factors contributing to the success of restored oyster
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Nova Southeastern University Nova Southeastern University
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3-25-2019
Factors Contributing to the Success of Restored Oyster Reefs in Factors Contributing to the Success of Restored Oyster Reefs in
the Choptank River of the Chesapeake Bay, Virginia the Choptank River of the Chesapeake Bay, Virginia
Tara L. Bardar Nova Southeastern University, [email protected]
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Capstone of Tara L. Bardar
Submitted in Partial Fulfillment of the Requirements for the Degree of
Master of Science
M.S. Coastal Zone Management
Nova Southeastern University Halmos College of Natural Sciences and Oceanography
March 2019
Approved: Capstone Committee
Major Professor: Joana Figueiredo, Ph.D.
Committee Member: Bernhard Riegl, Ph.D.
This capstone is available at NSUWorks: https://nsuworks.nova.edu/cnso_stucap/342
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Tara Bardar
Candidate for Masters in Coastal Zone Management
Factors Contributing to the Success of Restored Oyster Reefs in the Choptank River of the
Chesapeake Bay, Virginia
Advisors:
Dr. Joana Figueiredo
Dr. Bernhard Riegl
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Abstract
Populations of Crassostrea virginica, the Eastern oyster, have been declining since the late
1800s. While overharvesting is the primary cause of decline, the Eastern oyster is also facing the
threat of disease and habitat loss. As oyster populations decline, habitat suitable for oyster spats
declines as well, as these prefer to settle on the shells of other oysters that have formed reefs.
Knowing this, oyster restoration projects have been focused around testing methods that will
increase recruitment of spat and allow oyster reefs to form. A current and ongoing restoration
project in the Choptank River of the Chesapeake Bay, VA continually monitors the success of
restored and natural oyster reefs in that area. This study focuses on eight restoration sites in the
Choptank River and five environmental parameters (salinity, temperature, dissolved oxygen,
distance from shoreline, and acreage) that may or may not contribute to their success. All eight
restoration sites were deemed successful the last time that they were evaluated in 2016. The
environmental conditions in these sites were very similar to each other and seem to be within the
optimal range for the species, and thus, with the exception of temperature and salinity which
significantly helped explain differences in live biomass between restorations sites, all other
parameters did not contribute significantly to explain the differential success between sites.
More interestingly, controlled factors in this study, such as restoration treatment, substrate type
added, and number of spats planted per acre had a significant effect on the metrics that are used
to determine oyster success. Specifically, restoration projects using stone substrate and seed led
to higher average live biomass, density and shell volume than using seed only. We also
concluded that sites with salinity of 8.2 and lower temperature tend to generate higher live oyster
biomass. These environmental factors and methodological procedures should be taken into
account when selecting sites and implementing oyster restoration sites.
Keywords: Eastern oyster, population decline, harvesting, restoration, Chesapeake Bay,
environmental parameters, universal metrics
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Table of Contents
1. Introduction
a. Habitat, Life Cycle, Status, and Importance
b. Eastern Oyster Harvesting History and Methods
c. Causes of Decline
d. Restoration History
e. Restoration Methods
2. Methods
a. Environmental Parameters
b. Controlled Factors
c. Reef Success
3. Results
a. Environmental Parameters
• Average Live Density Across Reef
1. Salinity vs. Average Live Density
2. Temperature vs. Average Live Density
3. Dissolved Oxygen vs. Average Live Density
4. Distance from the Shoreline vs. Average Live Density
5. Acreage vs. Average Live Density
• Average Live Biomass Across Reef
1. Salinity vs. Average Live Biomass
2. Temperature vs. Average Live Biomass
3. Dissolved Oxygen vs. Average Live Biomass
4. Distance from the Shoreline vs. Average Live Biomass
5. Acreage vs. Average Live Biomass
• Average Shell Volume Across Reef
1. Salinity vs. Average Shell Volume
2. Temperature vs. Average Shell Volume
3. Dissolved Oxygen vs. Average Shell Volume
4. Distance from the Shoreline vs. Average Shell Volume
5. Acreage vs. Average Shell Volume
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d. Controlled Factors
• Restoration Treatment
1. Restoration Treatment vs. Average Live Density
2. Restoration Treatment vs. Average Live Biomass
3. Restoration Treatment vs. Average Shell Volume
• Substrate Type Added
1. Substrate Type Added vs. Average Live Density
2. Substrate Type Added vs. Average Live Biomass
3. Substrate Type Added vs. Average Shell Volume
• Spat Planted per Acre
1. Spat Planted per Acre vs. Average Live Density
2. Spat Planted per Acre vs. Average Live Biomass
3. Spat Planted per Acre vs. Average Shell Volume
4. Discussion
a. Reef Success
b. Environmental Parameters
c. Controlled Factors
5. Conclusions
6. Literature Cited
5
1. Introduction
Populations of the Eastern oyster, Crassostrea virginica, have declined drastically over the
last century (Rothschild et al., 1994). C. virginica has suffered a population decline due to over-
harvesting, disease, and habitat loss. However, the Eastern oyster is an extremely important
species that improves water quality (Coen et al., 2007) and provides food and habitat for other
marine life (Tolley and Volety, 2005). C. virginica is an iconic species along the East coast of
the United States. It has contributed millions of dollars to the economy in regions with large
oyster industries, like the Chesapeake and Delaware Bays (Grabowski et al, 2012), where oysters
have a rich economic history (Coen et al., 2007). For these reasons, experts and scientists have
been working to try and restore oyster populations by managing oyster harvest, creating
sanctuaries, and exploring ways for oysters to overcome disease.
a. Habitat, Life Cycle, Status, and Importance
Before the development of major cities, there was little sediment on the floors of bays
and inlets. This left exposed footholds for baby oysters, called spat, to attach themselves
to. The footholds allowed the Eastern oyster to successfully form reef habitats, which
they build up themselves using the shells of other oysters. These massive reef colonies
spanned hundreds of square miles up and down East coast of the United States, ranging
from Northern New Brunswick to the Gulf of Mexico on the Atlantic coast (Mackenzie,
1996).
N.E. Buroker (1983) describes C. virginica as an oviparous, dioecious species with a
long larval dispersal period and a sedentary adulthood. Adult Eastern oysters can live up
to 20 years along the East coast of North America, and females can produce between 15
and 114 million eggs in a single reproductive cycle (Buroker, 1983). Reproduction
begins when adult oysters release gametes into the water column, where fertilization
occurs. The timing and intensity of spawning is influenced by many environmental
factors, including food supply, temperature, and salinity (Dekshenieks et al, 1993).
Fertilization forms zygotes, which reach the free-swimming planktonic larval stage
(veliger) within 48 hours. The planktonic stage lasts between two and three weeks,
allowing for larval dispersal. The veliger evolves into the pediveliger stage, in which the
larvae will test the substratum on which they will attach themselves to live their lives as
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sedentary adults. The attachment phase happens between July and September on the
Atlantic coast of North America (Buroker, 1983).
Eastern oyster abundance has decreased in the Chesapeake Bay by 99.7% since the
early 1800’s and by 92% since 1980 (Wilberg et al. 2011). 85% of oyster habitat has
been lost globally, while the majority of the remaining natural oyster populations are in
poor condition (Beck et al., 2011). Population decline can have devastating effects on a
species. For instance, habitat fragmentation can cause a loss in genetic diversity, a
reduced ability to adapt, and an increased chance of total extinction (Smee et al., 2013).
Modern oyster populations have shorter lifespans than pre-colonial oysters and are also
affected by disease. Therefore, current populations of oysters are unable to maintain or
build natural reefs in the same way as their ancestors (Mann et al,. 2009), further
decreasing their chances of survival. In order to stop and potentially reverse the
devastation done to oyster populations on the East Coast, especially in regions like the
Chesapeake Bay, restoration projects have been put into place. It is imperative that an
effort is made to restore oyster populations, as C. virginica is considered a keystone
species in some areas. Oysters provide direct and indirect ecosystem services such as
water filtration and nutrient cycling (Munroe et al., 2017). The large amounts of water
that oysters filter can affect water column processes (Luckenbach et al., 1999), and the
hard substrate they provide acts as habitat for juvenile fish, aids in sediment stabilization,
and dissipates wave energy (Munroe et al., 2017). Additionally, Piazza et al. (2005)
found that small fringing reefs may be a useful tool in protecting shorelines in low-energy
environments.
b. Eastern Oyster Harvesting History and Methods
Native Americans were the first
to utilize the oyster as a resource in
the United States, using the species
for food and other uses such as
tools, jewelry, and currency
(Luckenbach et al., 1999).
Sustainable harvesting practices and
small populations allowed Native Figure 1: Oyster removal from a creek in Keyport, New Jersey, 1910
(MacKenzie, 1996)
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Americans to enjoy oysters as a resource without depleting or causing much damage to
the population (Luckenbach et al., 1999). For instance, before the arrival of European
settlers in the early 1600’s, members of the Algonquian tribe only used three “oyster”
islands in New York Harbor to acquire food. The Oyster Islands were named by Dutch
Settlers and are now home to the Statue of Liberty (National Park Service, 2015). The
production peak of the Eastern oyster harvesting industry in the US was between 1880
and 1910. During this time, the US produced 160 million pounds of oyster meat per
year, which was more than all other countries combined at that time (Mackenzie, 1996).
For the 1800s and much of 1900s, oyster production was centered in the Chesapeake Bay.
Early small-scale harvesting methods (hand picking, raking, tonging) expanded into
industrial fisheries in the late 1800s. At this time, vessels were fitted with engines and
propellers, and the use of trains allowed for transportation of large quantities of oysters
into populous towns and cities (MacKenzie Jr., 1996). Tongs were likely the first tool
used for oyster harvesting, as their first recorded use in Eastern North America was in the
early to mid 1700s (MacKenzie Jr., 1996). Rothschild et al. (1996) point out that “hand-
tong oystering can cover only a limited area per oyster fisherman per day and can only
operate at depths no greater than 6 m,” so this did not have too much of a destructive
effect on the oyster population. In the early 1800s, harvestmen began attaching dredges
to sailing vessels, which could be operated in deeper water than hand tongs. As
technologies advanced, steam engines were installed to vessels between the late 1800s
and early 1900s to pull the dredges, which expanded production (MacKenzie Jr., 1996).
c. Causes of Decline
As the human population grew in the Northeast, oysters started becoming scarce near
cities due to heavy harvest and environmental degradation, but demand continued to
increase. Northern oystermen began transporting oysters from the Chesapeake Bay to
Northern ports to meet these increasing demands, and soon there were shucking houses in
every oyster producing state on the East Coast (MacKenzie Jr., 1996). Although
overharvesting is the major cause of population decline in the Eastern oyster, disease has
also contributed to the dwindling numbers.
MSX, Haplosporidium nelsoni, is a protozoan parasite that was first observed in 1957
in the Delaware Bay (Burreson, 2000). MSX reached the Chesapeake Bay by 1959, and
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within two years, 90% of oysters growing in high salinity areas in the two bays were
killed by the disease. Haplosporidium nelsoni is now present along the Atlantic coast
from Maine to Florida, and the continuing presence and virulence of this pathogen has
prevented oyster populations from recovering in the lower Chesapeake Bay.
Dermo, Perkinsus marinus, is an endoparasite that was initially discovered from the
Gulf of Mexico along the Southeast lower United States into the lower Chesapeake Bay
in the late 1940s to early 1950s (Cook et al. 1998). Dermo was also found in the
Delaware Bay in the mid 1950’s when large numbers of oyster seed were being imported
from the lower Chesapeake, which is likely how it was introduced that far North. An
embargo was placed on imported oysters in 1959, and P. marinus prevalence in the
Delaware Bay decreased, as the temperature was too low to sustain a viable population of
the parasite (Cook et al. 1998). Between 1990 and 1992, a range of extension led to
Dermo outbreaks over a 500 km range North of the Chesapeake Bay. Additionally, a
sharp warming trend allowed small undetected numbers of the disease to outbreak. With
increasing winter water temperatures, outbreaks in Northeast US will continue and likely
increase (Cook et al. 1998). While high salinity waters encourage faster oyster growth,
the number of oysters lost to disease also increases with salinity (Kraeuter et al., 2007).
According to MacKenzie Jr. (2007), there were three main causes to oyster decline
from 1890 to 1940. First, consumers found out that oysters could contain pathogens,
causing a decline in demand. Second, there were three economic depressions that
occurred during this half of a century. And third, oysters were unable to endure the
damage to their populations, both biologically and physically, caused by predation,
storms, harvesting by dredge, channel dredging, etc. Additionally, some areas, like the
Delaware Bay, experienced low recruitment and population abundance reduction, which
has reduced shell input and heightened shell loss rates (Powell et al., 2006). Shell
dissolution has also become a problem. As the oceans and associated bodies of water
(bays) become more acidic with climate change and ocean acidification, the low pH
increases shell dissolution rates (Waldbusser et al., 2011).
d. Restoration History
In 2004, the National Research Council found that “oyster resource management
programs have historically been directed toward maintaining a sustainable oyster fishery
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and producing fishery-dependent revenues. Only recently has there been a shift in
management objectives toward rehabilitation of impaired resources and habitat to restore
ecological function.” In the past, the oyster industry has measured successful restoration
by increased harvests. However, in order to fully understand the importance of oysters as
a keystone species, success must also be measured by evaluating the ecological benefits
of the Eastern oyster (National Research Council, 2004). On the topic of Eastern oyster
ecosystem services, Baggett et al. (2014) found that “benefits include production of fish
and invertebrates of commercial, recreational and ecological significance, water quality
improvement, removal of excess nutrients from coastal ecosystems, and stabilization
and/or creation of adjacent habitats such as seagrass beds and salt marshes.” Also, Meyer
and Townsend (2000) found that settlement at created reefs typically exceeds that of
natural reefs. Unfortunately, regardless of increased restoration efforts, restored reefs
have not been monitored to an extent that allows for comparison in many cases (Baggett
et al., 2014). Additionally, Kennedy et al. (2011) point out that “limited monitoring
efforts, a lack of replicated post-restoration sampling, and the effects of harvest on some
restored bars hinders evaluations of the effectiveness of restoration activities.”
Restoration can be difficult, especially because it is challenging to predict recruitment
and limit disease impact (Mann and Powell, 2007). However, there are records of
successfully restored reefs, and some evidence as to what makes those reefs successful.
For instance, Schulte et al. (2009) determined that a major influence upon success is reef
height. Oysters that were higher above the river bottom in this study showed increased
size and density.
e. Restoration Methods
In order to measure success of an oyster restoration project, a set of “universal
metrics” should be utilized for all oyster restoration projects. This allows for the
assessment of the basic performance of restoration projects (Baggett et al., 2014).
Baggett et al. (2014) suggested that these universal metrics be reef areal dimension, reef
height, oyster density, and oyster size-frequency distributions. Additionally, Baggett et
al. (2014) suggested universal environmental variables that should also be monitored:
water temperature, salinity, and dissolved oxygen.
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Oyster shell is the preferred substrate for spat settlement, but supply is limited, so a
variety of alternative substrates have been used (George et al., 2015). Reef substrates
that have been used for restoration projects include unconsolidated oyster shell,
embedded oyster shell, and Oyster Castles. Oyster Castles have been proven to recruit,
retain, and host an oyster biomass four times higher than that of unconsolidated and
embedded shell (Theuerkaufet al., 2015). Alternative substrates have been considered
and studied to determine success rates. For instance, Soniat and Burton (2005) found a
clear preference of oyster larvae for limestone over sandstone at high salinity and high-
larval abundance, and at low salinity and low-larval abundance, which helped to
determine that sandstone does not appear to be a suitable alternative to limestone as a
cultch for oysters.
Surf clam shell was also explored as an option, but Nestlerode et al. (2007) found that
oyster shell supported greater growth and survival and had the highest degree of
structural complexity. Additionally, Dunn et al. (2014) found that it is beneficial to use
non calcium carbonate materials, like concrete, especially in high salinity areas where the
boring sponge Cliona is abundant and can infest calcium carbonate structures. There
seems to be a current trend toward using more engineered approaches to restoration (like
concrete), as opposed to material dump installations (like shell or limestone) (LaPeyre et
al., 2014). Appropriate substrate selection is extremely important when it comes to the
success oyster restoration projects.
Introducing a non-native oyster species to aid in success has also been considered and
carried out in some restoration projects. However, introducing a non-native oyster into
the US Atlantic coast estuaries may not be the best option, especially in light of
promising successes within sanctuaries (Powers et al., 2009).
Additionally, Hanke et al. (2017) determined that intertidal reefs cannot be
considered a uniform whole and may have different habitat characteristics. They found
increased density toward inner locations on the reef they studied. Grizzle and Ward
(2016) found that the two factors most strongly affecting restoration success are (1)
sedimentation and (2) site location relative to a natural reef. They recommended three
methods to increase oyster restoration success:
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(1) The reef base needs enough shell to reach height of at least 0.3m over as much
of the site as possible;
(2) Maximize the reef “edge;”
(3) Site selection should be in close proximity (<0.5km) to a healthy natural reef,
which could act as a potential larval source
Involving citizens in oyster restoration projects has also proven effective and is a
great way to spread knowledge on the topic. Brumbaugh et al. (2000) found that
“stocking strategically located broodstock reefs with hatchery produced oysters grown by
citizens” is an effective restoration strategy in the Chesapeake Bay.
2. Methods
a. Environmental Parameters
This study aims to compare the success of multiple oyster restoration sites in the
Harris Creek tributary of the Choptank River on Maryland’s Eastern shore of the
Chesapeake Bay by comparing universal metrics laid out by the Oyster Metrics
Workgroup (2011). These metrics are summarized as follows by the Maryland
Interagency Oyster Restoration Workgroup of the Sustainable Fisheries Goal
Implementation Team (2013):
A successfully-restored reef should:
• have a minimum mean density of 50 oysters and 50 g dry weight/m2 covering at
least 30% of the target restoration area at 6 years
post restoration;
• have two or more age classes present; and
• exhibit stable or increasing spatial extent, reef height and shell budget.
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In 2013, the Maryland Interagency Oyster Restoration Workgroup of the Sustainable
Fisheries Goal Implementation Team finalized the Harris Creek Oyster Restoration
Tributary Plan. From this plan, 377 acres of oyster reef was constructed (Maryland
Interagency Oyster Restoration Workgroup of the Sustainable Fisheries Goal
Implementation Team, 2013) and is still being monitored to this day. This study
compares the parameters potentially affecting oyster settlement and growth, such as
temperature, salinity, and dissolved oxygen using “Eyes on the Bay” via maryland.gov,
which is a public record of all tidal water quality data information for the state of
Maryland (Figure 2). There are three monitoring stations in Harris Creek: Harris Creek
Upstream, Harris Creek Profiler, and Harris Creek Downstream (Figure 3). This project
focuses on evaluating the success of restoration sites that are within close proximity to
these water quality monitoring stations. The most up to date data on the Harris Creek
sites is from the year 2016 and can be found in the 2016 oyster reef monitoring report by
the Maryland Oyster Restoration Interagency Workgroup of the Sustainable Fisheries
Goal Implementation Team. The temperature, salinity, and dissolved oxygen data that
was collected were range values for each monitoring station from September 3rd, 2013
(the first day that data was available at these stations after the reefs were built) to
December 31st, 2016, because 2016 was the last year that the Harris Creek restoration
sites were monitored. It is worth noting that all selected sites used the same substrate
Figure 2: Water quality monitoring stations in the
Chesapeake Bay (eyesonthebay.dnr.maryland.gov).
Figure 3: Water quality monitoring stations in Harris
Creek (eyesonthebay.dnr.maryland.gov).
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method (mixed shell). The sites
selected for this study are labeled
in Figure 4 and are as follows:
• Mixed shell sites close
to Harris Creek
Upstream station: H28,
H30, H31
• Mixed shell sites close
to Harris Creek Profiler
station: H21, H25
• Mixed shell sites close
to Harris Creek
Downstream station:
H35, H36, H37
The distance of each site from
the shoreline was also measured
using an interactive map on the
Maryland Department of Natural Resources website (Figure 5), created by the Oyster
Recovery Partnership (2017). The distance was estimated by measuring the closest
Figure 4: Location and reef number for each reef monitored in Harris
Creek in 2016 (Maryland Oyster Restoration Interagency Workgroup
of the Sustainable Fisheries Goal Implementation Team, 2017).
Figure 5: Harris Creek Interactive Map (Oyster Recovery Partnership,
2017).
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distance (in feet) between each reef site and the closest shoreline, which was then
converted into meters.
Additionally, acreage values were provided in table 3.1.4 of the 2016 Oyster
Monitoring Report. Dependent variable values were also provided in the 2016 Oyster
Monitoring Report (Maryland Oyster Restoration Interagency Workgroup of the
Sustainable Fisheries Goal Implementation Team, 2017).
R Studio was used to evaluate the effect of the environmental parameters, i.e.
temperature (C), salinity (ppt), dissolved oxygen (mg/L), distance from the shoreline
(meters), and acreage (acres) on the average live density across the reef (number of
oysters/m2), average live biomass across the reef (g dry weight/m2), amount of year
classes present (a whole number ranging from 0 to 3), and average shell volume across
the reef (L/m2). These metrics of oyster reef restoration were provided in the 2016
Oyster Reef Monitoring Report by the Maryland Oyster Restoration Interagency
Workgroup of the Sustainable Fisheries Goal Implementation Team. These were the
only available quantitative data given that aligned with the universal metrics laid out by
the Oyster Metrics Workgroup. The shell volume will not be determined until 2019, and
the reef height and reef footprint were given as “Yes” or “No” values as to whether they
were stable and/or increasing.
b. Controlled Factors
There are other factors that influence the success of restored oyster reefs other than
environmental parameters. In the Harris Creek restoration project that this study focuses
on, these factors include restoration treatment, substrate type added, and spat planted per
acre (millions). This data can be found in Table 4 of the 2016 Oyster Reef Monitoring
Report (Maryland Oyster Restoration Interagency Workgroup of the Sustainable
Fisheries Goal Implementation Team, 2017, Table 1). The restoration treatment was
either “substrate and seed” or “seed only”. Substrate type added was one of the
following: stone, mixed shell, or none. Spat planted per acre was measured in millions.
R Studio was used to determine if these controlled factors had a significant effect on the
average live density, the average live biomass, and the average shell volume of restored
reefs H18 through H47. Every reef of the “2013 Harris Creek Monitoring Cohort” was
evaluated to determine the effect of the controlled factors. The effect of “restoration
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treatment” on the average live density across the reef (number of oysters/m2), average
live biomass across the reef (g dry weight/m2), amount of year classes present (a whole
number ranging from 0 to 3), and average shell volume across the reef (L/m2was
determined using t-tests (or Mann-Whitney Wilcoxon tests if the parametric assumptions
were not met). The effect of “substrate type” on these same metrics of oyster restoration
success was determined using one-way ANOVAs (or Kruskal-Wallis test if the
Table 1: Restoration treatment information for Harris Creek reefs monitored in 2016; Factors relevant to this study:
Restoration treatment, substrate type added, and spat planted per acre (millions) (Maryland Oyster Restoration
Interagency Workgroup of the Sustainable Fisheries Goal Implementation Team, 2017).
16
parametric assumptions were not met). When significant differences between substrate
types were found, post-hoc multiple comparison tests (Tukey test, or if assumptions were
not met, its equivalent non-parametric test) were performed. Lastly, to determine if spat
planted per acre (millions) had an effect on the metrics of oyster restoration success,
regressions were performed; if the relationship was not linear, it was instead modeled
with the non-linear model that provided the best fit (determined using the Akaike
Information Criteria).
c. Reef Success
All of the selected reef sites were last evaluated in 2016. The Harris Creek
restoration project will not be fully “complete” until the year 2019, so the most up to date
measures of success were used to determine if the sites were successful up to 2016. Each
reef in this study met not only the minimum threshold requirements for oyster density (15
oysters per m2 over 30% of reef area) and biomass (15 g dry weight per m2 over 30% of
reef area), they met the target density and biomass for a restored reef. Each reef also had
multiple age classes present, and the height and footprint of each reef were
stable/increasing. Data on shell volume will not be determined until 2019. Therefore,
each reef was considered successful, having met all metrics laid out by the Maryland
Interagency Oyster Restoration Workgroup of the Sustainable Fisheries Goal
Restoration
Site
Did reef
meet
minimum
threshold
density in
Fall
2016?
Did
reef
meet
target
density
by Fall
2016?
Did reef
meet
minimum
threshold
oyster
biomass
for Fall
2016?
Did reef
meet
target
oyster
biomass
by Fall
2016?
Were
multiple
year
classes
present?
Was shell volume
stable/increasing?
Was reef height
stable/increasing?
Was reef
footprint
stable/increasing?
H28 YES YES YES YES YES TBD 2019 YES YES
H30 YES YES YES YES YES TBD 2019 YES YES
H31 YES YES YES YES YES TBD 2019 YES YES
H21 YES YES YES YES YES TBD 2019 YES YES
H25 YES YES YES YES YES TBD 2019 YES YES
H35 YES YES YES YES YES TBD 2019 YES YES
H36 YES YES YES YES YES TBD 2019 YES YES
H37 YES YES YES YES YES TBD 2019 YES YES
Table 2: Reef Success Summary, Adapted from Tables 6-8 in Maryland Oyster Restoration Interagency Workgroup of the Sustainable Fisheries Goal Implementation Team (2017).
17
Implementation Team (2013), except shell budget because data is not yet available
(Maryland Oyster Restoration Interagency Workgroup of the Sustainable Fisheries Goal
Implementation Team, 2017). See Table 2 for summary.
3. Results
a. Environmental Parameters
With all of the restored reefs being considered successful using the oyster metrics, the
study became a question of how the environmental factors influenced each of the
quantitative metrics. A multiple linear regression was performed to determine if each
environmental factor had a significant influence upon each of the measured oyster
metrics.
Restoration
Site
Average live
density across
reef (#/m2)
Average live
biomass across
reef (g dry wt/m2)
# of year
classes present
Average shell volume
across reef (L/m2)
H28 30.05 32.95 3 8.27
H30 53.42 51.11 3 13.3
H31 129.57 88.39 3 20.5
H21 100.62 137.44 3 21.07
H25 68.89 109.4 3 17
H35 63.35 92.01 3 13.82
H36 51.86 77.74 3 14.6
H37 56.94 64.32 3 13.46
Restoration
Site
Salinity
(ppt)
Temp (°C) DO (mg/L) Distance (m) Acreage
(acres)
H28 13.36 20.22 8.06 116.16 2.46
H30 13.36 20.22 8.06 68.58 0.97
H31 13.36 20.22 8.06 167.64 0.73
H21 14.28 20.25 8.4 548.64 2.01
H25 14.28 20.25 8.4 548.64 3.13
H35 14.09 20.25 8.12 1031.75 1.82
H36 14.09 20.25 8.12 640.08 2.06
H37 14.09 20.25 8.12 685.8 2.1
Table 3: Environmental parameter data gathered from each Restoration Site
Table 4: Available quantitative metrics from each restoration site
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• Average Live Density Across Reef
1. Salinity vs. Average Live Density
Since the relationship between salinity and average live density was not linear,
even after multiple transformation attempts, a Generalized Additive Model
(GAM) was performed to analyze their relationship. The levels of salinity in the
restored sites did not significantly contribute to explain average live density
(p=0.77) Figure 6 & 7).
2. Temperature vs. Average Live Density
Since the relationship between temperature and average live density was not
linear, even after multiple transformation attempts, a Generalized Additive Model
(GAM) was performed to analyze their relationship. The levels of temperature in
the restored sites did not significantly contribute to explain average live density
(p=0.643) Figures 8 & 9.
3. Dissolved Oxygen vs. Average Live Density
Figure 6: Relationship between salinity (ppt)
and Predicted Average Live Density (number
per m2)
Figure 7: Salinity (ppt) vs. Average
Live Density (number per m2)
Figure 8: Relationship between temperature
(°C) and Predicted Average Live Density
(number per m2)
Figure 9: Temperature (°C) vs.
Average Live Density (number per m2)
19
Since the relationship between dissolved oxygen and average live density was
not linear, even after multiple transformation attempts, a Generalized Additive
Model (GAM) was performed to analyze their relationship. The levels of
dissolved oxygen in the restored sites did not significantly contribute to explain
average live density (p=0.39) Figures 10 & 11.
4. Distance from the Shoreline vs. Average Live Density
Since the relationship between distance from the shoreline and average live
density was not linear, even after multiple transformation attempts, a Generalized
Additive Model (GAM) was performed to analyze their relationship. The levels
of distance from the shoreline in the restored sites did not significantly contribute
to explain average live density (p=0.885) Figures 12 & 13.
5. Acreage vs. Average Live Density
Since the relationship between acreage and average live density was not
linear, even after multiple transformation attempts, a Generalized Additive Model
(GAM) was performed to analyze their relationship. The levels of acreage in the
Figure 10: Relationship between dissolved oxygen (mg/L) and Predicted
Average Live Density (number per m2)
Figure 11: Dissolved Oxygen (mg/L)
vs. Average Live Density (number per
m2)
Figure 12: Relationship between
distance from the shoreline (m) and
Predicted Average Live Density (number per m2)
Figure 13: Distance from the shoreline
(m) vs. Average Live Density (number
per m2)
20
restored sites did not significantly contribute to explain average live density
(p=0.239) Figures 14 & 15.
• Average Live Biomass Across Reef
1. Salinity vs. Average Live Biomass
Since the relationship between salinity and average live biomass was not
linear, even after multiple transformation attempts, a Generalized Additive Model
(GAM) was performed to analyze their relationship. The levels of salinity in the
restored sites significantly contributed to explain average live biomass (p=0.0357)
Figures 16 & 17.
2. Temperature vs. Average Live Biomass
Since the relationship between temperature and average live biomass was not
linear, even after multiple transformation attempts, a Generalized Additive Model
(GAM) was performed to analyze their relationship. The levels of temperature in
Figure 14: Relationship between acreage (acres) and Predicted Average
Live Density (number per m2)
Figure 15: Acreage (acres) vs. Average Live Density (number per m2)
Figure 16: Relationship between salinity (ppt) and Predicted Average
Live Biomass (g dry weight per m2)
Figure 17: Salinity (ppt) vs. Average Live Biomass (g dry weight per m2)
21
the restored sites significantly contributed to explain average live biomass
(p=0.0104) Figures 18 & 19.
3. Dissolved Oxygen vs. Average Live Biomass
Since the relationship between dissolved oxygen and average live biomass
was not linear, even after multiple transformation attempts, a Generalized
Additive Model (GAM) was performed to analyze their relationship. The levels
of dissolved oxygen in the restored sites did not significantly contribute to explain
average live biomass (p=0.0615) Figures 20 & 21.
4. Distance vs. Average Live Biomass
Since the relationship between distance and average live biomass was not
linear, even after multiple transformation attempts, a Generalized Additive Model
(GAM) was performed to analyze their relationship. The levels of distance in the
restored sites did not significantly contribute to explain average live biomass
(p=0.144) Figures 22 & 23.
Figure 18: Relationship between
temperature (°C) and Predicted Average
Live Biomass (g dry weight per m2)
Figure 19: Temperature (°C) vs.
Average Live Biomass (g dry weight per m2)
Figure 20: Relationship between dissolved oxygen (mg/L) and Predicted
Average Live Biomass (g dry weight
per m2)
Figure 21: Dissolved oxygen (mg/L)
vs. Average Live Biomass (g dry
weight per m2)
22
5. Acreage vs. Average Live Biomass
Since the relationship between acreage and average live biomass was not
linear, even after multiple transformation attempts, a Generalized Additive Model
(GAM) was performed to analyze their relationship. The levels of acreage in the
restored sites did not significantly contribute to explain average live biomass
(p=0.704) Figures 24 & 25.
• Year Classes Present
Every reef site in this study had all three year classes present: market (>76 mm),
small (40-75 mm) and spat (<40 mm). Because the number of year classes present
was the same across all of the restored reefs, an analysis was unable to be performed
to determine if salinity, temperature, dissolved oxygen, distance, and acreage had any
effect on the number of year classes present.
• Average Shell Volume Across Reef
1. Salinity vs. Average Shell Volume
Figure 22: Relationship between
distance from the shoreline (m) and Predicted Average Live Biomass (g dry
weight per m2)
Figure 23: Distance from the shoreline
(m) and Average Live Biomass (g dry
weight per m2)
Figure 24: Relationship between acreage (acres) and Predicted Average
Live Biomass (g dry weight per m2)
Figure 25: Acreage (acres) vs. Average Live Biomass (g dry weight per m2)
23
Since the relationship between salinity and average shell volume was not
linear, even after multiple transformation attempts, a Generalized Additive Model
(GAM) was performed to analyze their relationship. The levels of salinity in the
restored sites did not significantly contribute to explain average shell volume
(p=0.158) Figures 26 & 27.
2. Temperature vs. Average Shell Volume
Since the relationship between temperature and average shell volume was not
linear, even after multiple transformation attempts, a Generalized Additive Model
(GAM) was performed to analyze their relationship. The levels of temperature in
the restored sites did not significantly contribute to explain average shell volume
(p=0.2) Figures 28 & 29.
3. Dissolved Oxygen vs. Average Shell Volume
Since the relationship between dissolved oxygen and average shell volume
was not linear, even after multiple transformation attempts, a Generalized
Additive Model (GAM) was performed to analyze their relationship. The levels
of dissolved oxygen in the restored sites did not significantly contribute to explain
average shell volume (p=0.179) Figures 30 & 31.
Figure 26: Relationship between
salinity (ppt) and Predicted Average
Shell Volume (L/m2)
Figure 27: Salinity (ppt) vs. Average Shell Volume (L/m2)
Figure 28: Relationship between
temperature (°C) and Predicted
Average Shell Volume (L/m2)
Figure 29: Temperature (°C) vs.
Average Shell Volume (L/m2)
24
4. Distance vs. Average Shell Volume
Since the relationship between distance and average shell volume was not
linear, even after multiple transformation attempts, a Generalized Additive Model
(GAM) was performed to analyze their relationship. The levels of distance in the
restored sites did not significantly contribute to explain average shell volume
(p=0.851) Figures 32 & 33.
5. Acreage vs. Average Shell Volume
Since the relationship between acreage and average shell volume was not
linear, even after multiple transformation attempts, a Generalized Additive Model
Figure 32: Relationship between
distance from the shoreline (m) and Predicted Average Shell Volume
(L/m2)
Figure 33: Distance from the shoreline (m) vs. Average Shell Volume (L/m2)
Figure 34: Relationship between acreage (acres) vs. Predicted Average
Shell Volume (L/m2)
Figure 35: Acreage (acres) vs. Average Shell Volume (L/m2)
Figure 30: Relationship between
dissolved oxygen (mg/L) and Predicted
Average Shell Volume (L/m2)
Figure 31: Dissolved Oxygen (mg/L) vs. Average Shell Volume (L/m2)
25
(GAM) was performed to analyze their relationship. The levels of acreage in the
restored sites did not significantly contribute to explain average shell volume
(p=0.539) Figures 34 & 35.
b. Controlled Factors
• Restoration Treatment
1. Restoration Treatment vs. Average Live Density
The restoration treatment significantly affected the average live density across
the reef. Mann-Whitney Wilcoxon test two-tailed, p= 0.0007152, Figure 36).
2. Restoration Treatment vs. Average Live Biomass
The restoration treatment significantly affected the average live biomass
across the reef. Mann-Whitney Wilcoxon test two-tailed, p= 0. 001451, Figure
37).
3. Restoration Treatment vs. Average Shell Volume
Figure 36: Restoration Treatment vs. Average Live Density (number per m2) Boxplot
Figure 37: Restoration Treatment vs. Average
Live Biomass (g dry weight per m2) Boxplot
26
The restoration treatment significantly affected the average shell volume
across the reef. Mann-Whitney Wilcoxon test two-tailed, p= 0.01369, Figure 38).
• Substrate Type Added
1. Substrate Type Added vs. Average Live Density
Substrate type significantly influenced the average live density (Kruskal-
Wallis test, p= 1.289×10-5, Figure 39). Next, a non-parametric multiple
comparisons post-hoc test was performed. This determined that there is a
significant difference between the “mixed shell – stone” group and the “none –
stone” group, but there is no significant difference between the “mixed shell –
none” group.
2. Substrate Type Added vs. Average Live Biomass
Figure 38: Restoration Treatment vs. Average Shell Volume (L/m2) Boxplot
Figure 39: Substrate Type Added vs. Average Live Density (number per m2) Boxplot
27
Substrate type significantly influenced the average live biomass (Kruskal-
Wallis test, p= 3.505×10-5, Figure 40). Next, a non-parametric multiple
comparisons post-hoc test was performed. This determined that there is a
significant difference between the “mixed shell – stone” group and the “none –
stone” group, but there is no significant difference between the “mixed shell –
none” group.
3. Substrate Type Added vs. Average Shell Volume
Substrate type significantly influenced the average shell volume (Kruskal-
Wallis test, p= 0.01944, Figure 41). Next, a non-parametric multiple comparisons
post-hoc test was performed. This determined that there is a significant difference
between the “none – stone” group, but there is no significant difference between
the “mixed shell – none” group or the “mixed shell – stone” group.
• Spat Planted per Acre (millions)
1. Spat Planted per Acre vs. Average Live Density
Figure 40: Substrate Type Added vs. Average
Live Biomass (g dry weight per m2) Boxplot
Figure 41: Substrate Type Added vs. Average
Shell Volume (L/m2) Boxplot
28
Spat planted per acre significantly impacts the average live density (p =
5.23×10-7). See Figure 42.
2. Spat Planted per Acre vs. Average Live Biomass
Spat planted per acre significantly impacts the average live biomass (p =
3.2×10-7). See Figure 43.
3. Spat Planted per Acre vs. Average Shell Volume
Spat planted per acre significantly impacts the average shell volume (p =
7.56×10-9). See Figure 44.
Figure 42: Spat Planted per Acre (millions) vs.
Average Live Density (number per m2)
logarithmic model
Figure 43: Spat Planted per Acre (millions) vs.
Average Live Biomass (g dry weight per m2)
logarithmic model
Figure 44: Spat Planted per Acre (millions) vs.
Average Shell Volume (L/m2) logarithmic
model
29
4. Discussion
a. Reef Success
Since every restoration site in this study was successful, it can be concluded that the
Harris Creek section of the Choptank River in the Chesapeake Bay is a suitable area for
oyster restoration. Using this knowledge, scientists looking to start restoration projects
can compare the conditions in Harris Creek to the conditions of potential restoration sites
to help determine suitable areas for restoration.
b. Environmental Parameters
Temperature and salinity significantly contributed to explain the differences in
average live biomass between restoration sites. Specifically, the salinity of 8.2 and the
smallest temperature (30.5°C) yielded the highest live biomass. Because the
environmental conditions are very similar between restorations sites, and likely within the
optimal levels for the oysters, many of the analyses performed in this study did not find
that the environmental parameters (salinity, temperature, dissolved oxygen, distance from
the shoreline, and acreage) significantly influenced the success of restored reefs..
c. Controlled Factors
It is not surprising that many of the controlled factors in this study (restoration
treatment, substrate type added, and spat planted per acre) have a significant influence on
the quantifiable universal metrics that define a successfully restored reef (average live
density across reef, average live biomass across reef, and average shell volume across
reef). The restoration treatment used significantly affected all of the quantifiable metrics,
likely because adding substrate and seed, not just seed, increases the chances of oyster
spat finding a suitable surface to attach to. The substrate type added (stone, mixed shell,
and none) also significantly affects the quantifiable metrics. Adding stone as substrate
(instead of mixed shells or not adding anything) led to higher average live density,
average live biomass, and average shell volume. Adding mixed shells led to similar
average shell size as stone, but significantly lower average live density and biomass.
Finally, the number of spat planted per acre significantly affected all of the quantifiable
metrics, which is predictable because the more spat planted per acre, the more oysters
you would expect to grow in an area. Specifically, planting about seven million spat per
acre yielded the highest live density, and planting about 12 million spat per acre led to the
30
highest live biomass. Additionally, planting about 13 million spat per acre yielded the
highest shell volume.
5. Conclusions
Human activities have severely impacted the oyster population in the Chesapeake Bay. From
overharvesting to the inadvertent introduction of disease, anthropogenic impacts upon the marine
environment have had devastating effects on the population of the Eastern oyster. Restoration
projects, like the one in Harris Creek of the Choptank River of the Chesapeake Bay, provide
hope for the oyster population. The fact that the reefs in this study are all successful is a
reminder that oysters are a resilient species. With the help of restoration projects, hopefully
Eastern oyster populations will continue to grow, and the species will rebound in the future.
31
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