Changing predator-prey dynamics:

effects of crown conch population increases on oyster persistence

by Harriet S. Booth

B.S. in Marine Biology, Brown University

A thesis submitted to

The Faculty of

the College of Science of

Northeastern University

in partial fulfillment of the requirements

for the degree of Master of Science

April 4th 2017

Thesis directed by

David Kimbro

Assistant Professor of Marine Ecology

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Acknowledgements

First and foremost, I would like to thank my advisor, Dr. David Kimbro, for his guidance

and support over the past three years. During my graduate career, he gave me intellectual

freedom while holding me to a high standard of work quality. I would also like to thank Dr.

Timothy Pusack, as well as Nicole Peckham and Matt Farnum, for helping me conduct the

fieldwork for this project.

I would like to thank my committee members, Dr. Randall Hughes, Dr. Geoff Trussell,

and especially Dr. Tarik Gouhier, for their interest in my research and for providing advice on

analyzing and developing my research results. I would also like to thank Dr. Torrie Hanley for

her constant encouragement, as well as my other labmates, Tanya Rogers and Karen Aerni, for

their insight, assistance, and companionship. Additionally, I thank all faculty and staff at the

Marine Science Center, as well as the funding sources for this research. Finally, I would like to

acknowledge my family and friends for their unwavering support, generosity, and kindness

during my graduate career and beyond.

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Abstract of Thesis

Predators commonly structure natural communities, but the exact outcomes of predation can be

highly variable and context-dependent. While high predator densities are assumed to deplete

prey populations, prey may persist if foraging constraints or intraspecific predator interactions

suppress predator foraging efficiency. Thus, evaluating the impacts of predators depends on

understanding the factors that moderate predation rates. Along the Atlantic coast of Florida,

outbreaks of the predatory crown conch (Melongena corona) have contributed to the recent

degradation of oyster reefs. Despite predictions of oyster population collapse as a result of

increased predation pressure, reefs have persisted in a state of reduced mean adult oyster size and

living oyster biomass. To quantify conch predation rates and determine whether a prey size

refuge may be driving oyster persistence, we conducted multi-year surveys and field experiments

that evaluated whether conchs exhibit size-selective feeding on oysters, as well as how the

relative densities of oysters and conchs affect the conch functional response. Our experiments

demonstrated that conchs selectively prey on large oysters, indicating the absence of a size

refuge, but that per capita predation rates were significantly suppressed at high conch densities,

likely due to intraspecific interference. Conchs exhibited a strongly predator-dependent response

to changes in oyster density, with a ratio-dependent model explaining almost 60 % of the

variation in per capita prey consumed. Our experimental results of conch size-selective predation

on larger oysters closely aligned with our observational findings that mean adult oyster size was

significantly reduced at high conch densities. However, these larger-scale survey results

indicated that prey density alone is the best predictor of predator-driven oyster mortality on

natural reefs, contradicting the strong predator-dependence in our experimental conch functional

response. This mismatch between our experimental and observational results indicates that conch

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interference may operate primarily when high conch densities coincide with low oyster densities,

and that this predator-dependence may be overwhelmed by other factors at a larger scale. Our

study suggests that oyster persistence is not driven by a size refuge, but that intraspecific

predator interference that suppresses predation rates may be important at high conch densities.

As the frequency and extent of predator outbreaks continue to increase with global

environmental change, understanding the factors that moderate predator-prey dynamics will help

resource managers predict and manage predation effects on ecologically and economically

important species.

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Table of Contents

Acknowledgements ......................................................................................................................... ii

Abstract of Thesis .......................................................................................................................... iii

Table of Contents ............................................................................................................................ v

List of Tables ................................................................................................................................. vi

List of Figures ............................................................................................................................... vii

Introduction ..................................................................................................................................... 1

Methods........................................................................................................................................... 4

Results ........................................................................................................................................... 10

Discussion ..................................................................................................................................... 11

Tables and Figures ........................................................................................................................ 17

References ..................................................................................................................................... 23

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List of Tables

1 Seven functional response models that describe how predator density and prey density

affect the number of prey consumed per predator. For each model, we report the

corrected Akaike Information Criterion (AICc), the difference in AICc score to the most

parsimonious model (dAICc), the AICc weights (w), and an adjusted R2 value. All

models include a variable for the attack rate (a, units: 1/d), handling time (h, units:

1/prey) for a given predator density (P) and prey density (N). In the BD, CM, and HV

models the parameters c and m describe the magnitude of predator facilitation (c < 0, m <

1) or interference (c > 0, m >1)…………………………………………….……………17

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List of Figures

1 Map of six study sites in the MRE with the inset depicting the location of the MRE (star

symbol) within the Floridian ecoregion…………………………………………………18

2 Survey results showing a) the mean density of conchs (+/- SE) and b) living oyster

biomass (+/- SE) from 2014-2016 across six field sites in the MRE……………………19

3 a) Results of the size selection experiment showing observed (white bars) and expected

(black bars) total number of oysters consumed by conchs based on oyster size group, and

b) survey results showing the relationship between mean adult oyster size and mean

conch density across six sites in the MRE from 2014 to 2016……………………......... 20

4 Fitted functional response curves of the ratio-dependent model (black lines) for multiple

densities of crown conchs and oysters. The densities of conchs were 1 (open circles, solid

line), 3 (grey filled circles, dashed line), and 5 (black filled circles, dotted line)………21

5 Survey results showing the relationship between (a) predator-driven oyster mortality and

oyster density, and b) predator-driven oyster mortality and the ratio of oyster density to

conch density across sites S1 and S2 (sites with the highest conch abundances) from 2014

to 2016…………………………………………………………………………………...22

1

Introduction

Predation is a pervasive process that can strongly affect the trophic structure and ecosystem

functioning of communities (Paine 1966; Estes and Palmisano 1974; Hixon 1991). However, the

effects of predation can be highly variable and context-dependent, driven by both abiotic and

biotic factors (Chamberlain et al. 2014). Environmental factors, such as habitat heterogeneity,

stress, and prey refuges, often drive these variable predation outcomes (Sih 1985; Menge and

Sutherland 1987; Persson and Eklov 1995), but even in consistent environmental conditions, the

outcomes of predation can be complex. For instance, variation in prey density strongly shapes

predator effects, with predation rates typically increasing with prey density before saturating due

to the constraints of search and handling times (Holling 1959; Gross et al. 1993). Predator

functional responses may be altered further by variation in predator density (Sih et al. 1998).

While cooperative hunting between predators can lead to higher per capita feeding rates than

those predicted from the additive effects of individual predators (e.g. Thiebault et al. 2016),

competitive interference between predators can reduce per capita feeding rates (Beddington

1975; Soluk 1993; Kratina et al. 2009; de Villemereuill and Lopez-Sepulcre 2011). Thus,

improving our ability to predict predation outcomes fundamentally depends on understanding the

effects of variation in both prey and predator densities.

Predation effects become increasingly important to understand, as well as increasingly

difficult to predict, at high predator densities, such as during predator outbreaks. High densities

of predators are assumed to have deleterious effects on prey populations (reviewed in Silliman et

al. 2013), but prey can persist if density-dependent constraints on predator foraging success, such

as predator intraspecific interference, reduce consumption rates. These predation constraints

often operate most strongly when high densities of predators correspond with low densities of

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prey. For example, Katz (1985) found that intraspecific interference between predatory snails at

varying densities had little effect on the consumption of barnacles except at the intersection of

low barnacle abundance and high snail abundance. The density-dependent mechanisms of

predator-dependence, such as predator interference and spatial aggregation (Arditi and Akcakaya

1990), can be difficult to isolate from one another. Thus, the ratio of prey to predators has gained

popularity in describing predator-dependence in a more general form (Arditi and Ginzburg

1989), and can be an equally strong descriptor of predation rates as the pure densities of prey and

predators.

Although variation in predation effects may depend on the relative abundance of

predators and prey, the size of predators and prey may be equally important (Werner and Gilliam

1984; Aljetlawi et al. 2004). Because predators must capture and subdue their prey, they

typically exhibit size-selective predation (Iriarte et al. 1990). For example, green crabs (Carcinus

maenas) selectively consume medium-sized mussels, often rejecting small and large mussels

after a brief evaluation period (Jubb et al. 1983). This selection is due to the fact that the

common predator preference for larger, more profitable prey must be balanced by the costly time

and energy required to handle large prey (Scharf et al. 1998; Yamada and Boulding 1998). When

predators are incapable of subduing certain sizes of prey due to handling limitations (e.g. gape

width [Persson et al. 1996] or claw morphology [Yamada and Boulding 1998]), prey size refuges

are created. These size refuges can be critical for the maintenance of prey populations because

larger prey typically produce more offspring (Werner and Gilliam 1984; Turner and Trexler

1998; Aljetlawi et al. 2004). Thus, improving the predictive capability of certain predator-prey

dynamics requires a focus on the effects of size as well as density.

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Along the east and west coasts of Florida, crown conchs (Melongena corona) are an

important predator of oysters (Wilber and Herrnkind 1982). Over the past several years, crown

conch densities have increased in areas south of the Matanzas Inlet, St. Augustine because of

estuarine salinization (Garland and Kimbro 2015; Figs. 1, 2a). Recent experiments demonstrated

significant oyster mortality due to predation on reefs with high densities of conchs, while

observations demonstrated a significant reduction in biomass of living oysters on the same reefs

(Garland and Kimbro 2015). Because these areas have supported commercial oyster fisheries for

decades, and only recently developed increased salinity and conch abundances, it was assumed

that conch predation rendered the natural reefs useless for commercial harvest and would

eventually deplete the oyster population. Declines in oyster reef habitat have significant

consequences, as oysters provide essential services, including habitat provisioning for

commercially important invertebrates and finfish, coastal water filtration, shoreline stabilization,

and the removal of excess nitrogen (Ermgassen et al. 2012; Grabowski et al. 2012). Despite

predictions of collapse however, oyster reefs in regions with increased conch densities have

persisted in their altered state instead of continuing to decline (Fig. 2b).

To evaluate whether variation in predator and prey density and/or a size refuge from

predation could contribute to the maintenance of oyster populations in the presence of elevated

conch abundances, we conducted two field experiments that addressed the following questions:

1) Do conchs exhibit size-selective predation, thus creating a size refuge from predation for adult

oysters? 2) Do per capita predation rates of conchs display a saturating response across oyster

densities and how is this functional response affected by conch density? To test the relevance of

our small-scale experiments, we also analyzed multi-year field survey data that determined

whether the average size of adult oysters at sites with high conch densities conformed to

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predictions based on the experimental results, and whether estimates of predator-driven oyster

mortality show similar patterns to those in our functional response experiment.

Methods

Study system

This research was conducted on oyster reefs in the Guana Tolomato Matanzas National Estuarine

Research Reserve, which occurs within the Matanzas River Estuary (MRE) (29.671387°N,

81.215758°W; Fig. 1), near the University of Florida’s Whitney Laboratory for Marine

Bioscience in Marineland, FL. This area of the MRE contains shorelines dominated by intertidal

oyster reefs that border Spartina alterniflora salt marshes and mangroves (Avicennia germinans

and Rhizophora mangle). The predator species of focus was the crown conch (M. corona), which

occurs intertidally along the Atlantic and Gulf coasts of Florida (Hayes and Karl 2009), and the

prey species of focus was the eastern oyster, Crassostrea virginica.

Size-selective predation

To test if crown conchs display size-selective predation on oysters, we conducted a field choice

experiment where adult-sized conchs (75-85 mm) were offered oysters ranging in size from 25-

100 mm in shell length (longest distance from the umbo to the tip of the shell). The size range of

conchs was based on the mean size of individuals from 2015 field surveys (78.8 +/- 10.0 mm),

and the oyster size range was based on reproductively mature individuals (> 25 mm) to the

largest size that is still commonly observed on the reef (100 mm). Oysters were assigned to one

of three size classes: small (25-50 mm), medium (51-75 mm), and large (76-100 mm), with the

large size class corresponding to the legal market size of oysters in the region.

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At one site in the southern MRE (S2; Fig. 1), we selected two reefs that were separated

by 12 m and established eight experimental units at the midpoint of the intertidal distribution of

the oysters on each reef. Experimental units were spaced 2 m apart and consisted of a full six-

sided cage (0.5 x 0.5 x 0.3 m, 0.075 m3) constructed of vinyl-coated wire mesh (5 x 5 mm

openings). The cages were dug 5 cm into the reef and the bottoms were filled with mud and dead

oyster shell to mimic the natural reef. Two 0.25-inch PVC pipes were driven into the reef and

each cage was secured with cable ties to the pipes at two opposing corners of the plot.

All oysters and conchs used for the experiment were collected from local reefs and held

in aquaria with flow-through seawater 4-5 days prior to the start of feeding trials. During this

holding period, oysters were fed Instant Algae Shellfish Diet 1800 (Reed Mariculture Inc, San

Jose, CA) daily, following the manufacturer’s instructions of 3.6 ml per 100 g of oyster wet

weight. To standardize hunger levels, crown conchs were held without food for 3.5 days prior to

the start of feeding trials. Prior to deployment in the field, we attached single oysters to small

squares of plastic mesh with marine epoxy. To begin feeding trials, we deployed one conch in

each cage, and randomly selected three oysters from each size class (nine oysters total). We then

secured the oysters with cable ties (via the small mesh squares) to the top of a brick that was dug

into the center of the plot.

We conducted two trials (n = 16 total replicates) that lasted seven days, and we used new

conchs for each feeding trial. Cages were checked daily at low tide, at which time we counted

the number of oysters that were “gaping”. Gaping oysters are dead adult oysters that have their

two valves intact, but lack tissue in the internal cavity. This gaping can be a sign of mortality due

to stress or recent predation by a consumer that does not damage the shell, such as crown conchs.

During the daily checks, we replaced all gaping oysters with live oysters of the same size class to

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maintain a constant prey density and size structure (Abrams 1994; Abrams and Ginzburg 2000).

We measured and recorded exact shell length of all gaping oysters and replacement oysters. To

examine oyster mortality in the absence of a conch, we deployed three control cages per trial that

lacked a conch and had one oyster of each size class secured to a brick (n = six total replicates).

Over the course of both trials, a total of three control oysters died (one from each size class).

Therefore, we subtracted this mortality from control cages from the mortality in the predation

cages to isolate mortality due to predation from that due to the environment.

Functional response of crown conchs

To examine the effects of conch and oyster density on the predator functional response, we

conducted a 3 x 5 factorial experiment with conch density and oyster density as fixed factors on

the same reefs as the size selection experiment. The three treatment levels of conch density were

1, 3, and 5 conchs, and the five treatment levels of oyster density were 2, 6, 10, 14, and 18

oysters. These densities were comparable to natural densities of conchs at high-density southern

reefs, and represented oyster densities slightly below the average densities on natural reefs, as we

were constrained by the time it takes to check and replace oysters each low tide. For the first

trial, we randomly assigned treatments to 15 experimental plots (each 2 m apart) in a completely

randomized design, using the same cages and plot set-up as in the size selection experiment. We

deployed one Onset Hobo data logger near cages in a central location to record ambient water

temperature and salinity throughout the experiment (mean inundation time was 3.5 hours each

tide). For the second trial, however, we added an additional 15 cages to increase replication for

the trial. These cages were set up in the same way, and we randomly assigned the 15 treatments

to these additional cages for a total of three replicates.

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We used oysters from the medium and large size classes (50-80 mm), and the same size

range of conchs (75-85 mm) as in the size selection experiment. We attached oysters in pairs to

small squares of plastic mesh with marine epoxy and kept them in aquaria tanks prior to

deployment, feeding them the same Instant Algae Shellfish Diet 1800 (Reed Mariculture Inc,

San Jose, CA). Conchs were kept in aquaria tanks and held without food for the same 3.5-day

holding period to standardize hunger levels. To begin the experiment, oyster pairs were cable-

tied to the sides of three small bricks that were dug into the mud in each cage, and conchs were

deployed in a consistent location within each plot. Each of the two trials lasted 7 days and we

used new conchs for each feeding trial. Cages were checked daily at low tide, at which time all

gaping oysters were counted and replaced with living oysters within the target size range to

maintain constant prey densities. As in the size selection experiment, we deployed control cages

without conchs to examine oyster mortality in the absence of predators. We observed low

background mortality and subtracted this amount from mortality in treatment cages to obtain

mortality due solely to predation.

Field patterns

To examine the relationships among oyster size, conch predation, and conch density on natural

reefs within the MRE, we conducted field surveys from July 2014-2016. For these surveys, six

reefs in each field site were selected and divided into a low and a high intertidal zone, in which

was laid one 20 m transect. A 0.5 m x 0.5 m quadrat was placed in the center of each transect and

all reef material was collected for processing. We weighed the biomass of the entire sample

before measuring the shell length of the first 100 oysters. We then counted the remaining live

adults (> 25 mm) and juveniles (< 25 mm), as well as the number of gaping oysters in the

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sample. Since gapers can signify conch predation, we used the number of gapers/m2 as a proxy

for conch predation in our analyses of the observational data. Conch densities were measured by

walking the length of each transect and recording all individuals within a 1-m section on either

side of the transect.

Statistical analyses

For the size selection experiment, we conducted a chi square goodness of fit test to determine

whether conchs consumed an equal number of oysters in each of the three size classes. All

analyses were run in R version 3.1.1 (2014-07-10).

To determine how both prey density and predator density affect the functional response

of crown conchs, we used maximum likelihood estimation to fit seven functional response

models to the data (Table 1). These included Holling’s three prey-dependent models, which

assume no predator interactions; Type I assumes no predator handling time and thus a linear

increase in predation rates with increasing prey density; Type II includes a baseline predator

attack rate and handling time that limits the predator at high prey densities; Type III is similar to

Type II but is a sigmoid function that describes potential predator learning, predator switching

between prey types, predator aggregation, and other forms of variation (Holling 1959; Bolker

2008). The other four models incorporate some form of predator dependence (Skalski and

Gilliam 2001); 1) the Beddington-Deangelis model (BD; Beddington 1975; DeAngelis et al.

1975), in which the predator attack rate is affected by predator density, 2) the Crowley-Martin

model (CM; Crowley and Martin 1989), in which both attack rate and handling time are affected

by predator density, 3) the Hassell-Varley model (HV; Hassell and Varley 1969), which is

similar to the BD model but allows for a non-linear effect of predator density on attack rate, and

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4) a ratio-dependent model, in which the attack rate depends on the ratio of prey to predators

(RD; Arditi and Ginzburg 1989; Arditi and Ginzburg 2012).

We obtained the maximum likelihood estimates for the parameters of each of the predator

functional response models by using the ‘mle2’ function within the ‘bbmle’ package in R

(Bolker 2008). Specifically, we modeled the probability of each oyster being consumed by a

predator via a binomial distribution with probability ‘p’ calculated according to each of the

functional responses. We then used the Nelder-Mead algorithm in order to find the maximum

likelihood estimates. We constrained the functional response parameters, attack rate and

handling time, to be positive and nonzero. We then calculated the adjusted R2 values for all

models to account for their differing numbers of parameters, and determined which model

produced the most parsimonious fit by using the Akaike Information Criterion, corrected for

small sample sizes (AICc) (Burnham and Anderson 2002).

For the observational data, we evaluated how oyster density and conch density affect

conch predation rates on natural reefs by conducting a linear regression of the density of gaping

oysters as a function of the density of living oysters from 2014-2016. To confirm the linear

model was the best descriptor of the relationship, we compared the fit of the linear model to a

logarithmic model as well as a null model using AICc model selection. To evaluate the

importance of conch density in predicting the density of gaping oysters, we then conducted a

linear regression of the density of gaping oysters as a function of the ratio of oyster density to

conch density. The relationship between oyster density and conch density was linear, which

allowed us to use their ratio as our predictor variable of predator-dependence. Finally, we

conducted a third linear regression that evaluated the mean size of adult oysters as a function of

conch density across the same three years. The linear model was a poor fit for these data,

10

determined by checking the diagnostic plots, so we fit an exponential model to the data using the

‘nlsLM’ function in the R package ‘minpack.lm’. We confirmed the improved fit of the

exponential model over the linear model by using AICc model selection.

Results

Results from the size selection experiment showed that conchs preferentially consumed oysters

from the large size class (76-100mm) at a greater rate than oysters from either the medium or

small size classes (X2df=2 = 20.30, p < 0.001; Fig. 3a). Correspondingly, our survey results

showed that the size of adult oysters decreased significantly with increasing conch density (non-

linear regression, R2 = 0.40; Fig. 3b).

In the functional response experiment, we found that the conch functional response

increased with increasing oyster density, but that per capita predation rates were strongly reduced

with increasing conch density (Fig. 4). The ratio-dependent model best explained the predator

functional response (AICc weight = 0.43; R2 = 0.58; Fig. 4), with the predator-dependent BD,

CM, and HV models as the next strongest descriptors of the conch functional response (AICc

weight = 0.19, 0.20, 0.18; Table 1). All four predator-dependent models fit the data better than

Holling’s prey-dependent models, with each explaining almost 60 % of the variation in per

capita prey consumed (Table 1). Additionally, the parameter estimates that represent the nature

and magnitude of predator interactions in the BD, CM, and HV models provide evidence for

conch interference with c > 0 (BD, CM) and m > 1 (HV) (Table 1).

Contrary to our experimental results, field survey patterns indicated that oyster density

alone was the strongest predictor of the density of gaping oysters (i.e. our measure of predation)

on natural reefs (linear regression, p < 0.001, R2 = 0.71; Fig. 5a). The ratio of oyster density to

conch density did not significantly affect the density of gaping oysters and explained relatively

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little of the variation in the dataset (linear regression, p > 0.05, R2 = 0.013; Fig. 5b),

contradicting our experimental finding of a strongly predator-dependent functional response.

Discussion

This study suggests that the persistence of oysters in the presence of high conch densities is not

due to a prey size refuge, but may be due in part to antagonistic predator interactions that reduce

per capita predation rates, especially at the lowest ratios of oysters to conchs. In field

experiments, we found that individual adult conchs consistently selected market-size oysters (>

75mm) relative to smaller, non-market size oysters (< 75mm). This result was supported by the

finding in our multi-year field surveys that mean adult oyster size significantly decreased at sites

with abundant conchs. In our functional response experiment, we found that conch predation

rates increased linearly with oyster density, but the slope of this relationship was strongly

influenced by predator density, with increasing density of conspecifics suppressing predation

rates. This could potentially be due to intraspecific interference that creates additional constraints

on search and handling times. Foraging restrictions can be compensated for by cooperative

hunting behaviors (Macdonald 1983), but our experimental results indicated an inhibitory effect

of multiple predators driving the reduced predation rates of conchs at high densities. This

functional response was best described by the ratio of oysters to conchs, but other predator-

dependent models explained oyster-conch dynamics similarly well, all indicating interference as

a likely driving mechanism. Despite this experimental evidence for predator-dependence,

predator-driven oyster mortality on natural reefs was best predicted by oyster density alone. This

suggests that conch interactions may influence predation rates at high conch densities, but that

the effects of predator density may be overwhelmed at larger scales by the importance of prey

abundance and accessibility.

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The size relationship between predators and prey can stabilize population dynamics and

promote persistence, as prey often use growth to obtain a refuge from predation. For example,

intertidal mussel populations can persist in the presence of predatory sea stars by growing out of

vulnerable size classes (Paine 1976), while gape limitations in freshwater predatory fish increase

survival of large prey (Eklov and Diehl 1994; Persson et al. 1996). Although large oysters

experience a predation refuge from other predators, such as stone crabs (Brown and Haight

1992) and blue crabs (Brown 1997), our results show that conchs inhibit a stabilizing effect of a

size refuge by preferentially selecting the largest oysters. Conch feeding mechanisms do not

appear to be affected by oyster size or defenses, as they pry open the shell and use their

proboscis to consume the internal tissue (Menzel and Nichy 1958); in our experiments, even the

smallest adult conchs in our experimental size range (75 mm) preyed on large oysters. Conchs

have few predators because of their strong, effective shell (Garland and Kimbro 2015), which

means their foraging time is unrestricted by trade-offs with predation risk (Werner et al. 1983).

An increase in conch abundance and size-selective feeding could select for smaller individuals in

the population, ultimately negatively impacting reproductive output of adult oysters. Therefore, it

is possible that the lack of size refuge can help explain both the reduced size of oysters and the

reduced biomass of living oysters in our study sites (Garland and Kimbro 2015).

Given the absence of a size refuge from predation, it is interesting to consider why oyster

size and biomass have not continued to decline in recent years, despite continued increases in

conch densities (Fig. 2a, b). Persistence of oysters in the face of high conch abundances could be

due to recruitment from other source populations, or in part because of successful reproduction

of the smaller oysters remaining on reefs. Additionally, the negative effects of conch predation

could be dampened by foraging constraints driven by increased predator density. Our

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experimental results indicated that conch predation rates increased strongly with oyster density,

but this linear functional response decreased and showed greater saturation as conch density

increased. This suggests that antagonistic conch interactions may extend the time it takes to kill

and ingest oysters, overwhelming the increased encounter rate at high prey densities. With the

ratio-dependent model as the best fit in our model selection, as well as evidence for interference

in the parameter estimates of the other predator-dependent models (Table 1), our experimental

results indicate strong predator-dependence in the conch functional response.

Predator-dependence in trophic interactions can arise from factors such as aggressive

social interactions, prey anti-predator behavior that increases with predator density, or predator

aggregation due to resource heterogeneity (Arditi and Akcakaya 1990; Turchin 2003). Because

oysters lack effective defenses against conchs (Garland and Kimbro 2015), we hypothesize

predator dependence in the conch functional response derives from interactions that intensify

with increased predator density and aggregative behavior. High predator densities can lead to

non-additive predation effects via facilitation or interference (Sih et al. 1998); our results

showing a decrease in per capita feeding rates with increasing conch densities provides possible

evidence for the latter, where intraspecific interference between conchs may be occurring. While

other gastropods often occur in dense aggregations (Butler 1985; Silliman and Zieman 2001) and

exhibit group feeding behavior (Butler 1985; Fodrie et al. 2008), crown conchs are mainly

individualistic feeders. When conchs are forced to aggregate at high densities, they may

indirectly interfere with each other by depleting their shared prey resource (Free et al. 1977;

Abrams and Walters 1996; Abrams 2015), or they may directly inhibit conspecifics in the

process of searching for and handling prey. Search time may be increased if conchs “waste” time

encountering conspecifics instead of their oyster prey (Arditi and Akcakaya 1990; Kratina et al.

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2009; Hossie and Murray 2016), or if the chemical cues of an attacked oyster attract conchs

(Hathaway and Woodburn 1961), and divert their attention from closer, more optimal prey.

Additionally, conch interactions may increase handling time if physical interference between

individuals impedes the process of prying open oysters and consuming their internal tissue.

Previous studies have demonstrated these various mechanisms of intraspecific interference in

reducing per capita predation rates both in theoretical models (e.g. Tyutyunov et al. 2008), as

well as in empirical studies across a range of taxa (e.g. Crowley and Martin 1989; Soluk et al.

1993; Zimmerman et al. 2015).

While our experimental results suggested that the conch functional response was strongly

predator-dependent, our observational results indicated that oyster mortality on natural reefs was

best predicted by oyster density alone (Fig. 5a). These contradictory results suggest that

predator-dependence in the form of interference may be important only in extreme cases of high

conch densities and low oyster densities, and that predator density may be less significant than

prey density at driving oyster mortality at larger scales. Our experimental conch densities

represented conch populations at the two high-density southern sites, while our oyster densities

were slightly below mean field densities due to experimental constraints. This low ratio of prey

to predators has been previously suggested to intensify density-driven predation constraints

(Katz 1985), and may have highlighted the specific conditions when predator interference

operates most strongly in this system. The density-dependent mechanisms that often drive

predator-dependence – predator interference (e.g. Sand et al. 2012; Hossie and Murray 2016) and

spatial clustering of predators (Cosner et al. 1999) – were both apparent in our experiment, thus

potentially explaining the strength of predator density in describing the conch functional

response at this relatively small scale.

15

The degree that ecological interactions impact their surrounding community can vary

based on scale, often creating mismatches between small-scale experiments and broad patterns in

nature (Wiens 1989; Levin 1992; Ives et al. 1993; Abrams 2015). The manipulation of

population densities and habitat resources within caging experiments can lead to difficulties in

extrapolating results to broader spatio-temporal scales (Carpenter 1996; Schindler 1998). For

example, in nature conchs may actively spread out to avoid intraspecific interactions, potentially

explaining the greater dependence on prey density than predator interactions in driving conch

predation rates at the larger scales in our field surveys. However, we did see the importance of

prey density in our experiment with the linear increase in conch predation rates with increasing

oyster density (Fig. 4). The linear nature of this functional response suggests that conchs are not

significantly limited by handling time, which was supported by the low parameter estimates of

handling time generated by our models (Table 1). While Type 1 functional responses are largely

attributed to filter feeders whose search and handling times are effectively one (Hassell 1978;

Jeschke et al. 2004), low predator handling times can also be due to factors that inhibit organisms

from reaching total satiation, like prey habitat refuges (Chan et al. 2017). In our system, conchs

may not reach total satiation because of the degraded reef conditions (i.e. reduced living oyster

biomass) that may increase the time it takes to travel between optimal oyster clusters. A lack of

satiation could explain the linear functional response of conchs that was consistent at both the

experimental and observational scales. In areas lacking density-dependent constraints on

predation rates and where conchs increase their consumption strongly with oyster density, oyster

persistence may be more strongly linked to factors such as juvenile recruitment from surrounding

reefs. Because of the variable effects of scale, it becomes especially important to compare

experimental findings to larger-scale patterns in nature to accurately predict the impacts of

16

changing predator densities on their surrounding communities.

In conclusion, our study highlights several factors that may influence predation

outcomes, an important consideration in accurately estimating functional responses as well as

predicting the dynamics of predator and prey populations over time. By evaluating predator-

driven oyster mortality across several years, and comparing this with controlled experimental

manipulations, we found that the negative effects of high conch densities may be limited by

intraspecific interactions constraining per capita predation rates. These negative predator

interactions, as well as factors like recruitment dynamics, may help oyster populations persist,

despite their lack of size refuge and the resulting decrease in the abundance of large oysters on

natural reefs. With increases in predator outbreaks and climate-induced range expansion of

species like crown conchs (Hayes and Karl 2009; Silliman 2013), empirical studies such as ours

provide valuable insight into sources of variation in predator-prey interactions and how we can

account for these factors to better predict the effects of changing predation intensities.

17

Tables and Figures

Table 1. Seven functional response models that describe how predator density and prey density

affect the number of prey consumed per predator. For each model, we report the corrected

Akaike Information Criterion (AICc), the difference in AICc score to the most parsimonious

model (dAICc), the AICc weights (w), and an adjusted R2 value. All models include a variable

for the attack rate (a, units: 1/d), handling time (h, units: 1/prey) for a given predator density (P)

and prey density (N). In the BD, CM, and HV models the parameters c and m describe the

magnitude of predator facilitation (c < 0, m < 1) or interference (c > 0, m > 1).

Model Formula Parameter Estimate dAIC W R2

Holling

Type 1

(H1)

���� = �� a 0.1588 23.0 <0.001 0.181

Holling

Type II

(H2) ��� =

��

1 + �ℎ�

a

h

0.1760

0.0463 25.1 <0.001 0.164

Holling

Type III

(H3) ���� =

���

1 + �ℎ��

a

h

0.0451

0.3197 21.3 <0.001 0.166

Beddington

-DeAngelis

(BD)

���, � =��

1 + ℎ� + �� − 1�

a

h

c

0.2977

0.0036

0.6760

1.7 0.19 0.567

Crowley-

Martin

(CM)

���, � =��

1 + ℎ� + � + ℎ��� − 1�

a

h

c

0.7480

0.0111

1.4836

1.6 0.20 0.569

Hassell-

Varley

(HV) ���, � =

��

ℎ� + �

a

h

m

0.2995

0.0038

1.8140

1.7 0.18 0.567

Ratio-

dependent

(RD)

���, � =��/

1 + ℎ�/

a

h

0.3884

0.0246 0.0 0.43 0.583

18

Figure 1. Map of six study sites in the MRE with the inset depicting the location of the MRE

(star symbol) within the Floridian ecoregion.

19

Figure 2. Survey results showing a) the mean density of conchs (+/- SE) and b) living oyster

biomass (+/- SE) from 2014-2016 across six field sites in the MRE.

20

Figure 3. a) Results of the size selection experiment showing observed (white bars) and

expected (black bars) total number of oysters consumed by conchs based on oyster size group,

and b) survey results showing the relationship between mean adult oyster size and mean conch

density across all six sites in the MRE from 2014 to 2016.

21

Figure 4. Fitted functional response curves of the ratio-dependent model (black lines) for

multiple densities of crown conchs and oysters. The densities of conchs were 1 (open circles,

solid line), 3 (grey filled circles, dashed line), and 5 (black filled circles, dotted line).

22

Figure 5. Survey results showing the relationship between (a) predator-driven oyster mortality

and oyster density, and b) predator-driven oyster mortality and the ratio of oyster density to

conch density across sites S1 and S2 (sites with the highest conch abundances) from 2014 to

2016.

23

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