Diet Analysis of Maumee River Fishes Using Cytochrome C Oxidase (Coi) Dna Metabarcoding ― Insights Into a Critical Time of Year

Home , Bonytail chub, Branchiostegus japonicus, Caenis latipennis, Emerald shiner, Ephemera simulans, Freshwater drum

DIET ANALYSIS OF MAUMEE RIVER FISHES USING CYTOCHROME C OXIDASE (COI) DNA METABARCODING ― INSIGHTS INTO A CRITICAL TIME OF YEAR

Megan G. Shortridge

A Thesis

Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE

December 2016

Committee:

Jeff Miner, Advisor

Daniel Heath

R. Michael McKay

Christine Mayer

Megan Shortridge

iii ABSTRACT

Jeffrey Miner, Advisor

In recent years, DNA barcoding, the sequencing of a common marker region for taxonomic identification, has become integrated into U.S. agency protocols and procedures.

Chapter 1 provides an overview of areas where DNA barcoding is currently being used by U.S. agencies to address questions of management concern; the benefits and limitations of using barcoding in an agency setting are considered, as well as how the technology may evolve in the near future. A diet metabarcoding study was then conducted in Chapter 2, which addressed a question of fisheries management concern, the diet of Maumee River fishes at an important time of year using cytochrome c oxidase (COI) DNA metabarcoding, with a particular focus on detecting predation on early life stages (ELS) of walleye (Sander vitreus). DNA amplified from the homogenized gut contents of fishes captured in the Maumee River during early spring was analyzed using next generation sequencing. Walleye eggs and larvae were present when predators were collected, although at lower densities than previously reported at peak density in the

Maumee River. Despite the presence of walleye ELS in the system, the number of fishes with sequences assigned to walleye was lower than initially expected. One female white perch

(Morone americana), one male white bass (Morone chrysops), and two emerald shiners

(Notropis atherinoides) that were caught in the spawning grounds (Orleans Park) had gut content sequences assigned to walleye. Relatively low density of walleye in the system, the presence of alternative prey items (e.g., chironomids), lower overall feeding intensity by predator fishes near the onset of spawning, and/or turbidity in the Maumee River acting as a predation refuge may explain the lower than expected predation on walleye ELS, however, this requires further investigation and confirmation. Overall, sequences assigned to 7 phyla of metazoans were detected using DNA metabarcoding, including 9 genera of chironomids. Unexpected diet items

iv were encountered, including potential predation on the bryozoan, Plumatella casmiana, by emerald shiner. This study reinforced the utility of DNA barcoding in providing insight where morphological identification is difficult as described in Chapter 1, but also points to areas where methods need improvement.

v Dedicated to all those who pursue science to better understand our world, gain insight on our past, and prepare for our future. This thesis is dedicated to those with an endless curiosity for the natural world.

“More broadly, DNA barcoding allows a day to be envisioned when every curious mind, from professional biologists to schoolchildren, will have easy access to the names and biological attributes of any species on the planet,” (Hebert & Gregory 2005).

vi ACKNOWLEDGMENTS

Numerous people have been instrumental in making this thesis possible, first of which, are my family. Thanks especially to my mom, dad, brother, aunts, uncles, and grandparents for your endless support and encouragement. Thanks for putting up with gill nets being power- washed and repaired in long driveways, and for even donning waders once or twice to lend a hand. Thank you to my partner, Phil, for your support, love, and endless patience and encouragement. I could not have done it without you.

Thank you to my funding sources for making this work possible: Toledo Naturalists'

Association and Jeff Schaeffer and the U.S. Geological Survey (USGS). This thesis would not be here without your support and I cannot thank you enough.

A big thank you to my lab mates and to undergrads who have helped with this research intellectually and technically. A special thanks to Rich Budnik, Jake Miller, Jamie Justice,

Mandy Nourse, Jamie Russell, Dani McNeil, and Kevin Bland for assistance sampling. Also, thanks to my friend and fellow grad student Scott Mastrocinque for your friendship during these years and encouragement.

Thank you to my advisor, Jeff Miner, my committee members, and to Scott Rogers for all of your help and advice. Thank you to Kyle Wellband for being such a patient teacher at

Windsor, and for showing me techniques that made this study possible. The learning curve was steep, but I learned a lot.

Thank you to the Ohio Department of Natural Resources for help in collecting organisms used during this study, and for fun and educational times out on the river. A special thanks to

Mike Wilkerson and ODNR District 2. vii

TABLE OF CONTENTS

Page

CHAPTER I: THE APPLIED USE OF DNA BARCODING IN REGULATORY SCIENCE:

CURRENT USES, PRESENT PROBLEMS, AND FUTURE APPLICATIONS ...... 1

Abstract…………………………………………...... 1

Introduction…………………………………………………………………………… 2

DNA Barcoding Background and Methods…………………………………………… 3

DNA Barcoding Aids in Protection of Public Safety………………………………… 7

DNA Barcoding and Biosecurity ...... 10

DNA Barcoding in Biomonitoring and Biodiversity………………………………… 19

Challenges and Limitations of DNA Barcoding, and Future Directions ...... 22

Quantitativeness in DNA barcoding studies…………………………………. 22

Portability, affordability, and speed in DNA barcoding ...... 25

The challenge of incomplete reference databases……………………………. 28

Conclusions ...... 29

CHAPTER II: DIET ANALYSIS OF MAUMEE RIVER FISHES USING

CYTOCHROME C OXIDASE (COI) DNA METABARCODING ― INSIGHTS INTO A

CRITICAL PERIOD ...... 30

Abstract ...... 30

Introduction ...... 31

Methods...... 38

Collection of predators ...... 38

Timing of larval walleye drift ...... 40 viii

Extraction of predator gut contents ...... 40

DNA extraction, library preparation, and sequencing ...... 42

Bioinformatics and data analysis ...... 44

Results ...... 47

Discussion ...... 56

LITERATURE CITED ...... 75

APPENDIX A: IACUC APPROVAL FOR PROTOCOL 14-006 ...... 149

APPENDIX B: ACRONYMS AND ABBREVIATIONS USED IN THESIS ...... 150 ix

LIST OF FIGURES

Figure Page

1.1 The general workflow for taxonomic identification of an organism using DNA

barcoding ...... 6

1.2 Amplification curve during quantitative real-time PCR (qRT-PCR) reaction ...... 18

1.3 Applications of DNA Barcoding by agencies ...... 24

2.1 Developmental stages of Sander vitreus ...... 37

2.2 Map of the two fish collection locations on the Maumee River, a major tributary of Lake

Erie in Ohio ...... 39

2.3 Processing workflow for analysis of next generation dataset ...... 46

2.4 Mean daily density of larval walleye ± 1 standard deviation in Orleans Park (OP) and

Buttonwood Park (BP) (spawning grounds) on selected dates in 2014 with sample size

represented in parentheses ...... 53

2.5 Mean density of walleye larvae ± 1 standard deviation in the Rossford Marina (RM) area

on selected dates in 2014 with sample size in parentheses ...... 54

2.6 Percent of total sequences from each predator species assigned to each metazoan phylum

With sample size for each species represented in parentheses ...... 55

B.1 Sequence length frequency distribution of original Ion Torrent PGM dataset produced

using FastQC Report (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) via

Galaxy ...... 124

B.2 Sequence quality score frequency distribution of original Ion Torrent PGM dataset

produced using FastQC Report (http://www.bioinformatics.babraham.ac.uk/projects/

fastqc/) via Galaxy ...... 125 x

Figure Page

N.1 Temperature data generated from NOAA Great Lakes Real-Time Currents Monitoring

station gl0201 on the Maumee River (41°37.748' N, 83°31.813' W) from March 15 to

May 15 of 2014 ...... 148 xi

LIST OF TABLES

Table Page

2.1 Primers and adapters used to generate the Maumee River fish diet metabarcoding

dataset ...... 43

A.1 Summary of capture location, sex, sample size, and total length for predator fishes

Present in the final next generation sequencing dataset ...... 122

C.1 Mean daily abundance of walleye eggs (mean number of eggs in benthic samples) ± 1

standard deviation collected at Orleans Park (OP) and Buttonwood Park (BP) (spawning

grounds) during April and May of 2014 with sample size in parentheses...... 126

C.2 Mean daily density (larvae/m3) of larval white suckers (Catostomus commersonii) at

Orleans Park (OP) and Buttonwood Park (BP) (spawning grounds) during April and May

of 2014 from surface samples with sample size in parentheses ...... 127

C.3 Days that presence of Morone spp. eggs and larvae were noted at Orleans Park (OP) and

Buttonwood Park (BP) (spawning grounds), and in the area from Rossford Marina (RM)

to the Maumee River mouth (areas downstream of spawning grounds) in April and May

of 2014 ...... 128

D.1 Number of sequences kept and removed at each processing step of the Maumee River

diet metabarcoding dataset ...... 129

D.2 Number of sequences represented by one, two, three, four, five, or greater than five

reads ...... 130

E.1 Taxonomic assignment of sequences from the stomach contents of white perch, Morone

americana, at Rossford Marina (n=12) ...... 131 xii

E.2 Taxonomic assignment of sequences from the stomach contents of white perch, Morone

americana, at Orleans Park (n=3) ...... 132

F.1 Taxonomic assignment of sequences from the stomach contents of white bass, Morone

chrysops, at Rossford Marina (n=11) ...... 134

F.2 Taxonomic assignment of sequences from the stomach contents of white bass, Morone

Chrysops, at Orleans Park (n=11) ...... 135

G.1 Taxonomic assignment of sequences from gut contents of emerald shiner, Notropis

atherinoides at Rossford Marina (n=20) ...... 137

G.2 Taxonomic assignment of sequences from gut contents of emerald shiner, Notropis

atherinoides at Orleans Park (n=28) ...... 138

H.1 Taxonomic assignment of sequences from the stomach contents of freshwater drum,

Aplodinotus grunnines (n=12, all from RM) ...... 140

I.1 Taxonomic assignment of sequences from the gut contents of one goldfish, Carassius

Auretus (n=1, from OP) ...... 142

J.1 Taxonomic assignment of sequences from the gut contents of common carp, Cyprinus

carpio (n=12, collected from both OP and RM) ...... 143

K.1 Taxonomic assignment of sequences from the gut contents of gizzard shad, Dorosoma

epedianum (n=8, from both OP and RM) ...... 144

L.1 Taxonomic assignment of sequences from the stomach contents of channel catfish,

Ictalurus punctatus (n=4, from OP) ...... 145

M.1 Taxonomic assignment of sequences from the gut contents of bluntnose minnow,

Pimephales notatus (n=12, all from OP) ...... 146 1

CHAPTER I: THE APPLIED USE OF DNA BARCODING IN REGULATORY SCIENCE:

CURRENT USES, PRESENT PROBLEMS, AND FUTURE APPLICATIONS

Abstract

In the past decade, DNA barcoding, the sequencing of genomic marker regions for taxonomic identification, has become an important tool in the toolbox of regulatory science.

DNA-based identification has been incorporated in multiple areas of regulatory concern in the

U.S., including the authentication of herbal medicines and food products, the prevention and management of invasive species, and routine biomonitoring. Although there are still limitations to using DNA barcoding in an agency setting, such as incomplete reference databases and issues with quantitativeness, DNA barcoding allows agency professionals to address questions that have traditionally been tedious or impractical to answer using morphology-based approaches.

DNA barcoding is already a feasible technology for many agency applications. From a cost perspective, next-generation sequencing of samples has been shown to be comparable to slightly less expensive than morphological identification. Additionally, recent studies indicate that reference databases are already comprehensive enough for large, ecologically and economically important regions such as the Great Lakes. This review chapter provides an overview of how

DNA barcoding has been applied by U.S. government agencies, examines current challenges and limitations, and considers how the technology may evolve in the future. Overall, DNA barcoding is readily implemented in protocols that seek to identify material from single species (e.g., identifying remains from birdstrikes or seafood products), however, the efficacy of barcoding bulk samples using next-generation sequencing (e.g., the metabarcoding of homogenized aquatic macroinvertebrate samples) requires further research and validation. Efforts should be made by 2 agency professionals to further incorporate DNA barcoding into agency protocols to enhance taxonomic identification efforts.

Introduction

Over ten years ago, Savolainen et al. envisioned a biodiversity-literate world enabled by a handheld, rapid DNA sequencer for identifying species (Savolainen et al. 2005). Despite the fact that this device has yet to appear, DNA barcoding, the amplification and sequencing of common genomic marker regions for taxonomic identification, is not a technique that regulatory science should dismiss lightly ― this method allows agency scientists to answer questions that are impractical to answer using traditional techniques. The enterprise of DNA barcoding has exploded since a paper by Hebert et al. proposed using a mitochondrial gene called cytochrome c oxidase (COI) for DNA-based identification of animals (Hebert et al. 2003a). While the number of broad disciplines that DNA barcoding has proven useful for has been impressive, some promises have not been fulfilled, and there are still many challenges to integrating DNA barcoding into routine monitoring programs. Despite limitations, it has been successfully used by

U.S. agencies such as the U.S. Fish and Wildlife Service (USFWS), Federal Aviation

Administration (FAA), National Oceanic and Atmospheric Administration (NOAA),

Environmental Protection Agency (EPA), Food and Drug Administration (FDA), and the U.S.

Department of Agriculture (USDA) to address questions of regulatory concern.

Although many excellent reviews exist on DNA barcoding (Valentini et al. 2009; Kress et al. 2015) and on next-generation sequencing (Shokralla et al. 2012), this review focuses on how DNA barcoding is currently being used by U.S. government agencies to address questions in food and drug safety, biomonitoring, and detection of invasive species. It also considers 3 potential limitations and challenges involved in using DNA barcoding for regulatory science, and how the technology may develop in the future.

DNA Barcoding Background and Methods

Technically simple to understand, DNA barcoding is a method that seeks to make taxonomic identifications by sequencing one or a few short regions of the genome, and comparing it to reference databases. Before DNA barcoding of eukaryotic life was widely used, the first culture-independent microbial diversity surveys in the 1990s using the 16S gene surprised many with the amount of uncharacterized diversity in the environment. From these studies, it is now recognized that only about half of bacterial phyla have members known from culture (Rappé & Giovannoni 2003). While 16S is typically used for barcoding prokaryotes, there are multiple genes that are used as barcoding regions for eukaryotes, with a fragment of the mitochondrial COI gene recognized for animals (Hebert et al. 2003b), a combination of fragments of the chloroplast genes rbcL and matK jointly recognized for land plants

(Hollingsworth et al. 2009), and the nuclear ITS gene recognized for fungi (Schoch et al. 2012).

There are two major efforts in the DNA barcoding enterprise: the first involves building barcode reference libraries, and the second involves using those libraries to identify unknown sequences

(Figure 1.1), typically using algorithms such as BLAST, which calculates similarity scores between sequences (Coissac et al. 2012). Building barcode libraries is a large task that involves collection, sequencing, and deposition of data for specimens in a database such as the Barcode of

Life Data Systems (BOLD, boldsystems.org) database (Hebert & Gregory 2005). BOLD serves as a central hub for barcoding data (Ratnasingham & Hebert 2007); recent, large-scale efforts to barcode species have rapidly increased the database’s depth and breadth (Puillandre et al. 2012; 4 species (Seto 2015). As reference databases expand, barcoding will be increasingly valuable to agency professionals.

DNA can be extracted from a variety of sources including the ethanol preservative that organisms are stored in (Shokralla et al. 2010), gut contents (Budarf et al. 2011), feces (Zeale et al. 2011), hair (Pilli et al. 2014), bone (Shirak et al. 2013), tissue samples (Wong & Hanner

2008), eggs (Briski et al. 2011), and even whole homogenized organisms (Piñol et al. 2014).

There are a variety of DNA extraction protocols used, ranging from manual tube extractions to more high-throughput, automated systems (Vidergar et al. 2014). The protocol that is used depends on cost and time considerations, and on quantity/quality concerns that may be specific to a particular sample type, such as aged and/or degraded samples (Knebelsberger & Stöger

2012).

After DNA is extracted, it is amplified in a thermocycler using the polymerase chain reaction (PCR), which uses short sequences of DNA (i.e., primers) that are complementary to conserved regions of DNA bounding the variable barcode region. The primers bind near the barcode region, allowing a thermostable enzyme to extend the sequence, and ultimately increasing the target sequence copy number. Once sequencing is finished, taxonomic identification occurs by comparing sequences with those in a reference database such as BOLD or NCBI’s GenBank (Figure 1.1). Modules for analysis of molecular data have been developed in languages such as Perl (Stajich & Birney 2000) and Python (Cock et al. 2009), and batch- scripting in a Unix/Linux environment can also be used (Dudley & Butte 2009) to analyze sequencing data.

There are two general approaches for analyzing samples using DNA barcoding, one that uses traditional, Sanger sequencing (Sanger et al. 1977) to amplify DNA from single organisms, 5 and another that uses next-generation sequencing to amplify DNA from multiple organisms in bulk/environmental samples simultaneously, a technique referred to as DNA metabarcoding

(Machida & Knowlton 2012).

Next-generation sequencing refers to new technologies that work by sequencing multiple

DNA templates in parallel, dramatically reducing the cost and speed of sequencing (Shendure &

Ji 2008). DNA metabarcoding has been particularly useful in diet studies, as DNA from individual gut or fecal content samples can be pooled into one sequencing run by ligating sample-specific Multiplex Identifier (MID) tags onto sequences, which can later be used to separate reads pertaining to individual samples (Coghlan et al. 2013).

Despite multiple approaches and methods, DNA barcoding efforts all essentially seek to derive taxonomic information from samples by comparing sequence data to reference databases of known taxonomic origin.

Figure 1.1. The general workflow for taxonomic identification of an organism using DNA barcoding. DNA from a sample is extracted, and target regions of the genome are amplified with PCR using group-specific or universal primers that are complementary to conserved regions of target DNA. Amplicons generated from PCR are sequenced, and then sequencing reads are cleaned up to remove primer/adaptor sequences and low quality reads using readily-available bioinformatics programs. Comparison of reads with sequences in a reference database (e.g., the Barcode of Life Database or GenBank) allows generation of a taxonomic match. Two general approaches are used; DNA barcoding uses traditional (Sanger) sequencing to generate sequences from a single organism, while DNA metabarcoding uses next-generation sequencing to sequence DNA derived from bulk samples containing multiple organisms.

DNA Barcoding Aids in Protection of Public Safety

Food fraud has been a societal concern for over 200 years, and adulteration of even basic staples was common and reported as early as 1820 (Collins 1993). Even with increased regulation of consumer products, food fraud is still observed, especially since there are great financial incentives to conduct it (Everstine et al. 2013; Kane & Hellberg 2015). Awareness of food fraud has recently increased, following highly public incidences such as the 2013 discovery of horsemeat in European Union products labeled pure beef (Food Standards Agency 2013).

DNA acts as a naturally built-in tag which follows raw materials during processing, making

DNA barcoding an effective tool for combating food fraud (Galimberti et al. 2013). Since substitution of a product with undeclared species can pose health risks to consumers (Tortorella et al. 2014; Biedermann et al. 2015), DNA barcoding can be an important tool for protecting public safety.

Many studies using DNA-based identification have suggested rampant mislabeling in the meat and seafood industry. In a case where a local family was sickened after eating a product mislabeled as monkfish, the FDA revealed via DNA barcoding that it actually was neurotoxin- containing pufferfish (Cohen et al. 2009). DNA barcoding has detected mislabeling of seafood species in 25 published studies (Wong & Hanner 2008; Ardura et al. 2010; Filonzi et al. 2010;

Barbuto et al. 2010; Hellberg et al. 2011; Carvalho et al. 2011, 2015; Huxley-Jones et al. 2012;

Miller et al. 2012; Cawthorn et al. 2012, 2015; Cline 2012; Haye et al. 2012; Cox et al. 2013; Di

Pinto et al. 2013; Maralit et al. 2013; Changizi et al. 2013; Cutarelli et al. 2014; Galal-Khallaf et al. 2014; Helyar et al. 2014; Armani et al. 2015a; de Brito et al. 2015; Armani et al. 2015b;

Khaksar et al. 2015; Vartak et al. 2015), and is therefore, apparently widespread. Although 8 concerns over seafood substitution is a global phenomenon, little regulatory action has resulted from such studies. Brazil is one of the only countries who has linked enforcement efforts with

DNA barcoding, and has fined establishments that mislabeled seafood on a small scale (Carvalho et al. 2015).

Although still in the early stages, there is great potential for DNA barcoding to be used in the United States for ensuring seafood and meat labeling compliance, and standardized protocols have already been developed (Handy et al. 2011). The FDA recently added DNA barcoding sequences to its web-based resource, the Regulatory Fish Encyclopedia, as a method for authentication of seafood products (Yancy et al. 2008). This represents a vast improvement over the previously used seafood identification method, isoelectric focusing on soluble muscle proteins, which relied upon differential migration of proteins in an electric field, ultimately generating species-specific banding patterns (Lundstrom 1977). Proteins are severely denatured during heat-sterilization (e.g., canning); however, DNA, which is more thermostable, can still be used (Mackie et al. 1999). In addition to undeclared species being present in seafood products,

DNA barcoding has also shown that there can be a high incidence (21%) of undeclared species in meat products being sold in the U.S. (Kane & Hellberg 2015). DNA barcoding may quickly become part of regulatory food surveillance systems, although, there will likely be challenges to coordinating and standardizing this technology in complex food production chains (Clark 2015).

Not only can barcoding be used to investigate mislabeling, but it can also breach information gaps in cases where food labels meet regulatory requirements but lack sufficient taxonomic resolution for a particular question. For example, the approved U.S. market name for all members of the Thunnus family is simply “tuna,” and therefore, seafood labeling 9 requirements are not species-specific (Food and Drug Administration 2015). Using DNA barcoding, Lowenstein et al. identified that there were significantly lower mercury concentrations in Yellowfin tuna sushi vs. Bluefin/Bigeye Tuna sushi, providing insight on a public health issue (Lowenstein et al. 2010). DNA barcoding can fill in such information gaps, and facilitate novel modes of inquiry for regulatory concerns.

Government agencies have also used DNA barcoding to improve aviation safety.

Collisions of birds with aircrafts (i.e., birdstrikes) is a public safety concern that costs the U.S.

$1.2 billion annually in damage and delays (Allan 2000). The species involved in birdstrikes are often identified by morphological identification of feather remains; however, in cases where recovered materials are low quality or quantity, identification is limited (Federal Aviation

Administration 2013). In 2003, the Federal Aviation Administration (FAA) and the U.S. Air

Force started a joint project to improve the avian COI reference collection for birdstrike identification. The pilot study was highly successful: over 47% of samples lacked sufficient feather material for morphological identifications, but still had blood or tissue that could be barcoded (Dove et al. 2008). DNA barcoding has already started appearing in FAA protocols and guidelines, and may soon become standard procedure (Federal Aviation Administration 2013).

Finally, recent DNA barcoding of herbal supplements has informed regulatory science on a potential high rate of substitution in the dietary supplement industry. A study of herbal supplements sold in the U.S. and Canada found a high proportion (59%) of undeclared plant species in products, including evidence of the use of fillers and some substitutions that could be hazardous to consumer health (e.g., substitution of St. John’s Wort with the herbal laxative, senna; Newmaster et al. 2013). The presence of undeclared ingredients in dietary supplements 10 could be hazardous to public health (Petroczi et al. 2011), and this is an area where DNA barcoding may eventually be used by U.S. agencies, although it currently is absent from agency protocols.

DNA-based analyses of dietary supplements, seafood, and meat products indicate that there may be more mislabeling in consumer products and foodstuffs than previously thought.

Other applications, such as identifying the species involved in birdstrikes, may also facilitate making management decisions important for public safety. While some agencies such as the

FDA and FAA have started implementing DNA barcoding into standard monitoring procedures, in the future, DNA barcoding may play a greater role in enforcement.

DNA Barcoding and Biosecurity

Invasive species cause billions of dollars annually in damage and losses in ecosystem services (Pejchar & Mooney 2009), the costs of which the public shoulders. As Lodge et al. recommend, policymakers should focus their efforts on preventing invasive species from becoming established (Lodge et al. 2006), because if detection occurs early, successful eradication of invasive species has occurred (Wimbush et al. 2009). DNA barcoding facilitates this early detection, as it allows detection of invasive species at low densities, and also enables distinguishing invasive from morphologically similar native flora and fauna (Darling & Mahon

2011).

In cases where differences between native and invasive species are highly subtle, and where the potential invader is unexpected, managers may not be as likely to detect them; in such cases, barcoding facilitates identification (Van de Wiel et al. 2009; Ghahramanzadeh et al. 2013).

This was the case the USDA faced in Florida with invasive armyworms; while there are nine native 11 species of these highly destructive Lepidopteran agricultural pests, distinguishing natives from invasives relies upon microscopic examination of adult male genitalia, and there are often no unambiguous keys for females or immatures (Nagoshi et al. 2011). In cases where identification of females and immatures is limited by morphology, barcoding has assisted managers in making species identifications (Chown et al. 2008; Ovalle et al. 2014).

In the past, developing detection methods were frequently done out of necessity, and only for a specific group of “likely” invaders (Armstrong and Ball 2005). However, there are inherent difficulties in predicting newly invasive species (Moles et al. 2008). Incorporating DNA barcoding into biosurveillance protocols facilitates detection of unexpected species. There already have been several novel documentations of invasive species during large-scale DNA barcoding biosurveillance surveys, including detection of four moth species (DeWaard et al. 2009, 2010), and a species of marine macroalgae (Saunders 2009). More frequently, DNA barcoding has been used to confirm morphology-based identification of suspected invasives, or used to detect range expansions in broad surveys (Bleeker et al. 2008; Scalici et al. 2009; Chiesa et al. 2013;

Evangelista et al. 2013; Oter et al. 2013). The use of DNA barcoding for biosurveillance is fairly recent, and over time it may become integrated into large-scale, routine surveys, allowing managers unprecedented ability to analyze the biota of an area.

In addition to facilitating detection, DNA barcoding also plays a role in prevention. In a world of globalized trade, understanding and managing invasive species pathways is challenging

(Hulme 2009). DNA barcoding elucidates invasive pathways by serving as a rapid identification tool, and has been applied to projects investigating multiple potential vectors, including ballast water (Briski et al. 2011; Zaiko et al. 2015), shipments of exotic pets (Padilla & Williams 2004), 12 the ornamental fish trade (Steinke et al. 2009; Collins et al. 2012, 2013; Amaral et al. 2013), aquatic plant shipments (Thum et al. 2012), and bait buckets (Mahon et al. 2014). As gaps in border security have been noted (Brasier 2008; Moffitt et al. 2010; Bacon et al. 2012), prompting some to suggest that the U.S. allocate more resources to inspections rather than to managing issues arising from a lack of inspections (Moffitt et al. 2010), DNA barcoding may play an important future role in invasive species prevention by identifying key introduction pathways.

Just as DNA barcoding has been applied to discrete organisms, molecular techniques have also frequently been applied to invasive species detection in water and soil samples.

Environmental DNA (eDNA) techniques have arisen to cope with the fact that visual and auditory surveys tend to be ineffective at low invasive species densities (Ficetola et al. 2008;

Dejean et al. 2012). Although barcodes can be recovered from DNA shed by organisms in the environment, there are concerns over DNA originating from non-living matter. Several studies have shown that DNA can persist long-term in soil samples (Cai et al. 2006; Andersen et al.

2012), and there is evidence that there can be a higher concentration of eDNA in sediments than in the water column (Turner et al. 2014b). Although studies indicate that the majority of eDNA in surface waters is rapidly degraded in about 3-10 days, under certain conditions, eDNA may be detectable even after 58 days (Strickler et al. 2015). Overall, this persistence highlights a potential downside of eDNA surveys that needs further evaluation ― DNA can come from non- viable organisms in the environment. The amplification of DNA from non-living organisms may limit the ability to discern whether or not viable organisms are transported by a vector, which may in some cases be required to be evaluated for compliance (International Maritime

Organization 2004). Zaiko et al. (2015) noted from their ballast water eDNA survey that eDNA was unable to provide estimates of organism viability, however, an increase in detection signal 13 over time may suggest that viable organisms are present (Zaiko et al. 2015). In addition to possible limitations due to eDNA’s persistence, there are also potential issues with false negatives in eDNA surveys; modeling exercises suggest there may be a high rate of false negatives in surveys when target DNA concentrations are low in the environment (Schultz &

Lance 2015). The possibility that eDNA persists in the environment and that false negatives can occur should be carefully considered by agencies conducting molecular-based surveys.

Despite caveats and limitations, using eDNA to detect invasive species has met with success in several agency programs, most notably the Asian carp eDNA program conducted by the USFWS (Jerde et al. 2011; Asian Carp Regional Coordinating Committee 2014). Able to reach a mass in excess of 40 kg (88 pounds), concern that carps of the genus

Hypophthalmichthys (“Asian carps”; Kolar et al. 2005) becoming established in the Great Lakes has led to implementation of a DNA-based monitoring program to combat Asian carp avoidance of traditional fisheries gear. While this program has been successful in detecting Asian carp, in the future, Asian carp detection may move from the lab back into the field; plans for developing a handheld, field-friendly device to detect Asian carp DNA in as little as 12 minutes have been made by the USGS (Asian Carp Regional Coordinating Committee 2014). The literature surrounding eDNA-based surveys is rapidly growing, and with new understandings of the nature of DNA in the environment, agency sampling and analysis methods are quickly adapting (Turner et al. 2014a; Ficetola et al. 2015; U.S. Fish and Wildlife Service 2015). For example, there are questions about whether or not eDNA is derived from live organisms given recent findings of its persistence; in light of this new understanding, eDNA monitoring in areas closest to the invasion front have been reduced since eDNA could be transported by boats, barges, or birds rather than by live carp (Asian Carp Regional Coordinating Committee 2014). This basin-wide 14 biosurveillance program has been used since 2009, and a newly constructed genetics lab ensures that future sample analysis will inform field-based management in a timely manner (Asian Carp

Regional Coordinating Committee 2014).

Increasingly, more quantitative PCR methods such as quantitative real-time PCR (qRT-

PCR) and droplet digital PCR (ddPCR) have been used by agencies to detect and semi- quantitatively estimate invasive species abundance (Castrillo et al. 2008). Advantages of qRT-

PCR over the traditional amplification and sequencing used in DNA barcoding include the ability to quantify the amount of target DNA in a sample, reduced sample processing time due to eliminating gel electrophoresis and sequencing steps, and increased specificity (Wilcox et al.

2013; Díaz-Ferguson et al. 2014; Amberg et al. 2015; Hunter et al. 2015). Wilcox et al. found that amplification of invasive brook trout occurred at concentrations as low as 0.5 target DNA molecules/μL using qRT-PCR, making qRT -PCR an extremely sensitive technique (Wilcox et al. 2013).

Generally, there are two types of fluorescence detection systems used in qRT-PCR. One system uses intercalation-based dyes such as SYBR Green, which bind to the minor groove of double-stranded DNA (dsDNA), and another uses hybridization-based dyes (e.g., TaqMan probes), which bind to part of the target DNA. Hybridization-based dyes are generally more expensive, but also more specific to the target DNA, since intercalation-based dyes bind non- discriminately to dsDNA, including non-specific PCR products such as primer dimers (Singh &

Roy-Chowdhuri 2016).

Of the two detection systems, TaqMan assays have become increasingly popular as a means to quantify invasive species target signal in samples (Bayha & Graham 2009; Bain et al. 15

2010; O’Meara et al. 2012; Takahara et al. 2013; Díaz-Ferguson et al. 2014; Hunter et al. 2015;

Dhami et al. 2016). Current use of qRT -PCR in invasive species research by agencies is primarily in the method development stage, but direct applications should soon follow. The

USDA Forest Service developed a TaqMan assay to quantify the amount of invasive brook trout

DNA present in water samples, in anticipation of using it to evaluate successful eradication after barrier construction and removal efforts (Wilcox et al. 2013). Likewise, the USFWS developed a

TaqMan assay for invasive silver carp, and found that the assay had several advantages over the previous method of PCR amplification followed by gel electrophoresis, including greater sensitivity and fewer false positives (Amberg et al. 2015). In addition to invasive species detection, qRT-PCR may also play a role in agency biocontrol research. The USDA Agricultural

Research Service (USDA ARS) developed a qRT-PCR assay for Beauveria bassiana, a fungal pathogen of the emerald ash borer that is applied to infected trees as part of a mycoinsecticide.

This assay shows promise for providing quantitative information on persistence of the fungus on ash tree leaves, and in the future, may inform agencies on optimal timing and frequency of application (Castrillo et al. 2008).

In a TaqMan assay, two primers amplify a short section of a target sequence during PCR, and a fluorescent probe binds to a site located within this amplified region; this probe consists of a fluorescent reporter dye and a quencher molecule. When reporter and quencher are located close to each other, the reporter’s fluorescent signal is absorbed by the quencher. However, during the extension stage of PCR, reporter and quencher are separated, allowing the reporter to fluoresce freely; over the course of the PCR reaction, as fluorescence increases, an amplification curve is generated. The cycle threshold (Ct) is the PCR cycle number where the reaction’s fluorescence level is distinguishable from the background fluorescence level; the Ct is inversely 16 related to the original amount of template DNA in the sample (Figure 1.2). By comparing the Ct values generated by reference standards of known copy number, the initial quantity of template

DNA in the unknown sample can be estimated (Wilcox et al. 2013). qRT-PCR has been shown to be semi-quantitative, but method development is ongoing. Loh et al. (2014) used qRT-PCR assays to quantify proportional abundances of larval fish species in mixed ichthyoplankton samples, with the intent of fisheries managers using the technique to monitor early life stages of invasive fish species in bulk samples. The method quantified proportional species abundance by using the average number of COI copies per larvae of each species. However, considerations such as variable sizes of larvae within a species and possibly different DNA extraction efficiencies with different larval fish morphotypes need to be explored in the future (Loh et al.

2014)

Droplet digital PCR (ddPCR) is another method that has arisen recently as a means to quantify the number of target DNA molecules in a sample; in ddPCR, sample DNA and reagents needed for the reaction are divided into thousands of partitions, some containing one or more target DNA molecules, and others not containing any template DNA. The partitions containing template DNA will produce a product following PCR, generating a positive end-point that can be measured (e.g., generation of fluorescence). The proportion of positive end points can be used to calculate the number of target DNA molecules originally present in the sample via probability analysis (Doi et al. 2015b). This technology has been used in several recent studies quantifying invasive species template DNA in samples (Nathan et al. 2014; Doi et al. 2015a; Simmons et al.

2016), and some have reported ddPCR to be less expensive, more rapid, and more consistent than qRT-PCR (Nathan et al. 2014). However, others have observed variability in performance, possibly related to the concentration of template DNA (Hayden et al. 2013). Like qRT-PCR, 17 method development for ddPCR is ongoing, however, there is some indication that ddPCR may outperform qRT-PCR in amplification of highly inhibited samples, and may offer simplified protocols since generation of standard curves are not needed for quantifying samples (Iwobi et al. 2016).

Although some agencies such as the USDA and the USFWS are beginning to incorporate

DNA-based monitoring into invasive species detection, there are still limitations and knowledge gaps about the effectiveness of eDNA surveys. However, in many cases where invasive species evade detection at low densities, eDNA may be the best available option. The utility of DNA barcoding to identify early life stages and novel invasive species has been well-documented in the literature, and barcoding could readily be incorporated into areas such as border inspections.

Since some of its earliest applications (Ficetola et al. 2008), DNA-based methods of invasive species detection have developed rapidly, and will likely be a key area of research in the future.

Figure 1.2. Amplification curve during quantitative real-time PCR (qRT-PCR) reaction. The hybridization probe (e.g., TaqMan probe), consisting of a fluorescent reporter dye and a quencher molecule, binds to a specific region on the template molecule. When reporter and quencher are located in close proximity, the quencher absorbs the reporter dye signal via fluorescent resonance energy transfer (FRET). During PCR, the DNA polymerase cleaves the probe during extension, separating quencher and reporter, and allowing the reporter to fluoresce freely. The PCR cycle where the amount of fluorescence detected goes higher than a defined threshold is referred to as the cycle threshold (Ct). At the Ct, the amount of fluorescence is clearly distinguishable from the background fluorescence level. The Ct is inversely proportional to the amount of initial target DNA in the sample, and by comparing the Ct from amplification curves derived from standards of known copy number, the amount of initial target DNA in the unknown sample can be estimated. 19

DNA Barcoding in Biomonitoring and Biodiversity

Sampling the aquatic environment is a nearly ubiquitous task that U.S. agencies conduct each year, and it is an effort that might be enhanced by integrating DNA barcoding into surveys.

Per annum, approximately $104 to 193 million is spent on sampling 19,500 lakes, streams, and wetlands in the United States; while fish and algae are also used, benthic macroinvertebrates are the most common bioindicator group for bioassessment (Stein et al. 2014). Metrics derived from these surveys may be based on species richness measures (e.g., total number of taxa, number of tolerant taxa) or may also take relative abundance into account (e.g., % EPT taxa, % grazers/scrapers), and are based on the assumption that environmental stressors such as pollution will be detected by changes in the macroinvertebrate community (Barbour et al. 1999). However, some have noted limitations to current morphology-based protocols in terms of time and taxonomic resolution. Due to samples being sent out to contracted labs at the end of the sampling season in batches, it can take months for a sample to be processed. A lack of available taxonomic specialists and the time required for sorting and identifying organisms can limit the ability to rapidly incorporate data into management decisions (Pfrender et al. 2010). Recently, DNA-based environmental monitoring has been encouraged to assist with some of these difficulties.

“Biomonitoring 2.0,” would rely on DNA barcoding to identify organisms to a finer taxonomic level, possibly producing more robust assessments (Baird & Hajibabaei 2012). Many state monitoring programs currently identify stream macroinvertebrates to courser taxonomic levels such as family or genus (Carter & Resh 2001; Division of Surface Water 2013); however, research suggests that within a genus, species can differ in pollution tolerance, and if their taxonomy were resolved, it might provide more information to policymakers in making 20 important assessments (Lenat 1993; Schmidt-Kloiber et al. 2006). However, species-specific data on pollution-tolerance needs development, as the current “pollution tolerance gap” limits the usefulness of barcoding data in biomonitoring. Sweeney et al. (2011) found that despite increased taxonomic resolution and higher estimates of diversity when using barcoding vs. expert taxonomist-derived stream macroinvertebrate data, metrics involving pollution tolerance did not differ, possibly due to a lack of species-level data on tolerance (Sweeney et al. 2011).

Despite some uncertainty as to whether barcoding generates more sensitive assessments than morphology, biomonitoring studies have ubiquitously reported that using barcoding increased taxonomic resolution, in part due to the ability of barcoding to identify small, immature, and damaged organisms that would otherwise have remained unidentified or have been identified at a courser level (Pilgrim et al. 2011; Sweeney et al. 2011; Carew et al. 2013; Jackson et al. 2014).

Due to biases discussed later in this review, estimates of actual or relative species abundance using DNA metabarcoding is not currently possible (Piñol et al. 2015). However, results have been promising that despite not being able to calculate metrics incorporating abundance, barcoding data still seems to support the same conclusions. For example, species- richness based estimates derived from DNA barcoding vs. morphology both detected differences between an impaired and unimpaired site (Sweeney et al. 2011). Additionally, efforts have been made to adapt indices which take into account abundance into versions that rely solely on presence-absence data. Such presence-only versions of indices have shown clear correlations with their abundance-weighted counterparts (Ranasinghe et al. 2012), such that the lack of quantitative species identification in metabarcoding may not be a major impediment to assessing biotic integrity. Others have also supported that presence-only versions of abundance-based indices are viable alternatives, but have noted other issues in using DNA barcoding; for example, 21

Aylagas et al. found that current reference databases were not sufficiently complete for calculating a presence-absence version of a commonly used marine benthic index (Aylagas et al.

2014). Overall, these preliminary findings suggest that despite the lack of quantitativeness in

DNA metabarcoding, improvements in reference databases may make it a valuable alternative to morphology-based biomonitoring. However, as with all applications of DNA barcoding, the ability to identify organisms is inextricably linked to the quality of reference databases.

Another key area of validation needed in metabarcoding bulk samples is determining whether rare species, in terms of biomass, are represented in a NGS dataset. To assess this,

Elbrecht and Leese (2015) homogenized stoneflies of known, variable COI haplotypes but of different biomasses, and investigated whether individuals of lower proportional biomass would be represented in the final sequencing dataset. Despite some of the stoneflies comprising only

0.023% of the overall sample biomass, sequences from all individuals were recovered, suggesting that rare taxa not being represented may not be a concern (Elbrecht & Leese 2015). In contrast, others have noted that less abundant species may disappear in environmental DNA samples (Kelly et al. 2014). The ability to detect rare species may be a limitation of DNA-based biomonitoring, and should be investigated further given these recent contradictory results.

In addition to being useful for biomonitoring, DNA barcoding can assist biodiversity surveys by linking early life stages to known adult stages of organisms. In this capacity, DNA barcoding has been used to link unknown species of larval fish to adult counterparts by NOAA

(Matarese et al. 2011), Unionid mussel larvae (i.e., glochidia) to vertebrate hosts (Boyer et al.

2011), and tadpoles to adult frogs (Grosjean et al. 2015) in biodiversity assessments. 22

Complementing traditional approaches, DNA barcoding shows promise for supporting bioassessment efforts. DNA barcoding could be integrated into many biomonitoring survey protocols with little procedural change: there is some indication that as long as samples are preserved in 95% ethanol initially, preservation for barcoding is generally sufficient, regardless of dilution later on (e.g., dilution into 70% ethanol) (Stein et al. 2013). Without needing to substantially change standard procedures, agencies could rapidly incorporate barcoding into biomonitoring protocols. However, in general, metabarcoding diversity is a rapidly evolving field of research where basic methodology is still under development (Mächler et al. 2016) and methods are likely to change rapidly in the years that follow.

Challenges and Limitations of DNA Barcoding, and Future Directions

Currently used by multiple agencies in the U.S. (Figure 1.3), DNA barcoding is quickly becoming integrated into regulatory science. However, there are still challenges that it faces including underrepresentation of some taxonomic groups in reference databases, and issues with making barcoding data quantitative. In the future, improvements in speed, portability, and cost- effectiveness of analyses may help further integrate DNA barcoding into agency compliance and ambient monitoring activities.

Quantitativeness in DNA barcoding studies

The ultimate goal of environmental assessment using DNA barcoding would be to make results quantitative; however, many studies currently report presence-absence data rather than relative or absolute species abundance. This is more of a concern for those who are using DNA metabarcoding for diet assessment or biomonitoring efforts, which use bulk samples as the source of DNA rather than barcoding individual organisms (Pompanon & Samadi 2015). There 23 have been multiple accounts of divergence between actual organism abundance and read abundance that have been attributed to several sources of bias. For example, comparison of the most abundant protists in a freshwater lake from morphological vs. next-generation sequencing

(NGS) data yielded different results; although diatoms were the most commonly observed group from the morphology-derived data, they were represented by only a few sequences in the NGS dataset (Medinger et al. 2010). Likewise, Pawlowski et al. (2014) suggested that the paucity of calcareous vs. organic-walled foraminiferans might be related to the relative ease of extracting

DNA from the latter vs. the former (Pawlowski et al. 2014). As reviewed by Pompanon et al.

(2012), the literature suggests that relative read abundance does not correspond well to relative biomass for diet analysis due to multiple potential technical and biological biases including mass-specific differences in target gene copy number (e.g., due to differences in tissue-cell density between types of prey tissues), differential digestion of prey tissues, the fact that individuals can have vast differences in biomass, differences in PCR amplification efficiency, 24

Figure 1.3. Applications of DNA Barcoding by agencies. A) Integration of DNA barcoding data into the

Regulatory Fish Encyclopedia by the FDA, with the intent of using it to detect seafood fraud (Yancy et al.

2008). B) Use by the FAA to identify highly damaged remains of birds and bats that have collided with aircraft (Dove et al. 2008). C) Aquatic macroinvertebrates are often used as bioindicators for monitoring ecosystem health, and the EPA has conducted proof-of-concept studies using DNA barcoding in biomonitoring (Pilgrim et al. 2011). D) Detection of invasives species (Asian carp) in environmental DNA surveys by the USFWS (Asian Carp Regional Coordinating Committee 2014). E) Linking larval stages of fish to known adult stages by NOAA for use in biomonitoring efforts (Matarese et al. 2011). F)

Development and testing of novel biocontrol agents by USDA ARS (Weber & Lundgren 2009). 25 sample pooling during library preparation, and time since ingestion (Pompanon et al. 2012).

While many diet and bioassessment studies have found discrepancies between biomass and read abundance in NGS datasets (Soininen et al. 2009; Deagle et al. 2010, 2013; Maruyama et al.

2014; Thomas et al. 2014; Elbrecht & Leese 2015), others have found a positive relationship between biomass/species abundance and read abundance (Takahara et al. 2012; Carew et al.

2013). This uncertainty points to a need for future investigation before NGS datasets can make quantitative assessments.

These studies ultimately indicate that caution should be exercised in interpreting NGS- based diet or eDNA derived data, and that, based on present knowledge, analyses can be considered semi-quantitative at best. However, this is an area where developments can be made.

Some investigators have tried alternative approaches for making analyses quantitative, such as the SNP-microsatellite method used by Carreon-Martinez (2013). In order to estimate the number of larval and juvenile yellow perch consumed by predator fishes, a probability curve was generated using microsatellite allele frequencies of the yellow perch population. From this curve, and based on the number of unique alleles present in predator gut contents, the number of larval yellow perch consumed could be estimated (Carreon-Martinez et al. 2014). Novel approaches such as this one as well as future techniques that avoid PCR-amplification altogether (and therefore eliminate PCR amplification bias; Zhou et al. 2013), may make quantitative comparisons a reality, but this is still down the road.

Portability, affordability, and speed in DNA barcoding

Agencies are frequently under budgetary constraints (Biber 2011), and methods must be reasonably quick, reliable, and inexpensive in order to be used in routine protocols. Using DNA 26 barcoding in an agency setting can help meet the demands of finite resources, while increasing capacity to identify species.

Several recent studies (Ji et al. 2013; Stein et al. 2014; Thompson & Newmaster 2014) have suggested that DNA barcoding can be comparable in price to less expensive than traditional morphological surveys, and that it can also offer faster turn-around times. Stein et al. (2014) conducted DNA barcoding of benthic macroinvertebrates via Sanger sequencing of individuals organisms and via NGS of bulk samples, and found conflicting results in terms of cost. Sanger sequencing was found to be 1.7 to 3.4 times the cost of morphological identification, and could be as high as 6 times the cost for certain groups such as diatoms, which require growing pure cultures for DNA extraction. NGS may be more viable, as costs were comparable to slightly less expensive than morphological identification. In terms of time, both approaches reduce the time required for analyses from a matter of months to a matter of weeks (Stein et al. 2014). Other large-scale comparisons have supported that DNA metabarcoding offers cost and time advantages over morphological approaches in biodiversity surveys. The workload in hours for analyzing three large datasets using morphology was over two times that of using metabarcoding

(Ji et al. 2013). Yet, the molecularly-derived data was still informative for three different management questions, illustrating that metabarcoding, despite being faster, still produced valuable data. A second study noted a 37% decrease in cost when conducting a metabarcoding vs. morphology-based vegetative survey, despite an increase in taxonomic resolution. Cost savings were due in part to the reduced cost of having teams partially vs. wholly composed of professional botanists, and savings due to not requiring several sampling trips in order to observe flora during different flowering times (Thompson & Newmaster 2014). 27

True portability of DNA barcoding has yet to be realized. In many agency settings, it would be convenient to have a portable method for species identification, as this would bypass the need to send samples out for sequencing and save time. Although Savolainen et al. envisioned a portable DNA barcoder (Savolainen et al. 2005), current technology does not meet that vision in terms of time, portability, or accuracy, although improvements are being made. In

May of 2015, start-up company Oxford Nanopore commercially released a new DNA sequencer, the MinION; this portable DNA sequencing device is about the size of a cellphone, and can be plugged into a computer for generating data in real-time. However, early evaluations of the technology have suggested that although the output and read length are adequate, the MinION is beset by a low level of accuracy, primarily due to insertion and deletion errors (Mikheyev & Tin

2014). Other attempts to make a handheld identification system have also been limited; although they have had increased accuracy, the devices test for specific taxonomic groups, and are not broadly applied to a variety of taxa as a true DNA barcoding device would. For example, Ulrich et al. (2015) devised a simple, inexpensive field RNA extraction method that when paired with a handheld analyzer, could differentiate seafood samples that contained grouper from those that were mislabeled; the procedure could be completed from sample preparation to results in 30 to

90 minutes (Ulrich et al. 2015). Portability may be enhanced by eliminating PCR altogether.

New techniques, such as NASBA (used by Ulrich et al. 2015 in their assay) and LAMP (Notomi et al. 2015) allow for isothermal amplification, rather than the cyclical temperature changes of traditional PCR, and may greatly simplify the amplification process. By obviating the need for bulky thermocyclers to amplify DNA, such isothermal amplification methods could one day be integrated in field-friendly sequencing technologies. It is also possible that in the future, microfluidic devices, which manipulate liquids at the submillimeter scale, could be used in 28 portable devices which could conduct extraction, amplification, and detection on the same analysis chip (Sackmann et al. 2014); microfluidics is starting to be used in DNA-based identification (Mahon et al. 2011; Rahman et al. 2015). At this point in time, there are several new technologies that show promise for one day becoming part of a more portable, field-friendly

DNA sequencer, but technology is not quite there yet.

The challenge of incomplete reference databases

Reference databases can be lacking for some taxonomic groups, and this can inhibit agency efforts to derive information via DNA barcoding. Previously, researchers have experienced limitations in making identifications due to incomplete reference databases for dipterans (Armstrong 2010), marine gastropods (Puillandre et al. 2009), and nematodes (Siddall et al. 2012); on the other hand, other groups such as economically important species of fish

(Hanner et al. 2011) and high-risk invasive invertebrate species (Briski et al. 2011) were found to be well-represented in databases. If found lacking for an application, agencies could take part in campaigns for improving the reference databases for those groups of interest. In fact, there already are such active DNA barcoding collaborations focused on improving reference databases for groups such as marine species (Trivedi et al. 2015), plant quarantine species (Bonants et al.

2010), and for flora and fauna of various regions (Zhou et al. 2011; Cardoni et al. 2015;

Gwiazdowski et al. 2015). However, for broad regions of interest such as the Great Lakes, there already exist sufficiently populated databases, according to preliminary assessments (Trebitz et al. 2015). Additionally, Pfrender et al. (2010) suggest that a coordinated effort between academia and agencies could easily complete reference databases for freshwater macroinvertebrates, a group used frequently for bioassessment, within a time period of 5 to 10 29 years (Pfrender et al. 2010); deficiencies in reference databases may be quickly remedied.

Although the possibility of incomplete database coverage for some taxa should be a consideration for agencies, it should not be a deterrent to using barcoding; many groups are well represented, and databases are rapidly growing due to large collaborative efforts that agencies themselves can take part of.

Conclusions

DNA-based identification has advanced extremely rapidly in recent years. DNA barcoding has been used in multiple agency applications, but currently, there has yet to be routine enforcement efforts in the United States using DNA barcoding. Future advances in DNA barcoding, such as improvements in the speed and cost effectiveness of analyses, in the portability of instruments, and in making DNA metabarcoding more quantitative may enhance the ability of government agencies to address some of the most egregious questions we face as a society today. While technology and protocols are developed sufficiently to incorporate into many agency procedures, caution should be exercised when attempting to make quantitative conclusions about bulk or environmental samples. However, in many applications, DNA barcoding is advantageous when morphology fails, and identifying the species present in fish fillets, herbal supplements, and bird strike remains are examples of applications where DNA barcoding is straightforward and readily-applied. The high prevalence of mislabeling of fish, meat, and herbal products, as revealed by DNA barcoding studies, may represent an area where agencies can use DNA barcoding to combat fraud. As long as limitations and uncertainties are taken into consideration, DNA barcoding is a powerful tool for identifying species, especially in cases where morphological identification fails. 30

CHAPTER II: DIET ANALYSIS OF MAUMEE RIVER FISHES USING CYTOCHROME C

OXIDASE (COI) DNA METABARCODING ― INSIGHTS INTO A CRITICAL PERIOD

Abstract

Early spring is a dynamic time for fishes in the Maumee River, and it is a period when numerous fishes enter the river to spawn. However, little is known about diet during this time, despite suggestions that the timing of spawning of one fish species and the migration into Lake Erie tributaries of other species can influence the extent of predation on fish early life stages (ELS).

This study sought to examine the diets of Maumee River fishes during early spring, with a special interest in discovering predation on ELS of walleye (Sander vitreus). Larval fish are quickly degraded morphologically in predator digestive tracts, and therefore, cytochrome c oxidase (COI) DNA metabarcoding of the gut contents of Maumee River fishes was conducted.

Walleye eggs and larvae were present when predators were collected, however, the number of fishes with sequences assigned to walleye was lower than initially expected. One female white perch (Morone americana), one male white bass (Morone chrysops), and two emerald shiners

(Notropis atherinoides) caught in the spawning grounds (Orleans Park) had sequences assigned to walleye. A relatively low density of walleye in the system, the presence of alternative prey items (e.g., chironomids), potentially lower overall feeding intensity by predator fishes near the onset of spawning, and/or turbidity in the Maumee River acting as a predation refuge may explain the lower than expected predation on walleye ELS; however, these explanations have yet to be evaluated. Sequences were recovered from 7 metazoan phyla, and the study confirmed molecularly the presence of several taxonomic groups, including 9 genera of chironomids. This study provided preliminary results that can be used to refine future molecular studies of early 31 spring diet in the Maumee River, and illustrated the utility of DNA barcoding to detect diverse prey items in animal gut contents. Future investigations should seek to confirm the observed lower than expected predation on walleye ELS in the Maumee River, and examine whether factors such as turbidity or predator spawning condition influence rates of predation.

Introduction

It has long been noted that recruitment, the addition of new individuals to a population, is highly variable from year to year in fishes (Sissenwine 1984; Gopalan et al. 1998; Karjalainen et al. 2000). In marine systems, it is widely believed that recruitment success is determined during early life history, and is regulated by processes such as predation and food-related mortality

(Leggett & Deblois 1994). Many theories have been developed to explain recruitment variability in marine fishes, and these same theories can be applied to Great Lakes populations (Pritt et al.

2014). Evidence suggests that abiotic factors are important regulators of recruitment by influencing food availability (i.e., “bottom-up processes”). Under that school of thought,

Cushing’s Match-Mismatch Hypothesis posits that recruitment is linked to food availability during the larval period, and that mismatches in temporal overlap between first-feeding larval fish and suitable prey can limit the recruitment strength of a cohort (Cushing 1972). Others have suggested the importance of another type of overlap, that of predators with prey, and have proposed that top-down processes primarily determine recruitment success. This school of thought has noted the importance of both the timing and extent of predation on early life stages

(ELS) (Kim & DeVries 2001; Berger & Wirth 2004; Mueller et al. 2006; Caroffino et al. 2010;

Vieira et al. 2012; Bachiller et al. 2015). 32

Piscine predation on ELS represents a large source of mortality for fishes in some systems: Johannessen observed an average of 15,000 to 20,000 herring eggs in cod stomachs, and concluded that predation could remove as much as 60% of total egg production in low deposition years (Johannessen 1980). Predation on eggs by piscine predators has been widely documented in marine/estuarine systems (Johannessen 1980; Richardson et al. 2011; Bachiller et al. 2015) as well as in rivers and lakes (Crowder 1980; Caroffino et al. 2010; Mychek-Londer et al. 2013); although there is abundant evidence of predation on eggs in the literature, few studies have quantitatively linked egg consumption to recruitment variation in fishes (Leggett & Deblois

1994), and predation is a poorly understood mechanism of recruitment variability.

Predation on larval fishes has been less frequently reported than on eggs, and some investigators have interpreted this as evidence that predation on larvae is less influential on recruitment than egg predation (Caroffino et al. 2010), despite others noting the difficulty of detecting predation on larval fishes (Kim & DeVries 2001). Indeed, studies that have examined predation on larval fishes generally have estimated predation indirectly (based on the disappearance of fish from laboratory feeding trials), have generally documented predation on larger bodied larvae (> ca. 8-10 mm TL), and have typically taken place under conditions where predator retrieval was shortly after ingestion, either due to the fact that it was a lab study or because predator collection occurred after mass release of larvae into the environment by investigators (Pepin et al. 1987; Cowan & Houde 1993; Rottiers & Johnson 1993; Huusko et al.

1996; Mason & Brandt 1996; Kim & DeVries 2001; Mueller et al. 2006; Haakana et al. 2007;

Haakana & Huuskonen 2009). The paucity of literature on field predation of larval fish is likely due to their rapid digestion; larvae are soft-bodied, lack fully ossified bones (see Figure 2.1), and have a large surface area to volume ratio (Great Lakes Fisheries Commission 1982). These 33 factors shorten digestion time, as confirmed by Schooley et al., who found that larval fish were essentially unidentifiable after only one hour post-ingestion at 20°C (Schooley et al. 2008).

Although visual examination of gut contents is commonly used to study predator-prey interactions (Hampton & Gilbert 2001; Zeldis et al. 2004; Jepsen et al. 2006; Costamagna &

Landis 2007; Flowers et al. 2011; Ohba 2011; Sakurai 2011; Yokota et al. 2011; Barnett et al.

2013; Furuichi 2014), for larval fish, it has proven impractical in the field, requiring thousands of stomachs to detect prey (Legler 2008). Piscivorous predation on larval fishes may be an underrepresented force contributing to recruitment variability in Great Lakes fishes.

Other methods such as stable isotope and fatty acid analyses provide information on diet, but have their own limitations, and generally provide more integrated assessments of long-term diet rather than the diet “snapshot,” that morphology offers. Stable isotopes of carbon (δ13C) and nitrogen (δ15N) can be used to estimate carbon flow and trophic position in food webs (Post

2002), and can be used in mixing models to estimate proportion of prey species to diet (Phillips

2001; Derbridge et al. 2012). However, in aquatic systems, a high degree of omnivory (Pusey et al. 2010; Blanchette et al. 2014) and isotopic signature overlap (Hardy et al. 2010) makes interpretation of data uncertain. Like stable isotopes, fatty acid (FA) biomarkers also can be used to estimate diet composition over longer periods of time (Galloway et al. 2012; Jo et al. 2013), and can be used quantitatively (Iverson et al. 2004). However, this method is also limited by the need to extensively sample the FA profiles of potential prey, and to know the consumer’s ability to synthesize or modify marker FAs (Galloway et al. 2012). These methods are ultimately not well-suited for capturing ephemeral events such as predation on fish ELS.

However, almost 90 years after Elton wrote about complex interactions in food webs

(Elton 1927), molecular methods have revolutionized trophic ecology (Carreon-Martinez & 34

Heath 2010), just as they have offered novel modes of inquiry in other areas of regulatory agency concern (reviewed in Chapter 1). DNA barcoding, the sequencing of genomic marker regions for taxonomic identification, has been successfully used to identify prey in stomach contents

(Bowser et al. 2013; Côté et al. 2013; Egeter et al. 2015), feces (Oehm et al. 2011; Zeale et al.

2011; Brown et al. 2012) , as well as regurgitated material (Barnett et al. 2010; González-Varo et al. 2014). DNA barcoding is highly sensitive, and even apparently empty stomachs can still contain amplifiable DNA (Hunter et al. 2012)N . ewly developed molecular techniques now allow detection of larval fish in fish digestive tracts for as long as 16 hours post-ingestion (Rosel

& Kocher 2002; Carreon-Martinez et al. 2011; Hunter et al. 2012; Ley et al. 2013).

New molecular studies of predation on ELS of fish in Lake Erie support that predation may play an important role in regulating recruitment. A recent application of DNA-based diet analysis in the western basin of Lake Erie studied predation on yellow perch larvae by fishes; it was estimated that 9% of yellow perch produced in the western basin of Lake Erie could be consumed within 30 days by age 1+ white perch, an invasive species (Carreon-Martinez 2012;

Carreon-Martinez et al. 2014).

Like yellow perch, walleye (Sander vitreus) is also an important sportsfish in Lake Erie, and a species that exhibits high interannual recruitment. The last strong year class was over a decade ago in 2003, and the population has since then had only infrequent moderate year classes to buoy the population (Kayle et al. 2015). There is strong evidence that abiotic variables influence walleye recruitment, including support for the importance of factors affecting the temporal overlap between larval fish and zooplankton production (May 2015), rates of spring warming (Busch et al. 1975), advection of larvae towards suitable nursery areas (Zhao et al.

2009), and the influence of stochastic events such as the destruction of eggs on spawning 35 grounds (Roseman et al. 2001). The Maumee River is an important tributary of Lake Erie; although it contributes less than 15% of the water that flows into Lake Erie’s western basin, it contributes more than half of the suspended sediment input (Manning et al. 2013). Lake Erie is highly productive, and has several recreationally and commercially important fisheries (Ohio

Division of Wildlife 2015); understanding recruitment variability is vital for maintaining these high quality fisheries. DNA barcoding has the potential for describing diet in the Maumee River,

Ohio, and for helping understand the role that predation may play in walleye interannual recruitment variability.

Some work has examined predation on walleye eggs in Lake Erie and its tributaries

(Schaeffer & Margraf 1987; Roseman et al. 1996, 2006); however, no studies have detected predation on walleye larvae. Additionally, several studies have suggested that abiotic and biotic factors may interact to determine year class success, such as increased cumulative predation on

ELS of walleye during years of slower incubation of eggs due to lower mean temperatures

(Roseman et al. 1996; Mion et al. 1998). There is also the possibility that incoming spawning fishes such as white bass (Morone chrysops), and the invasive white perch (Morone americana), which also use the Maumee River (Mapes et al. 2015), may consume walleye as larvae drift downstream to Lake Erie; white perch are already known to be walleye egg predators in the nearby Sandusky River (Schaeffer & Margraf 1987). In the Maumee River, small cyprinid minnows such as the bluntnose minnow (Pimephales notatus) and the emerald shiner (Notropis atherinoides), along with gizzard shad (Dorosoma cepedianum) are numerically dominant, while channel catfish (Ictalurus punctatus), freshwater drum (Aplodinotus grunniens), and common carp (Cyprinus carpio) are dominant in terms of relative biomass (Ohio Environmental

Protection Agency 2014). These abundant species also use the Maumee River as spawning 36 habitat (Mapes et al. 2015), and could contribute to recruitment variability in walleye via predation on walleye ELS.

Larval walleye abundance decreases from upstream to downstream in the Maumee River, and in-river mortality has been estimated to be as high as 65% (Dufour 2012); part of this mortality may be due to in-river predation on larvae. Studies have suggested that short-term feeding events prior to spawning can rapidly affect egg quality (e.g., short-term ingestion of essential fatty acids and subsequent deposition in eggs) (Watanabe et al. 1984; Fuiman & Faulk

2013); consumption of walleye ELS prior to spawning by Maumee River fishes may represent such a short-term feeding event.

In addition to the importance of understanding predation on ELS of fishes, having a better general understanding of the diet of fishes during this important period is of value to fisheries managers. Since molecular techniques can amplify remnant DNA from apparently empty stomachs (Carreon-Martinez et al. 2011), and are able to detect rare prey items (Jarman et al. 2013), they can generate data from very small sample sizes (Deagle et al. 2005; Baerwald et al. 2012). Pressures on ecological communities can have widespread effects on trophic web structure (Pauly et al. 1998; Harvey et al. 2012; McCary et al. 2016); having a baseline knowledge of trophic interactions informs managers seeking to mitigate stress on ecosystems, and to understand changes in complex trophic webs such as those of the Great Lakes (Madenjian et al. 2002) should a new stressor appear (e.g., introduction of Asian carp) (Zhang et al. 2016).

Therefore, DNA metabarcoding is an appropriate choice for assessing both general diet and predation on ELS of fishes. 37

In order to gain a better understanding of predation on walleye ELS and on alternative prey during this time of the year, a study using DNA metabarcoding was conducted using a short, 313 bp fragment of cytochrome c oxidase (COI) to amplify DNA from the gut contents of benthic and pelagic feeding fishes in the Maumee River at the time that ELS of walleye and other species were present.

Figure 2.1. Developmental stages of Sander vitreus; A) Early yolk sac larvae (ca. 6.8 mm TL),

B) Late-yolk sac larvae (ca. 8.5 mm TL), and C) Post-yolk sac larvae (ca. 11.1 mm TL; Great

Lakes Fisheries Commission 1982). Note that larval walleye are soft-bodied, have high surface area: volume ratios, and lack fully ossified bones, all factors that would make them quickly digested by fishes. 38

Methods

Collection of predators

The two sites on the Maumee River used in this study (Figure 2.2) differ in terms of bathymetry. The Orleans Park area (OP) serves as important spawning habitat for walleye and other species (Mion et al. 1998), and is generally shallow, with depths less than 1 m, while the

Rossford Marina area (RM) (located approximately 9 km downstream of OP) is deeper (ca. 2-4 m in depth). Predation on walleye eggs and larvae coming off of the gravel beds may occur at

OP, and on drifting larvae at RM, therefore, both locations were sampled for predators.

Seine, gill net, and electrofishing collections occurred on April 29 and on April 30 in the

OP area (41°33'37.50"N, 83°38'36.00"W) and seine and gill net collections occurred on May 6 in the RM area (41°36'58.35"N, 83°33'48.93"W). All collections occurred during daytime, except for evening electrofishing collections. Emerald shiners (Notropis atherinoides) and bluntnose minnows (Pimephales notatus), hereafter referred to as “small cyprinids,” were frozen whole after collection. Large-bodied fishes including channel catfish (Ictalurus punctatus), common carp (Cyprinus carpio), goldfish (Carassius auratus), gizzard shad (Dorosoma cepedianum), white bass (Morone chrysops), white perch (Morone americana), and freshwater drum

(Aplodinotus grunniens) were also captured. In all cases, large-bodied fishes were placed on ice or in coolers as soon as possible after collection; entire digestive tracts of large-bodied fishes were removed and frozen at -20 to -80°C until further processing of gut contents. Metadata on collected predators is summarized in the appendix (Table A.1).

Figure 2.2. Map of the two fish collection locations on the Maumee River, a major tributary of

Lake Erie in Ohio. Bathymetric perspective of the Maumee River with approximate depth and length of each section of the river noted (Bottom). Spawning habitat begins approximately at

Orleans Park, and continues upstream 28 km to Providence Dam.

Timing of larval walleye drift

Ichthyoplankton samples were collected from the Orleans Park (OP) and Rossford

Marina (RM) areas in order to affirm that walleye larvae were present at the time predators were collected. Ichthyoplankton and egg samples were collected at OP and at nearby Buttonwood Park

(BP) (located about 2.5 km upstream of OP, 41°32'54.45"N, 83°40'15.49"W), which were considered to be walleye spawning grounds, by wading into the river to a spot approximately

0.75 m deep, and sampling for 5 minutes per sample using a 0.5 m diameter, 500 μm ichthyoplankton net. Surface samples were taken in the top 0.5 m of the water column, and non- quantitative bottom samples were taken by kicking the area in front of the net while it was resting on the bottom for 5 minutes. Ichthyoplankton density was calculated from surface samples only, while the presence of eggs was noted semi-quantitatively from bottom samples.

Samples were collected in the morning (0800 to 1000 hours) and in the evening (1800 to 2000 hrs), but larval walleye densities from both time periods were used to calculate average daily density. During May, samples were collected downstream of the spawning grounds at RM using a 0.75 m diameter, 500 μm ichthyoplankton net for 3 to 5 minutes. On May 5, May 8, May 10, and on May 11, samples were collected in the RM area at the surface. A General Oceanics model flowmeter suspended on the mouth of the net was used to calculate the volume of water sampled.

Extraction of predator gut contents

Fish digestive tracts and small cyprinids were thawed until they could be dissected and manipulated. Effort was made to prevent samples from completely thawing in order to avoid degradation of DNA by enzymes that can be present in gut contents and in the environment

(Palka-Santini et al. 2003). Samples were processed in sterile petri dishes placed on a bed of ice 41 for the entire duration of processing. Between samples, fresh ice was used, and trays and the surrounding area were wiped with 20% or greater bleach solution followed by rinsing with

Nanopure RO/DI water. Metal equipment was washed with laboratory detergent, immersed in

95% ethanol, and flame sterilized (Côté et al. 2013). All other equipment was washed with detergent, placed in a bath of 20% or greater bleach solution for at least 30 minutes, and then rinsed vigorously with Nanopure to remove bleach.

Consumed material and whole predator digestive tracts of small cyprinids were homogenized due to small size and to avoid losing gut contents. For fishes containing true stomachs (channel catfish, white bass, white perch, and freshwater drum), entire stomach contents were homogenized. Consumed material from the entire digestive tract was homogenized for fishes lacking true stomachs (goldfish, common carp, and gizzard shad). For diet items that were large (e.g., digested crayfish material), a small piece (less than 3 mm2) was subsampled and left in the homogenate, while the remainder was stored and refrozen. Samples were homogenized in cold 95% ethanol using dissection scissors to cut material and using small, glass tissue homogenizers to grind material into small pieces. Homogenized bulk samples from gut contents were placed in plastic Nalgene bottles (for large-bodied fishes) or 1.5 mL microcentrifuge tubes for small cyprinids. Bulk samples were shaken vigorously, and a disposable transfer pipette was used to take a subsample of the gut content slurry and to place it in a fresh, 1.5 mL tube.

Subsamples were agitated with a motorized pestle pellet (Kimble Chase) to ensure homogeneity, and then centrifuged at 13,000 rpm for 8 minutes with a BioRad 16K microcentrifuge. The ethanol supernatant was discarded and subsamples were left to dry overnight. Subsamples were then desiccated in an Eppendorf Vacufuge for 8 minutes to ensure any remnant ethanol was removed. A small piece of dried tissue from each gut content subsample was placed in a well of 42 a 96-well assay plate; 150 μL of digestion buffer (100 mM NaCl, 50 mM Tris HCl, pH=8, 10 mM EDTA, pH=8, and 17.3 mM SDS) and 2 μL of proteinase K (20 mg/mL stock solution) were added to each well. Samples were then digested in a dry shaker set at 37°C for 4 hours with

300 rpm agitation to break up tissue. This protocol was previously successful when used by

Carreon-Martinez et al. to DNA barcode stomach contents from Lake Erie fishes (Carreon-

Martinez et al. 2011).

DNA extraction, library preparation, and sequencing

DNA was extracted at the Great Lakes Institute for Environmental Research (GLIER) using a Tecan Freedom Evo 150 Liquid Handling Platform via the plate-based extraction method

(Elphinstone et al. 2003). The classic COI DNA barcoding primers for animals amplify a 658 bp fragment (Folmer et al. 1994); however, DNA from gut contents can be much shorter due to degradation (Meusnier et al. 2008). Therefore, a modified primer set derived from the Folmer et al. primers was designed to target a 313 bp region of COI internal to the region amplified by the

Folmer primer set. This new universal primer set was used previously to amplify all metazoan sequences from the gut contents of coral reef fishes (Table 2.1; Leray et al. 2013). PCR amplification was conducted in a total volume of 25.0 μL and used 0.5 μL each of forward and reverse primers (10 μM working concentration), 2.5 μL of 10X Buffer, 0.5 μL of 10 μM dNTP,

0.5 μL of 50 mM MgCl2, 0.25 μL of bovine serum albumin (BSA), 0.1 μL of Taq polymerase (5 units/μL), and 1 μL of genomic DNA. PCR conditions were an initial denaturation step at 94°C for 2 minutes, and 10 cycles of denaturation at 94°C for 30 seconds, annealing at 60°C for 30 seconds, and extension at 72°C for 1 minute, followed by a final extension at 72°C for 5 minutes.

Amplicons were multiplexed using 10-12 bp multiplex identifiers (MIDs) that were adapted onto 43 amplicons during library preparation. 20 μL of amplified COI PCR product from each reaction was twice purified with an Agencourt Ampure XP kit for PCR cleanup of products. Purified PCR product (10 μL) from each well was pooled into a centrifuge tube. DNA was precipitated using

1/10th volume sodium acetate (3M, pH=5.2) and 1000 μL room temperature isopropanol, followed by a second precipitation step with 1000 μL of 75% ethanol. 10 μL of DNA from each plate was run on 1% agarose, and a band of the expected size excised and purified from the gel.

DNA was analyzed using an Agilent 2100 Bioanalyzer to ensure library sequence size and quality were appropriate, and sequenced using an Ion Torrent PGM Next Gen Sequencer with an

Ion 318™ Chip Kit. No blocking probes were used to block predator-specific DNA from amplifying in order to prevent possible co-blocking of fish prey DNA (Piñol et al. 2014).

Table 2.1. Primers and adapters used to generate the Maumee River fish diet metabarcoding dataset.

Primer Sequence (5’ to 3’) Reference Name

UniA ACCTGCCTGCCG Ion Torrent Tailed Primer (Adapter) mlCO1intF GGWACWGGWTGAACWGTWTAYCCYCC Forward primer (Leray et al. 2013)

jgHCO2198 TAIACYTCIGGRTGICCRAARAAYCA Reverse primer (Leray et al. 2013)

Ion Torrent GAT Ion Torrent Adapter Sequence Adapter

UniB ACGCCACCGAGC Ion Torrent Tailed Primer (Adapter) 44

Bioinformatics and data analysis

Sequences from the two 96-well plates were filtered using tools contained in Galaxy

(Blankenberg et al. 2010; Goecks et al. 2010; Martin 2011), and using scripts written in Python

(Figure 2.3). Quality and quantity of the dataset were assessed using FastQC (Figures B.1 and

B.2). Sequences that lacked a primer or adapter, were missing a MID, were < 80 bp in length, or had < 90% of its length with a PHRED score lower than 20 were removed from the dataset.

Duplicate sequences were removed, and sequences were queried against NCBI’s nucleotide database using BLAST (megablast). Sequences were not clustered based on similarity prior to

BLAST analysis. Blast-xml files generated from the BLAST search were converted into Excel files for analysis using tools on Galaxy as well as Python scripts written by the author. The dataset was reduced to only the top hit for each sequence by sorting hits by the highest percent identity match followed by the lowest e-value using an Excel macro, and then by using a Python script to subset the file. Visual inspection of the Excel files indicated that the putative taxonomic identity of the top hit was the same as the subsequent hit. BLAST results with less than a 95% identity match between the query and subject sequences were discarded

(filter_low_percent_id.py).

Taxonomic assignment and summary files were generated using scripts in Python (Figure

2.3). A script (fetch_taxa.py) used the GI number of each hit in the dataset in order to look up the

Taxonomy ID (TaxID) Number associated with it, and then used it to fetch taxonomic rank data from NCBI’s Taxonomy Database and dump it into the Excel files. Taxonomic assignments for reads were then combined and summarized for each predator species using a Python script to remove redundancy in taxonomy (summarize_taxa.py). 45 reads were then combined and summarized for each predator species using a Python script to remove redundancy in taxonomy (summarize_taxa.py).

Sequences were assigned manually to the appropriate taxonomic level in Excel. Reads that were related to fungi and bacteria were eliminated from the final dataset, as were obvious environmental contaminants, such as sequences from farm animals and humans (using remove_contaminants.py). Taxa unlikely to be present in the Maumee River based on knowledge of the species present from various guides (Keller & Krieger 2009; Stearns & Krieger 2010;

Barbiero & Warren 2011; Bolton 2012; Ohio Environmental Protection Agency 2014) were assigned at a higher taxonomic level (e.g., Notropis rubellus was assigned to the genus level,

Notropis). Taxa that were represented by only one sequence in the gut contents of a predator species were eliminated.

Figure 2.3. Processing workflow for analysis of next generation dataset. Pre-processing steps in columns 1 and 2 were conducted in Galaxy (usegalaxy.org), while steps in column 3 were conducted via NCBI (ncbi.nlm.nih.gov/) or with Python scripts written by the author. Tool attribution and/or source is represented in parentheses. 47

Results

Both walleye larvae (Figure 2.4) and eggs (Table C.1) were present at spawning areas and walleye larvae (Figure 2.5) were present in downstream areas of the Maumee River during predator collection periods. From April 28 to April 30, the period during which sampling for predators at OP occurred, mean larval walleye density ± 1 SD was 0.34 walleye/m3 ± 0.22 at OP and BP (spawning grounds, Figure 2.4), and on May 5 and May 8 (the period when RM predators were collected on May 6), mean larval walleye density ± 1 SD was 0.20 walleye/m3 ±

0.11 (Figure 2.5). Large-bodied white sucker larvae (Catostomus commersonii) were also recorded at low (generally < 0.1 larvae/m3) density in the spawning areas of the Maumee River

(Table C.2), and both Morone spp. eggs and larvae were observed as early as April 24 (Table

C.3).

Next generation sequencing of 133 fish gut contents from the Maumee River yielded

4,911,870 reads. After processing the dataset, 1% of the original sequences remained (48,610 unique reads, or 100,668 reads with duplicates included, Table D.1).

Sequences assigned to Sander vitreus were present in four fish from three species, all captured at the spawning grounds (OP) (Tables E.2, F.2, and G.2). One female white perch, one male white bass, and two emerald shiners had sequences assigned to walleye. The white bass also had sequences that were assigned to annelid worms, Moxostoma spp., white sucker

(Catostomus commersonii), and chironomids, and the white perch had sequences assigned to C. commersonii, Moxostoma spp., and annelid worms. Besides walleye, the two emerald shiners had sequences assigned to chironomids, mayflies, and rotifers. There were 37 reads assigned to

S. vitreus in the white perch, with 16 of those sequences represented by one read each; the three

48 sequences assigned to walleye in white bass were all represented by one read each (Tables E.2 and F.2). The two emerald shiners each had two sequences (both represented by one read) assigned to walleye (Table G.2).

Of all of the predator species, white perch (M. americana) (n =15, twelve from RM and three from OP) had the highest proportion of reads not assigned to ray-finned fishes: 39%, 50%, and 11% of the reads were assigned to annelids, arthropods, and ray-finned fishes, respectively

(Figure 2.6). In terms of frequency of occurrence, many of the white perch had sequences assigned to A. grunniens (67%), N. atherinoides (53%), M. macrolepidotum (33%), and D. cepedianum (20%). Of the sequences that were assigned to arthropods, the majority (99%) were assigned to chironomids, primarily from the genus Procladius. However, a small number of reads from a mayfly (Caenis latipennis) were found in one white perch, and 40% of the fish had sequences assigned to Bythotrephes longimanus. Sequences assigned to annelid worms and rotifers were present at low frequency (Tables E.1 and E.2).

In contrast to white perch, the reads from white bass (M. chrysops) (n=22, evenly split between RM and OP) were predominately (98%) assigned to ray-finned fishes, however, sequences were also assigned to arthropods, annelids, nemerteans, and rotifers (Figure 2.6).

Many white bass had sequences assigned to N. atherinoides, A. grunniens, C. carpio, and M. macrolepidotum (91%, 55%, 41%, and 18% of white bass, respectively). An OP male had reads assigned to P. flavescens, and a RM male had reads assigned to burbot (Lota lota). Most of the sequences that were assigned to arthropods were from chironomids, and sequences assigned to chironomids were present in 32% of the white perch, collectively from 4 genera. Sequences from caddisflies (Cheumatopsyche spp.), mayflies (E. simulans), and B. longimanus were detected at

49 low frequency. Sequences assigned to annelids were present at low frequency, and were assigned to L. terrestris and D. montifera. Trace numbers of reads assigned to nemerteans were found, as well as reads assigned to the rotifer, Brachionus urceolaris (Tables F.1 and F.2).

For sequences from emerald shiner (N. atherinoides) (n=48, collected from both OP and

RM), 63%, 26%, and 8% were assigned to ray-finned fishes, rotifers, and arthropods, respectively, with small numbers assigned to annelids, bryozoans, and to platyhelminths (Figure

2.6). 46%, 38%, 35%, and 19% of the emerald shiners had C. carpio, A. grunniens, P. notatus, and M. chrysops assigned to them, respectively. An OP individual had a sequence assigned to P. flavescens. Most of the arthropod sequences were assigned to chironomids; 42% of the emerald shiners had sequences assigned to chironomids, and overall, seven unique chironomid genera were present. 17% and 75% of the emerald shiners had sequences assigned to B. cepedianum and to rotifers, respectively, the majority of which were from RM. A potential platyhelminth parasite assigned to the trematode genus, Posthodiplostomum, was identified in three OP emerald shiners.

A nonindigenous parasitic cestode commonly known as the Asian tapeworm, Bothriocephalus acheilognathi, was found in one RM emerald shiner. Annelid sequences were assigned to L. terrestris and D. montifera. The bryozoan, Plumatella casmiana, was also detected in two RM emerald shiners (Tables G.1 and G.2).

Prey sequences from the gut contents of freshwater drum (A. grunniens) (n=11, all from the downstream “drift” habitat, RM) were primarily (61%) from ray-finned fishes, with arthropod sequences also making up a high proportion (36%) of the reads (Figure 2.6).

Sequences were assigned to several cyprinid species (C. carpio in 36% and N. atherinoides in

73% of freshwater drum). Shorthead redhorse (Moxostoma macrolepidotum) was detected in

36% of the freshwater drum, and low numbers of reads assigned to M. americana and M. chrysops were also detected in four (36%) of these predators. Arthropod sequences were primarily (>99%) from chironomids, but Bythotrephes longimanus, an invasive cladoceran, was also present in two small (<150 mm TL) freshwater drum. Four of these predators (36%) had reads assigned to chironomids from a total of 4 chironomid genera. Small numbers of reads assigned to nemerteans and oligochaetes were also found in a few fish (Table H.1).

While only one individual was sampled, reads from the goldfish (C. auretus) collected from OP were assigned to several fishes, including smallmouth buffalo (Ictiobus bubalus), bigmouth buffalo (Ictiobus cyprinellus), and largemouth bass (Micropterus salmoides). One chironomid taxon was also present (Table I.1).

The majority (97%) of the reads from common carp (C. carpio) (n=12, from both OP and

RM) were from ray-finned fishes, however, a small number of reads assigned to annelids, arthropods, nemerteans, and rotifers were also recovered (Figure 2.6). Most (92%) common carp had N. atherinoides present. Two common carp also had Perca flavescens, a recreationally and commercially important sportfish species detected in gut contents (one common carp from each sampling location). Arthropod sequences were assigned to two chironomid genera and a terrestrial millipede species (Cylindroiulus caeruleocinctus) at low frequency. Lumbricus terrestris, a widely distributed species of earthworm, was detected in two common carp from

OP. Trace amounts of nemertean sequences were detected in one common carp, and a species of rotifer (Brachionus urceolaris) was detected in another common carp from RM (Table J.1).

Few reads in the final dataset were from gizzard shad (D. cepedianum) gut content samples (129 sequences from eight individuals, collected from both OP and RM, Table K.1).

Sequences from D. cepedianum were almost exclusively from ray-finned fishes (98%, Figure

2.6). In terms of frequency of occurrence, 75% and 63% of the gizzard shad had sequences assigned to freshwater drum and emerald shiner, respectively. Sequences assigned to shorthead redhorse (M. macrolepidotum) and the genus Moxostoma were detected in one OP gizzard shad.

One OP gizzard shad had sequences assigned to M. chrysops, and another from OP had sequences assigned to M. americana. A few sequences from a species of mayfly (Ephemera simulans) were detected in one OP gizzard shad (Table K.1).

Most (95%) of the reads from four I. punctatus caught at OP were from ray-finned fishes, however, sequences from annelid worms and arthropods were also detected (Figure 2.6). Most of the fish (75%) had emerald shiner detected in stomach contents, while sequences assigned to

Moxostoma sp./M. macrolepidotum, C. carpio, A. grunniens, P. notatus and Morone spp. were also detected. Mayfly (E. simulans), terrestrial millipede (C. caeruleocinctus), chironomid (P. bellus), earthworm ( L. terrestris), and leech (D. montifera) sequences were also detected (Table

L.1).

The majority (99%) of reads from bluntnose minnow (P. notatus) (n=12 individuals, all from OP) were from ray-finned fishes, with a low number of reads assigned to phyla Arthropoda and Platyhelminthes (Figure 2.6). Of those reads, most (61%) of the chordate reads were from genus Notropis (present in 100% of P. notatus), with sequences from C. carpio detected in two bluntnose minnows. Two bluntnose minnows had a read assigned to lake sturgeon (Acipenser fulvescens). Sequences assigned to B. longimanus and to a chironomid genus were found at low frequency (Table M.1).

Amplification of DNA likely derived from the gut lining of the predator fishes themselves was present in the NGS dataset ― of the reads remaining after filtering, 72% could be attributed to the predator gut itself (Figure N.1). Both of the species of small cyprinids, which had their entire digestive tracts homogenized, showed a high proportion of the total number of reads attributed to the predator species itself (“self reads”): N. atherinoides and P. notatus had 94 and 38% of reads assigned as self-reads, respectively. Species containing a true stomach had lower percentages of self-reads, with the exception of A. grunniens (93% of reads assigned as self-reads, Figure N.1). All other species had percentages of self-reads lower than 15% (Figure

N.1, C. auretus is not shown due to low sample size, but had 7% of its sequences assigned as self-reads).

9.0 ) 3 8.0

7.0

6.0

5.0

4.0

3.0

2.0

1.0

Mean Daily Larval Walleye Density (larvae/m Walleye Mean Daily Larval 0.0 May 4 May 6 May 8 May 9 May May 10 May 11 May April 29 April May 1 (3)May 2 (2)May 3 (4)May 5 (1)May 7 (2) May May 12 (2)May April 23 (2) April 24 (3) April 25 (2) April 26 (4) April 27 (5) April 28 (2) April 30 (3) April

Figure 2.4. Mean daily density of larval walleye ± 1 standard deviation in Orleans Park (OP) and

Buttonwood Park (BP) (spawning grounds) on selected dates in 2014 with sample size represented in parentheses. Five of the 35 samples were from BP, which is ca. 2.5 km upstream of OP, and also serves as important spawning habitat for fishes. Five minute surface samples were collected in the top 0.5 m of the water column with an ichthyoplankton net; morning and evening samples were combined to calculate daily density estimates. Collection of predators at OP occurred on

April 29 and April 30.

0.40 ) 3 0.35

0.30

0.25

0.20

0.15

0.10

0.05 Mean Daily Larval Walleye Density (larvae/m Walleye Mean Daily Larval 0.00 May 6 May 7 May 9 May 5 (4) May 8 (3)May May 10 (3)May May 11 (15)May

Figure 2.5. Mean density of walleye larvae ± 1 standard deviation in the Rossford Marina (RM) area on selected dates in 2014 with sample size in parentheses. Predators were collected at RM on May 6. Samples were collected via three to five minute tows with an ichthyoplankton net at the surface. The RM area was considered to be the area downstream of OP to a downstream bridge (located at 41°38’27.36”N, 83°32’1.00”W).

7429 4959 745 2770 6212 4405 128 100% 1785

90% Rotifera

80% Platyhelminthes 70% Nemertea 60%

50% Chordata

40% Bryozoa

Percent (%) of total reads total of (%) Percent 30%

20% Arthropoda

10% Annelida 0%

Figure 2.6. Percent of total sequences from each predator species assigned to each metazoan phylum with sample size for each species represented in parentheses. Reads from the predators themselves (“self-reads”) were excluded, and predators from both sampling locations (OP and

RM) were pooled. Total number of reads per species (self-reads excluded) represented above each bar.

Discussion

Based on previous evidence that there is extensive predation on walleye eggs in the

Sandusky River and Lake Erie reefs (Wolfert et al. 1975; Schaeffer & Margraf 1987; Roseman et al. 1996, 2006), we expected that there would also be high rates of predation on walleye early life stages (ELS) in the Maumee River. We sought to 1) determine which fishes were preying on walleye ELS and 2) determine whether alternative prey taxa were also present in the diets of fishes. However, only 3% (two emerald shiners, one female white perch, and one male white bass) of the 133 fishes in the dataset had sequences assigned to S. vitreus, all of which were captured in the spawning area, Orleans Park (OP). The low prevalence of S. vitreus in the diet may be due to a few factors, including relatively low density of walleye larvae and eggs in the river (Figures 2.4 and 2.5, Table C.1), reduced predation efficiency due to high turbidity, and/or low rates of feeding by spawning or near-spawning predators.

There was a lower density of walleye larvae in the Maumee River than previously reported at peak density. Mean daily larval walleye densities at OP (spawning area) during predator sampling (0.34 walleye/m3, Figure 2.4) were about a factor of 10 lower than peak larval walleye densities observed by Dufour downstream of the spawning grounds in 2011 (Dufour

2012); on the other hand, estimates of walleye egg abundance (although not quantitative) indicated that walleye eggs were available for consumption at OP when predators were collected

(Figure C.1 ). Larval walleye were also present in the Rossford Marina (RM) area (located ca. 9 km downstream of spawning grounds, Figure 2.2) while sampling (0.20 walleye/m3, Figure 2.5), and were similar to peak densities encountered downstream by Dufour in 2010 and 2011 (ca.

0.08 and 0.5 walleye/m3 in 2010 and 2011, respectively) (Dufour 2012). Lower density may be 57 one reason why high rates of predation were not found, and indeed, there is evidence that predation on larval fishes may only be detected at very high larval densities (Haakana et al.

2007). Additionally, the presence of other prey in the next generation sequencing (NGS) dataset

(e.g., chironomids and annelids) may explain apparently low observed rates of predation on walleye ELS; when alternative prey are available, lower rates of predation on larval fishes have been observed (Margulies 1990; Huusko et al. 1996; Cao et al. 2015).

Another potential explanation for low predation on walleye ELS may be that turbidity in the Maumee River acts as a predation refuge from visually feeding predators. Reichert et al.

2010 found that the turbid Maumee River plume enhances recruitment of larval yellow perch to the juvenile stage relative to the less turbid Detroit River plume, and noted that this differential survival did not appear to be due to bottom-up (i.e., enhanced prey availability) processes

(Reichert et al. 2010). A later study confirmed this differential survival (Carreon-Martinez et al.

2015), and, using molecular genetics to quantify predation rates, support ed that turbidity-related top-down effects underlie the survival difference between plumes (Carreon-Martinez et al.

2014). Turbidity at the Maumee River mouth is higher than in the Maumee Bay during spring

(ca. 3 to 4 times higher in NTU; Ecology and Environment Inc. 2014), potentially protecting downstream migrating larvae in the river. Research on how turbidity acts as a predation refuge suggests that it both reduces the success rate of visually feeding predators (Gregory & Levings

1998; Gadomski & Parsley 2005; Lehtiniemi et al. 2005; Ohata et al. 2011; Ferrari et al. 2014) and alters predator-avoidance behavior (i.e., increased larval fish foraging in turbid vs. clear water habitats in the presence of predators; Lehtiniemi et al. 2005; Pangle et al. 2012). Several studies have found (Gray et al. 2012; Salonen & Engström-Öst 2013) or proposed (Mion et al.

1998) negative effects of turbidity on fish eggs and larvae. However, if turbidity serves as a 58 predation refuge from visually feeding predators such as white perch, white bass, and emerald shiners, there may be a net positive aspect of increased turbidity for larval fishes. Whether turbidity also affects the foraging success of benthic feeding fishes in addition to planktivorous fishes is less studied in the literature, and it is not well-understood whether or not turbidity reduces benthic feeding success .(,i.e feeding success on fish eggs); however, some studies have shown less successful benthic foraging under high turbidity (Harvey & White 2008; Murray et al. 2016; Swanbrow Becker et al. 2016). Overall, higher turbidity may decrease the reaction distance of visually feeding predators (Miner & Stein 1996; Sweka & Hartman 2003) in the

Maumee River, reducing foraging efficiency on larvae and/or eggs. Whether turbidity is acting as a predation refuge in the river needs to be investigated further.

A third possible explanation for the low observed rates of predation on walleye ELS is that predators may have had reduced/ceased feeding with the onset of spawning. It has been frequently cited in the literature that spawning fishes cease or significantly reduce feeding during spawning (Schaeffer & Margraf 1987; Rutaisire & Booth 2005; Davoren et al. 2006). However, studies have also found the opposite behavioral response to spawning, and support that spawning fishes still actively feed (Watanabe et al. 1984; Peterson et al. 1999; Chen & Yang 2005;

Haakana et al. 2007; Michalsen et al. 2008; Harris & McBride 2009; Knapp et al. 2014; Butler et al. 2015). The explanation that predation rates on walleye ELS were low due to the cessation of feeding in spawning fishes is unlikely given the presence of other prey items in the NGS dataset.

An example of the discord between the expected cessation of feeding predicted from the literature (Schaeffer & Margraf 1987) and the presence of prey items in our NGS dataset is white perch. In the nearby Sandusky River, Schaeffer and Margraf (1987) noted a pattern of extensive white perch consumption of walleye eggs and few empty stomachs when temperatures were 8- 59

10°C, but as peak spawning occurred at 15-18°C, feeding rates dramatically slowed and the majority of stomachs were empty (Schaeffer & Margraf 1987). White perch and white bass at OP were captured when mean temperatures were still below 5°C, but those caught at RM could have ceased feeding at temperatures slightly below 15°C (Figure O.1). Although a large proportion of white bass and white perch (73%) were found to have highly digested gut contents (unpublished notes), the metabarcoding dataset supported that they were still feeding; 41 and 73% of the white bass and white perch had sequences assigned to arthropods and/or annelids, respectively, suggesting active feeding. Likewise, 50% of the emerald shiners had sequences suggesting active feeding on bryozoans, arthropods, and/or annelids, despite also having apparently highly digested gut contents.

These three possible explanations for low observed predation rates on walleye ELS are not mutually exclusive, but, of the three, 1) low density of walleye ELS/presence of alternative prey and 2) turbidity acting as a predation refuge seem the most likely explanations because predators appeared to still be actively feeding. While it may be possible in the future to differentiate predation on eggs vs. larvae (e.g., immunological detection of egg proteins; Krautz et al. 2003; Taylor 2004; Taylor & Danila 2005; Santos-Neto et al. 2010), we were unable to differentiate predation on walleye eggs vs. larvae because the DNA detected is the same between lifestages. However, the presence of benthic prey items (e.g., chironomids, mayflies, and annelids), as well as the fact that the four fishes containing walleye were found only in OP suggests egg predation, although predation on drifting larvae was possible.

Of the three species with walleye sequences in gut contents, two of them (emerald shiner and white bass) have not been previously shown to consume ELS of walleye. It is not 60 surprising that N. atherinoides was found to consume walleye ELS given that a congener is known to eat walleye eggs (Wolfert et al. 1975; Corbett & Powles 1986; Roseman et al. 2006), however, with their numerical abundance in spring, emerald shiners may represent an important source of mortality for walleye during their first weeks of life. Although no data could be found on their specific abundance in the Maumee River during early spring, Detroit River seining data indicated that spawning emerald shiners were the numerically dominant species in May, comprising 78% of the spring catch (Lapointe et al. 2007). In summer and fall, emerald shiners have also been found to be one of the most abundant species in the Maumee River (Ohio

Environmental Protection Agency 2014a). Despite this abundance and the fact that they have been noted to be egg predators in other lake systems (Schaap 1989), none of the focused studies that assessed predation on walleye eggs examined emerald shiner gut contents (Wolfert et al.

1975; Schaeffer & Margraf 1987; Roseman et al. 1996, 2006). The consumption of walleye ELS may also be an interesting ontogenetic reversal of predation because emerald shiners are important prey for age-1 and older walleye (Ohio Division of Wildlife 2015). Future research might look further into this relationship, and investigate whether growth rates of larval walleye influence the degree to which they are preyed upon by emerald shiners, and if they reach a predation “size refuge” (Olson 1996). Likewise, predation on walleye ELS by white bass has not been previously documented; studies found no evidence of walleye eggs in white bass stomachs

(Schaeffer & Margraf 1987; Roseman et al. 2006), and yet, another study found predation on unidentified fish larvae by white bass (Legler 2008). White perch appear to be more opportunistic than white bass, switching between benthic and pelagic prey when advantageous, and are known to be voracious fish egg predators (Schaeffer & Margraf 1987; Danehy & Ringler

1991; Roseman et al. 2006; Couture & Watzin 2008), including on walleye eggs (Schaeffer 61

1984; Schaeffer & Margraf 1987; Roseman et al. 2006). As white perch have been shown to consume larval fish under various turbidity levels (Monteleone & Houde 1992), and have been observed to consume fish larvae in Lake Erie (Legler 2008), they may well have been consuming larval walleye in this study. With large numbers of white bass and white perch entering the

Maumee River during early spring to spawn (Ohio Division of Wildlife 2015), a high degree of overlap between predators and prey could have a substantive effect on walleye interannual recruitment. Abiotic factors have been previously shown to affect the extent of predation on fish

ELS (Garrison et al. 2000; Taylor & Danila 2005), and future work should look at whether factors such as spring warming patterns alter the spatiotemporal overlap between walleye ELS and their potential predators and affect predation rates.

Other instances of predation on fish ELS may have been detected in this dataset. It is very possible that sequences assigned to white sucker (Catostomus commersonii) in one white perch and in one white bass were from white sucker eggs or larvae, because temperatures reached preferred spawning temperatures (Great Lakes Fisheries Commission 1982; Figure O.1), and white sucker larvae were detected at low (generally <0.1 larvae/m3) abundance at the spawning grounds (Table C.2). Predation on white sucker larvae would be likely given that planktivores are often positively size-selective (Pepin et al. 1987), and white sucker larvae at first hatch (8-10 mm TL) are one of the largest in the river (Mapes et al. 2015). The presence of burbot, Lota lota, in one white bass was unexpected, however, not impossible. Burbot spawn in both lakes and rivers at low temperatures, spawning frequently under the ice (McPhail &

Paragamian 2000), and larvae have been found previously in the Maumee River; burbot larvae were entrained at a power plant near the Maumee river mouth during mid-May (Ager et al.

2008). 62

Possible predation on ELS of yellow perch (Perca flavescens) by common carp, emerald shiner, and white bass was also detected. Yellow perch are generally believed to spawn along the southern shore of Lake Erie (Collingsworth et al. 2011), and although Mapes et al.

(2015) did not find yellow perch larvae in the Maumee River, they have been historically present

(Mapes et al. 2015). Based on temperature, yellow perch were likely spawning (Collingsworth &

Marschall 2011) in Lake Erie, and thus, yellow perch are possible, although unexpected prey in this river system. Common carp and emerald shiners are known egg predators (Schaap 1989;

Marsden 1997; García-Berthou 2001), and this could represent predation on yellow perch eggs, especially in the case of common carp. All of the predators with P. flavescens detected in gut contents except one common carp were caught in OP, suggesting possible egg predation.

Possible predation on redhorse suckers (Moxostoma spp.) by white perch, white bass, gizzard shad, channel catfish, and emerald shiner was detected. Of the 17 fishes that had redhorse suckers in their diet, all but two were captured during the latter part of the season at RM (the downstream “drift” habitat). Temperatures at that time (Figure O.1) were in the spawning range of redhorse species (Lane et al. 1996), and therefore, predation on redhorse sucker eggs is possible.

Besides ELS of fishes, other items present in the dataset included predation by emerald shiners on the cosmopolitan freshwater bryozoan, Plumatella casmiana, which are atypically documented as prey in freshwater diet studies; this species is present in the Great Lakes (Kipp et al. 2010), and Plumatella sp. have been previously noted to be present in the Maumee River near the sampling locations (Ohio Environmental Protection Agency 2014b). Based on habitat preference (growing on the undersides of submerged objects, at shallow depths; Rogick 1941), P. casmiana was likely to be encountered by foraging cyprinids. Bryozoans appear to be a rare diet 63 item not previously reported in emerald shiner diets, but one that is known to be consumed by other small fishes (Moreau et al. 2008; Watson et al. 2009).

Several potential parasites were detected in this dataset in addition to prey items. The presence of the Asian tapeworm ( Bothriocephalus acheilognathi) in one emerald shiner represents an expansion into a previously unknown host species in Lake Erie. The spread of B. acheilognathi into the Great Lakes has been recently noted: a bluntnose minnow (Pimephales notatus) caught in the Detroit River was found to be a host (Marcogliese 2008). The first intermediate host of the Asian tapeworm is typically a cyclopod, but others have shown that transmission of parasites can occur from fish to fish via piscivory (Hansen et al. 2007), and so species of fish do not necessarily need to be ones that consume copepods in order to be infected.

The primary hosts of the Asian tapeworm are common carp (Cyprinus carpio) and grass carp

(Ctenoparyngodon idellus; Dove & Fletcher 2000), but it can infect a variety of fish families, including ictalurids, centrarchids, and salmonids (as reviewed in Marcogliese 2008). Its recent discovery in Morone chrysops (Choudhury et al. 2006), an important piscivorous sportsfish in

Ohio that is to known to heavily consume emerald shiners (Ohio Division of Wildlife 2015), suggests that its presence in such an abundant minnow species should be investigated further.

Infection with Asian tapeworm can have a variety of negative effects, including decreased feeding rates, and reduced growth and condition (Hansen et al. 2006; Britton et al. 2011). While emerald shiners have recently been shown to be heavily infected with the Asian tapeworm in

Lake Winnipeg (Dick et al. 2014), this represents the first time it has been reported in Lake Erie emerald shiners. A parasite of the genus Posthodiplostomum was also found in emerald shiners and bluntnose minnows, however, this is not unexpected; Posthodiplostomum minimum (white grub) has been previously documented in Lake Erie emerald shiners (Dechtiar 1972), and 64

Posthodiplostomum spp. in bluntnose minnows (Locke 2010). As white grubs occur in the connective tissues of cyprinid guts (Lane & Morris 2010), the parasite was likely co-extracted in addition to prey while homogenizing the gut. The success of DNA metabarcoding in detecting parasites suggests that it may be used to complete Great Lakes trophic webs, as many have recently argued that trophic webs are incomplete without considering parasites (Lafferty et al.

2006; Dunne et al. 2013; Thieltges et al. 2013).

Nemerteans (ribbonworms) were detected in five predator species (freshwater drum, white bass, white perch, emerald shiner, and common carp). Although ribbonworms are primarily marine, there are 22 freshwater species (Sundberg & Gibson 2008). While ribbon worms are not commonly reported in freshwater fish diets, they have been noted in marine fish diets (McDermott 2001). Ribbonworms have been found on gravel and mud substrate in Lake

Huron (Hare & Carter 1977), supporting the possibility that this prey item could be encountered by fish in the Maumee River. Ribbonworms are almost exclusively scavengers or predators

(Hare & Carter 1977), so this represents an unexpected prey item rather than a parasite.

Interestingly, the presence of an invasive cladoceran, Bythotrephes longimanus, was detected in several species; nineteen of the 133 (14%) fishes from five species (freshwater drum, white bass, white perch, emerald shiner, and bluntnose minnow) in the dataset had sequences assigned to B. longimanus. B. longimanus spines are thought to deter predation by small fishes

(Barnhisel & Harvey 1995); however, Pothoven et al. (2009) found that emerald shiners as small as 42 mm were consuming B. longimanus (Pothoven et al. 2009). This finding is corroborated by our results; the smallest emerald shiner in our dataset (also 42 mm TL) had sequences assigned to B. longimanus. Although no record in the literature could be found of freshwater drum 65 consuming B. longimanus, freshwater drum have been known to consume zooplankton

(Griswold & Tubb 1977; Bur 1982; Morrison et al. 1997), and so consumption of B. longimanus is certainly possible. B. longimanus has been confirmed previously as prey for white bass and white perch (Bur et al. 1986). The presence of B. longimanus in two small cyprinid species is interesting because widespread consumption may have health implications for the small fishes; the caudal spines of B. longimanus can cause frequent gut puncturing (Compton & Kerfoot

2004). The widespread presence of this invasive zooplankter in the diet may also have metabolic implications; B. longimanus has been found to be lower in energy and some essential fatty acids relative to native copepods in the Great Lakes (Storch 2005).

Chironomids were also commonly identified prey in the dataset, and occurred in all species except gizzard shad, comprising, in total, nine genera. Six of the chironomid genera have been documented in the Maumee River (Ohio Environmental Protection Agency 2014b), while the remaining three are known to be present in Ohio (Bolton 2012). The importance of chironomids in the diet of A. grunniens found in this study (present in 33% of the freshwater drum) has been noted from prior Lake Erie diet studies (Griswold & Tubb 1977; Bur 1982;

Morrison et al. 1997) and in other river systems (Jacquemin et al. 2014), where it can make up half of the stomach’s volume. Chironomids also appeared to be important prey for emerald shiners, as 42% had chironomid sequences in gut contents; this source of prey for emerald shiners is supported by the literature (Ewers 1933; Fuchs 1967; Muth & Busch 1989; Pothoven et al. 2009). White bass and white perch were also found to consume chironomids, with 32% of white bass and all of the white perch containing chironomid-assigned sequences. Although white bass are widely considered to be piscivorous, chironomids have been noted in their diet 66

(Griswold & Tubb 1977; Bur & Klarer 1991; Ohio Division of Wildlife 2015), and chironomids have also been noted in the diet of white perch (Moring & Mink 2002; Couture & Watzin 2008).

In addition to chironomids, rotifers were another prey group that was found frequently in the dataset; in some cases, this may represent active foraging on rotifers, but in many cases, it may instead be secondary predation or incidental feeding. Rotifers were found at low frequency in common carp, white perch, and white bass, and at higher frequency in emerald shiners. All three genera of rotifers found in the NGS dataset (Branchionus, Keratella , and Synchaeta) are known to be frequently found in western Lake Erie (Barbiero & Warren 2011). Although common carp can consume zooplankton larger than 250 μm (Branchionus urceolaris, the most commonly detected rotifer, is in that size range at ca. 200-300 μm reported size) (Sibbing 1988), this is likely a case of either secondary predation or incidental intake, especially because it was detected in only one common carp. Likewise, the presence of rotifers in Morone sp. is most likely due to secondary predation (Couture & Watzin 2008). However, the frequency of occurrence of rotifers in emerald shiners (35% of all emerald shiners) and the fact that most

(76%) of those emerald shiners were captured in the deeper, RM area suggests possible active feeding on rotifers, despite the argument of some investigators that rotifers are not often consumed by fish (Pothoven et al. 2012). Yet, consumption of rotifers by small cyprinids in the same size range as emerald shiners has been reported in the literature (Watson et al. 2009), and in another lake system, rotifers were found to contribute ca. 10% of the diet of open-water emerald shiners (Schaap 1989). Based on the NGS dataset, the assumption that emerald shiners do not frequently consume rotifers may not be valid. 67

Overall, DNA dietary metabarcoding of fishes caught during early spring in the

Maumee River yielded sequences from 7 metazoan phyla, namely Annelida, Arthropoda,

Bryozoa, Chordata, Nemertea, Platyhelminthes, and Rotifera (Figure 2.6), reinforcing the utility of DNA barcoding for detecting diverse prey items in fish gut contents found by others (Carreon-

Martinez et al. 2011; Côté et al. 2013; Jo et al. 2014; Moran et al. 2015). Although Mion et al.

(1998) proposed that there may be a “predatory gauntlet” on walleye larvae in the Maumee River

(Mion et al. 1998), we did not find any evidence that such a gauntlet exists during downstream transport. However, this is an area that should be investigated further to see if other mechanisms

(e.g., turbidity acting as a predation refuge) may explain the lower than expected rates of predation found here. Timing of future studies should also attempt to sample during peak larval walleye abundance in order to investigate whether predation rates are higher when prey is most abundant.

This study supports two major advantages of using DNA metabarcoding to assess diet that have been cited previously in the literature: 1) Fish can sample biodiversity in an area as they forage and 2) DNA-based diet analysis detects quickly digested, soft-bodied prey items.

Fish can sample in areas that are cumbersome to sample, so analyzing prey taxa in fish diet is a way to supplement traditional biodiversity inventories (Callisto et al. 2002; Rosati et al. 2003; dos Santos et al. 2009; Maroneze et al. 2011; Jo et al. 2016); when prey are located in inaccessible/remote areas, or are at low density, the feces of generalist predators (e.g., white perch) can act as “biodiversity capsules” (Boyer et al. 2015). The sequences present in the NGS dataset made sense with consideration to the species known to be present in the Maumee River from traditional surveys (Ohio Environmental Protection Agency 2014b), suggesting that analyzing fish diet may be a way to sample river biodiversity. This study was also successful at 68 detecting soft-bodied prey. Digestion biases can affect interpretation of diet, and gelatinous and soft-bodied prey are digested quickly relative to hard-shelled prey or large chunks of muscle tissue (Arai et al. 2003; Sheffield & Grebmeier 2009; Brush et al. 2012). The faster digestion of soft-bodied prey has even been suggested to lead to false interpretations that a diet shift has occurred (Sheffield & Grebmeier 2009). However, DNA metabarcoding of diet can be advantageous in that it also detects consumption of soft-bodied/gelatinous prey (Deagle et al.

2009; O’Rorke et al. 2012a; Rolfe et al. 2014; Kartzinel & Pringle 2015). In our own dataset, small and soft-bodied prey items were detected, including mayflies, ribbonworms, chironomids, bryozoans, parasitic tapeworms, and rotifers.

Although this was a successful study, there were several potential issues with methods that arose, including high levels of self-reads from the predator guts themselves, possible crossover from other predator tissues, and some groups that were observed morphologically during processing, but not detected in the final molecular dataset.

Although the versatile primers from Leray et al. were successful in amplifying sequences from seven phyla, several groups were observed morphologically, but not in the NGS dataset. Although the primers were highly successful in amplifying sequences from mollusks in the Moorea (an island in the South Pacific) dataset, these taxa were marine species (Leray et al.

2013). Here, shells of gastropods were noted while morphologically examining common carp gut contents, yet no mollusk sequences were detected in the NGS dataset. Likewise, digested crayfish material (from Orconectes rusticus) was also identified visually in the stomach contents of channel catfish, but did not appear in the NGS dataset. This may be due to two factors that require investigation 1) as many studies have noted, “universal” primers are not truly universal 69

(Roy et al. 2010; Comtet et al. 2015; Piñol et al. 2015), and it is possible that some freshwater taxa may not amplify well with the primers that were used due to template-primer mismatches and 2) the likelihood that a taxon will show up in a final NGS dataset is a matter of probability

(Andersen et al. 2012; Carew et al. 2013), and seems to be influenced by how much material from a prey item gets into a gut content subsample. As noted in Chapter 1, there are conflicting results as to whether items that are at low relative abundance in samples are detected in NGS datasets (i.e., whether false negatives occur as a matter of probability). Gut content samples were well homogenized, and entire samples were shaken in ethanol to ensure homogeneity. Even so, it is not impossible that in taking a subsample of gut contents, tissue from some prey items (e.g., from crayfish) were not represented in the final subsample due to random chance. Future work should test the universal primer set with representative Lake Erie taxa to confirm amplification success.

Other issues with methods included possible contamination of samples with the sequences of other predators during collection or processing. Bleach is frequently used to decontaminate surfaces in highly contamination sensitive fields such as ancient DNA (aDNA) analysis (Richards et al. 1995; Rollo et al. 2002; Fulton 2012), and this was used to decontaminate equipment and surfaces used while processing diet samples in this study.

Analogous to aDNA studies where modern DNA is more preferentially amplified than highly degraded and short aDNA in samples (Fulton 2012), intact DNA from the gut lining of predators can be amplified preferentially over prey DNA when using universal primers (Shehzad et al.

2012). Although precautions were taken to prevent contamination between predators, it is possible that DNA from predators in the dataset present in other predator species is due to introduction of DNA during either capture or sample processing. However, it is unlikely that 70 prey sequences were transferred between samples due to the low likelihood that highly degraded prey sequences would be amplified. Instead, it is more likely that predator sequences were introduced into samples that had highly degraded gut contents, and thus, out-competed other sequences during PCR. This is apparent in certain species such as gizzard shad, from which most of the reads were assigned to fishes present as predators in the dataset (Table K.1) and in bluntnose minnows, where all had emerald shiner sequences detected in samples (Table M.1). As whole digestive tracts from emerald shiners and bluntnose minnows were both placed in petri dishes during dissection, it is possible that despite extensive bleaching, small amounts of remnant DNA from the predators themselves were present on the dishes and were amplified.

Certain interactions were plausible during this time; for example Morone spp. larvae and eggs were noted in the river during collections (Table C.3), and emerald shiners from RM had sequences assigned to Morone spp. in several gut content samples (Table G.1). However, due to the concern of small amounts of contamination entering samples during processing, a conservative approach was taken. For this reason, discussion of any sequences in the dataset that were from predator species examined in the study were reported but not discussed here. A future recommendation to increase certainty when using multiple predator species would be to dissect predators in different areas.

Although many taxa were detected in the NGS dataset, the ability to make detailed analyses of predator diets was partially hampered by a high proportion of “predator DNA swamping.” Higher quality predator sequences can be preferentially amplified during PCR, such that little or no prey sequences are present in the final dataset, deemed “predator DNA swamping” (King et al. 2008). Two general methods have been used to combat preferential amplification of predator sequences. The first method uses a restriction enzyme that cleaves a 71 predator sequence at a specific recognition site, and facilitates predator amplicon removal via gel electrophoresis (Maloy et al. 2011, 2013; Taguchi et al. 2014); the second method uses modified primers (“blocking primers”) that overlap with one of the universal primer binding regions and extend onto a region that is predator-specific; the blocking primers are modified such that they will not prime amplification (Vestheim & Jarman 2008; Bowser et al. 2013; Deagle et al. 2013).

However, there are concerns with both of these techniques. Restriction enzyme recognition sites are generally less than 6 bp long, and due to their shortness, it is possible that restriction enzymes might cut sites on prey amplicons and not just on predator amplicons; additionally, the design requirements for blocking primers can be restrictive (O’Rorke et al. 2012b). Piñol et al. (2014) amplified DNA from whole, homogenized generalist spiders without using blocking primers in order to avoid blocking primers non-selectively blocking prey amplicons; while more than 90% of the NGS dataset was co-amplified predator DNA, there were still ca. 60,000 reads attributed to prey items. Sequences that were phylogenetically close to the predator (i.e., other spiders) were amplified, and this ecological information could have been lost if blocking primers were used (Piñol et al. 2014). For this reason, in our dataset, blocking primers and restriction enzymes were not used in order to avoid co-blocking fish sequences (e.g., those of fish ELS). However, as in other studies (Shehzad et al. 2012), we experienced extensive predator DNA swamping as a result. Future extensions of this study may develop blocking primers to prevent some of this predator co-amplification, but caution should be exercised.

Although percentages of sequences and frequency of occurrence of taxa in predator species are discussed, it is important to understand that DNA metabarcoding is not quantitative for reasons discussed in Chapter 1, including PCR amplification biases, differential digestion of tissues, and tissue-specific differences in DNA density (Pompanon et al. 2012). If sequences 72 representing a certain taxon are abundant and are present in multiple individuals of a predator species, that taxon may be more prevalent in the diet; however, the number of sequences assigned to a taxon in an NGS dataset is not understood to be proportional to biomass in a way that would facilitate quantitative assessment (Soininen et al. 2009; Deagle et al. 2010, 2013;

Maruyama et al. 2014; Thomas et al. 2014; Elbrecht & Leese 2015).

Some metabarcoding protocols eliminate sequences represented by one or a few reads during filtering steps; the decision to not exclude low frequency reads took into consideration the debate over whether low frequency reads are purely errors or if they could represent rare species.

Many have recognized a trade-off between eliminating errors in sequencing datasets and recovering rare species that may only be represented by a few reads. While some have argued that low frequency reads are artifacts resulting from sequencing errors (Tedersoo et al.

2010), several papers have suggested that they can represent actual species. By spiking indicator species into plankton community samples, Zhan et al. (2013) found that some species spiked at low concentrations were only recovered as low frequency reads in the final sequencing dataset

(Zhan et al. 2013). Likewise, by retaining low frequency reads, Flynn et al. (2015) found that two species that were only represented by a single read each in the dataset were recovered (Flynn et al. 2015).

Clustering of sequences into operational taxonomic units (OTUs) is often completed as part of analyzing a metabarcoding dataset, and can be beneficial in a few contexts. One context is when reference databases are highly incomplete; in microbial community samples where reference databases can be lacking, a method of analysis independent from taxonomic assignment can be beneficial. Clustering sequences together into OTUs based on similarity 73 allows sequences to be compared with each other without directly assigning taxonomy; the number and size of the clusters can indicate information on sample diversity (Bonder et al.

2012). The other context is using sequences as a proxy for species in complex datasets; after reads are filtered, they are clustered into OTUs (often referred to as "OTU picking") in order to account for variation due to PCR and sequencing errors or biological variation (Flynn et al.

2015). Algorithms such as UCLUST, mothur, and UPARSE are used to cluster sequences together based on similarity, using a defined divergence threshold (often 3%) to determine the clusters (Bonder et al. 2012; Zhan et al. 2014).

Clustering was not performed in this dataset for a few reasons. First, the COI reference database for Great Lakes fauna has recently been noted to be fairly complete (Trebitz et al.

2015). Second, no metrics of diversity based on the size and number of clusters were meant to be computed from this dataset. Additionally, due to the low number of sequences in the final dataset, reads could be assigned to a putative taxonomic identity with relative ease; as Staats et al. mention, sequences can be directly queried in reference databases (as was done here) or they can be clustered to remove redundancy in the data, making the computationally intensive taxonomic assignment steps of processing less computationally demanding (Staats et al. 2016).

Due to the issues involved with undersplitting and oversplitting of taxa during OTU clustering

(Flynn et al. 2015), this approach to assigning taxonomic identification was taken.

Overall, this study was successful, but further investigation should work to minimize predator DNA swamping, ensure that cross-contamination between samples does not occur, confirm the apparently low predation on walleye ELS observed in the dataset, and investigate the factors behind it. We detected predation on walleye ELS by white perch, white bass, and emerald 74 shiners, and detected alternative prey such as chironomids, annelids, bryozoans, and arthropods.

There was a strong concordance between taxa present in the molecular dataset and those known to be present in the river. Overall, the strengths of DNA barcoding for addressing a question of regulatory concern (i.e., high interannual recruitment variability in walleye) that were noted in

Chapter 1 were confirmed in this study.

75 LITERATURE CITED

Ager D, Marttila D, Patrick P (2008) Bay Shore Power Plant Colling Water Intake Structure

Information and I&E Sampling Data. Toronto.

Allan JR (2000) The costs of bird strikes and bird strike prevention. In: Human conflicts with

wildlife: economic considerations. Proceedings of the Third NWRC Special Symposium.

(eds Clark L, Hone J, Shivik JA, et al.), pp. 29–46. Fort Collins, Colorado.

Amaral CRL, Brito PM, Silva DA, Carvalho EF (2013) Border biosecurity and the use of species

identification techniques in the ornamental fish trade: The case of the South American

freshwater pufferfishes. Forensic Science International: Genetics Supplement Series, 4,

e236–e237.

Andersen K, Bird KL, Rasmussen M et al. (2012) Meta-barcoding of “dirt” DNA from soil

reflects vertebrate biodiversity. Molecular Ecology, 21, 1966–1979.

Arai MN, Welch DW, Dunsmuir AL, Jacobs MC, Ladouceur AR (2003) Digestion of pelagic

Ctenophora and Cnidaria by fish. Canadian Journal of Fisheries and Aquatic Sciences, 60,

825–829.

Ardura A, Pola IG, Ginuino I, Gomes V, Garcia-Vazquez E (2010) Application of barcoding to

Amazonian commercial fish labelling. Food Research International, 43, 1549–1552.

Armani A, Guardone L, La Castellana R et al. (2015a) DNA barcoding reveals commercial and

health issues in ethnic seafood sold on the Italian market. Food Control, 55, 206–214.

Armani A, Guardone L, Castigliego L et al. (2015b) DNA and Mini-DNA barcoding for the

identification of Porgies species (family Sparidae) of commercial interest on the

76 international market. Food Control, 50, 589–596.

Armstrong K (2010) DNA barcoding: A new module in New Zealand’s plant biosecurity

diagnostic toolbox. EPPO Bulletin, 40, 91–100.

Asian Carp Regional Coordinating Committee (2014) Asian Carp Control Strategy Framework.

Washington, D.C.

Aylagas E, Borja A, Rodríguez-Ezpeleta N (2014) Environmental Status Assessment Using

DNA Metabarcoding: Towards a Genetics Based Marine Biotic Index (gAMBI). PLoS One,

9, e90529.

Bachiller E, Cotano U, Ibaibarriaga L, Santos M, Irigoien X (2015) Intraguild predation between

small pelagic fish in the Bay of Biscay: impact on anchovy (Engraulis encrasicolus L.) egg

mortality. Marine Biology, 162, 1351–1369.

Bacon SJ, Bacher S, Aebi A (2012) Gaps in Border Controls Are Related to Quarantine Alien

Insect Invasions in Europe. PLoS One, 7, e47689.

Baerwald MR, Schreier BM, Schumer G, May B (2012) Detection of Threatened Delta Smelt in

the Gut Contents of the Invasive Mississippi Silverside in the San Francisco Estuary Using

TaqMan Assays. Transactions of the American Fisheries Society, 141, 1600–1607.

Baird DJ, Hajibabaei M (2012) Biomonitoring 2.0: a new paradigm in ecosystem assessment

made possible by next-generation DNA sequencing. Molecular Ecology, 21, 2039–2044.

Barbiero RP, Warren GJ (2011) Rotifer communities in the Laurentian Great Lakes, 1983-2006

and factors affecting their composition. Journal of Great Lakes Research, 37, 528–540.

Barbour MT, Gerritsen J, Snyder B, Stribling JB (1999) Benthic Macroinvertebrate Protocols. In:

77 Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton,

Benthic Macroinvertebrates, and Fish, pp. 1–35. Washington, D.C.

Barbuto M, Galimberti A, Ferri E et al. (2010) DNA barcoding reveals fraudulent substitutions

in shark seafood products: The Italian case of “palombo” (Mustelus spp.). Food Research

International, 43, 376–381.

Barnett A, Redd KS, Frusher SD, Stevens JD, Semmens JM (2010) Non-lethal method to obtain

stomach samples from a large marine predator and the use of DNA analysis to improve

dietary information. Journal of Experimental Marine Biology and Ecology, 393, 188–192.

Barnett CA, Sugita N, Suzuki TN (2013) Observations of predation attempts on avian nest boxes

by Japanese martens (Mantes melampus). Mammal Study, 38, 269–274.

Barnhisel DR, Harvey HA (1995) Size-specific fish avoidance of the spined crustacean,

Bythotrephes: field support for laboratory predictions. Canadian Journal of Fisheries and

Aquatic Sciences, 52 , 768–775.

Berger J, Wirth R (2004) Predation-mediated Mortality of Early Life Stages: A Field Experiment

with Nymphs of an Herbivorous Stick Insect (Metriophasma diocles). Biotropica, 36, 424–

428.

Biber E (2011) The problem of environmental monitoring. University of Colorado Law Review,

83, 1–82.

Biedermann T, Fischer J, Yazdi A (2015) Mammalian meat allergy: a diagnostic challenge.

Allergo Journal International, 24, 81–83.

Blanchette ML, Davis AM, Jardine TD, Pearson RG (2014) Omnivory and opportunism

78 characterize food webs in a large dry-tropics river system. Freshwater Science, 33, 142–

158.

Blankenberg D, Gordon A, Von Kuster G et al. (2010) Manipulation of FASTQ data with

Galaxy. Bioinformatics, 26, 1783–1785.

Blaxter M, Mann J, Chapman T et al. (2005) Defining operational taxonomic units using DNA

barcode data. Philosophical Transactions of the Royal Society B: Biological Sciences, 360,

1935–1943.

Bleeker W, Klausmeyer S, Peintinger M, Dienst M (2008) DNA sequences identify invasive

alien Cardamine at Lake Constance. Biological Conservation, 141, 692–698.

Bolton MJ (2012) Ohio EPA Supplemental Keys to the Larval Chironomidae (Diptera) of Ohio

and Ohio Chironomidae Checklist. Groveport, Ohio.

Bonants P, Groenewald E, Rasplus JY et al. (2010) QBOL: a new EU project focusing on DNA

barcoding of Quarantine organisms. EPPO Bulletin, 40, 30–33.

Bonder MJ, Abeln S, Zaura E, Brandt BW (2012) Comparing clustering and pre-processing in

taxonomy analysis. Bioinformatics, 28, 2891–2897.

Bowser AK, Diamond AW, Addison JA (2013) From Puffins to Plankton: A DNA-Based

Analysis of a Seabird Food Chain in the Northern Gulf of Maine. PLoS One, 8, e83152.

Boyer S, Cruickshank RH, Wratten SD (2015) Faeces of generalist predators as “biodiversity

capsules”: a new tool for biodiversity assessment in remote and inaccessible habitats. Food

Webs, 3, 1–6.

Boyer SL, Howe AA, Juergens NW, Hove MC (2011) A DNA-barcoding approach to

79 identifying juvenile freshwater mussels (Bivalvia: Unionidae) recovered from naturally

infested fishes. Journal of the North American Benthological Society, 30, 182–194.

Brasier CM (2008) The biosecurity threat to the UK and global environment from international

trade in plants. Plant Pathology, 57, 792–808.

Briski E, Cristescu ME, Bailey SA, MacIsaac HJ (2011) Use of DNA barcoding to detect

invertebrate invasive species from diapausing eggs. Biological Invasions, 13, 1325–1340. de Brito MA, Schneider H, Sampaio I, Santos S (2015) DNA barcoding reveals high substitution

rate and mislabeling in croaker fillets (Sciaenidae) marketed in Brazil: The case of “pescada

branca” (Cynoscion leiarchus and Plagioscion squamosissimus). Food Research

International, 70, 40–46.

Britton JR, Pegg J, Williams CF (2011) Pathological and Ecological Host Consequences of

Infection by an Introduced Fish Parasite. PLoS ONE, 6, 1–8.

Brown DS, Jarman SN, Symondson WOC (2012) Pyrosequencing of prey DNA in reptile faeces:

analysis of earthworm consumption by slow worms. Molecular Ecology Resources, 12,

259–266.

Brush JM, Fisk AT, Hussey NE, Johnson TB (2012) Spatial and seasonal variability in the diet

of round goby (Neogobius melanostomus): stable isotopes indicate that stomach contents

overestimate the importance of dreissenids. Canadian Journal of Fisheries and Aquatic

Sciences, 69, 573–586.

Budarf AC, Burfeind D, Loh W, Tibbetts I (2011) Identification of seagrasses in the gut of a

marine herbivorous fish using DNA barcoding and visual inspection techniques. Journal of

80 Fish Biology, 79, 112–121.

Bur MT (1982) Food of freshwater drum in western Lake Erie. Journal of Great Lakes Research,

8, 672–675.

Bur M, Klarer D (1991) Prey selection for the exotic cladoceran Bythotrephes cederstroemi by

selected Lake Erie fishes. Journal of Great Lakes Research, 17, 85–93.

Bur MT, Klarer DM, Krieger KA (1986) First Records of a European Cladoceran, Bythotrephes

cederstroemi, in Lakes Erie and Huron. Journal of Great Lakes Research, 12, 144–146.

Busch W-DN, Scholl RL, Hartman WL (1975) Environmental Factors Affecting the Strength of

Walleye (Stizostedion vitreum vitreum) Year-Classes in Western Lake Erie, 1960–70.

Journal of the Fisheries Research Board of Canada, 32, 1733–1743.

Butler CM, Logan JM, Provaznik JM et al. (2015) Atlantic bluefin tuna Thunnus thynnus feeding

ecology in the northern Gulf of Mexico: A preliminary description of diet from the western

Atlantic spawning grounds. Journal of Fish Biology, 86, 365–374.

Cai P, Huang QY, Zhang XW (2006) Interactions of DNA with clay minerals and soil colloidal

particles and protection against degradation by DNase. Environmental Science &

Technology, 40, 2971–2976.

Callisto M, Vono V, Barbosa FAR, Santeiro SM (2002) Chironomidae as a food resource for

Leporinus amblyrhynchus (Teleostei: Characiformes) and Pimelodus maculatus (Teleostei:

Siluriformes) in a Brazilian reservoir. Lundiana, 3, 67–73.

Cao L, Liu J, Yu X et al. (2015) Size-dependent predation of fish larvae by jellyfish: an

experimental evaluation exemplified with the flounder Paralichthys olivaceus larvae and

81 the moon jellyfish Aurelia aurita medusae. Hydrobiologia, 754, 135–146.

Cardoni S, Tenchini R, Ficulle I et al. (2015) DNA barcode assessment of Mediterranean

mayflies (Ephemeroptera), benchmark data for a regional reference library for rapid

biomonitoring of freshwaters. Biochemical Systematics and Ecology, 62, 36–50.

Carew ME, Pettigrove VJ, Metzeling L, Hoffmann A a (2013) Environmental monitoring using

next generation sequencing: rapid identification of macroinvertebrate bioindicator species.

Frontiers in zoology, 10, 45.

Caroffino DC, Sutton TM, Elliott RF, Donofrio MC (2010) Predation on Early Life Stages of

Lake Sturgeon in the Peshtigo River, Wisconsin. Transactions of the American Fisheries

Society, 139, 1846–1856.

Carreon-Martinez L (2012) River plume effects on larval yellow perch (Perca flavescens)

survival and predation in the Western Basin of Lake Erie. University of Windsor.

Carreon-Martinez L, Heath DD (2010) Revolution in food web analysis and trophic ecology:

Diet analysis by DNA and stable isotope analysis. Molecular Ecology, 19 , 25–27.

Carreon-Martinez L, Johnson TB, Ludsin SA, Heath DD (2011) Utilization of stomach content

DNA to determine diet diversity in piscivorous fishes. Journal of Fish Biology, 78, 1170–

1182.

Carreon-Martinez LB, Walter RP, Johnson TB, Ludsin SA, Heath DD (2015) Benefits of Turbid

River Plume Habitat for Lake Erie Yellow Perch (Perca flavescens) Recruitment

Determined by Juvenile to Larval Genotype Assignment. PLoS One, 10, e0125234.

Carreon-Martinez LB, Wellband KW, Johnson TB, Ludsin SA, Heath DD (2014) Novel

82 molecular approach demonstrates that turbid river plumes reduce predation mortality on

larval fish. Molecular Ecology, 23, 5366–5377.

Carter JL, Resh VH (2001) After site selection and before data analysis: sampling, sorting, and

laboratory procedures used in stream benthic macroinvertebrate monitoring programs by

USA state agencies. Journal of the North American Benthological Society, 20, 658–682.

Carvalho DC, Neto DAP, Brasil BSAF, Oliveira DAA (2011) DNA barcoding unveils a high

rate of mislabeling in a commercial freshwater catfish from Brazil. Mitochondrial DNA, 22,

97–105.

Carvalho DC, Palhares RM, Drummond MG, Frigo TB (2015) DNA Barcoding identification of

commercialized seafood in South Brazil: A governmental regulatory forensic program.

Food Control, 50, 784–788.

Cawthorn D-M, Duncan J, Kastern C, Francis J, Hoffman LC (2015) Fish species substitution

and misnaming in South Africa: An economic, safety and sustainability conundrum

revisited. Food Chemistry, 185, 165–181.

Cawthorn DM, Steinman HA, Witthuhn RC (2012) DNA barcoding reveals a high incidence of

fish species misrepresentation and substitution on the South African market. Food Research

International, 46, 30–40.

Changizi R, Farahmand H, Soltani M, Darvish F, Elmdoost A (2013) Species Identification of

Some Fish Processing Products in Iran by DNA Barcoding. Journal of Agricultural Science

and Technology, 15, 973–980.

Chen Y, Yang Z (2005) Diets of Obscure Puffer (Takifugu obscurus) and Ocellated Puffer

83 (Takifugu ocellatus) during Spawning Migration. Journal of Freshwater Ecology, 20, 195–

196.

Chiesa S, Filonzi L, Vaghi M, Papa R, Marzano FN (2013) Molecular Barcoding of an Atypical

Cyprinid Population Assessed by Cytochrome b Gene Sequencing. Zoological Science, 30,

408–413.

Choudhury A, Charipar E, Nelson P et al. (2006) Update on the Distribution of the Invasive

Asian Fish Tapeworm, Bothriocephalus acheilognathi, in the U.S. and Canada.

Comparative Parasitology, 73, 269–273.

Chown SL, Sinclair BJ, van Vuuren BJ (2008) DNA barcoding and the documentation of alien

species establishment on sub-Antarctic Marion Island. Polar Biology, 31, 651–655.

Clark LF (2015) The current status of DNA barcoding technology for species identification in

fish value chains. Food Policy, 54, 85–94.

Cline E (2012) Marketplace substitution of Atlantic salmon for Pacific salmon in Washington

State detected by DNA barcoding. Food Research International, 45, 388–393.

Cock PJA, Antao T, Chang JT et al. (2009) Biopython: freely available Python tools for

computational molecular biology and bioinformatics. Bioinformatics, 25, 1422–1423.

Coghlan ML, White NE, Murray DC et al. (2013) Metabarcoding avian diets at airports:

implications for birdstrike hazard management planning. Investigative Genetics, 4, 1–10.

Cohen NJ, Deeds JR, Wong ES et al. (2009) Public health response to puffer fish (Tetrodotoxin)

poisoning from mislabeled product. Journal of Food Protection, 72, 810–817.

Coissac E, Riaz T, Puillandre N (2012) Bioinformatic challenges for DNA metabarcoding of

84 plants and animals. Molecular Ecology, 21, 1834–1847.

Collingsworth PD, Marschall EA (2011) Spatial and temporal patterns in maternal energetic

traits of yellow perch (Perca flavescens) in Lake Erie. Freshwater Biology, 56, 2500–2513.

Collingsworth PD, Marschall E a, Collingsworth PD, Marschall E a (2011) Identifying

relationships between catches of spawning condition Yellow Perch and environmental

variables in the western basin of Lake Erie. Transactions of the American Fisheries Society,

140, 31–36.

Collins EJT (1993) Food adulteration and food safety in Britain in the 19th and early 20th

centuries. Food Policy, 18, 95–109.

Collins RA, Armstrong KF, Holyoake AJ, Keeling S (2013) Something in the water: biosecurity

monitoring of ornamental fish imports using environmental DNA. Biological Invasions, 15,

1209–1215.

Collins RA, Armstrong KF, Meier R et al. (2012) Barcoding and border biosecurity: identifying

cyprinid fishes in the aquarium trade. PloS One, 7, e28381.

Compton JA, Kerfoot WC (2004) Colonizing Inland Lakes: Consequences of YOY Fish

Ingesting the Spiny Cladoceran (Bythotrephes cederstroemi). Journal of Great Lakes

Research, 30, 315–326.

Comtet T, Sandionigi A, Viard F, Casiraghi M (2015) DNA (meta)barcoding of biological

invasions: a powerful tool to elucidate invasion processes and help managing aliens.

Biological Invasions, 17, 905–922.

Corbett BW, Powles PM (1986) Spawning and larva drift of sympatric walleyes and white

85 suckers in an Ontario stream. Transactions of the American Fisheries Society, 115, 41–46.

Costamagna AC, Landis DA (2007) Quantifying predation on soybean aphid through direct field

observations. Biological Control, 42, 16–24.

Côté IM, Green SJ, Morris JA, Akins JL, Steinke D (2013) Diet richness of invasive Indo-Pacific

lionfish revealed by DNA barcoding. Marine Ecology Progress Series, 472, 249–256.

Couture SC, Watzin MC (2008) Diet of Invasive Adult White Perch (Morone americana) and

their Effects on the Zooplankton Community in Missisquoi Bay, Lake Champlain. Journal

of Great Lakes Research, 34, 485–494.

Cowan JH, Houde ED (1993) Relative predation potentials of scyphomedusae, ctenophores and

planktivorous fish on ichthyoplankton in Chesapeake Bay. Marine Ecology Progress Series,

95, 55–65.

Cox CE, Jones CD, Wares JP et al. (2013) Genetic testing reveals some mislabeling but general

compliance with a ban on herbivorous fish harvesting in Belize. Conservation Letters, 6,

132–140.

Crowder LB (1980) Alewife, rainbow smelt and native fishes in Lake Michigan: competition or

predation? Environmental Biology of Fishes, 5, 225–233.

Cushing DH (1972) The production cycle and the numbers of marine fish. Symposia of the

Zoological Society of London, 29, 213–232.

Cutarelli A, Amoroso MG, De Roma A et al. (2014) Italian market fish species identification and

commercial frauds revealing by DNA sequencing. Food Control, 37, 46–50.

Danehy R, Ringler N (1991) Influence of nearshore structure on growth and diets of yellow

86 perch (Perca flavescens) and white perch (Morone americana) in Mexico Bay, Lake

Ontario. Journal of Great Lakes Research, 17, 183–193.

Darling JA, Mahon AR (2011) From molecules to management: Adopting DNA-based methods

for monitoring biological invasions in aquatic environments. Environmental Research, 111,

978–988.

Davoren GK, Anderson JT, Montevecchi WA (2006) Shoal behaviour and maturity relations of

spawning capelin (Mallotus villosus) off Newfoundland: demersal spawning and diel

vertical movement patterns. Canadian Journal of Fisheries and Aquatic Sciences, 63, 268–

284.

Deagle BE, Chiaradia A, McInnes J, Jarman SN (2010) Pyrosequencing faecal DNA to

determine diet of little penguins: is what goes in what comes out? Conservation Genetics,

11, 2039–2048.

Deagle BE, Jarman SN, Pemberton D, Gales NJ (2005) Genetic screening for prey in the gut

contents from a giant squid (Architeuthis sp.). Journal of Heredity, 96, 417–423.

Deagle BE, Kirkwood R, Jarman SN (2009) Analysis of Australian fur seal diet by

pyrosequencing prey DNA in faeces. Molecular ecology, 18, 2022–38.

Deagle BE, Thomas AC, Shaffer AK, Trites AW, Jarman SN (2013) Quantifying sequence

proportions in a DNA-based diet study using Ion Torrent amplicon sequencing: which

counts count? Molecular Ecology Resources, 13, 620–633.

Dechtiar AO (1972) New Parasite Records for Lake Erie Fish.

Dejean T, Valentini A, Miquel C et al. (2012) Improved detection of an alien invasive species

87 through environmental DNA barcoding: the example of the American bullfrog Lithobates

catesbeianus. Journal of Applied Ecology, 49, 953–959.

Derbridge JJ, Krausman PR, Darimont CT (2012) Using bayesian stable isotope mixing models

to estimate wolf diet in a multi-prey ecosystem. Journal of Wildlife Management, 76, 1277–

1289.

DeWaard JR, Humble LM, Schmidt BC (2010) DNA barcoding identifies the first North

American records of the Eurasian moth Eupithecia pusillata (Lepidoptera: Geometridae).

Journal of the Entomological Society of British Columbia, 107, 25–32.

DeWaard JR, Landry J-F, Schmidt BC et al. (2009) In the dark in a large urban park: DNA

barcodes illuminate cryptic and introduced moth species. Biodiversity and Conservation,

18, 3825–3839.

Dick TA, Gallagher C, Watinson D, Franzin W (2014) Red River Delta Fish Parasite Results.

Division of Surface Water (2013) 2013 Updates to Biological Criteria for the Protection of

Aquatic Life: Volume III . Standardized Biological Field Sampling and Laboratory Methods

for Assessing Fish and Macroinvertebrate Communities.

Dove ADM, Fletcher AS (2000) The distribution of the introduced tapeworm Bothriocephalus

acheilognathi in Australian freshwater fishes. Journal of Helminthology, 74, 121–127.

Dove C, Rotzel N, Heacker M, Weigt L (2008) Using DNA Barcodes to Identify Bird Species

Involved in Birdstrikes. Journal of Wildlife Management, 72, 1231–1236.

Dudley JT, Butte AJ (2009) A Quick Guide for Developing Effective Bioinformatics

Programming Skills. PLoS Computational Biology, 5, e1000589.

88 Dufour M (2012) Quantification of Variability, Abundance, and Mortality of Maumee River

Larval Walleye (Sander vitreus) Using Bayesian Hierarchical Models. University of

Toledo.

Dunne JA, Lafferty KD, Dobson AP et al. (2013) Parasites Affect Food Web Structure Primarily

through Increased Diversity and Complexity. PLoS Biology, 11, e1001579.

Ecology and Environment Inc. (2014) Influence of Open-Lake Placement of Dredged Material

on Western Lake Erie Basin Harmful Algal Blooms. Lancaster, New York.

Egeter B, Bishop PJ, Robertson BC (2015) Detecting frogs as prey in the diets of introduced

mammals: a comparison between morphological and DNA-based diet analyses. Molecular

Ecology Resources, 15, 306–316.

Elbrecht V, Leese F (2015) Can DNA-Based Ecosystem Assessments Quantify Species

Abundance? Testing Primer Bias and Biomass—Sequence Relationships with an Innovative

Metabarcoding Protocol. PLoS One, 10, e0130324.

Elphinstone MS, Hinten GN, Anderson MJ, Nock CJ (2003) An inexpensive and high-

throughput procedure to extract and purify total genomic DNA for population studies.

Molecular Ecology Notes, 3, 317–320.

Elton C (1927) Animal Ecology. Macmillan Co., New York.

Evangelista D, Buss L, Ware JL (2013) Using DNA Barcodes to Confirm the Presence of a New

Invasive Cockroach Pest in New York City. Journal of Economic Entomology, 106, 2275–

2279.

Everstine K, Spink J, Kennedy S (2013) Economically motivated adulteration (EMA) of food:

89 common characteristics of EMA incidents. Journal of Food Protection, 76, 723–735.

Ewers LA (1933) Summary Report of Crustacea Used as Food by the Fishes of the Western End

of Lake Erie. Transactions of the American Fisheries Society, 63, 379–390.

Federal Aviation Administration (2013) Reporting Wildlife Aircraft Strikes. Washington, D.C.

Ferrari MCO, Ranåker L, Weinersmith KL et al. (2014) Effects of turbidity and an invasive

waterweed on predation by introduced largemouth bass. Environmental Biology of Fishes,

97, 79–90.

Ficetola GF, Miaud C, Pompanon F, Taberlet P (2008) Species detection using environmental

DNA from water samples. Biology Letters, 4, 423–425.

Ficetola GF, Pansu J, Bonin A et al. (2015) Replication levels, false presences and the estimation

of the presence/absence from eDNA metabarcoding data. Molecular Ecology Resources, 15,

543–556.

Filonzi L, Chiesa S, Vaghi M, Nonnis Marzano F (2010) Molecular barcoding reveals

mislabelling of commercial fish products in Italy. Food Research International, 43, 1383–

1388.

Flowers HJ, Bonvechio TF, Peterson DL (2011) Observation of Atlantic Sturgeon Predation by a

Flathead Catfish. Transactions of the American Fisheries Society, 140, 250–252.

Flynn JM, Brown EA, Chain FJJ, MacIsaac HJ, Cristescu ME (2015) Toward accurate molecular

identification of species in complex environmental samples: testing the performance of

sequence filtering and clustering methods. Ecology and Evolution, 5, 2252–2266.

Folmer O, Black M, Hoeh W, Lutz R, Vrijenhoek R (1994) DNA primers for amplification of

90 mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates.

Molecular Marine Biology and Biotechnology, 3, 294–299.

Food and Drug Administration (2015) The Seafood List.

Food Standards Agency (2013) FSA investigation into horse DNA found in some burgers.

National Archives.

Fuchs EH (1967) Life History of the Emerald Shiner, Notropis atherinoides, in Lewis and Clark

Lake, South Dakota. Transactions of the American Fisheries Society, 96, 247–256.

Fuiman LA, Faulk CK (2013) Batch spawning facilitates transfer of an essential nutrient from

diet to eggs in a marine fish. Biology Letters, 9, 20130593.

Fulton TL (2012) Setting Up an Ancient DNA Laboratory. In: Ancient DNA: Methods and

Protocols (eds Shapiro B, Hofreiter M), pp. 1–11. Spring LLC.

Furuichi S (2014) Field observation of predation on paper wasp nests by introduced terrestrial

slugs. Insectes Sociaux, 61, 95–96.

Gadomski DM, Parsley MJ (2005) Effects of Turbidity, Light Level, and Cover on Predation of

White Sturgeon Larvae by Prickly Sculpins. Transactions of the American Fisheries

Society, 134, 369–374.

Galal-Khallaf A, Ardura A, Mohammed-Geba K, Borrell YJ, Garcia-Vazquez E (2014) DNA

barcoding reveals a high level of mislabeling in Egyptian fish fillets. Food Control, 46,

441–445.

Galimberti A, De Mattia F, Losa A et al. (2013) DNA barcoding as a new tool for food

traceability. Food Research International, 50, 55–63.

91 Galloway AWE, Britton-Simmons KH, Duggins DO, Gabrielson PW, Brett MT (2012) Fatty

acid signatures differentiate marine macrophytes at ordinal and family ranks. Journal of

Phycology, 48, 956–965.

García-Berthou E (2001) Size and depth-dependent variation in habitat and diet of the common

carp (Cyprinus carpio). Aquatic Sciences, 63, 466–476.

Garrison LP, Michaels W, Link JS, Fogarty MJ (2000) Predation risk on larval gadids by pelagic

fish in the Georges Bank ecosystem. Spatial overlap associated with hydrographic features.

Canadian Journal of Fisheries and Aquatic Sciences, 57, 2455–2469.

Ghahramanzadeh R, Esselink G, Kodde LP et al. (2013) Efficient distinction of invasive aquatic

plant species from non-invasive related species using DNA barcoding. Molecular Ecology

Resources, 13, 21–31.

Goecks J, Nekrutenko A, Taylor J (2010) Galaxy: a comprehensive approach for supporting

accessible, reproducible, and transparent computational research in the life sciences.

Genome Biology, 11 , R86.

González-Varo JP, Arroyo JM, Jordano P (2014) Who dispersed the seeds? The use of DNA

barcoding in frugivory and seed dispersal studies. Methods in Ecology and Evolution, 5,

806–814.

Gopalan G, Culver DA, Wu L, Trauben BK (1998) Effects of recent ecosystem changes on the

recruitment of young-of-the-year fish in western Lake Erie. Canadian Journal of Fisheries

and Aquatic Sciences, 55, 2572–2579.

Gray SM, Chapman LJ, Mandrak NE (2012) Turbidity reduces hatching success in Threatened

92 Spotted Gar (Lepisosteus oculatus). Environmental Biology of Fishes, 94, 689–694.

Great Lakes Fisheries Commission (1982) Identification of larval fishes of the Great Lakes basin

with emphasis on the Lake Michigan drainage (N Auer, Ed,). Great Lakes Fisheries

Commission, Ann Arbor.

Gregory RS, Levings CD (1998) Turbidity Reduces Predation on Migrating Juvenile Pacific

Salmon. Transactions of the American Fisheries Society, 127, 275–285.

Griswold BL, Tubb RA (1977) Food of yellow perch, white bass, freshwater drum, and channel

catfish in Sandusky Bay, Lake Erie. Ohio Journal of Science, 77, 43–47.

Grosjean S, Ohler A, Chuaynkern Y, Cruaud C, Hassanin A (2015) Improving biodiversity

assessment of anuran amphibians using DNA barcoding of tadpoles. Case studies from

Southeast Asia. Comptes Rendus Biologies, 338, 351–361.

Gwiazdowski RA, Foottit RG, Maw HEL, Hebert PD (2015) The Hemiptera (Insecta) of Canada:

Constructing a Reference Library of DNA barcodes. PloS One, 10, 1–20.

Haakana H, Huuskonen H (2009) Predation of smelt on vendace larvae: Experimental and field

studies. Ecology of Freshwater Fish, 18, 226–233.

Haakana H, Huuskonen H, Karjalainen J (2007) Predation of perch on vendace larvae: diet

composition in an oligotrophic lake and digestion time of the larvae. Journal of Fish

Biology, 70, 1171–1184.

Hampton SE, Gilbert JJ (2001) Observations of insect predation on rotifers. Hydrobiologia, 446,

115–121.

Handy SM, Deeds JR, Ivanova NV et al. (2011) A Single-Laboratory Validated Method for the

93 Generation of DNA Barcodes for the Identification of Fish for Regulatory Compliance.

Journal of AOAC International, 94, 201–210.

Hanner R, Becker S, Ivanova NV, Steinke D (2011) FISH-BOL and seafood identification:

Geographically dispersed case studies reveal systemic market substitution across Canada.

Mitochondrial DNA, 22, 106–122.

Hansen SP, Choudhury A, Cole RA (2007) Evidence of Experimental Postcyclic Transmission

of Bothriocephalus acheilognathi in Bonytail Chub (Gila elegans). The Journal of

Parasitology, 93, 202–204.

Hansen S, Choudhury A, Heisey D et al. (2006) Experimental infection of the endangered

bonytail chub (Gila elegans) with the Asian fish tapeworm (Bothriocephalus

acheilognathi): impacts on survival, growth, and condition. Canadian Journal of Zoology,

84, 1383–1394.

Hardy CM, Krull ES, Hartley DM, Oliver RL (2010) Carbon source accounting for fish using

combined DNA and stable isotope analyses in a regulated lowland river weir pool.

Molecular Ecology, 19, 197–212.

Hare L, Carter JC. (1977) The Oligochaeta, Polychaeta and Nemertea of Parry Sound, Georgian

Bay. Journal of Great Lakes Research, 3, 184–190.

Harris JE, McBride RS (2009) American Shad Feeding on Spawning Grounds in the St. Johns

River, Florida. Transactions of the American Fisheries Society, 138, 888–898.

Harvey BC, White JL (2008) Use of benthic prey by salmonids under turbid conditions in a

laboratory stream. Transactions of the American Fisheries Society, 137, 1756–1763.

94 Harvey CJ, Williams GD, Levin PS (2012) Food Web Structure and Trophic Control in Central

Puget Sound. Estuaries and Coasts, 35, 821–838.

Haye PA, Segovia NI, Vera R, Gallardo MDLA, Gallardo-Escárate C (2012) Authentication of

commercialized crab-meat in Chile using DNA Barcoding. Food Control, 25, 239–244.

Hebert PDN, Cywinska A, Ball SL, DeWaard JR (2003a) Biological identifications through

DNA barcodes. Proceedings of the Royal Society Biological Sciences, 270, 313–321.

Hebert P, Gregory TR (2005) The Promise of DNA Barcoding for Taxonomy. Systematic

Biology, 54, 852–859.

Hebert PDN, Ratnasingham S, de Waard JR (2003b) Barcoding animal life: cytochrome c

oxidase subunit 1 divergences among closely related species. Proceedings of the Royal

Society London Biological Sciences, 270, S96–S99.

Hellberg RSR, Naaum AM, Handy SM et al. (2011) Interlaboratory evaluation of a real-time

multiplex polymerase chain reaction method for identification of salmon and trout species

in commercial products. Journal of Agricultural and Food Chemistry, 59, 876–884.

Helyar SJ, Lloyd HAD, De Bruyn M et al. (2014) Fish product mislabelling: Failings of

traceability in the production chain and implications for Illegal, Unreported and

Unregulated (IUU) fishing. PLoS One, 9, e98691.

Hollingsworth PM, Forrest LL, Spouge JL et al. (2009) A DNA barcode for land plants.

Proceedings of the National Academy of Sciences, 106, 12794–12797.

Hulme PE (2009) Trade, transport and trouble: Managing invasive species pathways in an era of

globalization. Journal of Applied Ecology, 46, 10–18.

95 Hunter E, Taylor N, Fox CJ, Maillard M, Taylor MI (2012) Effectiveness of TaqMan probes for

detection of fish eggs and larvae in the stomach contents of a teleost predator. Journal of

Fish Biology, 81, 320–328.

Huusko A, Vuorimies O, Sutela T (1996) Temperature and light-mediated predation by perch on

vendace larvae. Journal Of Fish Biology, 49, 441–457.

Huxley-Jones E, Shaw J, Fletcher C, Parnell J, Watts PC (2012) Use of DNA Barcoding to

Reveal Species Composition of Convenience Seafood. Conservation Biology, 26, 367–371.

International Maritime Organization (2004) International Convention for the Control and

Manangement Of Ships’ Ballast Water and Sediments, 2004.

Iverson SJ, Field C, Bowen WD, Blanchard W, Bowen WDON (2004) Quantitative Fatty Acid

Signature Analysis: a New Method of Estimating Predator Diets. Ecological Monographs,

74, 211–235.

Jackson JK, Battle JM, White BP et al. (2014) Cryptic biodiversity in streams: a comparison of

macroinvertebrate communities based on morphological and DNA barcode identifications.

Freshwater Science, 33, 312–324.

Jacquemin SJ, Pyron M, Allen M, Etchison L (2014) Wabash River Freshwater Drum

Aplodinotus grunniens Diet: Effects of Body Size, Sex, and River Gradient. Journal of Fish

and Wildlife Management, 5, 133–140.

Jarman SN, McInnes J, Faux C et al. (2013) Adélie penguin population diet monitoring by

analysis of food DNA in scats. PLoS One, 8, e82227.

Jepsen N, Holthe E, Okland F (2006) Observations of predation on salmon and trout smolts in a

96 river mouth. Fisheries Management and Ecology, 13, 341–343.

Jerde CL, Mahon AR, Chadderton WL, Lodge DM (2011) “Sight-unseen” detection of rare

aquatic species using environmental DNA. Conservation Letters, 4, 150–157.

Ji Y, Ashton L, Pedley SM et al. (2013) Reliable, verifiable and efficient monitoring of

biodiversity via metabarcoding. Ecology Letters, 16, 1245–1257.

Jo H, Ventura M, Vidal N et al. (2016) Discovering hidden biodiversity: the use of

complementary monitoring of fish diet based on DNA barcoding in freshwater ecosystems.

Ecology and Evolution, 6, 219–232.

Jo H-S, Yeon I, Lim C et al. (2013) Fatty acid and stable isotope analyses to infer diet of

Antarctic toothfish caught in the southern Ross Sea. CCAMLR Science, 20 , 21–36.

Johannessen A (1980) Predation on herring (Clupea harengus) eggs and young larvae. Bergen,

Norway.

Kane DE, Hellberg RS (2015) Identification of Species in Ground Meat Products Sold on the

U.S. Commercial Market using DNA-Based Methods. Food Control, 59, 158–163.

Karjalainen J, Auvinen H, Helminen H et al. (2000) Unpredictability of fish recruitment:

interannual variation in young-of-the-year abundance. Journal of Fish Biology, 56, 837–

857.

Kartzinel TR, Pringle RM (2015) Molecular detection of invertebrate prey in vertebrate diets:

trophic ecology of Caribbean island lizards. Molecular Ecology Resources, 15, 903–914.

Kayle K, Oldenburg K, Francis J, Markham J (2015) Lake Erie Walleye Management Plan 2015-

2019.

97 Keller TS, Krieger KA (2009) Taxonomic Atlas of the Caddisfly Larvae (Class Insecta: Order

Trichoptera) Recorded at the Old Woman Creek National Estuarine Research Reserve and

State Nature Preserve, Ohio. Tiffin, Ohio.

Kelly RP, Port JA, Yamahara KM, Crowder LB (2014) Using Environmental DNA to Census

Marine Fishes in a Large Mesocosm. PLoS One, 9 , e86175.

Khaksar R, Carlson T, Schaffner DW et al. (2015) Unmasking seafood mislabeling in U.S.

markets: DNA barcoding as a unique technology for food authentication and quality

control. Food Control, 56, 71–76.

Kim GW, DeVries DR (2001) Adult Fish Predation on Freshwater Limnetic Fish Larvae: A

Mesocosm Experiment. Transactions of the American Fisheries Society, 130, 189–203.

King RA, Read DS, Traugott M, Symondson WOC (2008) Molecular analysis of predation: a

review of best practice for DNA-based approaches. Molecular Ecology, 17 , 947–963.

Kipp R, Bailey SA, MacIsaac HJ, Ricciardi A (2010) Transoceanic ships as vectors for

nonindigenous freshwater bryozoans. Diversity and Distributions, 16, 77–83.

Knapp JM, Aranda G, Medina A, Lutcavage M (2014) Comparative assessment of the

reproductive status of female Atlantic bluefin tuna from the Gulf of Mexico and the

Mediterranean Sea. PLoS One, 9, e98233.

Knebelsberger T, Stöger I (2012) DNA Extraction, Preservation, and Amplification. In: DNA

Barcodes: Methods and Protocols, Methods in Molecular Biology, pp. 311–338.

Kolar CS, Chapman DC, Courtenay WR et al. (2005) Asian Carps of the Genus

Hypophthalmichthys (Pisces, Cyprinidae)―A Biological Synopsis and Environmental Risk

98 Assessment.

Krautz MC, González M, Castro LR (2003) Detection of anchoveta (Engraulis ringens Jenyns

1842) eggs in euphausiid diets using immunoassays (ELISA). Journal of Experimental

Marine Biology and Ecology, 294, 27–39.

Kress WJ, García-Robledo C, Uriarte M, Erickson DL (2015) DNA barcodes for ecology,

evolution, and conservation. Trends in Ecology & Evolution, 30, 25–35.

Lafferty KD, Dobson AP, Kuris AM (2006) Parasites dominate food web links. Proceedings of

the National Academy of Sciences, 103, 11211–11216.

Laforest BJ, Winegardner AK, Zaheer OA et al. (2013) Insights into biodiversity sampling

strategies for freshwater microinvertebrate faunas through bioblitz campaigns and DNA

barcoding. BMC Ecology, 13, 1–17.

Lane RL, Morris JE (2010) Biology, Prevention, and Effects of Common Grubs (Digenetic

trematodes) in Freshwater Fish. Ames, Iowa.

Lane JA, Portt CB, Minns CK (1996) Spawning Habitat Characteristics Of Great Lakes Fishes.

Lapointe NWR, Corkum LD, Mandrak NE (2007) Seasonal and Ontogenic Shifts in

Microhabitat Selection by Fishes in the Shallow Waters of the Detroit River, a Large

Connecting Channel. Transactions of the American Fisheries Society, 136 , 155–166.

Leggett WC, Deblois E (1994) Recruitment in marine fishes: Is it regulated by starvation and

predation in the egg and larval stages? Netherlands Journal of Sea Research, 32, 119–134.

Legler ND (2008) Effects of Lake Erie Habitat Variation and Digestion Rate on Feeding in

Freshwater Fishes. University of Windsor.

99 Lehtiniemi M, Engström-Öst J, Viitasalo M (2005) Turbidity decreases anti-predator behaviour

in pike larvae, Esox lucius. Environmental Biology of Fishes, 73, 1–8.

Lenat DR (1993) A biotic index for the southeastern United States: derivation and list of

tolerance values, with criteria for assigning water-quality ratings. Journal of the North

American Benthological Society, 12, 279–290.

Leray M, Yang JY, Meyer CP et al. (2013) A new versatile primer set targeting a short fragment

of the mitochondrial COI region for metabarcoding metazoan diversity: application for

characterizing coral reef fish gut contents. Frontiers in Zoology, 10, 1–14.

Ley G, Saltzgiver MJ, Dowling TE et al. (2013) Use of a Molecular Assay to Detect Predation

on an Endangered Fish Species. Transactions of the American Fisheries Society, 143, 49–

54.

Locke S (2010) A molecular approach to the community ecology of parasites of freshwater fish.

Concordia University, Montreal, Quebec.

Lodge DM, Williams S, Macisaac HJ et al. (2006) Biological Invasions: Recommendations for

U.S. Policy and Management. Ecological Applications, 16, 2035–2054.

Lowenstein JH, Burger J, Jeitner CW et al. (2010) DNA barcodes reveal species-specific

mercury levels in tuna sushi that pose a health risk to consumers. Biology Letters, 6, 692–

695.

Lundstrom RC (1977) Identification of fish species by thin-layer polyacrylamide gel isoelectric

focusing. Fishery Bulletin, 75, 571–576.

Machida RJ, Knowlton N (2012) Ways to Mix Multiple PCR Amplicons into Single 454 Run for

100 DNA Barcoding. In: DNA Barcodes: Methods and Protocols Methods in Molecular

Biology. (eds Kress WJ, Erickson DL), pp. 3–8. Springer Science + Business Media, LLC,

New York, New York.

Mächler E, Deiner K, Spahn F, Altermatt F (2016) Fishing in the Water: Effect of Sampled

Water Volume on Environmental DNA-Based Detection of Macroinvertebrates.

Environmental Science and Technology, 50, 305–312.

Mackie IM, Pryde SE, Gonzales-Sotelo C et al. (1999) Challenges in the identification of species

of canned fish. Trends in Food Science and Technology, 10, 9–14.

Madenjian CP, Fahnenstiel GL, Johengen TH et al. (2002) Dynamics of the Lake Michigan food

web, 1970– -2000. Canadian Journal of Fisheries and Aquatic Sciences, 59, 736–753.

Mahon AR, Barnes MA, Senapati S et al. (2011) Molecular detection of invasive species in

heterogeneous mixtures using a microfluidic carbon nanotube platform. PLoS ONE, 6, 1–5.

Mahon AR, Nathan LR, Jerde CL (2014) Meta-genomic surveillance of invasive species in the

bait trade. Conservation Genetics Resources, 6 , 563–567.

Maloy AP, Culloty SC, Bolton-Warberg M, Fitzgerald R, Slater JW (2011) Molecular

identification of laser-dissected gut contents from hatchery-reared larval cod, Gadus

morhua: a new approach to diet analysis. Aquaculture Nutrition, 17, 536–541.

Maloy AP, Culloty SC, Slater JW (2013) Dietary analysis of small planktonic consumers: a case

study with marine bivalve larvae. Journal of Plankton Research, 35, 866–876.

Manning NF, Mayer CM, Bossenbroek JM, Tyson JT (2013) Effects of water clarity on the

length and abundance of age-0 yellow perch in the Western Basin of Lake Erie. Journal of

101 Great Lakes Research, 39, 295–302.

Mapes RL, Dufour MR, Pritt JJ, Mayer CM (2015) Larval fish assemblage recovery: A

reflection of environmental change in a large degraded river. Restoration Ecology, 23, 85–

93.

Maralit BA, Aguila RD, Ventolero MFH, Perez SKL, Santos MD (2013) Detection of mislabeled

commercial fishery by-products in the Philippines using DNA barcodes and its implications

to food traceability and safety. Food Control, 33, 119–125.

Marcogliese DJ (2008) First Report of the Asian Fish Tapeworm in the Great Lakes. Journal of

Great Lakes Research, 34, 566–569.

Margulies D (1990) Vulnerability of larval white perch, Morone americana, to fish predation.

Environmental Biology of Fishes, 27, 187–200.

Maroneze DM, Tupinambás TH, Alves CBM et al. (2011) Fish as ecological tools to

complement biodiversity inventories of benthic macroinvertebrates. Hydrobiologia, 673,

29–40.

Marsden JE (1997) Common Carp Diet Includes Zebra Mussels and Lake Trout Eggs. Journal of

Freshwater Ecology, 12, 491–492.

Martin M (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads.

EMBnet.journal, 17, 10–12.

Maruyama A, Nakamura K, Yamanaka H, Kondoh M, Minamoto T (2014) The Release Rate of

Environmental DNA from Juvenile and Adult Fish. PLoS One, 9, e114639.

Mason DM, Brandt SB (1996) Effect of alewife predation on survival of larval yellow perch in

102 an embayment of Lake Ontario. Canadian Journal of Fisheries and Aquatic Sciences, 53,

1609–1617.

Matarese AC, Spies IB, Busby MS, Orr JW (2011) Early larvae of Zesticelus profundorum

(family Cottidae) identified using DNA barcoding. Ichthyological Research, 58, 170–174.

May C (2015) The Importance of Early Life Processes to Future Growth and Recruitment in

Lake Erie Walleye. The Ohio State University.

McCary M, Mores R, Farfan M, Wise D (2016) Invasive plants have different effects on trophic

structure of green and brown food webs in terrestrial ecosystems : a meta-analysis. Ecology

Letters, 19, 328–335.

McDermott JJ (2001) Status of the Nemertea as prey in marine ecosystems. Hydrobiolgia, 7–20.

McPhail JD, Paragamian VL (2000) Burbot Biology and Life History. In: Burbot: Biology,

Ecology, and Management (eds Paragamian VL, Willis DW), pp. 11–23. Fisheries

Management Section of the American Fisheries Society.

Medinger R, Nolte V, Pandey RV et al. (2010) Diversity in a hidden world: potential and

limitation of next-generation sequencing for surveys of molecular diversity of eukaryotic

microorganisms. Molecular Ecology, 19, 32–40.

Meusnier I, Singer GAC, Landry J-F et al. (2008) A universal DNA mini-barcode for

biodiversity analysis. BMC Genomics, 9, 214–217.

Michalsen K, Johannesen E, Bogstad B (2008) Feeding of mature cod (Gadus morhua) at the

spawning grounds in Lofoten. ICES Journal of Marine Science, 65, 571–580.

Mikheyev AS, Tin MMY (2014) A first look at the Oxford Nanopore MinION sequencer.

103 Molecular Ecology Resources, 14, 1097–1102.

Miller D, Jessel A, Mariani S (2012) Seafood mislabelling: comparisons of two western

European case studies assist in defining influencing factors, mechanisms and motives. Fish

and Fisheries, 13, 345–358.

Miner JG, Stein RA (1996) Detection of Predators and Habitat Choice by Small Bluegills:

Effects of Turbidity and Alternative Prey. Transactions of the American Fisheries Society,

125, 97–103.

Mion JB, Stein RA, Marschall EA (1998) River discharge drives survival of larval walleye.

Ecological Applications, 8, 88–103.

Moffitt LJ, Stranlund JK, Osteen CD (2010) Securing the Border from Invasives: Robust

Inspections under Severe Uncertainty. Economics Research International, 2010, 1–9.

Moles AT, Gruber MM, Bonser SP (2008) A new framework for predicting invasive plant

species. Journal of Ecology, 96, 13–17.

Monteleone DM, Houde ED (1992) Vulnerability of striped bass Morone saxatilis Waldbaum

eggs and larvae to predation by juvenile white perch Morone americana Gmelin. Journal of

Experimental Marine Biology and Ecology, 158, 93–104.

Moreau S, Péron C, Pitt KA et al. (2008) Opportunistic predation by small fishes on epibiota of

jetty pilings in urban waterways. Journal of Fish Biology, 72, 205–217.

Moring JR, Mink LH (2002) Anadromous alewives, Alosa pseudoharengus, as prey for white

perch, Morone americana. Hydrobiologia, 479, 125–130.

Morrison TW, Lynch WE, Dabrowski K (1997) Predation on Zebra Mussels by Freshwater

104 Drum and Yellow Perch in Western Lake Erie. Journal of Great Lakes Research, 23, 177–

189.

Mueller GA, Carpenter J, Thornbrugh D (2006) Bullfrog Tadpole (Rana Catesbeiana) and Red

Swamp Crayfish (Procambarus Clarkii) Predation on Early Life Stages of Endangered

Razorback Sucker (Xyrauchen Texanus). The Southwestern Naturalist, 51 , 258–261.

Murray GPD, Stillman RA, Britton JR (2016) Habitat complexity and food item size modify the

foraging behaviour of a freshwater fish. Hydrobiologia, 766, 321–332.

Muth KM, Busch W-DN (1989) Food of forage fishes in western Lake Erie, 1975-76. Journal of

Great Lakes Research, 15, 217–222.

Mychek-Londer JG, Bunnell DB, Stott W et al. (2013) Using Diets to Reveal Overlap and Egg

Predation among Benthivorous Fishes in Lake Michigan. Transactions of the American

Fisheries Society, 142, 492–504.

Nagoshi RN, Brambila J, Meagher RL (2011) Use of DNA barcodes to identify invasive

armyworm Spodoptera species in Florida. Journal of Insect Science, 11, 1–11.

Newmaster SG, Grguric M, Shanmughanandhan D, Ramalingam S, Ragupathy S (2013) DNA

barcoding detects contamination and substitution in North American herbal products. BMC

Medicine, 11, 1–13.

Notomi T, Mori Y, Tomita N, Kanda H (2015) Loop-mediated isothermal amplification

(LAMP): principle, features, and future prospects. Journal of Microbiology, 53, 1–5.

O’Rorke R, Lavery S, Chow S et al. (2012a) Determining the diet of larvae of western Rock

Lobster (Panulirus cygnus) using high-throughput DNA sequencing techniques. PLoS One,

105 7, e42757.

O’Rorke R, Lavery S, Jeffs A (2012b) PCR enrichment techniques to identify the diet of

predators. Molecular Ecology Resources, 12, 5–17.

Oehm J, Juen A, Nagiller K, Neuhauser S, Traugott M (2011) Molecular scatology: How to

improve prey DNA detection success in avian faeces? Molecular Ecology Resources, 11,

620–628.

Ohata R, Masuda R, Ueno M, Fukunishi Y, Yamashita Y (2011) Effects of turbidity on survival

of larval ayu and red sea bream exposed to predation by jack mackerel and moon jellyfish.

Fisheries Science, 77, 207–215.

Ohba S (2011) Field observation of predation on a turtle by a giant water bug. Entomological

Science, 14, 364–365.

Ohio Division of Wildlife (2015) Ohio’s Lake Erie Fisheries 2014. Fairport and Sandusky.

Ohio Environmental Protection Agency (2014a) Biological and Water Quality Study of the

Maumee River and Auglaize River 2012-2013. Columbus, Ohio.

Ohio Environmental Protection Agency (2014b) Appendices to the Biological and Water Quality

Study of the Maumee and Auglaize Rivers Biological and Water Quality Study 2012-2013.

Ohio Environmental Protection Agency, Columbus, Ohio.

Olson MH (1996) Predator-prey interactions in size-structured fish communities: implications of

prey growth. Oecologia, 108, 757–763.

Oter K, Gunay F, Tuzer E et al. (2013) First record of Stegomyia albopicta in Turkey determined

by active ovitrap surveillance and DNA barcoding. Vector-Borne and Zoonotic Diseases,

106 13, 753–761.

Ovalle TM, Parsa S, Hernández MP, Lopez-Lavalle LAB (2014) Reliable molecular

identification of nine tropical whitefly species. Ecology and Evolution, 4, 3778–3787.

Padilla DK, Williams SL (2004) Beyond ballast water: aquarium and ornamental trades as

sources of invasive species in aquatic ecosystems. Frontiers in Ecology and the

Environment, 2, 131–138.

Palka-Santini M, Schwarz-Herzke B, Hösel M et al. (2003) The gastrointestinal tract as the

portal of entry for foreign macromolecules: fate of DNA and proteins. Molecular Genetics

and Genomics, 270, 201–215.

Pangle KL, Malinich TD, Bunnell DB, DeVries DR, Ludsin SA (2012) Context-dependent

planktivory: interacting effects of turbidity and predation risk on adaptive foraging.

Ecosphere, 3, 1–18.

Pauly D, Christensen V, Dalsgaard J, Froese R, Torres F (1998) Fishing down marine food webs.

Science, 279, 860–863.

Pawlowski J, Lejzerowicz F, Esling P (2014) Next-Generation Environmental Diversity Surveys

of Foraminifera: Preparing the Future. Marine Biological Laboratory, 227, 93–106.

Pejchar L, Mooney HA (2009) Invasive species, ecosystem services and human well-being.

Trends in Ecology & Evolution, 24, 497–504.

Pepin P, Pearre SJ, Koslow JA (1987) Predation on Larval Fish by Atlantic Mackerel, Scomber

scombrus, with a Comparison of Predation by Zooplankton. Canadian Journal of Fisheries

and Aquatic Sciences, 44, 2012–2018.

107 Peterson MS, Nicholson LC, Snyder DJ et al. (1999) Growth, Spawning Preparedness, and Diet

of Cycleptus meridionalis (Catostomidae). Transactions of the American Fisheries Society,

128, 900–908.

Petroczi A, Taylor G, Naughton DP (2011) Mission impossible? Regulatory and enforcement

issues to ensure safety of dietary supplements. Food and Chemical Toxicology, 49, 393–

402.

Pfrender ME, Hawkins CP, Bagley M et al. (2010) Assessing macroinvertebrate biodiversity in

freshwater ecosystems: advances and challenges in DNA-based approaches. The Quarterly

Review of Biology, 85 , 319–340.

Phillips DL (2001) Mixing models in analyses of diet using multiple stable isotopes: a critique.

Oecologia, 127, 166–170.

Pilgrim EM, Jackson SA, Swenson S et al. (2011) Incorporation of DNA barcoding into a large-

scale biomonitoring program: opportunities and pitfalls. Journal of the North American

Benthological Society, 30, 217–231.

Pilli E, Casamassima R, Vai S et al. (2014) Pet fur or fake fur? A forensic approach.

Investigative Genetics, 5, 1–18.

Piñol J, Mir G, Gomez-Polo P, Agustí N (2015a) Universal and blocking primer mismatches

limit the use of high-throughput DNA sequencing for the quantitative metabarcoding of

arthropods. Molecular Ecology Resources, 15, 819–830.

Piñol J, Mir G, Gomez-Polo P, Agustí N (2015b) Universal and blocking primer mismatches

limit the use of high-throughput DNA sequencing for the quantitative metabarcoding of

108 arthropods. Molecular Ecology Resources, 15, 819–830.

Piñol J, San Andrés V, Clare EL, Mir G, Symondson WOC (2014) A pragmatic approach to the

analysis of diets of generalist predators: the use of next-generation sequencing with no

blocking probes. Molecular Ecology Resources, 14, 18–26.

Di Pinto A, Di Pinto P, Terio V et al. (2013) DNA barcoding for detecting market substitution in

salted cod fillets and battered cod chunks. Food Chemistry, 141, 1757–1762.

Pompanon F, Deagle BE, Symondson WOC et al. (2012) Who is eating what: diet assessment

using next generation sequencing. Molecular Ecology, 21, 1931–1950.

Pompanon F, Samadi S (2015) Next generation sequencing for characterizing biodiversity:

promises and challenges. Genetica, 143, 133–138.

Post DM (2002) Using Stable Isotopes to Estimate Trophic Position: Models, Methods, and

Assumptions. Ecology, 83, 703–718.

Pothoven SA, Vanderploeg HA, Höök TO, Ludsin SA, Sprules G (2012) Hypoxia modifies

planktivore–zooplankton interactions in Lake Erie. Canadian Journal of Fisheries and

Aquatic Sciences, 69 , 2018–2028.

Pothoven SA, Vanderploeg HA, Ludsin SA, Höök TO, Brandt SB (2009) Feeding ecology of

emerald shiners and rainbow smelt in central Lake Erie. Journal of Great Lakes Research,

35, 190–198.

Pritt JJ, Roseman EF, O’Brien TP (2014) Mechanisms driving recruitment variability in fish:

comparisons between the Laurentian Great Lakes and marine systems. ICES Journal of

Marine Science, 71, 2252–2267.

109 Puillandre N, Bouchet P, Boisselier-Dubayle MC et al. (2012) New taxonomy and old

collections: integrating DNA barcoding into the collection curation process. Molecular

Ecology Resources, 12, 396–402.

Puillandre N, Strong E, Bouchet P et al. (2009) Identifying gastropod spawn from DNA

barcodes: possible but not yet practicable. Molecular Ecology Resources, 9, 1311–1321.

Pusey BJ, Arthington AH, Stewart-Koster B, Kennard MJ, Read MG (2010) Widespread

omnivory and low temporal and spatial variation in the diet of fishes in a hydrologically

variable northern Australian river. Journal of Fish Biology, 77, 731–753.

Rahman MM, Ali ME, Hamid SBA et al. (2015) Lab-on-a-Chip PCR-RFLP Assay for the

Detection of Canine DNA in Burger Formulations. Food Analytical Methods, 8, 1598–

1606.

Ranasinghe JA, Stein ED, Miller PE, Weisberg SB (2012) Performance of Two Southern

California Benthic Community Condition Indices Using Species Abundance and Presence-

Only Data: Relevance to DNA Barcoding. PLoS One, 7, e40875.

Rappé MS, Giovannoni SJ (2003) The Uncultured Microbial Majority. Annual Review of

Microbiology, 57, 369–394.

Ratnasingham S, Hebert PDN (2007) BOLD: The Barcode of Life Data System

(www.barcodinglife.org). Molecular Ecology Notes, 7, 355–364.

Reichert JM, Fryer BJ, Pangle KL et al. (2010) River-plume use during the pelagic larval stage

benefits recruitment of a lentic fish. Canadian Journal of Fisheries and Aquatic Sciences,

67, 987–1004.

110 Richards MB, Sykes BC, Hedges REM (1995) Authenticating DNA Extracted From Ancient

Skeletal Remains. Journal of Archaeological Science, 22, 291–299.

Richardson DE, Hare JA, Fogarty MJ, Link JS (2011) Role of egg predation by haddock in the

decline of an Atlantic herring population. Proceedings of the National Academy of Sciences,

108, 13606–13611.

Rogick MD (1941) Studies on Fresh-Water Bryozoa. X. The Occurrence of Plumatella casmiana

in North America. Transactions of the American Microscopical Society, 60, 211–220.

Rolfe AK, Kurta A, Clemans DL (2014) Species-level analysis of diets of two mormoopid bats

from Puerto Rico. Journal of Mammalogy, 95, 587–596.

Rollo F, Ubaldi M, Ermini L, Marota I (2002) Otzi’s last meals: DNA analysis of the intestinal

content of the Neolithic glacier mummy from the Alps. Proceedings of the National

Academy of Sciences of the United States of America, 99, 12594–12599.

Rosati TC, Johansen JR, Coburn MM (2003) Cyprinid fishes as samplers of benthic diatom

communities in freshwater streams of varying water quality. Canadian Journal of Fisheries

and Aquatic Sciences, 60, 117–125.

Rosel PE, Kocher TD (2002) DNA-based identification of larval cod in stomach contents of

predatory fishes. Journal of Experimental Marine Biology and Ecology, 267, 75–88.

Roseman EF, Taylor WW, Hayes DB et al. (1996) Walleye egg deposition and survival on reefs

in western Lake Erie (USA). Annales Zoologici Fennici, 33, 341–351.

Roseman EF, Taylor WW, Hayes DB, Jones AL, Francis JT (2006a) Predation on Walleye Eggs

by Fish on Reefs in Western Lake Erie. Journal of Great Lakes Research, 32, 415–423.

111 Roseman EF, Taylor WW, Hayes DB, Jones AL, Francis JT (2006b) Predation on Walleye Eggs

by Fish on Reefs in Western Lake Erie. Journal of Great Lakes Research, 32, 415–423.

Roseman EE, Taylor WW, Hayes DB, Knight RL, Haas RC (2001) Removal of walleye eggs

from reefs in western Lake Erie by a catastrophic storm. Transactions of the American

Fisheries Society, 130 , 341–346.

Rottiers D, Johnson J (1993) Digestion of larval American shad by cyprinids. Ecology of

Freshwater Fish, 2, 147–151.

Roy S, Tyagi A, Shukla V et al. (2010) Universal plant DNA barcode loci may not work in

complex groups: A case study with Indian Berberis species. PLoS One, 5 , e13674.

Rutaisire J, Booth AJ (2005) Reproductive biology of ningu, Labeo victorianus (Pisces:

Cyprinidae), in the Kagera and Sio Rivers, Uganda. Environmental Biology of Fishes, 73,

153–162.

Sackmann EK, Fulton AL, Beebe DJ (2014) The present and future role of microfluidics in

biomedical research. Nature, 507, 181–189.

Sakurai R (2011) Field observation of predation on adult lepidopterans by the tree sparrow

Passer montanus saturatus. Entomological Science, 14, 162–165.

Salonen M, Engström-Öst J (2013) Growth of pike larvae: Effects of prey, turbidity and food

quality. Hydrobiologia, 717, 169–175.

Sanger F, Nicklen S, Coulson AR (1977) DNA sequencing with chain-terminating inhibitors.

Proceedings of the National Academy of Sciences, 74, 5463–5467. dos Santos AFGN, Racca-Filho F, dos Santos LN, Araújo FG (2009) El pez Trachelyopterus

112 striatulus (Siluriformes: Auchenipteridae) como herramienta de muestreo de la

entomofauna en un embalse tropical. Revista de Biologia Tropical, 57, 1081–1091.

Santos-Neto JR, Mezencio JM, Chagas AT, Michereff-Filho M, Serrão JE (2010) Use of

serological techniques for determination of Spodoptera frugiperda (JE Smith) predators

(Lepidoptera: Noctuidae). Neotropical Entomology, 39, 420–423.

Saunders GW (2009) Routine DNA barcoding of Canadian Gracilariales (Rhodophyta) reveals

the invasive species Gracilaria vermiculophylla in British Columbia. Molecular Ecology

Resources, 9, 140–150.

Savolainen V, Cowan RS, Vogler AP, Roderick GK, Lane R (2005) Towards writing the

encyclopedia of life: an introduction to DNA barcoding. Philosophical transactions of the

Royal Society of London Biological Sciences, 360, 1805–1811.

Scalici M, Chiesa S, Gherardi F et al. (2009) The new threat to Italian inland waters from the

alien crayfish “gang”: The Australian Cherax destructor Clark, 1936. Hydrobiologia, 632,

341–345.

Schaap PRH (1989) Ecology of the Emerald Shiner Notropis atherinoides Rafinesque in

Dauphin Lake, Manitoba. University of Manitoba.

Schaeffer JS (1984) The White Perch in Lake Erie: The Ecology of an Invading Species. The

Ohio State University.

Schaeffer JS, Margraf FJ (1987) Predation on fish eggs by white perch, Morone americana, in

western Lake Erie. Environmental Biology of Fishes, 18, 77–80.

Schmidt-Kloiber A, Graf W, Lorenz A, Moog O (2006) The AQEM/STAR taxalist - A pan-

113 European macro-invertebrate ecological database and taxa inventory. Hydrobiologia, 566,

325–342.

Schoch CL, Seifert KA, Huhndorf S et al. (2012) Nuclear ribosomal internal transcribed spacer

(ITS) region as a universal DNA barcode marker for Fungi. Proceedings of the National

Academy of Sciences, 109, 6241–6246.

Schooley JD, Karam AP, Kesner BR et al. (2008) Detection of Larval Remains after

Consumption by Fishes. Transactions of the American Fisheries Society, 137, 1044–1049.

Schultz MT, Lance RF (2015) Modeling the Sensitivity of Field Surveys for Detection of

Environmental DNA (eDNA). PLoS One, 10, 1–16.

Seto C (2015) Guelph barcoding conference highlights need for “library of life.” Guelph

Mercury.

Sheffield G, Grebmeier JM (2009) Pacific walrus (Odobenus rosmarus divergens): Differential

prey digestion and diet. Marine Mammal Science, 25, 761–777.

Shehzad W, Riaz T, Nawaz MA et al. (2012) Carnivore diet analysis based on next-generation

sequencing: application to the leopard cat (Prionailurus bengalensis) in Pakistan. Molecular

Ecology, 21, 1951–1965.

Shendure J, Ji H (2008) Next-generation DNA sequencing. Nature Biotechnology, 26, 1135–

1145.

Shirak A, Barkai O, Lernau O et al. (2013) DNA Barcoding Analysis of Fish Bones from a

Shipwreck Found at Dor, Israel. Israeli Journal of Aquaculture, 873, 1–5.

Shokralla S, Singer G, Hajibabaei M (2010) Direct PCR amplification and sequencing of

114 specimens’ DNA from preservative ethanol. Biotechniques, 48, 233–234.

Shokralla S, Spall JL, Gibson JF, Hajibabaei M (2012) Next-generation sequencing technologies

for environmental DNA research. Molecular Ecology, 21, 1794–1805.

Sibbing FA (1988) Specializations and limitations in the utilisation of food resources by the carp,

Cyprinus carpio: a study of oral food processing. Environmental Biology of Fishes, 22,

161–178.

Siddall ME, Kvist S, Phillips A, Oceguera-Figuero A (2012) DNA Barcoding of Parasitic

Nematodes: Is it Kosher? Journal of Parasitology, 98, 692–694.

Sissenwine MP (1984) Why do fish populations vary? In: Exploitation of Marine Communities

(ed May RM), pp. 59–94. Springer Berlin Heidelberg.

Sneath PH, Sokal RR (1962) Numerical taxonomy. Nature, 193, 855–860.

Soininen EM, Valentini A, Coissac E et al. (2009) Analysing diet of small herbivores: the

efficiency of DNA barcoding coupled with high-throughput pyrosequencing for deciphering

the composition of complex plant mixtures. Frontiers in Zoology, 6, 1–9.

Staats M, Arulandhu AJ, Gravendeel B et al. (2016) Advances in DNA metabarcoding for food

and wildlife forensic species identification. Analytical and Bioanalytical Chemistry, 408,

4615–4630.

Stajich J, Birney E (2000) The Bioperl project: motivation and usage. ACM SIGBIO Newsletter,

13–14.

Stearns AM, Krieger KA (2010) Atlas of the Aquatic Oligochaete Worms (Phylum Annelida:

Class Clitellata: Superorder Microdrili) Recorded at the Old Woman Creek National

115 Estuarine Research Reserve and State Nature Preserve, Ohio. Tiffin, Ohio.

Stein ED, Martinez MC, Stiles S, Miller PE, Zakharov E (2014) Is DNA barcoding actually

cheaper and faster than traditional morphological methods: results from a survey of

freshwater bioassessment efforts in the United States? PLoS One, 9, e95525.

Stein ED, White BP, Mazor RD, Miller PE, Pilgrim EM (2013) Evaluating Ethanol-based

Sample Preservation to Facilitate Use of DNA Barcoding in Routine Freshwater

Biomonitoring Programs Using Benthic Macroinvertebrates. PLoS One, 8 , 1–7.

Steinke D, Zemlak TS, Hebert PDN (2009) Barcoding nemo: DNA-based identifications for the

ornamental fish trade. PLoS One, 4, e6300.

Storch AJ (2005) The role of invasive zooplankton in the diets of Great Lakes planktivores. State

University of New York.

Strickler KM, Fremier AK, Goldberg CS (2015) Quantifying effects of UV-B, temperature, and

pH on eDNA degradation in aquatic microcosms. Biological Conservation, 183, 85–92.

Sundberg P, Gibson R (2008) Global diversity of nemerteans (Nemertea) in freshwater.

Hydrobiologia, 595, 61–66.

Swanbrow Becker LJ, Brooks EM, Gabor CR (2016) Effects of turbidity on anti-predator

response and foraging behavior in the fountain darter, Etheostoma fonticola. American

Midland Naturalist, 175, 55–63.

Sweeney BW, Battle JM, Jackson JK, Dapkey T (2011) Can DNA barcodes of stream

macroinvertebrates improve descriptions of community structure and water quality? Journal

of the North American Benthological Society, 30, 195–216.

116 Sweka JA, Hartman KJ (2003) Reduction of reactive distance and foraging success in

smallmouth bass, Micropterus dolomieu, exposed to elevated turbidity levels.

Environmental Biology of Fishes, 67, 341–347.

Taguchi T, Miura Y, Krueger D, Sugiura S (2014) Utilizing stomach content and faecal DNA

analysis techniques to assess the feeding behaviour of largemouth bass Micropterus

salmoides and bluegill Lepomis macrochirus. Journal of Fish Biology, 84, 1271–1288.

Takahara T, Minamoto T, Yamanaka H, Doi H, Kawabata Z (2012) Estimation of Fish Biomass

Using Environmental DNA. PLoS One, 7, e35868.

Taylor DL (2004) Immunological detection of winter flounder (Pseudopleuronectes americanus)

eggs and juveniles in the stomach contents of crustacean predators. Journal of Experimental

Marine Biology and Ecology, 301, 55–73.

Taylor DL, Danila DJ (2005) Predation on winter flounder (Pseudopleuronectes americanus)

eggs by the sand shrimp (Crangon septemspinosa). Canadian Journal of Fisheries and

Aquatic Sciences, 62 , 1611–1625.

Tedersoo L, Nilsson RH, Abarenkov K et al. (2010) 454 Pyrosequencing and Sanger sequencing

of tropical mycorrhizal fungi provide similar results but reveal substantial methodological

biases. New Phytologist, 188, 291–301.

Thieltges DW, Amundsen PA, Hechinger RF et al. (2013) Parasites as prey in aquatic food

webs: Implications for predator infection and parasite transmission. Oikos, 122, 1473–1482.

Thomas AC, Jarman SN, Haman KH, Trites AW, Deagle BE (2014) Improving accuracy of

DNA diet estimates using food tissue control materials and an evaluation of proxies for

117 digestion bias. Molecular Ecology, 23, 3706–3718.

Thompson KA, Newmaster SG (2014) Molecular taxonomic tools provide more accurate

estimates of species richness at less cost than traditional morphology-based taxonomic

practices in a vegetation survey. Biodiversity and Conservation, 23, 1411–1424.

Thum RA, Mercer AT, Wcisel DJ (2012) Loopholes in the regulation of invasive species:

genetic identifications identify mislabeling of prohibited aquarium plants. Biological

Invasions, 14, 929–937.

Tortorella V, Masciari P, Pezzi M et al. (2014) Histamine poisoning from ingestion of fish or

scombroid syndrome. Case Reports in Emergency Medicine, 2014, 1–4.

Trebitz AS, Hoffman JC, Grant GW, Billehus TM, Pilgrim EM (2015) Potential for DNA-based

identification of Great Lakes fauna: match and mismatch between taxa inventories and

DNA barcode libraries. Nature Publishing Group, London, UK.

Trivedi S, Aloufi AA, Ansari AA, Ghosh SK (2015) Role of DNA Barcoding in Marine

Biodiversity Assessment and Conservation: An update. Saudi Journal of Biological

Sciences, 23, 161–171.

Turner CR, Miller DJ, Coyne KJ, Corush J (2014a) Improved Methods for Capture, Extraction,

and Quantitative Assay of Environmental DNA from Asian Bigheaded Carp

(Hypophthalmichthys spp.). PLoS One, 9, 1–20.

Turner CR, Uy KL, Everhart RC (2014b) Fish environmental DNA is more concentrated in

aquatic sediments than surface water. Biological Conservation, 183, 93–102.

U.S. Fish and Wildlife Service (2015) Quality Assurance Project Plan eDNA Monitoring of

118 Bighead and Silver Carps. Bloomington, MN.

Ulrich RM, John DE, Barton GW et al. (2015) A handheld sensor assay for the identification of

grouper as a safeguard against seafood mislabeling fraud. Food Control, 53, 81–90.

Valentini A, Pompanon F, Taberlet P (2009) DNA barcoding for ecologists. Trends in Ecology

& Evolution, 24, 110–117.

Vartak VR, Narasimmalu R, Annam PK, Singh DP, Lakra WS (2015) DNA barcoding detected

improper labelling and supersession of crab food served by restaurants in India. Journal of

the Science of Food and Agriculture, 95, 359–366.

Vestheim H, Jarman SN (2008) Blocking primers to enhance PCR amplification of rare

sequences in mixed samples – a case study on prey DNA in Antarctic krill stomachs.

Frontiers in Zoology, 5, 12.

Vidergar N, Toplak N, Kuntner M (2014) Streamlining DNA Barcoding Protocols: Automated

DNA Extraction and a New cox1 Primer in Arachnid Systematics. PLoS One, 9, e113030.

Vieira EA, Duarte LFL, Dias GM (2012) How the timing of predation affects composition and

diversity of species in a marine sessile community? Journal of Experimental Marine

Biology and Ecology, 412, 126–133.

Watanabe T, Itoh A, Murakami A et al. (1984) Effect of Nutritional Quality of Diets Given To

Broodstock on the Verge of Spawning on Reproduction of Red-Sea Bream. Bulletin of the

Japanese Society of Scientific Fisheries, 50, 1023–1028.

Watson JM, Sykes C, Bonner TH, Ward FA, Booker JF (2009) Foods of age-0 Rio Grande

Silvery Minnows (Hybognathus amarus) reared in hatchery ponds. The Southwestern

119 Naturalist, 54, 475–479.

Weber DC, Lundgren JG (2009) Detection of predation using qPCR: effect of prey quantity,

elapsed time, chaser diet, and sample preservation on detectable quantity of prey DNA.

Journal of Insect Science, 9, 1–12.

Van de Wiel CCM, Van der Schoot JV, Van Valkenburg JLCH, Duistermaat H, Smulders MJM

(2009) DNA barcoding discriminates the noxious invasive plant species, floating pennywort

(Hydrocotyle ranunculoides L.f.), from non-invasive relatives. Molecular Ecology

Resources, 9, 1086–1091.

Wimbush J, Frischer ME, Zarzynski JW, Nierzwicki-Bauer SA (2009) Eradication of colonizing

populations of zebra mussels (Dreissena polymorpha) by early detection and SCUBA

removal: Lake George, NY. Aquatic Conservation: Marine and Freshwater Ecosystems, 19,

703–713.

Wolfert D, Busch WD., Baker CT (1975) Predation by fish on walleye eggs on a spawning reef

in western Lake Erie, 1969-1971. Ohio Journal of Science, 75, 118–125.

Wong EH-K, Hanner RH (2008) DNA barcoding detects market substitution in North American

seafood. Food Research International, 41, 828–837.

Yancy HF, Zemlak TS, Mason JA et al. (2008) Potential use of DNA barcodes in regulatory

science: applications of the Regulatory Fish Encyclopedia. Journal of Food Protection, 71,

210–217.

Yokota T, Machida M, Takeuchi H et al. (2011) Anti-predatory performance in hatchery-reared

red tilefish (Branchiostegus japonicus) and behavioral characteristics of two predators:

120 Acoustic telemetry, video observation and predation trials. Aquaculture, 319, 290–297.

Zaiko A, Martinez JL, Schmidt-Petersen J et al. (2015) Metabarcoding approach for the ballast

water surveillance – An advantageous solution or an awkward challenge? Marine Pollution

Bulletin, 92, 25–34.

Zeale MRK, Butlin RK, Barker GLA, Lees DC, Jones G (2011) Taxon-specific PCR for DNA

barcoding arthropod prey in bat faeces. Molecular Ecology Resources, 11 , 236–244.

Zeldis J, Robinson K, Ross A, Hayden B (2004) First observations of predation by New Zealand

Greenshell mussels (Perna canaliculus) on zooplankton. Journal of Experimental Marine

Biology and Ecology, 311, 287–299.

Zhan A, He S, Brown E a. et al. (2014a) Reproducibility of pyrosequencing data for biodiversity

assessment in complex communities. Methods in Ecology and Evolution, 5, 881–890.

Zhan A, Hulák M, Sylvester F et al. (2013) High sensitivity of 454 pyrosequencing for detection

of rare species in aquatic communities. Methods in Ecology and Evolution, 4, 558–565.

Zhan A, Xiong W, He S, MacIsaac HJ (2014b) Influence of artifact removal on rare species

recovery in natural complex communities using high-throughput sequencing. PLoS One, 9,

e96928.

Zhang H, Rutherford ES, Mason DM et al. (2016) Forecasting the Impacts of Silver and Bighead

Carp on the Lake Erie Food Web. Transactions of the American Fisheries Society, 145,

136–162.

Zhao Y, Jones ML, Shuter BJ, Roseman EF (2009) A biophysical model of Lake Erie walleye

(Sander vitreus) explains interannual variations in recruitment. Canadian Journal of

121 Fisheries and Aquatic Sciences, 66, 114–125.

Zhou X, Li Y, Liu S et al. (2013) Ultra-deep sequencing enables high-fidelity recovery of

biodiversity for bulk arthropod samples without PCR amplification. GigaScience, 2, 1–12.

Zhou X, Robinson JL, Geraci CJ et al. (2011) Accelerated construction of a regional DNA-

barcode reference library: caddisflies (Trichoptera) in the Great Smoky Mountains National

Park. Journal of the North American Benthological Society, 30, 131–162.

122

Table A.1. Summary of capture location, sex, sample size, and total length for predator fishes

present in the final next generation sequencing dataset. Fishes were captured at Orleans Park on

April 29 and April 30, and at Rossford Marina on May 6. The range and mean total length (TL)

is expressed to the nearest 1 mm, and ND means no data.

Range (Mean) for Sample Predator species Capture location Sex Predator Length Size (TL, mm) Aplodinotus grunniens Rossford Male 4 135-473 (297) Aplodinotus grunniens Rossford Female 1 493 Aplodinotus grunniens Rossford Unknown 6 129-530 (349) Total 11 129-530 (338) Carassius auretus Orleans Female 1 243 Total 1 243 Cyprinus carpio Orleans Female 1 441 Cyprinus carpio Orleans Male 4 389-502 (430) Cyprinus carpio Orleans Unknown 2 500-564 (532) Cyprinus carpio Rossford Male 1 600 Cyprinus carpio Rossford Unknown 4 246-510 (387) Total 12 246-600 (449) Dorosoma cepedianum Orleans Female 2 349-385 (367) Dorosoma cepedianum Orleans Male 3 315-337 (328) Dorosoma cepedianum Orleans Unknown 1 382 Dorosoma cepedianum Rossford Male 1 259 Dorosoma cepedianum Rossford Unknown 1 310 Total 8 259-385 (334) Ictalurus punctatus Orleans Male 4 390-527 (470) Total 4 390-527 (470) Morone americana Orleans Female 1 272 Morone americana Orleans Male 1 205 Morone americana Orleans Unknown 1 ND Morone americana Rossford Female 2 205-239 (222) Morone americana Rossford Male 7 115-242 (215) Morone americana Rossford Unknown 3 152-246 (204) Total 15 115-272 (217) Continued

123

Table A.1 Continued

Range (Mean) for Capture Sample Predator species Sex Predator Length (TL, Location Size mm) Morone chrysops Orleans Female 1 267 Morone chrysops Orleans Male 10 260-466 (320) Morone chrysops Rossford Male 10 241-342 (291) Morone chrysops Rossford Unknown 1 251 Total 22 241-466 (299) Notropis atherinoides Orleans Unknown 28 42-58 (50) Notropis atherinoides Rossford Unknown 20 42-52 (47) Total 48 42-58 (48) Pimephales notatus Orleans Unknown 12 46-70 (58) Total 12 46-70 (58)

Total, all species 133 42-600 (196)

124

Figure B.1. Sequence length frequency distribution of original Ion Torrent PGM dataset produced using FastQC Report (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) via

Galaxy.

125

Figure B.2. Sequence quality score frequency distribution of original Ion Torrent PGM dataset produced using FastQC Report (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) via

Galaxy.

126

Table C.1. Mean daily abundance of walleye eggs (mean number of eggs in benthic samples) ± 1 standard deviation collected at Orleans Park (OP) and Buttonwood Park (BP) (spawning grounds) during April and May of 2014 with sample size in parentheses. ND = no data collected on date. These data are exclusively from bottom samples, which are semi-quantitative, and were collected by disturbing the area in front of an ichthyoplankton net for 5 minutes with the net resting on the bottom.

Date Mean # walleye eggs/sample ± 1 SD (sample size)

from OP and BP spawning grounds bottom samples April 22 165.0 (n=1) April 23 80.0 (n=1) April 24 2.0 ± 1.4 (n=2) April 25 72.5 ± 87.0 (n=2) April 26 101.3 ± 120.1 (n=4) April 27 233.5 ± 361.3 (n=4) April 28 294.5 ± 214.3 (n=2) April 29 ND

April 30 174.7 ± 112.7 (n=3) May 1 255.0 ± 240.4 (n=2) May 2 2325.0 ± 72.1 (n=2) May 3 773.0 ± 844.7 (n=4) May 4 ND May 5 119.0 (n=1) May 6 ND May 7 4.5 ± 3.5 (n=2)

127

Table C.2. Mean daily density (larvae/m3) of larval white suckers (Catostomus commersonii) at

Orleans Park (OP) and Buttonwood Park (BP) (spawning grounds) during April and May of

2014 from surface samples with sample size in parentheses. ND = No data collected on date.

Samples were collected for five minutes in the top 0.5 m of the water column using an ichthyoplankton net.

Date Mean white sucker larvae/m3 ± 1 SD (sample size) in OP and BP spawning grounds surface samples April 23 0 ± 0 (n=2) April 24 0 ± 0 (n=3) April 25 0 ± 0 (n=2) April 26 0 ± 0 (n=4) April 27 0.01 ± 0.02 (n=5) April 28 0 ± 0 (n=2) April 29 ND April 30 0.01 ± 0.02 (n=3) May 1 0.06 ± 0.06 (n=3) May 2 0.07 ± 0.01 (n=2) May 3 0.08 ± 0.03 (n=4) May 4 ND May 5 0.17 (n=1) May 6 ND May 7 0.15 ± 0.22 (n=2) May 8 ND May 9 ND May 10 ND May 11 ND May 12 1.50 ± 2.13 (n=2)

128

Table C.3. Days that presence of Morone spp. eggs and larvae were noted at Orleans Park (OP) and Buttonwood Park (BP) (spawning grounds), and in the area from Rossford Marina (RM) to the Maumee River mouth (areas downstream of spawning grounds) in April and May of 2014.

Note that this is qualitative information, and dates where Morone spp. were not recorded does not mean that they were necessarily absent from the river system.

OP and BP RM to River Mouth (Spawning grounds) (Downstream of Spawning grounds) Observation Dates noted Observation Dates noted Morone spp. April 26, May 3, May 5, May Morone spp. No dates noted eggs 7 eggs Morone spp. April 24, May 3, May 5, May Morone spp. May 5, May 8, May 11 larvae 12 larvae

129

Table D.1. Number of sequences kept and removed at each processing step of the Maumee River diet metabarcoding dataset. The percent of the original dataset remaining at each step is represented in parentheses. Sequences removed during the “Demultiplexing” step lacked a

Multiplex Identifier (MID), and could not be attributed to a sample. Any reads that lacked UniA,

UniB, the forward primer, or the reverse primer were removed from the dataset in the “Adaptor removal” step. Reads less than 80 bp in length were removed during the “Length Filter” step.

Any read that did not have at least 90% of its length with a PHRED quality score ≥ 20 was discarded during the “Quality Filter” step. Duplicate reads were removed during the “Collapse” step. The “Blast” step discarded any read lacking a significant match to reference database sequences. Any reads with ˂ 95% identity match were removed during the “Percent Identity >

95%” step. With duplicate sequences included, the final number of sequences remaining in the dataset post-processing was 100,668 sequences. After removal of duplicates, 48,610 sequences remained.

Blast Dataset Percent Percent removal Adaptor Adaptor Collapse Length Filter Length Quality Filter Quality Original Fastq Original Identity > 95% Identity > Demultiplexing Kept (% 4911870 4078020 425971 188064 124876 65320 56643 48610 original) (100%) (83%) (9%) (4%) (3%) (1%) (1%) (1%) Removed 0 833850 3652049 237907 63188 59556 8677 8033

130

Table D.2. Number of sequences represented by one, two, three, four, five, or greater than five reads. This was calculated after filtering steps in Table D.1 and after removing contaminant sequences such as those from Homo sapiens .

Number of Reads Total Number of % Total Number per Sequence Reads of Reads

1 40214 84.9%

2 3592 7.6% 3 1233 2.6% 4 585 1.2% 5 312 0.7% > 5 1454 3.1% Total: 47390 100%

131

Table E.1. Taxonomic assignment of sequences from the stomach contents of white perch,

Morone americana, at Rossford Marina (n=12). Sequences were assigned to the lowest

taxonomic rank level possible. Number of reads total assigned to the same taxon and the number

of individuals with a particular taxon in the dataset also represented. The maximum and

minimum percent identity (PID) between query and subject sequences for each taxon also

shown, with the mean PID in parentheses.

Rank Number of Number of Range PID Taxon level reads predators (Mean PID) Phylum

Chordata Moronidae family 68 7 95.1-100.0 (97.1) Sciaenidae family 20 3 95.9-98.8 (98.2) Moxostoma genus 23 3 97.3-100.0 (99.4) Dorosoma genus 8 3 98.4-99.7 (99.2) Cyprinus genus 1 1 99.4 Morone americana species 688 12 96.1-100.0 (98.7) Cyprinus carpio species 218 2 95.9-100.0 (99.0) Aplodinotus grunniens species 157 10 95.8-100.0 (99.0) Moxostoma macrolepidotum species 39 4 97.3-100.0 (99.4) Notropis atherinoides species 11 5 98.7-100.0 (99.4) Morone chrysops species 1 1 95.2 Phylum

Arthropoda Chironomidae family 1112 5 95.2-100.0 (98.4) Procladius genus 7 1 97.8-100.0 (99.3) Chironomus genus 2 1 99.4-99.7 (99.5) Procladius bellus species 2455 5 95.3-100.0 (98.9) Bythotrephes longimanus species 50 6 95.8-100.0 (99.3) Procladius denticulatus species 32 1 98.7-100.0 (99.3) Continued

132

Table E.1 Continued

Glyptotendipes meridionalis species 10 1 97.7-99.7 (99.2) Caenis latipennis species 5 1 97.8-99.7 (98.4) Phylum Annelida Oligochaeta subclass 1 1 99.4 Vejdovskyella genus 1 1 99.0 Desserobdella montifera species 2898 1 95.5-100.0 (99.0) Phylum Nemertea Nemertea Phylum 1 1 99.4 Phylum Rotifera Keratella cochlearis species 42 1 97.1-98.7 (97.8) Brachionus urceolaris species 2 2 97.8-99.7 (98.7)

Table E.2. Taxonomic assignment of sequences from the stomach contents of white perch,

Morone americana, at Orleans Park (n=3). Sequences were assigned to the lowest taxonomic

rank level possible. Number of reads total assigned to the same taxon and the number of

individuals with a particular taxon in the dataset also represented. The maximum and minimum

percent identity (PID) between query and subject sequences for each taxon also shown, with the

mean PID in parentheses.

Rank Number of Number of Range PID Taxon level reads predators (Mean PID) Phylum

Chordata Moronidae family 4 1 95.7-95.7 (95.7) Sciaenidae family 2 1 98.2-99.0 (98.7) Moxostoma genus 11 1 98.6-100.0 (99.6) Catostomus commersonii species 250 1 95.5-100.0 (99.2) Sander vitreus † species 37 1 98.2-100.0 (99.2) Continued

133

Table E.2 Continued

Morone americana species 32 3 97.8-100.0 (98.7) Notropis atherinoides species 10 3 96.8-100.0 (99.0) Moxostoma macrolepidotum species 6 1 99.0-99.7 (99.4) Phylum Arthropoda Paratanytarsus genus 14 1 98.9-100.0 (99.7) Phylum

Annelida Lumbricus terrestris species 3 1 99.6-100.0 (99.8) Desserobdella montifera species 1 1 98.4

† Of the 37 reads assigned to Sander vitreus in the white perch, 16 of the sequences were represented by one read and three sequences were represented by 3, 5, and 13 reads.

134

Table F.1. Taxonomic assignment of sequences from the stomach contents of white bass,

Morone chrysops, at Rossford Marina (n=11). Sequences were assigned to the lowest taxonomic

rank level possible. Number of reads total assigned to the same taxon and the number of

individuals with a particular taxon in the dataset also represented. The maximum and minimum

percent identity (PID) between query and subject sequences for each taxon also shown, with the

mean PID in parentheses.

Rank Number of Number of Range PID Taxon level reads predators (Mean PID) Phylum Chordata Sciaenidae family 24 1 95.3-98.8 (96.5) Cyprinidae family 3 1 98.0-100.0 (98.7) Moronidae family 1 1 99.1 Morone genus 142 9 96.3-100.0 (99.1) Moxostoma genus 7 3 97.3-100.0 (98.7) Cyprinus genus 3 1 99.4 Notropis genus 1 1 95.4 Aplodinotus grunniens species 2106 8 95.1-100.0 (99.1) Notropis atherinoides species 1302 10 95.0-100.0 (99.0) Cyprinus carpio species 845 6 95.2-100.0 (99.1) Morone chrysops species 254 10 95.8-99.7 (99.2) Lota lota species 6 1 99.1-100.0 (99.7) Moxostoma macrolepidotum species 4 2 99.7 Ictiobus cyprinellus species 3 1 98.7-99.7 (99.3) Morone americana species 2 2 95.1-98.7 (96.9) Ictiobus bubalus species 1 1 98.7 Phylum Arthropoda Chironomidae family 1 1 99.6 Chironomus genus 45 3 98.5-100.0 (99.7) Continued

135

Table F.1 Continued

Bythotrephes longimanus species 8 2 97.9-100.0 (99.4) Procladius bellus species 3 2 100.0

Chironomus crassicaudatus species 2 1 99.0 Phylum Annelida Desserobdella montifera species 8 1 98.4-99.7 (99.1) Phylum Nemertea Nemertea phylum 3 1 99.4-99.7 (99.5) Phylum Rotifera Brachionus urceolaris species 7 1 96.9-99.0 (98.4)

Table F.2. Taxonomic assignment of sequences from the stomach contents of white bass,

Morone chrysops at Orleans Park (n=11). Sequences were assigned to the lowest taxonomic rank

level possible. Number of reads total assigned to the same taxon and the number of individuals

with a particular taxon in the dataset also represented. The maximum and minimum percent

identity (PID) between query and subject sequences for each taxon also shown, with the mean

PID in parentheses.

Rank Number of Number of Range PID Taxon level reads predators (Mean PID) Phylum

Chordata Sciaenidae family 6 1 97.9 Moronidae family 4 2 95.3-99.2 (96.6) Morone genus 39 4 98.4-99.6 (99.1) Moxostoma genus 7 1 100.0 Aplodinotus grunniens species 324 4 95.4-100.0 (99.0) Notropis atherinoides species 157 10 97.1-100.0 (98.9) Morone chrysops species 81 7 97.3-100.0 (99.2) Catostomus commersonii species 15 1 98.1-100.0 (99.3) Continued

136

Table F.2 Continued

Perca flavescens species 8 1 96.8-99.2 (98.2) Moxostoma macrolepidotum species 5 2 99.3-100.0 (99.6) Cyprinus carpio species 5 3 97.4-100.0 (99.4) Sander vitreus † species 3 1 100.0 Ictalurus punctatus species 3 1 99.4-100.0 (99.8) Morone americana species 2 1 95.1-98.4 (96.7) Phylum

Arthropoda Chironomidae family 1 1 99.6 Tribelos genus 2 1 99.3-99.7 (99.5) Paratanytarsus genus 1 1 99.6 Cheumatopsyche analis species 1 1 99.7 Cheumatopsyche campyla species 1 1 99.7 Ephemera simulans species 1 1 99.3 Phylum Annelida Lumbricus genus 1 1 99.3 Lumbricus terrestris species 26 1 98.3-100.0 (99.7) Phylum Nemertea Nemertea phylum 1 1 99.7 Phylum Rotifera Procladius bellus species 3 1 99.0-100.0 (99.5) Brachionus urceolaris species 2 1 99.1-100 (99.5)

† All three of the sequences assigned to Sander vitreus in the white bass were represented by one read each.

137

Table G.1. Taxonomic assignment of sequences from gut contents of emerald shiner, Notropis

atherinoides at Rossford Marina (n=20). Sequences were assigned to the lowest taxonomic rank

level possible. Number of reads total assigned to the same taxon and the number of individuals

with a particular taxon in the dataset also represented. The maximum and minimum percent

identity (PID) between query and subject sequences for each taxon also shown, with the mean

PID in parentheses.

Rank Number of Number of Range PID Taxon level reads predators (Mean PID) Phylum Chordata Cyprinidae family 7 3 95.9-100.0 (98.0) Moronidae family 1 1 95.7 Moxostoma genus 25 4 96.9-100.0 (99.6) Morone genus 12 7 98.8-99.6 (99.2) Notropis genus 7 4 96.1-99.7 (97.6) Cyprinus genus 2 2 96.8-99.4 (98.1) Notropis atherinoides species 22442 19 95.0-100.0 (98.9) Aplodinotus grunniens species 965 15 95.1-100.0 (99.2) Cyprinus carpio species 467 16 95.0-100.0 (98.7) Morone chrysops species 61 8 97.3-100.0 (99.1) Moxostoma macrolepidotum species 44 4 98.4-100.0 (99.5) Morone americana species 3 2 98.4 Pimephales notatus species 2 2 99.4-99.7 (99.5) Notropis buchanani species 1 1 97.4 Phylum

Arthropoda Chironomidae family 9 3 98.3-99.7 (99.0) Orthocladius genus 30 1 97.4-100.0 (99.5) Chironomus genus 5 3 99.4-100.0 (99.6) Bythotrephes longimanus species 117 7 98.4-100.0 (99.5) Procladius bellus species 10 5 98.7-100.0 (99.4) Procladius denticulatus species 2 2 99.0-100.0 (99.5) Continued

138

Table G.1 Continued

Chironomus crassicaudatus species 1 1 99.4 Phylum

Annelida Desserobdella montifera species 1 1 99.4 Phylum

Rotifera Brachionus urceolaris species 482 11 97.5-100.0 (99.1) Brachionus quadridentatus species 150 5 95.3-98.7 (96.9) Brachionus variabilis species 71 2 97.5-99.4 (98.4) Brachionus calyciflorus species 14 3 97.5-100.0 (98.8) Phylum

Platyhelminthes Bothriocephalus acheilognathi Species 30 1 97.9-100.0 (99.2) Phylum

Bryozoa Plumatella casmiana Species 15 2 98.0-99.2 (98.7)

Table G.2. Taxonomic assignment of sequences from gut contents of emerald shiner, Notropis

atherinoides at Orleans Park (n=28). Sequences were assigned to the lowest taxonomic rank

level possible. Number of reads total assigned to the same taxon and the number of individuals

with a particular taxon in the dataset also represented. The maximum and minimum percent

identity (PID) between query and subject sequences for each taxon also shown, with the mean

PID in parentheses.

Rank Number of Number of Range PID (Mean Taxon level reads predators PID) Phylum

Chordata Cyprinidae family 9 3 95.4-97.8 (96.9) Notropis genus 106 8 95.0-97.8 (96.5) Dorosoma genus 1 1 99.7 Notropis atherinoides species 21117 27 95.1-100.0 (99.0) Notropis buchanani species 453 7 96.5-100.0 (99.3) Continued

139

Table G.2 Continued

Pimephales notatus species 103 15 95.2-100.0 (98.7) Cyprinus carpio species 47 6 95.1-100.0 (96.5) Aplodinotus grunniens species 7 3 99.4-100.0 (99.7) Sander vitreus † species 4 2 98.4-99.0 (98.9) Perca flavescens species 4 1 98.4-99.7 (98.7) Morone chrysops species 1 1 99.7 Phylum

Arthropoda Paratanytarsus genus 45 6 98.8-100.0 (99.6) Xenochironomus genus 3 1 96.5-97.1 (96.8) Hydrobaenus genus 1 1 98.7 Bythotrephes longimanus species 2 1 99.0-100.0 (99.5) Ephemera simulans species 2 1 98.6-99.4 (99.0) Glyptotendipes meridionalis species 1 1 99.4 Phylum

Annelida Lumbricus terrestris species 9 1 98.9-100.0 (99.5) Phylum

Nemertea Nemertea phylum 1 1 99.7 Phylum

Rotifera Synchaeta genus 3 2 97.8-99.7 (98.7) Brachionus urceolaris species 7 2 97.8-100.0 (99.4) Procladius bellus species 1 1 99.7 Phylum Platyhelminthes Posthodiplostomum genus 10 3 99.0-100.0 (99.5)

† Each of the two emerald shiners had two sequences assigned to Sander vitreus, and all sequences were represented by one read each.

140

Table H.1. Taxonomic assignment of sequences from the stomach contents of freshwater drum,

Aplodinotus grunniens (n=12, all from RM). Sequences were assigned to the lowest taxonomic

rank level possible. Number of reads total assigned to the same taxon and the number of

individuals with a particular taxon in the dataset also represented. The maximum and minimum

percent identity (PID) between query and subject sequences for each taxon also shown, with the

mean PID in parentheses.

Rank Number of Number of Range PID Taxon level reads predators (Mean PID) Phylum Chordata Sciaenidae family 11 3 96.3-100.0 (97.8) Moronidae family 2 1 95.9 Moxostoma genus 95 3 97.6-100.0 (99.6) Morone genus 5 1 98.8-99.2 (99.0) Notropis genus 1 1 95.3 Aplodinotus grunniens species 22771 11 95.4-100.0 (99.2) Cyprinus carpio species 627 4 95.1-100.0 (99.0) Notropis atherinoides species 206 8 95.4-99.7 (99.1) Moxostoma macrolepidotum species 131 4 97.7-100.0 (99.4) Morone chrysops species 13 2 99.0-99.4 (99.2) Morone americana species 8 2 98.1-99.0 (98.7) Phylum Arthropoda Chironomidae family 44 3 97.9-99.7 (99.1) Chironomus genus 113 2 97.2-99.7 (98.9) Tribelos genus 2 1 100.0 Procladius bellus species 488 3 97.1-100.0 (99.3) Bythotrephes longimanus species 3 2 99.7-100.0 (99.9) Tanytarsus guerlus species 1 1 100.0 Continued

141

Table H.1 Continued

Phylum

Annelida Oligochaeta subclass 16 1 97.4-98.4 (98.0) Phylum

Nemertea Nemertea phylum 30 2 98.1-100.0 (99.4)

142

Table I.1. Taxonomic assignment of sequences from the gut contents of one goldfish, Carassius auretus (n=1, from OP). Sequences were assigned to the lowest taxonomic rank level possible.

Number of reads total assigned to the same taxon and the number of individuals with a particular taxon in the dataset also represented. The maximum and minimum percent identity (PID) between query and subject sequences for each taxon also shown, with the mean PID in parentheses.

Rank Number of Number of Range PID Taxon level reads predators (Mean PID) Phylum

Chordata Micropterus salmoides species 20 1 97.8-99.4 (98.9) Ictiobus bubalus species 3 1 99.7 Carassius auratus species 2 1 99.4-100.0 (99.7) Ictiobus cyprinellus species 2 1 99.0-99.4 (99.2) Phylum Arthropoda Chironomus genus 2 1 99.7-100.0 (99.8)

143

Table J.1. Taxonomic assignment of sequences from the gut contents of common carp, Cyprinus

carpio (n=12, collected from both OP and RM). Sequences were assigned to the lowest

taxonomic rank level possible. Number of reads total assigned to the same taxon and the number

of individuals with a particular taxon in the dataset also represented. The maximum and

minimum percent identity (PID) between query and subject sequences for each taxon also

shown, with the mean PID in parentheses.

Rank Number of Number of Range PID Taxon level reads predators (Mean PID) Phylum

Chordata Notropis atherinoides species 2379 11 95.5-100.0 (99.1) Aplodinotus grunniens species 1843 3 97.8-100.0 (99.3) Perca flavescens species 26 2 95.2-100.0 (99.2) Morone americana species 16 1 97.8-99.7 (98.8) Cyprinus carpio species 7 3 95.4-100.0 (97.3) Pimephales notatus species 4 2 99.0-100.0 (99.7) Notropis buchanani species 3 1 99.0-100.0 (99.6) Phylum

Arthropoda Paratanytarsus genus 2 1 99.7-100.0 (99.8) Cylindroiulus caeruleocinctus species 7 1 98.7-100.0 (99.5) Procladius bellus species 1 1 99.7 Phylum

Annelida Lumbricus terrestris species 71 2 98.7-100.0 (99.8) Nais bretscheri species 1 1 99.7 Phylum Nemertea Nemertea phylum 2 1 99.7-100.0 (99.8) Phylum Rotifera Brachionus urceolaris species 50 1 98.5-100.0 (99.3)

144

Table K.1. Taxonomic assignment of sequences from the gut contents of gizzard shad,

Dorosoma cepedianum (n=8, from both OP and RM). Sequences were assigned to the lowest

taxonomic rank level possible. Number of reads total assigned to the same taxon and the number

of individuals with a particular taxon in the dataset also represented. The maximum and

minimum percent identity (PID) between query and subject sequences for each taxon also

shown, with the mean PID in parentheses.

Rank Number of Number of Range PID Taxon level reads predators (Mean PID) Phylum

Chordata Sciaenidae family 1 1 95.6 Morone genus 8 1 98.0-99.6 (99.0) Moxostoma genus 8 1 99.3-100.0 (99.8) Aplodinotus grunniens species 52 6 98.7-100.0 (99.3) Notropis atherinoides species 20 5 98.1-99.7 (98.9) Moxostoma macrolepidotum species 17 1 99.0-99.7 (99.6) Morone chrysops species 12 1 98.1-99.7 (99.2) Morone americana species 8 1 95.1-99.3 (98.4) Dorosoma cepedianum species 1 1 99.3 Phylum

Arthropoda Ephemera simulans species 2 1 99.0

145

Table L.1. Taxonomic assignment of sequences from the stomach contents of channel catfish,

Ictalurus punctatus (n=4, from OP). Sequences were assigned to the lowest taxonomic rank level

possible. Number of reads total assigned to the same taxon and the number of individuals with a

particular taxon in the dataset also represented. The maximum and minimum percent identity

(PID) between query and subject sequences for each taxon also shown, with the mean PID in

parentheses.

Rank Number of Number of Range PID Taxon level reads predators (Mean PID) Phylum Chordata Moxostoma genus 2 1 100.0 Aplodinotus grunniens species 466 1 95.4-100.0 (99.2) Cyprinus carpio species 152 1 98.1-100.0 (99.0) Ictalurus punctatus species 101 2 96.3-100.0 (98.6) Notropis atherinoides species 75 3 98.1-100.0 (99.2) Moxostoma macrolepidotum species 4 1 98.1-99.7 (99.1) Pimephales notatus species 2 2 98.7-99.7 (99.2) Morone chrysops species 2 1 98.4-99.0 (98.7) Morone americana species 1 1 99.0 Phylum

Arthropoda Ephemera simulans species 22 1 97.6-99.7 (98.9) Cylindroiulus species 8 1 98.2-100.0 (99.2) caeruleocinctus Procladius bellus species 4 1 98.7-99.7 (99.4) Phylum Annelida Lumbricus terrestris species 6 1 97.7-100.0 (99.4) Desserobdella montifera species 1 1 99.4

146

Table M.1. Taxonomic assignment of sequences from the gut contents of bluntnose minnow,

Pimephales notatus (n=12, all from OP). Sequences were assigned to the lowest taxonomic rank

level possible. Number of reads total assigned to the same taxon and the number of individuals

with a particular taxon in the dataset also represented. The maximum and minimum percent

identity (PID) between query and subject sequences for each taxon also shown, with the mean

PID in parentheses.

Rank Number of Number of Range PID Taxon level reads predators (Mean PID) Phylum

Chordata Cyprinidae family 12 1 95.0-96.0 (95.3) Notropis genus 16 2 95.5-96.9 (96.4) Notropis atherinoides species 5168 12 95.1-100.0 (98.8) Pimephales notatus species 3861 7 95.1-100.0 (99.0) Notropis buchanani species 876 3 95.4-100.0 (99.2) Notropis volucellus species 34 2 95.5-97.8 (96.5) Cyprinus carpio species 11 2 95.2-98.9 (97.2) Acipenser fulvescens species 2 2 99.4-99.7 (99.5) Phylum Arthropoda Paratanytarsus genus 5 1 99.0-100.0 (99.6) Bythotrephes longimanus species 4 1 99.4-100.0 (99.6) Phylum

Platyhelminthes Posthodiplostomum genus 84 3 97.6-100.0 (98.9)

147

100 24567 46911 90 80 70 60 50 40 10073 30 20 846 10 8221 5480 4412 129

Reads assigned to predator (% total reads) total(% predatorto assigned Reads 0

Figure N.1. Percent of total reads from each predator species that were assigned to the predator species itself and were likely derived from predator gut lining DNA. 72,193 sequences out of the

100,668 sequences (72%) from all predator species could be attributed to predator contamination. Black and gray bars represent fish species with and without true stomachs, respectively. Sample size is represented in parentheses. Carassius auretus not shown due to low number of reads and low sample size (n=29 sequences total from one fish). Total number of reads from each species shown above bar.

148

C) 20 °

5 Average daily temperature ( temperature daily Average

0 May 4 May April 4 April May 14 May April 14 April 24 April March 15 March 25 March

Figure O.1. Temperature data generated from NOAA Great Lakes Real-Time Currents

Monitoring station gl0201 on the Maumee River (41°37.748' N, 83°31.813' W) from March 15 to May 15 of 2014. Average daily temperature ± 1 standard deviation shown. Days in white

(April 29, April 30, and May 6) are the dates that predators were collected from Orleans Park

(April) and from Rossford Marina (May).

149

APPENDIX A: IACUC APPROVAL FOR PROTOCOL 14-006 150

APPENDIX B: ACRONYMS AND ABBREVIATIONS USED IN THESIS

BLAST Basic Local Alignment Search Tool BOLD Barcode of Life Data Systems BP Buttonwood Park bp Base pairs BSA Bovine Serum Albumin COI Cytochrome c oxidase

Ct Cycle Threshold DNA Deoxyribonucleic acid dNTP Deoxyribonucleotide ddPCR Droplet Digital PCR dsDNA Double-stranded DNA eDNA Environmental DNA EDTA Ethylenediaminetetraacetic acid ELS Early Life Stage(s) EPA Environmental Protection Agency EPT Ephemeroptera, Plecoptera, Trichoptera FA Fatty acid FAA Federal Aviation Administration FDA Food and Drug Administration FRET Fluorescent Resonance Energy Transfer LAMP Loop mediated isothermal amplification MID Multiplex Identifier NASBA Nucleic acid sequence based amplification NCBI National Center for Biotechnology Information Continued 151

APPENDIX B CONTINUED

NGS Next Generation Sequencing NOAA National Oceanic and Atmospheric Administration NTU Nephelometric Turbidity Units OP Orleans Park OTU Operational Taxonomic Unit PCR Polymerase chain reaction PID Percent Identity qRT-PCR Quantitative Real-Time PCR RM Rossford Marina RNA Ribonucleic acid RO/DI Reverse osmosis deionized RPM Revolutions per minute SDS Sodium dodecyl sulfate TL Total Length Tris HCl Trisaminomethane hydrochloride USDA United States Department of Agriculture

USDA United States Department of Agriculture ARS Agricultural Research Service USFWS United States Fish and Wildlife Service USGS United States Geological Survey