Open Mueller MS Thesis.Pdf

The Pennsylvania State University

The Graduate School

Department of Ecosystem Science and Management

METHODOLOGIES IN ASSESSING BENTHIC MACROINVERTEBRATE COMMUNITIES

A Thesis in

Wildlife and Fisheries Science

Sara Jaye Mueller

Submitted in Partial Fulfillment of the Requirements for the Degree of

Master of Science

December 2016

The thesis of Sara Mueller was reviewed and approved* by the following:

Jay R. Stauffer Jr. Distinguished Professor of Ichthyology Thesis Advisor

Elizabeth Boyer Associate Professor of Water Resources

Gregory A. Hoover Senior Extension Associate, Department of Entomology

Aaron Aunins Contractor, United States Geological Survey, Special Signatory

Michael G. Messina Head of Department of Ecosystem Science and Management

*Signatures are on file in the Graduate School

iii

ABSTRACT

Benthic macroinvertebrates have been used for over a century in biomonitoring.

Biomonitoring is the use of biological measures to determine the state of or to evaluate changes in the environment. This surveillance method is commonly used as part of water quality monitoring. Agencies vary in their approach in collecting, sorting, and identifying benthic macroinvertebrates. The objective of this study is to compare entire collections of benthic macroinvertebrates collected by researchers in 2015 to computer-simulated data of subsamples of 100, 200, and 300 organisms. The computer simulated data represent protocols used by the

National Park Service and the Pennsylvania Department of Environmental Protection. The

Brillouin Index was calculated for each site and subsample permutation. An ANOVA analysis of the Brillouin Index by site showed that there is a statistically significant difference (p<0.05) between the use of 100 and 200 organisms with the Brillouin Index for 200 being greater.

Additionally, the ANOVA analysis showed that 45.5% (5/11) of the time, that there is a statistically significant difference (p<0.05) between the use of 200 and 300 organisms with the

Brillouin Index for 300 being greater. Lastly, a one-sample t-test revealed that there is a statistically significant difference (p<0.05) between the use of 300 organisms (or 200 if 300 organisms were not obtained) and entire samples with the Brillouin Index for entire samples being greater at all but one site. These results indicate there could be valuable information missing from traditionally collected benthic macroinvertebrate samples for which DNA based technologies may improve macroinvertebrate bioassessments of aquatic ecosystems.

Several recent studies have investigated the applicability of the mitochondrial genes cytochrome oxidase 1 (COI), 12S rDNA, and 16S rDNA for barcoding of various aquatic fauna including fish and iv aquatic invertebrates using artificial pools of individuals in the laboratory. Generally, molecular methods have shown an increase in taxonomic resolution compared to the morphological analyses. However, current limitations in barcoding primer design result in certain taxonomic groups being over or under- represented in the sequencing results. In addition, little work has been done to see how genetic based estimates of diversity based on sequencing of environmental DNA samples extracted from sediment or water samples compare to traditional morphological based analyses. This study tested 55 primer pairs targeting both mitochondrial 16S rDNA and COI genes. The COI primers, despite incorporation of numerous ambiguous nucleotides to facilitate application to wide taxonomic breadth, did not amplify several taxa tested. However testing of 16S primers amplified DNA missed by COI. The results of this study make a large contribution to the effort of building up a robust 16S rDNA barcode database for aquatic invertebrate metabarcoding, through the generation and curation of 74 unique sequences.

TABLE OF CONTENTS

List of Figures ...... v

List of Tables ...... vi

Acknowledgements ...... vii

Chapter 1 Introduction ...... 1

What is Biomonitoring? ...... 1 Organisms in Biomonitoring ...... 3 Fishes ...... 3 Macrophytes ...... 4 Benthic Macroinvertebrates ...... 4 Diatoms ...... 6 Other Organisms ...... 7 Multiple Organisms ...... 7 New Trends in Biomonitoring ...... 8 Literature Cited ...... 10

Chapter 2 Comparison of Historic Assemblages and Sampling Methods ...... 14

Abstract ...... 14 Introduction ...... 15 Methods ...... 16 Study Sites ...... 16 National Park Survey Methods ...... 19 Benthic Macroinvertebrate Sampling ...... 20 Analysis ...... 21 Results ...... 24 Discussion ...... 28 Literature Cited ...... 32

Chapter 3 Use of 16S mitochondrial rDNA for barcoding of Benthic Macroinvertebrates .... 36

Abstract ...... 36 Introduction ...... 37 Methods ...... 40 COI Primer Design ...... 40 Amplification and sequencing of invertebrate samples for reference library construction ...... 42 Results ...... 44 Discussion ...... 45 Literature Cited ...... 47

Appendix A. Comprehensive Benthic Macroinvertebrate List ...... 54 vi

Appendix B. Comparison of Taxa Between Studies ...... 63

Appendix C. Families used to create Primer Miner output ...... 72

Appendix D. Primer Pairs Tested ...... 77

Appendix E. DNA Library Sequences with BLAST results ...... 81

vii

LIST OF FIGURES

Figure 1. Overview of study sites throughout the National Capital Region Network National Parks. Park boundaries

are indicated in green while survey sites are indicated by triangles...... 18

Figure 2. Brillouin Index for each site across studies. Black bars represent the 2015 study, white bars represent

National Park collections, light grey bars represent 100 simulated organisms, dark grey bars represent 200

simulated organisms, and medium grey bars represent 300 simulated organisms...... 22

Figure 3. Results of ANOVA test boxplots of Brillouin's Index for simulated data at each site...... 23

Figure 4. PrimerMiner COI consensus sequence alignments. The first 50 basepairs are enlarged to illustrate A as red,

T as green, C as blue, and G as yellow basepairs...... 41

viii

LIST OF TABLES

Table 1. Summary of site locations throughout National Capital Region Network National Parks ...... 17

Table 2. Summary of site conditions in the National Capital Region Network National Parks ...... 18

Table 3. Number of taxa across sites collected in this study ...... 24

Table 4. Number of taxa collected across sites by NPS ...... 25

Table 5. Comparison of unique taxa collected at each site in 2015 and by the National Park Service ...... 26

Table 6. Brillouin’s Index calculated for each site across studies ...... 27

Table 7. One-sample t-test and Tukey Honest Significant Difference test p-values ...... 28 ix

ACKNOWLEDGEMENTS

I would like to thank my committee, Jay R. Stauffer, Jr., Gregory Hoover, and Elizabeth

Boyer for their guidance and expertise in pursuing my Masters degree and perfecting my science.

I also extend my appreciation to Aaron Aunins and the imaginative Timothy King, who were pivotal in the completion of this project, for their wisdom and support. The scientists and staff at the US Geological Survey and the National Park Service willingly provided encouragement and laboratory/technical support throughout my project. Thank you to the Stauffer Laboratory technicians who spent countless hours picking and sorting bugs by my side. Lastly, I would not have been able to complete this tome without the incredible Lisa Harrington and Peter Liese who watched my dog so that I could dream. 1

Chapter 1

Introduction

What is Biomonitoring?

Benthic invertebrates have been used for over a century in biomonitoring (Bonada et al.

2006; Chang et al. 2014). Biomonitoring is the use of biological measures to determine the state of an ecosystem (Hao 1996; Gerhardt 2002; Yagow et al. 2006; Dolédec and Statzner 2010; Buss et al. 2015) or to evaluate changes in the environment (Herricks and Schaeffer 1985; Gerhardt

2002; Bae et al. 2005; Yagow et al. 2006; Friberg et al. 2011). This surveillance method is commonly used as part of water quality monitoring, which includes monitoring the physical, chemical, and biological aspects of an aquatic ecosystem (Bae et al. 2005). Together, these measurable characteristics, called ecological indicators, allow for human impacts on aquatic habitats to be measured and analyzed (Goetz and Fiske 2008; Dolédec and Statzner 2010).

Biomonitoring emerged from the need to solve human health issues associated with water contamination during the rise of industrialization (Cairns and Dickson 1977; Bonada et al. 2006;

Friberg et al. 2011). Bacteria and other microorganisms were monitored as they largely contributed to the human health issues of the era (Bonada et al. 2006; Friberg et al. 2011). Prior to biomonitoring, water testing relied entirely on chemical testing; therefore, biomonitoring is important in that it can catch what anything other than daily chemical testing misses (Yagow et al. 2006; Beyene et al. 2009). The birth of biological monitoring (as attributed by Thieneman

1914), and the use of the Saprobien system of biomonitoring, is often credited to Kolkwitz and 2 Marsson in 1902 (Dolédec and Statzner 2010; Chang et al. 2014). They believed that different aquatic taxa were sensitive to various levels of putative pollutants (Bonada et al. 2006). Today scientists realize that alone, any one ecological indicator is not enough to determine the impact of a stressor on an environment. Therefore, biological characters that are considered more sensitive than physiochemical characters are now employed (Yagow et al. 2006; Beyene et al. 2009).

Today, aquatic ecosystems are threatened from anthropogenic sources such as agricultural runoff, water management systems, recreation, acid mine drainage and physical alteration of a stream or riparian zone (Yuan 2004; Hering et al. 2006; Beyene et al. 2009).

Despite these pressures, streams and rivers provide important ecosystem services and must maintain their biological integrity to continue to function (Dolédec and Statzner 2010; De

Bikuña et al. 2015).

The need for biomonitoring serves as a bridge between the natural and social sciences

(Friberg et al. 2011). The idea of ecosystem heath became a valuable notion in the 1970 with public pressure on government agencies and water authorities resulting in the U.S. Clean Water

Act (Bonada et al. 2006; Dolédec and Statzner 2010). Biomonitoring addresses federal, state, and local regulations of water quality standards (Álvarez-Cabria et al. 2010; Carter and Resh 2013) and therefore underpins the management and conservation of freshwater ecosystems (Friberg et al. 2011). 3 Organisms in Biomonitoring

Fishes

Fishes are indicators of overall stream health and are traditional bioindicators for general habitat degradation and flow (Hering et al. 2006; Johnson et al. 2006). As mobile organisms, fishes can move throughout a variety of habitats (Attrill and Depledge 1997; Johnson et al.

2006). Their migratory nature makes them sensitive to changes over the entire river continuum

(Northcote 1998). Therefore, selected species of fish may reflect conditions of the entire watershed (Karr 1990).

Although changes in the community of fishes may directly indicate pollutants in a waterway, as the top of aquatic trophic schemes, they changes in the ichthyofauna may also indicate stresses in other parts of the ecosystem (Karr 1990; Hao 1996; Attrill and Depledge

1997; Yagow et al. 2006). Thus, this relationship allows fish to be a record of long-term stresses

(Karr 1990; Yagow et al. 2006). Sensitivities of fishes can easily be impacted by region, season, or life stage; so, fish populations should be interpreted carefully (Karr 1990). Therefore, it is best to use fish as bioindicators when using a multimetric approach (Doberstein et al. 2000).

A common multimetric method of assessing fish communities is an index of biotic integrity (IBI), which integrates community, population, and organismal characteristics to assess the biotic integrity of a stream (Karr 1990). Karr initially developed the IBI for streams in the

Midwest; however, it quickly became adopted nationwide with regional modifications (Karr

1990). The fish IBI provides a framework for the formation of biotic integrity indices for other organisms such as the benthic index of biotic integrity (B-IBI) (Kerans et al. 1992; Doberstein et al. 2000) and the periphytic index of biotic integrity (PIBI) (Yagow et al. 2006). 4 Macrophytes

Macrophytes include flowering plants, vascular cryptogams, bryophytes, and macro- algae (Hering et al. 2006). Like fish, macrophytes can be used as indicators of general stream health relative to overall habitat degradation on a site-by-site basis (Hering et al. 2006; Johnson et al. 2006; Friberg et al. 2011). In addition, macrophytes can assess temporal changes, nutrient enrichment, acidity, flow, and heavy metal concentrations (Hao 1996; Hering et al. 2006).

Recent trends have emerged in standardizing macrophyte protocols including the creation of the

Macrophyte Biological Index for Rivers (Hering et al. 2006; Dolédec and Statzner 2010).

Benthic Macroinvertebrates

Benthic macroinvertebrates are bottom-dwelling organisms in aquatic ecosystems that lack a backbone or an internal skeleton (Yagow et al. 2006). Macroinvertebrates includes aquatic insects, worms, mollusks, and crustaceans, which are all used in aquatic biomonitoring programs

(Heino 2005). This collective group of organisms is overwhelmingly the most common and widely used to assess water quality (Rosenberg and Resh 1993; Hao 1996; Yuan 2004; Bonada et al. 2006; Hering et al. 2006; Yagow et al. 2006; Buss et al. 2015; De Bikuña et al. 2015). Not only are macroinvertebrates attractive for biomonitoring purposes, but they are an important group of organisms relative to the function of aquatic ecosystems (Basset et al. 2004; Álvarez-

Cabria et al. 2010). Macroinvertebrates are an important link in aquatic food chains by providing a link between primary producers (e.g., diatoms) and predators (e.g., fish) (Barbour et al. 1999;

Johnson et al. 2006; Álvarez-Cabria et al. 2010; Brown et al. 2012). In addition to inclusion in a 5 tropic cascade, benthic macroinvertebrates serve functional roles such as shredding detritus and facilitating microbial interactions (Heino 2010).

Overall, benthic macroinvertebrates can be used to determine general stream health

(Hering et al. 2006; Yagow et al. 2006; De Jonge et al. 2008; Brown et al. 2012). They can also be used to evaluate human impacts (Álvarez-Cabria et al. 2010; De Bikuña et al. 2015) such as hydromorphological degradation, organic/nutrient pollution, acidification, and heavy metals

(Hao 1996; Böhmer et al. 2004; Hering et al. 2006; Johnson et al. 2006; De Jonge et al. 2008;

Álvarez-Cabria et al. 2010; Dolédec and Statzner 2010). Benthic macroinvertebrates tend to be less mobile than other aquatic organisms and are therefore, representative of conditions in a localized habitat (Barbour et al. 1999; Johnson et al. 2006; Yagow et al. 2006), although drift of these organisms may confound interpretations.

Other than serving as excellent bioindicators, the literature highlights three main reasons why benthic macroinvertebrates are the organism of choice in biomonitoring protocols: ubiquity, diversity, and the ease of collection (Rosenberg and Resh 1993; Barbour et al. 1999; Basset et al.

2004; Bonada et al. 2006; Gerth and Herlihy 2006; Hering et al. 2006; Yagow et al. 2006;

Friberg et al. 2011). Most commonly, benthic macroinvertebrate data are used in some form of multimetric index such as a pollution index, a diversity index, or a comparison index (Hao 1996).

Such indices include the Stream Condition Index (SCI), the Invertebrate Community Index (ICI), the Macroinvertebrate Aggregated Index for Streams (MAIS) or the Benthic Index of Biotic

Integrity (BIBI) which is based on Karr’s IBI for fish communities (Yagow et al. 2006). Some researchers classify macroinvertebrates environmentally for analysis (Pilarczyk et al. ; Kerans et al. 1992). The four frequently used categories are functional feeding group (Merritt and

Cummins 1996), tolerance values (Barbour et al. 1999), life cycle (Merritt and Cummins 1996), 6 and habitat/behavior (Karr et al. 1986). Although these above-mentioned strategies are frequently found in the literature, this list is by no means an exhaustive list of analytical methods as the scientific question dictates the best approach.

Diatoms

Diatoms are single-celled microscopic plants in the class Bacillariophyceae (Hao 1996;

Yagow et al. 2006). As primary producers in an ecosystem, diatoms serve as high quality food sources for other organisms such as benthic macroinvertebrates and fishes (Johnson et al. 2006;

Yagow et al. 2006). These organisms are short-lived and reproduce quickly, thus they can used as early warning bioindicators (Hao 1996; Yagow et al. 2006). Although seasonality and flow may impact diatom communities, diatoms may be more useful than other organisms because they are likely always present in an aquatic ecosystem unlike macroinvertebrates or fishes (Yagow et al. 2006; Beyene et al. 2009).

Diatoms serve as indicators of nutrient enrichment, salinity, acidity, and metal contamination as well as overall water quality (Cairns and Dickson 1977; Hering et al. 2006;

Johnson et al. 2006; De Jonge et al. 2008). Additionally, Beyene et al. (2009) suggest that diatoms are more robust bioindicators than macroinvertebrates for pollution associated with urban areas. Scientists may use peak biomass, species composition, or a variety of indices when evaluating water quality using diatoms (Yagow et al. 2006; Beyene et al. 2009; Justus et al.

2010). Although not explicitly used for diatoms, a Periphytic Index of Biotic Integrity (PIBI) has been developed for water quality surveys (Yagow et al. 2006). 7 Other Organisms

Hao’s (1996) review of bioindicator species mentioned the use of other organisms such as bacteria and viruses. While bacteria collection is a common practice in public access waters, there is no single best indicator species. Coliform is a common indicator bacteria, and bacteria as a whole should not be discounted as indicator organisms, but there are lots of different applications. Perhaps the use of obligate bacteria or bacteriophages would be more prudent (Hao

1996). Researchers may also choose to use viruses or obligate viruses as bioindicators.

Detectability and correlation with direct causes are still unreliable (Hao 1996).

Multiple Organisms

A study conducted in 2001 indicated that 77% of stream monitoring programs in the

United States only used one group of taxa to assess water quality (Carlisle et al. 2008). By 2005, the adoption of protocols using multiple assemblages seemed to be growing, although this practice had already been adopted throughout Europe (Johnson et al. 2006; Carlisle et al. 2008).

Due the complexity of aquatic ecosystems, it may be trite to believe that a single group of organisms can provide a complete picture of ecosystem health (Carlisle et al. 2008). Although fish, macrophytes, benthic macroinvertebrates, and diatoms are linked through trophic interactions, interassemblage responses to changes in the environment vary (Justus et al. 2010).

Therefore, it is wise to look at multiple taxonomic groups or surrogate indicator species from multiple assemblages (Heino 2010; Justus et al. 2010). With this strategy, time, money, effort, and expertise can be as focused as possible (Johnson et al. 2006; Heino 2010).

8 New Trends in Biomonitoring

Although there are several well established protocols for biomonitoring, ‘…a long history of use is not a sufficient reason to continue with a biomonitoring tool that is far from ideal’

(Bonada et al. 2006). Kiranyaz et al. (2011) have tested automatic image recognition software on aquatic phytoplankton with promising application to benthic macroinvertebrate identification.

Kiranyaz and others have found that taxonomists are reluctant to use this alternative method despite is viability. Additionally, as new stressors are introduced, new approaches will need to be developed to measure them (Friberg et al. 2011). This may include the use of molecular or biomarkers approaches (Friberg et al. 2011). One such tool is metagenomics, which is a DNA based tool that examines the entirety of DNA from all of the organisms in a sample (Dinsdale et al. 2008; Debroas et al. 2009). Moreover, the idea of functional diversity over biodiversity started to take hold in the scientific community in the 1970’s and 1980’s (Cairns and Dickson

1977; Heino 2005). Functional genomics looks at the entire diversity of genes in a sample, called functional potential, which has been promoted for use in biomonitoring by scientists such as

Creer et al. (2016), Bohmann et al. (2014), Dinsdale et al. (2008), and Baird et al. (2011).

Combined, molecular and functional biodiversity analyses could benefit from functional metagenomic techniques.

Current research methods for the genetic determination of benthic macroinvertebrate taxonomic diversity use the metabarcoding techniques applied to microbial communities.

Metabarcoding refers to the identification of multiple species in a sample using deoxyribonucleic acid (DNA) based techniques (Clarke et al. 2014; Deagle et al. 2014; Ficetola et al. 2015).

Simplified, metabarcoding is the process of DNA extraction from an environmental sample

(called eDNA) such as water, air, or soil (Cristescu 2014; Barnes and Turner 2016)., followed by 9 the identification of DNA barcodes, which are short gene sequences which are diagnostic among members of a taxonomic group, such as species of fish occupying a stream. These barcode sequences require the polymorphic and diagnostic region of the barcode to be flanked by conserved regions of DNA, where polymerase chain reaction (PCR) primers can be developed.

These DNA barcode sequences are used to create a reference library of known DNA sequences.

Using a universal primer targeting the conserved regions of the barcode, DNA is amplified from the environmental sample using PCR and sequenced via a high-throughput sequencing machine.

Bioinformatics processing, where the millions of barcodes are compared to the reference library, then reflects what taxa are present or absent in the sample. Ideally, metabarcoding would allow scientists to accurately identify more taxa faster at low levels of taxonomic resolution, and to provide a more complete picture of the health of an ecosystem (Chariton et al. 2010; Cristescu

2014). Metabarcoding would then meet the goal of biodiversity monitoring, gaining a comprehensive taxa list, in the most cost and time efficient manner possible (Wang et al. 2013).

10 Literature Cited

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Bae, Y. J., H. K. Kil and K. S. Bae. 2005. Benthic macroinvertebrates for uses in stream biomonitoring and restoration. KSCE Journal of Civil Engineering 9(1):55-63.

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Barnes, M. A. and C. R. Turner. 2016. The ecology of environmental DNA and implications for conservation genetics. Conservation Genetics 17(1):1-17.

Basset, A., F. Sangiorgio and M. Pinna. 2004. Monitoring with benthic macroinvertebrates: advantages and disadvantages of body size descriptors. Aquatic Conservation: Marine and Freshwater Ecosystems 14(S1):S43-S58.

Beyene, A., T. Addis, D. Kifle, W. Legesse, H. Kloos and L. Triest. 2009. Comparative study of diatoms and macroinvertebrates as indicators of severe water pollution: case study of the Kebena and Akaki rivers in Addis Ababa, Ethiopia. Ecological Indicators 9(2):381-392.

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Brown, L. R., J. T. May and M. Wulff. 2012. Associations of benthic macroinvertebrate assemblages with environmental variables in the Upper Clear Creek watershed, California. Western North American Naturalist 72(4):473-494.

Buss, D. F., D. M. Carlisle, T.-S. Chon, J. Culp, J. S. Harding, H. E. Keizer-Vlek, W. A. Robinson, S. Strachan, C. Thirion and R. M. Hughes. 2015. Stream biomonitoring using macroinvertebrates around the globe: a comparison of large-scale programs. Environmental monitoring and assessment 187(1):1-21. 11 Cairns, J. and K. L. Dickson. 1977. Recovery of streams from spills of hazardous materials. Recovery and Restoration of damaged ecosystems. University of Virginia Press, Charlottesville:24-42.

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Carter, J. L. and V. H. Resh. 2013. Analytical approaches used in stream benthic macroinvertebrate biomonitoring programs of State agencies in the United States.

Chang, F.-H., J. E. Lawrence, B. Rios-Touma and V. H. Resh. 2014. Tolerance values of benthic macroinvertebrates for stream biomonitoring: assessment of assumptions underlying scoring systems worldwide. Environmental monitoring and assessment 186(4):2135- 2149.

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De Jonge, M., B. Van de Vijver, R. Blust and L. Bervoets. 2008. Responses of aquatic organisms to metal pollution in a lowland river in Flanders: a comparison of diatoms and macroinvertebrates. Science of the Total Environment 407(1):615-629.

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Pilarczyk, M. M., P. M. Stewart and T. P. Simon. 12 Classification of Macroinvertebrate Assemblages of Great Lakes Coastal Wetlands for Use in the Development of Indices of Biological Integrity.

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Yuan, L. L. 2004. Assigning macroinvertebrate tolerance classifications using generalised additive models. Freshwater Biology 49(5):662-677.

Chapter 2

Comparison of Historic Assemblages and Sampling Methods

Abstract

Benthic macroinvertebrates have been used for over a century in biomonitoring.

National Park Service and the Pennsylvania Department of Environmental Protection. The

Brillouin Diversity Index was calculated for each site and subsample permutation. An ANOVA analysis of the Brillouin Index by site showed that there is a statistically significant difference

(p<0.05) between the use of 100 and 200 organisms with the Brillouin Index for 200 being greater. Additionally, the ANOVA analysis showed that about 50% of the time, that there is a statistically significant difference (p<0.05) between the use of 200 and 300 organisms with the

Brillouin Index for 300 being greater. Lastly, a one-sample t-test revealed that there is a statistically significant difference (p<0.05) between the use of 300 organisms (or 200 if 300 organisms were not obtained) and entire samples with the Brillouin Index for entire samples being greater at all but one site. There could be valuable information missing from traditionally 15 collected benthic macroinvertebrate samples for which DNA based technologies may improve macroinvertebrate bioassessments of aquatic ecosystems. Introduction

Introduction

Benthic aquatic invertebrates have been used for over a century in biomonitoring

(Bonada et al. 2006; Chang et al. 2014). Biomonitoring is the use of biological measures to determine the state of an ecosystem (Hao 1996; Gerhardt 2002; Yagow et al. 2006; Dolédec and

Statzner 2010; Buss et al. 2015) or to evaluate changes in the environment (Herricks and

Schaeffer 1985; Gerhardt 2002; Bae et al. 2005; Yagow et al. 2006; Friberg et al. 2011). This surveillance method is commonly used as part of water quality monitoring, which includes monitoring the physical, chemical, and biological aspects of an aquatic ecosystem (Bae et al.

2005). Together, these measurable characteristics, called ecological indicators, allow for impacts on aquatic habitats to be determined and measured (Goetz and Fiske 2008; Dolédec and Statzner

2010). Chapter one provides an overview of the use of macroinvertebrates and other organisms in biomonitoring.

The primary objective of this study was to compare macroinvertebrate D-frame kicknetting survey methods. A secondary objective of this study was to develop a comprehensive list of aquatic macroinvertebrates in thirteen streams throughout the National Capital Region

Network (NCRN) National Parks to serve as baseline data for future and ongoing ecological investigations in these parks. 16 Methods

Study Sites

There are eleven parks within the National Capital Region Network (NCRN): Antietam

National Battlefield (ANTI), Catoctin Mountain Park (CATO), Chesapeake and Ohio Canal

National Historical Park (CHOH), George Washington Memorial Parkway (GWMP), Harpers

Ferry National Historical Park (HAFE), Manassas National Battlefield Park (MANA), Monocacy

National Battlefield (MONO), National Capital Parks–East (NACE), Prince William Forest Park

(PRWI), Rock Creek Park (ROCR), and Wolf Trap National Park for the Performing Arts

(WOTR) (Fig. 1). These eleven parks are spread over three states and the District of Columbia.

Additionally, these eleven parks exist in six physiogeographic provinces (Norris 2009). These parks range widely in location from rural to urban settings. A summary of locations (Table 1) and site conditions (Table 2) has been provided.

17 Table 1. Summary of site locations throughout National Capital Region Network National Parks

Park name Site name Latitude Longitude County State

Antietam National Battlefield (ANTI) Sharpsburg Creek (SHCK) 39.454971 -77.737793 Washington MD

Chesapeake Ohio Canal (CHOH) Potomac River at Brunswick (BRUN) 39.30627 -77.614312 Frederick MD

Chesapeake Ohio Canal (CHOH) Potomac River at Williamsport (WILL) 39.598832 -77.829211 Washington MD

George Washington Memorial Parkway Turkey Run (TURU) 38.963623 -77.157501 Fairfax VA (GWMP)

Harper's Ferry (HAFE) Flowering Springs Run (FLSP) 39.29577 -77.792231 Jefferson WV

Manassas National Battlefield Park Youngs Branch (YOBR) 38.817849 -77.510242 Prince William VA (MANA)

Monocacy National Battlefield (MONO) Bush Creek (BUCK) 39.369119 -77.3855 Frederick MD

Monocacy National Battlefield (MONO) Visitor Center Creek (VCCR) 39.367842 -77.388236 Frederick MD

Oxon Hill Farm (OXON) Oxon Run (OXRU) 38.810858 -77.009346 Prince Georges MD

Prince William Forest Park (PRWI) Orenda Run (ORRU) 38.562813 -77.357536 Prince William VA

Prince William Forest Park (PRWI) Taylor Run (TARU) 38.575744 -77.375646 Prince William VA

Wolf Trap Park for the Performing Arts Courthouse Creek (CHCK) 38.936701 -77.262437 Fairfax VA (WOTR)

Wolf Trap Park for the Performing Arts Wolf Trap Creek (WOTR) 38.941038 -77.269033 Fairfax VA (WOTR)

18 Table 2. Summary of site conditions in the National Capital Region Network National Parks

Stream B-IBI Catchment Park name Site name Substrate Vegetation Order Region Area (Ha) ANTI SHCK 1 Highlands 319.138 C, S Ri CHOH BRUN 6 - - G, M Ri CHOH WILL 8 - - G, M E GWMP TURU 2 E. Piedmont 267.461 B, Si Ri HAFE FLSP 2 Highlands 2234.363 M, Si Ri MANA YOBR 3 E. Piedmont 1772.816 B, C, Si A MONO BUCK 3 E. Piedmont 8276.217 C, G, Lg Ri MONO VCCR 2 E. Piedmont 273.062 G, Si Ri OXON OXON 1 Coastal Plain 3465.946 C, G A PRWI ORRU 1 E. Piedmont 51.756 G, S Ri PRWI TARU 1 E. Piedmont 118.582 C, G Ri WOTR CHCK 1 Coastal Plain 404.741 C, Lg Ri WOTR WOTR 2 Coastal Plain 1025.819 C, S Ri C = cobble, S = sand, M = mud, G = gravel, B = bedrock, Si = silt, Lg = large rocks Ri = riparian only, E = emergent, A = algae

Figure 1. Overview of study sites throughout the National Capital Region Network National Parks. Park boundaries are indicated in green while survey sites are indicated by triangles. 19 National Park Survey Methods

In 2000, the National Park Service established the Inventory and Monitoring program. By the mid-2000s, the National Capital Region Inventory and Monitoring Network was established and funded by the National Park Service Water Resources Division. The goal of this program is to provide baseline monitoring of the natural resources of the parks within the network and assess the effectiveness of current management practices (Lookingbill et al. 2014). Therefore, the inventory process was completed to provide the science necessary to manage resources effectively. The National Park Service also viewed clean water resources as valuable to the visitor experience and for public health and safety. National Capital Region water resources face the same threats common to areas of human impact such as agriculture, timbering, acid precipitation, and sedimentation.

According to the NCRN Biological Stream Survey Protocol Version 2.0 (Norris and

Saunders 2009), in the National Capital Region parks, water resources are wide ranging from small headwater streams to the Potomac River. The NCRN Inventory and Monitoring Program assesses 37 streams ranging from first to fourth order wadable streams from agricultural, urban, and light industry development areas. Each site is sampled every six years, with an average of six sites sampled per year. Water chemistry, nutrient dynamics, surface water dynamics, physical habitat index, benthic aquatic macroinvertebrates, and fish are monitored throughout the year

(Norris and Saunders 2009).

Benthic macroinvertebrates are collected in accordance to the NCRN Biological Stream

Survey Protocol Version 2.0 (Norris and Saunders 2009). This protocol uses a multi-habitat approach using a D-frame kicknet as sampling gear. The National Park Service protocol is area- constrained, sampling 2 m2 of substrate. Some streams lack structure; therefore, samplers target 20 areas of the stream channel that are likely to have diverse taxa. Samples are preserved in the field in 70% ethanol and taken back to the laboratory for enumeration and identification. The sample is distributed onto a grid where squares are selected at random. The invertebrates within these randomly selected squares are identified until 100 organisms, plus or minus a margin of error, are selected. The National Park Service, considering cost and time constraints, has deemed 100 organisms as an appropriate sample size. The organisms selected are identified to the level of genus or the lowest taxonomic level possible. Oligochaetes and chironomids are slide mounted for identification. Samples are stored at the Maryland Department of Natural Resources field office. Data are used to calculate a benthic index of biotic integrity (B-IBI) in accordance with the Maryland Biological Stream Survey (MBSS).

Benthic Macroinvertebrate Sampling

Each study site was sampled in July or August of 2015. At each site, one pooled macroinvertebrate sample was taken with a D-frame kicknet. A 20 m reach of stream, with a variety of habitat types, was targeted. In streams lacking in different habitat types, any sort of structure (e.g. log, detritus) was targeted (Norris 2009). Kicks were taken in an upstream diagonal from right to left bank. Each sample was composed of nine 20-second kicks.

According to Frost et al. (1971), three one-minute kicks will sample 90% of benthic macroinvertebrate diversity. Our method allowed for more habitat types to be sampled than the

Frost et al. (1971) protocol, with the goal of increasing the level of diversity sampled. Each sample was preserved in 70% ethanol and returned to the lab for sorting and identification. All individuals from within each entire sample were separated and enumerated to gain a complete 21 census of the community. For each site, specimens identified to the same taxonomic group and level were stored in individual vials and identified to the lowest possible taxa using the dichotomous keys of Peckarsky (1990) and Merritt and Cummins (1996).

The ideal level of taxonomic resolution when using dichotomous keys was the level of genus. Some groups of benthic macroinvertebrates, however, cannot be identified to genus without further processing (e.g. slide mounting). For example, Oligochatea, Hirudinea,

Turbellaria, and Podacopa were only identified to this higher taxonomic classification. In any instance where the National Park Service identified these taxa to lower taxonomic units, the data were categorized to match the taxonomic resolution of this study. In addition to these taxa, organisms were only identified to family level if a character was missing, which prevented confident identification to genus. In the case of Chironomidae, only a sub-set of these individuals were slide mounted. Chironomids were sorted under a dissecting microscope into various morpho-species. Twenty-five percent, or up to 20 individuals, were slide mounted following

Simpson and Bode (1980), from each morpho-species group. The slide-mounted chironomids were identified to subfamily, or in the case of Chironominae, identified to tribe.

Analysis

The Brillouin Index, H, is a measure of species diversity for a collection that is a) a census of a population and b) is dependent on sample size as it is a direct measure of the diversity of a sample (Peet 1975; Stauffer et al. 1980; Washington 1984). The Brillouin index is not based on probability theory (Pielou 1966). The formula for the Brillouin Index, H, is:

1 !! ! = ln ! ! !!! !!! 22 First, Brillouin’s H was calculated for each site sampled in 2015 Second, Brillouin’s H was calculated for the National Park Inventory and Monitoring data (Figure 2). Lastly, fifty permutations of 100, 200, and 300 randomly selected organisms, without replacement, were generated from the macroinvertebrate data collected for this study. Brillouin’s H was again calculated for each permutation. A one-sample t-test was calculated for each site comparing the

2015 collections to the 300 organism simulated data (or 200 organism simulated data in the case of Oxon Run and Visitor Center Creek where there were fewer than 300 organisms) and the NPS collections to the 100 organism simulated data. After plotting the simulated data using boxplots

(Figure 3), an analysis of variance (ANOVA) and Tukey Honest Significant Difference test were used to test differences in means within sites based on the simulated data.

Figure 2. Brillouin Index for each site across studies. Black bars represent the 2015 study, white bars represent National Park collections, light grey bars represent 100 simulated organisms, dark grey bars represent 200 simulated organisms, and medium grey bars represent 300 simulated organisms.

23 2.4 2.3 1.8 2.0 2.3 2.2 1.6 1.9 2.2 1.4 2.1 1.8 2.1 1.2 1.7 2.0 2.0 1.0 1.6 1.9 1.9

BRUN_100 BRUN_200 BRUN_300 BUCK_100 BUCK_200 BUCK_300 CHCK_100 CHCK_200 CHCK_300 FLSP_100 FLSP_200 FLSP_300

1.60 2.6 1.20 2.4 1.55 2.5 1.15 2.3 1.50 1.10 2.4 1.45 2.2 1.05 1.40 2.3 1.00 2.1 1.35 0.95 2.2 2.0 1.30 0.90 2.1 1.25

ORRU_100 ORRU_200 ORRU_300 OXRU_100 OXRU_200 SHCK_100 SHCK_200 SHCK_300 TARU_100 TARU_200 TARU_300

1.8 2.4 2.1 1.45 1.7 1.40 2.0 2.3 1.35 1.6 1.9 1.30 2.2 1.8 1.5 1.25 2.1 1.20 1.7 1.4 1.15 1.6

TURU_100 TURU_200 TURU_300 VCCR_100 VCCR_200 WILL_100 WILL_200 WILL_300 WOTR_100 WOTR_200 WOTR_300

2.6 2.5 2.4 2.3 2.2

YOBR_100 YOBR_200 YOBR_300

Figure 3. Results of ANOVA test boxplots of Brillouin's Index for simulated data at each site.

24 Results

Across the thirteen sites collected in 2015 for the purposes of this study, there were 109 unique taxa identified (Appendix A). Of the 109 taxa, four were identified at a higher taxonomic classification: Oligochatea, Hirudinea, Turbellaria, and Podacopa. Additionally, five taxa represented subfamilies or tribes of the family Chironomidae. Another nine taxa represented distinct morpho-species of Gastropoda. Lastly, there were eleven instances in which individuals could not be identified beyond family level, and two families (Heptageniidae and Baetidae) were represented by several genera throughout the thirteen sites. The number of taxa at each site ranged from 13 to 38 taxa with an average of 27 taxa per site with a standard deviation of 8.57

(Table 3).

Table 3. Number of taxa across sites collected in this study

Park Site Number of Taxa Individuals sampled ANTI SHCK 17 1829 CHOH BRUN 37 1657 CHOH WILL 22 639 GWMP TURU 26 373 HAFE FLSP 27 830 MANA YOBR 34 411 MONO BUCK 33 1230 MONO VCCR 33 224 NACE OXON 13 223 PRWI ORRU 34 346 PRWI TARU 38 378 WOTR CHCK 22 520 WOTR WOTR 15 1107

25 In order to enable comparison, the National Park Service data were categorized to match how taxa for this study were identified. For example, Chironomidae genera were categorized to sub-family or tribe. Across the eleven sites collected by the National Park Service, there were 75 unique taxa identified. Sites ranged from nine to thirty taxa identified at each site with an average of 16 taxa per site with a standard deviation of 5.9 (Table 4).

Table 4. Number of taxa collected across sites by NPS

Park Site Number of Taxa Individuals sampled ANTI SHCK 9 117

GWMP TURU 10 118

HAFE FLSP 19 104

MANA YOBR 20 131

MONO BUCK 17 120

MONO VCCR 16 116

NACE OXON 12 113

PRWI ORRU 18 120

PRWI TARU 30 174

WOTR CHCK 12 119

WOTR WOTR 13 121

Two sites, Chesapeake and Ohio Canal National Historical Park (CHOH) at Brunswick and Williamsport, have not historically been sampled by the National Park Service and have been excluded from the presence/absence table comparing taxa between studies (Appendix B).

Across the eleven sites, there were a combined total of 133 unique taxa identified (Appendix B).

Sites ranged from 20 to 59 taxa identified at each site with an average of 34.5 taxa per site with a 26 standard deviation of 12.74 (Appendix B). The number of overlapping taxa from this study and

National Park Service surveys ranged from 3 to 10 taxa. The number of taxa unique to National

Park Service surveys ranged from 3 to 21. The number of taxa unique to this study ranged from 8 to 30 (Table 5).

Table 5. Comparison of unique taxa collected at each site in 2015 and by the National Park Service

Total Unique to Overlap Total species Total recorded Unique to Site recorded NPS between recorded by this study this study by NPS survey studies ANTI SHCK 20 17 11 11 3 6

GWMP TURU 29 26 19 10 3 7

HAFE FLSP 35 27 17 18 8 10

MANA YOBR 46 34 27 19 12 7

MONO BUCK 39 33 23 16 6 10

MONO VCCR 42 33 26 16 9 7

NACE OXON 20 13 10 10 7 3

PRWI ORRU 44 34 27 17 10 7

PRWI TARU 59 38 30 29 21 8

WOTR CHCK 25 22 14 11 3 8

WOTR WOTR 21 15 8 13 4 9

The Brillouin Index was calculated for each site across studies and ranged from 1.17 to

2.60 in the 2015 collections, from 0.70 to 2.23 in the NPS collections. The Brillouin Index ranged from 1.02 to 2.32, from 1.10 to 2.52 and from 1.12 to 2.60 for simulated data of 100, 200, and 300 organisms respectively (Table 6).

Table 6. Brillouin’s Index calculated for each site across studies

Simulated - 100 Simulated - 200 Simulated - 300 Park Site 2015 Coll. NPS Coll. organisms organisms organisms

CHOH BRUN 2.41437 - 2.0865876 2.2269448 2.2731262

MONO BUCK 2.31765 1.82834 2.0413894 2.1491514 2.2115482

WOTR CHCK 1.44256 1.43365 1.2195584 1.3573872 1.4096392

HAFE FLSP 2.01259 1.68026 1.7702002 1.9021588 1.9330166

PRWI ORRU 2.41729 1.82243 2.1314924 2.3188596 2.3809004

NACE OXRU 1.5278 1.61571 1.4367654 1.5168474 -

ANTI SHCK 1.16643 0.69873 1.0206056 1.0952978 1.1211358

PRWI TARU 2.59619 2.23028 2.3082538 2.4764256 2.5505244

GWMP TURU 2.07308 1.30584 1.8602678 2.0136162 2.0384146

MONO VCCR 2.40041 1.58081 2.181259 2.3188596 -

CHOH WILL 1.72638 - 1.5482438 1.6274338 1.667934

WOTR WOTR 1.43372 1.5443 1.277645 1.3501662 1.3684598

MANA YOBR 2.20635 2.03941 2.3187648 2.5197572 2.5970488

There were significant differences (p<0.05) in the Brillouin Index calculated for random selections of 100 and 200 simulated organisms. There were also significant differences (p<0.05) in the Brillouin Index calculated for random selections of 100 and 300 organisms. At six of the eleven sites, there were significant differences (p<0.05) in the Brillouin Index calculated for random selections of 200 and 300 organisms. At five of the eleven sites, there was not significant difference (p>0.05) (Table 7). 28 The one-sample t-test indicated that there is a significant difference (p<0.05) between the

Brillouin Index calculated for the 2015 collections and the randomly selected 300 organisms at each site where more than 300 organisms were collected in 2015 (Table 7). If fewer than 300 organisms were collected in 2015, Brillouin Index calculated for the 2015 collections was compared to that of the 200 simulated organisms. At one site, Young’s Branch, the Brillouin

Index calculated for the 2015 collection was lower than that of the 300 simulated organisms.

Table 7. One-sample t-test and Tukey Honest Significant Difference test p-values

2015 coll. v 100 org. v. 100 org. v. 200 org. v. Park Site Simulated 300 200 org. 300 org. 300 org. org. CHOH BRUN * * * * MONO BUCK * * * * WOTR CHCK * * * 0.0533891 HAFE FLSP * * * 0.1313043 PRWI ORRU * * * * NACE OXRU * (200) * - - ANTI SHCK * * * 0.1440904 PRWI TARU * * * * GWMP TURU * * * 0.2287242 MONO VCCR *(200) * - - CHOH WILL * * * * WOTR WOTR * * * 0.3635651 MANA YOBR * * * * * Indicating a p-value of <0.05, - indicating no comparison could be made

Discussion

Agencies often limit the number of individuals collected due to cost and time restraints

(Bonada et al. 2006), but at the cost of a complete data set. The results of the tests on the above data set illustrate that there may not always be a significant difference between the use of 200 29 and 300 organisms in a survey, there is always a difference in the use of 300 organisms and population census. Therefore, if an agency must limit itself in subsampling its efforts, the agency should select a subsample of at least 300 organisms although a complete census is preferable. It should be noted that this comparison is limited to this method of collecting macroinvertebrates and calculating the Brillouin Index. There are many other ways to collect macroinvertebrates

(e.g., Surber samplers) and metrics that can be used to assess diversity. This broadly includes measures using tolerance values, multimetric approaches (indices), and ecological metrics. The above method is broadly applicable and suggested for other studies.

The National Park Service data were not included in this comparison because we were unable to confirm identifications, which required presumed categorization of the data. While these data may be used to look at broad trends of biodiversity, a direct comparison to the 2015 collections and the simulated data would not be warranted. Based on the above conclusion, the

National Park collections of 100 organisms are insufficient.

Challenges and Future Studies

Morphological identification of benthic aquatic macroinvertebrates can be challenging

(Basset et al. 2004). Dichotomous keys rely on mature, entire specimens, which are often not present in a sample. Therefore, the results of morphological identification often result in mixed levels of taxonomic resolution. Therefore, measures of diversity using mixed levels of identification are conservative and underrepresent actual biodiversity of the system. The results of the 2015 collections for this study and the consolidated data from the NPS surveys are conservative in their measurement of ecosystem biodiversity (Table 5). Additionally, despite best efforts being made, it is likely that some cryptic species have been omitted. For example, 30 Wiggins (2015) does not recognize the genus Ceratopsyche as different from Hydropscyhe whereas Merritt and Cummins (1996) does. This further compounds the issue of an underrepresentation of diversity.

Some scientists argue over the level of taxonomic resolution required to measure differences in diversity. Attrill and Depledge (1997) reported aggregated marine invertebrates to phylum level and received sufficient measures of diversity. Chang et al. (2014) stated that identification to class, order, and family will produce different measures of diversity as each taxonomic level has different tolerance levels. The author also surmised differences in tolerance values at the species level although most of these are unknown. Gerth and Herlihy (2006), and the authors they site, further agree that macroinvertebrate bioassessments were sensitive to taxonomic resolution.

In addition to being able to correctly identify benthic macroinvertebrate specimens, there was an inconsistency in agency protocols as to how many specimens are needed to obtain a clear picture of diversity in an ecosystem. These results showed that it is best to take a complete census of the population rather than a subsample. Based on Table 8, 100 organisms was not sufficient for measures of diversity. Not only was sampling 200 organisms better, but an actual survey of 100 organisms followed the same trend of a difference in the Brillouin Index. Although the distinction between using 200 or 300 organisms may not be clear, (i.e. five out of seven sites provided no statistically significant difference), there is a marked difference in using 300 organisms and a complete sample. The more organisms sampled, the more accurate of a measure of diversity can be calculated.

The ideal solution to the challenges faced in this study is a tool that provides species resolution from a variety of forms of samples such as partial specimen, whole specimens, or 31 environmental DNA shed by specimens. DNA technologies such as metabarcoding are proving useful for addressing this biomonitoring need (Hajibabaei et al. 2011; Baird and Hajibabaei

2012). Future studies should focus on the use of genetic techniques to improve the biomonitoring process.

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Chapter 3

Use of 16S mitochondrial rDNA for barcoding of Benthic Macroinvertebrates

Abstract

The use of macroinvertebrates in bioassessments has gone relatively unchanged since its beginning in the early 1900s. Different agencies employ a variety of bioassessment protocols, which makes comparisons among studies difficult as methods for identification and quantitation of diversity are not standardized. Many aquatic invertebrate species have larval stages that are indistinguishable form each other, which can result in lower estimates of biodiversity. In addition, many adult species are difficult to identify to species level based on morphology as well. Therefore, an unambiguous method for species level identification is necessary to ensure the most accurate biodiversity assessment results. Several recent studies have investigated the applicability of the mitochondrial genes cytochrome oxidase 1 (COI), 12S rDNA, and 16S rDNA for barcoding of various aquatic fauna including fish and aquatic invertebrates using artificial pools of individuals in the laboratory. Generally, molecular methods have shown an increase in taxonomic resolution compared to the morphological analyses. However, current limitations in barcoding primer design result in certain taxonomic groups being over or under-represented in the sequencing results. In addition, little work has been done to see how genetic based estimates of diversity based on sequencing of environmental DNA samples extracted from sediment or water samples compare to traditional morphological based analyses. This study tested 55 primer pairs targeting both mitochondrial 16S rDNA and COI genes. The COI primers, despite 37 incorporation of numerous ambiguous nucleotides to facilitate application to wide taxonomic breadth, did not amplify several taxa tested. However testing of 16S primers amplified DNA missed by COI. The results of this study make a large contribution to the effort of building up a robust 16S rDNA barcode database for aquatic invertebrate metabarcoding, through the generation and curation of 74 unique sequences. In addition, the results highlight the difficulty in designing “universal” primers that amplify taxa from diverse groups with equal efficiency.

Introduction

Metabarcoding is a non-invasive genetic technique that allows for the identification of multiple species from environmental samples (Clarke et al. 2014; Cristescu 2014; Lawson

Handley 2015; Barnes and Turner 2016). While metabarcoding has been used for application in a wide variety of studies such as diet (Baird and Hajibabaei 2012) and invasive species monitoring (Bohmann et al. 2014), this tool is often useful in determining biodiversity of a system (Ficetola et al. 2015; Elbrecht and Leese 2016) as well as revealing cryptic species

(Lawson Handley 2015) and endangered species monitoring (Bohmann et al. 2014; Barnes and

Turner 2016). The environmental samples used in metabarcoding contain environmental DNA, called eDNA, which may come from many sources such air, water, soil, feces, and gametes

(Barnes et al. 2014; Bohmann et al. 2014; Deiner and Altermatt 2014). This is because organisms lose cells that then burst in the environment leaving DNA fragments behind (Barnes and Turner

2016). For example, Barnes et al. (2014) have found that DNA of some gastropods may persist in the environment for over three weeks whereas Deiner and Altermatt (2014) have determined that the threshold for detection of arthropod DNA may be anywhere from 15 to 50 m. There is 38 much that scientists do not understand about the variables impacting loose DNA in the environment, but the field is evolving rapidly (Barnes et al. 2014).

Several studies have demonstrated that amplifiable macroinvertebrate DNA can be extracted from a variety of environmental samples (Hajibabaei et al. 2011; Elbrecht et al. 2016).

For example DNA has been extracted from water, feces, soil, and air (Bohmann et al. 2014;

Deiner and Altermatt 2014; Cannon et al. 2015). Massively parallel next generation sequencing of this environmental DNA for the purposes of determining community composition began to be used in the in mid 2000’s (Hajibabaei et al. 2011) when it was acknowledged that genetic sequencing could complement traditional morphological identification (Carvan et al. ; Hajibabaei et al. 2007; Baird and Hajibabaei 2012). Initial studies used a mixture of specimens of known identity for which the recovery rate of the genetic method in relation to the morphological based method could be calculated (Hajibabaei et al. 2011; Mächler et al. 2014; Brandon-Mong et al.

2015). All of these studies used various primers targeting amplification of the COI gene because

COI tends to have a level of variability allowing species level discrimination (Baird and

Hajibabaei 2012; Dowle et al. 2015). Hebert et al. (2003) praises COI in that it is robust at the 5’ end and has experienced molecular evolution three times faster than other genes such as mitochondrial16S and 12S. In order to amplify across different taxonomic groups, these COI primers often have a large number of degenerate nucleotides that allow binding to regions of

DNA during PCR (polymerase chain reaction) that are not a perfect match. Despite their best efforts to design truly “universal” primers that amplify all taxa with equal efficiency, these researchers could not fully recover the complete list of taxa from which the known mix was composed (Hajibabaei et al. 2011; Elbrecht et al. 2016). Other researchers have tried other primer pairs, targeting other mitochondrial genes such as 16S mitochondrial rDNA, but again 39 have been unable to recover some taxa, suggesting there is no “universal” primer that will amplify all taxa with equal efficiency (Clarke et al. 2014; Elbrecht and Leese 2016).

For many metabarcoding studies, many unique DNA barcode sequences could be identified with the chosen primer, but the lack of a complete reference database hindered identification. Genetic databases such as BOLD or the nucleotide database in GenBank are repositories for barcode sequences, but are often biased towards certain taxonomic groups, and are incomplete (Shackleton and Rees 2015; Trebitz et al. 2015). Since most sequencing of macroinvertebrate DNA has been done with COI with the goal of contributing to the barcode of life database project, most of the entries in BOLD and GenBank are COI biased for this specific gene (Gibson et al. 2015; Creer et al. 2016). This hinders the utility of primers other than COI in new metabarcoding studies. The afore mentioned gaps in databases along with disagreements in a standard protocol (Cristescu 2014; Deiner and Altermatt 2014) and discovery of truly universal primers has kept metabarcoding from being a widespread tool applicable in the field for agencies thus far.

The purpose of this study was to develop a reference library for a future metabarcoding of eDNA of aquatic invertebrates in the mid-Atlantic region. First, specimens were sorted from samples collected from thirteen National Parks throughout the National Capital Region Network

(see Chapter 1). We tested new bioinformatics tools for metabarcoding primer design and tested

33 primers targeting COI. In accordance with the reservations regarding COI by Deagle et al.

(2014), we found that COI primers did not amplify the DNA of all of our voucher specimens.

Our barcoding efforts ended up using a primer set described by Elbrecht et al. (2016) that targeted the mitochondrial 16S rDNA gene, as these primers appeared to have the most success in amplification. Sanger sequencing was performed for select reference specimens. Overall, we 40 obtained unique sequences for 74 voucher specimens. Our future metabarcoding studies will utilize the foundation laid by this study.

Methods

COI Primer Design

PrimerMiner (Elbrecht and Leese 2016) was used to determine potential primers for amplification of the COI gene. In summary, PrimerMiner downloads relevant FASTA files from

GenBank based on a customized list of taxa. The list used contained all aquatic families of arthropods and some non-insects per Peckarsky (1990) and Merritt and Cummins (1996). If insufficient data existed at the family level, the entire order was downloaded (Appendix C).

FASTA sequences were aligned using Geneious (Version 9.1.4) using the multiple alignment option to determine a consensus sequence for each order. Consensus sequences were then aligned and scored using the PrimerMiner R package, resulting in a master sequence for which degenerate primers could be designed (Figure 1). 41

Trichoptera.fasta Podocopa.fasta Plecoptera.fasta Platyhelminthes.fasta Orthoptera.fasta Oligochaeta.fasta Odonata.fasta Neuroptera.fasta Megaloptera.fasta Lepidoptera.fasta Hymenoptera.fasta Hydrachnidia.fasta Hirudinea.fasta Hemiptera.fasta Gastropoda.fasta Ephemeroptera.fasta Diptera.fasta Copepoda.fasta Collembola.fasta Coleoptera.fasta Cladocera.fasta Cambaridae.fasta Bivalvia.fasta Asellidae.fasta Amphipoda.fasta

● 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300 310 320 330 340 350 360 370 380 390 400 410 420 430 440 450 460 470 480 490 500 510 520 530 540 550 560 570 580 590 600 610 620 630 640 650 660 670 680 690 700

Figure 4. PrimerMiner COI consensus sequence alignments. The first 50 basepairs are enlarged to illustrate A as red, T as green, C as blue, and G as yellow basepairs.

Initially, 11 degenerate primers were designed in PrimerMiner resulting in 16 pairs

(Appendix D). These primer pairs were tested on eDNA from Harper’s Ferry National Park (see

Chapter 2 for site details) with little success of amplification. Therefore, a degenerate version of the Leray primers was tested and proved somewhat successful. When amplifying DNA from specimens to begin library preparation, many taxa were not being amplified. Therefore, an additional 12 primers were designed and yielded 32 additional primer pairs. At this time a more degenerate Leray primer, three published 16S primers, and one published 12S primer were also tested to determine if primer mismatch or the template was the source of error. It was 42 determined that the template was not the source of error because the 16S and 12S primers amplified the DNA producing pronounced solid bands of the correct fragment size when checked on 1% agarose gel. In total, 55 primer pairs were tested, of which 51 were for COI (Appendix

D).

Amplification and sequencing of invertebrate samples for reference library construction

To build a customized DNA library for which to compare future metabarcoding data, one individual of each taxa from each site was used as a voucher specimen. The right metathoracic leg was removed if present. If not, the right mesothoracic leg was used. If a voucher specimen had no legs, as in the case of Dipterans, the middle third of the body was removed. Each voucher specimen and leg was placed in its own vial of 95% ethanol. The exception to this procedure is for the family Chironomidae in which case the top and bottom thirds were placed in 10% KOH and heated to 60º C for 45 minutes. Then, Chironomidae were slide mounted in glycerin to serve as a voucher specimen.

DNA was extracted from legs and abdominal segments using a combination of Qiagen and Omega DNA extraction kits. These two kits were used based on resource availability. Both kits use similar chemistries, and there did not appear to be any difference in the ability to obtain results from either kit based on comparisons of amplification success on a subset of DNA samples (data not shown). After DNA extraction was complete, PCR was conducted using the

16S_INS degenerate primer. The following master mix and thermal cycles were used throughout, including primer development. The master mix for each reaction contained 5 µL Buffer, 2.5 µL

MgCl2, 2 µL dNTPs, .63 µL BSA, .4 µL Taq, 3 µL Primers (1.5 µL of 5 µM each forward and 43 reverse), 1.47 µL PCR grade water per reaction. 10 µL of template was added to each reaction.

PCR reactions were placed on the BioRad Thermocycler for the following times: 95º C for 2:00 minutes, 34 cycles of 98º C for 0:20 seconds, 51 º C for 0:15 seconds, 72º C for 0:15, followed by 72º C for 1:00 and held at 12º C infinitely. Each sample was run on a 1% agarose gel to check for successful PCR amplification. Samples that failed to show a band were subjected to an additional 25 cycles using the 10 uL of the PCR product along with the same master mix composition and thermal cycling parameters, which usually resulted in the appearance of a single bright band. PCR product was cleaned of unincorporated nucleotides and free primers using

Ampure beads and the suggested cleaning protocol. Final DNA elution was done with 40 µL of

PCR grade water.

Ampure bead-cleaned PCR product was used as template for Sanger sequencing. The master mix for this reaction consisted of 2 µL BigDye (Life Technologies, USA), 5 µL PCR grade water, 1 µL of 5 µM primer, and 2 µL of template. The reactions were again placed on the

Biorad Thermocylcer for the following times: 95º C for 3:00, then 25 cycles of 95º C for 0:20 seconds, 51 º C for 0:20 seconds, 60º C for 4:00 minutes, followed by 60º C for 5:00 and an infinite hold at 12º C. The sequencing reaction product was cleaned with Axygen magnetic beads following the suggested cleaning protocol (Axygen Biosciences). Finally, 20 µL of cleaned pre-

Sanger sequencing reaction product were loaded onto an ABI 3130 capillary sequencer for sequencing. Exported run files from the ABI were imported into Sequencher. Forward and reverse reads were aligned and quality checked by eye. Consensus sequences were output in the

FASTA file format. Each sequence was then blasted against GenBank. Species level identification was assigned if sequences were 98% similar (Jo et al. 2015). Genus or higher level identification was assigned if GenBank and morphological identification matched. 44 Results

All of the COI primer sets failed to produce a cleanly amplified PCR product. Primer pair

8 (Appendix D) gave a faint but discernible band, but when primer pair 8 was compared alongside the degenerate Leray primer at a range of annealing temperatures (53°, 56°, and 58° C) where the Leray primer set gave the brightest band in all comparisons and appeared to be more efficient at amplifying eDNA than primer pair 8.

After initial testing of the newly designed primers on eDNA, we began amplifications of

DNA of reference specimens, with poor results for some taxa using the degenerate Leray primer.

Of the first attempted voucher specimen amplifications, eight of sixteen specimens did not amplify using the degenerate Leray primer. Therefore, we decided to reevaluate the choice of primer again. Upon testing published 16S and 12S primer to determine primer mismatch or template error, 16INS produced the brightest bands of PCR product on eDNA samples.

Additional COI primers were tested at this time, but failed to produce results as striking as 16

INS. Therefore, we proceeded with 16INS as published by Elbrecht et al. (2016).

Of the individual specimens sequenced, 74 produced alignable forward and reverse sequences that required minimal loss of sequence on the ends beyond the sequencing primer

(Appendix E). Some sequences were unable to be annotated such as those belonging to

Gastropoda and Bivalvia as well as a few Insecta because of sequencing errors that are currently unresolved.

BLAST results confirm the identification of some taxa whereas some sequences appear to be novel to GenBank. Seven individuals representing 4 genera were assigned to the correct order as the lowest taxonomic unit. Due to morphological identification, we know the genus for each of these individuals which are now new to GenBank. Nineteen individuals representing 10 45 genera were assigned to the correct family as the lowest taxonomic unit. Due to morphological identification, we also know the genus for each of these individuals which are now new to

GenBank. Eleven individuals representing 10 genera were assigned to the correct genus, which will supplement already existing sequences for these genera in GenBank. Ten individuals were assigned to the level of species with 98% or greater similarity. Our sequences will also supplement already existing sequences for these species in GenBank. Lastly, 27 individuals representing 14 genera did not return any BLAST results from GenBank and are completely new to the database.

Discussion

Aquatic biologists utilizing metabarcoding for biodiversity assessments are still on the hunt for a universal primer that can account for all taxa in a sample with equal efficiency. Our results of testing 51 novel COI primers sets clearly showed that COI is not a universal primer. In our hands, 16S was a better candidate as a universal primer. The aquatic realm is not unique to this quest or challenge or challenge for a universal primer.16S is already being successfully used for other organisms such as microbes (Cannon et al. 2015) and wildlife (Bohmann et al. 2014) while others are still struggling with the applicability of 16S (Dinsdale et al. 2008).

A few researchers who have produced papers denouncing the use of COI (Clarke et al.

2014; Deagle et al. 2014). This criticism of COI as a metabarcoding genes stems from the fact that the primer binding sites for COI are not highly conserved, resulting in uneven amplification of aquatic invertebrates from a chosen community. Alternatively, the mitochondrial 16S rDNA gene has been demonstrated to amplify more evenly across diverse taxa and have more 46 conserved primer binding sites than COI (Elbrecht et al. 2016). While COI is used for its ability to distinguish organisms at the species level, Hebert et al. (2003) have noted that 16S accumulates mutations slowly, which may not lead to sufficient taxonomic resolution, though recent research conducted by Elbrecht et al. (2016) recovered 41 of 42 insect taxa in a known mixture using 16S primers with 100% detection for Ephemeroptera, Plecoptera, and Trichoptera.

Elbrecht and Leese (2016) have published contrasting opinions on the use of COI and 16S. Two papers published in 2016 by Elbrecht extensively evaluated 16S and COI and potential metabarcoding primers. One paper praised 16S for its ability to identify more taxa than COI as well as its utility with Ephemeroptera, Trichoptera, and Plecoptera, which are indicators of high water quality (Elbrecht et al. 2016). Additionally, this paper praised the short length of 16S as useful when dealing with degraded DNA, which is often the case of eDNA samples. Elbrecht also recognized that the taxonomic resolution associated with 16S is still unknown. Clarke et al.

(2014) preformed an in silico review of COI primers and their results echoed the potential for primer mismatch as seen from our results. There are options beyond COI and 16S such as 12S and 18S, but their utility is vastly unknown (Creer et al. 2016).

In most metabarcoding projects, the overall goal is to get the most complete ecosystem picture at the best cost/work ratio. It is believed that metabarcoding is the future of monitoring biodiversity in aquatic ecosystems (Sweeney et al. 2011) and that it will become a cheaper and faster method of identification (Carew et al. 2013; Stein et al. 2014). As noted here, substantial work is needed to find new primers that are truly universal. The results of this study highlight the difficulty in designing “universal” primers that amplify taxa from diverse groups with equal efficiency. To get the most complete ecosystem picture at the best cost/work ratio, other methods may need to be explored. 47 Literature Cited

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Appendix A. Comprehensive Benthic Macroinvertebrate List

Park Site Order Family Lowest Taxonomic Unit Count ANTI SHCK Amphipoda Gammaridae Gammarus 896 ANTI SHCK Coleoptera Elmidae Optioservus 431 ANTI SHCK Coleoptera Elmidae Optioservus (Adult) 332 ANTI SHCK Coleoptera Elmidae Promoresia 19 ANTI SHCK Coleoptera Elmidae Promoresia (Adult) 4 ANTI SHCK Coleoptera Elmidae Stenelmis (Adult) 1 ANTI SHCK Decapoda Cambaridae Cambarus 7 ANTI SHCK Diptera Chironomidae Chironomini 6 ANTI SHCK Diptera Chironomidae Orthocladinae 11 ANTI SHCK Diptera Simuliidae Simulium 2 ANTI SHCK Ephemeroptera Baetidae Baetis 35 ANTI SHCK Ephemeroptera Baetidae Heterocloeon 6 ANTI SHCK Ephemeroptera Heptageniidae spp 6 ANTI SHCK Hemiptera Gerridae Limoporus 2 ANTI SHCK Hemiptera Veliidae Mesovelia 1 ANTI SHCK Isopoda Asellidae Caecidotea 58 ANTI SHCK Oligochaeta 30 ANTI SHCK Trichoptera Hydropsychidae Cheumatopsyche 2 ANTI SHCK Trichoptera Hydropsychidae Hydropsyche 7 CHOH BRUN Amphipoda Gammaridae Gammarus 81 CHOH BRUN Bivalvia Corbiculidae Corbicula 320 CHOH BRUN Bivalvia Spheriidae 21 CHOH BRUN Coleoptera Elmidae Dubiraphia 2 CHOH BRUN Coleoptera Elmidae Macronychus 8 CHOH BRUN Coleoptera Elmidae Stenelmis 111 CHOH BRUN Coleoptera Elmidae Stenelmis (Adult) 52 CHOH BRUN Coleoptera Psephenidae Psephenus 5 CHOH BRUN Decapoda Cambridae Orconectes 4 CHOH BRUN Diptera Chironomidae Chironomini 32 CHOH BRUN Diptera Chironomidae Orthocladinae 45 CHOH BRUN Diptera Chironomidae Tanypodinae 8 CHOH BRUN Diptera Chironomidae Tanytarsini 49 CHOH BRUN Diptera Simuliidae Simulium 1 CHOH BRUN Ephemeroptera Baetidae Baetis 11 CHOH BRUN Ephemeroptera Baetidae Heterocloeon 44 CHOH BRUN Ephemeroptera Caenidae Caenis 4 55 CHOH BRUN Ephemeroptera Heptageniidae Maccaffertium 19 CHOH BRUN Ephemeroptera Heptageniidae spp 39 CHOH BRUN Ephemeroptera Leptohyphidae Tricorythodes 253 CHOH BRUN Ephemeroptera Potamanthidae Anthopotamus 387 CHOH BRUN Gastropoda 4 CHOH BRUN Gastropoda Ancylidae 1 CHOH BRUN Gastropoda 1 CHOH BRUN Gastropoda 1 CHOH BRUN Hemiptera Gerridae Metrobates 54 CHOH BRUN Megaloptera Corydalidae Corydalus 18 CHOH BRUN Odonata Coenagrionidae Argia 5 CHOH BRUN Odonata Gomphidae Gomphus 1 CHOH BRUN Oligochaeta 6 CHOH BRUN Plecoptera Perlidae Agnetina 1 CHOH BRUN Trichoptera Glossosomatidae Glossosoma 15 CHOH BRUN Trichoptera Hydropsychidae Cheumatopsyche 17 CHOH BRUN Trichoptera Hydropsychidae Hydropsyche 3 CHOH BRUN Trichoptera Hydroptilidae Hydroptila 1 CHOH BRUN Trichoptera Lepdostomidae Lepidostoma 1 CHOH BRUN Trichoptera Leptoceridae Setodes 1 CHOH BRUN Trichoptera Philopotamidae Chimarra 31 CHOH WILL Amphipoda Gammaridae Gammarus 225 CHOH WILL Coleoptera Elmidae Dubiraphia 12 CHOH WILL Coleoptera Elmidae Dubiraphia (Adult) 1 CHOH WILL Coleoptera Elmidae Macronychus (Adult) 1 CHOH WILL Coleoptera Elmidae Optioservus 2 CHOH WILL Coleoptera Elmidae Stenelmis 18 CHOH WILL Coleoptera Elmidae Stenelmis (Adult) 4 CHOH WILL Decapoda Cambaridae Orcanectes 1 CHOH WILL Diptera Chironomidae Chironomini 222 CHOH WILL Diptera Chironomidae Orthocladinae 10 CHOH WILL Diptera Chironomidae Tanypodinae 14 CHOH WILL Ephemeroptera Baetidae Procloeon 6 CHOH WILL Ephemeroptera Caenidae Caenis 53 CHOH WILL Ephemeroptera Heptageniidae Maccaffertium 2 CHOH WILL Ephemeroptera Heptageniidae spp 1 CHOH WILL Ephemeroptera Heptageniidae Stenacron 1 CHOH WILL Ephemeroptera Potamanthidae Anthopotamus 3 CHOH WILL Gastropoda 1 CHOH WILL Hirudinea 11 CHOH WILL Isopoda Asellidae Caecidotea 6 CHOH WILL Odonata Coenagrionidae Enallagma 4 56 CHOH WILL Odonata Gomphidae Gomphus 2 CHOH WILL Oligochaeta 38 CHOH WILL Trichoptera Philopotamidae Dolophilodes 1 GWMP TURU Amphipoda Gammaridae Gammarus 13 GWMP TURU Coleoptera Elmidae Ancyronyx (Adult) 1 GWMP TURU Coleoptera Elmidae Macronychus (Adult) 1 GWMP TURU Coleoptera Elmidae Macronychus 1 GWMP TURU Coleoptera Elmidae Optioservus (Adult) 2 GWMP TURU Coleoptera Elmidae Stenelmis (Adult) 1 GWMP TURU Decapoda Cambaridae spp 2 GWMP TURU Diptera Chironomidae Chironomini 12 GWMP TURU Diptera Chironomidae Orthocladinae 4 GWMP TURU Diptera Chironomidae Tanypodinae 6 GWMP TURU Diptera Chironomidae Tanytarsini 3 GWMP TURU Diptera Simuliidae Prosimulium 1 GWMP TURU Diptera Tipulidae Antocha 1 GWMP TURU Diptera Tipulidae Limonia 1 GWMP TURU Ephemeroptera Baetidae Baetis 39 GWMP TURU Ephemeroptera Heptageniidae Maccaffertium 7 GWMP TURU Gastropoda 1 GWMP TURU Megaloptera Sialidae Sialis 1 GWMP TURU Odonata Calopterygidae Calopteryx 5 GWMP TURU Oligochaeta 4 GWMP TURU Plecoptera Chloroperlidae Utaperla 2 GWMP TURU Plecoptera Leutrichidae Leuctra 8 GWMP TURU Trichoptera Glossosomatidae Glossosoma 1 GWMP TURU Trichoptera Hydropsychidae Cheumatopsyche 126 GWMP TURU Trichoptera Hydropsychidae Hydropsyche 80 GWMP TURU Trichoptera Philopotamidae Chimarra 28 GWMP TURU Trichoptera Philopotamidae Dolophilodes 22 HAFE FLSP Amphipoda Crangonyctidae Crangonyx 14 HAFE FLSP Bivalvia Corbiculidae Corbicula 3 HAFE FLSP Coleoptera Elmidae Ancyronyx 2 HAFE FLSP Coleoptera Elmidae Dubiraphia 8 HAFE FLSP Coleoptera Elmidae Macronychus 91 HAFE FLSP Coleoptera Elmidae Macronychus (Adult) 50 HAFE FLSP Coleoptera Elmidae Stenelmis 324 HAFE FLSP Coleoptera Elmidae Stenelmis (Adult) 21 HAFE FLSP Diptera Chironomidae Chironomini 30 HAFE FLSP Diptera Chironomidae Orthocladinae 19 HAFE FLSP Diptera Chironomidae Tanypodinae 20 HAFE FLSP Diptera Chironomidae Tanytarsini 6 57 HAFE FLSP Diptera Empididae Hemerodromia 1 HAFE FLSP Diptera Tabanidae spp 6 HAFE FLSP Diptera Tipulidae Antocha 2 HAFE FLSP Diptera Tipulidae Tipula 2 HAFE FLSP Ephemeroptera Baetidae Pseudocloeon 11 HAFE FLSP Hirudinea 1 HAFE FLSP Isopoda Asellidae Lirceus 95 HAFE FLSP Megaloptera Corydalidae Nigronia 2 HAFE FLSP Odonata Aeshnidae Boyeria 2 HAFE FLSP Odonata Calopterygidae Hataerina 3 HAFE FLSP Odonata Gomphidae Gomphus 10 HAFE FLSP Oligochaeta 50 HAFE FLSP Trichoptera Hydropsychidae Cheumatopsyche 28 HAFE FLSP Trichoptera Hydropsychidae Hydropsyche 15 HAFE FLSP Trichoptera Philopotamidae Chimarra 2 HAFE FLSP Trichoptera Psychomiidae Lype 11 HAFE FLSP Turbellaria 1 MANA YOBR Bivalvia Corbiculidae Corbicula 7 MANA YOBR Coleoptera Dryopidae Helichus 1 MANA YOBR Coleoptera Elmidae Dubiraphia 1 MANA YOBR Coleoptera Elmidae Dubiraphia (Adult) 1 MANA YOBR Coleoptera Elmidae Optioservus 1 MANA YOBR Coleoptera Elmidae Stenelmis 165 MANA YOBR Coleoptera Elmidae Stenelmis (Adult) 23 MANA YOBR Coleoptera Psephenidae Ectopria 4 MANA YOBR Coleoptera Psephenidae Psephenus 12 MANA YOBR Diptera Chironomidae Chironomini 16 MANA YOBR Diptera Chironomidae Orthocladinae 30 MANA YOBR Diptera Chironomidae Tanypodinae 8 MANA YOBR Diptera Chironomidae Tanytarsini 20 MANA YOBR Diptera Tipulidae Antocha 2 MANA YOBR Diptera Tipulidae Tipula 6 MANA YOBR Ephemeroptera Baetidae Baetis 4 MANA YOBR Ephemeroptera Caenidae Caenis 16 MANA YOBR Ephemeroptera Heptageniidae spp 1 MANA YOBR Ephemeroptera Leptohyphidae Tricorythodes 3 MANA YOBR Gastropoda Ancylidae 6 MANA YOBR Gastropoda 3 MANA YOBR Gastropoda 3 MANA YOBR Hemiptera Gerridae Trepobates 3 MANA YOBR Hemiptera Veliidae Microvelia 1 MANA YOBR Hydracarina 1

58 MANA YOBR Megaloptera Corydalidae Corydalus 7 MANA YOBR Megaloptera Sialidae Sialis 1 MANA YOBR Odonata Coenagrionidae Argia 19 MANA YOBR Odonata Gomphidae Hagenis 2 MANA YOBR Odonata Gomphidae Stylogomphus 6 MANA YOBR Odonata Libellulidae Erythemis 1 MANA YOBR Oligochaeta 8 MANA YOBR Trichoptera Hydroptilidae Leucotrichia 1 MANA YOBR Trichoptera Leptoceridae Mystacides 6 MANA YOBR Trichoptera Leptoceridae Oecetis 2 MANA YOBR Turbellaria 20 MONO BUCK Amphipoda Gammaridae Gammarus 73 MONO BUCK Bivalvia Corbiculidae Corbicula 4 MONO BUCK Coleoptera Elmidae Dubiraphia (Adult) 1 MONO BUCK Coleoptera Elmidae Macronychus (Adult) 1 MONO BUCK Coleoptera Elmidae Macronychus 1 MONO BUCK Coleoptera Elmidae Optioservus (Adult) 5 MONO BUCK Coleoptera Elmidae Optioservus 6 MONO BUCK Coleoptera Elmidae Stenelmis 2 MONO BUCK Coleoptera Psephenidae Psephenus 3 MONO BUCK Decapoda Cambridae Orconectes 8 MONO BUCK Diptera Chironomidae Chironomini 71 MONO BUCK Diptera Chironomidae Orthocladinae 5 MONO BUCK Diptera Chironomidae Tanypodinae 5 MONO BUCK Diptera Chironomidae Tanytarsini 8 MONO BUCK Diptera Simuliidae Simulium 1 MONO BUCK Diptera Tipulidae Antocha 15 MONO BUCK Ephemeroptera Baetidae Acentrella 7 MONO BUCK Ephemeroptera Baetidae Baetis 9 MONO BUCK Ephemeroptera Caenidae Caenis 91 MONO BUCK Ephemeroptera Heptageniidae Maccaffertium 168 MONO BUCK Ephemeroptera Isonychiidae Isonychia 219 MONO BUCK Gastropoda Ancylidae 1 MONO BUCK Hemiptera Veliidae Rhagovelia 9 MONO BUCK Megaloptera Corydalidae Corydalus 7 MONO BUCK Megaloptera Corydalidae Nigronia 1 MONO BUCK Oligochaeta 6 MONO BUCK Trichoptera Glossosomatidae Glossosoma 1 MONO BUCK Trichoptera Hydropsychidae Cheumatopsyche 141 MONO BUCK Trichoptera Hydropsychidae Hydropsyche 271 MONO BUCK Trichoptera Hydroptilidae Hydroptila 1 MONO BUCK Trichoptera Hydroptilidae Leucotrichia 10 59 MONO BUCK Trichoptera Lepdostomidae Lepidostoma 1 MONO BUCK Trichoptera Leptoceridae Oecetis 1 MONO BUCK Trichoptera Philopotamidae Chimarra 76 MONO BUCK Trichoptera Polycentropodidae Polycentropus 1 MONO VCCR Bivalvia Spheriidae spp 1 MONO VCCR Coleoptera Dryopidae Helichus 1 MONO VCCR Coleoptera Elmidae Optioservus 1 MONO VCCR Coleoptera Elmidae Stenelmis (Adult) 4 MONO VCCR Coleoptera Hydrophilidae Hydrobius 3 MONO VCCR Coleoptera Psephenidae Psephenus 2 MONO VCCR Diptera Chironomidae Chironomini 81 MONO VCCR Diptera Chironomidae Diamesa 10 MONO VCCR Diptera Chironomidae Orthocladinae 19 MONO VCCR Diptera Chironomidae Tanypodinae 43 MONO VCCR Diptera Chironomidae Tanytarsini 5 MONO VCCR Diptera Culicidae 1 MONO VCCR Diptera Empididae Hemerodromia 1 MONO VCCR Diptera Tabanidae Chrysops 1 MONO VCCR Diptera Tipulidae Antocha 2 MONO VCCR Diptera Tipulidae Hexatoma 1 MONO VCCR Diptera Tipulidae Pseudolimnophila 15 MONO VCCR Diptera Tipulidae Tipula 4 MONO VCCR Hemiptera Gerridae Gerris 4 MONO VCCR Hemiptera Gerridae Trepobates 1 MONO VCCR Hemiptera Pleidae Neoplea 1 MONO VCCR Hemiptera Veliidae Microvelia 1 MONO VCCR Hemiptera Veliidae Rhagovelia 4 MONO VCCR Hirudinea 5 MONO VCCR Isopoda Asellidae Caecidotea 21 MONO VCCR Megaloptera Corydalidae Nigronia 1 MONO VCCR Odonata Gomphidae Stylogomphus 2 MONO VCCR Oligochaeta 30 MONO VCCR Trichoptera Hydropsychidae Cheumatopsyche 31 MONO VCCR Trichoptera Hydropsychidae Hydropsyche 3 MONO VCCR Trichoptera Philopotamidae Chimarra 2 MONO VCCR Trichoptera Polycentropodidae Polycentropus 2 MONO VCCR Trichoptera Psychomiidae spp 1 NACE OXRU Coleoptera Elmidae Stenelmis 1 NACE OXRU Decapoda Cambaridae spp 1 NACE OXRU Diptera Chironomidae Chironomini 84 NACE OXRU Diptera Chironomidae Tanypodinae 59 NACE OXRU Ephemeroptera Caenidae Caenis 1 60 NACE OXRU Gastropoda 1 NACE OXRU Hirudinea 4 NACE OXRU Odonata Libellulidae Erythemis 1 NACE OXRU Oligochaeta 35 NACE OXRU Podacopa 29 NACE OXRU Trichoptera Hydroptilidae Leucotrichia 1 NACE OXRU Trichoptera Leptoceridae Oecetis 4 NACE OXRU Turbellaria 2 PRWI ORRU Amphipoda Gammaridae Gammarus 2 PRWI ORRU Coleoptera Dryopidae Helichus 6 PRWI ORRU Coleoptera Elmidae Macronychus (Adult) 1 PRWI ORRU Coleoptera Elmidae Optioservus 1 PRWI ORRU Coleoptera Elmidae Stenelmis (Adult) 4 PRWI ORRU Coleoptera Elmidae Stenelmis 1 PRWI ORRU Decapoda Cambridae spp 3 PRWI ORRU Diptera Chironomidae Chironomini 66 PRWI ORRU Diptera Chironomidae Orthocladinae 10 PRWI ORRU Diptera Chironomidae Tanypodinae 8 PRWI ORRU Diptera Chironomidae Tanytarsini 8 PRWI ORRU Diptera Tabanidae Chrysops 1 PRWI ORRU Diptera Tipulidae Antocha 1 PRWI ORRU Diptera Tipulidae Hexatoma 4 PRWI ORRU Diptera Tipulidae Limnophila 2 PRWI ORRU Diptera Tipulidae Limonia 21 PRWI ORRU Diptera Tipulidae Pseudolimnophila 1 PRWI ORRU Diptera Tipulidae Tipula 4 PRWI ORRU Ephemeroptera Baetidae spp 2 PRWI ORRU Hemiptera Veliidae Microvelia 1 PRWI ORRU Hemiptera Veliidae Rhagovelia 13 PRWI ORRU Isopoda Asellidae Caecidotea 1 PRWI ORRU Megaloptera Corydalidae Nigronia 3 PRWI ORRU Odonata Calopterygidae Calopteryx 3 PRWI ORRU Odonata Cordulegastridae Cordulegaster 2 PRWI ORRU Oligochaeta 3 PRWI ORRU Plecoptera Leuctridae Leuctra 9 PRWI ORRU Plecoptera Perlidae Eccoptura 5 PRWI ORRU Plecoptera Perlidae spp 59 PRWI ORRU Trichoptera Hydropsychidae Diplectrona 79 PRWI ORRU Trichoptera Lepdostomidae Lepidostoma 2 PRWI ORRU Trichoptera Leptoceridae Mystacides 1 PRWI ORRU Trichoptera Philopotamidae Chimarra 10 PRWI ORRU Trichoptera Psychomiidae Lype 9 61 PRWI TARU Bivalvia Corbiculidae Corbicula 4 PRWI TARU Coleoptera Elmidae Dubiraphia (Adult) 6 PRWI TARU Coleoptera Elmidae Macronychus (Adult) 9 PRWI TARU Coleoptera Elmidae Optioservus 11 PRWI TARU Coleoptera Elmidae Optioservus (Adult) 3 PRWI TARU Coleoptera Elmidae Promoresia 2 PRWI TARU Coleoptera Elmidae Stenelmis 74 PRWI TARU Coleoptera Elmidae Stenelmis (Adult) 17 PRWI TARU Coleoptera Psephenidae Psephenus 5 PRWI TARU Diptera Chironomidae Chironomini 67 PRWI TARU Diptera Chironomidae Orthocladinae 3 PRWI TARU Diptera Chironomidae Tanypodinae 4 PRWI TARU Diptera Chironomidae Tanytarsini 19 PRWI TARU Diptera Simuliidae Simulium 1 PRWI TARU Diptera Tipulidae Antocha 3 PRWI TARU Diptera Tipulidae Dicronata 1 PRWI TARU Ephemeroptera Baetidae Acentrella 1 PRWI TARU Ephemeroptera Baetidae Baetis 5 PRWI TARU Ephemeroptera Baetiscidae Baetisca 11 PRWI TARU Ephemeroptera Caenidae Caenis 16 PRWI TARU Ephemeroptera Ephemerellidae spp 1 PRWI TARU Ephemeroptera Heptageniidae Heptagenia 2 PRWI TARU Ephemeroptera Heptageniidae spp 24 PRWI TARU Ephemeroptera Heptageniidae Stenonema 9 PRWI TARU Ephemeroptera Isonychiidae Isonychia 3 PRWI TARU Megaloptera Corydalidae Corydalus 1 PRWI TARU Odonata Aeshnidae Boyeria 2 PRWI TARU Odonata Coenagrionidae Amphiagrion 1 PRWI TARU Odonata Cordulegastridae Cordulegaster 1 PRWI TARU Odonata Gomphidae Hagenis 3 PRWI TARU Odonata Gomphidae Stylogomphus 5 PRWI TARU Plecoptera Perlidae Acroneuria 4 PRWI TARU Trichoptera Brachycentridae Brachycentrus 1 PRWI TARU Trichoptera Brachycentridae Micrasema 8 PRWI TARU Trichoptera Goeridae Goera 1 PRWI TARU Trichoptera Hydropsychidae Cheumatopsyche 17 PRWI TARU Trichoptera Hydropsychidae Hydropsyche 29 PRWI TARU Trichoptera Leptoceridae Mystacides 2 PRWI TARU Trichoptera Leptoceridae Oecetis 1 PRWI TARU Trichoptera Philopotamidae Chimarra 1 WOTR CHCK Bivalvia Spheriidae app 1 WOTR CHCK Coleoptera Elmidae Ancyronyx 3 62 WOTR CHCK Coleoptera Elmidae Ancyronyx (Adult) 19 WOTR CHCK Coleoptera Elmidae Stenelmis 2 WOTR CHCK Coleoptera Elmidae Stenelmis (Adult) 9 WOTR CHCK Decapoda Cambridae 1 WOTR CHCK Diptera Chironomidae Chironomini 3 WOTR CHCK Diptera Chironomidae Orthocladinae 16 WOTR CHCK Diptera Chironomidae Tanypodinae 3 WOTR CHCK Diptera Chironomidae Tanytarsini 11 WOTR CHCK Diptera Simuliidae Simulium 2 WOTR CHCK Diptera Tipulidae Antocha 4 WOTR CHCK Diptera Tipulidae Tipula 2 WOTR CHCK Ephemeroptera Baetidae Baetis 4 WOTR CHCK Gastropoda 1 WOTR CHCK Hemiptera Veliidae Microvelia 4 WOTR CHCK Hemiptera Veliidae Rhagovelia 6 WOTR CHCK Odonata Calopterygidae Calopteryx 9 WOTR CHCK Odonata Coenagrionidae Argia 2 WOTR CHCK Odonata Coenagrionidae Ishnura 5 WOTR CHCK Oligochaeta 8 WOTR CHCK Trichoptera Hydropsychidae Cheumatopsyche 328 WOTR CHCK Trichoptera Hydropsychidae Hydropsyche 73 WOTR CHCK Trichoptera Philopotamidae Chimarra 4 WOTR WOTR Coleoptera Elmidae Ancyronyx (Adult) 6 WOTR WOTR Coleoptera Elmidae Stenelmis (Adult) 6 WOTR WOTR Coleoptera Elmidae Stenelmis 2 WOTR WOTR Diptera Chironomidae Chironomini 324 WOTR WOTR Diptera Chironomidae Orthocladinae 66 WOTR WOTR Diptera Chironomidae Tanypodinae 12 WOTR WOTR Diptera Chironomidae Tanytarsini 40 WOTR WOTR Diptera Simuliidae Prosimulium 1 WOTR WOTR Diptera Tipulidae Antocha 2 WOTR WOTR Ephemeroptera Baetidae Baetis 25 WOTR WOTR Hemiptera Veliidae Rhagovelia 1 WOTR WOTR Odonata Calopterygidae Calopteryx 2 WOTR WOTR Oligochaeta 1 WOTR WOTR Trichoptera Hydropsychidae Cheumatopsyche 535 WOTR WOTR Trichoptera Hydropsychidae Hydropsyche 76 WOTR WOTR Trichoptera Philopotamidae Chimarra 8

63 Appendix B. Comparison of Taxa Between Studies

Lowest Taxonomic This NPS Park Site Order Family Unit Study Data ANTI SHCK Amphipoda Gammaridae Gammarus X X ANTI SHCK Coleoptera Elmidae Optioservus X X ANTI SHCK Coleoptera Elmidae Promoresia X

ANTI SHCK Coleoptera Elmidae Stenelmis X

ANTI SHCK Decapoda Cambaridae Cambarus X ANTI SHCK Diptera Chironomidae Chironomini X ANTI SHCK Diptera Chironomidae Diamesinae X ANTI SHCK Diptera Chironomidae Orthocladinae X X ANTI SHCK Diptera Chironomidae Tanytarsini X ANTI SHCK Diptera Dixidae Dixa X ANTI SHCK Diptera Simuliidae Simulium X ANTI SHCK Ephemeroptera Baetidae Baetis X X ANTI SHCK Ephemeroptera Baetidae Heterocloeon X ANTI SHCK Ephemeroptera Heptageniidae X ANTI SHCK Hemiptera Gerridae Limoporus X ANTI SHCK Hemiptera Veliidae Mesovelia X ANTI SHCK Isopoda Asellidae Caecidotea X X ANTI SHCK Oligochaeta X ANTI SHCK Trichoptera Hydropsychidae Cheumatopsyche X ANTI SHCK Trichoptera Hydropsychidae Hydropsyche X X GWMP TURU Amphipoda Gammaridae Gammarus X GWMP TURU Coleoptera Elmidae Ancyronyx X

GWMP TURU Coleoptera Elmidae Macronychus X

GWMP TURU Coleoptera Elmidae Optioservus X

GWMP TURU Coleoptera Elmidae Stenelmis X X GWMP TURU Decapoda Cambaridae X GWMP TURU Diptera Chironomidae Chironomini X GWMP TURU Diptera Chironomidae Diamesinae X GWMP TURU Diptera Chironomidae Orthocladinae X X GWMP TURU Diptera Chironomidae Tanypodinae X X GWMP TURU Diptera Chironomidae Tanytarsini X X GWMP TURU Diptera Simuliidae Prosimulium X GWMP TURU Diptera Simuliidae Simulium X GWMP TURU Diptera Tipulidae Antocha X GWMP TURU Diptera Tipulidae Limonia X GWMP TURU Ephemeroptera Baetidae Baetis X GWMP TURU Ephemeroptera Heptageniidae Maccaffertium X GWMP TURU Gastropoda X

GWMP TURU Hemiptera Gerridae Gerris X GWMP TURU Megaloptera Sialidae Sialis X

64 GWMP TURU Odonata Calopterygidae Calopteryx X GWMP TURU Oligochaeta X X GWMP TURU Plecoptera Chloroperlidae Utaperla X GWMP TURU Plecoptera Leutrichidae Leuctra X GWMP TURU Trichoptera Glossosomatidae Glossosoma X GWMP TURU Trichoptera Hydropsychidae Cheumatopsyche X GWMP TURU Trichoptera Hydropsychidae Hydropsyche X X GWMP TURU Trichoptera Philopotamidae Chimarra X GWMP TURU Trichoptera Philopotamidae Dolophilodes X X HAFE FLSP Amphipoda Crangonyctidae Crangonyx X X HAFE FLSP Bivalvia Corbiculidae Corbicula X HAFE FLSP Bivalvia Veneroida X HAFE FLSP Coleoptera Elmidae Ancyronyx X

HAFE FLSP Coleoptera Elmidae Dubiraphia X

HAFE FLSP Coleoptera Elmidae Macronychus X X HAFE FLSP Coleoptera Elmidae Stenelmis X

HAFE FLSP Coleoptera Gyrinidae Gyrinus X

HAFE FLSP Diptera Ceratapogonidae X HAFE FLSP Diptera Chironomidae Chironomini X X HAFE FLSP Diptera Chironomidae Orthocladinae X HAFE FLSP Diptera Chironomidae Tanypodinae X X HAFE FLSP Diptera Chironomidae Tanytarsini X X HAFE FLSP Diptera Empididae Hemerodromia X HAFE FLSP Diptera Tabanidae X HAFE FLSP Diptera Tipulidae Antocha X X HAFE FLSP Diptera Tipulidae Tipula X HAFE FLSP Ephemeroptera Baetidae Baetis X HAFE FLSP Ephemeroptera Baetidae Pseudocloeon X HAFE FLSP Gastropoda X HAFE FLSP Hemiptera Corixidae X HAFE FLSP Hirudinea X HAFE FLSP Isopoda Asellidae Lirceus X HAFE FLSP Megaloptera Corydalidae Nigronia X HAFE FLSP Odonata Aeshnidae Boyeria X HAFE FLSP Odonata Calopterygidae Calopteryx X HAFE FLSP Odonata Calopterygidae Hataerina X HAFE FLSP Odonata Gomphidae Gomphus X HAFE FLSP Oligochaeta X X HAFE FLSP Trichoptera Hydropsychidae Cheumatopsyche X X HAFE FLSP Trichoptera Hydropsychidae Hydropsyche X X HAFE FLSP Trichoptera Limnephilidae Pycnopsyche X HAFE FLSP Trichoptera Philopotamidae Chimarra X X HAFE FLSP Trichoptera Psychomiidae Lype X HAFE FLSP Turbellaria X MANA YOBR Amphipoda Crangonyctidae Crangonyx X 65 MANA YOBR Bivalvia Corbiculidae Corbicula X MANA YOBR Coleoptera Dryopidae Helichus X MANA YOBR Coleoptera Elmidae Dubiraphia X

MANA YOBR Coleoptera Elmidae Optioservus X

MANA YOBR Coleoptera Elmidae Stenelmis X X MANA YOBR Coleoptera Psephenidae Ectopria X MANA YOBR Coleoptera Psephenidae Psephenus X X MANA YOBR Diptera Chironomidae Chironomini X X MANA YOBR Diptera Chironomidae Orthocladinae X X MANA YOBR Diptera Chironomidae Tanypodinae X X MANA YOBR Diptera Chironomidae Tanytarsini X X MANA YOBR Diptera Empididae Neoplasta X MANA YOBR Diptera Ephydridae Scatella X MANA YOBR Diptera Simuliidae Simulium X MANA YOBR Diptera Tipulidae Antocha X MANA YOBR Diptera Tipulidae Tipula X MANA YOBR Ephemeroptera Baetidae Baetis X MANA YOBR Ephemeroptera Caenidae Caenis X MANA YOBR Ephemeroptera Heptageniidae X MANA YOBR Ephemeroptera Leptohyphidae Tricorythodes X MANA YOBR Gastropoda Ancylidae X

MANA YOBR Gastropoda X MANA YOBR Gastropoda X

MANA YOBR Gastropoda X

MANA YOBR Hemiptera Gerridae Trepobates X MANA YOBR Hemiptera Veliidae Microvelia X MANA YOBR Hydracarina X MANA YOBR Isopoda Asellidae Caecidotea X MANA YOBR Megaloptera Corydalidae Corydalus X MANA YOBR Megaloptera Sialidae Sialis X MANA YOBR Odonata Coenagrionidae Argia X MANA YOBR Odonata Gomphidae Hagenis X MANA YOBR Odonata Gomphidae Stylogomphus X MANA YOBR Odonata Libellulidae Erythemis X MANA YOBR Oligochaeta X X MANA YOBR Plecoptera Capniidae Capnia X MANA YOBR Plecoptera Perlidae Perlesta X MANA YOBR Trichoptera Hydropsychidae Cheumatopsyche X MANA YOBR Trichoptera Hydropsychidae Hydropsyche X MANA YOBR Trichoptera Hydroptilidae Leucotrichia X MANA YOBR Trichoptera Leptoceridae Mystacides X MANA YOBR Trichoptera Leptoceridae Oecetis X MANA YOBR Trichoptera Philopotamidae Chimarra X MANA YOBR Trichoptera Rhyacophilidae Rhyacophila X MANA YOBR Turbellaria X

66 MONO BUCK Amphipoda Crangonyctidae Crangonyx X MONO BUCK Amphipoda Gammaridae Gammarus X MONO BUCK Bivalvia Corbiculidae Corbicula X MONO BUCK Coleoptera Dytiscidae Heterosternuta X

MONO BUCK Coleoptera Elmidae Dubiraphia X X MONO BUCK Coleoptera Elmidae Macronychus X

MONO BUCK Coleoptera Elmidae Optioservus X X MONO BUCK Coleoptera Elmidae Stenelmis X

MONO BUCK Coleoptera Psephenidae Psephenus X MONO BUCK Decapoda Cambridae Orconectes X MONO BUCK Diptera Chironomidae Chironomini X X MONO BUCK Diptera Chironomidae Orthocladinae X X MONO BUCK Diptera Chironomidae Tanypodinae X MONO BUCK Diptera Chironomidae Tanytarsini X X MONO BUCK Diptera Empididae Clinocera X MONO BUCK Diptera Simuliidae Simulium X X MONO BUCK Diptera Tipulidae Antocha X X MONO BUCK Ephemeroptera Baetidae Acentrella X MONO BUCK Ephemeroptera Baetidae Baetis X X MONO BUCK Ephemeroptera Caenidae Caenis X MONO BUCK Ephemeroptera Heptageniidae Maccaffertium X X MONO BUCK Ephemeroptera Isonychiidae Isonychia X X MONO BUCK Gastropoda Ancylidae X MONO BUCK Hemiptera Veliidae Rhagovelia X MONO BUCK Megaloptera Corydalidae Corydalus X MONO BUCK Megaloptera Corydalidae Nigronia X MONO BUCK Oligochaeta X X MONO BUCK Plecoptera Nemouridae Amphinemoura X MONO BUCK Plecoptera Perlodidae X MONO BUCK Trichoptera Glossosomatidae Glossosoma X MONO BUCK Trichoptera Glossosomatidae Protoptila X MONO BUCK Trichoptera Hydropsychidae Cheumatopsyche X MONO BUCK Trichoptera Hydropsychidae Hydropsyche X MONO BUCK Trichoptera Hydroptilidae Hydroptila X MONO BUCK Trichoptera Hydroptilidae Leucotrichia X MONO BUCK Trichoptera Lepdostomidae Lepidostoma X MONO BUCK Trichoptera Leptoceridae Oecetis X MONO BUCK Trichoptera Philopotamidae Chimarra X MONO BUCK Trichoptera Polycentropodidae Polycentropus X MONO VCCR Amphipoda Gammaridae Gammarus X MONO VCCR Bivalvia Spheriidae X MONO VCCR Coleoptera Dryopidae Helichus X MONO VCCR Coleoptera Dytiscidae Heterosternuta X

MONO VCCR Coleoptera Elmidae Optioservus X

MONO VCCR Coleoptera Elmidae Stenelmis X X 67 MONO VCCR Coleoptera Hydrophilidae Hydrobius X MONO VCCR Coleoptera Psephenidae Psephenus X MONO VCCR Diptera Ceratapogonidae X MONO VCCR Diptera Chironomidae Chironomini X X MONO VCCR Diptera Chironomidae Diamesa X X MONO VCCR Diptera Chironomidae Orthocladinae X X MONO VCCR Diptera Chironomidae Tanypodinae X MONO VCCR Diptera Chironomidae Tanytarsini X X MONO VCCR Diptera Culicidae X MONO VCCR Diptera Empididae Hemerodromia X MONO VCCR Diptera Psychodidae Pericoma X MONO VCCR Diptera Simuliidae Prosimulium X MONO VCCR Diptera Simuliidae Simulium X MONO VCCR Diptera Tabanidae Chrysops X MONO VCCR Diptera Tipulidae Antocha X MONO VCCR Diptera Tipulidae Hexatoma X MONO VCCR Diptera Tipulidae Pseudolimnophila X MONO VCCR Diptera Tipulidae Tipula X X MONO VCCR Gastropoda X MONO VCCR Hemiptera Gerridae Gerris X MONO VCCR Hemiptera Gerridae Trepobates X MONO VCCR Hemiptera Pleidae Neoplea X MONO VCCR Hemiptera Veliidae Microvelia X MONO VCCR Hemiptera Veliidae Rhagovelia X MONO VCCR Hirudinea X MONO VCCR Isopoda Asellidae Caecidotea X MONO VCCR Megaloptera Corydalidae Nigronia X MONO VCCR Odonata Gomphidae Stylogomphus X MONO VCCR Oligochaeta X X MONO VCCR Plecoptera Perlodidae Isoperla X MONO VCCR Trichoptera Hydropsychidae Cheumatopsyche X MONO VCCR Trichoptera Hydropsychidae Diplectrona X MONO VCCR Trichoptera Hydropsychidae Hydropsyche X MONO VCCR Trichoptera Philopotamidae Chimarra X MONO VCCR Trichoptera Polycentropodidae Polycentropus X MONO VCCR Trichoptera Psychomiidae X NACE OXRU Bivalvia Spheriidae X NACE OXRU Coleoptera Elmidae Stenelmis X

NACE OXRU Decapoda Cambaridae X NACE OXRU Diptera Chironomidae Chironomini X X NACE OXRU Diptera Chironomidae Tanypodinae X X NACE OXRU Diptera Chironomidae Tanytarsini X NACE OXRU Diptera Psychodidae Psychoda X NACE OXRU Ephemeroptera Caenidae Caenis X NACE OXRU Gastropoda X

68 NACE OXRU Gastropoda X

NACE OXRU Hirudinea X X NACE OXRU Odonata Calopterygidae Calopteryx X NACE OXRU Odonata Coenagrionidae Argia X NACE OXRU Odonata Coenagrionidae Enallagma X NACE OXRU Odonata Libellulidae Erythemis X NACE OXRU Oligochaeta X X NACE OXRU Podacopa X NACE OXRU Trichoptera Hydroptilidae Leucotrichia X NACE OXRU Trichoptera Leptoceridae Oecetis X NACE OXRU Turbellaria X PRWI ORRU Amphipoda Crangonyctidae Crangonyx X PRWI ORRU Amphipoda Gammaridae Gammarus X PRWI ORRU Coleoptera Dryopidae Helichus X X PRWI ORRU Coleoptera Elmidae Macronychus X

PRWI ORRU Coleoptera Elmidae Optioservus X

PRWI ORRU Coleoptera Elmidae Oulimnius X

PRWI ORRU Coleoptera Elmidae Stenelmis X

PRWI ORRU Coleoptera Psephenidae Psephenus X

PRWI ORRU Decapoda Cambridae X

PRWI ORRU Diptera Ceratapogonidae X PRWI ORRU Diptera Chironomidae Chironomini X X PRWI ORRU Diptera Chironomidae Orthocladinae X X PRWI ORRU Diptera Chironomidae Tanypodinae X PRWI ORRU Diptera Chironomidae Tanytarsini X X PRWI ORRU Diptera Simuliidae Prosimulium X PRWI ORRU Diptera Simuliidae Simulium X PRWI ORRU Diptera Tabanidae Chrysops X PRWI ORRU Diptera Tipulidae Antocha X PRWI ORRU Diptera Tipulidae Hexatoma X X PRWI ORRU Diptera Tipulidae Limnophila X PRWI ORRU Diptera Tipulidae Limonia X PRWI ORRU Diptera Tipulidae Pseudolimnophila X PRWI ORRU Diptera Tipulidae Tipula X X PRWI ORRU Ephemeroptera Baetidae X

PRWI ORRU Ephemeroptera Ephemerellidae X PRWI ORRU Hemiptera Veliidae Microvelia X PRWI ORRU Hemiptera Veliidae Rhagovelia X PRWI ORRU Isopoda Asellidae Caecidotea X PRWI ORRU Megaloptera Corydalidae Nigronia X PRWI ORRU Odonata Calopterygidae Calopteryx X PRWI ORRU Odonata Cordulegastridae Cordulegaster X PRWI ORRU Oligochaeta X PRWI ORRU Plecoptera Chloroperlidae Haploperla X PRWI ORRU Plecoptera Leuctridae Leuctra X

69 PRWI ORRU Plecoptera Nemouridae Amphinemoura X PRWI ORRU Plecoptera Perlidae Eccoptura X PRWI ORRU Plecoptera Perlidae X

PRWI ORRU Plecoptera X PRWI ORRU Trichoptera Hydropsychidae Diplectrona X X PRWI ORRU Trichoptera Lepdostomidae Lepidostoma X PRWI ORRU Trichoptera Leptoceridae Mystacides X PRWI ORRU Trichoptera Limnephilidae Pycnopsyche X PRWI ORRU Trichoptera Philopotamidae Chimarra X PRWI ORRU Trichoptera Psychomiidae Lype X PRWI TARU Bivalvia Corbiculidae Corbicula X PRWI TARU Coleoptera Elmidae Dubiraphia X

PRWI TARU Coleoptera Elmidae Macronychus X

PRWI TARU Coleoptera Elmidae Optioservus X

PRWI TARU Coleoptera Elmidae Oulimnius X

PRWI TARU Coleoptera Elmidae Promoresia X

PRWI TARU Coleoptera Elmidae Stenelmis X X PRWI TARU Coleoptera Psephenidae Psephenus X X PRWI TARU Diptera Ceratapogonidae X PRWI TARU Diptera Chironomidae Chironomini X X PRWI TARU Diptera Chironomidae Orthocladinae X X PRWI TARU Diptera Chironomidae Tanypodinae X PRWI TARU Diptera Chironomidae Tanytarsini X X PRWI TARU Diptera Simuliidae Prosimulium X PRWI TARU Diptera Simuliidae Simulium X X PRWI TARU Diptera Simuliidae Stegopterna X PRWI TARU Diptera Tipulidae Antocha X PRWI TARU Diptera Tipulidae Dicronata X PRWI TARU Diptera Tipulidae Hexatoma X PRWI TARU Ephemeroptera Baetidae Acentrella X PRWI TARU Ephemeroptera Baetidae Acerpenna X PRWI TARU Ephemeroptera Baetidae Baetis X PRWI TARU Ephemeroptera Baetiscidae Baetisca X PRWI TARU Ephemeroptera Caenidae Caenis X PRWI TARU Ephemeroptera Ephemerellidae X X PRWI TARU Ephemeroptera Heptageniidae Heptagenia X PRWI TARU Ephemeroptera Heptageniidae Maccaffertium X PRWI TARU Ephemeroptera Heptageniidae Stenonema X PRWI TARU Ephemeroptera Heptageniidae X X PRWI TARU Ephemeroptera Isonychiidae Isonychia X PRWI TARU Ephemeroptera Leptophlebiidae X PRWI TARU Megaloptera Corydalidae Corydalus X PRWI TARU Megaloptera Corydalidae Nigronia X PRWI TARU Odonata Aeshnidae Boyeria X PRWI TARU Odonata Coenagrionidae Amphiagrion X

70 PRWI TARU Odonata Cordulegastridae Cordulegaster X PRWI TARU Odonata Gomphidae Gomphus X PRWI TARU Odonata Gomphidae Hagenis X PRWI TARU Odonata Gomphidae Stylogomphus X PRWI TARU Oligochaeta X PRWI TARU Plecoptera Chloroperlidae Haploperla X PRWI TARU Plecoptera Leuctridae Leuctra X PRWI TARU Plecoptera Nemouridae Amphinemoura X PRWI TARU Plecoptera Perlidae Acroneuria X PRWI TARU Plecoptera Perlidae X PRWI TARU Plecoptera Taeniopterygidae Taeniopteryx X PRWI TARU Trichoptera Brachycentridae Brachycentrus X PRWI TARU Trichoptera Brachycentridae Micrasema X PRWI TARU Trichoptera Goeridae Goera X PRWI TARU Trichoptera Hydropsychidae Cheumatopsyche X PRWI TARU Trichoptera Hydropsychidae Diplectrona X PRWI TARU Trichoptera Hydropsychidae Hydropsyche X PRWI TARU Trichoptera Lepdostomidae Lepidostoma X PRWI TARU Trichoptera Leptoceridae Mystacides X PRWI TARU Trichoptera Leptoceridae Oecetis X PRWI TARU Trichoptera Philopotamidae Chimarra X PRWI TARU Trichoptera Polycentropodidae Polycentropus X PRWI TARU Trichoptera Psychomiidae Lype X PRWI TARU Trichoptera Rhyacophilidae Rhycophila X WOTR CHCK Bivalvia Spheriidae X WOTR CHCK Coleoptera Elmidae Ancyronyx X

WOTR CHCK Coleoptera Elmidae Stenelmis X

WOTR CHCK Decapoda Cambridae X

WOTR CHCK Diptera Ceratapogonidae X WOTR CHCK Diptera Chironomidae Chironomini X X WOTR CHCK Diptera Chironomidae Diamesinae X WOTR CHCK Diptera Chironomidae Orthocladinae X WOTR CHCK Diptera Chironomidae Tanypodinae X X WOTR CHCK Diptera Chironomidae Tanytarsini X X WOTR CHCK Diptera Simuliidae Simulium X X WOTR CHCK Diptera Tipulidae Antocha X WOTR CHCK Diptera Tipulidae Tipula X WOTR CHCK Ephemeroptera Baetidae Baetis X X WOTR CHCK Gastropoda X

WOTR CHCK Hemiptera Veliidae Microvelia X WOTR CHCK Hemiptera Veliidae Rhagovelia X WOTR CHCK Odonata Calopterygidae Calopteryx X WOTR CHCK Odonata Coenagrionidae Argia X WOTR CHCK Odonata Coenagrionidae Ishnura X WOTR CHCK Oligochaeta X X

71 WOTR CHCK Trichoptera Hydropsychidae Cheumatopsyche X X WOTR CHCK Trichoptera Hydropsychidae Hydropsyche X X WOTR CHCK Trichoptera Philopotamidae Chimarra X WOTR CHCK Trichoptera Philopotamidae Dolophilodes X WOTR WOTR Coleoptera Elmidae Ancyronyx X

WOTR WOTR Coleoptera Elmidae Macronychus X X WOTR WOTR Coleoptera Elmidae Stenelmis X

WOTR WOTR Diptera Ceratapogonidae X WOTR WOTR Diptera Chironomidae Chironomini X X WOTR WOTR Diptera Chironomidae Diamesinae X X WOTR WOTR Diptera Chironomidae Orthocladinae X X WOTR WOTR Diptera Chironomidae Tanypodinae X X WOTR WOTR Diptera Chironomidae Tanytarsini X X WOTR WOTR Diptera Simuliidae Prosimulium X WOTR WOTR Diptera Simuliidae Simulium X WOTR WOTR Diptera Tipulidae Antocha X WOTR WOTR Diptera Tipulidae Tipula X WOTR WOTR Ephemeroptera Baetidae Baetis X X WOTR WOTR Gastropoda X WOTR WOTR Hemiptera Veliidae Rhagovelia X WOTR WOTR Odonata Calopterygidae Calopteryx X WOTR WOTR Oligochaeta X X WOTR WOTR Trichoptera Hydropsychidae Cheumatopsyche X WOTR WOTR Trichoptera Hydropsychidae Hydropsyche X X WOTR WOTR Trichoptera Philopotamidae Chimarra X

72 Appendix C. Families used to create Primer Miner output

Order Family Collembola Poduridae Collembola Onychiuridae Collembola Hypogastruidae Collembola Isotomidae Collembola Entomobryidae Collembola Sminthuridae Ephemeroptera Acanthametropodidae Ephemeroptera Ameletidae Ephemeroptera Siphlonuridae Ephemeroptera Metretopodidae Ephemeroptera Baetidae Ephemeroptera Ametropodidae Ephemeroptera Oligoneuriidae Ephemeroptera Isonychiidae Ephemeroptera Pseudironidae Ephemeroptera Heptageniidae Ephemeroptera Arthropleidae Ephemeroptera Ephemerellidae Ephemeroptera Leptohyphidae Ephemeroptera Tricorythidae Ephemeroptera Neoephemeridae Ephemeroptera Caenidae Ephemeroptera Baetiscidae Ephemeroptera Leptophlebiidae Ephemeroptera Behnigiidae Ephemeroptera Potamanthidae Ephemeroptera Ephemeridae Ephemeroptera Euthyplocidae Ephemeroptera Palingeniidae Ephemeroptera Polymitarcyidae Odonata Petaluridae Odonata Gomphidae Odonata Aeshnidae Odonata Cordulegastridae Odonata Macromiidae Odonata Corduliidae Odonata Libellulidae Odonata Calopterygidae 73 Odonata Palatystictidae Odonata Protoneuridae Odonata Coenagrionidae Orthoptera Acrididae Orthoptera Tetrigidae Orthoptera Tridactylidae Orthoptera Tettigoniidae Orthoptera Gryllidae Orthoptera Gryllotalpidae Plecoptera Pteronarcyidae Plecoptera Peltoperlidae Plecoptera Taeniopterygidae Plecoptera Nemouridae Plecoptera Leuctridae Plecoptera Capniidae Plecoptera Perlidae Plecoptera Perlodidae Plecoptera Chloroperlidae Hemiptera Hydrometridae Hemiptera Macroveliidae Hemiptera Veliidae Hemiptera Ceratocombidae Hemiptera Dipsocoridae Hemiptera Schizopteridae Hemiptera Gerridae Hemiptera Belostomatidae Hemiptera Nepidae Hemiptera Pleidae Hemiptera Naucoridae Hemiptera Corixidae Hemiptera Notonectidae Hemiptera Mesoveliidae Hemiptera Hebridae Hemiptera Saldidae Hemiptera Gelastocoridae Hemiptera Ochteridae Megaloptera Sialidae Megaloptera Corydalidae Neuroptera Sisyridae Trichoptera Philopotamidae Trichoptera Psychomyiidae Trichoptera Xiphocentronidae 74 Trichoptera Ecnomidae Trichoptera Polycentropodidae Trichoptera Dipseudopsidae Trichoptera Hydropsychidae Trichoptera Rhyacophilidae Trichoptera Hydrobiosidae Trichoptera Glossosomatidae Trichoptera Hydroptilidae Trichoptera Phryganeidae Trichoptera Brachycentridae Trichoptera Lepidostomatidae Trichoptera Apataniidae Trichoptera Limnephilidae Trichoptera Goeridae Trichoptera Rossianidae Trichoptera Uenoidae Trichoptera Beraeidae Trichoptera Sericostomatidae Trichoptera Helicopsychidae Trichoptera Molannidae Trichoptera Calamoceratidae Trichoptera Leptoceridae Lepidoptera Crambidae Lepidoptera Noctuidae Lepidoptera Tortricidae Lepidoptera Coleophoridae Lepidoptera Cosmopterigidae Lepidoptera Nepticulidae Lepidoptera Stigmellidae Coleoptera Amphizoidae Coleoptera Gyrinidae Coleoptera Carabidae Coleoptera Haliplidae Coleoptera Dytiscidae Coleoptera Noteridae Coleoptera Hydroscaphidae Coleoptera Sphaeriusidae Coleoptera Georissidae Coleoptera Histeridae Coleoptera Ptiliidae Coleoptera Hydrophilidae Coleoptera Staphylinidae 75 Coleoptera Melyridae Coleoptera Salpingidae Coleoptera Tenebrionidae Coleoptera Hydraenidae Coleoptera Psephenidae Coleoptera Dryopidae Coleoptera Scritidae Coleoptera Elmidae Coleoptera Ptilodactylidae Coleoptera Eulichadidae Coleoptera Limnichidae Coleoptera Lutrochidae Coleoptera Chrysomelidae Coleoptera Curculionidae Hymenoptera Pompilidae Hymenoptera Scelionidae Hymenoptera Diapriidae Hymenoptera Ichneumonidae Hymenoptera Braconidae Hymenoptera Chalcididae Hymenoptera Mymaridae Hymenoptera Trichogammatidae Hymenoptera Eulophidae Hymenoptera Pteromalidae Hymenoptera Figitidae Diptera Axymyiidae Diptera Blephariceridae Diptera Ceratopogonidae Diptera Chironomidae Diptera Corethrellidae Diptera Culicidae Diptera Deuterophlebiidae Diptera Dixidae Diptera Nymphomyiidae Diptera Psychodidae Diptera Ptychopteridae Diptera Simuliidae Diptera Tanyderidae Diptera Thaumaleidae Diptera Tipulidae Diptera Athericidae Diptera Dolichopodidae 76 Diptera Empididae Diptera Oreoleptidae Diptera Pelecorhynchidae Diptera Stratiomyidae Diptera Tabanidae Diptera Canacidae Diptera Coelopidae Diptera Dryomyzidae Diptera Ephydridae Diptera Helcomyzidae Diptera Heterocheilidae Diptera Muscidae Diptera Phoridae Diptera Sarcophagidae Diptera Scathophagidae Diptera Sciomyzidae Diptera Syrphidae Amphipoda Crangonyctidae Amphipoda Gammaridae Amphipoda Haustoriidae Amphipoda Talitridae Decapoda Cambaridae Gastropoda Asellidae Oligochaeta Bivalvia Hirudinea Platyhelmenthis Hydrachnidia Podocopa Cladocera Copepoda

77 Appendix D. Primer Pairs Tested

Tm Length # Primer Pair Sequence (°C) (bp) 1 NCR138F THTAYAAYDYDRTHGTHACHGC 50.3 331 NCR469R AARTTDAYDGMNCCTADRATWG 49.2

2 NCR138F THTAYAAYDYDRTHGTHACHGC 50.3 400 NCR539R DGMYCANRYRAATARDGD 48.1

3 NCR172F TAATTTTYTTYATRGTWATRCC 44.3 297 NCR469R AARTTDAYDGMNCCTADRATWG 49.2

4 NCR172F TAATTTTYTTYATRGTWATRCC 44.3 367 NCR539R DGMYCANRYRAATARDGD 48.1

5 NCR208F GDTTYGGDAAYTGRYTDRTHCC 53.5 261 NCR469R AARTTDAYDGMNCCTADRATWG 49.2

6 NCR208F GDTTYGGDAAYTGRYTDRTHCC 53.5 386 NCR594R ATDGCHCCNGCHARNACDGG 59.7

7 NCR208F GDTTYGGDAAYTGRYTDRTHCC 53.5 331 NCR539R DGMYCANRYRAATARDGD 48.1

8 NCR235F TRHTDGGDGCHCCWGAYATRGC 58.6 359 NCR594R ATDGCHCCNGCHARNACDGG 59.7

9 NCR264F CGWHTWAAYAAYWTRAGWTTYTG 46.8 205 NCR469R AARTTDAYDGMNCCTADRATWG 49.2

10 NCR264F CGWHTWAAYAAYWTRAGWTTYTG 46.8 375 NCR639R CRAARAADGWDGTRTTWADRTTWCG 50.8

11 NCR264F CGWHTWAAYAAYWTRAGWTTYTG 46.8 330 NCR594R ATDGCHCCNGCHARNACDGG 59.7

12 NCR264F CGWHTWAAYAAYWTRAGWTTYTG 46.8 275 NCR539R DGMYCANRYRAATARDGD 48.1

13 NCR349F CNGGNTGRACWGTNTAYCC 53.7 290 NCR639R CRAARAADGWDGTRTTWADRTTWCG 50.8

14 NCR349F CNGGNTGRACWGTNTAYCC 53.7 327 NCR676R TGYTGRWAWARRATDGGRTC 49.4

15 NCR349F CNGGNTGRACWGTNTAYCC 53.7 245 NCR594R ATDGCHCCNGCHARNACDGG 59.7

16 NCR349F CNGGNTGRACWGTNTAYCC 53.7 190 NCR539R DGMYCANRYRAATARDGD 48.1

17 CRmICO1intF GGIACHGGHTGAACHRTYTAYCC 58.0 364 jgHCO2198 ATKTGRAGWCCYACKGGTTTTTTRGT

2.1 16INSF RGACGAGAAGACCCTATARA 51.0 ~157 16INSR ACGCTGTTATCCCTAAARGTA 51.6

2.2 16Sar_L CGCCTGTTTATCAAAAACAT 49.2 ~150 16SbrH CCGGTCTGAACTCAGATCACGT 58.6

TGTAAAACGACGGCCAGTCAAACTGGGAT 2.3 M13U12SF 65.2 430 TAGATACCC CAGGAAACAGCTATGACCGAGGGTGACG M13U12SR 71.1 GGCGGTGTGT

TGTAAAACGACGGCCAGTACCGTGCAAAG 2.4 M13U16SF 66.4 500 GTAGCATAAT CAGGAAACAGCTATGACCTCCGGTCTGAA M13U16SR 65.9 CTCAGATCAC

2.5 miCOIntF_short ACIGGITGRACIRTITAYCC 59.2 350 jgHCO2198_short TCIGGRTGICCRAARAAYCA 58.9

2.6 NCR27F ACHYTATASTTHWTITTHGG 45.4 219 NCR246R GGRAADGCYATRTCDGG 50.2

2.7 NCR27F ACHYTATASTTHWTITTHGG 45.4 252 NCR279R GGIARIARYCARAAHCT 50.4

2.8 NCR27F ACHYTATASTTHWTITTHGG 45.4 327 NCR354R GGIGGRTACACDGTTCA 53.5

2.9 NCR138F THTAYAAYDYDRTHGTHACHGC 50.3 216 NCR354R GGIGGRTACACDGTTCA 53.5

2.10 NCR138F THTAYAAYDYDRTHGTHACHGC 50.3 331 NCR469R_short TTDAYDGMICCTARRATDG 49.0

2.11 NCR141F AAYDYIRTHGTHACHGC 48.7 213 79 NCR354R GGIGGRTACACDGTTCA 49.0

2.12 NCR141F AAYDYIRTHGTHACHGC 48.7 328 NCR469R_short TTDAYDGMICCTARRATDG 49.0

2.13 NCR147F ATHGTHACHGCHCAYGC 52.2 207 NCR354R GGIGGRTACACDGTTCA 53.5

2.14 NCR147F ATHGTHACHGCHCAYGC 52.2 322 NCR469R_short TTDAYDGMICCTARRATDG 49.0

2.15 NCR172F TAATTTTYTTYATRGTWATRCC 44.2 182 NCR354R GGIGGRTACACDGTTCA 53.5

2.16 NCR172F TAATTTTYTTYATRGTWATRCC 44.3 279 NCR469R_short TTDAYDGMICCTARRATDG 49.0

2.17 NCR208F GDTTYGGDAAYTGRYTDRTHCC 53.5 146 NCR354R GGIGGRTACACDGTTCA 53.5

2.18 NCR208F GDTTYGGDAAYTGRYTDRTHCC 53.5 261 NCR469R_short TTDAYDGMICCTARRATDG 49.0

2.19 NCR213F GGHAAYTGRYTDVTICC 50.1 256 NCR469R_short TTDAYDGMICCTARRATDG 49.0

2.20 NCR213F GGHAAYTGRYTDVTICC 50.1 326 NCR469R_short TTDAYDGMICCTARRATDG 48.1

2.21 NCR213F GGHAAYTGRYTDVTICC 50.1 381 NCR594R ATDGCHCCNGCHARNACDGG 59.7

2.22 NCR235F TRHTDGGDGCHCCWGAYATRGC 58.6 234 NCR469R_short TTDAYDGMICCTARRATDG 49.0

2.23 NCR246F CCHGAYATRGCHTTYCC 50.2 223 NCR469R_short TTDAYDGMICCTARRATDG 49.0

2.24 NCR246F CCHGAYATRGCHTTYCC 50.2 293 NCR539R DGMYCANRYRAATARDGD 48.1

2.25 NCR246F CCHGAYATRGCHTTYCC 50.2 348 NCR594R ATDGCHCCNGCHARNACDGG 59.7

2.26 NCR264F CGWHTWAAYAAYWTRAGWTTYTG 46.8 205 NCR469R_short TTDAYDGMICCTARRATDG 49.0

2.27 NCR279F AGDTTYTGRYTIYTICC 50.2 190 NCR469R_short TTDAYDGMICCTARRATDG 49.0

2.28 NCR279F AGDTTYTGRYTIYTICC 50.2 260 NCR539R DGMYCANRYRAATARDGD 48.1

2.29 NCR279F AGDTTYTGRYTIYTICC 50.2 315 NCR594R ATDGCHCCNGCHARNACDGG 59.7

2.30 NCR279F AGDTTYTGRYTIYTICC 50.2 360 NCR639R CRAARAADGWDGTRTTWADRTTWCG 50.8

2.31 NCR279F AGDTTYTGRYTIYTICC 50.2 381 NCR660R TGYTGRWAHARRATDGG 45.1

2.32 NCR349F CNGGNTGRACWGTNTAYCC 53.7 331 NCR660R TGYTGRWAHARRATDGG 45.1

2.33 NCR354F TGAACHGTITAYCCICC 53.5 185 NCR539R DGMYCANRYRAATARDGD 48.1

2.34 NCR354F TGAACHGTITAYCCICC 53.5 240 NCR594R ATDGCHCCNGCHARNACDGG 59.7

2.35 NCR354F TGAACHGTITAYCCICC 53.5 258 NCR639R CRAARAADGWDGTRTTWADRTTWCG 50.8

2.36 NCR354F TGAACHGTITAYCCICC 53.5 306 NCR660R TGYTGRWAHARRATDGG 45.1

2.37 NCR354F TGAACHGTITAYCCICC 53.5 322 NCR676R TGYTGRWAWARRATDGGRTC 49.4

2.38 NCR_BF2 GCHCCHGAYATRGCHTTYCC 57.1 363 NCR_BR1 ARYATDGTRATDGCHCCIGC 56.2 81

Appendix E. DNA Library Sequences with BLAST results

Park Site Taxa Sequence Blast Result % Accession GCTTGATATCATATCCTGTTTAAGTTGTTTAGATGT TTATGTTTTTTATAGAATTTGGTATTTTATTGGGGT GATAGGAAGATAAGAAAAACTCTTTTTAATAAAGT PRWI TARU Acroneuria Acroneuria lycorias 90 EF623251.1 TTGTACATTGATTTATGGTTTATTGATCCGTTAATG GCGATTATAAGACTAAGT

TCTTTATATAGATAAATTACGGTTAATTAGACGTTT CCCCCCCGTTGTCCACATTATATTTAGTTGGGGTG ACGCGAAGACATAAACTCTTTTTTCTTAAACTTATT PRWI TARU Acentrella Baetis canariensis 82 KF438112.1 TATAAAGGAACATGACCCCGTCAGCCCATCCCAAA ATTAAGT

TCTTTATATTAATAATTTTAATAATGATTTAGTTGG TTATTATTGTTAGATTTTTTAATATTTTATTGGGGT GATAGGAAGATAGAATAAACTCTTTTTAATTTATA CHOH BRUN Agnetina Agnetina capitata 89 EF623241.1 TACAATGATTTTTGAAATTTTGATCCGTTATTAGCG ATTATAAGACTAAGT

GCTTTACATTATATTTTTTAATATTTCTTGGTTAAA TATTTTGATATTAATTTATCTATTGTTTTGTTGGGG TGACAATAAAATATGATTAACTTTTTATAAAACTT PRWI TARU Argia Argia moesta 100 JX121208.1 ACATTTATTTATGAATTTTTGATCCAATATTATTGA TCATTAGATTAAGT

GTTTAATATTTTAAGGAAAATTTTATTTTAAGAAT AATATTTTAATTTTTTTTTTTAAGTATTTTGTTGGG GTGATGAAAAGATTAATTTAACTCTTTTTTTATTTT WOTR WOTR Ancronyx Elminae sp. 84 EF209454.1 TACATAAATAAATGATTTTTTGATCCAATTTTTTTG ATTATAAGAATAAAT

82 TTTTTGAAATAATATAATATTTATATATTTTCTTTT GTTGGGGCGACAAATTAGCATTATAACCTAATTAT CHOH BRUN Ancylidae AAATAATTATATCGATTATTTTAGTAAAAATTAAA Ferrissia rivularis 100 DQ452042.1 T

GCTTAATAATTATTTTAAATAAGAGTATAGGAGAA GTTTAATTATTTAAATAAATTATTTTGTTGGGGTGA ACAAGAAAATAATTTAACTTTTCTTAAATAAAATC CHOH BRUN Anthopotamus Anthopotamus sp. 99 AY749766.1 ATAAATTTATGAATTTATGATCCATTATTAATGGA TTAAAAGATTAAGT

GCTTTATAGTTTTATTTTATTATTTATAAAGTTTAT TTTAAATTTTATTAAAAAAACTATTTTGTTGGGGTG ACAATAAAATTTAATTAACTTTTATTATTTTATTAC Rhipidia PRWI TARU Antocha 91 KT970063.1 ATTGATTTATGAGTTTTAGATCCATTTTTAATGATT chenwenyoungi AAAAAATTAAGT

GCTTTATAGTTTTATTTTATTATTTATAAAGTTTAT TTTAAATTTTATTAAAAAAACTATTTTGTTGGGGTG ACAATAAAATTTAATTAACTTTTATTATTTTATTAC Rhipidia WOTR WOTR Antocha 91 KT970063.1 ATTGATTTATGAGTTTTAGATCCATTTTTAATGATT chenwenyoungi AAAAAATTAAGT

TCTTTATATAGCAAGACCTTCAGCTATTAGCCGCT ACCACTATGGGTTATTAACATATTTAGTTGGGGTG ACACGAAGTCCATAAACCACTTCTTTTTACACAAA CHOH BRUN Baetis No matches AATTAAAGGGTAAATGACCCGACAAGTCGATCAA AAGATTAAGT

TCTTTATGTTGTAAGACCTCCCGCTTGTAGACGTTA ATTTTAGTGGTCCTTAAAACATTTAGTTGGGGTGA CATGAAGTCATAAACACTTCTTTCCATACGAAAAT Baetis PRWI TARU Baetis 87 KF438121.1 TAAAGGGTAAATGACCCGACGCGTCGATCAAAAA pseudorhodani ATTAAGT

83 TCTTTATGTTGTAAGACCTCCCGCTTGTAGACGTTA ATTTTAGTGGTCTCTTAAAACCATTTAGTTGGGGT GACATGAAGTCATAAACACTTCTTTCCATACGAAA Baetis WOTR WOTR Baetis 88 KF438121.1 ATTAAAGGGTAAATGACCCGACGCGTCGATCAAA pseudorhodani AAATTAAGT

TCTTTATAGTTGTCTTTTTATTTTATTTTGTTGAGGC TAAAATAAAAATTAAAACTATTCTGTTGGGGCGAC Isonychia ussurica, LC114381.1, PRWI TARU Baetisca AGGAAGATAAATCAAACTCTTTCTTCACAAAAACA 83 Isonychia japonica LC114357.1 CCAATGCGTGAATTATTGATCCGTTTT

GCTTTATATTTTTATTATTAATTTAGTATAGATTAT TATTATTTATTATTAATTGAAATATTTTGTTGGGGT GACAAAAATATATAAATAACTTTTTTATTTTATTTA PRWI TARU Boyeria Boyeria irene 98 AF266091.1 CATTGATATATGATTATTAGATCCAATCTTATTGAT TGTTAGATTAAGT

TTTTTATAATTTTATTATTTAAAAAATTTATATTTA AATAATTAAATAAATAAAATTAGTTTATTGGGGCG ATAAAAAAATTAATTTAACTTTTTTTTATAATTTAT PRWI TARU Brachycentrus No matches TTTAATTAAAATTTTATTTTAAAAGAGTTATTATTA ATAATTAAAAGAATAAAA

GCTTTACTAATGGTGTTGTTAGGCTCTTTAGAAGA ATTTTAGCCTAACCAATCTGGTTAGTTAAATTGGG GTGATTGGAAAATAATAAACTTTTCTTAAAATAAC NACE OXON Caenis Caenis sp. 97 AY749763.1 AATTATTTTTGGAATTATGATCCTTTATTATGGATT TTAAGATCAAGT

GCTTTATTAAAAAATTTATTAAATCTTTAGTTGAG GTTAAATTTAAATGATTATTTAATTATATTGGGGT GATAAGAAAATAAGAAAACTTTTCTTATAATAACA PRWI TARU Caenis Caenis sp. 78 GQ502451.1 TAGATTTTTGGAAAAATGATCTTTTAATAAAGATT AGAAGATTAAGT

GCTTTAAGGTTTATTTTTATTGGTTATTATTATTTA ATTTTTTAAGCCATTAATTTGACCTTTTTGTTGGGG Calopteryx WOTR WOTR Calopteryx 100 AF170960.1 TGACAAAAATATATAACTAACTTTTATTATTTATTT maculata TACATTAATTTATGATTTTTAGATCCATTATTATTG 84 ATTATTAGACCAAGT

ACTTTATATTTATAATATAGTAGTTAGTTTTATTTA AGGGTATTATTTTAGAGTATTTGGTTGGGGTGACA AGGATAAAATATTAAATAACTGTCTTTTTTTTTTAC NACE OXON Cambaridae Procambarus clarkii 99 KT036444.1 AGTGATGTTTGGTTTAATAGATCCTAAAAGGGATT AAAGATTAAGT

TTTTAATTAAAAATTTTTAAATATATAATTTTAAAT TTAATTTATAAATGTTTAATTTGATTGGGGTGATTG TTAAATTTAATTAACTTTAATTTATAATAAAATTTT CHOH BRUN Cheumatopsyche No matches AATTAAAGAAATTGACGAATAAAGGATTTAATTTG TTGGATTAAAAGAATAAAT

TTTTAATTAAAAATTTTTAAATAAATAATTTTAAAT TTAATTTATAAATTTTTAATTTGATTGGGGTGATTG TTAAATTTAATTAACTTTAATTTTTAGTCAAATTTT PRWI TARU Cheumatopsyche No matches AATTAGAGAAATATTGTAGATAAATGATTTAATTT GTTGGATTAAAAGAATAAAT

TTTTAATTAAAAATTTTTAAGTATATAATTTTAAAT TTAATTTATAAATATTTAATTTGATTGGGGTGATTG TTAAATTTAATTAACTTTAATTTATAATAAAATTTT WOTR WOTR Cheumatopsyche No matches AATTAAAGAGATTAATAAGTAAATGATTTAATTTG TTGGATTAAAAGAATAAAT

TTTTTATATAATAGAAGATATTGTTATTTATTATTA TATTTAGTTGGGGTGACTAAAAAATTAAACAAATT CHOH BRUN Chimarra TTTTTATTGTGCATACATTTATTAATGAATGATTTT No matches

TCTAATTGTATTAAATATAAATTAAAT

TTTTTATATGGAGGATAAATAAATTTATTTTCTTAT ATTTAGTTGGGGTGACTAAAAAATTAATTTAATTT PRWI TARU Chimarra TTTTATTATTTTTACATTTATTTGTGGGTTTACTATT No matches

CCAATTTTATTCGAGTTTAAAATAAAT

85 TTTTATATATTTAAAATTAAATTCATTTAATTTTAA ATATTTGATTGGGGTGATTAAAAAATTAAGATAAT WOTR WOTR Chimarra TTTTTTATTTTTTTTACATTAATTAATGGATTTAATT No matches

TCTAATATTATTAATTTGAATTTAAAT

GCTTTATAATAAAAATTAGTTTTATTTTTTTAGAAT ATTAAAAATTTAATTTATTTGTTATTTAATTGGGGT GATTTAAAGATTTATTAAACTCTTTTATGTTGTATT PRWI TARU Chironomidae Chironomus tepperi 83 KC177440.1 TTTAATTATAATTATTTTTGAAGATCCTAAAGTTTT GGATTTATAAATTAAGT

GCTTTATATTTAATTATTTAATAAATTTTTGATAAT ATTATTATTTATTATTTAATAAAATATTTTGTTGGG GTGACAAAAATATATAATTAACTTTTTTATTTATAT Cordulegaster PRWI TARU Cordulegaster 100 EU477689.1 AACATTGATATATGAAAAATAGATCCAGTTTTACT maculata GATTATTAGAACAAGT

TTTTTATAATTTTTTATTTATAAATAATTTGGTTGA TAGAAGTTTTTAGGTAAGGAATTATTTTATTGGGG TGATAAATAAATTTAATAAACTTTATAAAATATAA PRWI TARU Corydalus Corydalus cornutus 100 EU734853.1 ACATTAATGTATGAATTATTGATCCATTTTTAGTGA TTATAAGATTAAAT

GCTTTATAATTTTTATTTTATAATTTATAAAGAATA TTTAAAATTTTATTTTAATAATTATTTTGTTGGGGT GACAGTAAGATTTAAAAAACTCTTATTATTTTTAT PRWI TARU Dicronata Pedicia albivitta 92 EU005435.1 ACATTGATTTATAATTATCATCCATTTTTAATGATT AAAAAATTAAGT

GTTTTATAAATTTAATTTAATGTAATTTTAAGAATT AAATTTTCTTTGTTTTAAATTTATTTTGTTGGGGTG ATGAAAAGATTTAATTAACTCTTTTTTTATTTGAAC Stenelmis grossa, HQ629798.1, CHOH BRUN Dubiraphia 99 ATAAATTTATGATAAATTGATCCATTTTTTATGATT Dubiraphia sp. DQ267445.1 ATAAGAATAAAT

GTTTTATAAATTTAATTTAATGTAATTTTAAGAATT AAATTTTCTTTGTTTTAAATTTATTTTGTTGGGGTG Stenelmis grossa, HQ629798.1, PRWI TARU Dubiraphia 99 ATGAAAAGATTTAATTAACTCTTTTTTTATTTGAAC Dubiraphia sp. DQ267445.2 ATAAATTTATGATAAATTGATCCATTTTTTATGATT 86 ATAAGAATAAAT

ACTTTATAAGGTATAAGTTAATTTTTTAGAAGTTTG ATAAAGTTTTATTTAAGTCTTATTCTGTTGGGGTGA PRWI TARU Ephemerellidae TGGGGTAATAAAATAACTTACCTTAAATATAACAT Serratella serrata 99 FJ443045.1 AAATACATGATTAAAAGAACCATTTGTGATGATTA TAAGATTAAGT TCTTTATATAACTTAATATAATTATTTATATATTAT TATATTTATATTAATTTGTATTTTGTTGGGGTGACA Plathemis AGAATATAAAATTAACTTTTTTATTTATTTTACATT AF037185.1, NACE OXON Erythemis subornata, 99 GATTTATGAATACTAGATCCATTATTATTGATTATT AF037184.1 Plathemis lydia AGATTAAGT

TCTTAATATAATTTTTATTTATAAAAATTATTTTTA AAATTATTTTAAAAGTAAAAATTTTATTTTATTGG GGTGATATAAAAATTAAATTAACTTTTTATAATAT PRWI TARU Goera No matches TTTACTTAAATTAAAGAAATATAAATTTTTGATCT ATTATTAATAAATAAAAGAATAAGA

GCTTTATAATTAGTATTTTAATTGTTTTTTGTAATA TAAATTATGATTATAATTTTAATTATTTTGTTGGGG TGACAAAAATATATATTTAACTTTTTTATTTAATTG Gomphus externus, EU477655.1, CHOH BRUN Gomphus 100 ACATTGATTTATGAATGTTAGATCCAGTCTTATTG Gomphus sp. EU055048.1 ATTGTTAGATTAAGT

GCTTTATAGCTTACGTTATAATTAATTTTGGATATA TAATTGTTATTTATGGCTTTGGCTATTTTGTTGGGG TGACAAAAATATATAATTAACTTTTTGATTTATAT PRWI TARU Hagenis Hagenius sp. 99 EU055043.1 AACATTGATTTATAGTTTATTAGATCCATTCATTAT TGATTGGTTAGAACAAGT

TCTTAATATAGTAATTTTGGGATAAACTAGTCGCC ATCCACCCCAATATGTTAATATTTAGTTGGGGTGA CGCGAAGACATAAACTCTTTTTCCTAAAACTTAAA CHOH BRUN Herterocloeon No matches TTAAAGGTTTAATGACCCGGTACCCGATCAAAAGT TTAAGT

87 GCTTTATATTTTTCTATACTTTAGTTTATTTAGTAG GAGTTTAATTTTGTAAAGAAAGTATTTCGTTGGGG TGACGGGAAGATAAAAAGAACTCTTCTTAACTGTA PRWI TARU Heptagenia Heptagenia sp. 87 AY749762.1 ACATAAATTCGTGATTTAATTGATCCATTAATAAT GATTATAAGACTAAGT

GCTTTATATAAAATATATATTCAATATAATTATATT CGATTGGGGCGATCTAGGTTCATAATAAACAACCT NACE OXON Hirudinea Erpobdella obscura 79 JQ821464.1 ATAAATCCTTTGATTTATAATTCATTTTATTGATCT AACATAACCAAAAAATTAAGC TTTTAATTGATAAATTAAATTAAAGTTATTTTAATT TAATGTATTATATAAATTGATAATTTTTTTATCAAA TTGATTGGGGTGATTATTAAATTTAATTAACTTTAT CHOH BRUN Hydropsyche TTTAATATAATTGAATATTTTAATTAAAAGATTTA No matches

AATTTTAAAAGTGATTTAATTTTGTGGATTATATG ATTAAAT

TTTTAATTTTTTTTTATTTTTAATTATCTCAACTATT ATGTATATAATTATAAAAATTAAATAAAATTTAGT TGGGGTGATTTATAAATTTTAAAAACTTTAATTTAT PRWI TARU Hydropsyche TAATATAAAAAATTTAAGTATGAAATAAAAATTTT No matches

AATAAAATTTATTGATTTAGTTGTATGGATTAAAA GATTAAAT

TTTTAATTATTTAAAAAAATTTAATTATTAATAAAT GTGTGTTATATATACTAATAAATAATTTTTTTTATA ATTTAATTGGGGTGATTATTAAATTTAATTAACTTT WOTR WOTR Hydropsyche ATTTAATAAAATTAGAAAATTTAGATTAAAAAATT No matches

TAAAATAAAAATTAATGATTTAGTTTTATGGATTA AATGATTAAAT

TCTTAATAGTGTATAAATATAGTCAAGTTAGATAG AACTGGACTAGATTTAAAAGGTATTCTGTTGGGGT GACAGGAAAATAAAAGTAACTTTTCTTTTTAAAGA PRWI TARU Isonychia Isonychia sp. 99 AY749761.1 ACACGGATGAGTGATTAAATGATCCATTTATTGTG ATTAGAAGACTAAGT

88 TCTTTATAGTTAAAAATTTATTTAATAAATTATATT TAAATTAATTAAAATAAATTAATTATTTTATTGGG GTGATAAAAAAATTTAATAACTTTTTTAGTAAAAA CHOH BRUN Lepidostoma No matches AATTTAGATAAAAAATTAAATTAATTTGAATTATT TAATTTTATATAATAATTAATAGAAAAGA

GCTTTACACTTTCCTCTATCTTAATTCGTTTAGTAG GAGTTAATTTTTGTGGAGGGAGTGTTTCGTTGGGG TGACGAGAAGATAGAATAAACTCTTCTTTACGTAA CHOH BRUN Maccaffertium Stenonema sp. 94 AY749769.1 AACACAACTGAGTGATTTGATTGATCCATTTTTAA TGATTAAAAGACTAAGT

GTTTTATATTGTTAGATTATTTTTATTTAAGGAATT TAATTTTGAATTGTTTAGCAATATTTTGTTGGGGTG ATAGAAAGATTAAATAAACTCTTTTTTTATTATTAC CHOH BRUN Macronychus Elminae sp. 86 EF209442.1 ATAAATTTATGAATTTTTGATCTGGTTGTTTGTGAT TATAAGAATAAAT

GTTTTATATTGTTAGATTATTTTTATTTAAGGAATT TAATTTTGAATTGTTTAGCAATATTTTGTTGGGGTG ATAGAAAGATTAAATAAACTCTTTTTTTATTATTAC PRWI TARU Macronychus Elminae sp. 86 EF209442.1 ATAAATTTATGAATTTTTGATCTGGTTGTTTGTGAT TATAAGAATAAAT

AGTTTACTTAATTTATTAATTTTAATTTTTTGTTTAT AAAATTTATTTTTAGAATGTTAAGTTTTGTTGGGGT GACATTAAAATTTTTTTAACTTTTAATTATTTTTTC CHOH BRUN Metrobates No matches ATTAATTAATGTTTTTATGATCCAGAATTTTTGATT ATAAGTTTAACT

TTTTTATTTTTTTTAAATTAATTTATTAGTTTAAAA AAATTTTATTGGGGTGATAAAAAAATTTTAGAAAC PRWI TARU Mystacides TTTTTTATATTGTATTCTTTTATTTAAGTCTTATTTT No matches

AATTTAAATTTTAAATATTAAATAAAA

TTTTTACTTTTCTTTAAATATTTTAAATTATTATTGA AAAATTTTATTGGGGTGATAAAAAAATTAAATAAA NACE OXON Oecetis No matches CTTTTTTTTATATTAACATTTATTAATGAATTTATT GAACTTTAATTTAAAAATTTAGAATAAAA 89

TTTTAATTTTTTAATTTTTAAATTTTTAGATTAAAA AATTATATTGGGGGGATACAAAAATTTAAAAAACT PRWI TARU Oecetis TTTTTTTAAAAAAGCATTTATTAATGAATTATTTAT No matches

CTTTAAATTAAAAATTTAAATAAAAA

GCTTTACCCATTAACCCTTAAGTTATAAGCTAATT GGTTCGGTTGGGGCGACCAAGGAACACAAAACAT Limnodrilus JF783979.1, NACE OXON Oligochaeta CCTTATTCAATTAGATATACAAATCAAATCAAAGA claparedeanus, 89 AF325984.1 TCCTTATTTAAGATCATAGAAATAAGC Limnodrilus cervix

GCTTTACTAAAAACTCTCAAACTTAGAGTCTAATA AGTTCGGTTGGGGCGACCAAGGAATTCTAAACATC Limnodrilus CHOH BRUN Oligochaeta CTTTTCCAGATAAGACTTTCTAGTCATCTTTATGAT 79 EU160491.1 hoffmeisteri CCTATTTATAGATCACAGAATCAAGC

GCTTGATTCTAACCTATAAAACTTATTCGATAGAA TTTGGTTGGGGCGACCCAGGAATATTAATCATCCT WOTR WOTR Oligochaeta TCCTAAAATAGATATATAAATCCCTCACATGACCC Eukerria saltensis 98 AF406590.1 TTAAAAGATCAATAGAAAGT

GTTTTATAGTACAAAGAATTAATTTTTGGAGGAAT TTAATTTTATTGTTTTTTTACTATTTTATTGGGGTG ATAAAAAGATTAATTTAACTCTTTTTTTATTTTATC Optioservus sp. , EF209461.1, PRWI TARU Optioservus 88 ATTGATAAATGTATAATTGATCCATTTTTTATGATT Oulimnius echinatus GU935679.1 ACAAGAATAAAT

ACTTAATCTTTAATCCCTTTTAGGATCAATAATTCA AATATTATTGTAAAAATAAAAGACAGTTACCCTTT CTTATATCCTTGTCGCCCCAACAAAATAATTAAAA CHOH BRUN Orconectes Orconectes virilis 99 AF235989.1 TAGTAATTTTAAAATAAAATTATCTTCCACCTTAA AATATAAAGT

90 GTTTTATAATATATTGTATTTATTTTTAGAGGAATT TAATTTTATTATTTTTTATATTATTTTATTGGGGTG PRWI TARU Promoresia ATGGAAAGATCTACTTAACTCTTTTTTTATTTTATC Optioservus sp. 86 EF209461.1 ACTAATAAGTGTATTATCGATCCATTTTTTATGATT ATAAGAATAAAT TCTTTATAATTTAATTAATAATAATTATAAAAAAT ATTAAAATTGTTATTTATTAAATTATTTTATTGGGG TGATATTAAAATTTAATAAACTTATAATTTAATTTA WOTR WOTR Prosimulim No matches TATACATTAATTTATGAATAAATTGATCCATTAAT AATGATTAAAAATTTAAGT

GTTTTATATTAATTTATTTTTAATTTTTAAGGATTT AATTTTTATTTTTAAATTAATATTTTATTGGGGTGA TAGAAAGATTAAATTAACTCTTTTTTTATTTTTACA CHOH BRUN Psephenus Elminae sp. 86 EF209442.1 TAGATTAGTGATTTTTTGATCCAAGTTTTTTGATTA TAAGATTAAAT

GTTTTATATTAATTTATTTTTAATTTTTAAGGATTT AATTTTTATTTTTAAATTAATATTTTATTGGGGTGA TAGAAAGATTAAATTAACTCTTTTTTTATTTTTACA PRWI TARU Psephenus Elminae sp. 86 EF209442.1 TAGATTAGTAGATTTTTTGATCCAAGTTTTTTGATT ATAAGATTAAAT

AATTTATTTTCTTTATAAAGTTTTTTTTTGGTTTTTA AAAGATTTATTTAGAATGGGAAATTTTGTTGGGGT GACAGTAAAATTTTAATAACTTTTATTTATTTTTTC WOTR WOTR Rhagovelia Rhagovelia sp. 95 EU871178.1 ATTAATTTATGTTTTGATCCAGTTTTATTGATTATA AGATTAATT

TTTTTATTATTAAAAAATAATTAATTATTTTTTAAT AATTTTATTGGGGTGATGAAAAAATTTAAATAACT CHOH BRUN Setodes TTTTTATAAAAAATTCATAAATTTATGTAAATATA No matches

GATTAATTTTAAATTAAAAAATTAATTTAAAA

GCTTTATATAATTTATATTTAAGTTATTAAGATTTT TTAAACTTAATTATTTTATTATATTTTGTTGGGGTG PRWI TARU Simulim ACAATAAAATTTATAAAACTTTTATTTTTAATTTAC No matches

ATTTATTTATGAGTATATGATCCAGTTTTATTGATT ATAAATTTAAGT 91

GTTTTATTATTTTTTTAAGTTGAACCATTGAGGAAT TTAATTTTTTGATTTTTTAGAATAATTTTGTTGGGG TGATGAAAAGATTAAATTAACTCTTTTTTTATTGTT CHOH BRUN Stenelmis Stenelmis sp. 87 DQ266474.1 ACATTTATTTATGAATTTTTGATCCATTTTTTATGA TTATAAGAATAAAT

GTTTTATTATTTTTTTGAGTTGAATATTGAGGAATT TAATTTTTTGATTTTTTAGAATAATTTTGTTGGGGT GATGAAAAGATTTAATTAACTCTTTTTTTATTGTTA NACE OXON Stenelmis Stenelmis sp. 87 DQ266474.1 CATTTATTTATGAATTTTTGATCCATTTTTTATGAT TATAAGAATAAAT

GTTTTATTATTTTTTTGAGTTGAACATTGAGGAATT TAATTTTTTGATTTTTTAGAATAATTTTGTTGGGGT GATGAAAAGATTAAATTAACTCTTTTTTTATTGTTA PRWI TARU Stenelmis Stenelmis sp. 87 DQ266474.1 CATTTATTTATGAATTTTTGATCCATTTTTTATGAT TATAAGAATAAAT

GTTTTATTATTTTTTTGAGTTGAACATTGAGGAATT TAATTTTTTGATTTTTTAGAATAATTTTGTTGGGGT GATGAAAAGATTAAATTAACTCTTTTTTTATTGTTA WOTR WOTR Stenelmis Stenelmis sp. 87 DQ266474.1 CATTTATTTATGAATTTTTGATCCATTTTTTATGAT TATAAGAATAAAT

92 GCTTTACACTTTTCTACATCTTAATTCGTTTAGTAG GAGTTAATTTTTGTGAAGGGAGTGTTTCGTTGGGG PRWI TARU Stenonema TGACGAGAAGATAGAAGAAACTCTTCTTTTAAACA Stenonema sp. 91 AY749769.1 AACCACAGATGAGTGATTTAATTGATCCATTTTTA ATGGATTAAAAGACCAAGT GCTTTATATTTATGGTTTTAATTAGGTTTTGTTTAT TATTATTATTTACGGTCATAATTATTTTGTTGGGGT Stylogomphus GACAAAAATATATAATCAACTTTTTGATTAAATTT EU477665.1, PRWI TARU Stylogomphus albistylus, Lanthus 100 ACATTGATTTATGAATTTAAGATCCAATCTTATTG AF266055.1 albistylus ATTGCTAGACTAAGT

GCTTTATAATTTTATAATATAATTTATAAAGAATA ATTTAAATTTTATTAAAAAAATTATTTTGTTGGGGT GACAATAAAATTTATTGAACTTTTATTATTTAATTA WOTR WOTR Tipula Tipula cockerelliana 94 KT970065.1 CATTGATTTATGATTTATTGATCCAGTTTTATTGAT TAAAAAATTAAGT

GCTTTATTAATAAGACGAAGAAACTGGAAAATAT AATATTTAGTATTAATTATGTTGGGGTGAATAATG CHOH BRUN Tricorythodes AAATAAATAAACTTTTTTAATATAAACATATATAA No matches ATGAATAATAGATCTTAAGTTACATAAAGAATAAG T