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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
2
© 2010 John Steven Hargrove
3
To my parents for their unconditional love and support
4
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
6
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
7
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
8
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
9
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
11
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
13
(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,
14
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
15
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.
16
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,
17
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
18
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
19
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
20
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.
21
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
22
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
23
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
24
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
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
Molecular Identification
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
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
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
VIS100P5 Sipunculid* Aspidosiphoniformes* Aspidosiphonidae 77
VIS099P4 Sipunculid* Aspidosiphoniformes* Aspidosiphonidae 99 VIS094P1 Sipunculid* Aspidosiphoniformes* Aspidosiphonidae Aspidosiphon laevis 79
VIS089P5 Sipunculid* Aspidosiphoniformes* Aspidosiphonidae 79
VIS088P1 Sipunculid Decapoda Aeglidae 85
VIS086P3 Sipunculid* Aspidosiphoniformes* Aspidosiphonidae 79
VIS086P5 Sipunculid* Aspidosiphoniformes* Aspidosiphonidae 72
VIS083P1 Sipunculid Decapoda Aeglidae 82
VIS080P5 Sipunculid* Aspidosiphoniformes* Aspidosiphonidae 89
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
Prey Visual DNA Visual & DNA Visual DNA Visual & DNA
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
Prey Visual DNA Visual & DNA Visual DNA Visual & DNA
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.