Factors Contributing to the Success of Restored Oyster ...

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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

https://nsuworks.nova.edu/cnso_stucap/342

1

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

2

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

3

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


• 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

4

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

6

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)

7

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

8

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

9

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.

10

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


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.

12

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


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).

13

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


to Harris Creek Profiler

station: H21, H25


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).

14

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.


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

15

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


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








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


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

18

• 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


(GAM) was performed to analyze their relationship. The levels of acreage in the

Figure 10: Relationship between dissolved oxygen (mg/L) and Predicted


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



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



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


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


(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.


temperature (°C) and Predicted Average



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



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


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


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



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



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


of dissolved oxygen in the restored sites did not significantly contribute to explain

average shell volume (p=0.179) Figures 30 & 31.


salinity (ppt) and Predicted Average

Shell Volume (L/m2)

Figure 27: Salinity (ppt) vs. Average Shell Volume (L/m2)


temperature (°C) and Predicted

Average Shell Volume (L/m2)



24

4. Distance vs. Average Shell Volume

Since the relationship between distance and average shell volume was not


(GAM) was performed to analyze their relationship. The levels of distance in the


(p=0.851) Figures 32 & 33.

5. Acreage vs. Average Shell Volume

Since the relationship between acreage and average shell volume was not



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)


dissolved oxygen (mg/L) and Predicted


Figure 31: Dissolved Oxygen (mg/L) vs. Average Shell Volume (L/m2)

25



(p=0.539) Figures 34 & 35.


• 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.


logarithmic model


Average Live Biomass (g dry weight per m2)

logarithmic model


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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