrap replicates (number of trees drawn) with replacement and the number of splits
was equal to two. I assessed model fit using the OOB metrics described previously.
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Fish community diversity
Diversity indices provide information on species richness and associated evenness
of diversity for a given ecosystem (Kwak and Peterson 2007). In addition, diversity
indices are useful for assessing assemblage structure and the associated interactions with
the physical environment. For example, diversity can be compared between sites of
varying ecosystem integrity, such as habitat heterogeneity, water quality, or nonnative
species. Shannon’s diversity index (H’; Shannon and Weaver 1949) is one of many
common indices applied to examining freshwater fish assemblages. I used Shannon’s
index, because it is sensitive to the abundance of rare species in a community, and rare
species are of overall interest herein. When I compared diversity between simple and
complex sites, there were species that only occurred in complex sites and were typically
low in abundance. I calculated Shannon’s index for each sampling reach as:
𝐻′ = ∑ (𝑝𝑖)(𝑙𝑜𝑔𝑒𝑝𝑖)𝑠𝑖=1
Where, s is the number of species, pi is the proportion of the total sample represented by
the ith species. I used a one-way analysis of variance (ANOVA) to detect any significant
differences of diversity between the different levels of habitat complexity.
Stable isotope analysis
Food web structure of an ecosystem provides useful information on the different
interactions between species, trophic niches, and the ecosystem integrity of the
surrounding physical environment. I began by correcting the carbon and nitrogen isotopic
signatures for upper trophic consumers using a common herbivorous invertebrate (Order:
Ephemeroptera, functional feeding group: scraper) as a baseline to compare between my
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sampling sites classified as either simple or complex, and alluvial or canyon. Thus, I used
the following equations to correct for basal resource variation using carbon and nitrogen
isotopes for each sample:
𝛿13𝐶𝑐𝑜𝑟 =𝛿13𝐶𝑖− 𝛿13𝐶𝑚𝑒𝑎𝑛𝑐ℎ
𝐶𝑅𝑐ℎ
Where, 𝛿13𝐶𝑖 is the carbon isotope signature of consumer i, 𝛿13𝐶𝑚𝑒𝑎𝑛𝑐ℎ is the mean
common herbivore carbon isotopic signature from the site where consumer i was
collected, and 𝐶𝑅𝑐ℎ is the carbon range of the common herbivores. I corrected nitrogen
values using:
𝛿15𝑁𝑐𝑜𝑟𝑗,𝑖 = 𝛿15𝑁𝑗,𝑖 − (𝛿15𝑁𝑐ℎ,𝑖 − 𝛿15𝑁𝑐ℎ,𝑚𝑖𝑛
)
Where, 𝛿15𝑁𝑗,𝑖 is the nitrogen signature of consumer j at site i, 𝛿15𝑁𝑐ℎ,𝑖 is the mean 15N
of common herbivores at site i, and 𝛿15𝑁𝑐ℎ,𝑚𝑖𝑛 is the minimum mean common
herbivore of all sites (from Cabana and Rasmussen 1996 and Walsworth et al. 2013). I
also calculated the carbon and nitrogen isotope ranges to supplement the corrected carbon
and nitrogen values and to provide information on the trophic length of the fish
community (NR) and the range of basal resources used by consumers (CR) using the
following equations:
𝑁𝑅 = 𝛿15𝑁𝑐𝑜𝑟𝑚𝑎𝑥 − 𝛿15𝑁𝑐𝑜𝑟𝑚𝑖𝑛
𝐶𝑅 = 𝛿13𝐶𝑐𝑜𝑟𝑚𝑎𝑥 − 𝛿13𝐶𝑐𝑜𝑟𝑚𝑖𝑛
Using my corrected carbon and nitrogen values, I then calculated the trophic
position for each consumer using the equation adapted from Post (2002):
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𝑇𝑃𝑖 =𝛿15𝑁𝑐𝑜𝑟𝑖−𝛿15𝑁𝑐𝑜𝑟𝑐ℎ
3.4+ 2
Where, 𝑇𝑃𝑖 is the trophic position for each consumer i, 𝛿15𝑁𝑐𝑜𝑟𝑖 is the corrected nitrogen
signature of consumer i, and 𝛿15𝑁𝑐𝑜𝑟𝑐ℎ is the corrected nitrogen signature of common
herbivorous invertebrates. I assumed trophic fractionation of 15N to be 3.4‰ with every
increase in trophic position (Minagawa and Wada 1984) and the common herbivorous
invertebrates I assumed to have a trophic position of two. I used the calculated trophic
positions to determine food chain length of both simple and complex, and alluvial and
canyon habitat sampling sites.
I analyzed carbon and nitrogen isotopes of upper level consumers (all fish species
caught) using community multivariate ellipses, or SIBER (Stable Isotope Bayesian
Ellipses in R). SIBER metrics use a Bayesian framework to calculate convex hulls, or the
area of all species plotted in a C-N bivariate space (Layman et al. 2007; Jackson et al.
2011). Using SIBER, I estimated the 15N range (NR) and 13C range (CR), total area
(TA) of convex hull, and mean and standard deviation nearest neighbor distance (NND,
SDNND) for the entire fish communities within simple and complex, and alluvial and
canyon sampling sites. The NR represents the vertical structure of a food web and a
larger range suggests more trophic diversity. Estimating the CR range provides a measure
of the diversity for basal resources and an increased range indicates multiple types of
basal resources. The TA measures the total isotopic niche space occupied and provides a
measure of trophic diversity. Finally, the NND and the SDNND are measures of the
‘evenness’ of species within a food web structure, where smaller values for each metric
suggests increased trophic redundancy and a more even distribution of ecological feeding
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niches.
I also used Bayesian mixing models of the carbon, nitrogen, and hydrogen stable
isotope data to predict patterns in resource partitioning or dietary preferences (Parnell et
al. 2010). Mixing models estimate the proportion of dietary items contributed to upper
trophic levels of interest. I used the Stable Isotope Mixing Models in R (simmr) package
(Parnell et al. 2010) to estimate the basal resources contributed to upper trophic
consumers (fish and aquatic invertebrates). For basal resources, I used aquatic (using the
common herbivore isotope signals), the combined three levels of terrestrial input: native
and nonnative vegetation, and periphyton. I first analyzed stable isotope food webs for
each type of habitat complexity: complex and simple, and alluvial or canyon. I then used
mixing models to observe any broad differences in the proportion of dietary items (pk)
between vegetation types across all habitat complexities. In addition, I analyzed separate
mixing models of each habitat complexity. For each model, I ran 500,000 iterations,
discarded 100,000 and retained one of every 10 iterations for analysis. I chose not to
include the residual error term for each simmr mixing model as there were sampling
periods where I only caught one individual (Parnell et al. 2010); however, I report the
95% confidence intervals for each pk mean estimate and assumed non-overlapping
intervals to indicate a more robust estimate of each pk. I performed and analyzed all
models using the free statistical software R (R Core Team 2017) and assumed models to
be significant at the P < 0.05 level.
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Results
Habitat Complexity
The relative proportions of mesohabitat types between simple and complex sites
appeared to differ, where complex sampling sites tended to contain all types of
mesohabitat present in some proportion (e.g., backwaters, forewaters, embayments,
riffles, runs, pools, side pools, side channels, and boulder bars). In contrast, simple sites
contained only embayments, riffles, runs, pools, and side channels. Run mesohabitats
were 22% greater in simple sites compared to complex (Figure 3.2b).
Having determined the total area of simple and complex sites and the relative
proportions of mesohabitat for each habitat classification, I next used a random forest
classification using the relative proportion of mesohabitat types to predict habitat
heterogeneity for each sampling reach (complex n = 9, simple n = 3). Overall, the
validation of fit of the model was high for the PCC, specificity, sensitivity, kappa, and
AUC (PCC = 91.7%, specificity = 100%, sensitivity = 66.7%, kappa = 0.75, AUC =
0.89). I then examined the variable importance plot for the mesohabitat predictors, and it
appeared backwater mesohabitats were the most important variable for predicting the
level of complexity for a sampling site (Figure 3.4).
Fish Abundance and Diversity
I captured 16 species (total n for complex sites = 1464 individual fish; total n for
simple sites = 247) across all sampling sites (Appendix, Tables A2 and A3). I collected
only two nonnative species in either simple or complex sampling sites: channel catfish
(Ictalurus punctatus; total n = 57) and common carp (Cyprinus carpio; total n = 55), each
of which were in relatively low abundance compared to the rest of the catch. I captured
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two generalist, saline-tolerant species in high abundance in each simple and complex site,
red shiner (Cyprinella lutrensis; total n = 529) and Tamaulipas shiner (Notropis braytoni;
total n = 542). In addition, I caught three Rio Grande silvery minnow (Hybognathus
amarus) in one complex sampling site.
Total catch per unit effort (CPUE; fish m-2) across all seasons was significantly
greater in complex habitats (CPUE = 0.52 ± 0.59 fish m2 [mean ± standard deviation];
repeated measures ANOVA, p < 0.001) and canyon (CPUE = 0.51 ± 0.59 fish m2;
repeated measures ANOVA, p < 0.001) sampling sites than in simple and alluvial sites
(Table 3.2, Figure 3.5a). March and May sampling events contained the greatest CPUE
(CPUEMarch = 0.74 ± 0.34 fish m2; CPUEMay = 1.69 ± 0.41 fish m2; p < 0.001 Two-way
ANOVA; Appendix, Figure A2). January, October, and June all demonstrated similar
CPUE and did not differ significantly.
Fish diversity appeared slightly higher in simple sites than complex sites (Figure
3.5b); however, the mean Shannon’s Index did not differ significantly between each type
of habitat complexity (H’simple = 1.42 ± 0.31, H’complex = 1.35 ± 0.52; repeated measures
ANOVA, p = 0.53). In contrast, canyon sites were significantly greater in fish diversity
than alluvial sites (H’canyon = 1.47 ± 0.38, H’alluvial = 0.88 ± 0.68; repeated measures
ANOVA, p < 0.05).
Abundance of Primary Productivity
As an indicator of primary productivity, I analyzed the chlorophyll a abundance
for all sampling sites across seasons and for three different types of algae: seston, soft
sediment, and periphyton, or attached algae (Figure 3.6). The concentration of Chl a
appeared to be greater in complex sampling sites compared to simple, but these
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differences were not significant (repeated measures ANOVA p > 0.05). Similarly,
alluvial sampling sites appeared to contain greater concentrations of Chl a for both seston
and soft sediment algae abundance than canyon-bound sites. Periphyton abundance in
contrast, appeared to be greater in the canyon-bound sites. I observed no significant
differences in Chl a concentrations across seasons; however, summer sampling events
appeared to have greater concentrations of Chl a than either fall or winter sampling
events (Appendix, Figure A4).
Terrestrial and Aquatic Invertebrate Relative Abundance
I analyzed the relative abundances of terrestrial and aquatic invertebrates between
complex and/or simple sites, alluvial and/or canyon sites, vegetation types, and by season
(e.g., sampling months; Appendix, Tables A4 and A5). There were no significant
differences in relative abundances of terrestrial invertebrates between either simple,
complex, alluvial, or canyon-bound sampling sites, or between seasons and vegetation
types (e.g, native, nonnative, or open). However, the relative abundances of terrestrial
invertebrates between simple or complex sampling sites were significantly greater at the
Family classification-level (p-value < 0.01, repeated measures one-way ANOVA) than at
the Order classification-level. Terrestrial abundance at the family level, however, did not
appear to differ between nonnative and native vegetation types (p-value = 0.9868,
repeated measures one-way ANOVA). When I tested for seasonality (e.g., sampling
months), there appeared to be significant differences between the sampling months of
March and October (total relative abundanceMarch = 3.78, total relative abundanceOctober =
2.03; p-value < 0.01), but not May, June, or January. The terrestrial invertebrate relative
abundance was highest in March and June (total relative abundanceJune = 4.00), and
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lowest in January (total relative abundanceJanuary = 0.036).
I analyzed functional feeding groups of the aquatic invertebrate catch between
complex and simple sites, alluvial and canyon sites, and season (sampling reach ncomplex =
9, aquatic invertebrate total ncomplex = 8604; sampling reach nsimple = 3, aquatic
invertebrate total nsimple = 947). The ‘scraper’ functional feeding group was 45.1% and
42.5% and significantly greater in relative abundance (p-value = 0.05) than all other
feeding groups in the complex and canyon sampling sites (mean scraper relative
abundancecomplex = 0.65; mean scraper relative abundancecanyon= 0.62; Figure 3.7a,b). The
relative abundance of the remaining four functional feeding groups was variable but
similar between simple and complex sites, and canyon and alluvial sites. I observed no
effect of season on the relative abundances of functional feeding groups (p–value = 0.23).
Stable isotope analyses
Overall, complex sampling sites appeared to contain a shorter food chain length
than simple sites (FCLcomplex = 3.99, FLCsimple = 4.81). Canyon-bound sites appeared to
contain a longer food chain length than alluvial sites (FCLcanyon = 4.81, FCLalluvial = 2.45).
I also compared the FCLs of species occurring in both complex and simple sampling
sites, and found species demonstrating a longer FCL in simple sites compared to complex
sites (Figure 3.8).
The trophic diversity (NR) appeared to be greater in complex (NRcomplex = 10.6)
and canyon sampling sites (NRcanyon = 9.25), and lower in simple (NRsimple = 5.49) and
alluvial sites (NRalluvial = 2.57; Table 3.3). Similarly, basal resources appeared to be more
diverse in complex and canyon sites (CRcomplex = 1.83, CRcanyon = 1.83) than in simple
and/or alluvial sites (CRsimple = 0.506, CRalluvial = 0.710). Total area (TA) of community
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isotopic hulls appeared to be greater in complex and canyon sites compared to simple and
alluvial sites (TAcomplex = 9.32, TAcanyon = 8.49, TAsimple = 1.87, TAalluvial = 0.986). The
trophic redundancy (NDD) appeared to be lower in simple and alluvial sites than
complex and canyon sites (NDDsimple = 0.416, NDDalluvial = 0.446, NDDcomplex = 0.844,
NDDcanyon = 0.715). Finally, I compared the evenness of trophic niche diversity
(SDNDD), which is less influenced by sample size, and observed higher SDNDD values
for complex and canyon sites than simple and alluvial sites (SDNDDcomplex = 0.749,
SDNDDcanyon = 0.611, SDNDDsimple = 0.322, SDNDDalluvial = 0.0954).
Overall, the trophic niche breadth of fish, aquatic and terrestrial invertebrates was
wide and more complex in complex and canyon sites relative to simple and alluvial sites
as demonstrated by stable isotope bi-plots (Figure 3.9). Periphyton in particular was
much less in depleted in 13C in the complex and canyon sites relative to the simple and
alluvial. In addition, fish tended to hold a greater diversity of trophic positions and higher
trophic positions in the complex and canyon sites, but there was considerable overlap and
variability. There was only one fish, the Rio Grande silvery minnow, that appeared to
rely more on periphyton as a carbon source. All other fishes appeared to rely on a
combination of terrestrial and aquatic macroinvertebrates.
When I compared isotopic trophic niche space of fish and invertebrates across
vegetation type (native, nonnative, open), the fish community overall appeared to be
more dependent on terrestrial-based food resources than aquatic macroinvertebrates
(Figure 10). Aquatic macroinvertebrates (e.g., common herbivorous consumers),
appeared to be more depleted in the carbon isotope and may also be partially reliant upon
terrestrially-based sources of food. Fishes, however, demonstrated very little variability
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in isotopic niche space across vegetation type, and did not appear to be trophically
influenced by nonnative vegetation.
Mixing models provide a more detailed and accurate description of the dietary
contribution of fishes as compared to general trophic positions. The mixing model results
demonstrated aquatic food resources potentially contributing to a higher proportion of
dietary (pk) items consumed by fish between complex, simple, and canyon sites (mean
complex paquatic = 0.453 ± 0.098, mean simple paquatic = 0.617 ± 0.099, mean canyon
paquatic = 0.569 ± 0.080) than terrestrial food resources (mean complex pterrestrial = 0.398 ±
0.116, mean simple pterrestrial = 0.211 ± 0.115, mean canyon pterrestrial = 0.237 ± 0.094;
Table 3.4, Figure 3.11). Terrestrial food resources appeared to contribute to a higher
proportion of dietary items consumed by fish in alluvial sites than aquatic food resources
(mean alluvial paquatic = 0.335 ± 0.077, mean alluvial pterrestrial = 0.636 ± 0.082; Figure
3.11). Periphyton did contribute to fish diets, but the proportion was small, albeit not
variable (Table 3.4).
A similar pattern appeared when I ran the mixing model using the hydrogen
isotope data, where complex, simple, and canyon sites (mean complex paquatic = 0.641 ±
0.046, mean simple paquatic = 0.780 ± 0.102, canyon paquatic = 0.711 ± 0.045) appeared to
contribute to a higher proportion of dietary items for fish than terrestrial food resources
(mean complex pterrestrial = 0.359 ± 0.046, mean simple pterrestrial = 0.220 ± 0.102, mean
canyon pterrestrial = 0.289 ± 0.045; Table 3.5, Figure 3.12). When I tested the mixing
model using aquatic invertebrates as the top consumers and terrestrial vegetation (native
and nonnative) and periphyton as the basal resources, native vegetation appeared to
contribute to a higher proportion of dietary items for aquatic invertebrates than nonnative
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vegetation or periphyton for complex, simple, and canyon sites (Table 3.6; Figure 3.13).
Periphyton appeared to contribute to a higher proportion of dietary items for aquatic
invertebrates in alluvial sites compared to canyon, complex, or simple sampling sites.
Discussion
The highly regulated and dewatered Rio Grande along the US-Mexico border has
had a century’s long history of human use and modification (Scurlock 1998; Schmidt et
al. 2003). As a result, the modern flow regime is greatly reduced hydrologically, and the
river channel has become narrowed and simplified associated with the modified flow
regime and continued sediment supply (Dean and Schmidt 2011). The physical
degradation (e.g., low flows, increased sediment accumulation) of this region has likely
contributed to the reduction of many native riverine species through the loss of viable and
connected habitat, poor water quantity and quality, and nonnative plants and animals.
Though the physical changes (e.g., hydrology and geomorphology) have been well
documented (e.g., Schmidt et al. 2003; Dean and Schmidt 2011, 2013; Dean et al. 2016,
Blythe and Schmidt 2018), little is known or understood about the aquatic ecological
structure and function of the Rio Grande in the Big Bend region. In this study, I examined
how the modified flow and sediment regimes of the Rio Grande in the Big Bend region
have influenced the native fish community and aquatic food web structure.
Understanding if and how the native fish community and food resources have changed in
response to this physical degradation may provide unique insight on how to better
manage water resource use for the conservation and rehabilitation of native fish species
endemic to the Rio Grande in the Big Bend region.
I used a multi-faceted approach involving habitat availability analysis, fish and
84
macroinvertebrate community and abundance comparisons, and stable isotope techniques
to compare 12 sampling sites with different levels of habitat complexity a priori
classified as either complex or simple, and/or canyon and alluvial. Using this type of
comparison created a study design that used space for time substitution, wherein complex
habitats represent large scale conditions immediately after a reset flood and simple
habitats represent conditions long after a reset flood. I observed complex sites containing
multiple types of habitat in some proportion, generally having both deep and shallow
slow habitats (e.g., backwaters, pools), and deep and shallow fast habitats (e.g., runs,
riffles). In contrast, simple sites contained only runs, pools, riffles, and relatively few
embayments. The levels of habitat complexity I classified also appear to be best predicted
by shallow mesohabitats (e.g., backwaters). In addition, complex sites contained greater
abundances of fish, an important functional feeding group of macroinvertebrates (e.g.,
scrapers), and greater trophic diversity. CPUE for fish and the relative abundance of
scrapers were greater in complex and canyon sites than alluvial and/or simple sites.
Complex and canyon sites also appeared to support multiple ecological feeding niches
(e.g. trophic diversity) compared alluvial and/or simple sites. Similarly, fish consumers
may be more dependent on aquatic-based food resources than terrestrial-based food
resources. Collectively, these results indicate that the hydrologic and geomorphic
degradation that has occurred within the Rio Grande extend to fish habitat and food web
structure, confirming that complex and heterogeneous habitat is likely one of the factors
limiting the viability and persistence of the native fish community in relation between
simple and complex habitats changing with time.
The once wide, meandering, multi-threaded alluvial valleys of the Rio Grande in
85
the Big Bend region likely have been affected the most by the altered hydrologic and
sediment regimes, and have presently become narrowed, single-threaded channels. In
addition, the alluvial valleys are narrowing at a faster rate compared to canyon-bound
regions following a recent 2008 reset flood (Dean et al. 2016; T. Blythe, in progress).
These large scale geomorphic changes also appear to manifest in my study. Notably,
most of the complex sampling sites were located within the canyon-bound portions of the
river. Thus, the geomorphic processes of channel narrowing occurring in the alluvial
valleys may not be having as great of an impact on habitat heterogeneity in the canyon-
bound sites.
Habitat heterogeneity is important for sustaining diverse fish communities,
because it provides protection from predators, reduces competition, and provides
necessary resources or refugia for critical life stages (Gorman and Karr 1978; Nagayama
and Nakamura 2018). Habitat heterogeneity may also influence growth patterns for
fishes. The synergistic effects of adequate food resources and vegetated habitats allowed
for increased growth and subsequent survival from predators in an estuarine fish species
compared to habitat sites with no vegetation (Levin et al. 1997). Similarly, increased
turbidity and varying habitat substrates decreased the reaction time and subsequent
consumption rates for roach (Rutilus rutilus L.), a freshwater fish native to many
European waters, potentially affecting growth for roach within more turbid habitat
environments (Murray et al. 2016). Based purely on these results, I would expect there to
be lower abundance and diversity of fish and macroinvertebrates in sites of lower habitat
heterogeneity associated with the decreased availability of food, limited growth, and
predation.
86
Somewhat surprisingly, fish diversity was not significantly different between
complex and simple sampling sites. Diversity serves as an abundance-weighted measure
of presence or absence, and though the individuals present may be similar between
different levels of habitat heterogeneity, their abundances can be dramatically lower yet
still demonstrate a similar diversity score. In addition, observed abundance was much
greater in complex and canyon sites relative to simple and alluvial sites, and these sites
may serve as a potential source of native fishes to other habitat types. It would follow that
the same set of species may be colonizing sub-optimal habitats but in much lower
abundance, and indeed, CPUE was lower in simple and alluvial sites. This pattern has
also been shown to serve as a form of population persistence. When optimal spring pool
habitat was unavailable or disconnected, the Arkansas darter (Etheostoma cragini)
remained in sub-optimal pools until large spring floods re-scoured new pool habitat
allowing the darter to recolonize and increase in population once again (Labbe and
Fausch 2000). The combined effects of climate change and flow alterations were also
predicted to alter viable spawning and rearing habitat for West Balkan trout (Salmo
fariodes) in the southwestern Balkan river of Greece, ultimately decreasing population
abundances and distributions of this endemic trout in the Balkan river (Papadaki et al.
2016). Proximity to viable habitat therefore likely controls the persistence of native fish
populations and communities, thus demonstrating how fishes and other aquatic biota
require complimentary habitat types (pools, riffles, backwaters) that are accessible to
complete their life cycle and persist (White and Rahel 2008).
Riverine food web structure is inherently influenced by the physical template,
and decreases in the amount or availability of habitat associated with alterations to the
87
physical template ultimately affects the biotic community composition, food resources,
and other ecosystem functions (Ledger et al. 2013; Datry et al. 2014; McHugh et al.
2015). Thus, I would expect food web structure to become more simplified with
increased habitat loss and degraded habitat complexity. Streams within the Waimakariri
and Rakaia river basins in Canterbury, New Zealand experienced significantly low
minimum flows during many parts of the year, ultimately creating smaller habitat size
and availability in many regions within these basins (McHugh et al. 2015). The food web
structure responded in kind to decreased habitat size and availability by becoming shorter
in food chain length, smaller in trophic niche area, and smaller in species richness. In this
study, food chain length and community trophic structure were linked to habitat
heterogeneity and the physical template of the Rio Grande. For example, the higher
trophic range (TA and NR) for complex and canyon sites suggests these sites have
increased trophic diversity, or multiple species of upper-level consumers (e.g., primary-,
secondary-, tertiary-, or quaternary-consumers). Thus, reaches more complex in habitat
may allow for more variability in ecological feeding niches, because of the increased area
available to better partition necessary resources (Walsworth et al. 2013). It has also been
shown that larger ecosystems may contain longer food chain lengths and increased
trophic diversity because of their ability to contain larger areas available for resources,
and they may also facilitate more ecosystem processes because of this increased area
(Takimoto and Post 2013). Basal resource use also appeared to be more diverse within
complex and canyon sites, which follows that a ‘larger’ or more ‘complex’ ecosystems
may facilitate niche specialization (trophic diversity) and provide abundant and diverse
food resources.
88
Given that trophic and basal resource diversity were greater in complex and
canyon sites, it was surprising to observe a shorter food chain length in these types of
habitat compared to simple and alluvial sites. However, river food web structure and
function is incredibly heterogeneous and dynamic in nature because food webs in any
given location are affected by upstream processes, terrestrial influences, and seasons (in
temperate regions; Baxter et al. 2005). In addition, while their abundance appears to be
relatively low in comparison to other desert rivers similarly affected (e.g., Colorado
River, San Rafael River, and many others), nonnative fish species are present within the
Rio Grande. Nonnative species have been demonstrated to significantly alter the food
web structure by narrowing the isotopic niche space of native species and increasing the
food chain length, often with added predation and competition, in many freshwater
systems (Walworth et al. 2013; Sagouis et al. 2015). A nonnative, top-level predator
altered a previous complex food web structure by increasing food chain length and
omnivory in a small stream located in Sussex, England (Woodward and Hildrew 2001,
2002). Though this invasion did not necessarily reduce the native top-level predators
present immediately, competition ultimately narrowed the isotopic niche space. Thus, my
results suggest food web chain length may be more influenced by other biotic factors,
potentially including upstream processes and nonnative species, than by abiotic factors
such as habitat complexity at a reach scale.
Also contrary to what I expected, my results suggest trophic redundancy was
higher in simple and alluvial sites than complex and canyon sites. Thus, complex and
canyon sites appear to be more divergent in terms of trophic niche and perhaps contain a
less stable food web structure. However, complex and canyon sites still demonstrated a
89
more diverse, preferred food resource base. In addition, nonnative species have also been
shown to simplify food web structure, and increased trophic redundancy may be a result
of elevated competition or predation on native species by nonnative individuals (Sagouis
et al. 2015; Busst and Britton 2017). The food web structure of the San Rafael River, a
small desert river in southeastern Utah was impacted by the synergistic effects nonnative
species and habitat loss (Walsworth et al. 2013). The food chain length was shorter and
isotopic niche narrower in regions of the San Rafael with increased abundance of
nonnative fish species and degraded habitat. It is important to note, however, that
synthetic food web metrics such as trophic redundancy may not vary in association with
local study sites as used herein, but rather vary over a larger area of river and again are
affected by both upstream and downstream processes (Fausch et al. 2002).
River ecosystems strongly interact with their riparian zones through the
movement and exchange of both organic and inorganic materials (e.g., invertebrates, leaf
litter; Baxter et al. 2005). Thus, the establishment of nonnative riparian species may alter
the types of organic and inorganic materials input to the river, potentially changing the
forms of carbon and food resources available for uptake by aquatic consumers (Mineau et
al. 2011). Nonnative giant reed has become well established in the riparian zone of the
Rio Grande within the Big Bend region. I therefore examined potential alterations to the
food resources available associated with the increased nonnative riparian vegetation
abundance and habitat simplification. Overall, nearly all fish species analyzed were
remarkably similar and had low variability in isotopic niche space when compared across
vegetation type (native, nonnative, and open).
While upper trophic consumers (e.g., fish) did not appear to be trophically
90
influenced by nonnative vegetation, aquatic macroinvertebrates appeared more depleted
in the 13C element and could be partially reliant upon terrestrially-based sources of food
in canyon-bound reaches of the Rio Grande in the Big Bend region. Surprisingly, native
vegetation appeared to contribute a higher proportion to the dietary items consumed by
aquatic invertebrates than either nonnative vegetation or periphyton. This result is
contrary to my prediction of nonnative riparian vegetation having a greater influence on
the food web function of the Rio Grande. However, the likely greater abundance of both
native and nonnative riparian species due to availability of habitat to become well
established (e.g., inset floodplains) can still affect the food web structure. For example, if
the riparian leaf litter is poor in terms of food quality for macroinvertebrates (e.g.,
necessary nutrients), the aquatic macroinvertebrate community could experience shifts in
abundance and diversity. The nonnative Rhododendron ponticum plant was shown to
decrease relative abundance and diversity of the aquatic macroinvertebrate communities
within multiple streams in Western Ireland, ultimately limiting ecosystem functioning by
decreasing decomposition rates and suppressing algal production (Hladyz et al. 2011).
Similarly, nonnative Russian olive (Elaeagnus angustifolia) impaired ecosystem
functioning by creating a large influx of relatively recalcitrant nonnative leaf litter into a
small stream, ultimately decreasing the ability of consumers to process this large influx of
leaf litter (Mineau et al. 2012). Thus, the food web structure of the Rio Grande may be
more influenced by bottom-up effects, where the potentially threatened macroinvertebrate
community may become a limited resource for fish consumers.
Contrary to what I appeared to observe based on trophic position and isotopic
niche space, the mixing model demonstrated that aquatic macroinvertebrates provided a
91
greater contribution to the proportion of dietary items consumed by fish across most sites
except for alluvial sites. Similarly, the mixing model results comparing terrestrial
vegetation and periphyton illustrated periphyton appearing to contribute to a greater
proportion of dietary items to aquatic invertebrate consumers than either native or
nonnative vegetation. These results are concurrent with the functional feeding group
results, where scrapers were significantly greater in abundance than other functional
feeding groups present. Scraper abundance has been shown to be dependent on increased
concentrations of chlorophyll a on cobble substrates (e.g., periphyton; Hawkins and
Sedell 1981). In addition, scraper abundance can also be negatively impacted by
increased sedimentation rates. For example, in four low-gradient, alluvial streams located
in the Missouri Ozark Highlands, scraper abundance was decreased in the alluvial
streams with elevated sedimentation rates (Rabeni et al. 2005). Thus, a high density of
scrapers in complex, simple, and canyon sites may indicate increased abundance of
periphyton, or attached algae, and lower turbidity due to sedimentation within these sites.
Interestingly, based on my stable isotope bi-plots, the federally endangered Rio Grande
silvery minnow appeared to be more dependent on periphyton as a source of food and
was only captured in a complex site. Periphyton forms by integrating inorganic materials
from the water, and thus, its nutrient quality can be influenced by the overall water
quality (Sekar et al. 2002; Azim et al. 2005). Because of its high nutrient quality,
periphyton is often a preferred source of food for many fish species. Thus, having
observed a specialized fish species potentially consuming a greater abundance of
nutrient-rich periphyton in complex and canyon sites further contributes to the knowledge
of these sites containing higher quality food resources.
92
As the Rio Grande through the Big Bend region is hydrologically dynamic (e.g.,
tributary flash flood events, dam releases associated with monsoonal events), the River
Wave Concept (Humphries et al. 2014) may also explain, in part, the patterns I observed
regarding the dietary proportions I modeled. The River Wave Concept suggests the
timing and sources of either autochthonous or allochthonous production to a river’s food
web structure and function is largely dependent on the ‘river wave’, or the flood pulses of
a river. This concept was applied to three major rivers in Texas of varying stream flow
and sediment regimes: the Brazos, Guadalupe, and Neches rivers (Roach and Winemiller
2015). The physical characteristics (e.g., sediment supply) and the timing and magnitude
of flood pulses predicted when each river was more dependent on either allochthonous or
autochthonous sources of energy. These patterns may also hold for the Rio Grande as the
hydrologic and associated sediment environments during my sampling events may have
allowed the fish and macroinvertebrates communities to consume more autochthonous
sources of energy. In addition, I assumed the isotopic ratios of consumer tissues was
reflective of the resource availability during the time of sampling, and isotopes can reflect
a longer time period of consumption (e.g., up to one month for fishes; Vander Zanden et
al. 2001; Post 2002).
Implications My results have important implications for native fish conservation and
understanding food web structure in exotic rivers that cross desert lands, especially those
exotic rivers that have been significantly dewatered. Specifically, I have provided an
example of how food web structure and native fish communities in such rivers are
inextricably linked with their local physical environment, which is driven by both
93
hydrologic and sediment supply processes. By linking the physical processes with the
ecology of this system, I provide additional impetus to find innovative solutions for water
management that can maintain channel complexity and perhaps rehabilitate and restore
the native aquatic biodiversity for this unique river. I have demonstrated that availability
of complex habitat is likely a limiting factor for native fishes, and the availability of
complex habitat also affects the structure of the food web, potentially including food
availability for fishes. Increasing flood magnitude and duration to levels that evacuate
sediment and rewiden the channel, reconnecting the river to its floodplain, and improving
water quality (not addressed here) may allow populations of sensitive native fish species,
such as the federally endangered Rio Grande silvery minnow, to be restored and
maintained within this region.
Acknowledgments
Funding for this study was provided by the U.S. Geological Survey, South Central
Climate Science Center, the National Park Service, the Ecology Center at Utah State
University, and by the U.S. Geological Survey, Utah Cooperative Fish and Wildlife
Research Unit (in-kind). I would like to thank G. Thiede, B. Laub, and members of both
the Fish Ecology Lab and Lake Ecology lab their generous help in the field and overall
guidance with my project. In addition, I would like to thank J. Brahney for their guidance
and reviews throughout my project. Any use of trade, firm, or product names is for
descriptive purposes only and does not imply endorsement by the United States
Government.
94
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102
Tables and Figures
Table 3.1. Mean, minimum, and maximum instantaneous discharge (in cubic meters per
second, m3 s-1) of the Rio Grande at the US Geological Survey (USGS) Castolon
(08374550) and Rio Grande Village (08375300) gaging stations and the International
Boundary and Water Commission (IBWC) Dryden Crossing (08376400) gaging station
for each field sampling date over the years 2016 to 2017 within the Big Bend region.
Sampling Dates
Mean Discharge
(m3 s -1)
Minimum
Discharge (m3 s -1)
Maximum
Discharge (m3 s -1)
Castolon Gage
March 1-11, 2016 1.61 1.37 2.11
May 26-30, 2016 2.38 2.02 2.71
October 17-21, 2016 23.4 22.4 23.8
January 11- 20, 2017 17.9 15.7 20.3
Rio Grande Village
March 1-11, 2016 2.97 2.40 3.46
May 26-30, 2016 4.73 0.977 28.1
October 17-21, 2016 23.4 22.4 23.8
January 11- 20, 2017 22.1 16.1 61.2
Dryden Crossing
June 24-30, 2017 8.11 7.06 10.9
Table 3.2. Summary statistics (average and standard deviation) of the catch per unit
effort (fish m-1) for the four different sites of habitat heterogeneity sampled along the Rio
Grande in the Big Bend region.
Complexity
Average
CPUE (fish m-1)
Standard Deviation
CPUE (fish m-1)
complex 0.519 0.576
simple 0.400 0.504
alluvial 0.392 0.056
canyon 0.510 0.590
103
Table 3.3. Layman community metrics (Layman et al. 2007) from the stable isotopic
analysis of the fish community for the four different sites of habitat heterogeneity within
the Rio Grande in the Big Bend region (NR = nitrogen isotopic range, CR = carbon
isotopic range, TA = total area of community hull calculated, MNND = mean nearest
neighbor distance, SDNND = standard deviation nearest neighbor distance).
Complexity NR CR TA MNND SDNND
complex 10.6 1.83 9.32 0.845 0.749
simple 5.49 0.586 1.87 0.416 0.322
alluvial 2.57 0.710 0.986 0.446 0.095
canyon 9.25 1.83 8.49 0.715 0.611
Table 3.4. Mean and standard deviation (SD) for the proportion of dietary items (aquatic,
terrestrial, or periphyton) consumed by fish calculated for each of the four different sites
of habitat heterogeneity using a three-source stable isotope mixing model (simmr).
Complexity Mean Paquatic ± SD Mean Pterrestrial ± SD Mean Pperiphyton ± SD
Complex 0.453 ± 0.098 0.398 ± 0.116 0.149 ± 0.024
Simple 0.617 ± 0.099 0.211 ± 0.115 0.172 ± 0.026
Alluvial 0.335 ± 0.077 0.636 ± 0.082 0.029 ± 0.014
Canyon 0.569 ± 0.080 0.237 ± 0.094 0.194 ± 0.020
Table 3.5. Mean and standard deviation (SD) for the proportion of dietary items (aquatic
or terrestrial) consumed by fish calculated for each of the four different sites of habitat
heterogeneity using the hydrogen isotope and a two-source mixing model (simmr).
Complexity Mean Paquatic ± SD Mean Pterrestrial ± SD
Complex 0.641 ± 0.046 0.359 ± 0.046
Simple 0.780 ± 0.102 0.220 ± 0.102
Alluvial 0.423 ± 0.068 0.577 ± 0.068
Canyon 0.711 ± 0.045 0.289 ± 0.045
Table 3.6. Mean and standard deviation (SD) for the proportion of dietary items (aquatic,
terrestrial, or periphyton) consumed by aquatic invertebrates calculated for each of the
four different sites of habitat heterogeneity using a three-source mixing model (simmr).
Complexity Mean Pnative ± SD Mean Pnonnative ± SD Mean Pperiphyton ± SD
Complex 0.304 ± 0.257 0.292 ± 0.114 0.404 ± 0.170
Simple 0.301 ± 0.244 0.299 ± 0.161 0.399 ± 0.206
Alluvial 0.309 ± 0.259 0.291 ± 0.128 0.400 ± 0.182
Canyon 0.445 ± 0.292 0.243 ± 0.122 0.312 ± 0.197
104
Figure 3.1. Study area map of the Rio Grande through the Big Bend region. Sampling
sites within Big Bend National Park are designated by the park boundary, those outside
the boundary is the Lower Canyons portion of the Big Bend region.
105
Figure 3.2. (a) Conceptual diagram illustrating the differences between simple and
complex sampling sites and the mesohabitats used to infer the level of complexity. (b)
Relative proportion of the total area (m2) summed for each mesohabitat type between all
simple and complex sampling sites.
106
Figure 3.3. Average depth and velocity for all mesohabitats mapped within two different
discharge ranges that occurred over the entire period of sampling. Box represents the
interquartile range, line indicates the median, whiskers are 1.5 the interquartile range, and
black circles indicate outliers.
Figure 3.4. Variable importance plot for the random forest classification model. The
increased ‘mean decreased accuracy’ values indicate the mesohabitats most important for
predicting the level of complexity.
107
Figure 3.5. (a) The average catch per unit effort for the four different types of habitat
sites sampled in the Rio Grande within the Big Bend region. The solid line denotes the
median, box indicates the 25th and 75th percentiles, and ‘whiskers’ represent 1.5 of the
interquartile range. (b) Shannon’s diversity index for the four different types of habitat
sites sampled in the Rio Grande within the Big Bend region. Box and whisker data
structure described previously in other figures.
108
Figure 3.6. Chlorophyll a abundance estimates for seston, soft sediment, and periphyton
alga across the four different levels of habitat heterogeneity sampled along the Rio
Grande in the Big Bend region. Top panels represent Chlorophyll a concentrations
between complex and simple sampling sites, and bottom panels represent Chlorophyll a
concentrations between alluvial and canyon sampling sites. Box and whisker data
structure described previously in other figures.
109
Figure 3.7. (a) Average relative abundance estimates for each functional feeding group
across complex and simple sampling sites. (b) Average relative abundance estimates for
each functional feeding group across alluvial and canyon sampling sites. The mean is
represented by the symbol ‘x’, the median is the solid line within the box, the box
represents the 25th and 75th interquartile range, and the ‘whiskers’ denote 1.5 of the
interquartile range.
110
Figure 3.8. Calculated trophic positions (food chain lengths) of fish species occurring in
both simple and complex sampling sites.
111
Figure 3.9. Food web structure for the four different habitat sites in the Rio Grande in the
Big Bend region. The food web structure was determined by calculating the mean stable
isotope signatures of individual fish species, common herbivorous macroinvertebrates
(aquatic), terrestrial invertebrates (terrestrial), and attached algae (periphyton). Bars for
each point represent the 95% confidence intervals for each mean organism calculated
(e.g., fish species, aquatic invertebrates, terrestrial invertebrates, and periphyton).
112
Figure 3.10. Stable isotope ellipses grouped and calculated for the entire fish community,
terrestrial invertebrates, and aquatic invertebrates across vegetation types (e.g., native,
nonnative, and open) in the Rio Grande in the Big Bend region. Border of ellipses (area)
are portrayed as the 95% confidence interval for each organism type (e.g., fish, aquatic
macroinvertebrates, and terrestrial invertebrates).
113
Figure 3.11. Carbon and Nitrogen stable isotope mixing model results (using the simmr
package in R) for the four different types of habitat heterogeneity sites, where the mean
proportion of dietary items (Proportion) contributed by either aquatic (common
herbivorous invertebrates), terrestrial (terrestrial invertebrates), or attached algae
(periphyton) to fish consumers was estimated using a Bayesian framework. Box and
whisker data structure described previously in other figures.
114
Figure 3.12. Hydrogen stable isotope mixing model results (using the simmr package in
R) for the four different types of habitat heterogeneity sites, where the mean proportion
of dietary items (Proportion) contributed by either aquatic (common herbivorous
invertebrates) or terrestrial (terrestrial invertebrates) to fish consumers was estimated
using a Bayesian framework. Box and whisker data structure described previously in
other figures.
115
Figure 3.13. Carbon and Nitrogen stable isotope mixing model results (using the simmr
package in R) for the four different types of habitat heterogeneity sites, where the mean
proportion of dietary items (Proportion) contributed by either native (native terrestrial
leaf litter), nonnative (nonnative terrestrial leaf litter), or attached algae (periphyton) to
aquatic invertebrate consumers was estimated using a Bayesian framework. Box and
whisker data structure described previously in other figures.
116
CHAPTER 4
CONCLUSIONS
The Rio Grande varies along its longitudinal length both in its physical and
ecological structure, as well as the anthropogenic impacts that affect the Rio Grande.
Thus, the Rio Grande is a unique river in which to understand how the interactions
between the physical environment and human impacts affect the aquatic ecological
integrity and associated native fish diversity of a large river system. Collectively, the
results of my project indicate that the hydrologic and geomorphic degradation that has
occurred within the Rio Grande extend to fish habitat and food web structure, confirming
complex and heterogeneous habitat is likely one of the factors limiting the viability and
persistence of the native fish community of the Rio Grande in far west Texas.
For Chapter II, we compared two different segments with different problems,
allowing us to explore the ways in which each region functioned and affected the native
fish diversity for the Rio Grande in each segment. Both the Big Bend and Forgotten
Reach native fish richness was significantly influenced by active channel width; however,
each model was influenced by differing hydrologic variables. For the Big Bend,
baseflows and low flow events may affect native fish richness to a greater degree than
within the Forgotten Reach. The Forgotten Reach native fish richness appeared to be
largely influenced by poor water quality and flash flood events, likely because any
change in flow magnitude has a greater impact within the Forgotten Reach due to its
significantly lower base flows. Given the Big Bend region is a gaining reach due to the
Rio Conchos and the large Edwards-Trinity groundwater aquifer, it follows that the Big
Bend region native fish richness is not as affected by poor water quality conditions
117
compared to the Forgotten Reach. Overall, our modeling results from this chapter
allowed us to test the links between the physical components of a river ecosystem with
the ecological components, and identify important limiting factors for native fish richness
associated with the degree of degradation within each segment of the Rio Grande.
The Big Bend region is diverse in terms of river landscapes containing simple
reaches, whether they are naturally simple or simple because of channel narrowing, and
complex reaches. For Chapter III, I found physical habitat differs between these types of
reaches such that complex reaches have a greater diversity of mesohabitats compared to
simple (specifically backwaters). Subsequently, the fauna appears to be influenced by the
degree of habitat heterogeneity within the Rio Grande in the Big Bend region. Fish
abundance and diversity differs between canyon and alluvial sites, but not between
simple and complex sites. Aquatic food resources (e.g., aquatic invertebrates and
periphyton) also appeared to be influenced by the level of habitat heterogeneity. The
‘scraper’ functional feeding group for aquatic invertebrates appeared to be greater in
abundance in complex sites than simple, and between canyon and alluvial sites. In
contrast, terrestrial sources of food (e.g., terrestrial invertebrates) appeared to be more
influenced by seasonality than complexity.
Food web structure in the Big Bend region appeared to be linked with the physical
template as well, where food web structure appeared to be more complex within complex
sites than simple sites. Interestingly, food web function was more associated with native
or nonnative terrestrial vegetation than habitat complexity. Native vegetation appeared to
contribute to a greater proportion of dietary items for aquatic invertebrates, and
subsequently, aquatic invertebrates appeared to contribute to a greater proportion of
118
dietary items for fish consumers. It is important to note, however, that nonnative
vegetation tended to be greater in abundance within simple and alluvial sites than
complex or canyon reaches, and nonnative vegetation has been treated for eradication in
Boquillas Canyon, one of my main sampling canyons within the Big Bend region.
The Rio Grande is a complex river having undergone significant hydrologic and
associated geomorphic changes. My thesis explored this complexity from an ecological
perspective, and illustrates the importance of considering both the physical and ecological
components of a riverine ecosystem. Having used a modeling approach to link the
physical template to ecosystem structure and function in Chapter II, we were then able to
test these modeling results in Chapter three using field observations from the Big Bend
region. My thesis confirms stream flows are a critical variable influencing ecosystem
structure (e.g., faunal assemblages and abundances) and function (e.g., food resource
partitioning and pathways), and habitat heterogeneity is an important predictor of native
fish richness and abundance. We now have a better understanding for native fish
conservation through understanding food web structure and function in large, dewatered
rivers. Overall, my thesis provides an impetus for finding innovative water management
solutions to rehabilitate and restore native aquatic abundance, distribution, and
biodiversity through restoration to physical habitat and improved water quality.
The innovative water management solutions I would suggest are specific for each
region studied in my thesis, the Forgotten Reach and the Big Bend region. For the
Forgotten Reach, improving water quality would likely be the best solution to restoring
the aquatic faunal assemblages and abundances for this degraded segment. In a perfect,
yet unrealistic world, I would suggest we remove Elephant Butte Dam entirely, and
119
demand that the agriculture within Colorado and New Mexico be changed to cultivate
crops less dependent on large water infrastructure and more suited to the biomes they are
cultivated in (e.g., Hatch chilis in New Mexico). In a more realistic world, I would
suggest a significant proportion of the water in Elephant Butte be designated for
environmental flows and emulate a more natural flow regime conducive to improving
water quality, increasing flood magnitudes for native fish to cue in to, and providing
greater habitat heterogeneity in the Forgotten Reach. In addition, I would also suggest
redesigning municipal and agricultural flow infrastructure so that discharges into the river
are treated extensively to improve water quality (e.g., eliminate point sources of salinity
or sulfates, for example).
For the management recommendations in the Big Bend, we need stream flows
that can restore base flow magnitudes and increase habitat heterogeneity. Thus, I would
suggest the United States and Mexico work on an agreement to increase the minimum
release magnitudes from both Luis L. León Dam and Elephant Butte Dam. If we could
maintain greater levels of flow following a reset flood, when habitat is at its most
complex, we would likely observe a reduction in the degree of channel narrowing
throughout the Big Bend region. Like the Forgotten Reach, I would also suggest a
management solution to emulate the natural flow regime (e.g., Blythe and Schmidt 2018).
A simulated spring flood pulse may allow access to the Rio Grande’s floodplain and
subsequently, sensitive fish species such as the Rio Grande silvery minnow, may be able
to re-establish and thrive in the Big Bend region once again.
120
APPENDIX
121
121
Table A1. Average water quality parameters for each mesohabitat mapped in every one-kilometer sampling site and month of
sampling. Month of Sampling Mesohabitat Water Temperature (C) Specific Conductance (µS/cm) Total Dissolved Solids (mg L-1) pH Dissolved Oxygen (mg L-1) Dissolved Oxygen (%)
March backwater 20.3 2310.1 1.5 8.5 8.6 92.5
embayment 20.3 2065.8 1.3 8.2 8.1 89.9
forewater 17.1 2030.0 1.3 8.3 9.1 94.8
pool 20.4 2424.1 1.7 8.3 8.5 92.3
riffle 20.6 2403.0 1.6 8.5 8.2 91.4
run 20.1 2388.5 1.6 8.5 8.6 94.2
side pool 17.3 3068.0 2.0 9.6 9.4 98.7
May backwater 25.7 1308.7 0.9 8.0 8.2 103.6
embayment 26.3 1254.8 0.8 8.2 8.0 99.2
forewater 27.3 1262.3 0.8 8.1 7.9 100.6
pool 27.2 1267.6 0.8 8.1 7.6 95.9
riffle 27.7 1269.0 0.8 8.1 7.7 98.2
run 26.3 1160.9 0.8 8.1 7.5 93.9
side channel 26.6 1263.3 0.8 8.1 7.8 97.7
side pool 30.5 1276.0 0.8 8.2 7.4 100.1
October backwater 26.6 1308.0 0.9 NA 6.5 81.6
embayment 26.5 1385.0 0.9 NA 7.5 93.5
forewater 25.5 1404.5 0.9 NA 4.8 59.0
riffle 25.9 1392.8 0.9 NA 6.1 75.9
run 25.1 1422.7 0.9 NA 6.1 74.8
side channel 26.0 1440.0 0.9 NA 7.3 90.9
122
Table A1. (cont.) Month of Sampling Mesohabitat Water Temperature (C) Specific Conductance (µS/cm) Total Dissolved Solids (mg L-1) pH Dissolved Oxygen (mg L-1) Dissolved Oxygen (%)
January backwater 18.3 1607.0 1.0 7.7 7.2 77.1
embayment 15.7 1640.0 1.1 7.9 7.4 74.8
forewater 16.8 1608.0 1.0 NA 8.1 83.3
pool 16.7 1611.5 1.0 NA 7.3 75.1
riffle 15.9 1626.2 1.1 NA 8.3 83.7
run 16.0 1625.7 1.1 NA 8.5 86.4
side channel 15.0 1643.8 1.1 NA 8.5 84.4
June backwater 30.2 1069.8 NA 8.1 7.6 101.3
embayment 29.9 1240.5 NA 8.3 7.7 101.7
forewater 31.2 1196.3 NA 8.3 8.0 109.7
pool 29.3 1279.4 NA 8.2 7.1 93.1
riffle 29.1 1266.7 NA 8.3 7.5 97.6
run 30.3 1177.6 NA 8.1 7.6 101.6
side channel 30.3 1086.0 NA 8.5 7.9 105.2
side pool 31.2 969.0 NA 7.8 7.3 98.0
122
123
Table A2. Average total length for all fish species caught between complex and simple sampling sites (Min is minimum length, Max
is maximum length). In addition, those denoted with I are nonnative species.
Complex
Simple
Species
n
Mean
length
(mm)
Min
length
(mm)
Max
length
(mm)
n
Mean
length
(mm)
Min
length
(mm)
Max
length
(mm)
Blue sucker 175 37.3 16 80 17 33.5 22 105
Channel catfish (I)* 31 157.7 24 441 23 70.7 27 623
Common carp (I) 49 29.3 18 65 6 34.2 25 57
Flathead catfish 3 201.3 75 360 - - - -
Freshwater drum - - - - 1 188 188 188
Gizzard shad 8 42.9 35 50 - - - -
Longear sunfish 3 44.7 39 52 4 34 29 37
Longnose dace 7 51.0 42 90 1 75 75 75
Longnose gar 7 222.7 42 656 2 509 450 568
Mexican tetra 53 42.6 21 68 12 43.2 29 51
Red shiner 489 39.4 22 63 41 44.2 33 61
Rio Grande silvery minnow 3 62.7 61 66 - - - -
River carpsucker 124 44.6 18 211 34 37.4 22 95
Speckled chub 46 40.2 25 52 18 48.7 39 63
Tamaulipas shiner 472 36.1 16 67 85 42 22 66
Western mosquitofish 18 33.6 21 51 2 41 38 44
*Channel catfish may not be a nonnative species but its own Rio Grande catfish species
123
124
Table A3. Average total length for all fish species caught between alluvial and canyon sampling sites (Min is minimum length, Max is
maximum length). In addition, those denoted with I are nonnative species.
Alluvial
Canyon
Species
n Mean
Length
(mm)
Min
length
(mm)
Max length
(mm)
n Mean length (mm) Min length (mm) Max length (mm)
Blue sucker - - - - 192 36.9 16 105
Channel catfish (I)* 1 134 134 134 53 120.4 24 623
Common carp (I) - - - - 55 29.8 18 65
Flathead catfish - - - - 3 201.3 75 360
Freshwater drum - - - - 1 188 188 188
Gizzard shad - - - - 8 42.9 35 50
Longear sunfish - - - - 7 38.6 29 52
Longnose dace 1 75 75 75 7 51 42 90
Longnose gar - - - - 9 286.3 42 656
Mexican tetra 8 46.4 42 51 57 42.2 21 68
Red shiner 194 40.2 22 63 336 39.6 22 62
Rio Grande silvery
minnow
- - - - 3 62.7 61 66
River carpsucker 3 57.7 33 95 155 42.8 18 211
Speckled chub 26 44.1 26 63 38 41.6 25 51
Tamaulipas shiner 47 42.8 24 57 510 36.5 16 67
Western mosquitofish - - - - 20 34.3 21 51
*Channel catfish may not be a nonnative species but its own Rio Grande catfish species
124
125
Table A4. Total sum for all orders and families of terrestrial invertebrates caught in both
complex and simple sites as well as native and nonnative vegetation, and open sites.
complex
Simple
Coleoptera native nonnative open native nonnative open
N Order 819 785 1119 27 50 192
Curculionidae
2
Dryopidae 1 5 4
Dytiscidae 5
2
Elmidae 13 19 29
Hydrophilidae 1
8
Hydroscaphidae 18 21
Noteridae
1
Sphaeriusidae 9 4 17
Staphylinidae 41 234 52
Diptera
N Order
6960 6384 9470 478 1013 1878
Canacidae
1
Cecidomyidae 2
Ceratopogonidae 1447 984 1912
154 260
Chironomidae 5182 5032 6902
113 320
Culicidae 3
1
Empididae 1
Ephydridae
9 5
Muscidae 20 28 24
1 1
Phoridae 47 36 27 2
Sciomyzidae 5 1
1
Simullidae 344 207 470
3
Tipulidae 14 13 52
1
Ephemeroptera
N Order 723 820 1292 2 132 132
Baetidae 9 2
Behningiidae 611 917 1085
Caenidae 103 22 135
4
Ephemerellidae 2 4 11
Hemiptera
N Order 600 375 299 40 97 54
Corixidae
61 1
Reduviidae 1
Hymenoptera
N Order
243 247 81 34 44 10
Braconidae
1
Trichogrammatidae 12 2 6 2
126
Table A4. (cont.) Lepidoptera
N Order 567 769 287 177 103 38
Crambidae
9 2
Noctuidae 1 3
Mecoptera
N Order
1
Bittacidae 1
Neuroptera
N Order
30 23 10 2 1 2
Myrmeleontidae
2 1
Myrmeliodae 3 6
Odonata
N Order
1 0 1
Lestidae 1
1
Orthoptera
N Order
1 2 7 0 0 1
Tridactylidae
1 4
1
Trichoptera
N Order
5437 4121 4207 1029 654 687
Hydropsychidae 186 258 105 24 18 5
Hydroptilidae 4846 3726 3707 312 136 178
Leptoceridae 63 37 33 15 8 3
Thysanoptera
N Order 28 6 16 6 11 6
Isoptera
N Order 26 9 4 0 1 2
Collembola
N Order 22 16 1 1 5 1
Arachnida
N Order 7 3 3 0 3 0
Table A5. Average relative abundance of functional feeding groups for the four different
sites of habitat heterogeneity sampled along the Rio Grande in the Big Bend region.
Complexity
Filtering
Collectors
Gathering
Collectors
Predators
Scrapers
Shredders
complex 0.141 0.139 0.062 0.649 0.009
simple 0.235 0.202 0.260 0.293 0.010
alluvial 0.350 0.185 0.190 0.263 0.011
canyon 0.128 0.149 0.096 0.619 0.008
127
Table A6. Shannon’s diversity index for terrestrial invertebrates sampled over the
varying levels of habitat heterogeneity and across vegetation type for the Rio Grande
within the Big Bend region.
Habitat Type
All Complex
Native
Nonnative
Open
Shannon's
Diversity Index
1.47
1.52
1.55
Simple
Shannon’s
Diversity Index
1.56
1.57
1.67
Lower Canyons
Shannon's
Diversity Index
1.06
1.17
0.96
Figure A1. Total area calculated for each type of habitat heterogeneity. I show the mean
(x), median, interquartile range (25th and 75th percentiles), and outliers calculated for each
habitat classification.
128
Figure A2. The average catch per unit effort for each sampling month between the years
2016 and 2017. March and May contained the most significant (p-value < 0.05) CPUE
compared to January, June, and October sampling months.
129
Figure A3. Species evenness calculated for the fish communities sampled among the
varying levels of habitat heterogeneity. Evenness measure is based on the calculated
relative abundance for each complex, simple, alluvial, and canyons (Boquillas Lower
Canyons) sampling sites.
130
Figure A4. Chlorophyll a abundance estimates for seston, soft sediment, and periphyton
alga across sampling seasons.