To my parents for their unconditional love and...

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VISUAL AND MOLECULAR ANALYSIS OF FRENCH GRUNT STOMACH CONTENTS FROM ST. JOHN, U.S. VIRGIN ISLANDS

By

JOHN STEVEN HARGROVE

A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2010

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© 2010 John Steven Hargrove

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To my parents for their unconditional love and support

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ACKNOWLEDGMENTS

I would like to thank my advisor, Dr. Daryl Parkyn for his generous investment of

knowledge, energy, and patience that has allowed this thesis to come to fruition. Being

able to work aside an accomplished and intelligent scientist is both an educational as

well as humbling experience. I would also like to offer sincere thanks to all the

members of my committee, co-chair James Austin, Debra Murie, and Amanda

Demopoulos who have provided critical insight into the development and execution of

my master‟s research. A large number of people have directly and indirectly contributed

their expertise and hard work to this project ranging from sample collection and

processing to explaining lab techniques, and for this I am very grateful. I would like to

extend a sincere thank you the members of the Murie/Parkyn, Austin, and Demopoulos

labs for their efforts and camaraderie. I am greatly indebted to my parents, Rob and

Ann Hargrove for all of the sacrifices they have made on my behalf as well as the

seemingly limitless support they have offered over the years. Lastly, I would like to

thank Ashley Houston who has offered love, support and understanding throughout this

whole process.

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TABLE OF CONTENTS page

ACKNOWLEDGMENTS .................................................................................................. 4

LIST OF TABLES ............................................................................................................ 7

LIST OF FIGURES .......................................................................................................... 8

LIST OF ABBREVIATIONS ............................................................................................. 9

ABSTRACT ................................................................................................................... 10

CHAPTER

1 GENERAL INTRODUCTION .................................................................................. 12

2 VISUAL ANALYSIS OF FRENCH GRUNT STOMACH CONENTS ........................ 16

Introduction ............................................................................................................. 16

Methods .................................................................................................................. 18 Study Site and French Grunt Collections.......................................................... 18

Stomach Content Analysis ............................................................................... 20 Niche Breadth and Diet Overlap ....................................................................... 22

Multivariate Comparisons Using Fish Size and Sample Site ............................ 22 Results .................................................................................................................... 23

French Grunt Collections .................................................................................. 23 Stomach Content Analysis ............................................................................... 24

Diet by Sampling Location ................................................................................ 25 Diet by Fish Size .............................................................................................. 25

Niche Breadth and Diet Overlap ....................................................................... 27 Multivariate Comparisons Using Fish Size and Sample Site ............................ 27

Discussion .............................................................................................................. 28

3 MOLECULAR ANALYSIS OF FRENCH GRUNT STOMACH CONTENTS ............ 42

Methods .................................................................................................................. 47 DNA Extraction ................................................................................................. 47

Polymerase Chain Reaction (PCR) .................................................................. 47 Factors Influencing PCR Success and DNA Sequencing ................................. 48

Molecular Identification ..................................................................................... 49 Comparison of Techniques ............................................................................... 51

Results .................................................................................................................... 52 DNA Extractions ............................................................................................... 52

Polymerase Chain Reaction and DNA Sequencing .......................................... 52 Molecular Identification ..................................................................................... 53

Factors Influencing PCR and Sequencing Success ......................................... 54

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Comparison of Techniques ............................................................................... 56 Discussion .............................................................................................................. 56

Polymerase Chain Reaction and DNA Sequencing .......................................... 57 Factors Influencing PCR and Sequencing Success ......................................... 58

Molecular Identification ..................................................................................... 61

4 CONCLUSION ........................................................................................................ 75

LIST OF REFERENCES ............................................................................................... 77

BIOGRAPHICAL SKETCH ............................................................................................ 89

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LIST OF TABLES

Table page 2-1 Summary of the numbers of Haemulon flavolineatum collected by gear type

from two sampling trips (May 2008 and June 2009) made on St. John, U.S. Virgin Islands. ..................................................................................................... 35

2-2 Occurrence (%FO) and numerical abundance (%N) of prey sampled from Haemulon flavolineatum stomach contents collected from St John, US Virgin Islands in May/June 2008 and June 2009. ......................................................... 36

2-3 Frequency of occurrence (%O) and numerical abundance (%N) for prey items recovered from Haemulon flavolineatum stomach contents by sampling location ............................................................................................................... 37

2-4 Diet of Haemulon flavolineatum sampled catalogued by numerical abundance (N%) and frequency of occurrence (O%) based on two size groups ................................................................................................................ 38

3-1 A list of studies that have used DNA-based techniques to examine the stomach contents of vertebrate predators. ......................................................... 66

3-2 Stomach content items of Haemulon flavolineatum catalogued by prey type and the corresponding numbers of organisms that were successfully extracted, amplified and sequenced ................................................................... 66

3-3 A list of the number of DNA sequences generated by taxa for potential prey items collected through bulk sampling and opportunistic sampling conducted on St. John Island, U.S. Virgin Islands. .............................................................. 67

3-4 List of identifications generated via visual and molecular analysis of stomach content items recovered from Haemulon flavolineatum collected in the U.S. Virgin Islands ...................................................................................................... 68

3-5 Comparison of frequency of occurrence (%FO) and percent numerical abundance (%N) for prey recovered from Haemulon flavolineatum stomach contents based on visual, molecular, and a combined visual/molecular analysis. ............................................................................................................. 70

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LIST OF FIGURES

Figure page 2-1 Size distribution of all Haemulon flavolineatum collected from St. John, U.S.

Virgin Islands in May of 2008 and June 2009. .................................................... 39

2-2 The percent of Haemulon flavolineatum stomach contents collected from St. John, U.S. Virgin Islands containing prey items at different collection times. ..... 39

2-3 Cumulative prey curve representing the number of novel prey orders recovered withby fish stomachs .......................................................................... 40

2-4 Multi-dimensional scaling of percent numerical abundance of stomach contents for Haemulon flavolineatum using the Bray-Curtis Index of Similarity by sample collection site ..................................................................................... 41

3-1 A neighbor joining tree showing the degree of sequence relatedness for stomach contents and potential prey .................................................................. 73

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LIST OF ABBREVIATIONS

BSA Bovine Serum Albumin

COI Cytochrome Oxidase I

DNA Deoxyribonucleic acid

FB Fish Bay sample site

PCR Polymerase Chain Reaction

TR Tektite Reef sample site

VD Virgin Islands Environmental Resource Station Dock

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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the

Requirements for the Degree of Master of Science

VISUAL AND MOLECULAR ANALYSIS OF FRENCH GRUNT STOMACH CONTENTS FROM ST. JOHN, U.S. VIRGIN ISLANDS

By

John Steven Hargrove

December 2010

Chair: Daryl Parkyn Cochair: James Austin Major: Fisheries and Aquatic Sciences

Fish stomachs are routinely examined to understand trophic interactions and the

roles that fish play within their community. Despite the utility of such studies, intrinsic

limitations confound reliable identification of consumed prey. Examples include

differential rates of digestion and physical structures, such as pharyngeal teeth, which

can result in certain prey items being rendered unidentifiable and subsequently

underrepresented in diet studies. The present study analyzed the stomach contents of

French grunt (Haemulon flavolineatum), a reef fish that forages on soft-bodied prey

items including polychaete and sipunculid worms that are ground up by its pharyngeal

teeth. French grunt (n = 99) were collected from St. John, U.S. Virgin Islands (USVI)

over two sampling events (June 2008 and May 2009), 51 of which contained stomach

contents. Fish were collected from seagrass beds and coral reefs and ranged in size

from 57-188 mm ( = 119.4 mm, S.D. = 4.0 mm). Conventional visual analysis of the

stomach contents indicated that sipunculid worms were most abundant numerically

followed by decapod crustaceans, polychaete worms, and unidentifiable prey. As a

supplemental approach to visual analysis, polymerase chain reaction (PCR) was used

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to amplify fragments of the cytochrome oxidase I (COI) gene region from prey tissue

recovered from fish stomachs. DNA sequences generated from PCR products were

compared to records from GenBank as well as a database of potential prey sequences

collected in the USVI to establish taxonomic identification. DNA extracted from 195

prey items produced 48 DNA “barcode” sequences and prey items identified as

sipunculids via this molecular technique were placed with a high level of confidence

(based on sequence similarity) at the species level. For approximately half of the

samples for which barcodes were generated, taxonomic resolution was potentially

increased when compared with visual analysis alone. Several factors, including DNA

concentration, the presence of contaminants, digestion code, and prey type were

examined to explain the observed differences in PCR amplification and DNA

sequencing. Prey type alone was determined to have a significant impact on PCR and

sequencing success (Fisher‟s Exact Test, p = 0.020). Results from this study illustrate

the utility of and potential pitfalls associated with molecular analysis applied to diet

analysis of a generalist, carnivorous fish.

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CHAPTER 1 GENERAL INTRODUCTION

An accurate description of marine fish diets is critical to furthering our

understanding of the trophic relationships between fish and their environment.

Management agencies and researchers routinely conduct investigations of fish diets

that can be as simple as establishing an inventory of what prey items are consumed or

as complex as measuring dietary responses to changes in the environment (Bowen

1992). Historically, the standard technique for describing the diet involves visually

identifying the contents of fish stomachs and summarizing prey items by numerical

abundance, frequency of occurrence, volume and/or weight (Hyslop 1980). Despite

widespread use, there are limitations inherent to visual techniques that can bias results.

For example, the rate at which different types of prey digest in the stomach varies, and

as a result some prey items, such as those with hard parts, will be physically identifiable

for longer time periods than others (Gannon 1976, Murie and Lavigne 1991, Berens and

Murie 2008). As a result, some prey types may be underrepresented or not identified at

all. When such data is extrapolated to the population level, a relatively small bias can

present a misleading picture. Tissue biomarkers, whereby an organism is identified by

chemical rather than morphological properties, represent an alternative option to collect

dietary information from prey that is macerated or thoroughly digested. The

development of a reliable, non-visual method to analyze stomach contents could

potentially reduce bias when quantifying the diet of fish who consume soft-bodied prey

types that digest rapidly or prey that lack characteristic hard parts used in identification.

Alternatives to visual analysis of stomach contents include, but are not limited to,

stable isotope analysis (Cocheret de la Morinière et al. 2003a), fatty acid analysis

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(Iverson et al. 2002), clonal antibodies (Ohman et al. 1991; Feller 1992) and

polymerase chain reaction (PCR) (Rosel and Kocher 2002; Smith et al. 2005). Stable

isotope and fatty acid analysis have been used to establish the trophic placement of

predators (Budge et al. 2002), examine changes in diet through time (Cocheret de la

Morinière et al. 2003a), and track shifts in the foraging of fish populations spatially

(Hadwen et al. 2007). These methodologies represent powerful tools for tracking the

flow of nutrients within and between ecosystems. However they typically do not provide

species-level dietary information (Iverson et al. 2004). In addition, isotope signatures

and fatty acid profiles rely on elements accrued over days to months, which precludes

the direct comparison of results with those collected via stomach content analysis

(Tieszen et al. 1983). The use of polyclonal antibodies to study fish diets have only

been applied in a limited number of studies (Ohman et al. 1991, Feller 1992), largely

explained by the extensive development times and labor-intensive requirements of this

method (Chen et al. 2000, Mayfield et al. 2000, Symondson 2002).

PCR-based techniques are appealing alternatives to visual analysis of stomach

contents because the process can be highly sensitive and specific (Symondson 2002,

Sheppard et al. 2005, Deagle et al. 2007). PCR-based methods employ a series of

chemical reactions that produce millions of copies of targeted DNA fragments and as a

result only minute amounts of tissue from prey are required for analysis. The use of

DNA sequences to discriminate between closely related species is now commonplace

and illustrates that DNA-based approaches can be highly specific (Hebert et al. 2003).

Although DNA decays as it is digested, the constituent nucleotides often remain intact

and can be recovered by targeting relatively short DNA fragments (Rollo et al. 2002,

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Agusti et al. 2003). Within the last five years the number of researchers using DNA-

based techniques to study predator-prey interactions has increased dramatically and

the method has been applied to birds, terrestrial and marine invertebrates, and fish

(Scribner and Bowman 1998, Jarman et al. 2004, Blankenship and Yayanos 2005,

Smith et al. 2005, Deagle et al. 2005b, Deagle 2006, Pons 2006, Redd et al. 2008,

Clare et al. 2009, Valentini et al. 2009).

To date, little focus has been directed towards teleost diets using non-traditional

methods of stomach content analysis. Grunts (Hameulidae) are an appealing study

animal because a information on their general diets has been established and although

important prey types have been identified, fine scale taxonomic data have been limited

to selected species (Randall 1967). French grunt, (Haemulon flavolineatum) are

ecologically important marine fish distributed throughout the Caribbean Sea (Randall

1967, Bohlke and Chaplin 1993). French grunt utilize multiple different habitat types,

beginning their lives in sea grass beds and mangrove prop roots and eventually

becoming daytime residents of coral reefs (Helfman and Schultz 1984, Nagelkerken et

al. 2000b, Cocheret de la Morinière et al. 2002). Both juveniles and adults make

crepuscular migrations to seagrass beds adjacent to coral reefs where they forage on

benthic infauna (Helfman et al. 1982, Helfman and Schultz 1984, Nagelkerken et al.

2000a). In the West Indies, French grunt represent the most abundant fish species on

coral reefs and transfer a significant amount of nutrients via food consumed from

seagrass beds that is subsequently defecated onto coral reefs (Randall 1967, Meyer

and Schultz 1985). Diet analysis of French grunt has been performed in various parts

of the Caribbean and soft-bodied invertebrates including polychaetes and sipunculids

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comprise a significant portion of the consumed prey items (Davis 1967, Randall 1967,

Estrada 1986, Hein 1999). French grunt possess pharyngeal teeth that are used to

macerate prey items prior to entering the stomach, resulting in a high incidence of

unidentifiable prey items observed in stomach content analysis. Given their significance

in the coral reef, seagrass, and mangrove habitats and a general lack of species-level

resolution in identified prey items, French grunt are an interesting study species for the

application of DNA-based techniques for analysis of diet.

The overall goal of this study was to examine the diet of French grunts collected

from multiple locations on St. John, U.S. Virgin Islands. Specific objectives were to: 1)

use multiple methodologies to increase the taxonomic resolution of prey items

consumed by French grunt, and 2) compare their diet obtained through visual analysis

with that from PCR-based techniques when applied to the same stomach content

samples.

Data collected from visual analysis of stomach contents were used to test for

changes in diet by sampling event, sampling location and fish size as measured by

niche breadth and niche overlap (Chapter 2). Amplification of DNA recovered from prey

items was analyzed via a barcoding approach to generate identifications for unknown

prey items and confirm known individuals (Chapter 3). Finally, the utility and

shortcomings of a DNA barcoding approach applied to fish diets in the context of French

grunt forms the foundation of Chapter 4.

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CHAPTER 2 VISUAL ANALYSIS OF FRENCH GRUNT STOMACH CONENTS

Introduction

French grunt occur in waters less than 60 meters deep in the western Atlantic

from South Carolina to Brazil, including parts of the Gulf of Mexico (Bohlke and Chaplin

1993). They are common throughout much of their range and in the West Indies are

one of the most abundant fish species observed on coral reefs (Randall 1967) . French

grunt undergo ontogenetic shifts in habitat use, beginning as pelagic larvae, settling into

nursery areas as juveniles, and eventually becoming coral reef residents as adults

(Helfman and Schultz 1984, Cocheret de la Morinière et al. 2003a, Cocheret de la

Morinière et al. 2003b, Nagelkerken and van der Velde 2004a, Nagelkerken and Velde

2004b). Newly settled larvae inhabit interstitial spaces in seagrass beds and occur as

solitary individuals. Juveniles aggregate into schools according to size and utilize

mangrove roots, patch reefs, and structures within seagrass beds as nursery habitat

(Cocheret de la Morinière et al. 2003a, Cocheret de la Morinière et al. 2003b). As

individuals approach sexual maturity, they form resting schools on coral reefs and

eventually move to offshore habitats (Helfman et al. 1982, Meyer and Schultz 1985).

French grunt feed via a relatively non-selective winnowing behavior, whereby

potential prey and non-nutritive debris are separated within the buccal cavity (Dennis

1992). Retained prey items are macerated by depressor muscle movement of the

pharyngeal teeth before entering the stomach (Wainwright 1989, Dennis 1992).

Although identifying macerated prey species can be difficult, several studies have

documented the diet of French grunt throughout their range, including the Netherland

Antilles (Cocheret de la Morinière et al. 2003a, Cocheret de la Morinière et al. 2003b,

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Nagelkerken and van der Velde 2004a, Nagelkerken and Velde 2004b), Puerto Rico

(Austin and Austin 1971, Dennis 1992), Haiti (Beebe and Tee-Van 1928), Florida (Davis

1967, Hein 1999), Columbia (Estrada 1986), and the U.S. Virgin Islands (Randall 1967).

Changes in French grunt diet have been documented as fish grow larger and transition

through habitats (Dennis 1992, Cocheret de la Morinière et al. 2002, Cocheret de la

Morinière et al. 2003a). Pre-juvenile fish feed during the day on planktonic copepods

within the nursery habitat (Gaut and Munro 1983, Cocheret de la Morinière et al.

2003a). Juveniles begin making migrations to seagrass beds to forage at night on

benthic invertebrates, which includes tanaids, decapod crabs and shrimp, and

polychaete worms (Dennis 1992).

Adults form resting schools on coral reefs and make crepuscular migrations to

grass beds and sand patches where they forage on benthic invertebrates (Randall

1967, Estrada 1986, Dennis 1992). Randall (1967) examined the stomachs of adult

French grunt from the U.S. Virgin Islands and Puerto Rico and found the most important

prey items volumetrically were polychaete worms, crabs and sipunculids. In contrast,

Cocheret de la Moriniére (2003a) examined French grunt in Curacao collected from

different habitat types and found that reef-inhabiting adults mainly fed on decapods

crabs and fishes. In bay habitats, which were occupied by juvenile fish, tanaids,

copepods, and decapod crustaceans dominated the diet volumetrically. Further work

conducted in Curacao (Nagelkerken et al. 2000a) indicated that adult French grunt

forage primarily on tanaids, copepods, and mysids, with amphipods and gastropods

contributing a smaller part of the diet. Estrada (1986) examined 174 individuals

collected from Santa Maria, Columbia, and concluded there were two main dietary

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groups. Smaller French grunt (30 – 110 mm total length) consumed primarily

gastropods, harpacticoid copepods, polychaetes, and decapod crustaceans while larger

individuals (>111 mm total length) foraged on gastropods, chitons, scaphopods,

decapod crustaceans, polychaetes and sipunculids.

The relative importance of prey items determined by numerical abundance and

volume varied some between studies; however, unidentifiable prey items were routinely

encountered. Dennis (1992) recorded at least one unidentified prey item in 263 out of

330 (79%) stomachs examined, and Cocheret de la Moriniére et al. (2003a) found

unidentified material constituted up to 58% of total stomach contents by volume for

particular sized fish. French grunt were therefore selected as the study species for two

reasons: 1) their ecological importance within the seagrass and coral reef habitats as

predators of benthic invertebrates and transporters of nutrients between habitats, and 2)

because although previous studies have examined French grunt diets, there is still a

lack of genus or species-level identifications for certain prey types. The goal of this

chapter was to quantify the diet of French grunt through visual analysis of stomach

contents and test for differences in diet across sampling events, habitat type and fish

size.

Methods

Study Site and French Grunt Collections

The diets of French grunt in this study were collected from the southern shore of

St. John Island, U.S. Virgin Islands as part of a larger study examining habitat

connectivity of coral reef, mangrove, and seagrass communities. The southern shore of

St. John Island has a National Park, National Monument, and unprotected waters, and

these differing levels of protection can potentially influence the abundance of fish within

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and between habitats (Rogers and Beets 2001). Diet analysis of French grunt, along

with stable isotope and telemetry data, will be used to understand how fish utilize

various habitats and what if any potential impacts the National Parks and Monuments

have on these processes.

French grunt were collected from the southern shore of St. John Island, U.S. Virgin

Islands, USA, in June of 2008 and May/June of 2009. For each individual, the time of

day, date, GPS location, fork length (FL), depth (meters), and gear type was recorded.

In 2008, collections were made throughout the day to determine periods of peak

foraging and once this was established then sampling was targeted towards times with

greatest chance of collecting prey-filled stomachs. In total, fish were collected from two

reef habitats (Tektite and Fish Bay) and one seagrass habitat (adjacent to the VIERS

Dock).

Grunts were collected using multiple gears including hook and line, trap, pole

spear, and hand net. Hook and line sampling consisted of sabiki rigs with multiple

hooks baited with squid. Fish traps (1 m long, 1 m wide, and 0.5 m in height) with mesh

size of ~7.5 cm2 were set on sand patches near reef habitats, baited using cat food

(Kozy Kitten), and set overnight. Both spear fishing and hand net collections were

conducted by divers using scuba gear. Collections by hand net utilized a modified 4 m

cast net (brail and hand lines removed) to capture French grunt.

Fish captured alive were brought to the surface and their stomach contents were

obtained via gastric lavage following Murie and Parkyn (2001). A 3-mm diameter

polyethylene tube, with a round, plastic bead attached to the distal portion (to prevent

chafing or puncturing of the stomach wall), was inserted into the fish‟s stomach attached

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to a 4 L Chapin (Batavia, NY) sprayer pressurized tank. Filtered sea water from the

sprayer tank was pumped in pulses as the wand was moved back and forth to loosen

prey items and regurgitated contents were deposited onto a 210 µm sieve (W.S. Tyler,

Mentor, OH, U.S. Standard Sieve Series #70) to drain away fluids. Stomach contents

were collected and put into individually labeled sterile sampling bag. Samples were

kept on ice in the field prior to freezing in the lab. For fish < 75 mm FL, a 10 mL syringe

rigged with a ball-inflating needle (Spalding, Alexander City, AL) was used in the place

of the pressurized sprayer. Fish harvested using a pole spear were placed directly into

individual plastic bags underwater and placed on ice at the surface prior to

transportation to the Virgin Islands Environmental Resource Station (VIERS) laboratory.

Speared grunts were dissected, and contents from the esophagus to the upper intestine

were removed for visual analysis.

Stomach Content Analysis

Stomach contents were transported to University of Florida Fisheries and Aquatic

Sciences facilities where they were individually thawed and examined using a Leica MZ

12.5 stereomicroscope. Diet items were identified to the lowest taxonomic level using

relevant identification keys (Manning 1969, Fauchald 1977, Abele and Kim 1989,

Kensley and Shotte 1989, Thomas 1993, Cutler 1994, Hendler et al. 1995, Heard et al.

2003). Individual prey items were catalogued by fish number, given a distinct

identification number and photographed using a microscope-based digital imaging

system (Motic Images V 3.2). Once sorted, individual items were rinsed with deionized

water and placed into individual 1.5 mL Eppendorf vials containing 100% non-denatured

ethanol as a preservative for PCR work.

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Recovered food items were catalogued by percent frequency of occurrence (%O)

and percent by numerical abundance (%N). Percent frequency of occurrence was

defined as the total number of stomachs containing a particular prey type divided by the

total number of stomachs containing food. Numerical abundance was calculated as the

number of each prey type from all stomachs divided by the number of all prey items

from all stomachs. Partial prey items were counted based on the presence of

diagnostic characters, such as a pair of crustacean eyes or a sipunculid introvert, in

order to standardize enumeration of prey items. Volumetric and weight measurements

were not taken because individual prey types were in various states of digestion or

fragmented and represented only partial organisms, as well as to minimize processing

time between thawing and immersion of prey into DNA preservative. Data were

analyzed using Microsoft Excel for diet analysis and statistical tests were computed

using R version 2.11.1 (R Development Team 2010).

A cumulative prey curve, which tracks the number of new prey items

encountered per analyzed stomach, was generated to assess the adequacy of sample

size (Hurtubia 1973, Ferry and Caillet 1996). A random number generator [Excel

function RAND()] was used to determine the order in which analyzed stomachs were

included in the prey curve. This process was iterated twenty five times (bootstrapping)

to establish an average number of novel prey items per stomach. Standard error was

calculated by dividing the standard deviation of the number of novel prey in the each

stomach by the square root of the sample size (or number of iterations) for that

stomach. The total number of stomachs was then plotted as a function of the number of

new prey items in each stomach. If the curve of this graph reaches an asymptote then

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an adequate number of stomachs have been examined and the diet is considered

adequately described (Hurtubia 1973, Ferry and Caillet 1996).

Digestion codes were assigned to prey items recovered from stomach contents

on a scale from 1 to 6, with 1 representing minimal digestion (0-5%) increasing to 6

(>90%), an almost fully digested state. A general guide was developed for the various

prey types adapted from previous studies (Jackson et al. 1998, Berens 2005).

Digestion code data were collected to examine any potential correlation with DNA

amplification and sequencing success (Chapter 3).

Niche Breadth and Diet Overlap

Comparisons between sampling years and habitat types were calculated using

numerical abundance. Niche breadth was calculated using Levin‟s standardized index

(BA) to determine if differences in sampling locations and collection year influenced the

exploited food resources (Hurlbert 1978, Krebs 1999a). This index returns values that

range on a scale from 0 (narrow niche) to 1 (broad niche).

Diet overlap for sampling event, habitat type and fish size was calculated using

Morisita‟s index of similarity (C (Krebs 1999a). Morisita‟s index was selected because

of low bias with varied samples sizes and large numbers of resource states (Smith and

Zaret 1982). Both sets of calculations were performed using Ecological Methodology

software package (Krebs 1999b).

Multivariate Comparisons Using Fish Size and Sample Site

Stomach content data were subjected to non-metric multi-dimensional scaling

(MDS) using the software package PRIMER_E v6.1.6 (Clarke and Gorley 2006) to test

for differences in diet by sampling site and fish size. MDS ordination was applied to %N

calculated for each of the 51 fish stomachs and prey items were grouped into taxonomic

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order to facilitate comparisons. Prey items coded as unidentified and unidentified

crustacean were removed to minimize potential bias of multivariate analysis and values

of other dietary categories were not adjusted upwards to correspond with 100% %N.

An arcsine transformation was applied to %N prior to applying data to a Bray-Curtis

index of similarity to correct for non-normality (Zar 1984). An analysis of similarity

(ANOSIM) test was used to test the significance of differences in diet by location of

sample collection and fish size. The species mainly responsible for differences in the

Bray-Curtis index by sample location and fish size was assessed using a similarity of

percentages (SIMPER) test. Both ANOSIM and SIMPER analysis were performed in

the statistical program PRIMER_E v6.1.6.

Results

French Grunt Collections

A total of 99 fish were collected during two sampling trips in June of 2008 (n = 69)

and May/June of 2009 (n = 30). Multiple gear types were employed in both years with

the majority of fish collected using a hand net (Table 2-1). Overall, sampled French

grunt ranged in size from 57-188 mm fork length (Figure 2-1) ( = 119 mm ± 40.3 mm (1

S.D.). Fish collected in 2008 spanned a larger range of sizes and displayed a bimodal

distribution with peaks around 70 mm and 170 mm. Samples from 2009 were

significantly larger on average than 2008 (Welch‟s t-test = -2.92, DF = 76.5, p = 0.004)

and were distributed evenly across sizes from 80 mm to 180 mm. Multiple habitat types

were sampled with 69 (70%) fish collected from coral reefs [Tektite Reef – (TR), and

Fish Bay – (FB)] and 30 (30%) from seagrass beds [VIERS Dock – (VD)]. Fish

collected in 2009 were collected from either Tektite Reef or VIERS dock while samples

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from 2009 were collected from either Tektite Reef or Fish Bay. Fish collected on coral

reefs sites (FB & TR) were significantly larger compared to seagrass beds (VD)

(Welch‟s t-test = -10.94, DF = 85.1, p < 0.001). Samples collected at different times

during the day in 2008 indicated morning hours yielded more stomachs filled with prey

and subsequently all collections in 2009 occurred prior to 0900h EST (Figure 2-2).

Stomach Content Analysis

In 2008, a total of 69 fish were collected and 26 (38%) stomachs contained prey

items. From 2009, a total of 30 fish were collected and 25 (83%) had stomach contents.

Regardless of digestion code, all prey items recovered were included in the diet

analysis. French grunt collected in 2009 contained more prey items on average (2.6 ±

2.15 in 2008 versus. 7.2 ± 4.4 in 2009) and had a greater diversity of prey types.

Unidentified crustaceans were commonly encountered in both years based on

occurrence (46% and 48%) and number (19% and 10%) for 2008 and 2009 respectively

(Table 2-2). Polychaete worms were abundant by occurrence (23% and 20%) as were

harpacticoid copepods (23% and 28%, for 2008 and 2009 respectively). Numerically

unidentified prey items (10% and 14%) and unidentified polychaete worms (7% and 3%)

were commonly encountered prey items. Sipunculid worms showed the largest

difference between years based on both occurrence (15% and 96%) and number (12%

and 35%) for 2008 and 2009 samples respectively. Unidentified prey items were less

common by occurrence in samples from 2008 (31%) relative to 2009 (68%).

Stomatopods and Leptocheliidae tanaid crustaceans were absent from stomachs

collected in 2008 but present in 2009 samples (16% stomatopods and 20% tanaid by

occurrence and 2% and 3% by number).

25

Diet by Sampling Location

Stomach contents of French grunt were divided by sampling location to examine

potential trends. In total, fish were collected from two coral reef sites, Fish bay (n=9

stomachs) and Tektite Reef (n = 27 stomachs), and one seagrass bed, VIERS Dock

(n=15 stomachs). Prey types were pooled into broader taxonomic categories

(Amphipoda, Copepoda, etc.) to allow a comparative analysis. Several similarities were

observed among diets collected at the three sites. Unidentified crustaceans were

abundant by %O (53%, 56%, and 37%) and %N (16%, 15%, 10%) for VIERS Dock,

Fish Bay, and Tektite Reef respectively (Table 2-3). Unidentified prey items were also

abundant at all locations (47% VD, 56% FB, and 48% TR) by occurrence.

Distinct differences by location were also revealed with fish only being consumed

at the VIERS Dock location (13%O and 16%N). Foraminifera (4%O and 3%N) and

polyplacophora (4%O and 1%N) were only observed in stomach contents collected from

Tektite Reef. Stomatopods (11%O for both locations) and tanaid crustacean (11%O FB

and 48%O TR) were identified only from coral reef samples. Sipunculid worms were

consumed at all three locations, with more individuals consumed by occurrence and

number in Fish Bay (89%O and 32%N) and Tektite reef (67%O and 35%N) relative to

VIERS Dock (13%O and 12%N).

Diet by Fish Size

To facilitate comparisons with previous diet work (Hein 1999), French grunts

were divided into two size groups [A = <90 mm and B = >90 mm standard length (SL)]

to examine size-specific patterns in the occurrence and frequency of different prey items

in their diets. These size groups were selected because previous work has suggested

that nocturnal foraging begins during a mid-juvenile phase or about 90 mm SL (Hein

26

1999). Therefore, fork lengths of French grunt collected in this study were converted to

standard length (SL) using the regression FL = 1.04 * SL + 4.04 (Hein 1999). In total,

size group A (<90 mm SL) contained 25 individuals and group B (≥90 mm SL) had 26

individuals.

For French grunts <90 mm SL, the most important prey items numerically were

sipunculids (18%), unidentified crustaceans (17%), copepods (10%), fish (10%) and

polychaetes (9%) (Table 2-4). By occurrence, unidentified crustaceans also dominated

(52%) followed by copepods (28%), polychaetes (24%), shrimps (24%), and sipunculids

(24%). French grunts ≥90 mm SL, sipunculid worms were most important by number

(35%), followed by unidentified prey items (16%), unidentified crustaceans (10%) and

tanaid crustaceans (6%). By occurrence, sipunculid worms were consumed by almost

all individuals (85%), followed by unidentified prey types (69%), unidentified

crustaceans (42%) and ophiuroids and tanaids (both 27%). Both shrimp and crabs

were more commonly consumed by smaller individuals than the relatively larger fish

(44%O and 23%O, respectively).

The cumulative prey curve constructed based on all diet items consumed by all

collected fish did not appear to reach an asymptote (Figure 2-3). This indicated that

additional French Grunt stomach samples would be needed to adequately describe

diets. Over 19 novel prey orders were recovered from the various stomach samples

and standard error values were smallest for the initial and final stomachs examined

(Figure 2-3). Although an asymptote was not reached few additional taxa (<2) were

discovered once 30 stomachs were examined.

27

Niche Breadth and Diet Overlap

Overall, diet niche breadth between sampling years was considerably different

(0.19 and 0.52 for 2008 and 2009, respectively). The number of resource states utilized

was higher in 2009 (38) compared to 2008 (18), however, fish diets from 2009 were

dominated by three resources (sipunculids, unidentified crustaceans and unidentified

prey items). Niche overlap as calculated using Morisita‟s simplified index for the two

sampling years was “moderate” (C0.62) based on Krebs (1999a) criteria.

When analyzed by location, niche breadth was greatest at VIERS Dock (0.64)

followed by Fish Bay (0.41) and Tektite Reef (0.31). The number of resource states

was similar among sites (VD=13, FB=14, TR =17), with 8 resources frequently used

(cutoff proportion 0.05) at VIERS Dock and Fish Bay and only 5 resources on Tektite

Reef. Niche overlap was greater between Fish Bay and Tektite Reef (0.91) compared

with Fish Bay to VIERS Dock (0.70) or Tektite Reef to VIERS Dock (0.63).

By fish size, the niche breadth was greater for smaller individuals (0.64) relative

to larger ones (0.27) as calculated by Levin‟s standardized measure. Both groups

utilized an array of prey types (Group A = 14, B=18), however, the diet of larger

individuals was more restricted in that few prey types were consumed in large quantities

(4 vs. 8 prey types for A and B, respectively). A moderate amount of diet overlap

between groups was observed according to Morisita‟s simplified index of overlap

(C0.73).

Multivariate Comparisons Using Fish Size and Sample Site

Once unidentified crustaceans and unidentified prey were removed from analysis,

the number of samples by site were VIERS Dock = 13, Tektite reef = 25, and Fish Bay =

28

9. Size group A (< 90 mm FL) consisted of 22 stomach contents and size group B (>90

mm FL) contained 25 stomachs samples. Previous analysis indicated larger individuals

were restricted to reef sites and smaller individuals were primarily collected from VIERS

Dock, and as a result size and site were tested together by ANOSIM. Diets were

significantly different (ANOSIM, R = 0.132, p = 0.3%) by the combined size and location

factor. Significant pairwise difference was observed between <90 mm FL VIERS Dock

and >90 mm FL Tektite Reef (ANOSIM, R = 0.315, p = 0.1%, SIMPER dissimilarity =

89.1%) with differences in sipunculids, polychaetes, and tanaids abundance accounting

for 27.6%, 8.9% and 8.1% of the observed dissimilarity. Small and large fish sizes from

Tektite Reef had significantly different diets (ANOSIM, R = 0.182, p = 2.7%, SIMPER

dissimilarity = 77.6%) with key differences noted in the abundance of sipunculids,

copepods, and tanaids explaining 26.7%, 12.2, and 10.8% of the dissimilarity (Figure 2-

4). Only fish <90 mm FL were collected from VIERS Dock and consumed crabs,

polychaetes, and harpacticoid copepods. Fish <90 mm FL from Tektite Reef had a diet

composed of harpacticoid copepods, sipunculids, and shrimp while fish >90 mm FL

from the same location ate sipunculids and tanaids. All fish from Fish Bay were >90

mm FL and consumed sipunculids, ophiuroids, and amphipods.

Discussion

French grunt collected from southern shore of St. John Island, U.S. Virgin

Islands, consumed a diverse array of benthic invertebrates consisting of nonmotile

infauna and motile prey including small crabs and shrimp. Differences were observed in

diet based on fish size and sampling location. Both small and large fish consumed

similar prey items, however, the relative importance of certain prey types varied by size

29

group. This observation could be due to differences in the abundance of infaunal prey

at the different sampling locations. Dennis (1992) noted that size related differences in

the diet of 3 French grunt size classes were probably due solely to habitat changes,

since different size grunts feeding in the same habitat had no difference in consumed

prey items. Furthermore, French grunt foraging strategy and morphology does not

change dramatically between the juvenile and adult stage as evidenced by the weak

relationship between prey size and body size, with larger fish continuing to consume

small infaunal prey (Dennis 1992). Novel prey items were observed in fish collected

from both coral reefs and VIERS dock, with the majority of prey types being common to

both locations. Overall, sipunculid worms, unidentified crustaceans, polychaetes and

copepods represented the four most commonly encountered prey types by both

numerical abundance and frequency of occurrence.

This study is generally consistent with previous diet studies conducted in the

northern Caribbean region. Dennis (1992) analyzed the diets of 330 French grunt from

Puerto Rico and concluded that polychaete worms, sipunculids, gastropods and shrimp

were the four most important prey types by blotted wet weight measurements.

Similarly, Randall (1967) found that for adult French grunts collected in the U.S. Virgin

Islands and Puerto Rico, polychaetes were the most important prey type volumetrically,

followed by crabs and sipunculid worms. However, Estrada (1986) examined French

grunt diets from the southern Caribbean region in Columbia, and concluded that

gastropods were the most commonly consumed prey item in both small (30 – 110 mm

TL) and large (>111 mm TL) fish determined by frequency of occurrence. The next

most important prey groups for smaller individuals were harpacticoid copepods,

30

polychaetes and decapod crustaceans whereas chitons, scaphopods, decapods, and

polychaetes worms represented the next most frequently consumed prey for larger

individuals. Relative to other grunt species Estrada (1986) noted that French grunt in

general consumed more worms (sipunculids and polychaetes).

One key difference between the current study and previous studies is the apparent

importance of gastropods in the diet of French Grunt from St. John USVI. Gastropods

were found in fish collected from all sample sites, fish sizes and both 2008 and 2009

sampling events. However, their abundance was limited to low levels in both %N and

%O. Various studies conducted in the Netherland Antilles of the southern Caribbean

found sipunculid worms and polychaetes were ingested at insignificant levels (< 2% by

volume) (Cocheret de la Morinière et al. 2003a, Cocheret de la Morinière et al. 2003b,

Nagelkerken and van der Velde 2004) which is a stark contrast from the diet of fish from

the present study which was dominated numerically by sipunculids and to a lesser

extent polychaete worms. Diets from French grunt collected on coral reefs were

dominated volumetrically by decapods crabs and prey fishes, while French grunt from

nursery habitats consumed primarily tanaid crustaceans, copepods and decapods

crustaceans (Cocheret de la Morinière et al. 2003a, Cocheret de la Morinière et al.

2003b). In contrast, French grunt seldom consumed fish prey in St. John USVI. An

important note regarding the studies conducted in the Antilles is that fish were spatially

segregated by size but adults did not make crepuscular migrations to forage in adjacent

seagrass beds.

The number and type of prey items recovered from French grunt stomach contents

were found to vary by year. Numerically, the primary diet items of French grunt

31

collected in 2008 were unidentified crustaceans, unidentified prey, fish and copepods,

which were evenly distributed amongst the sampled French grunt with the exception of

baitfish. Fish as prey were limited to two French grunt stomachs sampled in 2008, both

collected from the same sampling area 5 days apart. A potential explanation for this

observation was the presence of large schools of silversides (Atheriniformes) present at

the VIERS dock location during this period (personal observation). The most important

prey items recovered from stomach samples collected in 2009 were sipunculids,

unidentified crustaceans, unidentified prey and tanaids in terms of numerical abundance

and frequency of occurrence. Sipunculids occurred in almost all stomach samples

obtained in 2009 (96%) and also were the most important prey item numerically (35%).

Differences in stomach contents by collection year might be explained by the strategy

used to gather samples. Collections made in 2008 were completed throughout the day

to determine periods of peak foraging, which appeared to be prior to dawn. All sampling

conducted in 2009 occurred in early morning hours to capture fish as they were

returning from nighttime foraging events and could explain why more prey items were

recovered on average in 2009 than 2008.

When considering disparities in stomach contents by collection year it is

important to note that differences existed in the sampling sites as well as the size

distribution of sampled fish. Fish collected in 2008 were significantly smaller on

average than samples collected from 2009 and the difference in size of fish by collection

year was likely an artifact of sampled habitat. Fish from coral reefs were significantly

larger on average than those collected in the seagrass bed, consistent with

32

observations reported for French grunt habitat use throughout their range (Dennis 1992,

Hein 1999).

When stomachs were divided by sampling location, sipunculid worms were found

to be important numerically and by occurrence in both coral reef habitat types but were

consumed much less frequently from collected in seagrass habitats. Stomatopods and

tanaids were only observed in stomach contents of French grunt collected on reefs.

Quantitative measures of prey densities in the different sample sites would be helpful to

elucidate if prey were consumed due to higher relative abundance or active selection

through measures such as electivity (Ivlev 1961).

When stomach contents were divided into groups by fish size, fish <90 mm SL

was the only size class to consume prey fish. This observation can potentially be

explained by the abundance of small silversides (Atheriniformes) present in the

sampling area where fish <90 mm SL were collected. Fish >90 mm SL consumed the

only ophiuroids recovered from stomach contents, however, none of these individuals

represented whole individuals but only the distal portions of arms. This was also noted

by Estrada (1986) who observed that fish >111 mm TL were the only size of French

grunt to consume echinoderms.

In terms of overall diet, sipunculid worms were the most commonly consumed

prey item by French grunts both numerically and by occurrence collected in this study.

The majority of sipunculid worms counted in this analysis were partial organisms and

consisted of little more than the distal portion of the sipunculid introvert, suggesting that

numerically their importance might be overestimated. Both unidentified crustaceans

and unidentified prey items were found in almost half of all individuals stomachs

33

analyzed and represented important prey types numerically. Prey items coded as

unidentified crustaceans or unidentified prey encompassed a wide range of sizes and

without gravimetric or volumetric measurements minimal inference can be made

regarding the dietary importance of theses prey types. Harpacticoid copepods were

found in a large portion of the stomachs, however, they likely contributed little nutritional

value to the diet of French grunt given their small size (<1 mm total length). Additional

information in the form of ingested prey weight and caloric content of the different prey

types would be beneficial for minimizing bias associated with categorizing diet based on

prey number and for understanding which prey contribute most nutritionally.

Dietary niche breadth, when calculated for all individuals, was moderate,

decreasing with fish size. This suggests that either fewer prey types are exploited or

that diets are heavily influenced by specific prey types consumed in large quantities. As

would be expected, similarity between niche breadth diets was greatest between the

two reef sampling sites and to a lesser extent seagrass habitat. This may be indicative

of opportunistic foraging on locally abundant prey resources and is supported by the

wide differences in diet of French grunt observed throughout their distributional range.

The diet varied when sampling site and size were compared using MDS ordination

and ANOSIM analysis of %N data. The diets of fish were not tightly clustered by

sampling site or size, suggesting that variation in diet occurred within and between each

of the locations and size classes (Figure 2-4). Although schools of H. flavolineatum

might rest over the same reef or structure, multiple foraging locations may exist within a

given sampling site and fish collected from different schools might consume different

prey types based on abundance within a given sand patch. These results were not

34

consistent with observations made by Hein (1999) who noted that different sized grunts

feeding in the same habitat had no difference in prey items consumed. It is important to

note that stomach contents in the current study were pooled across sampling events

(2008 and 2009) and overall sample size was small. SIMPER analysis indicated that

the numerically dominant prey items for fish collected from reef sites were sipunculids,

while few sipunculids were recovered from the VIERS Dock site. Analysis of diet by fish

size revealed that sampling site and fish size were correlated as all fish from VIERS

Dock and Fish Bay belonged to one size group (small and large fish size groups,

respectively). Additional fish stomachs from individuals spanning a range of sizes from

each sample site would be needed in order to determine if size and sample site affect

diets independently.

The high percent of stomach contents with unidentified prey items and the fact that

previous studies also had a high proportion of unidentified prey provides justification for

the optimizing collections to times shortly after feeding has occurred, as well as

exploring alternative means of identification, such as molecular analysis that does not

rely on morphological characteristics to generate identifications.

35

Table 2-1. Summary of the numbers of Haemulon flavolineatum collected by gear type

from two sampling trips (May 2008 and June 2009) made on St. John, U.S. Virgin Islands.

Hook & Line Spear Hand Net Trap Total

2008 9 0 45 15 69 2009 0 10 20 0 30

Totals 9 10 65 15 99

36

Table 2-2. Occurrence (%FO) and numerical abundance (%N) of prey sampled from Haemulon flavolineatum stomach contents collected from St John, US Virgin Islands in May/June 2008 and June 2009.

2008 2009

Order Suborder/Infraorder Family %O %N %O %N Algae 3.85 - 8.00 - Chitons - - 4.00 0.56 Crustaceans

Amphipods Gammaridea Gammaridae 3.85 1.47 12.00 1.67 Hyperiidea - - 4.00 0.56 Unidentified - - 8.00 1.11 Copepods Harpacticoida 23.08 10.29 16.00 2.78 Crabs Anomura Galatheidae - - 4.00 0.56 Paguridae 3.85 1.47 - - Brachyura Unidentified - - 4.00 1.67 Hippidae Emerita sp. - - 4.00 0.56 Unidentified 15.38 5.88 4.00 0.56 Isopods Flabellifera Cymothoidae - - 8.00 1.11 Unidentified - - 4.00 0.56 Valvifera Idoteidae 3.85 1.47 4.00 0.56 Mysids Mysidae 3.85 1.47 4.00 0.56 Ostracods - - 4.00 0.56 Shrimp Caridea Alpheidae 11.54 4.41 4.00 0.56

Unidentified 3.85 1.47 4.00 0.56 Penaeidae - - 4.00 0.56 Unidentified 7.69 2.94 4.00 1.11

Stomatopods - - 16.00 2.22

Tanaids Tanaidomorpha Leptocheliidae - - 20.00 2.78 Unidentified - - 16.00 2.22 Unidentified - - 16.00 2.22

Unidentified 46.15 19.12 48.00 10.00

Fish Atheriniformes 7.69 11.76 - - Forams - - 4.00 2.22 Gastropods Cerithiidae Bittiolum sp. 3.85 1.47 - -

Cyclichnidae Acteocina sp. - - 4.00 0.56 Unidentified - - 4.00 1.11

Ophiuroids Ophiocomidae - - 8.00 1.11 Ophiodermatidae - - 8.00 1.67 Unidentified 3.85 1.47 8.00 1.11

Annelids Unidentified 11.54 4.41 12.00 1.67 Polychaeta Arabellidae 3.85 1.47 4.00 0.56 Glyceridae - - 4.00 0.56 Unidentified 19.23 7.35 20.00 2.78

Sipunculids Aspidosiphonidae - - 12.00 1.67 Unidentified 15.38 11.76 96.00 35.00

Sediment 26.92 - - - Unidentified 30.77 10.29 68.00 14.44

Totals % (Number)

100 (26)

100 (68)

100 (25)

100 (180)

37

Table 2-3. Frequency of occurrence (%O) and numerical abundance (%N) for prey items recovered from Haemulon flavolineatum stomach contents by sampling location. Fish Bay and Tektite Reef represent coral reef sampling sites while VIERS dock was a boat dock surrounded by shallow (<2 m) sea grass bed.

Seagrass VIERS Dock

Coral Reef

Fish Bay

Coral Reef Tektite Reef

Prey O% N% O% N% O% N%

Algae 6.67 - - - 7.41 - Chitons - - - - 3.70 0.71 Crustaceans

Amphipods 6.67 2.04 33.33 5.00 11.11 2.14 Copepods 20.00 6.12 11.11 3.33 22.22 5.00 Crabs 26.67 8.16 22.22 10.00 3.70 0.71 Isopods 6.67 2.04 33.33 5.00 3.70 0.71 Mysids 6.67 2.04 11.11 1.67 - - Ostracods - - - - 3.70 0.71 Shrimp 20.00 6.12 22.22 5.00 18.52 3.57 Stomatopods - - 11.11 1.67 11.11 2.14 Tanaids - - 11.11 1.67 48.15 8.57 Unidentified 53.33 16.33 55.56 15.00 37.04 10.00

Fish 13.33 16.33 - - - - Forams - - - - 3.70 2.86 Gastropods 6.67 2.04 11.11 1.67 7.41 1.43 Ophiuroids - - 44.44 6.67 11.11 2.86 Annelids 6.67 2.04 - - 18.52 3.57

Polychaetes 26.67 10.20 22.22 3.33 22.22 4.29 Sipunculids 13.33 12.24 88.89 31.67 66.67 35.00 Sediment 26.67 - 44.44 - 18.52 - Unidentified 46.67 14.29 55.56 8.33 48.15 15.71

Totals % (Numbers)

100 (15)

100 (48)

100 (9)

100 (60)

100 (27)

100 (140)

38

Table 2-4. Diet of Haemulon flavolineatum sampled in May 2008 and June 2009 in St. John, U.S. Virgin Islands, catalogued by numerical abundance (N%) and frequency of occurrence (O%) based on two size groups (<90 mm SL and >90 mm SL).

<90 mm SL (Group A)

>90 mm SL (Group B)

Prey %O %N %O %N

Algae 4.00 - 7.69 - Chitons - - 3.85 0.60 Crustaceans

Amphipods 8.00 2.44 19.23 2.99 Copepods 28.00 9.76 11.54 2.40 Crab 20.00 6.10 7.69 3.59 Isopods 8.00 2.44 11.54 1.80 Mysids 4.00 1.22 3.85 0.60 Ostracods - - 3.85 0.60 Shrimp 24.00 7.32 15.38 2.99 Stomatopods - - 15.38 2.40 Tanaids 8.00 3.66 26.92 5.99 Unidentified 52.00 17.07 42.31 10.18

Fish 8.00 9.76 - - Forams - - 3.85 2.40 Gastropods 4.00 1.22 11.54 1.80 Ophiuroids - - 26.92 4.79 Annelids 12.00 3.66 11.54 1.80

Polychaetes 24.00 8.54 23.08 3.59 Sipunculids 24.00 18.29 84.62 35.33 Sediment 28.00 - 23.08 - Unidentified 28.00 8.54 69.23 16.17

Totals % (Numbers)

100 (25)

100 (81)

100 (26)

100 (167)

39

Figure 2-1. Size distribution of all Haemulon flavolineatum collected from St. John, U.S. Virgin Islands in May of 2008 and June 2009.

Figure 2-2. The percent of Haemulon flavolineatum stomach contents collected from St.

John, U.S. Virgin Islands containing prey items at different collection times.

40

Figure 2-3. Cumulative prey curve representing the number of novel prey orders

recovered with the addition of more fish stomachs for all French grunts collected in 2008 and 2009. Stomach order was randomized and bootstrapped 25 times to establish standard error bars.

41

Figure 2-4. Multi-dimensional scaling of percent numerical abundance of stomach contents for Haemulon flavolineatum using the Bray-Curtis Index of Similarity by sample collection site. Ellipses represent 50% diet similarity between individuals.

42

CHAPTER 3 MOLECULAR ANALYSIS OF FRENCH GRUNT STOMACH CONTENTS

Morphological-based identification of fish stomach contents can be difficult,

particularly in situations when prey items are thoroughly digested or lack diagnostic

characters (Gannon 1976, Hyslop 1980, Chapter 2). Bias can be introduced into food

habit descriptions as a result of differential rates of digestion, whereby some prey types

pass through the digestive tract more rapidly than others (Hyslop 1980). Modifications

to sampling methods, such as collecting samples shortly after peak foraging times, can

help to maximize data quality. One way to circumvent problems associated with visual

analysis of stomach contents is to consider different techniques, including those

described below.

Alternative approaches to studying the interactions between fish and their prey

include stable isotope analysis (Cocheret de la Morinière et al. 2003a, Sarà and Sarà

2007), fatty acid analysis (Iverson et al. 2002, Budge et al. 2006), serum antibodies

(Ohman et al. 1991, Feller 1992) and DNA-based methods (Rosel and Kocher 2002,

Smith et al. 2005). Stable isotope and fatty acid analysis have been used to infer

trophic level interactions (Budge et al. 2002), shifts in diet (Cocheret de la Morinière et

al. 2003a), and changes in foraging location for fish populations (Hadwen et al. 2007).

Although capable of fine-scale resolution (Iverson et al. 2002), these methodologies do

not typically provide species-level dietary information. Furthermore, data collected via

fatty acid and stable isotope analysis can be influenced by factors such as the type of

tissue samples and energy balance of the animal, potentially confounding interpretation

of results (Thiemann 2009). To date, the use of polyclonal antibodies to study fish diets

has been limited (Ohman et al. 1991, Feller 1992), in part because this process requires

43

a long time to develop appropriate antibodies and has intensive labor requirements

(Chen et al. 2000, Mayfield et al. 2000, Symondson 2002).

DNA-based approaches using the polymerase chain reaction (PCR) technique

hold particular promise in identifying prey items recovered from the stomachs of

predators (Blankenship and Yayanos 2005; Deagle 2006). First, PCR is a highly

sensitive process and can be successfully executed with minute amounts of sample

material (Hajibabaei et al. 2005). In addition, DNA-based methods can be both specific

and selective, capable of distinguishing between closely related species (Hebert et al.

2003), and selectively amplifying DNA from a targeted source in a sample of pooled

DNA (Jarman et al. 2004, Jarman et al. 2006). Comprehensive collections of DNA

sequences from known individuals have been generated and used as reference

databases to successfully identify unknown individuals at the species-level (Hebert et al.

2003, Ward et al. 2005, Lowenstein et al. 2009). Unlike fatty acid and stable isotope

analysis, DNA-based methods do not rely on elements or lipids accrued over weeks to

months and therefore generate data on the same time scale as visual analysis

(snapshot in time). To date, DNA-based techniques have been successfully used in

field studies to determine the diets of several invertebrate and vertebrate species

(DeWoody et al. 2001, Rosel and Kocher 2002, Saitoh et al. 2003, Smith et al. 2005,

Casper et al. 2007, Deagle et al. 2007).

The PCR process involves the replication of a targeted gene region, which is

directed by strands of oligonucleotides known as primers. DNA-based studies of

predator-prey interactions have utilized a variety of target genes, from both the nuclear

and mitochondrial (mtDNA) genomes, and have employed different primer types

44

including “universal”, “group”, and species-specific primer sets (see King et al. 2008 for

review). Two general approaches have been utilized in studies using DNA to identify

prey recovered from stomach contents. The first approach involves the use of primers

that selectively amplify DNA from a user-defined target source (species or group of

animals) to generate presence or absence data (e.g. Augusti et al. 2003). The second

molecular approach uses polymerase chain reactions to produce DNA copies via

universal primers, which amplify the same gene region across a wide range of

organisms. Subsequent identification is accomplished through comparisons of DNA

sequences (Poinar et al. 2001), restriction enzyme analysis (Asahida et al. 1997), or

hybridization techniques (Rosel and Kocher 2002) applied to PCR products.

Initial investigations of stomach content analysis using DNA-based techniques

have been performed primarily with terrestrial insects (Coulson et al. 1990, Gokool et al.

1993, Zaidi et al. 1999, Chen et al. 2000, Symondson 2002, Agusti et al. 2003, Kasper

et al. 2004). Earlier works utilized group or species-specific primers to selectively

amplify prey DNA from pooled sources including homogenized predators (and therefore

the prey in their stomachs)(Zaidi et al. 1999) or homogenized stomachs (Asahida et al.

1997). The feasibility of DNA-based methodology has been examined via experimental

feeding trials (Chen et al. 2000, Agusti et al. 2003), which paved the way for field-based

applications.

To date, a limited number of studies have used genetic identification of prey

remains recovered from vertebrate stomach contents (Table 3-1). Scribner and

Bowman (1998) used microsatellites loci to identify unknown prey recovered from

glaucous gulls (Larus heperboreus). Results from this study revealed gulls consumed

45

greater amounts of goslings than previously known from conventional visual analysis

(Scribner and Bowman 1998). Rosel and Kocher (2002) developed a PCR-based

assay to detect the remains of larval Atlantic cod (Gadus morhua) in homogenized

stomach contents from predatory fish. The assay utilized cod-specific primers to

amplify short DNA fragments which were then screened on a dot-blot hybridization

procedure to facilitate high throughput analysis. Results from captive feeding

experiments indicated prey DNA was recoverable up to 12 hours post-ingestion and the

assay was successful in detecting cod DNA from predator‟s stomachs collected in the

field (Rosel and Kocher 2002). Smith et al. (2005) applied universal primers to prey

items removed from the stomachs of pelagic fishes and identified prey remains by

comparing generated sequences to those available in publicly accessed databases

(GenBank). Unlike earlier studies, specific prey items were targeted using universal

primers, which can be appropriate in situations where dietary items are unknown a priori

or the diversity of prey types is expected to be high (Clare et al. 2009). Through

targeting a standardized gene region using universal primers, these researchers

employed a DNA barcoding approach.

DNA barcoding refers to the use of large-scale databases of DNA sequences

from a standardized gene region to identify organisms at the species level (Hebert et al.

2003, Savolainen et al. 2005). The mitochondrial gene cytochrome oxidase I (COI) has

been selected as the appropriate marker for metazoans (Hebert et al. 2003), and

campaigns, such as the Consortium for the Barcode of Life (CBOL) and Barcode of Life

Data Systems (BOLD), have generated hundreds of thousands of sequences from

vouchered specimens identified by taxonomists (Ratnasingham and Hebert 2007). The

46

utility of such databases is many fold (Schander and Willassen 2005), with species-level

identification of unknown individuals being one application relevant to molecular studies

of food habits. Some researchers have argued that using a single-gene approach, as is

used in barcoding, is not appropriate as a sole means to identify species (Ebach and

Holdrege 2005). However, when used in conjunction with conventional taxonomy, well

sampled barcode databases have been used successfully to generate species-level

identifications from samples of unknown origin (Clare et al. 2009, Lowenstein et al.

2009).

Various elements of DNA-based techniques are well-suited for molecular

analysis of stomach contents. However, there are potential pitfalls that can limit

success including a lack of quality DNA, contamination, and the presence of PCR

inhibitors (King et al. 2008). Certain complications, such as PCR inhibition, can be

reduced through the use of PCR including Bovine Serum Albumin (BSA). However,

poor quality DNA that is characteristic of many molecular diet studies (Deagle et al.

2006) can be difficult to overcome. One solution involves applying species-specific

primers that target short regions of DNA that can increase the chances of recovering

DNA sequences from degraded samples (Hajibabaei et al. 2006, Meusnier et al. 2008).

Similarly, primers targeting a specified group of organisms have been implemented in

several studies to reduce the likelihood of amplifying host DNA (Jarman et al. 2004,

2006, Casper et al. 2007).

The goal of this chapter was to generate DNA barcodes and subsequent

identifications based on these sequences for prey items recovered from the stomach

47

contents of French grunt. Factors contributing to the success of DNA sequencing were

examined, including prey type, digestion state of prey item, DNA quality and quantity.

Methods

DNA Extraction

Individual prey items were stored at -20 ºC in 1.5 mL Eppendorf tubes filled with 95-

100% non-denatured ethanol (EtOH) following visual analysis of stomach contents

(Chapter 2) and stored. DNA was extracted from prey items that contained extractable

material, with items such as sediment, algae and shells excluded from molecular

analysis. Extractions were performed using Qiagen PureGene DNA Extraction Kit

(Valencia, Ca) following manufacturer‟s instructions (Qiagen 2007). Extraction tools

were flamed between samples and wiped clean using 70% EtOH. Extraction blanks

(containing no DNA) were included to screen for potential cross-contamination. DNA

was initially re-suspended in 50 µL rehydration solution (10mM TRIS/1mM EDTA, pH

7.4) for all samples and dilutions were made when necessary to standardize template

concentration at 20 ng/µL. Concentration of DNA template as well as the ratio of

absorbance at 260 nm and 280 nm (A260/280) was measured for each sample using a

Nanodrop spectrophotometer (Nanodrop ND-100, Wilmington, Delaware).

Polymerase Chain Reaction

Several primer sets were initially screened for suitable amplification of the

cytochrome oxidase I (COI) gene region across a variety of prey types. Ultimately, a

universal primer set LCO1490 (5‟-GGTCAACAAATCATAAAGATATTGG-3‟) and

HCO2198 (5‟-TAAACTTCAGGGTGACCAAAAAATCA-3‟) was selected to amplify a

710-bp region of the mitochondrial genome (Folmer et al. 1994). PCR reactions were

performed in 25-µL reaction volumes containing 5µL of 5x PCR buffer, 2 µL DNA

48

template (20 ng/µL), and final concentrations of 2.5 mM MgCl2, 0.2 mM dNTP‟s

(Promega, Madison, WI), 1 unit of Taq polymerase (Promega, Madison, WI), 0.3 µM of

each primer, and 0.4 mg/mL Bovine Serum Albumin (Invitrogen, Frederick, MD).

Cycling parameters were: 92 ºC for 2 min, then 5 cycles of 92 ºC for 40 s, 40 ºC for 40

s, and 72 ºC for 1 min 30 s, followed by 35 cycles of 92 ºC for 40 s, 50 ºC for 1 min, and

68 ºC for 1 min 30 s, with a final extension step at 72 ºC for 10 min. PCR products were

examined under ultraviolet light following electrophoresis at 110 V and 400 milliamps for

1 hour on an ethidium bromide-stained, 1.5% agarose gel. Gel results were scored

based on intensity (strong, faint, smear) to determine if product was adequate for DNA

sequencing. Products with either strong or faint were selected for DNA sequencing.

Positive PCR products were cleaned using ExoSAP-IT chemistry (Applied

Biosystems, Foster City, CA) at a ratio of 2µL ExoSAP to 25 µL PCR product to remove

unbound primers and nucleotides. Bidirectional sequences were generated using

BigDye terminator sequencing chemistry (Applied Biosystems, Foster City, CA) and

electrophoresis was performed on an Applied Biosystems 3130xl Genetic Analyzer.

Generated sequences were aligned using CLC DNA Workbench program (CLC bio,

Cambridge, MA).

Factors Influencing PCR Success and DNA Sequencing

Several factors were examined to explain the observed trends in the success and

failure of PCR amplification and DNA sequencing. Prey type, DNA quantity, A260/280

ratio, and digestion code (Chapter 2) were tested to see if values for amplified or

sequenced prey items differed significantly from items that did not. For two of these

factors, DNA quantity and A260/280 ratio, continuous data were placed into two bins; 0 =

(2.0 > A260/280 <1.6), 1 = (1.8 < A260/280 < 2.0), corresponding to “contaminants present”,

49

“pure DNA”, respectively. In the case of A260/280, values appreciably <1.8 or >2.0 may

indicate the presence of protein or phenol contamination (Wilfinger 1997). Similarly, the

absolute values of extracted DNA were placed into the following groups; 0 = (<0 ng/µL),

1 = (0-50 ng/µL), 2 = (51-200 ng/µL), 3 = (>200 ng/µL), representing “little or no DNA”,

“low”, “intermediate”, and “high” concentrations of DNA respectively. Differences were

tested using a Fisher‟s Exact Test to compensate for low sample sizes and differences

were considered significant at the α = 0.05 level.

Molecular Identification

Identification of prey items based on DNA sequences was accomplished using

two complimentary approaches: sequence similarity and phylogenetic relatedness.

Sequence similarity was accomplished through queries of two molecular databases, the

National Center for Biotechnology Information (NCBI) database (GenBank)

(http://www.ncbi.nlm.nih.gov/genbank/) and a local database with DNA sequences

generated from potential prey items collected in the USVI. Molecular identifications

were generated within the program Geneious v5.1.7 (Drummond et al. 2010) using the

Basic Local Alignment Search Tool (BLAST) to search all nucleotide records from

GenBank for somewhat similar sequences (blastn) (Altschul et al. 1997). A second

series of BLAST searches were performed against a local database of COI gene

sequences generated from potential prey items (described below). All search results

were combined into one list and matches receiving the highest E-value were selected

as molecular identifications. In situations where sequence similarity was >97%, prey

items were identified at the species level.

Phylogenetic analysis was performed on a reference data set of COI gene

sequences from potential prey items, selected GenBank sequences, and sequences

http://www.ncbi.nlm.nih.gov/genbank/

50

generated from stomach content items to produce a graphical display of genetic

distance between samples used to confirm results generated via BLAST searches of

GenBank. Prey items in the USVI were collected through two techniques, bulk

sediment sampling and opportunistic collection of invertebrates from in and around the

sampling sites. Bulk sampling was collected from Lameshur Bay and Tektite Reef by

USGS scientists on SCUBA using garden trowels to scoop the top 2 cm of sediment

from approximately a 10 cm2 with the samples placed into Ziploc bags. Bulk samples

were then sieved though a 300 µm screen using sea water and sorted by taxonomic

order while animals were still alive or stored in a container with 95% ethanol and then

sorted later. Opportunistic sampling of invertebrates was accomplished using hand nets

while on SCUBA on and near coral reef sites. Extractions were performed on a subset

of all sorted invertebrates for which multiple individuals were collected and a reference

specimen was retained.

Cytochrome oxidase I (COI) sequences greater than 500 base pairs in length

were downloaded from GenBank for major prey groups including Sipunculida,

Polychaeta, Stomatopoda, Copepoda, Tanaidacea, Echinodermata, and Gastropoda

and added to the database that included sequences from potential prey. A subset of

these downloaded sequences was retained for analysis based on initial phylogenetic

similarity and minimal redundancy. All stomach content sequences were aligned using

the Clustal X algorithm within the program MEGA version 5 (Tamura et al. 2007) using a

gap opening and extension penalties of 10 and 6.66, respectively. Phylogenetic trees

were generated using the neighbor-joining method (Saitou and Nei 1987) selecting the

Kimura 2-Parameter nucleotide substitution model in MEGA. Node support was

51

calculated with 5000 bootstrap pseudo replicates and the neighbor-joining trees were

used as a graphical representation of uncorrected genetic distance to confirm patterns

observed from BLAST searches.

Comparison of Techniques

Prey identifications generated by visual (Chapter 2) and molecular analysis (this

chapter) via BLAST queries were examined for significant differences. Numerical

abundance and frequency of occurrence were calculated based on visual and molecular

analysis alone as well as for a combined approach. The numerical abundance and

frequency of occurrence data for the combined approach was calculated using the best

available prey description from either visual or molecular analysis. In instances where a

conflict occurred, it was assumed that visual analysis was correct (e.g. visual =

sipunculid, molecular = decapods. Using this information, Morisita‟s (1959) index of

similarity was used to compare the numerical abundance of prey items identified to the

class level by each technique and values of the index range from 0 (no similarity) to 1

(complete similarity) (Krebs 1999a). Niche overlap was calculated using a simplified

Morisita‟s simplified index of overlap, again using numerical abundance data (Krebs

1999a). Prey groups with no class level identification, such as unidentified and

unidentified crustaceans, were omitted from analysis. Calculations for both similarity

and overlap indices were calculated using the Ecological Methodology software

package (Krebs 1999b). Significant differences in class level identifications based on

frequency of occurrence data generated by visual analysis and GenBank queries were

tested using a Wilcoxon Mann-Whitney test (Krebs 1999). Calculations were performed

using the statistical package R and differences were considered significant at the α =

0.05 level.

52

Results

DNA Extractions

In total, DNA was extracted from 195 unique prey items (Table 3-2).

Foraminifera were excluded from extraction due to difficulties associated with DNA

isolation and a high incident of contamination in molecular studies (Pawlowski 2000).

For other taxa, including fish, copepods, sipunculids and tanaids, not all samples were

extracted because a subset of individuals was retained for a reference collection. Not

all unidentified crustaceans, crabs and copepods were extracted because several of

these individuals represented appendages or exoskeletons with insufficient tissue for

DNA extractions.

Polymerase Chain Reaction and DNA Sequencing

In total, DNA from 70 of 195 (35.9%) extracted prey items were successfully

amplified using the Folmer primer set across 542 PCR attempts. Sequences were

successfully generated for 48 unique prey items (24.6% of extracted prey) recovered

from 28 stomachs (54.9% of 51 stomachs) with an average number of 1.71 sequences

generated per stomach (range 1-5, S.D. = 1.05). Sequences were generated from a

total of 13 of 44 extracted (29.5%) prey in 2008 with an average of 1.18 sequences

generated per stomach (range 1-3, S.D. = 0.60). Thirty-five sequences were generated

from 2009 samples (of 151 extracted prey, 23.3%) with an average of 2.05 sequences

generated per stomach (range 1-5, S.D. = 1.14).

Two additional sequences were generated for prey items VIS089P11 and

VIS082P1 and represented contamination from a mussel project performed in the lab

and these samples were excluded from all statistical analysis.

53


DNA from potential prey items collected through bulk sampling were extracted,

sequenced, and then compared to sequences from French grunt stomach contents via

BLAST queries. In total, DNA was extracted from 71 prey items recovered from the

bulk samples along with 22 prey items recovered through opportunistic sampling which

resulted in 19 DNA sequences (Table 3-3).

BLAST searches of all sequences from GenBank and the local database of

potential prey produced identifications for all 48 prey items, with sequence similarity

values ranging from 73% to 99% and a mean assignment value of 83.4% (S.D.= 6.5%,

Table 3-4). Overall agreement of identifications generated using the two methods was

high, with 29 prey items (60.4%) being placed in the same taxonomic class. A total of

20 prey items were identified as Aspidosiphoniformes sipunculids of which three

sequences were greater than 97% similar to records found in GenBank. These

sequences were identified at the species level. Ten prey items were identified via

molecular techniques as decapods crustaceans with 6 of these prey items belonging to

the family Aeglidae. DNA sequences from two stomach content items were most

closely related to sequence records from potential prey and included an unidentified

crab (VIS048P2) that was most closely matched with a spider crab, Mithrax

cinctimanus, and a tanaid (VIS095P10) that was identified as Leptochelia sp. based on

both visual and molecular analysis. Order level classification was not available for 33

sequenced prey items based on visual analysis alone and 23 of these prey items

(69.7%) had potentially increased taxonomic resolution based on molecular

identifications. Thirteen prey had conflicting identifications generated via the two

54

techniques (e.g. visual id = unidentified crustacean, molecular id = sipunculid worm,

Table 3-4).

A neighbor joining tree was generated using all potential prey item sequences

along with 52 sequences from GenBank representing stomatopods, shrimps, crabs,

fish, polychaetes, sea urchins, and gastropods. The resulting neighbor joining tree

confirmed several identifications generated from GenBank searches and offered a

graphical display of sequence relatedness for stomach content items and potential prey

items (Figure 3-1). Prey items VIS092P5 (visually an unidentified crustacean) and

VIS045P1 (unidentified annelid) were placed with high branch support (>99) in a clade

of stomatopods. Prey item VIS080P9 (unidentified) fell out in a clade of

Phascolosomatidae sipunculids, and items VIS076P13 (sipunculid), VIS080P5

(sipunculid), VIS082P3 (unidentified crustacean), and VIS099P4 (sipunculid) were

placed into a clade with Aspidosiphonidae sipunculids. Sequence data from prey item

VIS076P11 (unidentified polychaete) was most similar to an unidentified polychaete

collected in the USVI and VIS095P10 (Leptochelia sp. tanaid) formed a tight clade with

Leptocheliidae tanaids also collected as potential prey in the USVI. These observations

were consistent with results from GenBank searches.

Factors Influencing PCR and Sequencing Success

Prey type was determined to have a significant effect on the ability to amplify

(Fisher‟s Exact Test, p = 0.020) but not sequence (Fisher‟s Exact Test, p = 0.211)

stomach content items. Overall there was high variability in the proportions of items that

were amplified (range = 0.0 – 1.0, = 0.41, S.D. = 0.32) by prey group with fish, shrimp

and copepods amplifying most frequently and stomatopods and isopods most

infrequently. All gastropod (n=2), ophiuroid (n=3), and copepod (n=3) samples that

55

amplified were sequenced while half of unidentified prey, one-third of tanaids, and one-

quarter of annelid prey types returned successful sequences (Table 3-2).

The A260/280 ratios were placed into bins corresponding to the presence or absence

of potential contaminants. A total of 124 prey items returned values indicating the

presence of contaminants and 69 individuals contained “pure” DNA. Results from a

Fisher Exact Test showed no significant difference in the ability to amplify prey by

A260/280 bin (p = 0.878). Similarly, no significant differences were observed in

sequences generated for the three A260/280 bins (p = 0.733).

The digestive state of the stomach content prey items assigned during visual

analysis (Chapter 2) was tested against amplification and sequencing success. Almost

half of all prey items (51%) were assigned digestion codes of 5 or 6 corresponding to

mostly digested or almost fully digested states. Approximately 15% of recovered prey

items were considered either minimally digested or freshly eaten. However,

amplification and sequencing success did not differ significantly across digestion states

(amplification, Fisher‟s Exact Test, p = 0.449; sequencing, Fisher‟s Exact Test, p =

0.654).

DNA concentrations were measured for each prey item and placed into bins

representing “little or no DNA”, “low”, “intermediate”, and “high” concentrations of DNA.

Only a small percent (11%) of prey items in the category “little or no DNA” were

successfully amplified relative to prey with “high” concentrations (47%). Overall, there

was no significant difference in the numbers of prey items that amplified across DNA

concentrations (Fisher‟s Exact Test, p = 0.199). Similarly, no significant difference was

56

detected in sequencing success across DNA concentration bins (Fisher‟s Exact Test, p

= 0.204).

Comparison of Techniques

The frequency of occurrence for prey items varied between visual and molecular

analysis and a combined resulted in a decrease in the number of unidentified taxa and

an increase in the number of sipunculids that were placed at the family level (Table 3-5).

Several prey items identified visually as sipunculids were placed at the species level

based on molecular analysis. The proportion of stomach containing several prey

groups remained unchanged based on a combined approach including isopods,

copepods, and echinoderms. When numerical abundance was examined it was noted

that the number of unidentified crustacean prey items decreased (visual - 12.5%,

combined – 10.5%) based on a combined visual and molecular approach. Similarly, the

number of unidentified taxa was reduced (visual - 13.7%, combined 11.7%) while the

number of sipunculids attributed to the family Aspidosiphonidae increased.

Morisita‟s index of similarity (CH = 0.98) was high indicating that both methods

generated similar classification for the catalogued prey items. Niche overlap between

visual and molecular identifications indicated that prey were detected in similar

proportions by both methods (CH = 0.94). Prey frequency data for the two techniques

were tested using a Wilcoxon Mann-Whitney test and indicated a significant difference

in the frequency that the various prey items were consumed (W = 279, p = 0.03) based

on visual and molecular analysis.

Discussion

DNA barcodes were successfully generated for a subset of extracted prey items

recovered from the stomach contents of French grunt. Identifications based on DNA

57

barcodes were successfully used to identify prey items potentially to the species level.

For approximately half of the barcoded samples, taxonomic resolution was increased

via molecular analysis compared with visual analysis. Despite limited sequencing

success, molecular analysis of stomach contents was able to produce species level and

higher taxonomic identifications for prey that lacked diagnostic characteristics relative to

visual analysis alone.

Polymerase Chain Reaction and DNA Sequencing

Overall, PCR success, as measured by the percentage of samples that positively

amplified, was low, with 36% of extracted prey items producing positive results.

Discerning the reason why a given sample failed to amplify can be difficult, and potential

causes is likely a combination of the presence of PCR inhibitors, primer incompatibility,

low quality or quantity template, and human error (Burkardt 2000, Bartlett and Stiling

2003).

Human error can be difficult to rule out, however it can be minimized through

proper training, education, and experience (Burkardt 2000). To that end, no increase in

PCR success was documented through time in the course of this study. Positive and

negative controls, a sample with no DNA and another with high quality DNA, were

included in each individual PCR to screen against potential contamination and failed

PCR reaction, respectively.

Stomach samples in general are subject to exposure to a variety of PCR inhibitors

including digestive enzymes derived from the predator itself and reagents that contact

samples during DNA extraction and purification (Waits and Paetkau 2005). Two

approaches were employed to minimize problems; adding 10 mg/mL Bovine Serum

Albumin (BSA) into each PCR reaction, and the dilution of extracted DNA and PCR

58

inhibitors to a non-detrimental level. BSA serves to facilitate DNA amplification through

binding to inhibitors and has been documented to increase the success of conventional

PCR reactions in the presence of various inhibiting agents (Kreader 1996, McGregor et

al. 1996, Al-Soud and Radstrom 2000). Serial dilution of prey DNA prior to PCR

amplification was applied to a subset (n=24) of samples to determine if PCR inhibitors

present in the extracted DNA might be inhibiting PCR success (Wilson 1997). Samples

were selected based on at least one failed PCR and a minimum of 60 ng/µL DNA

measured directly after extraction which suggested the presence of ample DNA for PCR

amplification. DNA was diluted to final concentrations of 10 ng/µL for the 24 samples,

and 12% of previously unamplified samples showed positive results suggesting the

presence of PCR inhibitors for selected samples. This approach was not expanded

because only a subset of samples met the requirements for DNA dilution.

Factors Influencing PCR and Sequencing Success

Several factors were examined to explain the observed trends in PCR

amplification and DNA sequencing success. The ability to amplify stomach contents

was significantly different for the various prey types. Three prey groups, unidentified

prey, unidentified crustaceans, and sipunculids consisted of 115 extractions and

returned an average success rate of 33%, while the majority of prey types had less than

ten observations with highly variable success rates. A possible explanation for the

observed significance could be explained by primer fidelity, whereby some prey are

more likely to amplify using a given primer pair. Universal primers developed by Folmer

(1994) were chosen as the primary set to screen gut contents and potential prey

because of their widespread application in barcoding (Hoareau and Boissin, Hebert et

al. 2003) and documented ability to amplify various phyla (Folmer et al. 1994). Despite

59

being labeled universal primers certain animal groups can be more likely to amplify

using a given primer set and these issues are typically only realized for groups with

extensive phylogenetic data (Halanych and Janosik 2006). Some groups such as

Eunicidae polychaetes have been shown to amplify poorly using the Folmer primers but

unfortunately, quantitative data on the ability for these primers to amplify across all taxa

is not available (Halanych and Janosik 2006).

To address issues associated with primer fidelity a small subset of the stomach

samples were screened with alternate primer pairs to determine if the lack of

amplification was marker specific. Two markers, one a degenerate form of the Folmer

primers (Meyer 2004) and a second that targeted an alternate mitochondrial gene (16S)

(Palumbi 1991) were screened against two types of prey DNA, ones that had been

previously amplified and others that had not been amplified. Previously amplified

samples tested positive at the alternate loci and only a small number of previously

unamplified DNA samples were successfully amplified using the alternate markers.

Although not tested statistically, these results suggested that problems could be

associated with poor quality template and not the selected primers.

DNA quantity as measured by a spectrophotometer was utilized to determine if

samples with lower DNA yields were equally likely to amplify and sequence as those

with higher concentrations. Paradoxically, the ability to successfully amplify and/or

sequence were not strongly correlated with DNA concentration. There are several

possible reasons that could explain this: 1) DNA concentration was standardized prior to

PCR at 20 ng/µL; 2) given the exponential nature of PCR, even samples with small

quantities of DNA template can be successfully amplified; and 3) quantities of extracted

60

DNA as determined via spectrophotometer, may be influenced by the presence of

proteins and other chemicals that increase the optical density of the test liquid (Glasel

1995).

Absorbance ratios have been used to evaluate the quality of extracted nucleic

acids since the 1970‟s (Wilfinger 1997) and proteins as early as the 1940‟s (Warburg

and Christian 1942). No correlation was observed between prey items whose A260/280

ratios suggested pure DNA and those with potential contaminants. Although a

commonly used technique to appraise DNA purity (Sambrook and Russell 2001) several

critics have pointed out that factors such as pH can dramatically influence absorbance

at 260 nm and 280 nm (Wilfinger 1997). A260/280 ratios are used as “rules of thumb”,

which might explain why the associated variability of these values might be too great to

prohibit fine scale statistical analysis.

Interestingly, the state of digestion for each prey item did not appear to

significantly impact the ability of prey to amplify or sequence. Prey that received low

states of digestion (Code 1) had a greater percentage of items amplify and sequence

relative to more advanced states, however, the differences in amplification success

were small for prey items with intermediate and advanced states of digestion. A

potential explanation for the lack of significance in these tests includes difficulty with

assigning digestion codes to partial organisms as well as a diversity of prey types.

Fragmented or partial organisms, where a small portion of the animal was ingested

(<10%), were common for sipunculids and some crustaceans. A combination of percent

of total animal present and physical characteristics, such as the presence or absence of

appendages were used to assign digestion codes, however, digestion codes are

61

qualitative and assigning codes in the above described situation proved difficult. In

addition, unlike previous projects (e.g. Berens 2005), digestion codes were not created

for all prey types encountered but instead general guides were developed for major prey

types.


Molecular identification of prey items recovered from French grunt stomach

contents was successful in many respects and demonstrates proof of concept. Several

sipunculid prey items were identified to the species level with a high degree of certainty.

In some the instances, no low level classifications could be made based on visual

analysis alone and a combined visual and molecular approach resulted in increased

taxonomic resolution. In other cases, specimens that were not identifiable visually were

also not identified using a barcoding approach.

When results for frequency of occurrence were calculated for each technique

alone as well as a combined visual and molecular approach, several important

observations were noted. The number of unidentified prey items decreased and the

alternate identifications generated via molecular analysis were biologically feasible.

When a combined approach was used, more sipunculids were placed at least to the

family level which is particularly helpful given that sipunculids are more rapidly digested

versus hard-bodied prey and digested individuals often lack characteristic traits used in

identification. Some prey groups, including isopods, mysids, and forams had

proportions that were not influenced by molecular analysis because either DNA was not

extracted or sequences were not generated from the extracted DNA. Interestingly, fish

recovered from stomach contents were placed as silversides based on visual analysis

while molecular analysis indicated the same prey items were herrings. These two fish

62

orders are distinctly different and it was unclear if this error was a result of unavailable

sequence data or an incorrect identification of otoliths and fish skeletal remains. Similar

patterns were observed based on numerical abundance of prey items from a combined

visual and molecular approach. Crustaceans were more commonly detected using a

combined approach versus visual analysis alone. More sipunculids were placed at the

family and species level based on a combined approach and fewer prey items were

unidentified.

Measures of similarity and niche overlap suggested that the two approaches

generated identifications that were highly similar based on the numerical abundance of

major prey groups. It is likely that this high level of overlap is explained, in part, by

using class-level taxonomy for the catalogued prey items. Because visual analysis

produced few species or genus-level identifications, no analysis could be performed at

these lower taxonomic levels. Percent frequency of occurrence data tested by a

Wilcoxon Mann-Whitney test indicated that the proportions of prey items were

significantly different when visual results were compared to those from molecular

analysis. This observation could be explained by the lack of sequence data for certain

prey groups (forams, isopods, and mysids) that stemmed from either a lack of

extractable DNA or an inability to sequence extracted DNA.

Results from neighbor joining analysis generally agreed with results from BLAST

searches of GenBank and potential prey sequences. A large group of stomach content

items (VIS100P1, VIS099P9, VIS100P5, VIS076P12, VIS089P3, VIS094P1, and

VIS086P3) were most closely related to sipunculids, consistent with BLAST results. In

63

addition, prey item visually identified as a Leptochelidae tanaid grouped most closely

with tanaid specimens collected and sequenced in the USVI.

Disagreements in identification based on visual and molecular techniques

comprised a significant portion of the prey items analyzed. Potential explanations for

these discrepancies include coamplification of DNA from other organisms from the

same stomach (Deagle 2006), lack of pertinent sequence data allowing for accurate

identification (Elias et al. 2007), and potential amplification of nuclear mitochondrial

DNA (Dunshea et al. 2008, Buhay 2009). Universal primers were used to amplify DNA

from prey items that were isolated from pooled stomach contents and due to their

“universal” nature the primers could have amplified foreign DNA attached to a prey item

(Deagle 2006). Originally it was thought that flushing individual prey items with

deionized water prior to immersion in ethanol would reduce the probability of this

occurring. The use of group-specific primers in future studies could allow for that use of

pooled stomach samples thereby reducing the number of samples (50 stomachs vs.

195 individual prey) and producing presence absence data for functional prey groups

(Casper et al. 2007, Deagle et al. 2007, Tollit et al. 2009).

The present lack of available sequence data represents a critical limitation to

molecular analysis of diet because the accuracy of identification is dependent upon

having relevant sequences for comparison (Casiraghi et al. 2000, Meyer and Paulay

2005, Elias et al. 2007). One prey item, a tanaid crustacean (VIS95P10), was recovered

in a relatively undigested state and identified visually as a member of the genus

Leptochelia. Queries of GenBank sequence data revealed that only one COI sequence

was available for an unclassified tanaid crustacean (GenBank Accession Number:

64

AF520452.1) and without a reference database of potential prey this stomach content

item would not have been correctly identified.

Finally, the lack of successful identification observed in this study could be

explained by the presence of mitochondrial sequence data that has been inserted in the

nuclear genome, referred to as nuclear mitochondrial pseudogenes or NUMTs (Zhang

and Hewitt 1996). Because these insertions are subject to different evolutionary

constraints than their true mitochondrial counterparts, a sequence generated from the

nuclear can confound dietary analysis and make interpretation of results difficult

(Dunshea et al. 2008).

GenBank was selected over other genetic databases (e.g. BOLD systems,

www.barcodinglife.org/) for several reasons. The benefits of GenBank are that

sequences can be downloaded and data analysis is transparent (e.g. closest match

sequences can be viewed and downloaded). In addition results are generated using

local alignments that are quick and can outperform global alignments used by other

search engines in certain circumstances (Lowenstein et al. 2009). One drawback of

using GenBank is that because the database is user driven, questionable or erroneous

data can be uploaded and not realized due to the absence of “sequence moderators”.

Bridge et al. (2003) determined that up to 20% of fungal sequences available on

GenBank may be unreliable due to incorrect species identification. Furthermore, in the

absence of related sequences in the database, search results can be of little value

(Pertsemlidis and Fondon 2001). A second database, the Barcode of Life Database

(BOLD), was not used in the final analysis because despite having more stringent

standards for sequence submission fewer sequences were available at the time of

http://www.barcodinglife.org/

65

writing this thesis. The lack of sequence data for more obscure organisms could result

in identifications of little or no biological merit.

This research has shown that DNA-based techniques have the potential to

provide new and additional information to diet studies reliant on visual methodologies

alone. The PCR-based approach is a sensitive process and was successfully used to

amplify and sequence DNA from prey items that were <10 mm in length. The sensitivity

of this process served as a double-edged sword and resulted in the amplification of

DNA likely from non-target sources of DNA. The time involvement associated with

optimizing PCR conditions and selecting the appropriate primers for amplification was

not miniscule as were the associated costs of sequencing PCR products. The single

largest limitation to the successful identification of prey items was the lack of DNA

sequences from potential prey items from the coral reef and seagrass environment. As

barcoding projects are completed and databases become more extensive, so will the

ability of researchers to reliably use PCR-based techniques as a tool to identify the diet

of marine fish.

66

Table 3-1. A list of studies that have used DNA-based techniques to examine the stomach contents of vertebrate predators.

Study Vertebrate species Genetic markers used

Scribner and Bowman 1998

Glaucous gulls Microsatellite

DeWoody et al. 2001 Darter and sunfish Microsatellite Rosel and Kocher 2002 Unspecified marine fish Species-specific primers

Rollo et al. 2002 Neolithic glacier mummy Group-specific Smith et al. 2005 Istiophoridae, Scombridae,

Sphyraenidae, Xiphiidae Species-specific primers

Table 3-2. Stomach content items of Haemulon flavolineatum catalogued by prey type and the corresponding numbers of organisms that were successfully extracted, amplified and sequenced for the cytochrome oxidase I gene region.

Identification Number of individuals Extracted Amplified Sequenced

Chitons 1 0 0 0 Crustaceans

Amphipods 7 6 3 2 Copepods 12 3 3 3 Crab 11 9 3 2 Isopods 5 5 0 0 Mysids 2 2 0 0 Ostracoda 1 0 0 0 Shrimp 11 11 7 4 Stomatopods 4 3 0 0 Tanaids 13 9 3 1 Unidentified Crustacean 31 27 9 6

Fish 8 3 3 2 Forams 4 0 0 0 Gastropods 4 3 2 2 Ophiuroids 8 8 3 3 Annelida 6 6 4 1

Polychaetes 13 12 2 1 Sipunculids 74 58 20 17 Unidentified 33 30 8 4

Grand Total 248 195 70 48

67

Table 3-3. A list of the number of DNA sequences generated by taxa for potential prey items collected through bulk sampling and opportunistic sampling conducted on St. John Island, U.S. Virgin Islands.

Prey Type Number of Individuals Description

Amphipoda 3 Gammaridae, Unidentified

Bivalve 2 Unidentified Copepoda 1 Harpacticoida

Crab 2 Unidentified Polychaeta 6 Unidentified Ostracoda 2 Unidentified

Shrimp 1 Unidentified Tanaid 2 Leptocheliidae

Total 19

68

Table 3-4. List of identifications generated via visual and molecular analysis of stomach content items recovered from Haemulon flavolineatum collected in the U.S. Virgin Islands. Molecular identifications were generated through BLAST searches of all nucleotide records from GenBank and a reference data set of potential prey sequences. Species identifications in italics represent identifications derived from local prey sequences and asterisks represent samples with matching visual and molecular identifications.

Sample ID Visual ID GenBank Order GenBank Family GenBank Species Max Ident

VIS099P9 Amphipod Aspidosiphoniformes Aspidosiphonidae 78

VIS089P3 Amphipod – Gammaridae Aspidosiphoniformes Aspidosiphonidae 80

VIS045P1 Annelid – Unidentified Stomatopoda

92

VIS085P9 Copepod – Harpacticoida Pseudomonadales Pseudomonadaceae 79

VIS079P6 Copepod – Harpacticoida Cyclopoida

78 VIS048P2 Crab – Unidentified Decapod* Decapoda* Majidae Mithrax cinctimanus 82

VIS009P1 Crab – Unidentified Decapod* Decapoda* Portunidae 83

VIS007P2 Fish – Atheriniformes Clupeiformes Clupeidae 86

VIS019P5 Fish – Atheriniformes Clupeiformes Clupeidae 86

VIS095P4 Gastropod – Acteocina sp. Cephalaspidea Retusidae 82

VIS093P1 Ophiuroid – Ophiodermatidae* Ophiuroidea*

77

VIS080P2 Ophiuroid – Ophiodermatidae Aspidosiphoniformes Aspidosiphonidae 79

VIS029P1 Ophiuroid – Ophiurida Aspidosiphoniformes Aspidosiphonidae 75

VIS019P2 Polychaete – Arabellidae Decapoda Aeglidae 82

VIS076P11 Polychaete – Errantia* Aciculata* Glyceridae 82

VIS031P1 Shrimp – Alpheidae* Decapoda* Palaemonidae 85

VIS084P2 Shrimp – Caridea* Decapoda* Aeglidae 83

VIS019P4 Shrimp – Caridea* Decapoda* Aeglidae 82

VIS015P2 Shrimp – Unidentified* Decapoda* Aeglidae 82

VIS100P1 Sipunculid* Aspidosiphoniformes* Aspidosiphonidae 78


VIS099P4 Sipunculid* Aspidosiphoniformes* Aspidosiphonidae 99 VIS094P1 Sipunculid* Aspidosiphoniformes* Aspidosiphonidae Aspidosiphon laevis 79


VIS088P1 Sipunculid Decapoda Aeglidae 85



VIS083P1 Sipunculid Decapoda Aeglidae 82


69

Table 3-4. Continued Sample ID Visual ID GenBank Order GenBank Family GenBank Species Max Ident

VIS079P3 Sipunculid Aspidosiphoniformes Aspidosiphonidae 91 VIS076P13 Sipunculid* Aspidosiphoniformes* Aspidosiphonidae Aspidosiphon laevis 98 VIS076P12 Sipunculid* Aspidosiphoniformes* Aspidosiphonidae 78 VIS081P4 Sipunculid – Aspidosiphonidae* Aspidosiphoniformes* Aspidosiphonidae 79

VIS095P10 Tanaid - Leptochelia sp. * Tanaidacea* Leptocheliidae Leptochelia sp.* 97

VIS093P5 Unidentified Aciculata Sigaionidae 80

VIS085P7 Unidentified Cephalaspidea Haminoeidae 83

VIS080P9 Unidentified Phascolosomatiformes Phascolosomatidae Phascolosoma perlucens 99

VIS076P14 Unidentified Canalipalpata 84

VIS028P2 Unidentified Perciformes Haemulidae 93

VIS012P2 Unidentified Clupeiformes Clupeidae 86

VIS092P5 Unidentified Crustacean Stomatopoda 82

VIS085P6 Unidentified Crustacean Amphipoda Stenothoidae 84

VIS085P5 Unidentified Crustacean Aspidosiphoniformes Aspidosiphonidae 79

VIS082P3 Unidentified Crustacean Aspidosiphoniformes Aspidosiphonidae Aspidosiphon steenstrupii 97

VIS081P7 Unidentified Crustacean Pseudomonadales Pseudomonadaceae 81

VIS079P5 Unidentified Crustacean Decapoda Palaemonidae 81

VIS076P3 Unidentified Crustacean Aspidosiphoniformes Aspidosiphonidae 78

70

Table 3-5. Comparison of frequency of occurrence (%FO) and percent numerical abundance (%N) for prey species recovered from stomach contents of Haemulon flavolineatum collected from St. John Island, USVI. „Visual‟ represents results obtained from conventional visual analysis and „DNA‟ represents results obtained via BLAST searches of DNA sequences derived from prey items. „Visual and DNA‟ indicates results obtained by the combination of the two techniques.

%O %N

Prey Visual DNA Visual & DNA Visual DNA Visual & DNA

Algae 5.9 - 5.9 - - - Bacteria - 7.1 - - 4.2 -

Pseudomonadaceae - 7.1 - - 4.2 - Crustaceans 74.5 53.6 64.7 16.2 31.3 32.3

Amphipods 13.7 3.6 15.7 2.8 2.1 3.2 Gammaridea 7.8 3.6 7.8 1.6 - 1.6 Hyperiidea 2.0 - 2.0 0.4 - 0.4 Stenothoidae - 3.6 2.0 - 2.1 0.4 Unidentified Amphipod 3.9 - 3.9 0.8 - 0.8

Isopods 9.8 - 9.8 2.0 - 2.0 Cymothoidae 3.9 - 3.9 0.8 - 0.8 Flabellifera 2.0 - 2.0 0.8 - 0.4 Idoteidae 3.9 - 2.0 0.4 - 0.4 Unidentified Isopod 2.0 - 2.0 0.4 - 0.4

Mysidae 3.9 - 3.9 0.8 - 0.8 Copepods 19.6 3.6 19.6 4.8 2.1 4.8

Harpacticoida 19.6 - 19.6 4.8 - 4.8 Cyclopoida - 3.6 - - 2.1 -

Crabs 13.7 32.1 15.7 4.4 20.8 4.8 Aeglidae - 17.9 2.0 - 12.5 0.4 Galatheidae 2.0 - 0.0 0.4 - 0.4 Majidae - 3.6 2.0 - 2.1 0.4 Paguridae 2.0 - 2.0 0.4 - 0.4 Brachyura 2.0 - 2.0 1.2 - 1.6 Hippidae – Emerita sp. 2.0 - 2.0 0.4 - 0.4 Portunidae - 3.6 2.0 - 2.1 0.4

71

Table 3-5. Continued

%O %N


Unidentified Crab 10 - 3.9 2.0 - 0.8 Shrimps 19.6 7.1 21.6 4.4 4.2 4.8

Palaemonidae - 7.1 2.0 - 4.2 0.4 Alpheidae 7.8 - 5.9 1.6 - 1.6 Caridea 3.9 - 3.9 0.8 - 0.8 Panaeidae 2.0 - 2.0 0.4 - 0.4 Unidentified Shrimp 5.9 - 7.8 1.6 - 1.6

Stomatopoda 7.8 7.1 9.8 1.6 4.2 2.0 Tanaids 17.6 3.6 17.6 5.2 2.1 5.2

Leptocheliidae 9.8 3.6 5.9 2.4 2.1 2.4 Tanaidomorpha 7.8 - 5.9 1.6 - 1.6 Unidentified Tanaid 7.8 - 5.9 1.2 - 1.2

Unidentified Ostracod 2.0 - 2.0 0.4 - 0.4 Unidentified Crustacean 47.1 43.1 12.5 - 10.5

Fish 3.9 14.3 7.8 3.2 8.3 4.4 Clupeidae - 10.7 3.9 - 6.3 0.8 Haemulidae - 3.6 2.0 - 2.1 0.4 Atheriniformes 3.9 - 3.9 3.2 - 3.2

Forams 2.0 - 2.0 1.6 - 1.6 Molluscs 7.1 9.8 2.0 4.2 2.8

Chitonida 2.0 2.0 0.4 - 0.4 Gastropoda 1.6 7.1 7.8 1.6 4.2 2.0 Bittiolum sp. 2.0 - 2.0 0.4 - 0.4 Acteocina sp. 2.0 - 2.0 0.4 - 0.4 Haminoeidae - 3.6 3.9 - 2.1 0.4 Unidentified Gastropod 2.0 - 2.0 0.8 - 0.8

Echinoderms 13.7 3.6 13.7 3.2 2.1 3.2 Ophiocomidae 3.9 - 3.9 0.8 - 0.8 Ophiodermatidae 3.9 - 3.9 1.2 - 1.2 Unidentified Brittle Star 5.9 - 5.9 1.2 - 1.2

72

Table 3-5. Continued.

%O %N


Annelids 11.8 - 11.8 2.4 - 2.4 Unidentified Annelid 11.8 - 11.8 2.4 - 2.4

Polychaetes 23.5 7.1 27.5 5.2 6.3 6.0 Arabellidae 3.9 - 3.9 0.8 - 0.8 Glyceridae 2.0 - 2.0 0.4 - 0.4 Canalipalpata - 3.6 2.0 - 2.1 0.4 Glyceridae - 3.6 2.0 - 2.1 0.4 Sigaionidae - 3.6 2.0 - 2.1 0.4 Unidentified Polychaete 19.6 - 17.6 4.0 - 3.6

Sediment 13.7 - 25.5 - - 0.0 Sipunculids 54.9 50.0 54.9 29.8 43.8 29.4

Aspidosiphonidae 5.9 46.4 19.6 1.2 35.4 5.2 Aspidosiphon laevis - 7.1 3.9 - 4.2 0.8 Aspidosiphon steenstrupii - 3.6 2.0 - 2.1 0.4 Phascolosomatidae - 3.6 2.0 - 2.1 0.4 Phascolosoma perlucens - 3.6 2.0 - 2.1 0.4 Unidentified 51.0 - 47.1 28.6 - 22.2

Unidentified taxa 49.0 - 41.2 13.7 - 11.7

Percent total (Number of Individuals) 100 (51) 100 (28) 100 (51) 100 (248) 100 (48) 100 (248)

73

Figure 3-1. A neighbor joining tree showing the degree of sequence relatedness for the mitochondrial Cytochrome Oxidase I gene in a range of invertebrate and vertebrate species. Sequences generated from stomach contents are highlighted with solid arrows and potential prey are highlighted with dashed arrows. All other sequences were downloaded from GenBank. Bootstrap values were calculated based on 5000 replicates (data not shown). The tree was broken in two with the triangles representing collapsed branches. The triangles in this figure are expanded in the tree continued below.

74

Figure 3-1. Continued.

75

CHAPTER 4 CONCLUSION

Visually, the diet of French grunt sampled from the southern shore of St. John,

U.S. Virgin Islands, was generally consistent with other studies in the Caribbean

including Florida, Columbia, Puerto Rico, and the U.S. Virgin Islands (Randall 1967,

Estrada 1986, Dennis 1992, Hein 1999) but differed from studies in the Netherland

Antilles (Nagelkerken et al. 2000a, Cocheret de la Morinière et al. 2003a). Sipunculid

worms, polychaetes, copepods, and decapods crustaceans were important prey items

by number and frequency of occurrence in this study. Visual identification of prey items

recovered from French grunt were difficult to place at lower taxonomic level because

many prey items lacked characteristic hard parts resistant to digestion and pharyngeal

teeth enabled grunts to macerate prey items prior to entering the stomach (Wainwright

1989). French grunt play an important role in the coral reef, seagrass and mangrove

habitats as consumers of benthic infauna, transporters of nutrients, and as prey for

predators (Meyer et al. 1983, Meyer and Schultz 1985). Specific knowledge of diet is

important for understanding ecosystem processes (Diehl 1992) and anticipating how

anthropogenic forces might affect coral reef, seagrass and mangrove habitats (Meyer et

al. 1983, Holmlund and Hammer 1999). Additional instances where specific prey

information could prove useful includes potential competition for resources from

introduced species might have in near shore Caribbean waters (Carlton 1987, Schofield

et al. 2009), restoration of impacted populations (Marsh and Douglas 1997), and

estimations of carrying capacity based on quantitative food web data (Walters et al.

2000).

76

Molecular analysis of diet applied to French grunt stomach contents was

successfully able to place specific prey items at the genus or species level with a high

degree of certainty in selected instances. Although overall success, as measured by the

percentage of prey items amplified and sequenced was relatively low, this work illustrates

that PCR-based analysis of diet is a powerful tool capable of identifying specific prey and

can serve in the capacity to augment other prey identification methods. Future studies

may be able to minimize issues associated with contamination and sample size through

the use of species or group-specific primers applied to entire stomachs (Jarman et al.

2004, Casper et al. 2007, Deagle et al. 2007). As larger numbers of high-quality DNA

sequences become available for various marine taxa, molecular analysis of diet will be an

increasingly more prevalent and successful tool for studying the diets of marine

organisms.

Much of the DNA-based analysis of diet that is now being conducted focuses on

developing appropriate PCR primers, collecting relevant sequence data and applying lab-

based approaches to controlled systems (King et al. 2008). The application of next

generation DNA-based techniques such as pyrosequencing (Deagle et al. 2009), blocking

primers (Vestheim and Jarman 2008), and quantitative forms of PCR (qPCR) (Troedsson

et al. 2009) will help researches address more complex ecological questions.

Understanding the dynamics of how DNA digestion is affected by predator meal size,

temperature, and metabolic activity, for example, will help to explain the limitations and

potentials for PCR-based analysis of diet. Ultimately, PCR-based techniques might one

day be used to construct quantitative food webs and answer previously enigmatic trophic

interactions.

77

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BIOGRAPHICAL SKETCH

John Steven Hargrove was born in Kansas City, Missouri and raised in the central

valley of California. For his undergraduate education, John moved north to Seattle

where he attended the University of Washington and graduated in 2003 with a

bachelor‟s degree in fisheries and aquatic sciences. After several years of working as a

biological scientist onboard commercial fishing boats throughout Alaska, he moved to

Gainesville, Florida to attend the University of Florida‟s graduate program in fisheries

and aquatic sciences. John currently works as a biological scientist for the Wildlife

Ecology and Conservation Department at University of Florida.

To my parents for their unconditional love and...

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