MIAMI UNIVERSITY the Graduate School

MIAMI UNIVERSITY The Graduate School

Certificate for Approving the Dissertation

We hereby approve the Dissertation

Mohammed Abdullah Al-Saffar

Candidate for the Degree

DOCTOR OF PHILOSOPHY

______David J. Berg, Director

______Bruce J. Cochrane, Reader

______John C. Morse, Reader

______Michael J. Vanni, Reader

______Thomas O. Crist, Reader

______Mary C. Henry, Graduate School Representative ABSTRACT

CONSERVATION BIOLOGY IN POORLY STUDIED FRESHWATER ECOSYSTEMS: FROM ACCELERATED IDENTIFICATION OF WATER QUALITY BIOINDICATORS TO CONSERVATION PLANNING

Mohammed A. Al-Saffar

The Tigris and Euphrates Rivers and their tributaries form the arteries of life in the central part of the Middle East, where climate change and anthropogenic disturbance have been evident in recent decades. While the Tigris River has a long history of human use, the conservation status for the majority of its basin is poorly known. In addition, planning for conservation, given limited time, funds, and prior information, has remained a challenge. In my dissertation research, I sampled 53 randomly selected sites in the Kurdistan Region (the KR) of northern Iraq, a poorly studied region of the Upper Tigris and Euphrates freshwater ecoregion, for water quality bioindicators, mayflies (Insecta, Ephemeroptera), stoneflies (Plecoptera), and caddisflies (Trichoptera) (a.k.a. EPT). I identified the mayflies to the finest possible taxonomic level and created the first Iraqi checklist and larval key to nine families, nine subfamilies, 19 genera, and 13 subgenera, and supported it with 117 state-of-the-art scientific illustrations using fresh specimens collected during my study (Chapter 1). I performed an initial species morphological identification for mayflies and stoneflies, then identified them genetically after sequencing the full-length of the mitochondrial cytochrome oxidase subunit 1 (COI) gene (658 base pairs). I introduced Genetic Similarity Blocks (GSBs), a genetic-based analysis which was used along with morphology and other genetic-based analyses to overcome the taxonomic impediment and accelerate species identification. I delineated Operational Taxonomic Units (OTUs) using genetic-based analyses, then matched OTUs to delineate Species-Like Units (SpLUs). I compared and contrasted SpLUs morphologically and found five stonefly and more than 55 mayfly taxa, the majority of them being new records for Iraq, and many of them potentially new to science (Chapter 2). I identified 76 planning units within aquatic ecosystems in the KR and prioritized a subset of them for EPT conservation using my mayfly and stonefly data as well as caddisfly data from other studies. I identified samples of healthy aquatic habitats from this subset and used them along with various predictor variables to predict the distribution of healthy aquatic habitats across the entire KR. I identified one natural lake and 23 stream segments as habitats of conservation priority in the KR (Chapter 3). In my dissertation, I showed that in developing countries where knowledge about aquatic ecosystems and most extant species is unavailable, conservation priorities can still be identified after rapid assessment for water quality bioindicators.

CONSERVATION BIOLOGY IN POORLY STUDIED FRESHWATER ECOSYSTEMS: FROM ACCELERATED IDENTIFICATION OF WATER QUALITY BIOINDICATORS TO CONSERVATION PLANNING

A DISSERTATION

Presented to the Faculty of

Miami University in partial

fulfillment of the requirements

for the degree of

Doctor of Philosophy

in Ecology, Evolution, & Environmental Biology

Mohammed A. Al-Saffar

The Graduate School Miami University Oxford, Ohio

2016

Dissertation Director: David J. Berg

Mohammed Abdullah Al-Saffar

2016 TABLE OF CONTENTS GENERAL INTRODUCTION ...... 1 REFERENCES ...... 5 TABLES AND FIGURES ...... 10 Chapter 1: Identification Key to the Families, Subfamilies, Genera, and Subgenera of Mayfly Larvae (Insecta: Ephemeroptera) from the Kurdistan Region, Northern Iraq ..... 14 ABSTRACT ...... 14 INTRODUCTION ...... 14 METHODS...... 16 Study Area...... 16 Sampling and Preservation ...... 16 Morphological Identification and Construction of the Dichotomous Key ...... 17 RESULTS...... 18 List of Mayfly Larvae from the Kurdistan Region, Northern Iraq ...... 18 Identification Key to Mayfly Larvae from the Kurdistan Region, Northern Iraq ...... 20 REFERENCES ...... 61 Chapter 2: Genetics-Based Analyses Facilitate Delineating Unknown Faunas: A Case Study on Ephemeroptera and Plecoptera in the Headwaters of the Tigris River, Iraq ..... 69 ABSTRACT ...... 69 INTRODUCTION ...... 70 METHODS...... 72 Study Area...... 72 Sampling and Initial Morphological Identifications ...... 73 DNA Sequencing ...... 74 Genetic Similarity Blocks (GSBs) Analysis ...... 74 Refined Single Linkage (RESL) Analysis and Barcode Index Numbers (BINs) ...... 75 Automatic Barcode Gap Discovery (ABGD) ...... 76 Phylogenetic Analyses ...... 76 Guided Discovery of Species-Like Units (SpLUs) ...... 78 RESULTS...... 79 Genetic-Based Analyses ...... 79 Species Discovery ...... 81 DISCUSSION ...... 82

iii Unknown Diversity of Mayflies and Stoneflies from Northern Iraq ...... 82 Findings of this Study Compared to Previous Findings from Northern Iraq ...... 83 Findings of this Study Compared to the Known Fauna of Turkey ...... 84 Performance of Genetic-Based Analyses ...... 86 Genetic-Morphological Cross-Validation Facilitated Species Discovery ...... 87 Morphological Examination after Genetic-Based Analyses Is Critical ...... 88 Emphasizing Morphology Key Points and Discovering Others ...... 89 Flagging Species versus Subspecies Taxonomy ...... 90 Facilitating Cryptic and Controversial Species Delineation ...... 91 CONCLUSION ...... 92 REFERENCES ...... 93 TABLES AND FIGURES ...... 106 Chapter 3: Rapid Solution Given Limited Information is a Challenge in Conservation Biology: A Strategy to Identify and Prioritize Aquatic Hotspots of Conservation Concern in a Poorly Studied Region ...... 133 ABSTRACT ...... 133 INTRODUCTION ...... 134 METHODS...... 137 Study Area...... 137 Identifying Planning Units...... 138 Part 1: Prioritizing Sampled Planning Units for EPT Conservation ...... 139 Part 2: Predicting Planning Units of Healthy Aquatic Habitats across the KR ..... 141 RESULTS...... 143 Part 1: Prioritized Units for EPT Conservation ...... 143 Part 2: Predicted Units of Healthy Aquatic Habitats across the KR ...... 144 DISCUSSION ...... 146 Part 1: Prioritized Units for EPT Conservation ...... 146 Part 2: Predicted Units of Healthy Aquatic Habitats across the KR ...... 148 Current Conservation Status of KBAs and Aquatic Ecosystems in the KR ...... 150 Planning for Conservation in the KR ...... 150 CONCLUSION ...... 152 REFERENCES ...... 153 TABLES AND FIGURES ...... 164

iv SUMMARY AND GENERAL CONCLUSIONS ...... 189 REFERENCES ...... 193

v LIST OF TABLES

Table 0-1. GPS coordinates of 53 study sites in streams and tributaries of the upper Tigris River in the Kurdistan Region, northern Iraq...... 10 Table 2-1. Percent match of OTUs delineated by different genetic-based analyses...... 106 Table 2-2. Emphasized and discovered morphological key points to distinguish SpLUs of Iraqi stonefly and mayfly larvae...... 108 Table 2-3. Lists of Iraqi stoneflies and mayflies examined in this study...... 112 Table 2-4. Distribution of mayflies and stoneflies in the Kurdistan Region Northern Iraq...... 116 Table 2-5. Mayflies previously reported from northern Iraq, with updates, revisions, and confirmations...... 126 Table 2-6. List of Non-Iraqi Sequenced Mayfly taxa, available in the BOLD Systems and GenBank...... 127 Table 3-1. Sampled polyline stream segments (sampled-units) in the Kurdistan Region, northern Iraq. DD = GPS coordinates in decimal degrees...... 164 Table 3-2. Predictor variables used in 14 distribution models for healthy aquatic habitats in the Kurdistan region, northern Iraq...... 168 Table 3-3. Prioritized sampled-units for EPT conservation in the Kurdistan Region, northern Iraq, using MSC, MC, or UM...... 170 Table 3-4. Prioritized sub-basins for EPT conservation in the Kurdistan Region, northern Iraq, using MSC, MC, or UM. Priority scores were inherited from overlapping prioritized 19 sampled-units. Sub-basins with more than one prioritized sampled-unit received the score from the sampled-unit with the highest priority...... 172 Table 3-5. Fourteen distribution models for healthy aquatic habitats in the Kurdistan region, northern Iraq. Models were organized in a descending order, starting with the best model in the first row...... 174 Table 3-6. The most important predictor variables used in best distribution model for healthy aquatic habitats in the Kurdistan region, northern Iraq...... 176 Table 3-7. Predicted planning units of healthy aquatic habitats in the Kurdistan region, northern Iraq, identified using habitat distribution modeling...... 177 Table 3-8. Predicted sub-basins of healthy aquatic habitats in the Kurdistan region, northern Iraq, identified using habitat distribution modeling...... 179

vi LIST OF FIGURES

Figure 0-1. Map of the Kurdistan Region, northern Iraq, depicting the 53 Study Sites along the streams and tributaries of the Tigris River across four terrestrial ecoregions...... 12 Figure 0-2. Conceptual model of the dissertation...... 13 Figure 1-1. Prosopistoma: (a) carapace, dorsal; (b) carapace, thoracic sterna, and sternites 1-6, ventral...... 21 Figure 1-2. Electrogena: mesonotum and wingpads, dorsal...... 22 Figure 1-3. Isonychia (Isonychia): mesonotum, metanotum, and wingpads, dorsal...... 22 Figure 1-4. Ephemera (Ephemera): (a) first right gill, dorsal; (b) second right gill, dorsal; (c) left mandible, dorsal; (d) right fore leg, outer; (e) labium, ventral; (f) head, dorsal...... 24 Figure 1-5. Electrogena: left mandible, ventral...... 25 Figure 1-6. Caenis: mandibles, ventral...... 25 Figure 1-7. Oligoneuriella: head, dorsal...... 26 Figure 1-8. Electrogena: head, dorsal...... 26 Figure 1-9. Ecdyonurus (Ecdyonurus): head, dorsal...... 26 Figure 1-10. Isonychia (Isonychia): head, dorsal...... 26 Figure 1-11. Oligoneuriopsis: (a) right fore leg, dorsal; (b) left maxilla, ventral; (c) gills 1–3, ventral; (d) abdominal sternites and gills 1–3, ventral...... 28 Figure 1-12. Ecdyonurus (Ecdyonurus): (a) left fore leg, dorsal; (b) left maxilla, ventral...... 29 Figure 1-13. Electrogena: (a) left fore leg, dorsal; (b) left maxilla, ventral; (c) first left gill, dorsal; (d) second left gill, dorsal...... 30 Figure 1-14. Oligoneuriella: group of scale-like bristles on postero-medial part of sternite 3, ventral...... 30 Figure 1-15. Oligoneuriopsis: group of scale-like bristles on postero-medial part of sternite 3, ventral...... 31 Figure 1-16. Rhithrogena: (a) fore left leg, dorsal; (b) first right gill, ventral; (c) second right gill, ventral...... 32 Figure 1-17. Ecdyonurus (Ecdyonurus): (a) left fore leg, dorsal; (b) first left gill, dorsal; (c) second left gill, dorsal; (d) labium, ventral; (e) labrum, dorsal...... 33 Figure 1-18. Epeorus (Ironopsis): (a) first left gill, ventral; (b) second left gill, ventral. . 34 Figure 1-19. Epeorus (Epeorus): (a) first left gill, dorsal; (b) second left gill, dorsal...... 35 Figure 1-20. Ecdyonurus (Ecdyonurus): (a) pronotum, dorsal; (b) hypopharynx, ventral; (c) labrum, ventral; (d) left maxilla, ventral...... 36 Figure 1-21. Electrogena: (a) pronotum, dorsal; (b) head, frontal; (c) labrum, ventral; (d) left maxilla, ventral; (e) labium, ventral; (f) left hind leg, dorsal...... 37 Figure 1-22. Isonychia (Isonychia): (a) right fore leg, dorsal; (b) left maxilla, ventral. ... 38 Figure 1-23. Caenis: right fore leg, dorsal...... 38 Figure 1-24. Baetis (Baetis): (a) head, dorsal; (b) abdominal tergites 1–10, dorsal...... 39 Figure 1-25. Habrophlebia: head, dorsal...... 40

vii Figure 1-26. Caenis: terminal filaments, dorsal...... 40 Figure 1-27. Serratella: (a) cerci, dorsal; (b) postero-lateral margin of tergite 6, dorsal. . 40 Figure 1-28. Procloeon (Pseudocentroptilum): claw of right hind leg, outer...... 41 Figure 1-29. Centroptilum: claw of right hind leg, outer...... 41 Figure 1-30. Cloeon (Cloeon): labrum, dorsal...... 41 Figure 1-31. Baetis (Rhodobaetis): labrum, dorsal...... 42 Figure 1-32. Centroptilum: (a) first left gill, dorsal; (b) second left gill, dorsal...... 42 Figure 1-33. Procloeon (Procloeon): (a) first left gill, dorsal; (b) third left gill, dorsal; (c) thorax, showing fore wingpads only, lateral...... 44 Figure 1-34. Procloeon (Pseudocentroptilum): (a) first left gill, dorsal; (b) second left gill, dorsal; (c) thorax, showing fore and hind wingpads, lateral...... 44 Figure 1-35. Cloeon (Cloeon): (a) left maxilla, ventral; (b) first left gill, dorsal; (c) second left gill, dorsal...... 45 Figure 1-36. Cloeon (Intercloeon): left maxilla, ventral...... 46 Figure 1-37. Cloeon (Intercloeon): (a) labium, ventral; (b) first left gill, dorsal; (c) second left gill, dorsal...... 47 Figure 1-38. Cloeon (Similicloeon): (a) labium, ventral; (b) first left gill, dorsal; (c) second left gill, dorsal...... 48 Figure 1-39. Labiobaetis: (a) labium, ventral; (b) antenna...... 49 Figure 1-40. Baetis: antenna...... 49 Figure 1-41. Baetis: (a) labium, ventral; (b) left fore leg, ventral; (c) right mandible, ventral...... 51 Figure 1-42. Nigrobaetis: labium, ventral...... 52 Figure 1-43. Baetis (Baetis): (a) second right gill, dorsal; (b) postero-medial part of tergite 4, dorsal; (c) terminal filaments, dorsal; (d) claw of the right fore leg, dorsal...... 53 Figure 1-44. Baetis (Rhodobaetis): (a) third left gill, dorsal; (b) postero-medial part of tergite 5, dorsal; (c) terminal filaments, dorsal; (d) claw of the right fore leg, dorsal...... 54 Figure 1-45. Choroterpes (Choroterpes): (a) labrum, dorsal; (b) hypopharynx, ventral; (c) first right gill, dorsal; (d) gill 6, dorsal; (e) dorsal plate of gill 6, dorsal; (f) ventral plate of gill 6, dorsal...... 56 Figure 1-46. Habrophlebia: (a) labrum, dorsal; (b) hypopharynx, ventral; and (c) gill 4, dorsal...... 57 Figure 1-47. Caenis: (a) sclerotized plate-like gill on the right side of tergite 2, dorsal; (b) gill on the right side of tergite 3, dorsal; (c) mesonotum and wingpads, dorsal; (d) claw on fore left leg, outer; (e) labium and maxilla, ventral; (f) right hind leg, dorsal...... 59 Figure 1-48. Serratella: (a) dorsal plate of gill on the right side of tergite 6, dorsal; (b) ventral plate of gill on the right side of tergite 6, dorsal; (c) dorsal and ventral plates on the right side of tergite 6, dorsal; (d) left maxilla, ventral; (e) tergite 6, dorsal; (f) terminal filaments, dorsal...... 61

viii Figure 2-1. Neighbor-Joining trees with delineated OTUs using genetic-based analyses, delineated SpLUs, and the final names and status of taxonomic units in Iraq and the West Palaearctic...... 131 Figure 2-2. Relative accuracy of genetic-based analysis, measured as percent match of OTUs identified by each analysis to final delineated SpLUs...... 132 Figure 3-1. Conceptual model showing the main steps in this study. Twisted arrows denote information and data feeding. PSSs: Polyline-Stream-Segments; SPSSs: Sampled Polyline-Stream-Segments...... 182 Figure 3-2. Prioritized sampled-units for EPT conservation in the Kurdistan region, northern Iraq, using MSC or MC. The percentage of protected EPT taxa and the accumulated percentage of protected taxa are changing smoothly as we protect these sampled-units starting with the one with the highest priority. 183 Figure 3-3. Map of prioritized sampled-units for EPT conservation in the Kurdistan region, northern Iraq, using MSC or MC...... 184 Figure 3-4. Prioritized sampled-units for EPT conservation in the Kurdistan region, northern Iraq, using UM. The percentage of protected EPT taxa and the accumulated percentage of protected taxa are not changing smoothly as we protect these sampled-units starting with the one with the highest priority. 185 Figure 3-5. Map of prioritized sampled-units for EPT conservation in the Kurdistan region, northern Iraq, using UM...... 186 Figure 3-6. Map of predicted planning units of healthy aquatic habitats in the Kurdistan region, northern Iraq, identified using habitat distribution modeling...... 187 Figure 3-7. Map of predicted sub-basins of healthy aquatic habitats in the Kurdistan region, northern Iraq, identified using habitat distribution modeling...... 188

DEDICATION To my wife, Ghasak S. Al-Obaidi, My two children, Mehdi and Mesk, and My home country, IRAQ

x ACKNOWLEDGEMENTS My dissertation project would not have been possible without the guidance and support of many people. I would like to start out by thanking my supervisor, Dr. David J. Berg (Miami U.), for endless support and editorial and scientific guidance over more than five years conducting this research. I also thank him for giving me advice through my years as a PhD student and putting me on the right path for success. I would also like to thank Dr. Berg for instructing me in his course of Conservation Biology. I thank my PhD advisory committee: Dr. Mary C. Henry, Dr. Bruce J. Cochrane, Dr. Michael J. Vanni, and Dr. Thomas O. Crist (Miami U.), and Dr. John C. Morse (Clemson U.) for consultations and valuable editorial comments on the full dissertation. I also thank my committee and supervisor for their valued time and efforts along many years especially when I did my comprehensive exam and proposal defense. I would also like to thank John C. Morse for instructing me in two of his courses: Ecology and Taxonomy of Aquatic Insects in Eastern North America; and Taxonomy and Natural History of Southern Appalachian Mayflies, Stoneflies, and Caddisflies. I thank Bruce J. Cochrane again for instructing me in his course of Evolution and Population Genetics. I thank Thomas O. Crist for his seminar about Environmental and Risk Impact Assessment. I thank Miami University; the Graduate School, the Department of Biology, the Ecology, Evolution, and Environmental Biology PhD program, the Center for Bioinformatics and Functional Genomics, the BEST and King libraries, and the Graduate Student Association. The Benthic Macroinvertebrates’ Team in Nature Iraq (Mohammed A. Al-Saffar, Ali S. Al-Zubaidi and Ghazwan B. Al-Waili) conducted the field work as part of a project for the Key Biodiversity Areas (KBAs) in Iraq, funded by the Italian Ministry for the Environment, Land and Sea (IMELS). I thank Nature Iraq and the Twin Rivers Institute for Scientific Research (the American University of Iraq-Sulaimani), especially Azzam J. Alwash, Haithem Al-Hassani, Adel Hillawi, Araz M. Hamarash, Anna Bachmann for planning, facilitating, supporting, and funding the field and lab work in Iraq, and Ghasak S. Al-Obaidi, Ali M. Maher, Hussam J. Ali, Ali Kherallah, Haider Abid, Raid Abdulmehdi, Zana Jamal, and Lizan Nawzad for helping with the field and lab work in Iraq then transfering the specimens to the United States of America. Sequencing costs were supported by two grants from Miami University (Academic Challenge Research

xi Grants and Committee on Faculty Research), a grant from the Natural Sciences and Engineering Research Council of Canada (NSERC), and grants from Genome Canada through the Ontario Genomics Institute to Paul Hebert, University of Guelph. I thank Ghasak S. Al-Obaidi (Miami U.) for helping with cleaning and sorting the specimens as well as for preparing the scientific illustrations using Adobe Illustrator 2015. I thank John C. Morse for guidance and valuable training on scientific illustrations and key construction for mayflies. I thank Istvan Turcsanyi (Dynamac Corporation, Maryland, US) for helping with mayfly identification. I thank R. Edward DeWalt (University of Illinois at Urbana-Champaign, IL) for consultation with stonefly identification. I thank Bruce A. Steinly for providing dissecting and compound microscopes. I thank Yoshinori Tomoyasu (Miami U.) and his students for use of their microscopy and imaging facility. I thank Kaitlin Uppstrom Campbell and Ashley Walters (Miami U.) for testing the key. I thank Nikita J. Kluge (Saint-Petersburg State U.) for compiling a catalogue of literature about all mayfly taxa and making it available online (Ephemeroptera of the World; www.insecta.bio.spbu.ru/z/Eph-spp/Contents.htm). I thank the International Conferences on Ephemeroptera for making mayfly literature available online (Ephemeroptera Galactica; www.ephemeroptera-galactica.com). I thank R. Edward DeWalt et al. for making Plecoptera Species File available online (plecoptera.speciesfile.org). I thank the Barcode of Life Data (BOLD) Systems for providing valuable tools to build and contrast a library of COI sequences and run various genetic-based analyses. I thank the National Center for Biotechnology Information (NCBI) for making valuable sequences available on-line. I thank Erik M. Pilgrim of the U.S. Environmental Protection Agency in Cincinnati, Ohio, for his significant assistance and consultation with PCR and DNA sequencing. I thank Ghasak S. Al-Obaidi, Christy Jo Geraci (STEM Educator and Researcher), Xin Zhao (Chinese Center for DNA barcoding) and Kentaro Inoue (Miami U.) for help with DNA sequencing protocols and DNA purification lab work. I thank Andor J. Kiss (Miami U.) for use of the Center for Bioinformatics and Functional Genomics. I thank Cayla Morningstar (Miami U.) and Ashley Walters for help with DNA sequencing lab work. I thank Robbyn J.F. Abbitt and Jacqueline A. Housel for instructing me in their courses on Geographic Information Systems and technical support with ArcMap. Also, I

xii thank A. John Bailer for instructing me in his course of Environmental Analysis and Modeling. I thank Ashley Walters for consultation to run MaxEnt and SDMtoolbox. I thank the U.S. Fish and Wildlife Services especially Bradley Potter, John Rogner, and Craig Czarnecki (Upper Midwest and Great Lakes Landscape Conservation Cooperative, MI) and Gregory J. Soulliere (Upper Mississippi River and Great Lakes Region Joint Venture, MI) for the summer fellowship that allowed me to practice habitat distribution modeling on the Black Tern, one of the threatened birds in the Midwest. I thank Craig E. Williamson and Martin Henry H. Stevens for instructing me in their course of Ecosystem and Global Ecology. I thank Thomas O. Crist, David L. Gorchov, and Martin Henry H. Stevens for instructing me as an auditor in their course of Population and Community Ecology. I thank R. James Hickey, Robert A. Balfour, Yoshinori Tomoyasu, John Z. Kiss, Melany C. Fisk, Richard E. Lee, and Bruce J. Cochrane for opportunities to participate in their advanced seminars. I thank Ann L. Rypstra, Christy J. Geraci, and David J. Berg for employing me as a research assistant for the U.S.AID PEER project entitled PEER Research Experiences for Undergraduates (REU). Additionally, I would like to thank Berg Lab members for the friendly and excellent lab atmosphere: Nicole Adams, Jeff Moore, Kentaro Inoue, Ashley Walters, Cayla Morningstar, Danielle Holste, Trevor Williams, Kristina Taynor, and Emily Robinson. Finally, I thank my family who have been by my side in good and bad times and have been patient for more than five years; I could not have made it without them. I thank my wife, Ghasak S. Al-Obaidi, for supporting and encouraging me and providing the perfect atmosphere for my studies. I thank my two children, Mehdi and Mesk, who have been supportive throughout these years and understanding of my situation.

xiii GENERAL INTRODUCTION The Tigris River and its tributaries form an important part of the Upper Tigris and Euphrates freshwater ecoregion, an area under climate and anthropogenic pressure in the Middle East (Eden Again Group 2004 and 2005; Iraqi Ministry of the Environment et al. 2006a, 2006b, 2006c; Al-Saffar 2006 and 2007; Nature Iraq 2008; Mahir et al. 2009). The Tigris originates in the mountains of Turkey and flows for 1400 km throughout Iraq (~75% of its length), from its northernmost region (i.e., Kurdistan) to the southernmost part (i.e. the wetlands of southern Iraq). Along its long path, this river provides a diversity of services, such as food, drinking water, transportation, irrigation, and recreation (Iraqi Ministry of the Environment et al. 2006a, 2006b, 2006c; Iraqi Ministry of the Environment 2010). While the Tigris River has a long history of human use, most of its lentic and lotic ecosystems are located in poorly studied regions, with little information about the available biota and the locations of healthy, productive habitats. In Iraq, only aquatic plants, fish, and water birds have begun to receive attention. In contrast, other components of the biota, such as aquatic insects, mammals, reptiles, and amphibians have received little attention (Knees et al. 2009; Nature Iraq 2009; Rubec et al. 2009; Coad 2010; Iraqi Ministry of the Environment and Nature Iraq 2015). Consequently, the conservation status for the majority of the Tigris River basin and its biota remains poorly known and planning for conservation, given limited time, funds, and prior information, remains challenging. Given continuous climate change and anthropogenic disturbance along its basin, a rapid, efficient, and low-cost strategy to identify and prioritize healthy aquatic habitats for conservation is urgently needed. Using knowledge about bioindicator species, especially mayflies, stoneflies, and caddisflies [Insecta: Ephemeroptera, Plecoptera, and Trichoptera (EPT), respectively] is a common approach for addressing these needs. Ephemeroptera-Plecoptera-Trichoptera (EPT) are pollution-sensitive insects living in freshwater ecosystems when immature, while their adults are terrestrial flies (Merritt et al. 2008). They are the basis for most biomonitoring programs in operation worldwide (Reynoldson and Rosenberg 1997; Reif 2002; Morse et al. 2007) due to the following seven characteristics: they (1) are ubiquitous (Via-Norton et al. 2013); (2) are easily collected and handled (Rosenberg and Resh 1993); (3) are visible to the naked eye

1 with characteristics that are easily distinguished for identification at least to the family and subfamily levels (EVS Consultants 1993); (4) play a central role in moving energy through food webs (Nova Scotia Museum of Natural History 1993; Bass 1994; Rosenberg et al. 1997; Hoosier Riverwatch 2000; Vowell 2001); (5) have a narrow range of tolerance to pollution and habitat stressors (Reece and Richardson 1998; Thomson et al. 1999; Via-Norton et al. 2013); (6) have low motility (Hynes 1972; Culter and Leverone 1993); and (7) have relatively long life cycles which reflect conditions over an extended period of time (Rosenberg and Resh 1993). All of these characteristics make EPT communities sensitive to water quality, ecosystem stability, and diversity of aquatic food webs; overall, they are excellent measures of the quality of aquatic habitats. For instance, tracking EPT distribution and meta-population dynamics has been commonly used in recent decades to understand and analyze the effects of climate change and anthropogenic disturbance on freshwater ecosystems (DeWalt et al. 2012; Cao et al. 2013). While knowledge about EPT is well-established in Europe (Bauernfeind and Soldàn 2012) and North America (Merritt et al. 2008), information about them in the Tigris River basin in Iraq is quite limited, with no previous studies about stoneflies and only six studies about mayflies and caddisflies being reported from northern Iraq; five of these are 29–82 years old (Mosely 1934; Al-Zubaidi and Al-Kayatt 1986, 1987; Al- Zubaidi et al. 1987; Malicky 1987). More recently, my colleagues and I conducted a sixth study, which was the fourth for caddisflies in the Tigris river basin (Geraci et al. 2011). Since then, we have continued the identification process for caddisflies using the Barcode of Life Data (BOLD) Systems, along with morphology, to discover the presence of 12 species, eight morphospecies, and 13 haplogroups from Neighbour-Joining Trees (a.k.a. Operational Taxonomic Units (OTUs) delineated based on interspecific versus intraspecific genetic distance; unpublished data available in the BOLD Systems; www.boldsystems.org; Ratnasingham and Hebert 2007). Overall, information about caddisflies continues to develop using morphological and gentic species concepts, while information about stoneflies and mayflies remains relatively poor, with no records for any stoneflies, ~29-year-old records for 10-12 morphological species of mayflies, no

2 genetic data, doubtful identifications for two mayflies, no reliable checklists, and no keys or guides for identification being available. In my dissertation, I developed an approach to plan for conservation in the poorly studied Tigris River basin in Iraq using caddisfly data along with a rapid assessment for available stoneflies and mayflies. My colleagues at Nature Iraq and I conducted the survey for EPT biannually from summer 2007 to summer 2010 at 53 randomly selected sites spanning Kurdistan Region (KR) northern Iraq (Table 0-1), as part of a larger Key Biodiversity Areas (KBAs) survey conducted by Nature Iraq (Nature Iraq 2008; Mahir et al. 2009; Iraqi Ministry of the Environment and Nature Iraq 2015). The KR is an autonomous region of federal Iraq, consisting of three governorates: Dohuk, Erbil, and Sulaimani. It borders Syria to the west, Iran to the east, Turkey to the north, and the rest of Iraq to the south and southwest. This region has a strategic location as it occupies part of the Irano-Anatolian biodiversity hotspot, one of the more poorly studied of 35 hotspots globally (Conservation International 2005). In addition, the KR occupies part of the Upper Tigris and Euphrates freshwater ecoregion as well as parts of four of six terrestrial ecoregions (Figure 0-1) in the West Palaearctic realm (National Geographic Society 2008; Hoekstra et al. 2010); (1) Zagros Mountains Forest Steppe, (2) Middle East Steppe, (3) Eastern Mediterranean Conifer-Sclerophyllous-Broadleaf Forests, and (4) Mesopotamian Shrub Desert. The KR has diverse freshwater habitats, such as streams, mineral springs, waterfalls, wetlands, lakes, and reservoirs (Mahir et al. 2009). It plays a major role in the restoration of southern Iraqi wetlands, as it has most of the headwater streams feeding the wetlands (Eden Again Group.2004 and 2005). Furthermore, the KR has aesthetic, economic, and recreational values for Iraq (Iraqi Ministry of the Environment and Nature Iraq 2015). Overall, the KR is an important, yet poorly studied, region with serious need for species and habitat discovery, and conservation planning. The first objective of my dissertation was to identify the available mayflies of the KR and create the first checklist and larval key to the families, subfamilies, genera, and subgenera of the region. Key morphological characteristics were reviewed for each of these taxonomic levels using references from Europe, West Asia, North Africa, the Caucasus, the Trans-Caucasus, and the Middle East. Finally, an identification key was

3 constructed and supported by state-of-the-art Adobe Illustrator scientific illustrations based on specimens collected during this study (Chapter 1; Figure 0-2). Secondly, species identification of mayflies and stoneflies was accelerated using a combination of morphology and genetic-based analyses. First an initial rough morphological identification was performed (mostly to morphospecies). Next, the mitochondrial cytochrome oxidase subunit 1 (COI) gene was sequenced for ~ 350 specimens. Operational Taxonomic Units (OTUs) were delineated using each of five genetic-based analyses; OTUs were matched and Species-Like Units (SpLUs) were delineated based on agreement between genetic-based analyses. Finally, SpLUs were compared and contrasted morphologically against each other and against species and subspecies previously known from the West Palaearctic realm (Chapter 2; Figure 0-2). My third objective was to develop a plan to identify and prioritize healthy aquatic habitats for conservation in the KR using previously gathered caddisfly data (Geraci et al. 2011) and the data generated by my research to identify unique evolutionary taxa of mayflies and stoneflies. First, I divided the lotic and lentic ecosystems of the KR into 76 planning units based on topography and land use/land cover. Next, I compiled presence- absence data of EPT taxa for 33 of these units. I used Complementarity-Based Approaches (CBAs) to rank these habitats and prioritize them for EPT conservation. From the rankings, I selected a subset of habitats with the highest priority for EPT conservation due to high diversity and community uniqueness; this subset was considered to represent samples of healthy aquatic habitats in the KR; it was converted to occurrence points and used along with various predictor variables (including anthropogenic, environmental, and climate variables) to create a correlative distribution model for healthy aquatic habitats across the entire KR. This approach allowed me to “fill-the-gaps” and achieve my goal of uncovering the pattern of distribution of healthy aquatic habitats in the KR that merit conservation priority (Chapter 3; Figure 0-2). Through my dissertation, I demonstrated an approach to conservation biology in poorly studied freshwater ecosystems. I studied the morphology of mayflies in the KR and produced the first identification key in the region about their families, subfamilies, genera, and subgenera. I accelerated the identification of unique evolutionary taxa of mayflies and stoneflies using morphological, genetic, and phylogenetic species concepts.

4 Finally, I used the findings to plan for the conservation of healthy aquatic habitats after conducting a landscape analysis for EPT data and associated environmental variables. I showed that in developing countries where knowledge about aquatic ecosystems and most extant species is unavailable, conservation studies can still be conducted following rapid assessment of water quality bioindicators.

REFERENCES Al-Saffar, M.A. (2006). Macro-benthos. In Nature Iraq (2006). Water quality monitoring programme in Iraqi marshlands (p. 253). United Nations Environmental Program (UNEP) http://marshlands.unep.or.jp Al-Saffar, M.A. (2007). Interaction between the Environmental Variables and Benthic Macroinvertebrates Community Structure in Abu Zirig Marsh, Southern Iraq. M.Sc. Thesis, University of Baghdad, College of Science, Baghdad, Iraq. Al-Zubaidi, F., and Al-Kayatt, A. (1986). A preliminary survey of mayflies from the north of Iraq. Journal of Biological Science Research 17 (2): 147–151. Al-Zubaidi, F., and Al-Kayatt, A. (1987). A preliminary study of the Trichoptera caddisflies in the north of Iraq. Iraqi Journal of Science 28:439–444. Al-Zubaidi, F., Braasch, D., and Al-Kayatt, A. (1987). Mayflies from Iraq (Insecta, Ephemeroptera). Faunistische Abhandlungen Staatliches Museum für Tierkunde Dresden 14 (15): 179–184. Bass, D. (1994). Community structure and distribution patterns of aquatic macroinvertebrates in a tall grass prairie stream ecosystem. Proceedings of the Oklahoma Academy of Sciences 74: 3–10. Bauernfeind, E., and Soldàn, T. (2012). The Mayflies of Europe (Ephemeroptera). Apollo Books, Ollerup. Denmark. 781 pp. Cao, Y., DeWalt, R. E., Robinson, J. L., Tweddale, T., Hinz, L., and Pessino, M. (2013). Using Maxent to model the historic distributions of stonefly species in Illinois streams and rivers: the effects of regularization and threshold selections. Ecological Modelling 259: 30–39. Coad, B.W. (2010). Freshwater Fishes of Iraq. Pensoft Publishers, Sofia-Moscow. 294 pp.

5 Conservation International (2005). Biodiversity hotspots. Washington DC. www.conservation.org. Culter, J.K., and Leverone, J.R. (1993). Bay bottom habitat assessment, final report. Sarasota Bay National Estuary Program. Mote Marine Laboratory technical report, no 303, 69 pp. DeWalt, R.E., Cao, Y., Tweddale, T., Grubbs, S.A., Hinz, L., Pessino, M., and Robinson, J.L. (2012). Ohio USA stoneflies (Insecta, Plecoptera): Species richness estimation, distribution of functional niche traits, drainage affiliations, and relationships to other states. ZooKeys 178: 1–26. Eden Again Group (2004). Feasibility Study for the Restoration of the Mesopotamian Marshlands. Free Iraq Foundation Reports www.iraqfoundation.org, Washington, DC. Eden Again Group (2005). Water and Energy Project. Book 1: Abu Zirig Marshland Restoration Project. Baghdad, Iraq: Italian Ministry of Environment and Free Iraq Foundation Reports www.iraqfoundation.org EVS Consultants (1993). Guidelines for Monitoring Benthos in Freshwater Environments. Environment Canada, North Vancouver, B.C. Geraci, C.J., Al-Saffar, M.A., and Zhou, X. (2011). DNA barcoding facilitates description of unknown faunas: A case study on Trichoptera in the headwaters of the Tigris River, Iraq. Journal of the North American Benthological Society 30 (1): 163–173. Hoekstra, J.M., Molnar, J.L., Jennings, M., Revenga, C., Spalding, M.D., Boucher, T.M., Robertson, J.C., Heibel, T.J., and Ellison, K. (2010). The Atlas of Global Conservation: Changes, Challenges, and Opportunities to Make a Difference. Ed. J. L. Molnar. Berkeley: University of California Press. Hooser Riverwatch (2000). Volunteer Stream Monitoring Training Manual. Indiana’s Volunteer Stream Monitoring Program. Natural Resources Education Center. Indianapolis, IN 46216–1066. Hynes, H.B.N. (1972). The ecology of running waters. University of Toronto Press. Ontario, Canada.

6 Iraqi Ministries of Environment, Water Resources and Municipalities and Public Works (2006a). New Eden Master Plan for integrated water resources management in the marshlands areas, Volume I: Overview of Present Conditions and Current Use of the Water in the Marshlands Area, Book 1: Water resources. New Eden Group, Italy-Iraq. Iraqi Ministries of Environment, Water Resources and Municipalities and Public Works (2006b). New Eden Master Plan for Integrated Water Resources Management in the Marshlands Area (Volume I: Overview of Present Conditions and Current Use of the Water in the Marshlands Area, Book 4: Marshlands. New Eden Group, Italy- Iraq. Iraqi Ministries of Environment, Water Resources and Municipalities and Public Works (2006c). New Eden Master Plan for integrated water resources management in the marshlands areas, Annex III: Main Water Control Structures (Dams and Water Diversions) and Reservoirs. New Eden Group, Italy-Iraq. Iraqi Ministry of Environment (2010). National Report on Biodiversity in Iraq. Iraqi fourth national report to the Convention on Biological Diversity (CBD). Republic of Iraq, 160 pp. www.cbd.int/doc/world/iq/iq-nr-04-en.pdf Iraqi Ministry of Environment, and Nature Iraq (2015). The Inventory of Key Biodiversity Areas of Iraq. Sulaimani, Iraq: Nature Iraq. Manuscript in preparation. Knees, S., Zantout, N., Gardner, M., Neale, S., and Miller, A. (2009). Endemic Plant Species of Iraq (1st Draft Checklist prepared from a initial review of the Flora of Iraq and Flora Iranica) Edinburgh: Royal Botanical Gardens Edinburgh. Mahir, A.M., Radhi, A.G., Falih, H.A., Al-Obaidi, G.S., and Al-Saffar, M.A. (2009). Key Biodiversity Areas Survey of the Kurdistan, Northern Iraq: Water Quality Review- winter and summer 2008 Survey. Sulaimani, Iraq: Nature Iraq. Publication No. NI- 0209-004. Malicky, H. (1987). A survey of the caddisflies (Insecta: Trichoptera) of the Middle East. Pages 174–177 in F. Krupp, W. Schneider, and R. Kinzelbach (editors). Proceedings of the Symposium on the Fauna and Zoogeography of the Middle East. Dr Ludwig Reichert Verlag, Weisbaden, Germany

7 Merritt, R.W., Cummins, K.W., and Berg, M.B., eds. (2008). An Introduction to Aquatic Insects of North America, 4th Edition. Kendall/Hunt Publishing Company, Dubuque, IA. 1158 pp. Morse, J.C., Bae, Y.J., Munkhjargal, G., Sangpradub, N., Tanida, K., Vshivkova, T.S., Wang, B., Yang, L., and Yule, C.M. (2007). Freshwater biomonitoring with macroinvertebrates in East Asia. Frontiers in Ecology and the Environment 5(1): 33–42. Mosely, M.E. (1934). Trichoptera collected in the Kurdistan by Mr. B. P. Uvarov. Eos 10:120–123. National Geographic Society (2008). National Geographic Atlas of the Middle East, 2nd ed. Washington, DC. Nature Iraq (2008). Nature Iraq Field and Lab Report: Tanjero River Project I: Survey of water quality, sediment, benthic macroinvertebrates and fisheries. For the Qara Ali Dam Irrigation Project (QDIP) Environmental Impact Assessment. Sulaimani, Iraq: Nature Iraq. Publication No. NI-0508-01. Nature Iraq (2009). The Key Biodiversity Areas Program in Iraq: Objectives and scope 2004–2008. Rubec & Bachmann (Eds). Sulaimani, Iraq: Nature Iraq. Publication No. NI-0309-001. Nova Scotia Museum of Natural History (NSMNH) (1993). Land and Freshwater Invertebrates. Natural History of Nova Scotia 1: 288–297. Ratnasingham, S., and Hebert, P.D.N. (2007). BOLD: The Barcode of Life Data System. Molecular Ecology Notes 7: 355–364. www.boldsystems.org Reece, P.F., and Richardson, J.S. (1998). Seasonal Changes of Benthic Macroinvertebrate Communities in Southwestern British Columbia. Environment Canada: Environmental Conservation Branch, Aquatic and Atmospheric Science Division. FRAP Report Number: DOE- FRAP1998-33. Reif, A.G. (2002). Assessment of Stream Conditions and Trends in Biological and Water- Chemistry Data from Selected Streams in Chester County, Pennsylvania, 1981-97. U.S. Geological Survey Water-Resources Investigations Report 02-4242. 87pp.

8 Reynoldson, T.B., and Rosenberg, D.M. (1997). Benthic Invertebrate Community Structure. Proceedings of the 45th Annual Meeting of the North American Benthological Society, San Marcos, Texas. Rosenberg, D.M., and Resh, V.H. (1993). Freshwater Biomonitoring and Benthic Macroinvertebrates. Chapman & Hall, New York, 488 pp. Rosenberg, D.M., Davies, I.J., Cobb, D.G., and Wiens, A.P. (1997). Ecological Monitoring and Assessment Network (EMAN- Environment Canada) - Protocols for Measuring Biodiversity: Benthic Macroinvertebrates in Fresh Waters. Dept. of Fisheries & Oceans, Freshwater Institute, Winnipeg, Manitoba. 53 pp. Rubec, C., Alwash, A., and Bachmann, A. (2009). The Key Biodiversity Areas Project in Iraq: Objectives and scope 2004–2008. In: Krupp, F., Musselman, L.J., Kotb M.M.A., and Weidig I. (eds.) Environment, Biodiversity and Conservation in the Middle East. Proceedings of the First Middle Eastern Biodiversity Congress, Aqaba, Jordan, 20–23 October 2008. BioRisk 3: 39–53. Thomson, E.E., Peterson, C.H., and Summerson, H.C. (1999). Neuse ModMon investigations confirm recovery of Neuse River Estuary will be slow. Retrieved December 9th 2013, from The Water Resources Research Institute. NC State University. Via-Norton, A., Maher, A., and Hoffman, D. (2013). An Introduction to Benthic Macroinvertebrates. Retrieved December 9th 2013, from Service of Kentucky Water Watch website, Kentucky Division of Water, Frankfort. Vowell, J.L. (2001). Using stream bioassessment to monitor best management practice effectiveness. Forest Ecology and Management 143: 237–244.

9 TABLES AND FIGURES Table 0-1. GPS coordinates of 53 study sites in streams and tributaries of the upper Tigris River in the Kurdistan Region, northern Iraq.

Site ID Site Name Longitude (X) Latitude (Y) Governorate 1 Ashawa 43.113327 37.110486 Dohuk 2 Atrush_A 43.215674 36.895983 Dohuk 3 Atrush_B 43.353964 36.808749 Dohuk 4 Benavi 43.436500 37.218566 Dohuk 5 Deraloke 43.744090 37.037387 Dohuk 6 Dohuk Lake 43.000030 36.897882 Dohuk 7 Fishkhaboor_A 42.341137 37.050415 Dohuk 8 Fishkhaboor_B 42.414124 37.126088 Dohuk 9 Gali Zanta 43.530293 36.665267 Dohuk 10 Garagu 43.488575 36.900681 Dohuk 11 Gerbeesh 43.618863 36.779433 Dohuk 12 Sulav 43.473957 37.049296 Dohuk 13 Tajika 42.902077 37.094055 Dohuk 14 Altun Kopri 44.114876 35.719010 Erbil 15 Aski Kalak_A 43.905553 36.531996 Erbil 16 Aski Kalak_B 43.734571 36.337694 Erbil 17 Bahraka 44.316356 36.439524 Erbil 18 Barzan 44.158959 36.922957 Erbil 19 Bekhma 44.222415 36.656425 Erbil 20 Gali Ali Beg 44.467433 36.630580 Erbil 21 Jundyan 44.673145 36.624047 Erbil 22 Kherazook 44.392290 36.965820 Erbil 23 Taq Taq 43.792570 36.016705 Erbil 24 Ahmed Awa_C 46.088857 35.310706 Sulaimani 25 Awe Sar 46.114852 35.140430 Sulaimani

10 Site ID Site Name Longitude (X) Latitude (Y) Governorate 26 Bani Kani 45.592489 34.908983 Sulaimani 27 Bargalu 45.107929 35.933726 Sulaimani 28 Bequch 45.192037 35.824587 Sulaimani 29 Dara Ban 44.779524 36.354630 Sulaimani 30 Darbandikhan 45.668025 35.094758 Sulaimani 31 Darua Kotr 45.011399 36.328470 Sulaimani 32 Delezha_A 45.066792 35.389937 Sulaimani 33 Delezha_C 45.162675 35.569070 Sulaimani 34 Dole 45.207647 36.191820 Sulaimani 35 Dukan 44.962875 35.932599 Sulaimani 36 Halsho Upper 45.311511 36.206723 Sulaimani 37 Isa juction 45.436493 35.991167 Sulaimani 38 Kalar 45.386574 34.649013 Sulaimani 39 Kela Spi 45.286837 35.559927 Sulaimani 40 Kunamasi_A 45.449409 35.743799 Sulaimani 41 Mertka 45.113773 36.256920 Sulaimani 42 Penjween 45.947873 35.751962 Sulaimani 43 Qara Ali 45.712727 35.342839 Sulaimani 44 Qara Dagh 45.344209 35.326094 Sulaimani 45 Qarani Agha 44.766247 36.188870 Sulaimani 46 Qaziawa 45.170102 35.698931 Sulaimani 47 Qocha Blagh 44.973500 35.811009 Sulaimani 48 Said Sadiq 45.886036 35.409643 Sulaimani 49 Surchanar 45.376054 35.660736 Sulaimani 50 Tabban 44.854754 35.896083 Sulaimani 51 Tatapitch Kola 45.397685 35.880154 Sulaimani 52 Waraz 45.662635 35.754605 Sulaimani 53 Zalm 45.974504 35.303650 Sulaimani

Figure 0-1. Map of the Kurdistan Region, northern Iraq, depicting the 53 Study Sites along the streams and tributaries of the Tigris River across four terrestrial ecoregions.

Chapter 1: Produce an Identification Mayfly Key to the Families, Subfamilies, Specimens Genera, and Subgenera of Mayfly Larvae

Chapter 2: Use Morphological, Stonefly Genetic, and Phylogenetic Species Specimens Concepts to Accelerate Species Discovery

Mayfly and

Stonefly Data

Caddisfly Data: Geraci et al. (2011) + BOLD Systems Chapter 3: Plan for Conservation of Healthy Aquatic Habitats, while Conserving EPT Environmental Variables Data: various sources

Figure 0-2. Conceptual model of the dissertation.

13 Chapter 1: Identification Key to the Families, Subfamilies, Genera, and Subgenera of Mayfly Larvae (Insecta: Ephemeroptera) from the Kurdistan Region, Northern Iraq

ABSTRACT Taxonomy and identification of mayflies (Insecta: Ephemeroptera) in Iraq is very poor, with only two studies from northern Iraq conducted around 29 years ago. There are no published checklists and no keys or guides to mayfly identification available. I conducted the third study in the Kurdistan Region (the KR) of northern Iraq and created the first checklist and larval key to the families, subfamilies, genera, and subgenera. The checklist and key were produced after reviewing the diagnostic morphological characteristics for each of these taxonomic levels using references from Europe, West Asia, North Africa, the Caucasus, the Trans-Caucasus, and the Middle East. Finally, an identification key for nine families, nine subfamilies, 19 genera, and 13 subgenera was constructed and supported by 117 state-of-the-art Adobe Illustrator scientific illustrations using specimens collected during this study. Based on the findings of this research, at least 24 species of mayflies are inhabiting the KR.

Keywords: Larva, Dichotomous Key, Middle East, West Palaearctic

INTRODUCTION Ephemeroptera-Plecoptera-Trichoptera (so-called EPT) taxa are macroinvertebrates that spend most of their lifespans in immature stages, living in aquatic habitats (Merritt et al. 2008). These taxa are excellent bioindicators of water quality and general habitat conditions (Culter and Leverone 1993, Reif 2002), therefore they are the basis for most biological monitoring programs in operation worldwide (Reynoldson and Rosenberg 1997). Having delicate body-parts (such as the gills), and being sensitive to habitat destruction and changes in water quality, allows them to provide important information on the destructive effects of anthropogenic alterations of freshwaters; as such, they play the role of early warning systems for large-scale events such as global warming (Rosenberg and Resh 1993; Holt and Miller 2010). Therefore, building knowledge about

14 them is highly useful for conducting ecological and conservation studies in freshwater ecosystems. Mayflies (order Ephemeroptera) spend most of their lifespans as larva, which typically live for one year in streams with good to high water quality. They inhabit areas under decaying vegetation and rocks, or burrow into sediments. Most species are detritivores, grazers, and/or collector-gatherers that feed on detritus, diatoms, and algae, while a few of them are predators. Most larvae can be distinguished from other aquatic insects by having seven pairs of gills on the first seven abdominal segments. In addition, most of them have three long caudal/terminal filaments at the end of the abdomen. The larvae usually undergo many molts, with the last one producing a winged, sexually immature adult-like form, called a sub-imago. Sub-imagoes then emerge from water and undergo one additional molt to give the sexually mature adult, called an imago. (McCafferty 1981; Brittain 1982; Berner and Pescador 1988; Campbell 1990; Clifford 1991). While the taxonomy of mayflies is well-established in Europe (Bauernfeind and Soldàn 2012) and North America (Merritt et al. 2008), knowledge of them in Iraq is quite limited (Al-Saffar 2006 and 2007), with only two studies about mayflies being reported ~29 years ago from northern Iraq (Al-Zubaidi and Al-Kayatt 1986; Al-Zubaidi et al. 1987). A previous preliminary survey for mayfly larvae at only seven randomly selected sites resulted in identifying four species and one group-complex, while suggesting the potential presence of another eight species within six genera from five families (Al- Zubaidi and Al-Kayatt 1986). A second study recorded the presence of 11 species, two group-complexes, nine genera, and six families, including two species new-to-science which appear to be endemic to Iraq: Isonychia arabica Al-Zubaidi, Braasch, and Al- Kayatt 1987 and Oligoneuriella bicaudata Al-Zubaidi, Braasch, and Al-Kayatt 1987 (Al- Zubaidi et al. 1987). Collectively, from 1987 to 2016, no updates have been published and a total of only 12 species within 11 genera in seven families are known from northern Iraq. However, identification was uncertain for two of the 12 species, Baetis lutheri Muller-Liebenau 1967 and Rhithrogena expectata Braasch 1979. Therefore, knowledge about mayfly species in Iraq remains poor compared to other Middle Eastern countries such as Turkey, with no checklists, keys or guides to their identification being available.

15 The aim of my study was to increase knowledge of Iraqi mayflies, starting with the first list and first identification key for larvae of the families, subfamilies, genera, and subgenera of mayflies inhabiting the Kurdistan Region (the KR), northern Iraq. Creating this list and key will set the stage for future discoveries and description of species that are new to Iraq and science. It will also set the stage to train a new generation of scientists in Iraq on identifying mayflies and using them in future ecological studies, water quality biomonitoring, and conservation research.

METHODS Study Area The study area encompassed the entire KR, including the areas studied within the KR by Al-Zubaidi and Al-Kayatt (1986) and Al-Zubaidi et al. (1987). The Tigris River flows through the KR where five tributaries (Khabur River, Great Zab River, Little Zab River, Udhaim River, and Diyala River) flowing from the northeast to the southwest contribute to its water budget. Fifty-three sites (Table 0-1) located along these tributaries and other streams in the KR were sampled biannually (summer and winter) from summer 2007 to summer 2010 as part of a larger Key Biodiversity Areas (KBAs) survey conducted by Nature Iraq (Rubec et al. 2009; Mahir et al. 2009; Iraqi Ministry of the Environment and Nature Iraq 2015). Sampling covered a large part of the Upper Tigris and Euphrates freshwater ecoregion within the KR. This region also encompassed four terrestrial ecoregions: Zagros Mountains Forest Steppe, Middle East Steppe, Eastern Mediterranean Conifer-Sclerophyllous-Broadleaf Forests, and Mesopotamian Shrub Desert (National Geographic Society 2008; Hoekstra et al. 2010; Figure 0-1).

Sampling and Preservation Mayfly larvae from the selected streams were collected using standard benthic macroinvertebrate sampling tools for streams. In order to obtain a representative sample at each site, the highest possible number of microhabitats was sampled, using a kick net (sampling area of 1 m2 per replicate), a Surber sampler (sampling area of 0.09 m2 per replicate), and/or a Hess sampler (sampling area of 0.8 m2 per replicate). Length of sampled stream reaches ranged from 50-100m and 6-10 samples were taken in each. Specimens, ~ 10,000 individuals, were washed and sieved in the field through a 0.5 mm

16 nytex screen and then preserved in 70% ethanol. Upon returning to the laboratory, specimens were transferred to 95% ethanol and stored at room temperature.

Morphological Identification and Construction of the Dichotomous Key A dissecting microscope was used to sort specimens into morphspecies based on gross external morphological characters such as gill shape and placement, body size and shape, color patterns, etc. Identification to the genus and subgenus levels was performed after transferring specimens to a Stender Dish filled with 70% ethanol or Glycerin, using a Wild Heerbrugg M5 Stereomicroscope (up to 200x magnification power), a Carl Zeiss SteREO Discovery.V12 (up to 250x magnification power), a compound microscope (as needed), and publications from Europe, West Asia, North Africa, the Caucasus, the Trans-Caucasus, and the Middle East (Kluge 2016; Staniczek 2016) such as those by Schoenemund 1930; Kimmins 1942; Macan 1955a, 1955b, 1957, 1958, 1961, and 1979; Bogoescu 1958; Ikonomov 1961 and 1962; Landa 1969; Sowa 1973; Kazlauskas 1977; Soldán 1978; Belfiore 1983; Mol 1983; Elliott and Humpesch 1983; Malzacher 1984, 1986, 1992, and 1996; Jensen 1986; Kluge 1987 and 1997; Novikova and Kluge 1987 and 1994; Andrikovics 1988; Elliott et al. 1988; Hefti and Tomka 1989; Sartori 1991 and 1992; Studemann et al. 1992; Kluge and Novikova 1992; Alba-Tercedor and Zamora- Munoz 1993; Bauernfeind 1994 and 1995; Engblom 1996; Kluge 1997; Buffagni 1997 and 1999; Soldán and Landa 1999; Marie et al. 2000 and 2001; Bauernfeind and Humpesch 2001; Jacob 2003; Eiseler 2005; Malzacher and Staniczek 2006 and 2007; Webb and McCafferty 2008; Elliott and Humpesch 2010; Macadam and Bennet 2010; Küttner and Zimmermann 2011; Bauernfeind and Soldàn 2012; and Türkmen and Kazanci 2013. While examining the specimens morphologically, exemplars were dissected, detailed notes on their morphology were taken, and hand-sketches (using a 10x10 grid reticule mounted on 20 x and 25 x eyepieces) were made. In addition, three-dimensional (3D) pictures were taken and processed using Carl Zeiss SteREO Discovery.V12 and AxioVision 4 software. Finally, 3D pictures were imported to Adobe Illustrator 2015 to produce the final gray-scale digital scientific illustrations necessary to identify each genus and subgenus. Next, description notes and scientific illustrations were used to construct a preliminary dichotomous key to mayfly larvae inhabiting the KR. The final

17 key was produced after subsequent revisions using taxonomic literature from the West Palaearctic realm (including the references listed above), taking into account the main characteristics of mayfly families, subfamilies, genera, and subgenera. Available subfamilies and subgenera in the KR were keyed, when possible, to allow for finer identification of taxa and setting the stage for future species description. Extra characters were provided, where necessary, to distinguish the studied mayflies from other families, subfamilies, genera, and subgenera of the Western Palearctic realm that were not found in this study.

RESULTS List of Mayfly Larvae from the Kurdistan Region, Northern Iraq Following is a list of nine families, nine subfamilies, 19 genera, and 13 subgenera of mayfly larvae that I found in this study. Numbers in parentheses represent the distribution (occurrence number and sites ID) of genera and subgenera across the KR (Table 0-1 and Figure 0-1).

Family: Baetidae Leach 1815 Subfamily: Baetinae Leach 1815 Genus: Baetis Leach 1815 Subgenus: Baetis Leach 1815 (45 sites: 1, 2, 5,7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 51, 52, 53) Subgenus: Rhodobaetis Jacob 2003 (22 sites: 2, 14, 15, 23, 24, 25, 26, 27, 28, 29, 30, 32, 34, 35, 36, 37, 39, 40, 41, 48, 49, 52) Genus: Labiobaetis Novikova and Kluge 1987 (3 sites: 2, 15, 35) Genus: Nigrobaetis Novikova and Kluge 1987 (4 sites: 15, 24, 25, 35) Subfamily: Cloeoninae Newman 1853 Genus: Centroptilum Eaton 1869 (1 site: 26) Genus: Cloeon Leach 1815 Subgenus: Cloeon Leach 1815 (3 sites: 34, 42, 43) Subgenus: Intercloeon Kluge and Novikova 1992 (3 sites: 43, 45, 53) Subgenus: Similicloeon Kluge and Novikova 1992 (3 sites: 28, 40, 41)

18 Genus: Procloeon Bengtsson 1915 Subgenus: Procloeon Bengtsson 1915 (6 sites: 3, 6, 14, 16, 18, 23) Subgenus: Pseudocentroptilum Bogoescu 1947 (12 sites: 2, 3, 6, 14, 15, 18, 20, 21, 23, 30, 34, 41) Family: Caenidae Newman 1853 Subfamily: Caeninae Newman 1853 Genus: Caenis Stephens 1836 (44 sites: 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 28, 30, 31, 32, 33, 34, 35, 36, 37, 39, 40, 41, 42, 43, 44, 45, 48, 50, 51, 52, 53) Family: Ephemerellidae Klapálek 1909 Subfamily: Ephemerellinae Klapálek 1909 Genus: Serratella Edmunds 1959 (6 sites: 2, 14, 17, 29, 41, 49) Family: Ephemeridae Latreille 1810 Subfamily: Ephemerinae Latreille 1810 Genus: Ephemera Linnaeus 1758 Subgenus: Ephemera Linnaeus 1758 (4 sites: 4, 26, 32, 34) Family: Heptageniidae Needham 1901 Subfamily: Ecdyonurinae Ulmer 1920 (1905) Genus: Ecdyonurus Eaton 1868 Subgenus: Ecdyonurus Eaton 1868 (10 sites: 5, 14, 16, 18, 26, 34, 35, 48, 49, 51) Genus: Electrogena Zurwerra and Tomka 1985 (18 sites: 5, 12, 15, 16, 17, 24, 30, 34, 35, 37, 39, 40, 42, 44, 48, 49, 51, 53) Subfamily: Rhithrogeninae Lestage 1917 Genus: Epeorus Eaton 1881 Subgenus: Epeorus Eaton 1881 (9 sites: 5, 12, 14, 24, 28, 29, 39, 41, 49) Subgenus: Ironopsis Traver 1935 (2 sites: 29, 48) Genus: Rhithrogena Eaton 1881 (20 sites: 1, 2, 12, 13, 14, 15, 17, 18, 21, 24, 25, 26, 29, 30, 31, 34, 37, 39, 52, 53) Family: Isonychiidae Burks 1953 Genus: Isonychia Eaton 1871

19 Subgenus: Isonychia Eaton 1871 (5 sites: 2, 14, 17, 20, 33) Family: Leptophlebiidae Banks 1900 Subfamily: Atalophlebiinae Peters 1980 Genus: Choroterpes Eaton 1881 Subgenus: Choroterpes Eaton 1881 (1 site: 36) Subfamily: Habrophlebiinae Kluge 1994 Genus: Habrophlebia Eaton 1881 (4 sites: 24, 35, 39, 40) Family: Oligoneuriidae Ulmer 1914 Genus: Oligoneuriella Ulmer 1924 (9 sites: 2, 9, 14, 15, 17, 18, 21, 25, 41) Genus: Oligoneuriopsis Crass 1947 (2 sites: 2, 24) Family: Prosopistomatidae Lameere 1917 Genus: Prosopistoma Latreille 1833 (1 site: 17)

Identification Key to Mayfly Larvae from the Kurdistan Region, Northern Iraq I identified nine families, nine subfamilies, 19 genera, and 13 subgenera. The key was designed for mayfly larvae inhabiting the KR, taking into account the main characteristics of mayfly families, subfamilies, genera, and subgenera in the West Palearctic realm. It can be used with caution to identify mayfly larvae inhabiting neighboring regions, especially regions of Syria, Turkey, and Iran that are located within the same freshwater ecoregion and/or terrestrial ecoregions of the KR. Iron Eaton 1883 (reported by Al-Zubaidi and Al-Kayatt (1986) and Al-Zubaidi et al. (1987)) was reported in this key as Epeorus (Ironopsis) Traver 1935 (refer to the taxonomic updates mentioned in Bauernfeind and Soldàn 2012). For vocabulary and detailed morphological terms commonly used for mayfly larvae, I recommend referring to publications such as Kluge (1997), Merritt et al. (2008), and Bauernfeind and Soldàn (2012).

1 Mesonotum forming a carapace covering legs and abdominal tergites 1–6 completely, concealing them in dorsal view (Figure 1-1a). Wing-pads absent. Abdominal sternites 1–5 fused with each other and thoracic sternum (Figure 1-1b). Gills present on abdominal segments 1–6, all of them inserted dorsolaterally, and all enclosed/concealed in gill-chamber located between carapace and abdominal tergites 1–6 (Figure 1-1b) ......

20 ...... Family: Prosopistomatidae Lameere 1917 ...... Genus: Prosopistoma Latreille 1833

1a 1b

Figure 1-1. Prosopistoma: (a) carapace, dorsal; (b) carapace, thoracic sterna, and sternites 1-6, ventral.

1’ Mesonotum not forming carapace (Figures 1-2, 1-3, 1-47c), legs and all abdominal tergites (1–10) visible in dorsal view. At least one pair of wing-pads present (Figures 1-2, 1-3, 1-33c, 1-34c, 1-47c). Abdominal sternites 1–5 not fused with each other and thoracic sternum (Figures 1- 11d, 1-24b). Gills present on abdominal segments 1–7, 2–7, 3–7, or 4–7; all of them free, not enclosed/concealed in any chamber; most of them inserted laterally or dorsolaterally (Figures 1-24b, 1-48e); gills when available on abdominal segment 1 sometimes inserted ventrally or ventrolaterally (Figure 1-11d) ...... 2

21 2 3

Figure 1-2. Electrogena: mesonotum and wingpads, dorsal. Figure 1-3. Isonychia (Isonychia): mesonotum, metanotum, and wingpads, dorsal.

2 (1’) Gills on abdominal segments 2–7 bifurcate and each part lanceolate with fringed margin (Figure 1-4b). Each mandible with a thorn-like smooth narrow long tusk, as long as or longer than head (Figure 1-4c). Fore tibia distally broadened (Figure 1-4d). [Extra characters: Gills turned dorsally, covering part of the abdomen. Labial palps 3-segmented. Glossae rounded or oval (Figure 1-4e)] ...... Family: Ephemeridae Latreille 1810 Mandibular tusk round in cross section, slightly curved outwards, and without denticles (Figure 1-4c). Gill 1 vestigial and distinctly smaller than others (Figure 1-4a). Abdominal segments 7–9 elongate compared to other segments. Gills on Abdominal segment 7 clearly situated in the middle of lateral or dorso-lateral side ...... Subfamily: Ephemerinae Latreille 1810 Frontal projection of head bifurcate; antennal segments with whorls of fine setae (Figure 1-4f). Mandibular tusks easily visible in dorsal view, with only a few bristles, but without ramifications or secondary teeth (Figure 1-4c). Tarsal claws without teeth, but fine crenulations may exist. All tibiae broadened distally (Figure 1-4d). Seven pairs of gills situated in

22 the middle of the dorso-lateral side of the abdominal tergites 1–7. Abdominal gill 1 bifurcate, tubiform, and without fringed margins (Figure 1-4a). Abdominal gills 2–7 distinctly with a deep cleft and fringed margins (Figure 1-4b) ...... Genus: Ephemera Linnaeus 1758 Frontal projection of head bi-pointed. Mandibular tusks long and slender. Abdominal gills 2–7 usually vertically-oriented and their tips meet on dorsal side of abdomen ...... Subgenus: Ephemera Linnaeus 1758

4a 4b

23 4f

Figure 1-4. Ephemera (Ephemera): (a) first right gill, dorsal; (b) second right gill, dorsal; (c) left mandible, dorsal; (d) right fore leg, outer; (e) labium, ventral; (f) head, dorsal.

2’ (1’) Gills on abdominal segments 2–7 plate-like, operculate, semi-operculate, bifurcate, or forked (Figures 1-11c, 1-13d, 1-16c, 1-17c, 1-18b, 1-19b, 1- 32b, 1-33b, 1-34b, 1-35c, 1-37c, 1-38c, 1-43a, 1-44a, 1-45f, 1-46c, 1- 47a and b, 1-48a to c). If any of abdominal gills 2–7 operculate, semi- operculate, or plate-like, then its margin fringed with short or long hair- like setae, stout bristles, spines, or tubiform processes. If any of abdominal gills 2–7 bifurcate or forked, then each part smooth and without a fringed margin. Mandibles without any tusk (Figures 1-5, 1-6, 1-41c). Fore tibiae never broadened distally (Figures 1-11a, 1-12a, 1-13a, 1-16a, 1-17a, 1-22a, 1-23, 1-41b) ...... 3

24 6

Figure 1-5. Electrogena: left mandible, ventral. Figure 1-6. Caenis: mandibles, ventral.

3 (2’) Head capsule distinctly dorso-ventrally flattened. Eyes situated on dorsal side of head capsule (Figures 1-7 to 1-9; 1-21b) ...... 4

25 8 9

Figure 1-7. Oligoneuriella: head, dorsal. Figure 1-8. Electrogena: head, dorsal. Figure 1-9. Ecdyonurus (Ecdyonurus): head, dorsal.

3’ (2’) Head capsule not distinctly dorso-ventrally flattened. Eyes situated on lateral side of head capsule and extended downwards (Figures 1-10, 1- 24a, 1-25) ...... 10

Figure 1-10. Isonychia (Isonychia): head, dorsal.

4 (3) Inner margin of femur and tibia of fore legs with long filtering hair-like setae (Figure 1-11a). Basal part of maxilla with a tuft of tubular filiform gills (Figure 1-11b). Abdominal gill plates 2–7 chitinized plates that always shorter than respective abdominal segment (Figures 1-11c and d). Abdominal gill plate 1 chitinized or membranous and always inserted ventro-laterally (Figure 1-11d) ......

26 ...... Family: Oligoneuriidae Ulmer 1914 ...... 5

11a 11b

11c

27 11d

Figure 1-11. Oligoneuriopsis: (a) right fore leg, dorsal; (b) left maxilla, ventral; (c) gills

1–3, ventral; (d) abdominal sternites and gills 1–3, ventral.

4’ (3) Inner margin of femur and tibia of fore legs never with long filtering hair- like setae (Figures 1-12a, 1-13a, 1-16a, 1-17a). Basal part of maxilla never with a tuft of tubular filiform gills (Figures 1-12b, 1-13b, 1-20d, 1- 21d). Abdominal gill plates never supported with chitin (Figures 1-13c and d, 1-16b and c, 1-17b and c, 1-18a and b, 1-19a and b) and usually as long as or longer than respective abdominal segment. Plate of abdominal gills 1 always membranous (Figures 1-13c, 1-16b, 1-17b, 1- 18a, 1-19a). Abdominal gills 1 usually inserted laterally or dorsolaterally; sometimes inserted ventrally or ventro-laterally. [Extra characters: Terminal segment of maxillary palp broad and long (Figures 1-12b, 1-13b, 1-20d, 1-21d), but maxillary palp never visible from dorsal side of head. Gills 1–6, each with a plate and a tuft of delicate filaments (Figures 1-13c and d, 1-16b and c, 1-17b and c, 1-18a and b, 1-19a and b)] ......

28 ...... Family: Heptageniidae Needham 1901 ...... 6

12b

12a

Figure 1-12. Ecdyonurus (Ecdyonurus): (a) left fore leg, dorsal; (b) left maxilla, ventral.

13a

13b

29 13c 13d

Figure 1-13. Electrogena: (a) left fore leg, dorsal; (b) left maxilla, ventral; (c) first left gill, dorsal; (d) second left gill, dorsal.

5 (4) Plate of gills 1 sclerotized and usually longer than half of respective abdominal segment. Abdominal sternites 2–5 (6) with a postero-medial group of short, scale-like bristles, with bluntly rounded end (Figure 1- 14). Paracercus vestigial or well developed. Cerci always uni-colored, without a dark band in the middle ...... Genus: Oligoneuriella Ulmer 1924

Figure 1-14. Oligoneuriella: group of scale-like bristles on postero-medial part of sternite 3, ventral.

30 5’ (4) Plate of gills 1 membranous and usually shorter than half of respective abdominal segment (Figures 1-11c and d). Abdominal sternites 2–5 with a postero-medial group of long, stiff, sharply ended, scale-like bristles (Figure 1-15). Paracercus always well developed (equals 2/3 of cerci or longer). Cerci with or without dark band in the middle ...... Genus: Oligoneuriopsis Crass 1947

Figure 1-15. Oligoneuriopsis: group of scale-like bristles on postero-medial part of sternite 3, ventral.

6 (4’) Distal end of fore femora with dorsal projection (Figure 1-16a). Plate of gills on abdominal segment 1 positioned ventrally or ventro-laterally, usually greatly enlarged compared to other gills and extended on the ventral side of abdomen (Figures 1-16b and c, 1-18a and b). If the plate of gills 1 sub-equal or little larger than the plate of gills 2 (Figures 1-19a and b), then paracercus vestigial or missing ...... Subfamily: Rhithrogeninae Lestage 1917 ...... 7

31 16a

16b

16c

Figure 1-16. Rhithrogena: (a) fore left leg, dorsal; (b) first right gill, ventral; (c) second right gill, ventral.

6’ (4’) Distal end of fore femora without dorsal projection (Figure 1-17a). Plate of gills on abdominal segment 1 positioned laterally, never extending on the ventral side of abdomen, and usually smaller than the plate of gills 2 (Figures 1-17b and c). Paracercus always well developed, sub-equal to cerci or little shorter. [Extra characters: Glossae broadened in the middle and inner margin angular and clearly convex (Figures 1-17d, 1-21e). Labrum always broader than long (Figures 1-17e, 1-20c, 1-21c)] ......

32 ...... Subfamily: Ecdyonurinae Ulmer 1920 (1905) ...... 9

17a

17b 17c

17d

17e

Figure 1-17. Ecdyonurus (Ecdyonurus): (a) left fore leg, dorsal; (b) first left gill, dorsal; (c) second left gill, dorsal; (d) labium, ventral; (e) labrum, dorsal.

33 7 (6) Paracercus present ...... Genus: Rhithrogena Eaton 1881

7’ (6) Paracercus vestigial or missing ...... Genus: Epeorus Eaton 1881 ...... 8

8 (7’) Plate of gills 1 kidney-like and much larger than the plates of gills 2–7 (Figures 1-18a and b). For gills 1, left and right plates, inner margins meet ventrally ...... Subgenus: Ironopsis Traver 1935

18a

18b

Figure 1-18. Epeorus (Ironopsis): (a) first left gill, ventral; (b) second left gill, ventral.

8’ (7’) Plate of gills 1 heart-like and sub-equal or little larger than the plates of gills 2–7 (Figures 1-19a and b). For gills 1, left and right plates, inner margins widely separate and not meet ventrally ...... Subgenus: Epeorus Eaton 1881

34 19a 19b

Figure 1-19. Epeorus (Epeorus): (a) first left gill, dorsal; (b) second left gill, dorsal.

9 (6’) Pronotum with prominent postero-lateral processes, almost flat and heading backwards (Figure 1-20a) ...... Genus: Ecdyonurus Eaton 1868 Anterior borders and lateral tips of superlingua of hypopharynx with long fine setae (Figure 1-20b). Antero-ventral side of labrum usually with one row of stout, spine-like, sharp bristles (Figure 1-20c). Distal end of maxilla usually with one row of <20 comb-like sclerotized bristles (Figure 1-20d) ...... Subgenus: Ecdyonurus Eaton 1868

20a

20b

35 20d

20c

Figure 1-20. Ecdyonurus (Ecdyonurus): (a) pronotum, dorsal; (b) hypopharynx, ventral; (c) labrum, ventral; (d) left maxilla, ventral.

9’ (6’) Pronotum without postero-lateral processes (Figure 1-21a) ...... Genus: Electrogena Zurwerra and Tomka 1985 [Extra characters for Electrogena: Anterior margin of head normal and never thickened (Figure 1-21b). Medio-anterior margin of labrum straight or little concave, but never with conspicuous notch (Figure 1- 21c). Ventral side of maxilla with numerous scattered short bristles. Distal end of maxilla with a row of >13 comb-like sclerotized bristles (Figure 1-21d). Inner margin of glossae usually convex (Figure 1-21e). Only the ventral row of long hairs available on each of the hind and middle tibiae; some scattered very short thin hair may present along the lateral ridge, but never arranged in a conspicuous row (Figure 1-21f)]

21b

21a

21c 21d

21f

21e

Figure 1-21. Electrogena: (a) pronotum, dorsal; (b) head, frontal; (c) labrum, ventral; (d) left maxilla, ventral; (e) labium, ventral; (f) left hind leg, dorsal.

10 (3’) Inner margin of fore legs with long filtering hair-like setae. Distal end of fore tibiae with straight, relatively long, spur-like process that is heading internally (Figure 1-22a). Basal parts of fore coxae and maxillae with a tuft of tubular filiform gills (Figures 1-22a and b) ...... Family: Isonychiidae Burks 1953 ...... Genus: Isonychia Eaton 1871 Fore coxae with a tuft of multi-branched filamentous gills (Figure 1-22a) ...... Subgenus: Isonychia Eaton 1871

37 22b

22a

Figure 1-22. Isonychia (Isonychia): (a) right fore leg, dorsal; (b) left maxilla, ventral.

10’ (3’) Inner margin of fore legs never with long filtering hair-like setae. Fore tibia never with apico-internal process/spur (Figures 1-23, 1-41b). Basal parts of fore coxae and maxillae never with a tuft of tubular filiform gills (Figures 1-23, 1-35a, 1-36) ...... 11

Figure 1-23. Caenis: right fore leg, dorsal.

38 11 (10’) Inner side of cerci with one row of long hair-like setae (Figure 1-44c). Antennal length is usually more than 3 times longer than width of head. Lateral ocelli positioned posterior to lateral branches of the epicranial suture (Figure 1-24a). Postero-lateral margin of abdominal segments bluntly rounded or with short rounded spine, but never with a flat spine (Figure 1-24b) ...... Family: Baetidae Leach 1815 ...... 12

24a

24b

Figure 1-24. Baetis (Baetis): (a) head, dorsal; (b) abdominal tergites 1–10, dorsal.

11’ (10’) Inner and outer sides of cerci with long hair-like setae. If not, then each segment of cerci with a whorl of fine setae, stiff spine-like bristles, and/or spines (Figures 1-26, 1-27a). Antennal length is usually less than 2 times longer than width of head. Lateral ocelli positioned on lateral

39 branches of the epicranial suture (Figure 1-25). Postero-lateral margin of at least 2–3 abdominal segments with a flat spine (Figure 1-27b) ...... 21

27a

27b

Figure 1-25. Habrophlebia: head, dorsal. Figure 1-26. Caenis: terminal filaments, dorsal. Figure 1-27. Serratella: (a) cerci, dorsal; (b) postero-lateral margin of tergite 6, dorsal.

40 12 (11) Tarsal claws always long, ranging from 1/4 the length of tibiae to slightly longer than tibia, slender, slightly curved apically, smooth (Figure 1-28), with 1 row of denticles of different shapes, or 2 rows of denticles of the same shape (Figure 1-29). Length of antenna usually greater than length of head and thorax together. Dorsal side of labrum smooth or with scattered bristles, but never with transversal row of stout bristles (Figure 1-30) ...... Subfamily: Cloeoninae Newman 1853 ...... 13

Figure 1-28. Procloeon (Pseudocentroptilum): claw of right hind leg, outer. Figure 1-29. Centroptilum: claw of right hind leg, outer. Figure 1-30. Cloeon (Cloeon): labrum, dorsal.

12’ (11) Tarsal claws short, 1/4 the length of tibia at maximum, robust, apically hooked or bent, and with one row of denticles of the same shape but

41 usually of gradually increasing length (Figures 1-43d, 1-44d). Length of antenna usually sub-equal to length of head and thorax together or shorter. Dorsal side of labrum with transversal row of stout bristles (Figure 1-31) ...... Subfamily: Baetinae Leach 1815 ...... 18

Figure 1-31. Baetis (Rhodobaetis): labrum, dorsal.

13 (12) Abdominal gills 1–6, each composed of 1 leaf-like plate. Gills almost symmetric and with pointed distal end (Figures 1-32a and b) ...... Genus: Centroptilum Eaton 1869 [Extra characters for Centroptilum: Maxillary palp 3-segmented. For mandibles, incisor groups (2 groups of thick stout teeth) distinctly separated. Hind wing pads barely developed. Terminal filaments usually uniform in color, sometimes with faint brownish transversal area and weak brownish rings]

32a 32b

Figure 1-32. Centroptilum: (a) first left gill, dorsal; (b) second left gill, dorsal.

42 13’ (12) Abdominal gills 1–6, each composed of 2 plates of different size and usually different shape; dorsal plate may be represented as a vestigial dorsal flap. Gills not symmetric; some rounded ventral plates maybe close to symmetric, but their dorsal plates usually not rounded and not symmetric (Figures 1-33a and b, 1-34a and b, 1-35b and c, 1-37b and c, 1-38b and c) ...... 14

14 (13’) Gills 1 clearly smaller in size than gills 2–6. Gills with pinnate tracheization or venation (Figures 1-33a and b, 1-34a and b) ...... Genus: Procloeon Bengtsson 1915 ...... 15

14’ (13’) Gills 1 range from sub-equal to larger than gills 2–6. Gills with palmate tracheization or venation (Figures 1-35b and c, 1-37b and c, 1-38b and c) ...... Genus: Cloeon Leach 1815 ...... 16 15 (14) Dorsal plate in abdominal gills 1–6 represented as a vestigial dorsal flap (Figures 1-33a and b). Hind wing pads absent and not developed (Figure 1-33c) ...... Subgenus: Procloeon Bengtsson 1915

33a 33b

43 33c

Figure 1-33. Procloeon (Procloeon): (a) first left gill, dorsal; (b) third left gill, dorsal; (c) thorax, showing fore wingpads only, lateral.

15’ (14) Dorsal plate in abdominal gills 1–6 very well developed, with a width of 1/2 the ventral plate or greater (Figures 1-34a and b). Hind wing pads barely developed; although costal process well developed, hind wing pads still several times smaller than middle wing pads and usually concealed underneath (Figure 1-34c) ...... Subgenus: Pseudocentroptilum Bogoescu 1947

34a 34b

34c

Figure 1-34. Procloeon (Pseudocentroptilum): (a) first left gill, dorsal; (b) second left gill, dorsal; (c) thorax, showing fore and hind wingpads, lateral.

44 16 (14’) Maxillary palp composed of 3 segments (Figure 1-35a); segment 3 usually shorter than segment 2 and sometimes hard to distinguish. Ventral plate of abdominal gills 1–6 almost rounded in shape (length and width almost sub-equal) and almost symmetric (Figures 1-35b and c) ...... Subgenus: Cloeon Leach 1815

35a

35b 35c

Figure 1-35. Cloeon (Cloeon): (a) left maxilla, ventral; (b) first left gill, dorsal; (c) second left gill, dorsal.

16’ (14’) Maxillary palp composed of 2 segments only (Figure 1-36). Ventral plate of abdominal gills 1–6 not rounded in shape (length distinctly greater than width) and clearly asymmetric ...... 17

45 36

Figure 1-36. Cloeon (Intercloeon): left maxilla, ventral.

17 (16’) Distal end of segment 3 of labial palp slightly wider than distal end of segment 2. Segment 3 of labial palp distinctly truncate apically (Figure 1-37a). Gills 1 larger than any of gills 2–6. Gills dorsal plates very well developed (Figures 1-37b and c) ...... Subgenus: Intercloeon Kluge and Novikova 1992

37a

46 37b 37c

Figure 1-37. Cloeon (Intercloeon): (a) labium, ventral; (b) first left gill, dorsal; (c) second left gill, dorsal.

17’ (16’) Distal end of segment 3 of labial palp of the same width as distal end of segment 2 or slightly less. Segment 3 of labial palp barely truncate apically (Figure 1-38a). Gill 1 sub-equal or smaller than any of gills 2–6. Dorsal plates of gills much smaller than ventral plates and mostly vestigial, with < 1/3 the width of ventral plate at its maximum (Figures 1-38b and c) ...... Subgenus: Similicloeon Kluge and Novikova 1992

38a

47 38b 38c

Figure 1-38. Cloeon (Similicloeon): (a) labium, ventral; (b) first left gill, dorsal; (c) second left gill, dorsal.

[Extra characters for Cloeoninae genera reported in this key: Labial palp composed of 3 distinct segments. Segment 2 of labial palp with normal distal end (never with inner apical lobe); width of distal end of segment 2 never two times the width of distal end of segment 3. Labrum normal (with sub-equal widths of base and apex). Medial anterior margin of labrum straight or with a shallow notch (labrum never bi-lobed). Glossae and paraglossae sub-equal in length and glossae never truncate and always apically pointed]

18 (12’) Distal end of segment 2 of labial palp with noticeably developed inner lobe that may have a width of 1/2 the width of segment 3. Segment 2 of labial palp distinctly with curved, never straight, inner margin. Paraglossae 2.5–3 times the width of glossae (Figure 1-39a). Distal end of second antennal segment (pedicel) distinctly wider than the flagellum and almost forming a lobe (Figure 1-39b) ...... Genus: Labiobaetis Novikova and Kluge 1987

48 39b

39a

Figure 1-39. Labiobaetis: (a) labium, ventral; (b) antenna.

18’ (12’) Distal end of segment 2 of labial palp never with inner lobe. Distal end of segment 2 of labial palp sometimes slightly expanded and inner apical margin form a prominent apex. Segment 2 of labial palp distinctly with straight, never curved, inner margin. Width of paraglossae always less than 2 times the width of glossae. Distal end of antennal pedicel sub- equal in width to the next segment; the transition in size very smooth with no sign of lobed or swollen pedicel (Figure 1-40) ...... 19

Figure 1-40. Baetis: antenna.

19 (18’) Distal end of segment 3 of labial palp rounded or with one bluntly pointed distal corner (Figure 1-41a). Femoral villopore, a tuft of short simple setae situated antero-ventrally at the beginning of femora, always present (Figure 1-41b). Body never compressed laterally, circular in cross section (abdomen sometimes slightly compressed dorso-ventrally). Width of paraglossae sub-equal to glossae or slightly wider (Figure 1- 41a). Abdominal gills 1 and prostheca of right mandible always present (Figure 1-41c) ...... Genus: Baetis Leach 1815 ...... 20

41a

50 41b 41c

Figure 1-41. Baetis: (a) labium, ventral; (b) left fore leg, ventral; (c) right mandible, ventral.

19’ (18’) Distal end of segment 3 of labial palp truncate and with 2 distal corners (Figure 1-42). Femoral villopore absent and not developed. Body slightly compressed laterally, rounded in cross section. Paraglossa ranging from sub-equal up to 1.8 times broader than glossa (Figure 1-42). Abdominal gills 1 and prostheca of right mandible sometimes absent and not developed ...... Genus: Nigrobaetis Novikova and Kluge 1987

51 42

Figure 1-42. Nigrobaetis: labium, ventral.

20 (19) Abdominal gills with smooth or finely serrated margin, and never with stout pointed spine-like bristles (Figure 1-43a). First and/or second antennal segments (scape and/or pedicel) smooth or with thin delicate hair-like bristles (never with stout spine-like bristles). Posterior margin of tergites with apically pointed or rounded spines and never with spatulate spines (Figure 1-43b). Paracercus ranging from well developed (sub-equal or slightly shorter than cerci) to vestigial and composed of couple segments (Figure 1-43c). Tarsal claws sometimes with sub-apical hair-like short bristles (Figure 1-43d) ...... Subgenus: Baetis Leach 1815

52 43a

43b

43c

43d

Figure 1-43. Baetis (Baetis): (a) second right gill, dorsal; (b) postero-medial part of tergite 4, dorsal; (c) terminal filaments, dorsal; (d) claw of the right fore leg, dorsal.

20’ (19) Abdominal gills with stout pointed spine-like bristles (Figure 1-44a). First and/or second antennal segments and posterior margin of tergites with pointed or spatulate spines (Figure 1-44b). Paracercus always well developed (Figure 1-44c). Tarsal claws rarely with sub-apical hair-like short bristles (Figure 1-44d) ...... Subgenus: Rhodobaetis Jacob 2003

44a

44b

44d

44c

Figure 1-44. Baetis (Rhodobaetis): (a) third left gill, dorsal; (b) postero-medial part of tergite 5, dorsal; (c) terminal filaments, dorsal; (d) claw of the right fore leg, dorsal.

[Extra characters for Baetis, Labiobaetis, and Nigrobaetis: Outer margin of femora and tibiae without a continuous row of long sub-marginal

54 bristles. Body circular or rounded in cross section (never dorso-ventrally compressed and ventrally flattened)]

21 (11’) All pairs of abdominal gills (i.e., seven pairs) visible in dorsal view. Gills not sclerotized and positioned laterally (sometimes gills took a dorso- lateral position right at the posterior corner of tergites) ...... Family: Leptophlebiidae Banks 1900 ...... 22

21’ (11’) One to five pairs of plate-like abdominal gills visible in dorsal view. Gills positioned dorsally and at least one pair of gills sclerotized ...... 23

22 (21) Labrum with deep antero-medial notch, smooth or with fine denticles, and simple lateral bristles (Figure 1-45a). Lingua of hypopharynx with long and curved lateral processes (Figure 1-45b) ...... Subfamily: Atalophlebiinae Peters 1980 Gills on abdominal segment 1 simple and each composed of one plate, filiform, rarely bifurcate, and different in shape than all other gills (45c). Gills 2–7 similar in shape, each with rounded quadrangular dorsal and ventral plates, with incisions. Dorsal and ventral plates of gills 2–7 terminate in 3 apical processes (Figures 1-45d to f) ...... Genus: Choroterpes Eaton 1881 Apical processes of gills of different shapes. Middle process lanceolate and much longer than lateral processes (Figures 1-45d to f) ...... Subgenus: Choroterpes Eaton 1881

55 45a 45b

45c 45d

45e 45f

Figure 1-45. Choroterpes (Choroterpes): (a) labrum, dorsal; (b) hypopharynx, ventral; (c) first right gill, dorsal; (d) gill 6, dorsal; (e) dorsal plate of gill 6, dorsal; (f) ventral plate of gill 6, dorsal.

22’ (21) Labrum with shallow antero-medial depression (never with a notch), usually smooth, with lateral bristles, but without denticles (Figure 1-46a). Lateral bristles stout and blade-like. Lingua of hypopharynx without lateral processes; superlinguae of hypopharynx with apical process (Figure 1-46b) ...... Subfamily: Habrophlebiinae Kluge 1994

56 All gills (on abdominal segments 1–7) similar in shape, each composed of a dorsal and a ventral plate, with several long and slender processes (Figure 1-46c) ...... Genus: Habrophlebia Eaton 1881

46a 46b

46c

Figure 1-46. Habrophlebia: (a) labrum, dorsal; (b) hypopharynx, ventral; and (c) gill 4, dorsal.

23 (21’) Only one pair of sclerotized plate-like abdominal gills present; positioned on abdominal segment 2, visible in dorsal view, and different in shape than the gills on abdominal segments 3–7; operculate (covering flap-like or lid-like), with Y-like ridge, and without a baso-ventral tuft of filaments (Figures 1-47a and b). Hind wing pads missing (Figure 1-47c) ...... Family: Caenidae Newman 1853

57 Head capsule with 3 celli. Tarsal claws smooth or with fine sub-marginal teeth; hard to see using a magnification of 100x or less (Figures 1-47d and f). Labial palp 3-segmented (Figure 1-47e) ...... Subfamily: Caeninae Newman 1853 No ocellar tubercles on head. Apical segment of labial palp not broadened (Figure 1-47e). Fore coxae separated by a distance equal to the width of one coxa or less. Dorsal side of abdomen convex. Tarsal claws short and usually stout (Figures 1-47d and f) ...... Genus: Caenis Stephens 1836

47a

47b

47c 47d

58 47e

47f

Figure 1-47. Caenis: (a) sclerotized plate-like gill on the right side of tergite 2, dorsal; (b) gill on the right side of tergite 3, dorsal; (c) mesonotum and wingpads, dorsal; (d) claw on fore left leg, outer; (e) labium and maxilla, ventral; (f) right hind leg, dorsal.

23’ (21’) More than one pair of plate-like abdominal gills visible (at least partially) in dorsal view. These pairs of gills semi-operculate and positioned on abdominal segments 3 or 4–7 (Figures 1-48a to c, and e) ...... Family: Ephemerellidae Klapalek 1909 Gills present on only abdominal segments 2–7. Plate-like gills present on abdominal segments 3–7 ...... Subfamily: Ephemerellinae Klapalek 1909 Ventral plate of gill 6 with deep median cleft; cleft length 2/3 the length of the plate or longer (Figure 1-48b). Maxillary palps vestigial (with unclear segmentation) or with 3 short small segments (Figure 1-48d). Most abdominal tergites smooth, or with 2 sub-median tubercles (Figure 1-48e). Posterior margin of each segment (or every other two segments) of terminal filaments (cerci and paracercus) with whorls of short and long stiff spine-like bristles; lateral fine hair-like bristles present or absent (Figure 1-48f) ...... Genus: Serratella Edmunds 1959 [Extra characters for Serratella: Gill plate on segment 3 less than twice longer than broad, covering 1/2 of gill 4 or less. At least 1/3 of gill plate on abdominal segment 6 visible in dorsal view. Hind legs, each sub-

59 equal or shorter than abdominal length. Inner apical margin of fore tibia without pointed projection. Abdomen sub-equal or longer than head and thorax together. Abdominal tergite 9 sub-equal in length to segment 8 or shorter. Body with shallow tubercles or with 2 medial ridges on head, thorax, or abdomen (Figure 1-48e). Anterior margin of fore femora without projections. Posterior margin of fore femora with sparse short bristles and/or spines. Clypeus without clear transverse field of bristles heading forward]

48a 48b

48c

48d

60 48e

48f

Figure 1-48. Serratella: (a) dorsal plate of gill on the right side of tergite 6, dorsal; (b) ventral plate of gill on the right side of tergite 6, dorsal; (c) dorsal and ventral plates on the right side of tergite 6, dorsal; (d) left maxilla, ventral; (e) tergite 6, dorsal; (f) terminal filaments, dorsal.

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68 Chapter 2: Genetics-Based Analyses Facilitate Delineating Unknown Faunas: A Case Study on Ephemeroptera and Plecoptera in the Headwaters of the Tigris River, Iraq

Co-author: David J. Berg1 1 Department of Biology, Miami University, Hamilton, Ohio, 45011, USA

ABSTRACT Knowledge about water quality bioindicators, mayflies (Ephemeroptera) and stoneflies (Plecoptera), in Iraq is quite limited, with no previous studies about stoneflies and only two ~ 29-yr-old studies about mayflies from the Kurdistan Region of northern Iraq. This study is the first for Iraqi stoneflies and the third in terms of studying Iraqi mayflies. I sampled 53 sites in the Kurdistan Region from 2007-2010 and used a combination of morphology and genetic-based analyses to accelerate discover of mayfly and stonefly species. Genetic-based analyses were performed after sequencing 291-658 base pairs of the mitochondrial cytochrome oxidase subunit 1 (COI) gene for more than 350 specimens. First an initial rough morphological identification was performed (mostly to morphospecies), then Operational Taxonomic Units (OTUs) were delineated using genetic-based analyses; OTUs were matched and Species-Like Units (SpLUs) were delineated. SpLUs were compared and contrasted against each other and against species and subspecies known from the West Palaearctic. The results confirmed the presence of five stonefly and more than 55 mayfly taxa, the majority of them being new records for Iraq, and many of them being potentially new species and subspecies to science. Genetic Similarity Blocks (GSBs) Analysis, Automatic Barcode Gap Discovery (ABGD), and Poisson Tree Processes (PTP) model outperformed other analyses to delineate OTUs and contributed significantly to SpLU delineation. Matching genetic-based analyses results facilitated and accelerated SpLU delineation, which in turn guided more confident discovery of taxa, and played a significant role in confirming and/or discovering diagnostic morphological characters that were under-emphasized or overlooked in the taxonomic literature. Such multi-method approaches are especially important when investigating biodiversity in regions that are only now being studied.

69 Keywords: Mayfly, Stonefly, Taxonomic Impediment, Species Concepts, Integrative Taxonomy, COI Gene, Genetic-Based Analyses, Middle East, Kurdistan Region

INTRODUCTION Conservation biology and ecological research are heavily based on species taxonomy. However, after centuries of taxonomic work, a worldwide deficiency in trained taxonomists and taxonomic information remains. Consequently, research in conservation biology and ecology is often limited by this so-called “taxonomic impediment” (Hoagland 1996, de Carvalho et al. 2007; Bortolus 2008; Wheeler 2008). In recent decades, the taxonomic impediment has become a serious issue as millions of species on earth remain undiscovered, while climate change and anthropogenic disturbance are increasing dramatically and affecting most species and their natural habitats. Species-rich orders such as mayflies (Ephemeroptera), stoneflies (Plecoptera), and caddisflies (Trichoptera), a.k.a. EPT, consistent with a large percentage of the earth’s biodiversity, contain significant numbers of undescribed species (Brown and Lomolino 1998; Wilson 2007). In order to overcome the taxonomic impediment for species-rich groups such as EPT, traditional morphological identification methods need to be enhanced and accelerated using genetic information (Zhou 2009; Webb et al. 2012). In the last decade, animal scientists have started to use sequences of the Cytochrome c Oxidase subunit 1 (COI) gene (Hebert et al. 2004), known as the “barcode region”, along with different genetic-based analyses, such as Refined Single Linkage (RESL) Analysis, Automatic Barcode Gap Discovery (ABGD), Generalized Mixed Yule Coalescent (GMYC) Approach, and Poisson Tree Processes (PTP) model to accelerate species discovery through delineation of different haplotype groups and evolutionary taxa (Hajibabaei et al. 2007; Hajibabaei et al. 2011; Collins and Cruickshank 2012). These analyses have also been used as tools to assess biodiversity rapidly and to associate the various life-history stages of aquatic insects such as EPT (Zhou et al. 2007; Floyd et al. 2009; Zhou et al. 2009; Powell 2012). Refined Single Linkage (RESL) Analysis and ABGD are based on the genetic-species concept, as they use intraspecific versus interspecific genetic distance to differentiate species (Puillandre et al. 2012; Ratnasingham and Hebert 2013), while GMYC and PTP are related to the phylogenetic- species concept, as they depend on phylogenetic trees, coalescent and divergence times,

70 or the number of base substitutions to differentiate between species (Pons et al. 2006; Fujisawa and Barraclough 2013; Zhang et al. 2013). Scientists usually have increased confidence in species identification when the results of these analyses are consistent with each other and with morphological identification. Furthermore, these approaches may uncover cryptic species. Genetic-based analyses have proven to play a significant role in reducing the taxonomic impediment, through guiding and accelerating species identification (Monaghan et al. 2009; Vuataz et al. 2011). In developing countries such as those of the Middle East, the taxonomic impediment is very obvious due to an acute deficiency of taxonomists and evolutionary biologists. In Iraq, this situation is exacerbated by other impediments due to political instability and logistical issues (Al-Saffar 2006 and 2007). For stoneflies, no studies have been conducted and no information is available. For mayflies, only two ~29yr old studies have been conducted so far. The first study, which was a preliminary survey for mayfly larvae at seven random sites in northern Iraq, resulted in identification of four species and one group-complex, while suggesting the potential presence of eight additional species within six genera of five families (Al-Zubaidi and Al-Kayatt 1986). The second study in northern Iraq recorded the presence of 11 species, two group-complexes, nine genera, and six families, including two new species which appear to be endemic to Iraq: Isonychia arabica Al-Zubaidi, Braasch, and Al-Kayatt 1987 and Oligoneuriella bicaudata Al- Zubaidi, Braasch, and Al-Kayatt 1987 (Al-Zubaidi et al. 1987). Collectively, only 12 species have been identified from northern Iraq since 1987, exact species for group- complexes were not identified, and identification was uncertain for two of the 12 species. Information about caddisflies in northern Iraq is more extensive (Mosely 1934; Al- Zubaidi and Al-Kayatt 1987; Malicky 1987). In addition, recent DNA barcoding research in the Kurdistan region (KR), northern Iraq, uncovered high biodiversity, with many new records for Iraq, and many undescribed species (Geraci et al. 2011; Trichoptera of Iraq Project: Barcode of Life Data (BOLD) Systems, www.boldsystems.org, Ratnasingham and Hebert 2007). Collectively, 12 species, eight morphospecies, and 13 Neighbour- Joining-Tree haplogroups are currently known for caddisflies from this region. In this study, I conducted a rapid assessment for unique evolutionary taxa of stoneflies and mayflies in the KR, as a step toward solving the taxonomic impediment,

71 and creating a basis for conducting conservation and ecological studies in the aquatic ecosystems of the region. I used a variety of genetic-based analyses employing genetic and phylogenetic species concepts, and morphology hoping to find a consensus in species delineation. I hypothesized that integrating morphological, genetic, and phylogenetic species concepts would facilitate the delineation of Ephemaroptera and Plecoptera taxa. I predicted that streams in the KR are colonized by multiple mayfly and stonefly species, many of which will be new records for Iraq and some of which will be new to science. First, I performed an initial rough morphological identification (mostly for morphospecies). Next, I sequenced the COI gene for more than 350 specimens. I delineated Operational Taxonomic Units (OTUs) using each of five genetic-based analyses; Genetic Similarity Blocks (GSBs) Analysis (I introduced here), RESL, ABGD, GMYC, and PTP. I matched the OTUs and delineated the Species-Like Units (SpLUs) with increased confidence in taxa identification when OTUs from different analyses were consistent with each other and with morphological identification. Finally, I compared and contrasted the SpLUs against each other and against species and subspecies known from the West Palaearctic realm.

METHODS Study Area Five major tributaries (Khabur, Great Zab, Little Zab, Udhaim, and Diyala rivers), along with streams draining Mosul, enter the Tigris River as it flows from northwest-to- southeast along the western side of the KR. Fifty-three sites located along these streams and tributaries (Table 0-1) were sampled biannually (summer and winter) from summer 2007 to summer 2010 as part of a larger ‘‘Key Biodiversity Areas’’ survey conducted by Nature Iraq (Nature Iraq 2009; Rubec et al. 2009; Iraqi Ministry of the Environment 2010, Iraqi Ministry of the Environment and Nature Iraq 2015). Caddisfly specimens from twenty of these sites were used for a previous study of Iraqi caddisfly diversity in the KR (Geraci et al. 2011). Security concerns and logistical issues associated with war and civil unrest resulted in selection of sampling sites primarily based on the ability to access sites within the KR that were safe. As a result, the sampling “design” was somewhat haphazard and not necessarily representative of the breadth of diversity within these freshwater ecosystems. However, all five of these tributary systems were sampled in

72 multiple localities throughout their upper reaches and in their headwater streams, such that a large part of the Upper Tigris and Euphrates freshwater ecoregion within the KR was sampled. This sampling area encompassed four terrestrial ecoregions: Zagros Mountains Forest Steppe (44 sites sampled), Middle East Steppe (6), Eastern Mediterranean Conifer-Sclerophyllous-Broadleaf Forests (2), and Mesopotamian Shrub Desert (1; National Geographic Society 2008; Hoekstra et al. 2010; Figure 0-1).

Sampling and Initial Morphological Identifications Mayfly and stonefly larvae were collected using standard benthic macroinvertebrate sampling techniques. In order to obtain a representative sample at each site, the highest possible number of microhabitats was sampled, using a kick net (sampling area of 1 m2 per replicate), a Surber sampler (sampling area of 0.09 m2 per replicate), and/or a Hess sampler (sampling area of 0.8 m2 per replicate). Length of sampled stream reaches ranged from 50-100m and 6-10 samples were taken in each. Specimens were washed and sieved in the field through a 0.5 mm nytex screen and then preserved in 70% ethanol. Adult specimens were collected from some sites using a light trap and also preserved in 70% ethanol. Upon returning to the laboratory, all specimens (larvae and adults) were transferred to 95% ethanol and stored at room temperature. More than 10,000 individuals were collected from more than 318 samples, consisting overwhelmingly of mayflies, with only a small number of stoneflies collected. I used a stereomicroscope (Carl Zeiss SteREO Discovery V12 with maximum magnification of 250x), to examine the morphological traits (Chapter 1), and consulted identification keys and guides from the Palearctic realm (DeWalt et al. 2015; Kluge 2015; Staniczek 2015) to perform initial morphological identifications. Specimens preserved in 95% ethanol were sorted and identified to species, when possible. Specimens that could not be identified to species were identified to the genus or subgenus-level and then sorted into morphospecies based on gross external morphological characters such as gill shape and placement, body size and shape, prominent body features, and colors patterns. Next, species and morphospecies exemplars from each site (1–6 exemplars per morphospecies per site, including all available life-stages; usually male and female larvae, and sometimes adults and sub-imagoes) were chosen for DNA extraction and sequencing.

73 DNA Sequencing Total DNA was extracted from the leg(s) or the entire body depending on specimen size and preservation condition. DNA extraction, amplification, and sequencing were performed following standard protocols, using the following kits and materials: QIAGEN DNeasy Blood & Tissue Kit (for DNA purification-extraction); GoTaq Colorless Master Mix and Qiagen Taq PCR Core Kit (for polymerase chain reaction (PCR)); IBI Scientific Agarose Gel and Thermo Scientific 50X Tris-acetate-EDTA (TAE) Buffer (for Agarose Gel Electrophoresis and PCR products visualization and extraction); QIAquick Gel Extraction and E.Z.N.A. Gel Extraction Kits (for PCR products purification); and BigDye Terminator v3.1 (for bidirectional Cycle Sequencing of purified PCR products). For PCR, the entire COI gene (658 base pairs) was amplified with LCO1490 + HCO2198, LCO1490_t1 + HCO2198_t1, LepF1 + LepR1, LepF1_t1 + LepR1_t1, MLepF1 + MLepR1, C_LepFolF + C_LepFolR, and/or MEPTR1_t1 (Messing 1983; Folmer et al. 1994; Hebert et al. 2004; Hajibabaei et al. 2006; Zhou et al. 2009), using the following thermal cycle: (1) initial denaturation at 94˚C, for 2:30min; (2) denaturation at 94˚C for 30sec.; (3) annealing at 46˚C for 1min.; (4) extension at 72˚C for 1min.; (5) steps 2-4 were repeated an additional 34 times; (6) final extension at 72˚C for 10min.; and (7) indefinite hold at 4˚C. DNA sequences were edited using Geneious v5.4 (Drummond et al. 2011) and aligned with MUSCLE (Edgar 2004) using eBioX 1.6 (Barrio et al. 2009). Sequences were uploaded to the Barcode of Life Data (BOLD) Systems v3.6 (Ratnasingham and Hebert 2007) to check their quality, construct the Iraqi library, match life-history stages of species (if needed), and perform Refined Single Linkage (RESL) Analysis. All of the Iraqi sequences submitted to the BOLD system were required to pass a quality check that excluded a sequence if (1) it had less than 500 bp of the COI gene or had more than 1% ambiguous bases; (2) it had stop codons or improbable peptides (Finn et al. 2010); or (3) it matched sequences from bacteria or specific external contaminants (e.g., humans). Individual COI sequences were deposited and will be made public in the BOLD Systems.

Genetic Similarity Blocks (GSBs) Analysis Genetic Similarity Blocks (GSBs) Analysis, a taxonomy-independent approach based on the genetic species concept, was employed in this study. Each morphospecies’ sequence

74 in fasta format was uploaded individually to the BOLD Identification System (IDS) for COI and compared to all available sequences in the BOLD Systems (>3,000,000 sequences with lengths of 500-658bp, and provisional or full species names). The IDS search engine returned a list of the nearest 99 sequences (including, but not limited to, mayflies) based on pairwise genetic distance (a.k.a. uncorrected P-distance), starting with the most similar sequence. Each of the 99 sequences was listed with its available provisional or full species name and a score of % similarity showing how similar it was to the Iraqi morphospecies sequence. Names and % similarity for all 99 sequences were then copied to Microsoft Excel, where they were summarized manually to remove all taxa other than mayflies (or stoneflies) and keep one representative of each mayfly (or stonefly) taxon when there was more than one sequence with the same name; only the closest match (based on % similarity) was retained and the rest were discarded, resulting in a shorter list of matches (usually <15 species). This process was repeated for all Iraqi morphospecies sequences, and their provisional names and list of summarized matches were next used to construct a matrix in Microsoft Excel; each Iraqi morphospecies was listed in column 1 and its summarized matches in subsequent columns to the right, starting with the most similar match (with a name and % similarity in each cell). Iraqi morphospecies matched by the same or similar list of species were stacked on top of each other, taking into account the same or similar % similarity scores (<2% difference). Stacking all morphospecies resulted at the end in constructing a matrix of species genetic similarity. Examining this matrix visually resulted in detecting a clear pattern, where “blocks” within this matrix were separated from each other and delineated visually. Iraqi morphospecies within a given block were considered as one OTU; and morphospecies from different blocks were considered as different OTUs. After delineating blocks, if any species within a block was > 99% similar to the target sequence, it was considered a match.

Refined Single Linkage (RESL) Analysis and Barcode Index Numbers (BINs) In order to delineate the available Operational Taxonomic Units (OTUs) and assign them to new or already available Barcode Index Numbers (BINs) in the BOLD Systems, Refined Single Linkage (RESL) Analysis (Ratnasingham and Hebert 2013) was performed by (1) translating sequences to amino acids, aligning them to a Hidden

75 Markov Model (HMM) of the COI protein (Eddy 1998), and then translating the aligned amino acids back to nucleotides; (2) grouping the aligned nucleotide sequences with uncorrected pairwise distance (p-distance) of 2.2% or less into clusters of initial OTUs; (3) refining the clusters by collapsing neighbor OTUs (with <4.4% pairwise distance) into a single unit; (4) running Markov Clustering on the resulting OTUs to produce eight refinement options for each candidate OTU (i.e. candidate clustering schemes) using inflation parameters ranging from 1.0–2.4 at intervals of 0.2; (5) generating a Silhouette index score for each candidate clustering scheme, and selecting the one with the maximum score and reporting it as the delineated OTU; and (6) assigning unique codes for the delineated OTUs using the Barcode Index Number (BIN) System.

Automatic Barcode Gap Discovery (ABGD) To account for the rate of evolution and genetic divergence within genera, a fasta file for each genus was compiled from the Iraqi sequences and from sequences downloaded from GenBank (National Center for Biotechnology Information – NCBI). The fasta file was then used to perform Automatic Barcode Gap Discovery (ABGD) analysis at the ABGD website (http://wwwabi.snv.jussieu.fr/public/abgd/). This analysis was performed with two models of nucleotide evolution, Jukes-Cantor (JC69) and Kimura (K80) TS/TV 2.0, to delimit the available OTUs by comparing intraspecific versus interspecific genetic distances (Puillandre et al. 2012). I used the following settings: minimum pairwise genetic distance (Pmin) = 0.001, range of Pmax = 0.4–0.9, range of Steps = 100–1000, range of relative gap width or Distance MinSlope (X) = 1.0–1.5, and number of bins for distance distribution (Nb) = 100.

Phylogenetic Analyses Fasta files used in ABGD and outgroup sequences from GenBank were used to perform phylogenetic analyses using the Generalized Mixed Yule Coalescent (GMYC) approach (Pons et al. 2006; Fujisawa and Barraclough 2013) and Poisson Tree Processes (PTP) model (Zhang et al. 2013). For these analyses, I first identified the most appropriate substitution model for Bayesian analyses with BEAST v2.1.2 package (BEAUti, BEAST, and TreeAnnotator; Bouckaert et al. 2014) and MrBayes 3.2.2 (Ronquist et al. 2012) using Akaike/Bayesian Information Criterion (AIC/BIC) performed in Kakusan4 (Tanabe

76 2011); both indices, AIC and BIC, are derived from information theory, as they provide a means for model selection, by providing a relative estimate of the information lost when a given substitution model is used (Akaike1974 and 1976). When using substitution models to explain the data, adding parameters to increase complexity may result in overfitting. To solve this issue, both AIC and BIC introduce a penalty term for the number of parameters in the model. Therefore, AIC and BIC deal with the trade-off between the goodness of fit of the substitution models and their complexity (Aho et al. 2014). For GMYC, time-calibrated phylogenetic trees must be strictly ultrametric, bifurcating, and with no zero-length branches. I used the BEAST v2.1.2 package to construct the trees: Nexus files produced by Kakusan4 were imported to BEAUti and the following options were selected to prepare the xml files to run BEAST v2.1.2: (1) appropriate substitution model (alternating between HKY85 and GTR), 4-8 gamma categories, and 0.0–0.9 proportion of invariant sites following Kakusan4 results; (2) 8-10 million Markov chain Monte Carlo (MCMC) length; (3) 2–4 million trees pre-burn-in; (4) priors Yule model with default parameters; and (5) strict and relaxed molecular clock models. The quality of results was analyzed using Tracer v1.6 (Rambaut et al 2014). TreeAnnotator was used to burn-in the first 10–20 % of sampled trees and select the maximum clade credibility tree. This tree was converted to Newick format using FigTree v1.4.2 (Rambaut 2014) and used next in R 3.1.1 to perform single threshold GMYC analysis using the Ape and Splits packages (R Core Team 2014). GMYC was performed in R using the Chronos function with correlated, strict, and relaxed clock models, where the lambda parameter was set to 0.001–1.0. Poisson Tree Processes (PTP; Zhang et al. 2013) is an evolutionary model using rooted non-time-calibrated phylogenetic trees to delineate OTUs. To perform the maximum likelihood and Bayesian implementations of the Poisson Tree Processes model (mPTP and bPTP respectively), I constructed the required trees with the number of nucleotide substitutions reflected in branch lengths, using MrBayes 3.2.2. Analysis was performed using 1–3 million MCMC generations, with the number of MCMC generations selected based on analyzing the effective sampling sites and convergence of the parameter estimates by MrBayes 3.2.2 using Tracer v1.6 (Rambaut et al. 2014). Every

77 500th tree was saved (giving 2000–6000 in total), and the consensus tree was calculated after discarding the first 10% of the trees as burn-in. Recovered phylogenetic trees were edited and converted to Newick format using FigTree v1.4.2 (Rambaut 2014). Newick trees were then uploaded to the Poisson Tree Processes (PTP) web server (http://species.h-its.org/ptp), and mPTP and bPTP were performed with 500,000 MCMC generations and 10–20% pre-burn-in.

Guided Discovery of Species-Like Units (SpLUs) Neighbor-Joining trees were constructed using the BOLD Systems workbench (using pairwise genetic distance and MUSCLE alignment), and edited to visualize clusters of Iraqi sequences using FigTree v1.4.2 and TreeGraph 2.1.0 (Stöver and Müller 2010). These trees were then imported to Adobe Illustrator 2015 and edited again to add and align the results of GSBs, RESL, ABGD, GMYC, and PTP analyses. This strategy facilitated performing a visual examination for the results of all analyses, where the dominant pattern of agreement between genetic-based analyses (usually 3 matches or more) was used to delineate Species-Like Units (SpLUs). For the purpose of this study, an Operational Taxonomic Unit was any evolutionary taxon, a species or sub-species, delineated by each of the genetic-based analyses separately. A Species-Like Unit is an Operational Taxonomic Unit that is consistently delineated by most genetic-based analyses and it is most likely to be a species; it represents the highest agreement between genetic-based analyses. Relative accuracy of each genetic-based analysis to delineate SpLUs was calculated as percent match of OTUs, delineated by each genetic-based analysis, to SpLUs (number of OTUs matching SpLUs / total number of SpLUs * 100). Next, exemplars from SpLUs were examined morphologically to compare and contrast them against each other and against species and sub-species reported from the West Palaearctic realm. To do this, cleared specimens were preserved and relaxed in glycerin, body dissection was performed if necessary, and three-dimensional (3-D) pictures were taken for body-parts, if necessary, using Carl Zeiss SteREO Discovery V12. Publications of original species descriptions from Europe, West Asia, North Africa, the Caucasus, the Trans-Caucasus, and the Middle East (DeWalt et al. 2015; Kluge 2016; Staniczek 2016), were used to compare and contrast the Iraqi specimens, which facilitated the discovery of non-cryptic and cryptic SpLUs. Many references and

78 identification keys were used in this research. The following are the references that were most frequently used. For stoneflies, the works of Despax (1951), Hynes (1977), Lillehammer (1988), Brittain and Saltveit (1996), Zhiltzova (1997, 2003), Tierno de Figueroa et al. (2003), Zwick (2004), and Lubini et al. (2012). For mayflies, the works of Schoenemund (1930), Kimmins (1942), Macan (1955a, 1955b, 1957, 1958, 1961, 1979), Bogoescu (1958), Ikonomov (1961, 1962), Tshernova (1964), Landa (1969), Sowa (1973), Kazlauskas (1977), Soldán (1978), Belfiore (1983), Mol (1983), Elliott and Humpesch (1983), Malzacher (1984, 1986, 1992, 1996), Jensen (1986), Kluge (1987, 1997), Novikova and Kluge (1987, 1994), Andrikovics (1988), Elliott et al. (1988), Hefti and Tomka (1989), Sartori (1991, 1992), Studemann et al. (1992), Kluge and Novikova (1992), Alba-Tercedor and Zamora-Munoz (1993), Bauernfeind (1994, 1995), Engblom (1996), Kluge (1997), Buffagni (1997, 1999), Alba-Tercedor (1998), Soldán and Landa (1999), Marie et al. (2000, 2001), Bauernfeind and Humpesch (2001), Jacob (2003), Eiseler (2005), Malzacher and Staniczek (2006, 2007), Webb and McCafferty (2008), Elliott and Humpesch (2010), Macadam and Bennet (2010), Küttner and Zimmermann (2011), Bauernfeind and Soldàn (2012), and Türkmen and Kazanci (2013). For the purpose of this study, a Non-cryptic Species-Like Unit is a Species-Like Unit supported by morphological evidence; a Cryptic Species-Like Unit is a Species-Like Unit without support from morphological evidence. Finally, mayfly species and morphospecies initially identified but not successfully sequenced were also compared and contrasted with the others.

RESULTS Genetic-Based Analyses I successfully sequenced 347 mayflies and nine stoneflies and deposited sequences in the BOLD Systems database; sequences of additional specimens failed, likely because they were preserved at room temperature and in low-quality ethanol for many years after they were collected in Iraq. Genetic-based analyses successfully recognized all Iraqi sequences (291bp-658bp) and delineated their OTUs, except for the RESL analysis, which failed to recognize many sequences with 400 – 500bp (13 out of 28 sequences) and did not recognize any sequence with 400bp or less (17 sequences).

79 Genetic-based analyses identified between 42 and 66 OTUs (Figure 2-1). The Genetic Similarity Blocks analysis suggested the presence of 49 OTUs, while the RESL analysis assigned 42 OTUs to bins in the BOLD Systems. The Generalized Mixed Yule Coalescent (GMYC) Approach suggested the presence of 51 OTUs. Initial partitioning of ABDG (ABGD iP) delineated 45 OTUs, and recursive partitioning (ABGD rP1, ABGD rP2, and then ABGD rP3) resulted in delineating 54, 60, and then 62 OTUs with each additional step. Initial partitioning of PTP (PTP iP) delineated 51 OTUs, and recursive partitioning (PTP rP1, PTP rP2, and then PTP rP3) resulted in delineating 53, 55, and then 66 OTUs. Highly similar OTUs, with average percent match of up to 90%, were found between each of the following pairs of analyses: GSBs-ABGD rP1, GSBs-PTP rP1, and ABGD iP-PTP iP. (Tables 2-1a and b). Using average percent match of ≥80% as a threshold, ABGD rP1 consistently succeeded in matching other analyses in 86% of pairwise comparisons, followed by GSBs and GMYC (80%), PTP iP and PTP rP1 (71%), RESL (60%), ABGD iP and PTP rP2 (57%), then ABGD rP2 (29%), while ABGD rP3 and PTP rP3 consistently failed (Tables 2-1a and b). Matching OTUs of all analyses (from all partitioning levels, initial and recursive) resulted in delineating 51 SpLUs: 46 mayflies and 5 stoneflies (Figure 2-1). Relative accuracy of GSBs, RESL, ABGD, GMYC, and PTP in delineating SpLUs were 92%, 82%, 80–92%, 88%, and 73–98% respectively (Figure 2-2). GSBs, ABGD rP1, and PTP iP, rP1, and rP2 matched SpLUs at ≥90%. ABGD and PTP were highly accurate during the first recursive partitioning (rP1), while further recursive portioning in these analyses reduced their % match to SpLUs (Figure 2-2). Downloading sequences from the BOLD Systems and GenBank for known species from the West Palaearctic, and then using them along with Iraqi sequences in genetic-based analyses resulted in some or all analyses matching four taxa (Figure 2-1). Baetis (Rhodobaetis) braaschi consistently matched sequences in BOLD and GenBank identified as Baetis braaschi Zimmermann 1980. Nigrobaetis nr. digitatus consistently did not match a GenBank sequence identified as Nigrobaetis digitatus (Bengtsson, 1912), except when performing ABGD, which grouped them together during ABGD iP and continued to do so in all recursive partitioning levels. All genetic-based analyses matched Leuctra nr. digitata MAA01 to a sequence mined in GenBank which was also available

80 in the BOLD Systems and identified as Leuctra fusca (Linnaeus, 1758). For Leuctra nr. digitata MAA02, RESL assigned it to a BIN with taxonomic discordance, with 39 sequences (adults and larvae) identified as Leuctra fusca (Linnaeus, 1758) and 21 sequences (adults and larvae) identified as Leuctra digitata Kempny 1899.

Species Discovery The 51 delineated SpLUs were examined morphologically and compared and contrasted to species and sub-species known from the West Palaearctic. Morphological identification resulted in confirming most of them as non-cryptic taxa, after emphasizing morphological diagnostic characters, and discovering others (Table 2-2). Examining the Iraqi SpLUs that matched the taxa from GenBank and BOLD Systems resulted in confirming only Baetis (Rhodobaetis) braaschi. Nigrobaetis nr. digitatus was found to be close morphologically to Nigrobaetis digitatus. Examining Leuctra nr. digitata MAA01 morphologically confirmed that it is different from L. fusca and very similar to described larvae of L. digitata Kempny 1899. Examining Leuctra nr. digitata MAA02 morphologically resulted in discovering a different taxon than either Leuctra nr. digitata MAA01 or L. digitata. I uncovered five cryptic mayfly SpLUs, where morphological diagnostic characters grouped them together as one taxon, while genetic evidence separated them into five distinct taxa. In contrast, all five SpLUs of stoneflies and 41 of mayflies were compared and contrasted successfully, resulting in confirming the presence of non- cryptic taxa (Table 2-3; Figure 2-1). Exemplars of eight subimago SpLUs and five imago SpLUs were matched successfully to their other life-stages such as male and female larvae (Table 2-3). For male imagoes, the key morphological characters used to distinguish them were the shape and dimensions of genitalia, and the presence or absence of stripes and patterns on the pronotum and abdominal segments. I found that ~49 SpLUs were new records for Iraq, and ~45 were potentially undescribed taxa (Figure 2-1). Non-sequenced mayfly taxa (species and morphospecies) were compared and contrasted to SpLUs and species and sub-species already known from the West Palaearctic, identifying 10 taxa: two species and eight morphospecies (Table 2- 3). When combining the identified SpLUs with non-sequenced species and

81 morphospecies, the new records for Iraq reached ~58 taxa, and potential novel taxa reached ~54 (Table 2-3). I provide a full list of discovered Iraqi SpLUs and non-sequenced taxa and show their distribution across the terrestrial ecoregions sampled in the KR (Table 2-4; Figure 0- 1). Many SpLUs were very similar morphologically to known West Palaearctic species, therefore their names included “nr.”, as an abbreviation for “near”. Cryptic taxa were assigned provisional names with “cr.” as an abbreviation for “cryptic” (Tables 2-3 and 2- 4; Figure 2-1). Finally, I updated and revised the names and dates for species mentioned in Al-Zubaidi and Al-Kayatt (1986) and Al-Zubaidi et al. (1987) and confirmed the presence for some of them (Table 2-5). I found that eight taxa (four species, two genera, and two group-complexes) related to the SpLUs found in this study were previously reported from northern Iraq (Table 2-5). Another eight species reported previously from northern Iraq were not found in this study; however, I found similar SpLUs for five of them (Table 2-5).

DISCUSSION Unknown Diversity of Mayflies and Stoneflies from Northern Iraq My hypothesis was supported, as integrating multiple genetic-based analyses with morphological identification was highly successful in facilitating taxon delineation. The Kurdistan region was already expected to harbor high diversity of fauna including mayflies and stonrflies because it occupies part of the Irano-Anatolian biodiversity hotspot, one of 35 such hotspots on earth (Conservation International 2005). Five of the genera I found in the KR (Baetis, Caenis, Rhithrogena, Epeorus, and Ephemera) are among the 12 most diverse mayfly genera in the world (Barber-James et al. 2008). Examining the distribution of delineated SpLUs in the KR’s four terrestrial ecoregions was in most cases less helpful to find an answer for this unknown diversity of mayflies and stoneflies; spatial patterns for most SpLUs were not easy to detect and their distribution cover more than one ecoregion (Figure 0-1 and Table 2-4). However, this unknown diversity may be explained from an evolutionary and biogeographical prospective. The Kurdistan region has (1) a mountainous geography that is expected to lead to many vicariance events and (2) a unique location in the Palaearctic realm that is known for having high diversity of water quality bioindicator species compared to other

82 realms (Barber-James et al. 2008; de Moor and Ivanov 2008; Fochetti and Tierno de Figueroa 2008). Furthermore, the KR is located in the south-western part of the Palearctic realm, close to the Ethiopian realm. Therefore, the present fauna could display affinities with faunas from both realms as a result of introgressions from each.

Findings of this Study Compared to Previous Findings from Northern Iraq I found that eight taxa related to SpLUs (four species, two genera, and two group- complexes) were previously reported from northern Iraq (Table 2-5). Another eight species reported previously from northern Iraq were not found in this study; however, I found SpLUs that were similar to five of them (Table 2-5). Fortunately, sequences from the West Palaearctic realm for four of them (Baetis buceratus, B. vardarensis, Torleya major, and Serratella ignita) were available in the BOLD Systems and GenBank, and none matched the sequences of specimens collected in this study. Baetis buceratus was similar genetically and morphologically to Baetis (Baetis) nr. zdenkae, while Baetis vardarensis was similar only morphologically to Baetis (Baetis) MAA04 and MAA05 and very different genetically from both of them. Torleya major was different genetically and morphologically from all Spiny Crawler mayflies (family: Ephemerellidae) found in this study. Serratella ignita was similar genetically and morphologically to Serratella sp. MAA01. Epeorus zaitzevi and E. nigripilosus were found to be similar morphologically to Epeorus (E.) nr. zaitzevi and E. (Ironopsis) nr. nigripilosus respectively, while Rhithrogena expectata and Oligoneuriella tskhomelidzei were different morphologically from SpLUs of Rhithrogena and Oligoneuriella that were found in this study. Some genera and group-complexes were reported previously from northern Iraq, with no information about the available species (Table 2-5). My approach helped in identifying them genetically and morphologically. For example, Oligoneuriopsis sp. MAA01 was delineated as a unique SpLU and found to be clearly different than Oligoneuriopsis skhounate Dakki & Giudicelli 1980, which is the only species of this genus discovered so far in the West Palaearctic realm, from Morocco, Spain, and Algeria. Ephemera (Ephemera) sp. MAA01 and MAA02 were another example where unique SpLUs were distinguished genetically and morphologically from the closest species, such as Ephemera (Ephemera) danica Muller 1764, E. (E.) danica perpallida Thomas and Dia 2007, and E. (E.) lineata Eaton 1870. In addition, life history stages (male imago, male

83 and female sub-imagoes, and larvae) for Ephemera (Ephemera) sp. MAA01 were all matched for the first time in this study. For Isonychia, there are 14 species in the Palaearctic realm (Barber-James et al. 2013), and Isonychia (Isonychia) arabica Al-Zubaidi, Braasch & Al-Kayatt 1987 was only known from Iraq. I delineated the SpLU of the Isonychia (Isonychia) arabica? larva and it was easily distinguished from the closest species, which is Isonychia (Isonychia) ignota (Walker 1853). However, only the male subimago was described from northern Iraq (Al-Zubaidi et al. 1987). Therefore, I identified my SpLU as Isonychia (Isonychia) arabica?, assuming that this is the larval stage of it. Based on this result, it is recommended to collect and identify adults then matching them genetically with the larvae sequenced in this study.

Findings of this Study Compared to the Known Fauna of Turkey There are around 146 species of mayflies and more than 90 species of stoneflies reported so far from Turkey (Kazanci 2001; Kazanci 2008; Kazanci 2009; Kazanci 2012; Kazanci and Türkmen 2012; Türkmen and Kazanci 2013). The Kurdistan Region is geographically continuous with south-eastern Turkey; the Upper Tigris and Euphrates freshwater ecoregion connects the KR with this part of Turkey (Figure 0-1). Therefore, high similarity was expected to be found with the fauna listed from Turkey. However, all stoneflies and the majority of mayfly SpLUs did not match Turkish records. Only four mayfly species, Baetis braaschi Zimmermann 1980, Baetis lutheri Müller-Liebenau 1967, Electrogena kugleri (Demoulin 1973), and Caenis macrura Stephens 1835, were present in my samples and Turkish records. For Baetis lutheri, Iraqi specimens were not sequenced and their identifications were done based on morphology alone. Using sequences from the West Palaearctic realm available in the BOLD Systems and GenBank confirmed that Iraqi SpLUs are different from most species listed for Turkey. For example, 20 Baetis species, four Nigrobaetis, two Cloeon, and one Centroptilum species were reported from Turkey. Fortunately, sequences for more than half of them, from multiple countries within the West Palaearctic other than Turkey, were available from the BOLD Systems and GenBank (Table 2-6). Genetic-based analyses and morphological identification performed in this study confirmed that only Baetis braaschi matcheed Iraqi SpLUs genetically and morphologically.

84 Thirteen SpLUs found in this study showed morphological similarity to species already described from the Palaearctic realm (Table 2-3). Nine similar species were reported from Turkey (Baetis samochai, Baetis rhodani, Cloeon simile, Ecdyonurus ornatipennis, Epeorus zaitzevi, Epeorus nigripilosus, Nigrobaetis digitatus, Prosopistoma orhanelicum, and Rhithrogena znojkoi). Since the range of morphological variation was still under investigation in the Iraqi fauna, careful morphological identification was done in the light of genetic-based analyses. Fortunately, sequences for four similar species were found in GenBank and the BOLD Systems: sequences for Baetis rhodani, Cloeon simile, Nigrobaetis digitatus, and Leuctra digitata. Genetic-based analyses showed some similarity with the Iraqi SpLUs, but never matched them, except Nigrobaetis digitatus, which matched Nigrobaetis nr. digitatus using ABGD only. Although morphological diagnostic characters were found in all of these Iraqi SpLUs (Table 2-2), DNA sequences from Turkey and other Palaearctic regions are needed to improve species delineation. Many of the species discovered and described from Turkey such as Rhithrogena anatolica Kazanci 1985, R. sublineata Kazanci & Braasch 1988, Electrogena anatolica (Kazanci & Braasch 1986), E. boluensis Kazanci 1990, E. dirmil Kazanci 1990, E. hakkarica (Kazanci 1986), E. madli (Kazanci 1992), and E. necatii (Kazanci 1987) were described only as adults, and information about their DNA sequences and larval stage is not available. In addition, listing other species and reporting their availability in Turkey was only based on specimen morphology. Eleven SpLUs examined in this study were found in areas very close to the Turkish boarders, within the Iraqi side of border-crossing headwater streams of Tigris River that originated in Turkey and flowed to Iraq; these species are expected to be found in Turkey along the Turkish side of Tigris headwater streams (Tables 2-3 and 2-4; Figure 0-1). Based on my findings, fauna occupying southeastern Turkey especially Turkish areas neighboring northern Iraq will need to be sequenced and matched genetically to Iraqi sequences and all other sequences from Palaearctic realm, and then morphological analyses will need to be conducted to confirm their identity.

85 Performance of Genetic-Based Analyses Species-Like Units were identified following the similarity/consistency pattern of OTUs delineated by different genetic-based analyses at different partitioning levels (Figure 2-1; Table 2-1a and b). The higher the consistency between genetic-based analyses, the more confident the SpLU identification. My results showed that some genetic-based analyses were more consistent in delineating OTUs than others. Confidence in the identities of OTUs increased when several different methods reached similar conclusions Figure 2-1). This concordance among methods was especially high between GSBs, ABGD rP1, and PTP (from iP-rP2); over 90% of the time, these methods agreed in their identification of OTUs (Figure 2-2). Despite its simplicity, the GSB approach, which is related to the genetic-species concept, was one of the best analyses to delineate OTUs; GSB was highly consistent and comparable to highly sophisticated genetic-based analyses such as RESL and GMYC. GSB results were highly similar (≥80% match) to eight out of 10 analyses (RESL, GMYC, ABGD iP - rP3, and PTP iP - rP3). GSB contribution to delineated SpLUs was 92%, while GMYC was 88% and RESL only 82% (Figures 2-1 and 2-2). ABGD and PTP initial partitioning (iP) resulted in highly similar OTUs delineation (90% average percent match). Performing the first recursive portioning in both analyses resulted in delineating more OTUs that were highly similar (90% average percent match) to the OTUs delineated by GSB (Table 2-1b). The contributions of ABGD and PTP to delineating SpLUs were relatively high during the first recursive partitioning (rP1), while further recursive portioning reduced their OTUs detection accuracy, especially during the third recursive partitioning (rP3), which resulted in fewer matches to OTUs delineated by other analyses, and less contribution to delineating SpLUs (Table 2-1a and b, and Figure 2-2). In each of ABGD and PTP, as I move to the second recursive partitioning (rP2), the delineated OTUs were slightly different than rP1 (>90% average match), but the change from rP1 was remarkable as I move to rP3 especially in PTP (86% in ABGD and 73% in PTP). Although RESL analysis was very consistent with other analyses and all 42 OTUs/BINs were finally delineated as SpLUs, RESL missed nine OTUs and did not assign them to BINs because their sequence lengths were less than 400bp; RESL also

86 failed to delineate OTUs when there were only 1-2 sequences related to a given taxon (with little or no sequence variation); three or more sequences were needed to run step four, Marcov Clustering, which was required to refine OTU delineation (Ratnasingham and Hebert 2013). In many cases, multiple sequences per taxon with a length of >500 bp were not available, therefore the contribution of RESL to SpLUs delineation was reduced to only 82%. Eight of the nine missed OTUs were delineated successfully by all other analyses from the first round, leading to the discovery of SpLUs (Figure 2-1). The ninth missed OTU by RESL, Caenis macrura cr. MAA01, was detected by all other analyses, however, GMYC was not consistent with others and ABGD delineated it after performing ABGD rP1. For GMYC, although eight of the nine OTUs missed by RESL were delineated by this analyses, it failed in delineating six other OTUs that were delineated from the first round by most other analyses, including RESL (Figure 2-1). Based on my findings, whenever COI sequences are >400bp and there is no time constraint for species identification, a combination of GSBs, RESL, ABGD rP1, GMYC, and PTP rP1 are recommended. When COI sequences are <400bp and/or there is a time constraint for species identification (i.e., identification needs to be done quickly with the highest possible accuracy), it is recommended to use a combination of at least two of GSB, ABGD rP1, GMYC, and PTP rP, and the preferred of these are GSB, ABGD rP1 and PTP rP1.

Genetic-Morphological Cross-Validation Facilitated Species Discovery The results of genetic-based analyses and morphological identification worked in synergy and confirmed each other in most cases, which steered the discovery of taxa. The lack of a given result from one genetic-based analysis did not affect the delineation process, as it was compensated by results of other genetic-based analyses (Figure 2-1). For example, although its sequence length was 402bp, all genetic-based analyses, except RESL, contributed to identifying Protonemura sp. MAA01 as a unique SpLU and showed that the closest available taxon from the BOLD Systems and GenBank had sequences identified as Protonemura intricata (Ris 1902). Examining Protonemura sp. MAA01 morphologically confirmed these findings, and clear evidence was found to distinguish the Iraqi SpLU from Protonemura intricata (Table 2-2).

87 My approach also clearly showed that using the results of only one or two genetic-based analyses was not enough to accelerate the discovery of taxa, and they have to be “cross-validated” by other genetic-based analyses as well as morphology (Figure 2- 1). For example, RESL assigned Leuctra nr. digitata MAA02 to a BIN with taxonomic discordance, with 39 sequences (adults and larvae) identified as L. fusca and 21 sequences identified as L. digitata. These sequences were from different European countries: Germany, Norway, France, Finland, Bulgaria, and Belarus. The result of GSB was not helpful for solving this issue and unfortunately no sequence for L. digitata was mined in GenBank while the BOLD Systems’ sequences were private (only general information was available; sequences were not accessible). Therefore, ABGD, GMYC, and PTP were only helpful for confirming one RESL finding: that Leuctra nr. digitata MAA02 was different from the publically available Italian sequence identified as L. fusca. Although these findings did not provide a final answer, the morphological identification resulted in discovering another taxon that is very similar to described L. digitata Kempny 1899 and Leuctra nr. digitata MAA01.

Morphological Examination after Genetic-Based Analyses Is Critical In three of four sequence-matching cases (Figure 2-1), use of only genetic-based analyses would have led to misleading conclusions. For example, Leuctra nr. digitata MAA01 and MAA02 were easily identified as separate SpLUs using genetic-based analyses. However, all of these analyses matched Leuctra nr. digitata MAA01 to one incomplete COI sequence from Italy identified as L. fusca. Investigating the morphology of Leuctra nr. digitata MAA01 and MAA02 confirmed some results of genetic-based analyses and refuted others. Leuctra nr. digitata MAA01 and MAA02 were found to be different morphologically from each other, but comparing and contrasting Leuctra nr. digitata MAA01 to the described larvae of Leuctra fusca (Linnaeus, 1758) resulted in different findings than what was expected. Leuctra nr. digitata MAA01 was found to be different than L. fusca for many characters. In contrast, my SpLU was found to be very close to described larvae of L. digitata Kempny 1899, which is a Palaearctic species never reported before from the Middle East. This case was an example for how important the genetic-based analyses are to guide more focused morphological identification; such focused morphological identification in return validated the findings of genetic-based

88 analyses and rejected the match with L. fusca (Linnaeus, 1758), flagging the need for full sequence length for the COI gene (658bp) when performing these analyses.

Emphasizing Morphology Key Points and Discovering Others Taking into account the morphological variation observed with West Palaearctic species (Thomas et al. 1988; Dalkiran 2009), solving the taxonomic impediment using only morphology would have been extremely difficult and time-consuming. This challenge was evident with my specimens, given that most taxa were unknown and taxonomic publications about mayflies from Iraq and surrounding nations were lacking. Performing genetic-based analyses facilitated identification of SpLUs, which guided me to re- examine the specimens over and over and recognize discreet morphological traits that were previously overlooked. Genetic-based analyses significantly helped in solving the uncertainty issue witnessed earlier with morphospecies, and guided better examination for morphological characters. The accuracy of morphological identification witnessed a dramatic improvement in the light of genetic-based analyses and overlooked morphological evidence was discovered that served to remove uncertainty. Morphologically examining delineated SpLUs highlighted the need to add overlooked morphological features and emphasize others when doing species taxonomy and identification (Table 2-2). Overlooked morphology key points were newly discovered features that were not mentioned in the reviwed taxonomic literature; these features might have never been seen by taxonomists because described species might not have them. Emphasized morphology key points were found to vary between SpLUs, and they will need more attention by taxonomists when describing the delineated SpLUs as new species or sub-species (Table 2-2). Such discoveries highlight the need to re-visit the identification of species in these genera and provide more morphological details. Sometimes traditional morphological identification may overlook important traits due to dependence on visual examination and the use of easily spotted traits, without paying careful attention to hidden or small traits. For example, genetic-based analyses distinguished Leuctra nr. digitata MAA01 and MAA02 as unique taxa and re-examining them morphologically confirmed the difference between them. However, a poorly described species, L. digitata Kempny 1899, almost matched both of them morphologically. This species was barely distinguished from Iraqi

89 SpLUs by having long bristles scattered on abdominal segments 4-7. Unfortunately, many other characters of L. digitata were missing or not described in details in the taxonomy literature, diminishing the possibility of discovering more key points of similarity or dis-similarity. For example, description of L. digitata Kempny 1899 lacks details for many characters, such as the number and arrangement of bristles around the pronotum and number and arrangement of long abdominal bristles. In addition, it is totally missing other information such as the presence and configuration of long bristles on wing-pads, and the dimensions of hind legs (Table 2-2).

Flagging Species versus Subspecies Taxonomy Genetic-based analyses recognized the OTUs equally, with no a priori setting to differentiate species from sub-species. For each analysis, all sequences were identified using the same settings, therefore, OTU delineation can be considered as bias-free and not depending on taxonomists’ opinions. In addition, many studies (using RESL, ABGD, and GMYC) have considered their delineated OTUs as “provisional species”. In this study, two sub-species of the Small Squaregill mayflies, Caenis macrura macrura and C. m. helenica, were delineated by all genetic-based analyses as unique OTUs. After examining their distribution in the KR, I found that these sub-species inhabit the same ecoregion, which is Zagros Mountains Forest Steppe. As described by the International Commission on Zoological Nomenclature (1999), subspecies are identified based on taxonomists’ opinions; they are related to a taxonomic category that ranks below species and identify permanent geographically isolated races that can breed and producing fertile offspring if brought together. Geographic isolation was not found in the case of C. m. macrura and C. m. helenica. However, C. m. helenica occupied a small geographic range (in only three sites around Lake Dukan), while C. m. macrura was found in a wide geographic range (including sites around Lake Dukan), occupying 27 sites across all four terrestrial ecoregions in the KR. According to Malzacher (1986), C. macrura Stephens 1836 has three subspecies: C. m. macrura Stephens 1836, C. m. helenica Malzacher 1986, and C. m. minoica Malzacher 1986. The main difference known so far in morphology between C. m. macrura and C. m. helenica is the shape of microtrachia on the dorsal side of the body, especially on wingpads. Based on my findings, taxonomists are invited to integrate genetic-based analyses with morphology when classifying sub-species; the

90 shape of these microtrachia, if applicable, maybe emphasized and used to distinguish species.

Facilitating Cryptic and Controversial Species Delineation Based on my findings, it is recommended to integrate genetic-based analyses with morphology to solve the taxonomic impediment of cryptic and controversial species. In five cases, delineated SpLUs did not show morphological evidence, highlighting the presence of cryptic species and/or subspecies. For Nigrobaetis gracilis cr. MAA01 and MAA02, I initially identified them as a known species from the West Palaearctic, which is Nigrobaetis gracilis Bogoescu & Tabacaru 1957, but using the genetic-based analyses with sequences from my specimens and sequences for that species from the BOLD Systems and GenBank led to a different conclusion. Re-examining these SpLUs morphologically did not lead to the discovery of traits to distinguish between them and described Nigrobaetis gracilis. For Caenis macrura cr. MAA01-03, SpLUs related to the genus Caenis were delineated successfully by genetic-based analyses as separate taxa, but their morphology was the same as Caenis macrura macrura, even after dissecting the specimens and examining all parts of their bodies, including areas never mentioned in the taxonomy literatures, which highlighted the presence of cryptic taxa. Examining their distribution pattern in the KR, Nigrobaetis gracilis cr. MAA01 and MAA02 did not show any difference, as these specimens were only found in proximate streams in Zagros Mountains Forest Steppe. However, Caenis macrura cr. MAA01 showed clear geographic separation from Caenis macrura macrura and Caenis macrura cr. MAA03; it occupied four sites in an area in Zagros Mountains Forest Steppe where Caenis macrura macrura and Caenis macrura cr. MAA03 were not found (sites 38 - 52). Caenis macrura cr. MAA02 occupies more sites in the same area occupied by Caenis macrura cr. MAA01 and spread a little bit northwest to adjacent areas in this ecoregion and borders of Middle East Steppe where Caenis macrura macrura was found. Caenis macrura cr. MAA03 was the rarest Caenis SpLU, occupying only one site in the middle of Middle East Steppe that is already occupied by Caenis macrura macrura. According to Malzacher (1984 and 1992), and Alba-Tercedor & Zamora-Munoz (1993), Caenis macrura group-complex encompasses seven species, three of them (C. luctuosa (Burmeister, 1839), C. macrura Stephens, 1835, and C. martae Belfiore 1984)

91 are recorded from Turkey (Kazanci and Türkmen 2012). However, it has been well- documented that the taxonomic situation of this group-complex in the Mediterranean is confusing and unclear; taxonomists are not able to delineate the range of variability within a single species or distinguish subspecies from local forms or races (Malzacher 1986; Alba-Tercedor and Zamora-Muñoz 1993). Caenis macrura cr. MAA01 - MAA03 could be cryptic subspecies related to Caenis macrura and/or representatives of the three Mediterranean species related to the Caenis macrura group-complex (Caenis nachoi Alba-Tercedor & Zamora-Munoz 1993, C. luctuosa, and C. martae) and highly similar to C. macrura. These species are difficult to distinguish and their range of morphological variability is not clearly understood; they are most likely overlapping in their morphological characters and as such, this issue requires further investigation (Bauernfeind and Humpesch 2001, Bauernfeind and Soldàn 2012).

CONCLUSION Integrative taxonomy has been proposed in the last decade as a way of improving and accelerating species discovery (Dayrat 2005; Will et al. 2005). To perform integrative taxonomy and improve species delineation, scientists nowadays lean towards using morphological, genetic, and phylogenetic species concepts (Pante et al. 2014). Despite their varied perspectives, different species concepts usually deliver congruent decisions when the taxa being considered have evolved independently for a substantial interval (de Queiroz 2005). In my dissertation, I demonstrated the use of integrative taxonomy via five genetic-based analyses related to two different species concepts (genetic and phylogenetic species) and backed-up the findings using morphology (morphological species concept) through performing morphological-genetic cross-validation; I found this to be very important for understanding biodiversity in groups and regions that are not well-studied. I found that there is an unknown diversity of mayflies and stoneflies from northern Iraq, and genetic-morphological cross-validation was the key strategy that facilitated the discovery of these unique taxa. Similar approaches have been used in almost half of the integrative taxonomy studies published in the last decade; these have led to the discovery of a significant amount of overlooked biodiversity (Pante et al. 2014). My findings agree with other studies that used similar approaches. For instance, Kekkonen and Hebert (2014) tested

92 the congruence of OTUs resulting from the application of three genetic-based analyses related to genetic and phylogenetic species concepts, and the OTUs delineated by all analyses were considered as robust. The congruence among multiple analyses related to multiple species concepts can be viewed as supporting the robustness of species delineation due to their differing analytical approaches and theoretical bases (Carstens et al. 2013). Landry et al. (2013) and Lees et al. (2013) found that many species can initially be detected through DNA-based analysis and subsequently confirmed by morphological examination. Landry et al. (2013) also found that many species may first be detected through morphology and then the findings can be confirmed using genetic information. Results of genetic-based analyses are usually congruent with traditional taxonomy (Schmidt et al. 2015), however, exceptions are expected, especially when species with plastic-morphology or cryptic morphology are present (Mutanen et al. 2012a; Yang et al. 2012). Using multiple genetic-based analyses can solve the cryptic species issue and significantly accelerate the delineation process for non-cryptic taxa (Landry et al. 2013). Deficiency in morphological information and lack of trained taxonomists in many taxonomic groups (Pante et al. 2014) has led to the use of characters such as monophyly and sympatry as criteria for clarifying the status of OTUs, especially OTUs with discordance in boundaries (Kekkonen and Hebert 2014). However, morphological examination after performing genetic-based analyses is critical (Woodcock et al. 2013). Using the result of one genetic-based analyses only may lead to misleading conclusions if it is not matched with results from other genetic-based analysis and never “ground- truthed” using morphology (Yang et al. 2012). In addition, using genetic-based analyses are needed to emphasize and/or discover key morphological features that may be overlooked by taxonomists (Mutanen et al. 2012b). They are also needed to investigate species versus subspecies taxonomy (Mutanen et al. 2012a).

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105 TABLES AND FIGURES Table 2-1. Percent match of OTUs delineated by different genetic-based analyses. a. Numbers on top is percent match of each analysis to the analysis listed to the left, while numbers on bottom is the percent match of each analysis to the analysis to the right. For example, the 42 OTUs delineated by RESL analysis matched 78% of the 49 OTUs delineated by GSBs; the 49 OTUs delineated by GSBs matched 90% of the 42 OTUs delineated by RESL analysis.

GSBs 78 82 94 90 88 84 86 94 90 67 90 RESL 79 90 86 83 88 88 98 95 69 ABGD 89 73 89 84 80 91 96 91 87 62 iP ABGD 85 70 74 96 93 81 78 85 85 67 rP1 ABGD 73 60 63 87 97 75 67 73 73 60 rP2 ABGD 69 56 58 81 94 71 63 69 69 61 rP3 80 73 80 86 88 86 GMYC 86 88 84 65 PTP 82 73 84 82 78 76 86 96 92 71 iP PTP 87 77 77 87 83 81 85 92 96 75 rP1 PTP 80 73 71 84 80 78 78 85 93 80 rP2 PTP 50 44 42 55 55 58 50 55 61 67 rP3

106 b. Numbers on top is the average percent match of OTUs; For example, average percent match of OTUs of GSBs and RESL is 84%. For each of ABGD or PTP, comparisons between initial and recursive partitioning were excluded. Fraction on bottom represent the number of times the average percent match was ≥80% over the total number of comparisons (10 or seven). Percentage on bottom represent the percentage of comparisons with average percent match of ≥80%.

GSBs 84 85 90 82 79 82 84 90 85 59 8/10 RESL 76 80 73 70 80 80 87 84 56 ABGD 80% 6/10 81 74 69 86 90 84 79 52 iP ABGD 60% 4/7 91 87 84 80 86 84 61 rP1 ABGD 57% 6/7 95 82 73 78 77 57 rP2 ABGD 86% 2/7 79 70 75 74 59 rP3 29% 0/7 GMYC 86 87 81 57 PTP 0% 8/10 94 89 63 iP PTP 80% 5/7 94 68 rP1 PTP 71% 5/7 73 rP2 PTP 71% 4/7 rP3 57% 0/7 0%

107 Table 2-2. Emphasized and discovered morphological key points to distinguish SpLUs of Iraqi stonefly and mayfly larvae.

Genera and Emphasized morphological key points Discovered morphological key points Subgenera presence or absence of a fringe of hair on tibiae; presence, absence size of hind legs compared to fore and Leuctra Stephens, of long bristles on abdominal segments 4(5)-10; length of bristles middle legs; relative length and 1835 on cercal segments 5-7; number of bristles on front corners and configuration of long bristles on lateral side of Pronotum. abdominal segments 4(5)-10 shape and length of pronotal bristles; shape and dimensions of Protonemura paraprocts of male and female larvae; length of bristles on None Kempny, 1898 abdominal tergites presence or absence and the shape and presence or absence and shape of the lateral short projections dimensions of the hump on the postero- between coxae; length of paracercus compared to cerci and number medial edge of meta-thorax; number Baetis (Baetis) Leach of its segments; shape and dimentions of bristles on tergites; and configuration of spine-like bristles 1815 number and configuration of long bristles on labrum; shapes of on dorsal side of the second segment of bristles on third segment of labial palp; shapes, length, and labial palp; number and length of configurations of bristles on posterior edge of fore femora spines on dorsal side of second segment of labial palp number of spines on second segment of labial palp; number and configuration of long bristles on labrum; shape, length, and Baetis (Rhodobaetis) configuration of bristles on posterior edge of femora; dimensions None Jacob 2003 of maxillary palp segments; shape, dimentions, and configuration of bristles on posterior edge of tergites; presence or absence and shape and dimentions of bristles on tergites; shape and dimensions of second segment of labial palp; presence Labiobaetis or absence and shape of color pattern of body, especially on legs Novikova and Kluge None and abdomen; shapes, length, and configurations of bristles on 1987 posterior edge of fore femora

108 Genera and Emphasized morphological key points Discovered morphological key points Subgenera shape and dimensions of gills; color pattern on tergites; shape and Nigrobaetis length of tarsal claws; ratio of tarsal claws to tibiae; dimentions of Novikova and Kluge second and third segments of labial palp; bases of antenna almost None 1987 attached; shape and configuration of spines on posterior edge of tergites presence or absence of spines on lateral margin of abdominal Centroptilum Eaton segments; shape and dimentions of gills; presence or absence and ventral side of body with fine and 1869 configuration of colored bands on terminal filaments; length of relatively long hair tarsal claws compared to tibiae dimensions of third and second segments of labial palp; dimension Cloeon (Cloeon) of labrum; ratio of the lengths of legs; presence or absence and None Leach 1815 configuration of colored bands on terminal filaments; shape and dimentions of gills Cloeon (Intercloeon) dimensions of third and second segments of labial palp; shape and Kluge and Novikova dimentions of gills; color pattern on tergites; ; shape and position None 1992 of lateral spines on abdominal segments; dimensions of third and second segments of labial palp; shape and Cloeon dimentions of gills; dimensions of maxilla; shape of mandible at (Similicloeon) Kluge None the incisor groups; shape and position of lateral spines on and Novikova 1992 abdominal segments; presence or absence of dark band on cerci dimensions of third and second segments of labial palp; presence Procloeon or absence and shape of lateral and terminal spines on abdominal (Procloeon) None segments; ratio of tarsal claws to tibiae; shape and dimentions of Bengtsson 1915 gills

109 Genera and Emphasized morphological key points Discovered morphological key points Subgenera dimensions of third and second segments of labial palp; presence or absence and configuration of colored bands and rings on Procloeon terminal filaments; presence or absence and shape of lateral and (Pseudocentroptilum) None terminal spines on posterior margin of abdominal segments; ratio Bogoescu 1947 of tarsal claws to tibiae; color pattern on tergites; shape and dimentions of gills Caenis Stephens size, shape, and configuration of microtrachia on wingpads and None 1836 ventral side of gills on second abdominal segment number, shape, size, length, and configuration of tarsal claws denticles; presence and shape of bristles on them, distribution, length, size, shape, and direction of postero-medial projections (or presence or absence lamellate tongue- Serratella Edmunds tubercles) on abdominal segments; bands and color pattern on like swelling on the inner proximal 1959 body, especially legs and terminal filaments; shape and dimentions corner of gill plates of femora, especially hind femora, compared to other segments of legs; shape and dimentions of spines on femora; dimentions of segment 3 of maxillary palp compared to segment 2 shape and dimentions of front process of head; presence or absence Ephemera of stripes and pattern on pronotum and abdominal tergites; shape (Ephemera) Linnaeus and dimentions of fore femur; presence, absence, and number of None 1758 strong bristles on ventral side of antennal pedicle; lengths of gills and sripe patterns on them. shape and dimentions of pronotum; number of denticles od claws; Ecdyonurus shape of labrum; shape of spines on dorsal side of femur; shape (Ecdyonurus) Eaton None and color pattern of gills; stripes and color pattern on abdominal 1868 segments

110 Genera and Emphasized morphological key points Discovered morphological key points Subgenera presence or absence and number of spines on glossae; number of Electrogena maxillar spines and comb teeth; number and configuration of claw Zurwerra and Tomka denticles; shape and length of spines on femora; shape and None 1985 dimentions of labrum; shape of bristles on posterior edge of tergites; number and configuration of denticles of claws; shape of gills, Epeorus (Epeorus) especially gills 7; shape of segments of maxillary palp, especially None Eaton 1881 segment 2; number and shape of teeth on maxilla; length of spines on femora shape and dimentions of labrum; shape, number, and Epeorus (Ironopsis) configurations of teeth of mandibles; color pattern on abdominal None Traver 1935 segments shape and dimentions of labrum; number of comb-shaped bristles Rhithrogena Eaton of lacinia and number of teeth on them; presence or absence and None 1881 number and configuration of claws denticles; shape of spine-like bristles on dorsal side of hind femora; number of rows and lengths of long filtering bristle-like setae on Isonychia (Isonychia) fore legs; body color pattern, spots, stripes; dimensions of femur, None Eaton 1871 tibia, and tarsus of hind leg Choroterpes presence or absence and arrangment of transversal row of hair on (Choroterpes) Eaton None dorsal side of labrum; shape and dimentions of gills 1881 number and configuration of denticles of claws of fore legs; length Habrophlebia Eaton of claw of fore legs relative to tarsus; shape and configuration of None 1881 bristles on fore femur; size of gills relative to body size; number of filaments on gills; shape of labrum, especially anterior side

111 Genera and Emphasized morphological key points Discovered morphological key points Subgenera tuft of bristles on the lateral ventral Oligoneuriella Ulmer shape and dimensions of lateral abdominal spines; length of corner of the head and the bulb-like 1924 paracercus compared to cerci projection on lateral sides of abdominal sternite 9 shape and dimensions of labrum; spines shape on the surface of Oligoneuriopsis femora, number of teeth on tarsal claws; shape of bristles on None Crass 1947 tergites, location and shape of groups of abdominal bristles, shape of gill plates, and color pattern of body and cerci Prosopistoma length ratio of antenna segment 2 to 3-5, number of maxillary None Latreille 1833 bristles, and notal shield length along median suture

Table 2-3. Lists of Iraqi stoneflies and mayflies examined in this study.

No. Non-cryptic Species-Like Units (NcSpLUs) of Stoneflies 1 Leuctra sp. MAA01 2 Leuctra sp. MAA02 3 Leuctra nr. digitata MAA01 4 Leuctra nr. digitata MAA02 5 Protonemura sp. MAA01 No. Cryptic Species-Like Units (CSpLUs) of Mayflies 1 Caenis macrura cr. MAA01 2 Caenis macrura cr. MAA02 3 Caenis macrura cr. MAA03 4 Nigrobaetis gracilis cr. MAA01 5 Nigrobaetis gracilis cr. MAA02 No. Non-cryptic Species-Like Units (NcSpLUs) of Mayflies 1 Baetis (Baetis) sp. MAA01

112 2 Baetis (Baetis) sp. MAA02 3 Baetis (Baetis) sp. MAA03 4 Baetis (Baetis) sp. MAA04 5 Baetis (Baetis) sp. MAA05 6 Baetis (Baetis) nr. zdenkae 7 Baetis (Rhodobaetis) braaschi 8 Baetis (Rhodobaetis) nr. irenkae 9 Baetis (Rhodobaetis) nr. rhodani 10 Baetis (Rhodobaetis) sp. MAA01 11 Baetis (Rhodobaetis) sp. MAA02 12 Nigrobaetis nr. digitatus 13 Cloeon (Similicloeon) nr. simile 14 Procloeon (Procloeon) sp. MAA01 15 Procloeon (Pseudocentroptilum) sp. MAA01 16 Centroptilum sp. MAA01 17 Epeorus (Epeorus) nr. zaitzevi 18 Epeorus (Ironopsis) nr. nigripilosus 19 Ecdyonurus (Ecdyonurus) nr. ornatipennis 20 Rhithrogena nr. znojkoi 21 Rhithrogena paulinae 22 Rhithrogena sp. MAA01 23 Electrogena kugleri 24 Electrogena sp. MAA01 25 Electrogena sp. MAA02 26 Caenis macrura macrura 27 Caenis macrura helenica 28 Habrophlebia sp. MAA01 29 Habrophlebia sp. MAA02 30 Choroterpes (Choroterpes) sp. MAA01 31 Isonychia (Isonychia) arabica? 32 Serratella sp. MAA01

113 33 Serratella sp. MAA02 34 Serratella sp. MAA03 35 Ephemera (Ephemera) sp. MAA01 36 Ephemera (Ephemera) sp. MAA02 37 Prosopistoma nr. orhanelicum 38 Oligoneuriella sp. MAA01 39 Oligoneuriella sp. MAA02 40 Oligoneuriella bicaudata 41 Oligoneuriopsis sp. MAA01 No. Subimagoes’ SpLUs Matched to SpLUs of Larvae and/or Imagoes 1 Baetis (Baetis) sp. MAA01 2 Baetis (Rhodobaetis) sp. MAA02 3 Electrogena kugleri 4 Epeorus (Epeorus) nr. zaitzevi 5 Epeorus (Ironopsis) nr. nigripilosus 6 Ephemera (Ephemera) sp. MAA01 7 Habrophlebia sp. MAA02 8 Rhithrogena nr. znojkoi No. Imagoes’ SpLUs Matched to SpLUs of Larvae and Subimagoes 1 Baetis (Rhodobaetis) sp. MAA02 2 Baetis (Baetis) sp. MAA01 3 Caenis macrura macrura 4 Electrogena kugleri 5 Ephemera (Ephemera) sp. MAA01 No. SpLUs with morphological similarity to described species 1 Baetis (Baetis) nr. samochai 2 Baetis (Baetis) nr. zdenkae 3 Baetis (Rhodobaetis) nr. irenkae 4 Baetis (Rhodobaetis) nr. rhodani 5 Cloeon (Similicloeon) nr. simile 6 Ecdyonurus (Ecdyonurus) nr. ornatipennis

114 7 Epeorus (Epeorus) nr. zaitzevi 8 Epeorus (Ironopsis) nr. nigripilosus 9 Nigrobaetis nr. digitatus 10 Prosopistoma nr. orhanelicum 11 Rhithrogena nr. znojkoi 12 Leuctra nr. digitata MAA01 13 Leuctra nr. digitata MAA02 No. Mayfly SpLUs found close to the Turkish boarders 1 Baetis (Baetis) nr. zdenkae 2 Baetis (Baetis) sp. MAA03 3 Baetis (Rhodobaetis) sp. MAA03 4 Isonychia (Isonychia) arabica? 5 Electrogena sp. MAA02 6 Labiobaetis sp. MAA01 7 Oligoneuriella sp. MAA01 8 Oligoneuriopsis sp. MAA01 9 Procloeon (Pseudocentroptilum) sp. MAA02 10 Rhithrogena nr. znojkoi 11 Serratella sp. MAA01 No. Non-Sequenced Species of Mayflies 1 Baetis (Baetis) lutheri 2 Baetis (Baetis) nr. samochai No. Non-Sequenced Morphospecies of Mayflies 1 Baetis (Rhodobaetis) sp. MAA03 2 Cloeon (Cloeon) sp. MAA01 3 Cloeon (Cloeon) sp. MAA02 4 Cloeon (Intercloeon) sp. MAA01 5 Labiobaetis sp. MAA01 6 Labiobaetis sp. MAA02 7 Nigrobaetis sp. MAA01 8 Procloeon (Pseudocentroptilum) sp. MAA02

115 Table 2-4. Distribution of mayflies and stoneflies in the Kurdistan Region Northern Iraq. ) ) ) ) ) ) ) )

) ) ) )

Baetis Baetis Baetis Baetis Baetis Baetis Baetis Baetis ( ( ( ( ( ( ( ( irenkae rhodani zdenkae samochai Baetis Baetis Baetis Baetis lutheri Site ID braaschi nr. nr. nr. nr. nr. nr. sp. MAA01 sp. MAA02 sp. MAA03 sp. MAA04 sp. MAA05 sp. MAA01 Rhodobaetis Rhodobaetis Rhodobaetis Rhodobaetis nr. nr. ( ( ( ( Baetis Baetis Baetis Baetis Baetis Baetis Baetis Baetis 1 ● 2 ● ● 3 4 5 ● 6 7 ● 8 ● 9 ● 10 ● 11 ● 12 ● 13 ● 14 ● ● ● ● ● 15 ● ● ● ● 16 17 ● ● 18 ● ● ● 19 ● ● 20 ● 21 ● ● 22 23 24 ● ● ● ● 25 ● ● ● 26 ● ● ● ● ● ●

116 ) ) ) ) ) ) ) )

) ) ) )

Baetis Baetis Baetis Baetis Baetis Baetis Baetis Baetis ( ( ( ( ( ( ( ( irenkae rhodani zdenkae samochai Baetis Baetis Baetis Baetis lutheri Site ID braaschi nr. nr. nr. nr. nr. nr. sp. MAA01 sp. MAA02 sp. MAA03 sp. MAA04 sp. MAA05 sp. MAA01 Rhodobaetis Rhodobaetis Rhodobaetis Rhodobaetis nr. nr. ( ( ( ( Baetis Baetis Baetis Baetis Baetis Baetis Baetis Baetis 27 ● ● ● 28 ● ● 29 ● ● 30 ● ● ● 31 32 ● ● ● 33 ● 34 ● ● ● ● ● 35 ● ● ● ● ● 36 ● ● ● 37 ● ● ● ● 38 ● 39 ● ● 40 ● ● ● 41 ● ● ● ● 42 ● ● 43 ● 44 ● ● 45 ● 46 ● 47 ● 48 ● 49 ● ● ● 50 51 ● 52 ● ● 53 ● # of sites 1 1 17 13 1 12 11 19 13 10 1 1

117

) )

) sp. ) sp. sp. ) ) sp. sp. )

) sp. ) sp.

Cloeon Cloeon macrura macrura macrura macrura macrura

( ( Baetis Baetis Site ID Cloeon MAA02 MAA03 MAA01 MAA01 helenica macrura cr. MAA01 cr. MAA02 cr. MAA03 sp. MAA01 sp. MAA01 sp. MAA02 Choroterpes Choroterpes ( Intercloeon Rhodobaetis Rhodobaetis Caenis Caenis Caenis Caenis Caenis ( Cloeon Cloeon Centroptilum ( ( 1 2 ● ● 3 ● 4 ● 5 6 ● 7 ● 8 ● 9 ● 10 ● 11 ● 12 ● 13 ● 14 ● 15 ● 16 ● 17 ● 18 ● 19 ● 20 ● 21 ● 22 ● ● 23 ● 24 ● 25 ● 26 ● ● ● ● ● 27 28 ●

118

) )

) sp. ) sp. sp. ) ) sp. sp. )

) sp. ) sp.

Cloeon Cloeon macrura macrura macrura macrura macrura

( ( Baetis Baetis Site ID Cloeon MAA02 MAA03 MAA01 MAA01 helenica macrura cr. MAA01 cr. MAA02 cr. MAA03 sp. MAA01 sp. MAA01 sp. MAA02 Choroterpes Choroterpes ( Intercloeon Rhodobaetis Rhodobaetis Caenis Caenis Caenis Caenis Caenis ( Cloeon Cloeon Centroptilum ( ( 29 30 ● 31 ● 32 ● 33 ● 34 ● ● 35 ● 36 ● ● 37 ● ● 38 39 ● 40 ● 41 ● ● 42 ● ● 43 ● ● ● 44 ● 45 ● ● 46 47 48 ● ● 49 ● 50 ● 51 ● 52 ● ● 53 ● ● ● # of sites 2 5 3 4 14 1 27 1 1 2 1 3

119 )

sp. sp.

) ) nr. nr. ) sp. sp. sp. sp. ) nr. nr. )

) sp. sp. ) sp. ) ? ) nr.

Epeorus (

zaitzevi simile kugleri Site ID Cloeon MAA01 MAA02 MAA01 MAA02 MAA01 MAA02 Epeorus arabica Isonychia Isonychia Ephemera Ephemera nr. nr. ( nigripilosus Ecdyonurus Electrogena ornatipennis Ironopsis Ephemera Ephemera ( Ecdyonurus ( ( Electrogena Electrogena Similicloeon ( ( Habrophlebia Habrophlebia Epeorus 1 2 ● 3 4 ● 5 ● ● ● ● 6 7 8 9 10 11 12 ● ● ● 13 14 ● ● ● 15 ● 16 ● ● ● 17 ● ● 18 ● 19 20 ● 21 22 23 24 ● ● ● 25 26 ● ● 27 28 ● ●

120 )

sp. sp.

) ) nr. nr. ) sp. sp. sp. sp. ) nr. nr. )

) sp. sp. ) sp. ) ? ) nr.

Epeorus (

zaitzevi simile kugleri Site ID Cloeon MAA01 MAA02 MAA01 MAA02 MAA01 MAA02 Epeorus arabica Isonychia Isonychia Ephemera Ephemera nr. nr. ( nigripilosus Ecdyonurus Electrogena ornatipennis Ironopsis Ephemera Ephemera ( Ecdyonurus ( ( Electrogena Electrogena Similicloeon ( ( Habrophlebia Habrophlebia Epeorus 29 ● ● 30 ● 31 32 ● 33 ● 34 ● ● ● 35 ● ● ● 36 37 ● 38 39 ● ● ● 40 ● ● ● 41 ● ● 42 ● 43 44 ● 45 46 47 48 ● ● ● 49 ● ● ● 50 51 ● ● 52 53 ● # of sites 3 10 17 1 3 9 2 3 1 1 3 5

121 )

sp. sp. sp.

nr. sp. sp. sp. sp. sp. sp. ) sp. ) sp. gracilis gracilis

Site ID MAA01 MAA01 MAA02 MAA01 MAA02 MAA01 MAA01 digitatus bicaudata Procloeon Procloeon cr. MAA01 cr. MAA02 sp. MAA01 Procloeon Oligoneuriella Nigrobaetis Nigrobaetis Labiobaetis Labiobaetis ( Oligoneuriella Oligoneuriella Oligoneuriopsis Nigrobaetis Nigrobaetis Pseudocentroptilum ( 1 2 ● ● ● 3 ● 4 5 6 ● 7 8 9 ● ● ● 10 11 12 13 14 ● ● ● 15 ● ● ● 16 ● 17 ● ● 18 ● ● 19 20 21 ● ● 22 23 ● ● 24 ● ● 25 ● ● 26 27

122 )

sp. sp. sp.

nr. sp. sp. sp. sp. sp. sp. ) sp. ) sp. gracilis gracilis

Site ID MAA01 MAA01 MAA02 MAA01 MAA02 MAA01 MAA01 digitatus bicaudata Procloeon Procloeon cr. MAA01 cr. MAA02 sp. MAA01 Procloeon Oligoneuriella Nigrobaetis Nigrobaetis Labiobaetis Labiobaetis ( Oligoneuriella Oligoneuriella Oligoneuriopsis Nigrobaetis Nigrobaetis Pseudocentroptilum ( 28 29 30 ● 31 32 33 34 ● 35 ● ● 36 37 38 39 40 41 ● ● 42 43 44 45 46 47 48 49 50 51 52 53 # of sites 2 1 1 1 1 1 4 9 1 2 6 4

123

)

nr.

sp. sp. nr. sp. sp.

sp. sp. sp. sp. sp. sp. sp. digitata digitata nr. nr. Site ID znojkoi MAA01 MAA01 MAA01 MAA02 MAA03 MAA01 MAA02 MAA01 MAA02 paulinae Procloeon Leuctra Leuctra sp. MAA02 Rhithrogena orhanelicum Serratella Serratella Serratella Rhithrogena Rhithrogena Protonemura Prosopistoma Pseudocentroptilum Leuctra Leuctra ( 1 ● 2 ● ● ● 3 ● 4 ● 5 6 ● 7 8 9 10 ● 11 12 ● 13 ● 14 ● ● ● 15 ● ● ● 16 17 ● ● ● 18 ● ● 19 20 ● 21 ● ● 22 23 24 ● ● 25 ● ● 26 ● ●

124

)

nr.

sp. sp. nr. sp. sp.

sp. sp. sp. sp. sp. sp. sp. digitata digitata nr. nr. Site ID znojkoi MAA01 MAA01 MAA01 MAA02 MAA03 MAA01 MAA02 MAA01 MAA02 paulinae Procloeon Leuctra Leuctra sp. MAA02 Rhithrogena orhanelicum Serratella Serratella Serratella Rhithrogena Rhithrogena Protonemura Prosopistoma Pseudocentroptilum Leuctra Leuctra ( 27 28 29 ● ● 30 ● 31 ● ● 32 33 34 ● ● 35 36 37 ● 38 39 ● 40 ● 41 ● ● 42 43 44 45 46 47 48 49 ● ● ● 50 51 52 ● 53 ● # of sites 8 1 19 3 1 5 1 2 1 1 1 3 1

125 Table 2-5. Mayflies previously reported from northern Iraq, with updates, revisions, and confirmations.

Genera and Species (Al-Zubaidi and Al-Kayatt 1986; Al- Families Found in this study? Zubaidi et al. 1987) Baetis buceratus Eaton 1870 No, but similar taxon were found Baetis lutheri Muller-Liebenau 1967 (uncertain Yes, but was not sequenced Baetidae Leach 1815 identification) Baetis rhodani group (unidentified species) Yes Baetis vardarensis Ikonomov 1962 No, but similar taxa were found Caenidae Newman 1853 Caenis macrura group (unidentified species) Yes Serratella ignita Poda 1761 No, but similar taxon were found Ephemerellidae Klapálek 1909 Torleya major Klapálek 1905 No Ephemeridae Latreille 1810 Ephemera Linnaeus 1758 Yes Electrogena kugleri Demoulin 1973 Yes Epeorus nigripilosus Sinitshenkova 1976 No, but similar taxon were found Heptageniidae Needham 1901 Epeorus zaitzevi Tshernova 1981 No, but similar taxon were found Rhithrogena expectata Braasch 1979 (uncertain No identification) Isonychiidae Burks 1953 Isonychia arabica Al-Zubaidi & Braasch & Al-Kayatt 1987 Maybe Yes Oligoneuriella bicaudata Al-Zubaidi & Braasch & Al- Yes Kayatt 1987 Oligoneuriidae Ulmer 1914 Oligoneuriella tskhomelidzei Sowa & Zosidze 1973 No Oligoneuriopsis Crass 1947 Yes Note: Names of scientists and dates were updated using Bauernfeind and Soldan (2012), Barber-James et al. 2013, and Kluge (2015).

126 Table 2-6. List of Non-Iraqi Sequenced Mayfly taxa, available in the BOLD Systems and GenBank.

No. Non-Iraqi Sequenced Mayflies 1 Nigrobaetis digitatus (Bengtsson 1912) 2 Nigrobaetis gracilis (Bogoescu & Tabacaru 1957) 3 Nigrobaetis niger (Linnaeus 1761) 4 Centroptilum luteolum (Müller 1776) 5 Cloeon dipterum (Linne 1761) 6 Baetis lutheri Müller-Liebenau 1967 7 Baetis vardarensis Ikonomov 1962 8 Baetis alpinus (Pictet 1843) 9 Baetis fuscatus (Linnaeus 1761) 10 Baetis macani Kimmins 1957 11 Baetis vernus Curtis 1834 12 Baetis melanonyx Pictet 1843 13 Baetis rhodani (Pictet 1843) 14 Baetis braaschi Zimmermann 1980 15 Baetis buceratus Eaton 1870

127

128

129

130

Figure 2-1. Neighbor-Joining trees with delineated OTUs using genetic-based analyses, delineated SpLUs, and the final names and status of taxonomic units in Iraq and the West Palaearctic.

131 98 100 94 92 92 90 88 86 88 82 80 80 73

40 % Match to SpLUs

Figure 2-2. Relative accuracy of genetic-based analysis, measured as percent match of OTUs identified by each analysis to final delineated SpLUs.

132 Chapter 3: Rapid Solution Given Limited Information is a Challenge in Conservation Biology: A Strategy to Identify and Prioritize Aquatic Hotspots of Conservation Concern in a Poorly Studied Region

Co-author: David J. Berg1 1 Department of Biology, Miami University, Hamilton, Ohio, 45011, USA

ABSTRACT Setting priorities is an important step in conservation biology, not only to protect habitats but also to guide future activities such as habitat restoration projects. In developing countries such as Iraq, information about aquatic habitats is quite limited and identifying and prioritizing healthy habitats for conservation requires a multidisciplinary strategy to utilize available types of data efficiently. I divided lotic and lentic ecosystems of the Kurdistan Region (KR) northern Iraq into 76 planning units. Next, I compiled mayfly, stonefly, and caddisfly (Insecta: Ephemeroptera-Plecoptera-Trichoptera (EPT)) data for 33 of them; these 33 units were ranked for EPT conservation using a combination of conservation indices. From the ranking, I selected a subset of units with the highest priority for EPT conservation and considered them to represent samples of healthy aquatic habitats in the KR; these units were then used along with various landscape predictor variables in correlative distribution modeling for healthy aquatic habitats across the entire KR. This strategy resulted in identifying one lake and 23 units as the healthy aquatic habitats of conservation priority in the KR. This research showed that in developing countries where knowledge about aquatic habitats and most species inhabiting them is minimal, conservation studies can still be conducted using a combination of conservation and modeling methods based on the availability of data about water quality bioindicators.

Keywords: Ephemeroptera, Plecoptera, Trichoptera, Prioritization, Distribution Modeling, Kurdistan Region, Iraq

133 INTRODUCTION Freshwater ecosystems are among the most threatened ecosystems on earth due to climate change, anthropogenic disturbance, and high human demand on their ecological services (Heino et al. 2009; Woodward et al. 2010; Domisch et al. 2011; Friberg et al. 2011). Recent evidence has shown that climate change is altering freshwater ecosystems globally by changing river flows, the frequency of flood events, the frequency and intensity of droughts, and water temperatures in rivers and lakes (EEA 2012). In the Middle East, these changes are exacerbated by water stress and anthropogenic disturbance due to political instability, human population growth, dam construction, gravel mining, oil fields, agricultural runoff, sewage dumping, and urban development (Morris 1997; MacQuarrie 2004; Eden Again Group 2004 and 2005; Nature Iraq 2006; Al-Saffar 2007; Iraqi Ministry of the Environment et al. 2006a to c; Iraqi Ministry of the Environment 2010). Continuous aquatic habitat destruction due to climate change and anthropogenic threats will threaten all aquatic organisms; specialists and intolerant species will be the first to decline in abundance, distribution, and diversity; if no conservation efforts take place, then eventually many aquatic organisms will likely face extinction and this will lead to collapse of food webs and decrease in the quality of ecosystem services (Smith et al. 2014; IUCN 2015). Conservation efforts are usually limited by three factors: time, funds, and prior information (Pimm et al. 2015). Given these limitations, conserving all aquatic habitats is not possible; rather, conserving a subset of aquatic habitats may be reasonable and achievable. In particular, decision makers and stakeholders will always be interested in saving time and funds, through conserving the subset of habitats with the highest conservation value. Identifying the subset of aquatic habitats of conservation priority can be achieved easily in well-studied regions. However, in developing-understudied regions, such efforts are often impeded by additional factors (Eden Again Group 2004 and 2005; Nature Iraq 2006; Al-Saffar 2007; Iraqi Ministry of the Environment et al. 2006a to c; Mahir et al. 2009; Iraqi Ministry of the Environment 2010; Geraci et al. 2011): surveys are governed by security and logistic issues; people often have higher priorities for conservation, ecological knowledge and research are rare; many aquatic habitats are not surveyed; physico-chemical and biological data for surveyed aquatic habitats are scarce

134 and uncertain; datasets are sporadic, biased spatially and temporally, and landscape-level datasets are not available; many species are unknown and many others are still under taxonomic investigation; datasets are not final, mostly not available for sharing, and available data are in different forms. Given these impediments and the urgency of biodiversity issues, rapid low-cost methods for assessment of aquatic habitats and prioritization for conservation are needed. The use of bioindicator species, especially mayflies, stoneflies, and caddisflies (Insecta: Ephemeroptera-Plecoptera-Trichoptera, or EPT), as a measure of water quality and aquatic habitat quality is a common approach for addressing these issues (Tulloch et al. 2013; Suter and Cormier 2014; Everall et al. 2015; Nair et al. 2015). EPT are aquatic insects with benthic larval stages that are habitat- specific and typically occupy freshwater systems of only good-to-excellent water quality. These insects are highly intolerant of habitat destruction, pollutant contamination, altered water flow dynamics, river straightening, and degraded water quality (Merritt et al. 2008; Lubini et al. 2012). Therefore, EPT data can indicate aquatic habitat conditions without the need for detailed information about physico-chemical properties and biological components. The higher the diversity, rarity, and endemism of EPT, the higher the water quality and the healthier the food web; healthier aquatic habitats are likely to produce the highest quality of ecosystem services (Morse et al. 2007; Nair et al. 2015). As such, these habitats will merit the highest priority for conservation. The Upper Tigris and Euphrates freshwater ecoregion, encompassing parts of Iraq, Iran, Syria, and Turkey, is one of 53 freshwater ecoregions located in Europe and the Middle East (Abell et al. 2008). In Kurdistan Region (KR) of northern Iraq, this ecoregion is represented by Tigris River tributaries and streams, a few natural lakes such as Lake Dohuk, as well as reservoirs such as Lake Dukan and other smaller impoundments. One of the longest rivers in the Middle East (Kavvas et al. 2011), the Tigris originates in the mountainous region of southeastern Turkey and flows southward, where it receives more that 50% of its water budget while passing through Iraq (Kibaroglu and Ünver 2000). Just like the rest of the Upper Tigris and Euphrates freshwater ecoregion, biodiversity in the Tigris basin in the KR is poorly known, while climate change and anthropogenic threats are increasing dramatically, especially in the last decades, leading to serious degradation of aquatic habitats (MacQuarrie 2004; Mahir

135 et al. 2009; Iraqi Ministry of the Environment 2010; Zentner 2011; Geraci et al. 2011; Hattam 2013; Mulder et al. 2015). In contrast, terrestrial habitats and reservoirs have received more attention, with the identification of Key Biodiversity Areas (KBAs) that would serve to conserve birds, plants, fish, and some terrestrial mammals and vertebrates (Iraqi Ministry of the Environment and Nature Iraq 2015). Although aquatic invertebrates, such as EPT, are highly diverse and highly important for human persistence and welfare, they were not considered when KBAs were identified due to many impediments especially the Linnean (taxonomy), Wallacean (distribution), and Prestonian (abundance) shortfalls (Cardoso et al. 2011). Ephemeroptera-Plecoptera-Trichoptera are aquatic insects (with very delicate gills and body-parts) that are known for their sensitivity to, and intolerance of, climate change and anthropogenic disturbance (Fochetti and Tierno de Figueroa 2006). EPT conservation issues in the Nearctic, Afrotropical, and West Palaearctic realms, have garnered greater attention in the last decades (Barber-James et al. 2008). Recent evidence from the West Palaearctic showed that EPT (especially rare and endemic species) are facing extinction, reduction to small isolated populations, and dramatic declines in abundance, distribution, and diversity, due to habitat destruction and water pollution (Zwick 1992; Malzacher et al. 1998; Sánchez-Ortega and Tierno de Figueroa 1996; Guerold et al. 2000; Fochetti and Tierno de Figueroa 2004; Lubini et al. 2012). The KR is located in the West Palaearctic and the same factors contributing to EPT conservation issues are currently happening in the KR, while knowledge about these taxa is limited, conservation studies for them and their habitats are not available, and their conservation status is unknown. Extinction of these unique aquatic insects, besides being a great loss in biodiversity, will lead to ineffective future water quality biomonitoring programs, altered aquatic food webs, and ultimately, poor ecosystem services (Morse et al. 2007; Nair et al. 2015). Therefore, it is important to examine EPT diversity in the KR when conducting conservation studies, as a mean for identifying high quality aquatic resources. In this research, I describe a survey of aquatic habitats in the KR; my goal was to identify a subset of healthy (least-impaired, highly diverse, and highly productive) aquatic habitats that merit the highest priority for conservation in the KR. I predict that healthy aquatic habitats will be found in association with the KBAs, in unoccupied and

136 least-populated river-valleys along political and administrative boundaries and away from urban areas, where the fewest anthropogenic threats are encountered. I predict that conserving most unique evolutionary taxa of EPT in the KR will not require conserving all aquatic habitats in the region. Rather, only conserving a subset of healthy aquatic habitats will help conserve the majority of species. I employed a strategy for meeting this goal. First, I used indices of biodiversity to rank a set of surveyed aquatic habitats and prioritize them for conservation. From the ranking, I selected a subset of habitats with the highest EPT diversity and community uniqueness; this subset, which has the highest priority for EPT conservation, was considered to represent the healthy aquatic habitats in the KR. Second, to avoid data-bias and spatial-bias, I “filled-the-gaps” by modeling the distribution of healthy aquatic habitats across the entire KR, using the subset of healthy aquatic habitats I produced above as occurrence points. This approach allowed me to uncover the pattern of distribution of healthy aquatic habitats in the KR that would merit conservation priority. By ranking the KR’s aquatic habitats based on their priority for EPT conservation, I am hitting two targets in one shot; I am (1) using this low-cost method to achieve the goal of identifying the healthy aquatic habitats in the KR and (2) planning ahead for conserving potentially threatened, highly useful, aquatic species that should help to maintain the greatest possible ecosystem services for future generations.

METHODS Study Area Streams and tributaries of the Upper Tigris River, which are part of the Upper Tigris and Euphrates freshwater ecoregion, were sampled for this research. The upper Tigris River flows through the western side of the KR, where five main tributaries flowing from the northeast to the southwest contribute to its water budget; Khabur River, Great Zab River, Little Zab River, Udhaim River, and Diyala River (also called Sirwan River). Aquatic habitats along these tributaries, as well as streams draining Mosul, constitute the study area; they were sampled for EPT taxa biannually (summer and winter), at 53 randomly chosen sites (Table 0-1 and 3-1), from summer 2007 to summer 2010 as part of a larger ‘‘Key Biodiversity Areas’’ survey conducted by Nature Iraq (Nature Iraq 2009; Rubec et al. 2009; Iraqi Ministry of the Environment 2010, Iraqi Ministry of the Environment and Nature Iraq 2015). Lakes and reservoirs in the KR and most streams close to the Iraqi

137 borders with Turkey and Iran were not included in this survey due to logistic and security issues. Twenty of the sites were used in a previous study of Iraqi caddisfly diversity in the KR (Geraci et al. 2011).

Identifying Planning Units A map of the KR (with latitude and longitude coordinates) was scanned and imported into ArcMap 10.2.2, then the KR’s part of the upper Tigris lentic and lotic ecosystems was digitized. This included one natural lake, Lake Dohuk and three reservoirs, Lake Mosul, Lake Dukan, and Lake Darbandikhan, as well as networks of streams and tributaries in the KR. In addition, land use/cover information (such as agricultural, rural, urban, tourist, factory, gravel mining, and vegetation type) were compiled from videos, pictures, and field descriptions for the 53 study sites, which were visited during Nature Iraq’s surveys of Key Biodiversity Areas (KBAs) and other surveys in the KR from 2007 – 2010 (Nature Iraq 2009; Rubec et al. 2009; Iraqi Ministry of the Environment 2010, Iraqi Ministry of the Environment and Nature Iraq 2015). The natural lake and three reservoirs were considered as the main planning units for conservation of lentic ecosystems in the KR. Next, lotic ecosystems were divided into 72 polyline-stream-segments, and considered as the main planning units for conservation of lotic ecosystems in the KR. These units were delineated using the following strategy (Figure 3-1): (1) initial segmentation using sub-basin boundaries (Lehner and Grill 2013) to produce physically connected or not-connected stream reaches within each sub-basin; (2) when information about land use/cover was available for sub-basins, the initial segmentation for stream reaches was refined. Stream reaches within a sub-basin were combined with connected stream reaches of an attached sub-basin and considered as one unit if both had similar land use/cover. Stream reaches within a relatively large sub-basin with multiple land uses/covers were sub-divided into multiple units. Areas of land use/cover change were usually coincident with change in natural shape and elevation of the landscape such as the presence of mountain ranges, plains, valleys, fields, etc. For stream reaches within a relatively large sub-basin with multiple land use/cover types, topographic 2D and 3D base-maps (available in ArcMap 10.2.2 and Google Earth) were used to view areas of change in landscape features and determine the start and end points for units; and (3) for sub-basins with no land use/cover information, headwater streams

138 within a sub-basin were combined with connected stream reaches of an attached sub- basin and considered as one unit. If two attached sub-basins did not contain headwater streams, then no combination of stream reaches from the two sub-basins was done; all stream reaches within each sub-basin were considered as one individual unit. Finally, planning units were given identification codes, such as 1-1, referring to tributary number – segment number.

Part 1: Prioritizing Sampled Planning Units for EPT Conservation Presence-absence data for 94 unique EPT taxa (19 species (7 E and 12 T), 16 morphospecies (8 E and 8 T), 46 SpLUs (41 E and 5 P), and 13 T-OTUs) were compiled for the 53 study sites (from Chapter 2, Geraci et al. (2011), along with Trichoptera from the Iraq BOLD Systemss project (www.boldsystems.org; Ratnasingham and Hebert 2007). Next, data from study sites were transferred to overlapping units, which resulted in identifying 33 sampled polyline-stream-segments (sampled-units), each of which received data from 1 – 3 study sites (Table 3-1). Evaluation and prioritization of these sampled-units for EPT conservation was then performed jointly rather than independently through performing three independent Complementarity-based Approaches (CBAs; Vane-Wright et al. 1991; Moilanen 2007; Moilanen et al. 2009); (1) Minimum Set Coverage (MSC); (2) Maximal Coverage (MC); and (3) Utility Maximization (UM) (Figure 3-1). Following is the mathematical formula for each approach as well as the formula for Rarity-Weighted Richness Index (RWRI, Williams et al. 1996), which was a central calculation to perform UM; RWRI is an index integrating alpha and beta diversities which gives more weight to rare and endemic taxa when calculating richness (Master et al. 1998):

for all taxa … … … . … .. 1 where (ci) is conservation cost for sampled-unit (i); sampled-unit length/area size was

used as a proxy for conservation cost; (xi) is a control variable with a value of 1 for selected sampled-units and value of zero for not selected sampled-units; (Ns) is the total

number of sampled-units; (rij) is the occurrence level of taxon (j) in sampled-unit (i); used occurrence level was 1; and (Tj) is the target level for taxon (j).

139 ………………….. 2

where (Ij(z)) is an indicator function with a value of 1 when (z) is true (i.e., the target for taxon (j) is met; taxon (j) was conserved in at least 1 unit) and zero otherwise; and (B) is the conservation budget.

……………………….. 3 where (wj) is the weight given to taxon (j); (fj) is a function used to transform the representation to benefit (set to 1 as presence of each taxon is equally beneficial); and

∑ and it represents the level of taxa representation in selected sampled- units (set to 1 in present-absent data). 1 ……………………………………………………………………………..4

where (N) is the number of taxa found within a sampled-unit, (hi) is the number of sampled-units in which taxon (i) occurs, and (1/hi) equals (wj) from equation 3. Each of the three CBAs was performed independently to order sampled-units from the one with the highest conservation priority to the lowest. Percentage of conserved EPT taxa through protecting each sampled-unit and accumulated percentage of conserved taxa were calculated, starting with the sampled-unit with the highest priority and ending with the one with the least priority. For MSC, there was no budget constraint in order to conserve all EPT taxa in the KR, but there was a need to do so at the best minimum cost. The first selected sampled-unit was the one with the highest richness of EPT. When more than one sampled-unit had the same richness, the one with the least length/area size was selected and received the highest priority for conservation. The next step was to select a sampled-unit that complemented the first one and had the highest number of different taxa (i.e., had the highest richness of remaining taxa). When more than one sampled-unit had the same characteristics, the one with the least length/area size was selected and received the next priority for conservation. When sampled-units’ lengths were sub-equal (with a difference of < 2km), connectivity and then proximity to the first selected sampled-unit were considered. This process was continued until all EPT taxa in the KR were covered. For MC, a budget constraint was added to the principles used in MSC

140 (richness, complementarity, and sampled-units’ length (or area size), proximity, and connectivity). The budget constraint was: protecting <50% of total length of lotic ecosystems (rivers and streams) in the KR, while conserving the highest possible number of EPT taxa in the region. Next, UM was performed with the same budget constraint used in MC, however, prioritized sampled-units using UM were selected based on RWRI scores and complementarity, without taking into account sampled-units’ length (or area size), connectivity, and proximity. Given that the final total length of prioritized sampled- units could not exceed 50% of total length of lotic ecosystems in the KR, RWRI was calculated for each sampled-unit and then prioritized from the one with the highest score to the lowest taking into account complementarity. First, the sampled-unit with the highest score was selected and given the highest priority; the next sampled-unit was the one with the next highest score; when more than one sampled-unit had the same score, the one complimenting the sampled-unit with the higher priority was selected. Sub-basins in the KR were also prioritized for EPT conservation, using MSC, MC, or UM. Priority scores were inherited from overlapping prioritized sampled-units. Sub-basins with more than one prioritized sampled-unit received the score from the sampled-unit with the highest priority.

Part 2: Predicting Planning Units of Healthy Aquatic Habitats across the KR An approach was developed to predict all planning units with healthy aquatic habitats across the KR, including both sampled and nonsampled units. For the three sets of prioritized sampled-units for EPT conservation (produced by MSC, MC, and UM respectively), the upper 50% of units (the half most important for EPT conservation) was selected and combined to produce a final set of 11 units (1-1, 1-3, 2-1, 3-2, 3-3, 3-6, 3-8, 3-11, 4-3, 4-5, and 4-7); 1-3 and 4-3 were not prioritized by all CBAs in the upper 50%. The resultant 11 units were considered as samples of healthy aquatic habitats in the KR (Figure 3-1). These 11 units were converted from polyline to multipoint units and each point was considered as an occurrence point for a healthy aquatic habitat. Next, healthy habitat occurrence points (16741 points) along with 92 predictor variables (including various anthropogenic, environmental, and climate variables) were filtered and then used in correlative Maximum Entropy (MaxEnt) modeling (Harte 2011; Elith et al. 2011) for the distribution of healthy aquatic habitats across the KR (Figure 3-1).

141 Maximum Entropy (MaxEnt) modeling is an approach commonly used to predict the distribution of species or habitats (Phillips et al. 2005 and 2006; Pearson 2009). MaxEnt has been shown to be superior in comparison with previous methods of distribution modeling (Elith et al. 2006; Pearson et al. 2007; Phillips and Elith 2013) due to the following advantages (discussed in details in Phillips et al. 2006): (1) MaxEnt does not require absence data (a highly controversial parameter); instead, it uses a background of the available predictor variable data in the study area to predict presence or absence; (2) MaxEnt can utilize both continuous and categorical parameters while other methods cannot use categorical data; and (3) MaxEnt ensures that the estimated distribution agrees with the observed data and avoids making assumptions not supported by the data. The probability distribution gained from MaxEnt can show how similar the predicted habitats are to a set of points of healthy aquatic habitats; it is a spread-out distribution that is close to uniform and is subject to constraints imposed by the conditions of the study area (i.e., observed data for the distribution of healthy aquatic habitats and predictor variables). Python-based ArcGIS landscape analysis tools available in SDMtoolbox within ArcGIS 10.2.2 (Veloz 2009; Thuiller et al. 2009; Anderson and Raza 2010; Hijmans 2012; Brown 2014a and b) and Maxent 3.3.3k (Phillips et al. 2005 and 2006) were used to perform 14 HDMs in three phases (Figure 3-1): (i) pre-Maxent (data processing and preparation); (ii) Maxent (model creation, calibration, and validation); (iii) post-Maxent (model evaluation and best model selection). First, predictor variables were processed and converted to ASCII grids, then filtered to retain 18 variables with Pearson's Correlation Coefficient (R) <0.6 (Table 3-2). Next, Principal Component Analysis (PCA) was performed to find the principal components of the data (i.e., directions where there is more variance and directions where the data is most spread out). Spatial heterogeneity for the KR was then measured based on the variation of 18 selected variables (calculated at each raster cell) for each of the three layers produced by PCA, givening more weight for the PCA layer with the highest Eigenvalue (Brown 2014a and b). Graduated spatial rarefication for occurrence points was then performed within a spatial scale of 2-10km using five heterogeneity classes (Veloz 2009; Hijimans et al. 2012; Boria et al. 2014). All but one of the points close to each other and located within the same heterogeneity class were excluded. Next, spatially rarefied occurrence points for healthy aquatic habitats

142 (135 points) were used along with a grid of the KR to create an ASCII grid with a 2km buffer mask, which is a bias file masking part of the KR where all occurrence points are located (Phillips et al. 2009; Barbet‐Massin et al. 2012). Rarefied points were next spatially jackknifed to create spatially segregated, spatially independent, five-fold cross- validation points to be used in MaxEnt in training and evaluation of the models (Shcheglovitova and Anderson 2013). Fourteen Maxent models were tested using 135 spatially rarefied occurrence points, 18 predictor variables, background points (pseudo-absence) from outside the masked area, geographically structured five-fold cross validation, three regularization multipliers (0.5, 1, and 1.5), and five combinations of different Maxent features (linear, quadratic, hinge, product, and threshold). Finally, the best model was identified based on the highest weighted prediction, an independent statistical measurement combining scores for Omission Rate (OR), Area Under the Curve (AUC), as well as MaxEnt feature complexity (Radosavljevic and Anderson 2014; Brown 2014a and b). A logistic output raster produced by the best model reflected a similarity range of 0 – 88% when compared to the 135 sites used in modeling. The logistic output raster was imported to ArcGIS 10.2.2 and reclassified into 10 classes, where >50% habitat similarity was extracted and combined, and then spatially intersected with the 76 planning units in the KR (i.e., all 72 units, 1 natural lake, and 3 reservoirs). Stream units with predicted healthy aquatic habitats of >50% of the length, and lakes or reservoirs with predicted healthy aquatic habitats of >50% of the area, were considered as predicted planning units with healthy aquatic habitats in the KR. These units were prioritized for conservation using the percent of healthy aquatic habitat within each. Sub-basins in the KR were also prioritized for conservation following the priority of predicted planning units of healthy aquatic habitats. Sub-basins with more than one predicted planning unit of healthy aquatic habitats received the priority of the most important unit.

RESULTS Part 1: Prioritized Units for EPT Conservation I evaluated 33 sampled-units (67.5% of the total length of lotic ecosystems in the KR) and prioritized 19 of them (43.4% of the total length of lotic ecosystems in the KR) for EPT conservation. The same 19 sampled-units were selected by all three CBAs. Using

143 MSC, 3-2 received the highest priority followed by 1-1, 4-5, then 16 others. Using MC, these 19 sampled-units were given the same order of priority as MSC (Table 3-3; Figures 3-2 and 3-3). According to MSC or MC, protecting 3-2 only (0.5% of total length of lotic ecosystems in the KR) will help to conserve ~ 25% of EPT taxa in the KR. Using UM, these 19 sampled-units were prioritized differently; 1-1 received the highest priority followed by 3-2, 3-11, then 16 others (Table 3-3; Figures 3-4 and 3-5). Using UM, only three sampled-units kept their positions, while most of them changed position (Table 3-3). Priority for conservation (1) stayed the same in 3-6, 5-2, and 3-9; (2) went up in eight (1- 1, 3-11, 3-3, 3-8, 4-3, 3-7, 4-9, and 1-4); and (3) went down in other eight (3-2, 4-5, 4-7, 2-1, 3-10, 4-6, 1-3, and 4-1). According to UM, protecting 1-1 only (2.4% of total length of lotic ecosystems in the KR) will help to conserve more than 23% of EPT taxa in the region. Results of sampled-units’ prioritization for EPT conservation, using different CBAs, confirmed each other and showed that (1) Little Zab River has the highest number of prioritized sampled-units compared to other rivers in the KR (8 sampled-units), followed by Great Zab River (6 sampled-units), Diyala River (3 sampled-units), then Udhaim and Khabur Rivers (Table 3-3); (2) sampled-units 1-1 and 3-2 have the highest priorities for conservation and protecting them alone will conserve ~ 44% of EPT taxa in the KR (Table 3-3); (3) conserving the top 50% (top 10) of the prioritized sampled-units (19-21% of total length of lotic ecosystems in the KR) will conserve ~ 88% of EPT taxa, and protecting all prioritized sampled-units will help in conserving 100% of EPT taxa in the region (Table 3-3); and (4) 42 sub-basins out of 117 in the KR (54.5% of the KR area) were overlapping with the prioritized sampled-units. These sub-basins received their priority from overlapping prioritized sampled-units, and sub-basins 2080737390 and 2080726290 received the highest priority for EPT conservation (Table 3-4).

Part 2: Predicted Units of Healthy Aquatic Habitats across the KR I created 14 distribution models of healthy aquatic habitats in the KR, using different MaxEnt settings (Table 3-5), with 135 randomly selected sites along 11 sampled-units (1- 1, 1-3, 2-1, 3-2, 3-3, 3-6, 3-8, 3-11, 4-3, 4-5, and 4-7) with the highest conservation priority for EPT conservation, and 18 environmental variables with R<0.6. The best model was achieved using a regularization multiplier of 1.0 and linear, quadratic, and

144 hinge features; this model returned an AUC and weighted prediction of 0.83 and 0.73 respectively (Table 3-5). The best model used seven predictor variables (Table 3-6). The most important predictor variables, that contributed >80% to the gain (a measure of goodness of the fit), were density of rivers and streams, croplands density, DEM, and human population density respectively. Croplands density and DEM had the highest effect on AUC when permuted; permutation importance was > 26% (drop in training AUC) for each (Table 3-6). Predicted aquatic habitats have an association with high density of stream networks, low density of croplands, and areas of low elevation that seem to have higher human population density compare to other areas in the KR. Many of these habitats were predicted to be present along political and administrative boundaries that are shared between governorates, districts, sub-districts, and urban areas (cities and towns). The logistic output raster of the best model reflected a similarity range of 0.0 – 88% for modeled healthy aquatic habitats. I found that similarity ranges of 50-60%, 60- 70%, 70-80%, and 80-88% were available in 64%, 52%, 34%, and 9% of sub-basins in the KR respectively, while 68% of sub-basins in the KR had any similarity between 50% and 88%. I found that the 11 sub-basins with similarity range of 80-88% to be the only sub-basins with the full range of 50-88% similarity. I also found <1% of the KR sub- basins with a range of 60-80% similarity, 23% of the KR sub-basins with a range of 50- 80% similarity, 17% of the KR sub-basins with a range of 50-70% similarity, <2% of the KR sub-basins with a range of 60-70% similarity, and 15% of the KR sub-basins with a range of 50-60% similarity. Landscape with similarities of 50-88%, 60-88%, 70-88%, and 80-88% constituted 21.3%, 11.5%, 3%, and 0.6% of total area of the KR, respectively. I found that aquatic habitats with 50-88% similarity constituted 42.43 % of the total length of streams and rivers in the KR, 47.94 % of all sampled-units, 14.24 % of sampled-units that were not prioritized for EPT conservation, 53.30 % of sampled-units that were prioritized for EPT conservation, 62.28 % of sampled-units that were used in HDM, and 30.42 % of total length of not-sampled units in the KR. Based on the distribution and percent overlap of these predicted healthy aquatic habitats with my 76 planning units in the KR, I identified Lake Dohuk (~2141 km2; ~0.4% of all lake and

145 reservoir areas in the KR) and 23 stream units (~2603 km, ~43% of total length of streams and rivers in the KR) as the predicted planning units of healthy aquatic habitats in the KR; seven of these planning units (Lake Dohuk, 4-12, 4-14, 4-18, 4-22, 5-4, and 5- 9) were not sampled for EPT before, five of them (1-2, 1-6, 1-7, 5-1, and 6-4) were sampled but not prioritized for EPT conservation, and nine of them (1-1, 1-3, 2-1, 3-2, 3- 3, 3-6, 3-8, 4-3, 4-5) were used to create occurrence points for distribution modeling of healthy aquatic habitats (Table 3-7; Figure 3-6). Lake Dohuk received the highest priority for conservation followed by 3-2, 4-9, and then 21 other predicted planning units of healthy aquatic habitats. I found that 42 sub-basins out of 117 in the KR (47.8% of the KR area) were overlapping with the predicted planning units of healthy aquatic habitats in the KR. These sub-basins received their priority from overlapping units, and with sub- basins 2080711640 and 2080726290 receiving the highest priority for conservation (Table 3-8; Figure 3-7).

DISCUSSION Part 1: Prioritized Units for EPT Conservation I used three Complementarity-based Approaches (CBAs) to provide minimum sets of planning units to maintain EPT biodiversity at its maximum. When performing CBAs, I did not use descriptive data nor summary statistics such as richness so as to evaluate planning units independently, because protecting only the richest units will most likely result in conserving the same taxa around the KR and missing unique communities and many rare and endemic species (Vane-Wright et al. 1991; Margules and Pressey 2000; Justus and Sarkar 2002). Rather, I used richness or RWRI of each planning unit and evaluated units jointly based on taxa complementarity (Moilanen et al. 2009). I also took into account units’ proximities and connectivities when selecting the best solution for MSC and MC, because conserving a given unit will likely require conserving its connected or proximate unit upstream. In addition, proximity and connectivity of selected units are desired for many species assuming future benefits for population connectivity (Kool et al. 2013). Performing CBAs led to the identification of 19 sampled-units as those needing to be protected in order to conserve all EPT in the KR (Table 3-3). This result agreed with my earlier prediction that conserving all unique evolutionary taxa of EPT does not

146 require conserving all aquatic habitats in the KR. Although some differences in the set of units and/or their priorities when performing each CBA (because each approach has at least one different a priori criterion than others) were expected, the same 19 sampled- units were identified by all CBAs. In addition, MSC and MC prioritized them in the same order. MSC and MC used the same criteria to identify units except one, which was the budget constraint. Given the constraint I used with MC, this approach prioritized the 19 units just like MSC (Table 3-3; Figures 3-2 and 3-3). However, these units and their order may have been changed if I used a tighter constraint, such as a budget to protect <25% of total length of lotic ecosystems in the KR (for example, see Snyder et al. (1999) and Chadés et al. (2015)). For UM, there was a budget constraint, just like the case in MC, however, the prioritized units were selected based on RWRI scores and Complementarity, without taking into account units’ length (or area size), connectivity, and proximity (Moilanen 2007). Therefore, when UM was performed, the order of priority of the 19 units was changed; while 3 units kept their positions (3-6, 5-2, and 3-9), eight units (1-1, 3-11, 3-3, 3-8, 4-3, 3-7, 4-9, and 1-4) received higher priority due to the presence of rare and endemic taxa, and other eight segments received less priority due to the presence of more common taxa than other segments (Table 3-3; Figures 3-4 and 3-5). In countries such as Iraq, where ecological research is rare and data are missing for the vast majority of aquatic habitats, CBAs were only helpful to prioritize sampled- units, and produce a list of 19 units for conservation of EPT taxa. The top 50% of these units were considered to be samples of healthy aquatic habitats in the KR because each has a unique EPT community; however, richness and RWRI were not at the same level in all. In addition, given that studied segments were only sampled at 1-3 sites (selected randomly and surveyed prior to the delineation of units as planning units), some of the selected samples of healthy aquatic habitats may have been over-emphasized. Therefore, the distribution of healthy aquatic habitats in the sampled part of the KR remained incomplete and uncertain. In addition, 39 planning units in the KR were not sampled in this study, therefore large gaps were present and a full image of the distribution of healthy aquatic habitats across the KR remains unknown. To solve this issue, I conducted part two of this research and developed an approach to “fill-the-gaps” and predict the

147 distribution of healthy aquatic habitats in the entire the KR, along sampled and nonsampled lotic and lentic ecosystems.

Part 2: Predicted Units of Healthy Aquatic Habitats across the KR I produced 14 distribution models for healthy aquatic habitats across the KR and selected the best one (Tables 3-5 and 3-6). This procedure identified Lake Dohuk and 23 sampled stream lengths as the units of conservation concern in the KR, due to the presence of high percentages of healthy aquatic habitats along them (>50%); seven of these aquatic habitats (Lake Dohuk, 4-12, 4-14, 4-18, 4-22, 5-4, and 5-9) were not surveyed for EPT (Table 3-7; Figure 3-6). In addition, five of them (1-2, 1-6, 1-7, 5-1, and 6-4) were surveyed but were not prioritized for EPT conservation (Table 3-7; Figure 3-6). Lake Dohuk was recently identified as one of 41 Key Biodiversity Areas (KBAs) in the KR (Iraqi Ministry of the Environment and Nature Iraq 2015) and unit 6-4 represents one of the Mosul streams that flow through this natural lake and connect it to a larger KBA, which is a reservoir known as Lake Mosul. Units 1-2, 1-6, and 1-7 are all related to the Diyala River and parts of them are located within another large KBA that includes a reservoir known as Lake Darbandikhan; units 1-2 and 1-6 are parts of some streams feeding this reservoir, while unit 1-7 is a stream starting within KBA39, Qara Dagh, and feeding the Diyala River right after Darbandikhan Dam. Unit 5-1 is mostly located at the Iraqi-Turkish border, and it represents the last polyline stream segment in Khabour River that feeds the Tigris River where another KBA (KBA1: Fishkhaboor) was identified. Units 4-12 and 2-14, along with other identified units (4-5 and 4-6), form a network of streams that surround and overlap the largest KBA in the KR (KBA4: Barzan Area and Gali Balnda); this has often been proposed as a national park (Iraqi Ministry of the Environment and Nature Iraq 2015. Key biodiversity areas of the KR were identified recently, based on their roles in maintaining populations of birds, plants, and fish (Iraqi Ministry of the Environment and Nature Iraq 2015). They were selected at the landscape level and suggested to be used for spatial conservation prioritization efforts in the KR. I found that the KBAs that overlapped with identified units of healthy aquatic habitats are supporting various plants and animals of conservation concern. For instance, they support a critically endangered amphibian (Sharifi et al. 2009); critically endangered and endangered fish (Freyhof 2014a

148 and b); an endangered mammal (Khorozyan 2008); a vulnerable mammal (Weinberg et al. 2008); an endangered reptile (European Reptile & Amphibian Specialist Group 1996); an endangered bird (BirdLife International 2015); many biome-restricted birds (Iraqi Ministry of the Environment and Nature Iraq 2015); national endemic, near-endemic, and rare plant species (Iraqi Ministry of the Environment and Nature Iraq 2015); and a globally threatened habitat type (Riverine Forest of the Plains (Al-Ahrash); Iraqi Ministry of the Environment and Nature Iraq 2015). Identified units of conservation priority are expected to play a central role in connecting aquatic organisms of the KBAs and providing ideal foraging sites for them, especially fish, waterbirds, and aquatic mammals. For terrestrial organisms, these aquatic habitats are still vital parts of corridors and dispersal routes. Although the dispersal of many organisms might reflect random walk patterns (McRae 2006; McRae and Beier 2007), behavior and preference of habitats (Corbet 2004) is expected to play an important role with KBA species. Suitable habitat patches are expected to be used by KBA species in many ways depending on physico-chemical and biological factors such as the patch shape, size, location, presence of threats, and overall degree of suitability. Permeable patches situated along the way may connect meta-populations and be used for foraging and dispersal among them; these patches will form corridors if attached to each other (or located close enough for flying and/or swimming individuals). These will serve as the main connectivity routes allowing gene flow between established meta-populations. Although some units of conservation concern in the KR, such as 3-2, 5-1, and 4- 22, overlap with only one KBA (KBA26: Chami Razan, KBA1: Fishkhaboor, and KBA13: Gali Zanta & Garbeesh respectively), most units represent a network of corridors and permeable paths across the landscape, feeding almost half of the KBAs (20 KBAs) which constitute ~76% of total area size of KBAs in the KR, and connecting 18 KBAs, which constitute ~74% of total area size of KBAs in the KR. They provide the natural physical component in landscape connectivity (Merriam 1984), facilitating spatial contagion of habitats; the greater the number of connectivity routes and the better their quality, the more species will be able to move. Some segments, such as 1-1 and 4-6, were found to connect more than two KBAs that support many endangered and threatened species in the KR. For instance, unit 1-1, which is part of Diyala River, overlapped with

149 four KBAs; 40: Lake Darbandikhan; 41: Zalm; 42: Ahmed Awa; and 43: Hawraman Area. This unit flows through terrestrial ecoregions of Middle East Steppe and Zagros Mountains Forest Steppe, where various terrestrial habitat types, such as grassland, riverine forest of the plains, mountain oak forest, oak woodlands, and mountain riverine forest, get connected. Although a structurally connected landscape may not always be functionally connected, water-associated movement in most organisms is a central part in their metapopulation dynamics (Levins 1969 and 1970; Hanski 1998); the more species movement, the more connectivity between populations, and the greater potential for improving their conservation status.

Current Conservation Status of KBAs and Aquatic Ecosystems in the KR Conserving networks of healthy aquatic habitats that connect the KBAs in the KR should become a vital part of land management strategies. However, none of the KBAs or lotic ecosystems in the KR are currently protected, while habitat destruction and threats are evident in all. Most KBAs, such as the ones connected by unit 1-1, are threatened by gravel mining, livestock grazing, hunting, fishing, tourism, sewage and garbage pollution, and impoundment by dams. While unit 1-1 is currently not protected, direct anthropogenic threats to it were witnessed in KBA41, Zalm, where car washing, garbage pollution, burning in the riparian area, extending croplands into the riparian area, agricultural run-off, and animal grazing were evident (Iraqi Ministry of the Environment and Nature Iraq 2015). In addition, erosion and unmanaged tourism activities were very evident in KBA42, Ahmed Awa (Iraqi Ministry of the Environment and Nature Iraq 2015) and heavy metal contamination and sudden fish kills were evident especially in KBA40, Lake Darbandikhan (Mahir et al 2009; Iraqi Ministry of the Environment and Nature Iraq 2015).

Planning for Conservation in the KR As predicted, healthy aquatic habitats were found in river-valleys, along political and administrative boundaries, and many of them are shared between governorates, districts, sub-districts, and urban areas (cities and towns). Pioneer conservation efforts in regions such as the KR are expected to encounter issues related to jurisdiction by municipalities and points of human population settlement as well as administrative conflicts related to

150 planning units. Therefore, it is recommended that all administrative levels unify their efforts and use the same units for conservation planning across the KR; the most recommended units for conservation of healthy aquatic habitats would be the planning units identified in this study. These units were also prioritized for conservation based on the percentage of healthy aquatic habitats within each (Figure 3-6). In case my planning units cannot currently be used, my next recommendation is to use the sub-basins overlapping with them (Figure 3-7); conservation planning at the sub-basin level has been commonly used in the West Palaearctic realm, especially in Europe (Lehner and Grill 2013). Using governorates or districts as planning units for conservation will not be nearly as effective. In case sub-districts have to be used as planning units, conflicts will potentially happen. More than 89% of the prioritized units for EPT conservation and 88% of the units of healthy aquatic habitats were found to be overlapping with 2-5 sub- districts. These units are shared between or flowing through more than one sub-district. Conservation planning has to be coordinated between sub-districts with shared units of healthy aquatic habitats along their boundaries or flowing through both, otherwise “tragedy-of-the-commons” (Hardin 1968) will be the ultimate fate for these unique habitats. Upstream conservation efforts have to be coordinated with downstream efforts and management practices have to be unified; if not, habitats, time, efforts, and funds will be wasted. Finally, more than 50% of the prioritized units for EPT conservation and 74% of the units of healthy aquatic habitats were found to be overlapping with urban areas and most of these units are flowing through more than two urban areas. Most urban areas in the region are small developing villages, towns, and cities and they are not stretching along the full length of identified habitats of conservation concern (Mahir et al. 2009; Iraqi Ministry of the Environment and Nature Iraq 2015). Therefore, using cities as planning units will also cause potential administrative conflicts and lead to ovelooking many healthy aquatic habitats. The Kurdistan region is under accelerating development and conservation has to be incorporated in development plans. Sulaimani governorate has the highest priority for conservation of KBAs and healthy aquatic habitats compared to Erbil and Dohuk governorates. Urban Areas constitute ~ 4% of total area size of the KR, and ~45%, ~35, and ~20% of them are located in Sulaimani, Erbil, and Dohuk governorates respectively.

151 KBAs constitute ~ 11% of total area size of the KR, and ~50%, ~31, and ~19% of them are located in Sulaimani, Erbil, and Dohuk respectively. Almost 63% of prioritized units for EPT conservation, and 44% (~41% of total length) of predicted units of healthy aquatic habitats, are parts of Diyala, Udhaim, and Little Zab Rivers, which are flowing through Sulaimani governorate. Most of the sub-basins with high priority for EPT conservation are located in the middle and southeastern part of the KR, which is mostly Sulaimani (Table 3-4). Almost 45% of total area of predicted sub-basins of healthy aquatic habitats are located in Sulaimani (Table 3-8; Figure 3-7). Two sub-basins, 2080737390 and 2080726290, have the highest priority for EPT conservation, and protecting them alone will help in conserving 10 KBAs. These are large sub-basins attached to one another, they constitute 11.6% of the KR area size, and both are located in Sulaimani.

CONCLUSION Given the impact of climate change and anthropogenic disturbance on biodiversity and the urgency with which these issues need to be addressed, developing a rapid and low- cost conservation strategy to protect aquatic habitats and maintain their ecosystem services is needed (Terrado et al. 2016). To implement such a strategy in poorly studied ecosystems, surrogate species need to be used (Faith and Walker 1996; Tulloch et al. 2013; Jones et al. 2016) and genetic and phylogenetic data need to be integrated (Forest et al. 2015). Due to their important role in aquatic habitats as food for fish, amphibians, birds, and wildlife, as well as their important contribution to energy and nutrient processing (Suter and Cormier 2015), I used water quality bioindicators as surrogate species and integrated all of their data, including genetic data (OTUs and SpLUs), in order to identify and prioritize healthy aquatic habitats for conservation in the KR. I used multiple biodiversity indices related to CBAs and looked for the most important units in order to identify a subset that represent EPT biodiversity hotspots. My approach came to an agreement with many other conservation prioritization studies. For instance, Tolimieri et al. (2015) and Daru et al. (2015) used multiple biodiversity indices and looked to the consensus between them to identify biodiversity hotspots and use them as areas of conservation priority.

152 Complementarity-based Approaches have been found in the last decades to be the most efficient compared to other approaches such as hotspots of species richness and endemism and biogeographic zoning (Fox and Beckley 2005). Ranking units for conservation based on CBAs has also been found to outperform other methods in terms of cost-effectiveness and as a result, to have the potential to save many more species (Chadés et al. 2015). Biodiversity hotspots identified using CBAs are more productive, have greater resilience to disturbance, and improved ecosystem services relative to less diverse spots (Worm et al. 2006, Stachowicz et al. 2007, Duffy 2009, Palumbi et al. 2009), therefore these hotspots indicate healthy aquatic habitats that merit conservation priority. Focusing conservation efforts on biodiversity hotspots is the most efficient way to use limited resources to protect the most or rarest species (Myers et al. 2000). I considered the EPT diversity hotspots I identified in this research as a sample for healthy aquatic habitats in the KR. These hotspots were used in a correlative Habitat Distribution Model (HDM) under present environmental conditions to “fill-the-gaps” and predict the full distribution of EPT diversity hotspots (i.e., healthy aquatic habitats) across the KR (for similar approaches, see Guisan and Zimmermann 2000, Benito et al. 2014). To insure the best possible distribution modeling, occurrence points of healthy aquatic habitats were rarified and pseudo-absence (background) points were selected after “masking” the occurrence points using a bias-file. In addition, spatial jackknifing of occurrence and background points were performed and models were evaluated independently. This approach has been used recently in several studies (Brown and Yoder 2015; Brown et al. 2016a, 2016b). The best HDM resulted in identifying a lentic habitat and networks of lotic habitats in the KR as the units of healthy aquatic habitats that merit the conservation priority in the region. Such a network of aquatic habitats will need to be taken into account when planning for conservation of KBAs in the KR, as most of them connect and feed the KBAs with water and resources, and their predicted quality qualify them to serve as main corridors and paths for dispersal between the KBAs (for similar approaches, see Cuyckens et al. 2016 and Ochoa-Ochoa et al. 2016).

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163 TABLES AND FIGURES Table 3-1. Sampled polyline stream segments (sampled-units) in the Kurdistan Region, northern Iraq. DD = GPS coordinates in decimal degrees.

Tributary Segment Segment Site Latitude Longitude Tributary Name Site ID Site Name Number Number Code Number (DD) (DD) Diyala River 1 1 1-1 48 zal Zalm 35.3036 45.9745

Diyala River 1 1 1-1 49 aac Ahmed Awa_C 35.3107 46.0889

Diyala River 1 2 1-2 46 qaa Qara Ali 35.3428 45.7127

Diyala River 1 2 1-2 47 sas Said Sadiq 35.4096 45.8860

Diyala River 1 3 1-3 38 sur Surchanar 35.6607 45.3761

Diyala River 1 3 1-3 43 kes Kela Spi 35.5599 45.2868

Diyala River 1 4 1-4 53 kal Kalar 34.6490 45.3866

Diyala River 1 5 1-5 52 bak Bani Kani 34.9090 45.5925

Diyala River 1 6 1-6 51 aws Awe Sar 35.1404 46.1149

Diyala River 1 7 1-7 45 qad Qara Dagh 35.3261 45.3442

Diyala River 1 7 1-7 50 dar Darbandikhan 35.0948 45.6680

164 Tributary Segment Segment Site Latitude Longitude Tributary Name Site ID Site Name Number Number Code Number (DD) (DD) Udhaim River 2 1 2-1 42 dec Delezha_C 35.5691 45.1627

Udhaim River 2 1 2-1 44 dea Delezha_A 35.3899 45.0668

Little Zab River 3 1 3-1 23 alk Altun Kopri 35.7190 44.1149

Little Zab River 3 2 3-2 34 qob Qocha Blagh 35.8110 44.9735

Little Zab River 3 3 3-3 35 beq Bequch 35.8246 45.1920

Little Zab River 3 3 3-3 37 qaz Qaziawa 35.6989 45.1701

Little Zab River 3 4 3-4 30 tab Tabban 35.8961 44.8548

Little Zab River 3 4 3-4 31 duk Dukan 35.9326 44.9629

Little Zab River 3 5 3-5 32 bar Bargalu 35.9337 45.1079

Little Zab River 3 6 3-6 24 dab Dara Ban 36.3546 44.7795

Little Zab River 3 6 3-6 26 qra Qarani Agha 36.1889 44.7662

Little Zab River 3 7 3-7 25 dak Darua Kotr 36.3285 45.0114

Little Zab River 3 7 3-7 27 mer Mertka 36.2569 45.1138

165 Tributary Segment Segment Site Latitude Longitude Tributary Name Site ID Site Name Number Number Code Number (DD) (DD) Little Zab River 3 8 3-8 28 dol Dole 36.1918 45.2076

Little Zab River 3 8 3-8 29 hau Halsho Upper 36.2067 45.3115

Little Zab River 3 9 3-9 33 isj Isa juction 35.9912 45.4365

Little Zab River 3 9 3-9 36 tak Tatapitch Kola 35.8802 45.3977

Little Zab River 3 10 3-10 39 kua Kunamasi_A 35.7438 45.4494

Little Zab River 3 11 3-11 40 war Waraz 35.7546 45.6626

Little Zab River 3 11 3-11 41 pen Penjween 35.7520 45.9479

Great Zab River 4 1 4-1 22 tat Taq Taq 36.0167 43.7926

Great Zab River 4 2 4-2 16 aka Aski Kalak_A 36.5320 43.9056

Great Zab River 4 2 4-2 20 akb Aski Kalak_B 36.3377 43.7346

Great Zab River 4 3 4-3 17 bek Bekhma 36.6564 44.2224

Great Zab River 4 3 4-3 21 bah Bahraka 36.4395 44.3164

Great Zab River 4 4 4-4 18 gab Gali Ali Beg 36.6306 44.4674

166 Tributary Segment Segment Site Latitude Longitude Tributary Name Site ID Site Name Number Number Code Number (DD) (DD) Great Zab River 4 4 4-4 19 jun Jundyan 36.6240 44.6731

Great Zab River 4 5 4-5 14 baz Barzan 36.9230 44.1590

Great Zab River 4 5 4-5 15 khe Kherazook 36.9658 44.3923

Great Zab River 4 6 4-6 8 sul Sulav 37.0493 43.4740

Great Zab River 4 6 4-6 9 der Deraloke 37.0374 43.7441

Great Zab River 4 7 4-7 5 ben Benavi 37.2186 43.4365

Great Zab River 4 8 4-8 10 gar Garagu 36.9007 43.4886

Great Zab River 4 9 4-9 12 ger Gerbeesh 36.7794 43.6189

Great Zab River 4 9 4-9 13 gaz Gali Zanta 36.6653 43.5303

Great Zab River 4 10 4-10 7 ata Atrush_A 36.8960 43.2157

Great Zab River 4 10 4-10 11 atb Atrush_B 36.8087 43.3540

Khabur River 5 1 5-1 2 fib Fishkhaboor_B 37.1261 42.4141

Khabur River 5 2 5-2 3 taj Tajika 37.0941 42.9021

167 Tributary Segment Segment Site Latitude Longitude Tributary Name Site ID Site Name Number Number Code Number (DD) (DD) Khabur River 5 2 5-2 4 ash Ashawa 37.1105 43.1133

Mosul Streams 6 1 6-1 1 fia Fishkhaboor_A 37.0504 42.3411

Mosul Streams 6 4 6-4 6 dul Dohuk Lake 36.8979 43.0000

Table 3-2. Predictor variables used in 14 distribution models for healthy aquatic habitats in the Kurdistan region, northern Iraq.

Predictor Variable Source Website National Aeronautics and Space Digital Elevation Model (DEM); 30 arc-seconds Administration (NASA), National Geospatial- www.webgis.com (~1 km) Intelligence Agency (NGA), and United States Geological Survey (USGS); 2009 Distribution of Key Biodiversity Areas (KBAs) Iraqi Ministry of Environment and Nature Iraq; www.moen.gov.iq 2015 www.natureiraq.org Density of Key Biodiversity Areas (KBAs)

Human population density

Distribution of human settlement points United Nations (UN): Office for the

Coordination of Humanitarian Affairs www.unocha.org Rivers and streams distribution (OCHA); 2004 and 2014

Rivers and streams density

168 Predictor Variable Source Website

All croplands distribution

All savannas distribution

All shrublands distribution

Distribution of barren and sparsely vegetated area

Croplands distribution National Aeronautics and Space modis‐ Croplands density Administration (NASA): Moderate Resolution

land.gsfc.nasa.gov Imaging Spectroradiometer (MODIS); 2010 Grasslands distribution

All urban and all barren density

Urban areas distribution

Open shrublands distribution

Open shrublands density

169 Table 3-3. Prioritized sampled-units for EPT conservation in the Kurdistan Region, northern Iraq, using MSC, MC, or UM.

Conservation Accumulated % of % of conserved Conservation Accumulated % sampled- Priority % of conserved conserved Tributary Name EPT using Priority of conserved unit ID using MSC EPT using EPT using MSC or MC using UM EPT using UM or MC MSC or MC UM Little Zab River 3-2 1 25.53 25.53 2 20.21 43.62

Diyala River 1-1 2 18.09 43.62 1 23.40 23.40

Great Zab River 4-5 3 11.70 55.32 5 10.64 71.28

Little Zab River 3-6 4 8.51 63.83 4 8.51 60.64

Little Zab River 3-11 5 7.45 71.28 3 8.51 52.13

Great Zab River 4-7 6 5.32 76.60 8 5.32 82.98

Little Zab River 3-3 7 3.19 79.79 6 3.19 74.47

Udhaim River 2-1 8 3.19 82.98 9 3.19 86.17

Little Zab River 3-8 9 3.19 86.17 7 3.19 77.66

Diyala River 1-3 10 2.13 88.30 18 1.06 98.94

Khabur River 5-2 11 2.13 90.43 11 2.13 90.43

Great Zab River 4-3 12 2.13 92.55 10 2.13 88.30

170 Conservation Accumulated % of % of conserved Conservation Accumulated % sampled- Priority % of conserved conserved Tributary Name EPT using Priority of conserved unit ID using MSC EPT using EPT using MSC or MC using UM EPT using UM or MC MSC or MC UM Little Zab River 3-10 13 1.06 93.62 15 1.06 94.68

Little Zab River 3-9 14 1.06 94.68 14 1.06 93.62

Great Zab River 4-1 15 1.06 95.74 19 1.06 100.00

Great Zab River 4-6 16 1.06 96.81 17 1.06 97.87

Little Zab River 3-7 17 1.06 97.87 12 1.06 91.49

Diyala River 1-4 18 1.06 98.94 16 2.13 96.81

Great Zab River 4-9 19 1.06 100.00 13 1.06 92.55

171 Table 3-4. Prioritized sub-basins for EPT conservation in the Kurdistan Region, northern Iraq, using MSC, MC, or UM. Priority scores were inherited from overlapping prioritized 19 sampled-units. Sub-basins with more than one prioritized sampled-unit received the score from the sampled-unit with the highest priority.

Overlapping Sub-Basin’s Conservation Sub-Basin’s Conservation Sub-Basin ID sampled-unit(s) Priority using MSC or MC Priority using UM 2080726290 3-2 1 2 2080737390 1-1 2 1 2080709450 4-5 3 5 2080721620 3-6 4 4 2080724440 3-9 5 3 2080728080 3-11 5 3 2080728160 3-11 5 3 2080728390 3-11 5 3 2080728400 3-11 5 3 2080706170 4-6 6 8 2080703660 4-7 6 8 2080733480 2-1 8 9 2080721500 3-7 9 7 2080704570 5-1 11 11 2080705870 5-2 11 11

172 Overlapping Sub-Basin’s Conservation Sub-Basin’s Conservation Sub-Basin ID sampled-unit(s) Priority using MSC or MC Priority using UM 2080706040 4-10 11 11 2080705210 5-2 11 11 2080705220 5-2 11 11 2080714340 4-3 12 10 2080714080 4-3 12 10 2080714140 4-3 12 10 2080727510 3-9 13 15 2080727520 3-10 13 15 2080728150 3-10 13 15 2080724360 3-9 14 14 2080723880 3-9 14 14 2080723860 4-1 15 19 2080711890 4-4 16 17 2080709440 4-6 16 17 2080706800 4-6 16 17 2080706690 4-6 16 17 2080706250 4-6 16 17 2080753740 1-4 18 16

173 Overlapping Sub-Basin’s Conservation Sub-Basin’s Conservation Sub-Basin ID sampled-unit(s) Priority using MSC or MC Priority using UM 2080747550 1-4 18 16 2080746280 1-4 18 16 2080746330 1-4 18 16 2080715610 4-9 19 13 2080715620 4-9 19 13 2080715060 4-9 19 13 2080715020 4-9 19 13 2080714450 4-9 19 13 2080714330 4-8 19 13

Table 3-5. Fourteen distribution models for healthy aquatic habitats in the Kurdistan region, northern Iraq. Models were organized in a descending order, starting with the best model in the first row.

Regularization Weighted MaxEnt Feature(s) AUC Multiplier Prediction 1 linear, quadratic, and hinge 0.8296 0.7302

1 hinge 0.8296 0.7290

0.5 linear, quadratic, and hinge 0.8370 0.7289

1.5 linear, quadratic, and hinge 0.8593 0.7284

174 Regularization Weighted MaxEnt Feature(s) AUC Multiplier Prediction 0.5 hinge 0.8074 0.7276

1.5 hinge 0.8370 0.7255

1 linear, quadratic, hinge, product, and threshold 0.8222 0.7168

0.5 linear, quadratic, hinge, product, and threshold 0.5704 0.6982

0.5 linear & quadratic 0.8667 0.6831

1 linear & quadratic 0.8741 0.6821

1.5 linear & quadratic 0.8741 0.6811

0.5 linear 0.8963 0.6732

1 linear 0.8963 0.6727

1.5 linear 0.8889 0.6720

175 Table 3-6. The most important predictor variables used in best distribution model for healthy aquatic habitats in the Kurdistan region, northern Iraq.

Percent contribution Permutation Predictor Variable to the gain importance Density of rivers and streams 34.7 15.5

Croplands density 20.1 26.3

DEM 18.7 26.3

Human population density 10.1 4.9

Open Shrublands density 7.9 16.2

KBAs density 7.8 10.3

Density of all urban and all barren areas 0.7 0.6

176 Table 3-7. Predicted planning units of healthy aquatic habitats in the Kurdistan region, northern Iraq, identified using habitat distribution modeling.

% of Healthy Prioritized for Used as Waterbody Unit Priority for Sampled Potentially New Aquatic EPT input for Name ID Conservation for EPT? Overlooked? Discovery? Habitat conservation? HDM? Lake Dohuk LD 100.00 1 No No No Yes

Little Zab River 3-2 99.80 2 Yes Yes Yes

Great Zab River 4-9 94.23 3 Yes Yes No

Khabur River 5-4 90.58 4 No No No Yes

Great Zab River 4-3 82.73 5 Yes Yes Yes

Great Zab River 4-12 79.32 6 No No No Yes

Great Zab River 4-14 78.94 7 No No No Yes

Great Zab River 4-22 78.69 8 No No No Yes

Diyala River 1-1 76.06 9 Yes Yes Yes

Little Zab River 3-8 75.43 10 Yes Yes Yes

Khabur River 5-1 73.57 11 Yes No No Yes

177 % of Healthy Prioritized for Used as Waterbody Unit Priority for Sampled Potentially New Aquatic EPT input for Name ID Conservation for EPT? Overlooked? Discovery? Habitat conservation? HDM? Khabur River 5-9 73.08 12 No No No Yes

Diyala River 1-3 72.36 13 Yes Yes Yes

Mosul Streams 6-4 71.74 14 Yes No No Yes

Diyala River 1-6 67.40 15 Yes No No Yes

Great Zab River 4-18 67.16 16 No No No Yes

Diyala River 1-2 66.98 17 Yes No No Yes

Great Zab River 4-5 59.90 18 Yes Yes Yes

Little Zab River 3-6 57.49 19 Yes Yes Yes

Little Zab River 3-3 56.29 20 Yes Yes Yes

Diyala River 1-7 54.22 21 Yes No No Yes

Udhaim River 2-1 53.98 22 Yes Yes Yes

Great Zab River 4-6 53.46 23 Yes Yes No

Khabur River 5-2 50.96 24 Yes Yes No

178 Table 3-8. Predicted sub-basins of healthy aquatic habitats in the Kurdistan region, northern Iraq, identified using habitat distribution modeling.

Sub-Basin ID Waterbody Name Overlapping Unit 2080711640 Lake Dohuk LD 2080738910 Diyala River 1-7 2080738900 Diyala River 1-7 2080737450 Diyala River 1-6 2080737390 Diyala River 1-1 2080738550 Diyala River 1-6 2080738670 Diyala River 1-6 2080739040 Diyala River 1-6 2080738780 Diyala River 1-6 2080739730 Diyala River 1-6 2080739620 Diyala River 1-6 2080733480 Udhaim River 2-1 2080726290 Little Zab River 3-2 2080715610 Great Zab River 4-9 2080715620 Great Zab River 4-9 2080715060 Great Zab River 4-9

179 Sub-Basin ID Waterbody Name Overlapping Unit 2080715020 Great Zab River 4-9 2080714450 Great Zab River 4-9 2080714330 Great Zab River 4-9 2080714340 Great Zab River 4-3 2080714210 Great Zab River 4-22 2080721620 Little Zab River 3-6 2080721500 Little Zab River 3-8 2080714080 Great Zab River 4-3 2080714140 Great Zab River 4-3 2080711640 Mosul Streams 6-4 2080711890 Great Zab River 4-6 2080713360 Great Zab River 4-18 2080709450 Great Zab River 4-5 2080709440 Great Zab River 4-6 2080706800 Great Zab River 4-6 2080706690 Great Zab River 4-6 2080706050 Great Zab River 4-12 2080705320 Khabur River 5-1

180 Sub-Basin ID Waterbody Name Overlapping Unit 2080706250 Great Zab River 4-6 2080703560 Great Zab River 4-14 2080704570 Khabur River 5-1 2080704490 Khabur River 5-1 2080705870 Khabur River 5-2 2080706040 Khabur River 5-2 2080705210 Khabur River 5-2 2080705220 Khabur River 5-2 2080703610 Khabur River 5-9

181

Figure 3-1. Conceptual model showing the main steps in this study. Twisted arrows denote information and data feeding. PSSs: Polyline-Stream-Segments; SPSSs: Sampled Polyline-Stream-Segments.

182 100.0 90.0 80.0 70.0 60.0 50.0 40.0

Percentage of EPT 30.0 20.0 10.0 0.0 3‐2 1‐1 4‐5 3‐6 3‐11 4‐7 3‐3 2‐1 3‐8 1‐3 5‐2 4‐3 3‐10 3‐9 4‐1 4‐6 3‐7 1‐4 4‐9 12345678910111213141516171819 Sampled Planning Unit

% of EPT entities to be conserved Accumulated % of EPT entities to be conserved

Figure 3-2. Prioritized sampled-units for EPT conservation in the Kurdistan region, northern Iraq, using MSC or MC. The percentage of protected EPT taxa and the accumulated percentage of protected taxa are changing smoothly as we protect these sampled-units starting with the one with the highest priority.

183

Figure 3-3. Map of prioritized sampled-units for EPT conservation in the Kurdistan region, northern Iraq, using MSC or MC.

184 100.0 90.0 80.0 70.0 60.0 50.0 40.0

Percentage of EPT 30.0 20.0 10.0 0.0 1‐1 3‐2 3‐11 3‐6 4‐5 3‐3 3‐8 4‐7 2‐1 4‐3 5‐2 3‐7 4‐9 3‐9 3‐10 1‐4 4‐6 1‐3 4‐1 12345678910111213141516171819 Sampled Planning Unit

% of EPT entities to be conserved Accumulated % of EPT entities to be conserved

Figure 3-4. Prioritized sampled-units for EPT conservation in the Kurdistan region, northern Iraq, using UM. The percentage of protected EPT taxa and the accumulated percentage of protected taxa are not changing smoothly as we protect these sampled-units starting with the one with the highest priority.

185

Figure 3-5. Map of prioritized sampled-units for EPT conservation in the Kurdistan region, northern Iraq, using UM.

186

Figure 3-6. Map of predicted planning units of healthy aquatic habitats in the Kurdistan region, northern Iraq, identified using habitat distribution modeling.

187

Figure 3-7. Map of predicted sub-basins of healthy aquatic habitats in the Kurdistan region, northern Iraq, identified using habitat distribution modeling.

188 SUMMARY AND GENERAL CONCLUSIONS The Tigris River has a long history of human utilization, however conservation status for the majority of its basin and fauna remained poorly known. In addition, planning for conservation given limited time, funds, and prior information remains a challenge. Given continuous climate change and anthropogenic disturbance along its basin (Eden Again Group 2004 and 2005, Iraqi Ministry of the Environment et al. 2006a, 2006b, 2006c, Al- Saffar 2006 and 2007, Nature Iraq 2008, Mahir et al. 2009), a rapid and efficient, low- cost, strategy to identify and prioritize healthy aquatic habitats for conservation is urgently needed. A common approach for addressing these needs is using knowledge about bioindicator species, especially mayflies, stoneflies, and caddisflies (Insecta: Ephemeroptera-Plecoptera-Trichoptera (EPT) respectively); powerful tools in ecology and conservation biology have been implemented after building knowledge about them (DeWalt et al. 2012, Cao et al. 2013). Unfortunately, information about EPT in the Tigris River basin in Iraq is quite limited (Mosely 1934; Al-Zubaidi and Al-Kayatt 1986, 1987; Al-Zubaidi et al. 1987; Malicky 1987). Although information is under development for caddisflies (Geraci et al. 2011; BOLD Systems; www.boldsystems.org; Ratnasingham and Hebert 2007), knowledge about stoneflies and mayflies remained relatively poor with no updates, no genetic data, doubtful identifications, no official checklists, and no keys or guides to their identification being available. While identifying and prioritizing healthy aquatic habitats for conservation is needed, EPT conservation is also an important issue. Recent evidence has shown that EPT are threatened by extinction due to many factors such as climate change, habitat destruction, and water pollution (Zwick 1992; Sánchez- Ortega and Tierno de Figueroa 1996; Malzacher et al. 1998; Guerold et al. 2000; Fochetti and Tierno de Figueroa 2004 and 2006; Barber-James et al. 2008; Lubini et al. 2012). EPT extinctions, besides leading to great loss in biodiversity, will alter the aquatic food webs and stop water quality biomonitoring programs; therefore, conserving them (with special care for rare and endemic species) and their unique habitats is also necessary. In my dissertation, I developed an approach to discover healthy aquatic habitats after discovering EPT taxa and the habitats important for their conservation. I employed my study in one of the poorly studied regions in the Tigris River basin of Iraq, after using available caddisflies’ data and conducting a rapid assessment for available stoneflies and

189 mayflies. My first objective was to identify the available mayflies and create the first checklist and larval key. Key morphological characteristics were reviewed for Iraqi mayflies using references from Europe, West Asia, North Africa, the Caucasus, the Trans-Caucasus, and the Middle East. Finally, the identification key for nine families, nine subfamilies, 19 genera, and 13 subgenera was constructed and supported by scientific illustrations using fresh specimens collected during this study (Chapter 1). Secondly, species identification for mayflies and stoneflies was accelerated using a combination of morphology and genetic-based analyses using the full-length of the mitochondrial cytochrome oxidase subunit 1 (COI) gene for ~350 specimens. Several genetic-based analyses were used to delineate Operational Taxonomic Units (OTUs) which were matched and compared with morphology to identify Species-Like Units (SpLUs). Finally, SpLUs were compared and contrasted morphologically against each other and against species and subspecies known so far from West Palaearctic realm. This approach allowed me to discover the presence of five stonefly and 56 mayfly taxa, the majority of them being new records for Iraq, and many of them undescribed (Chapter 2). In my dissertation, I used northern Iraq as a case-study in which I attempted to integrate genetic data with morphology to address the taxonomic impediment. I found this to be very important for understanding biodiversity in groups and regions that are not well-studied. There is an unknown diversity of mayflies and stoneflies from northern Iraq, and genetic-morphological cross-validation was the key strategy that facilitated the discovery of these unique taxa. Rapid and more confident assessment for mayflies and stoneflies occurred when taking their genetic data into account. In addition, using genetic-based analyses allowed the discovery of key morphological points that may have been overlooked by taxonomists. In addition, the results of traditional morphological identification may not be accurate without their integration with genetic-based analyses (Woodcock et al. 2013). Cryptic and controversial species delineation can be facilitated using genetic analyses (Mutanen et al. 2012a; Yang et al. 2012). Using morphological characters only may decelerate the delineation in many cases; it may also lead to misleading results (i.e., when species with plastic morphology or cryptic species are present). Morphological identification of larvae can also be hindered by the fact that many are rather delicate, especially mayflies, and the structures critical for confident

190 identification such as gills, legs, and caudal filaments are commonly damaged or missing (Webb et al. 2012). In contrast, using multiple genetic-based analysis can accelerate the delineation process significantly. However, morphological examination after performing genetic-based analyses is critical (Landry et al. 2013; Pante et al. 2014). Using the result of one genetic-based analyses only may lead to misleading conclusions if it was never matched with results from other genetic-based analysis and never “ground-truthed” using morphology (Chapter 2). In my dissertation, I found that whenever COI sequences were >400bp and there was no time constraint for species identification, a combination of GSBs, RESL, ABGD rP1, GMYC, and PTP rP1 performed well. When COI sequences were <400bp and/or there was a time constraint for species identification (i.e., identification need to be done quickly with the highest possible accuracy), it is recommended to use a combination of at least two of the preferred methods. However, this study also showed clear evidence that building an international library with the full length of the COI sequence can accelerate species identification significantly without the need to conduct multiple genetic-based analyses (Ratnasingham and Hebert 2007). Full sequences are needed and the BOLD Systems is likely to play an active role in informing and connecting researchers having similar or conflicting results, to facilitate sharing information and experience, and solve perplexing identification issues. In addition, researchers should continue to submit sequences even if they do not have final identification for them. It is also critical to share other information about sequences including geographic and photographic information (Ratnasingham and Hebert 2013). The BOLD Systems has the potential to solve larger issues related to species versus subspecies and non-cryptic versus cryptic species (Kekkonen and Hebert 2014). The greater the number of sequences submitted to BOLD, the closer the library is to completion, and the more useful it becomes (Chapter 2). The third objective of my dissertation was to develop a plan to identify and prioritize healthy aquatic habitats for conservation in the KR using available caddisfly data and the data generated in this study about unique evolutionary taxa of mayflies and stoneflies. I divided the lotic and lentic ecosystems of the KR into planning units, and then compiled presence-absence data of EPT for a set of them. I used Complementarity- based Approaches to rank these habitats and prioritize them for EPT conservation. This

191 approach led to the discovery of 19 aquatic habitats that are important to conserve all EPT in the KR. From these 19 habitats, I selected a subset with the highest priority for EPT conservation; this subset was used along with various predictor variables to perform a correlative distribution modeling for healthy aquatic habitats across the entire the KR. This approach allowed me to “fill-the-gaps” and achieve my goal of uncovering the distribution pattern of healthy aquatic habitats across the KR. I concluded that Lake Dohuk and 23 stream segments should merit conservation priority in the KR. By ranking the KR’s aquatic habitats based on their priority for EPT conservation, I was able to hit two targets in one shot; I (1) used this low-cost method to achieve my goal of predicting the healthy aquatic habitats in the KR and (2) planned ahead for conserving potentially threatened, highly useful, aquatic species that should help to maintain the greater possible ecosystem services for the next generations (Chapter 3). In my dissertation, I provided a list of habitats of conservation concern for EPT taxa and another list for the healthiest aquatic habitats across the KR. The list of habitats of conservation priority for EPT provided minimum subset of complementary sites to maintain EPT biodiversity at its maximum in the KR. The predicted list of healthy aquatic habitats of conservation concern provided a set of habitats that potentially support the highest water quality in the KR and the highest diversity of aquatic fauna and flora. These lists can be adopted by decision-makers and stakeholders to plan for conservation in Iraq (see Albuquerque and Beier 2015). Surveying and ground-truthing the predicted habitats is highly recommended at this point (Chopra et al. 2001). As soon as my prediction is confirmed, the next step would be to plan for conservation. This will be a key step towards maintaining freshwater ecosystem services (Albuquerque and Beier 2015), and conserving highly important unique evolutionary taxa before they face extinction (Chapter 3). In my dissertation, I used Complementarity-based Approaches (CBAs), one of the principles of Spatial Conservation Prioritization (Leathwick et al. 2010). Further research in the KR may take other principles into account, such as adequacy and persistence, cost- efficiency, threat and vulnerability, conservation value, and flexibility (Moilanen et al. 2009). However, implementing such additional principles will need cooperation from conservation agencies and administrative authorities in the KR and this may take

192 prolonged time to be done (Faith et al. 2004). Given the fact that none of the KBAs or the prioritized aquatic habitats are currently protected, while climate change and anthropogenic disturbance are on-going factors (Iraqi Ministry of the Environment and Nature Iraq 2015), implementation of conservation plans is urgently needed, at least for the habitats of highest priority. In addition, conservation plans should continue surveys for EPT taxa and update their data frequently (Morse et al. 2007). Maintaining physico- chemical properties and biological components in aquatic habitats is related to monitoring high water quality and healthy food webs (Nair et al. 2015), both of which are indicated by high diversity of sensitive aquatic insects, especially EPT taxa (Chapter 3). Through my dissertation, I demonstrated an approach to conservation biology in poorly studied freshwater ecosystems. I showed that in developing countries where knowledge about aquatic ecosystems and most extant species is unavailable, conservation studies can still be conducted after a rapid assessment for the water quality bioindicators. I hope that this approach to conservation biology encourages researchers from Iraq and other developing countries to build knowledge about the invertebrates and vertebrates in aquatic ecosystems, before then using this knowledge to discover the most important habitats and plan for their conservation.

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