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Theses and Dissertations

12-2020

Neogene and Quaternary Events Shaped Diversification and Speciation in Bhutanese Rheophilic Fishes of the Family Nemacheilidae (Cypriniformes) and Sisoridae (Siluriformes)

Karma Wangchuk University of Arkansas, Fayetteville

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Citation Wangchuk, K. (2020). Neogene and Quaternary Events Shaped Diversification and Speciation in Bhutanese Rheophilic Fishes of the Family Nemacheilidae (Cypriniformes) and Sisoridae (Siluriformes). Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/3900

This Thesis is brought to you for free and open access by ScholarWorks@UARK. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of ScholarWorks@UARK. For more information, please contact [email protected]. Neogene and Quaternary Events Shaped Diversification and Speciation in Bhutanese Rheophilic Fishes of the Family Nemacheilidae (Cypriniformes) and Sisoridae (Siluriformes)

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Biology

Karma Wangchuk Sri Venkateswara Veterinary University Bachelor of Fishery Science, 2011

December 2020 University of Arkansas

This thesis is approved for recommendation to the Graduate Council.

______Marlis R. Douglas, Ph.D. Thesis Director

______Michael E. Douglas, Ph.D. Thesis Co-Director

______David P. Philipp, Ph.D. Jeff F. Pummill, Director, AHPCC Committee Member Committee Member

ABSTRACT

Biogeography of the Himalayan region [to include the Qinghai-Tibetan Plateau (QTP)] evolved over a ~30M year span, catalyzed by the collision of the Indian and Eurasian plates. The resulting uplift produced major ecological and climatic effects, that in turn drove the diversification of biodiversity. As a result, the QTP is designated as a global biodiversity hotspot particularly vulnerable to cumulative climatic effects, including shrinking distributions, declining numbers, and local extinctions. Understanding how the biodiversity within the Himalaya/ QTP was established and maintained is a necessary first step in prioritizing conservation efforts.

Fishes in global montane regions, such as the Himalaya, are at an elevated risk to climate change, in that their natural histories reflect adaptations to local conditions such as water temperature and flow regimes. An historic baseline for the specialized freshwater fishes of the region, in tandem with a contemporary understanding of their trajectory, is needed to promote collaboration among conservation and management agencies in regional countries, an activity that is to date unfortunately missing. One approach is to derive an historic baseline for these fishes by quantifying their biogeographies, including their dispersals and diversifications.

My thesis evaluates the phylogenetic relationships within two families of fishes [i.e.,

Loaches (Nemacheilidae) and Asian catfish (Sisoridae)] whose Grinnellian niches (i.e., their habitat and its accompanying behavioral adaptations) identify them as ‘rheophilic’ (i.e., inhabiting swiftly flowing water). I specifically evaluate the distribution of these fishes within the drainages of Bhutan, where aquatic biodiversity is relatively undefined. The diversification and speciation in both study groups reflect the geomorphic evolution of the Himalaya/QTP. My results indicate Bhutanese drainages maintain undiagnosed variation that is allocated to species-groups within each family.

ACKNOWLEDGEMENT

I would like to express my sincere gratitude to my co-advisors and mentors, Drs. Marlis R.

Douglas and Michael E. Douglas, endowed professors in Biological Sciences at the University of Arkansas, for taking me under their collective wings and training me to be a cognizant and diligent researcher. Their support and generosity predate my graduate school tenure at

University of Arkansas. Early on, they encouraged Sonam (my wife) and I to pursue graduate school. This soon extended to preparing us for the GRE exam, and finally facilitating our transition into the United States. In graduate school, they guided me through course work, trained me in laboratory techniques, and helped me with the research endeavors that ultimately led to the completion of this thesis. They were always supportive in helping to resolve any issue or problem that I faced. I always emerged from our meetings motivated and determined to succeed. I could not have imagined having better advisors and mentors for my Masters’ study.

In addition to my advisors, I would like to extend my gratitude to Dr. David P. Philipp, co- founder of the Fisheries Conservation Foundation and a larger-than-life fisheries scientist who initially sparked my idea of a master’s degree in the United States, provided unforgettable fishing adventures in the U.S., and served as outside member on my thesis committee. His influence on a Senior Livestock Production Officer in the tiny town of Haa (northwestern

Bhutan), is only matched, if not surpassed by the support and encouragement of Ms. Julie E.

Claussen, Director of Operations and co-founder of the Fisheries Conservation Foundation. Her grueling hikes to reach field locations coupled with death-defying river trips made field work educational and fun. She guided me through my first presentations at an international fisheries conference, provided a voice of reason and sounding board whenever I overthought simple steps or upcoming scientific challenges. Both are long-standing colleagues of my advisors, and in fact brought my advisors to Bhutan to add a genetic perspective to their ongoing research on

Golden Mahseer migrations. This serendipitous alignment of Bhutanese and U.S. professionals,

and a shared interest in conserving Bhutan's biodiversity, sparked my career as a fisheries scientist and established my initial connection with my soon-to-be advisors, their molecular ecology lab at University of Arkansas, and the research activities they engaged in.

I would also like to thank Mr. Jeff F. Pummill, Director of Strategic Initiatives & User

Services, Arkansas High Performance Computing Center, and Adjunct Graduate Faculty in

Biological Sciences for also being a member of my committee. His programming course provided the coding foundation for several of the analyses necessary to complete my research.

His positive attitude and timely emails expressing support for Sonam and I were especially reassuring, and particularly so during the trying times of the pandemic lockdown.

I am also grateful to other professional for their contribution to my growth as a scientist.

Dr. David R. Edds, emeritus Professor at Emporia State University (KS), shared his profound knowledge of Himalaya fishes with me. As a Fulbright Scholar in Nepal during 1996, Dr. Edds collected numerous species now held in the University of Kansas Museum of Natural History

(Lawrence KS). In addition, several professors in Biological Sciences at University of Arkansas were instrumental in my development in STEM disciplines: Drs. Andrew Alverson, William

Etges, Kenneth Kvamme, and Dan Magoulick provided invaluable instruction on programming, ecological genetics, GIS, and biological statistics.

I also wish to acknowledge my fellow lab mates in the Douglas Lab: Tyler Chafin,

Bradley Martin, Zachery Zbinden, Whitney Murchison, Timothy Goodhart, Sonam Wangmo, Riri

Retnaningtyas, Katelyn McDaniels and Griffin McDaniels for stimulated discussions, brain- storming sessions and light-hearted banter amongst fellow graduate students. Tyler and Zach were not only great colleagues that introduced me to the graduate student culture, but also as peers that selflessly offered scientific training and mentorship to a novice fish geneticist in the lab. And a special nod to Tim, who became a close friend, particularly during trying times such

the first few weeks in graduate school where we struggled to navigate the challenges of graduate courses, and during the lock-down/pandemic-induced months of social distancing.

I would also like to recognize my connection to the National Research and Development Centre for Riverine & Lake Fisheries, Department of Livestock, Ministry of Agriculture & Forests, Royal

Government of Bhutan, for granting a “Leave of Absence” that allowed me to pursue and successfully complete my Master’s Thesis program at University of Arkansas. I particularly acknowledge Mr. Singye Tshering, the Program Director of NRDCR&LF and my first supervisor.

He always fostered professional growth in his staff and encouraged my desire to engage in this unknown journey to obtain a Masters in the U.S.

My research was made possible through generous endowments to the University of

Arkansas: The Bruker Professorship in Life Sciences (MRD), the 21st Century Chair in Global

Change Biology (MED), and a Graduate Assistantship (KW). In addition, field logistics and tissue sampling were facilitated by a grant to the Bhutan Trust Fund for Environmental

Conservation (BTFEC). The Fulbright College of Arts & Sciences, University of Arkansas, provided a Graduate Teaching Assistantship and tuition waivers for the duration of my graduate education. Without this financial support, my graduate education would not have been possible.

Most importantly, I would like to thank my parents Namgo Dori and Pem, for supporting and encouraging me to pursue my aspirations, be it my formal education, graduate studies, or life in general. Finally, I would like to thank my companion in life and science, Ms. Sonam

Wangmo for being the source of my inspiration and emotional support at every step of my personal and academic life. And, last but not least, a heartfelt thanks to my two daughters,

Tshering Yeetsho Namgo and Choezom Mendarava, to whom our absence from their immediate lives for two long years must have seemed an eternity, but who's mere presence on

Earth motivated me to continue and persevere, even if the challenges of completing a graduate degree in the United States seemed at times insurmountable.

TABLE OF CONTENTS

INTRODUCTION ...... 1

Reference ...... 4

CHAPTER 1 ...... 5

Introduction ...... 5

Methods and Materials ...... 7

Results ...... 10

Discussion ...... 13

References ...... 18

Tables ...... 23

Figures ...... 27

CHAPTER 2 ...... 31

Introduction ...... 31

Methods and Materials ...... 32

Results ...... 36

Discussion ...... 37

Conclusion ...... 43

References ...... 44

Tables ...... 49

Figures ...... 53

CONCLUSION ...... 60

Reference ...... 62

INTRODUCTION

The Himalaya [to include the broader Qinghai-Tibetan Plateau to the north (hereafter QTP)] comprises a so-called global “third pole” in that it serves as the world’s third largest reservoir for frozen freshwater, beyond the Arctic and Antarctic (Yao et al., 2012). Biogeography of the region evolved substantially over a span of the last ~30M years, catalyzed by a collision between the Indian and the Eurasian plates at that time. The resulting uplift of the Plateau promoted major ecological and climatic ramifications, which in turn dictated patterns of diversification and lineage turnover. As a result, the QTP also serves as a stronghold for endemism and a hotspot for biodiversity (Myers et al., 2000). Understanding the intrinsic and extrinsic factors responsible for the establishment and maintenance of a global biological hotspot (e.g., the Himalaya/QTP) is a necessary first step in prioritizing conservation efforts for the region (Favre et al., 2015). A proactive (rather than reactive) conservation management paradigm, therefore, necessitates an historical baseline from which projections can be made with respect to the availability of ecosystem services under future climatic or environmental changes.

Fishes in montane regions such as the Himalaya are at a particularly higher risk to climate change, due to their specific natural histories that reflect adaptations to local conditions such as water temperature and flow regime (Comte & Grenouillet, 2015; Isaak et al., 2016).

With regard to the latter, ‘rheophilic’ species, i.e., those preferring fast flowing currents and rocky substrates (e.g., torrents, rapids, and chutes), are of particular interest because of their innate endemism to specific mountain environments. They have evolved morphological and physiological adaptations that allow them to subsist within these violently shifting habitats.

Bhutan, as a component of the QTP, lies in the Eastern Himalaya and shares its western, southern and eastern boundaries with northern India. Rheophilic fishes in Bhutan

1 include (but are not limited to) the families Nemacheilidae (Cypriniformes) and Sisoridae

(Siluriformes). The former is a family of freshwater loach, while the latter is a family of Asian catfish, both primarily endemic to the Himalaya and QTP. Each exemplifies the impact of geomorphic evolution within the QTP as a driver of diversification and speciation, and thus form ideal study systems with which to examine patterns of historical biogeography and phylogeography within the Himalaya.

Herein, I examined phylogenetic relationships and patterns of diversification in the above two families, principally focusing on drainages of Bhutan, where aquatic biodiversity is relatively undefined. In Chapter I, I contrasted patterns of taxonomic and phylogenetic diversity among

Bhutanese nemacheilids, specifically focusing on the genus Schistura. To do so, I employed four different species delimitation methods applied to mitochondrial (mt)DNA sequences representing the cytochrome oxidase subunit I (COI) gene (also referred to as the DNA barcoding gene). Those sequences were combined with sequences acquired from an online molecular genetic database (i.e., GENBANK©) that allowed me to increase the phylogenetic breadth of my study. My results identified the presence of five putative species out of the seven

Nemacheilidae listed from Bhutan, and further suggest three potential undescribed species (or evolutionary significant units; ESUs) as well.

In Chapter II, I examined the mode of diversification in a second group of rheophilic fishes, the sisorid catfishes, to build inferences regarding the manner by which the uplift impacted the rapid radiation of this group. As with Chapter I, I generated novel COI gene sequences that were combined with GENBANK© sequences, (again) allowing the phylogenetic breadth of my study to be expanded by incorporating other individuals and species previously identified as such from neighboring countries (i.e., India, Nepal, Bangladesh, and the People’s

Republic of China). These additional references allowed me to demonstrate strong evidence of

2 vicariant speciation among Bhutanese sisorids, also with three potentially undescribed species

(or evolutionary significant units; ESUs) being present.

3 Reference

Comte, L., & Grenouillet, G. (2015). Distribution shifts of freshwater fish under a variable climate: Comparing climatic, bioclimatic and biotic velocities. Diversity and Distributions, 21, 1014–1026. https://doi.org/10.1111/ddi.12346

Favre, A., Päckert, M., Pauls, S. U., Jähnig, S. C., Uhl, D., Michalak, I., & Muellner-Riehl, A. N. (2015). The role of the uplift of the Qinghai-Tibetan plateau for the evolution of Tibetan biotas. Biological Reviews of the Cambridge Philosophical Society, 90, 236–253. https://doi.org/10.1111/brv.12107

Isaak, D. J., Young, M. K., Luce, C. H., Hostetler, S. W., Wenger, S. J., Peterson, E. E., Ver Hoef, J. M., Groce, M. C., Horan, D. L., & Nagel, D. E. (2016). Slow climate velocities of mountain streams portend their role as refugia for cold-water biodiversity. Proceedings of the National Academy of Sciences of the United States of America, 113, 4374–4379. https://doi.org/10.1073/pnas.1522429113

Myers, N., Mittermeler, R. A., Mittermeler, C. G., da Fonseca, G. A. B., & Kent, J. (2000). Biodiversity hotspots for conservation priorities. Nature, 403, 853–858. https://www.nature.com/articles/35002501

Yao, T., Thompson, L. G., Mosbrugger, V., Zhang, F., Ma, Y., Luo, T., Xu, B., Yang, X., Joswiak, D. R., Wang, W., Joswiak, M. E., Devkota, L. P., Tayal, S., Jilani, R., & Fayziev, R. (2012). Third Pole Environment (TPE). Environmental Development, 2, 52–64. https://doi.org/10.1016/j.envdev.2012.04.002

4 CHAPTER 1

Himalayan Uplift Shaped Diversification in Stone Loach (Nemacheilidae)

Introduction

The Qinghai-Tibetan Plateau in central Asia (QTP: historically termed ‘Tibetan Plateau’) is colloquially referred to as ‘the roof of the world’ (Keay, 1982). The QTP has an average elevation of >4,000 m above sea level (asl) and tectonic uplifts that shaped it also promoted major shifts in the region’s habitat and climate. These events also impacted distribution of biodiversity (An et al., 2001; Peng et al., 2006) and promoted speciation events (Lu et al., 2014;

Macey et al., 1998; Peng et al., 2006). As a result, it is appropriately designated as a global biodiversity hotspot (Myers et al., 2000).

Topographically, the region is primarily defined by mountains ranging from 300 to

8,500m elevation (Dahal et al., 2017), although numerous valleys also exist with dissimilar micro-climates. These geographic features often represent discontinuities not only in the distribution of species, but also in their genetic structure (Lim et al., 2011). For example, small montane mammals with limited dispersal ability seemingly exhibit higher levels of endemism and genetic differentiation. Inadequate systematic sampling across much of the Himalaya, however, has left extensive gaps regarding the presence and distribution of biodiversity (Dahal et al., 2017).

The QTP and surrounding regions are regarded as the “third pole,” because they contain the greatest amount of global ice outside of the Arctic and Antarctic regions (Yao et al., 2012).

These glaciers account for an estimated 14.5% of the global total (Pörtner et al., 2019) and represent the headwater source for nine of the largest rivers in Asia, with ecosystem services provided to >1.5 billion people (Yao et al., 2012). The region, however, is warming at a rate

5 significantly higher than the global average, as reflected in a 6X increase in the pace of glacier retreat (Tewari et al., 2012). This, in turn, increases precipitation, expands glacial lakes, and diminishes permafrost levels (Liu et al., 2018).

Understanding the drivers behind such biological hotspots may help prioritize conservation efforts, to include predicting the impact and extent of climate change on ecosystem services. To do so requires systematic knowledge of endemic biodiversity, as well as the application of diverse analytical approaches that appropriately link organisms and environments (Favre et al., 2015). Montane fishes are at an elevated risk with regard to climate change, given their natural history adaptations with regard to water temperature and current velocity (Comte & Grenouillet, 2015; Isaak et al., 2016). Unfortunately, the fact that the biodiversity of the QTP remains insufficiently studied represents a major obstacle with regard to ongoing conservation and management efforts within and among countries of the trans-

Himalaya.

Bhutan in the Eastern Himalaya is a component of the QTP (Fig. 1) and shares its western, southern and eastern boundaries with northern India. Although it is considered a global exemplar with regard to conservation, having allocated >60% of existing forest cover into reserves (Government of Bhutan, 2008), the biodiversity found within its rivers and streams has neither been quantified nor explicitly included in these mandates. Thus, data on fish biodiversity and species distributions are incomplete, a situation that looms large given the sampling difficulties inherent in its remote and logistically-challenging terrain (Barman et al., 2018).

Furthermore, most fish taxonomic studies in the region employ fisheries-based morphometric and meristic characters (Anganthoibi & Vishwanath, 2010; Darshan et al., 2011; Lalramliana et al., 2014), and such data often lack the necessary resolving power to identify cryptic biodiversity.

6 One such problematic group is the Nemacheilidae, a family of freshwater loach

(Superfamily Cobitoidea, Order Cypriniformes) broadly distributed throughout Asia (Kottelat,

2012; Zhu, 1989). The genus Schistura, in particular, contains numerous morphologically and ecologically similar species that display scant morphometric and habitat differentiation. At best,

Schistura represents a “species complex,” and at worst, an artificial assemblage (Bănărescu &

Nalbant, 1995; Maurice, 1990; Menon, 1999; Prokofiev, 2010). A variety of genetic markers can be employed to identify distinct species, as well as to delimit boundaries within such groups (de

Mazancourt et al., 2019; Feng et al., 2019). One approach, DNA barcoding, has been particularly useful in this regard (Elliott & Davies, 2014; Hebert et al., 2003), and it has the power to resolve taxonomic diversity and relationships within the Schistura complex.

In this study, I applied DNA barcoding to examine taxonomic diversity among Bhutanese loaches in general, with specific focus on Schistura. I sequenced the mitochondrial (mt)DNA cytochrome oxidase subunit I (COI) gene in an attempt to resolve the taxonomic ambiguity that exists in this group. To identify potential species boundaries, sequence data were analyzed using several approaches: Automatic Barcode Gap Discovery (ABGD), Poisson Tree Process

(PTP; two methods), and Generalized Mixed Yule Coalescent (GMYC). The focal areas of my study were: 1) to taxonomically place Bhutanese nemacheilids among other members of the family; 2) to clarify the relationships among Bhutanese species groups; and 3) to develop a potential timeline of divergence for Bhutanese nemacheilids, nested within the geologic history of the QTP.

Methods and Materials

Study taxa

The freshwater loach family Nemacheilidae (Cypriniformes) contains >600 mostly small, benthic species (Eschmeyer & Fong, 2020). All are morphologically adapted to high-gradient streams

7 and each displays a limited dispersal capacity (Chen et al., 2019). Many share a morphologically similar bauplan (i.e., a generalized phenotype that characterizes a group of organisms) (Nalbant & Bianco, 1998). Standard meristic counts and morphometric measurements overlap considerably among members in this group, and these characters offer scant taxonomic resolution (Chen et al., 2019).

Sample collection, DNA extraction and amplification

Fin clips were acquired non-lethally during 2016-18 from 51 specimens collected at 12 sites across various drainages in Bhutan (Fig. 1, Table 1). Sampling and tissue collection were approved by the Department of Forests & Parks Services, Ministry of Agriculture & Forests,

Royal Government of Bhutan, as well as the University of Arkansas Institutional Animal Care and Use Committee (IACUC# 17064). Voucher specimens are housed at the National Research

& Development Centre for Riverine & Lake Fisheries, (NRDCR&LF; Haa, Bhutan). DNA was extracted using Qiagen© DNeasy kits, following manufacturer’s instructions. The mtDNA COI gene was amplified using published primers (Ali et al., 2013), then sequenced using BIGDYE©

[ver.3.1, Applied Biosystems Inc. (ABI), Forest City, CA, USA]. Data were generated on an ABI

Prism 3700 Gene Analyzer (W. M. Keck Center, University of Illinois, Champaign).

Sequence alignment and phylogenetic analyses

Sequences were edited manually using SEQUENCHER v.5.4.6. and aligned using MUSCLE v.3.8

(Edgar, 2004). An additional 20 sequences were acquired from GenBank© (17 Nemacheilidae + three outgroups; Table 1). Three taxa were selected as outgroups: Anguilla bengalensis

(Anguilliformes), Myxocyprinus asiaticus (Cypriniformes) and Carpoides carpio (Cypriniformes).

The latter two were selected based on the availability of fossil calibration data. Phylogenetic relationship were inferred using Maximum Likelihood (ML: IQ-TREE v.2; Nguyen et al., 2015) and Bayesian Inference (BI: MRBAYES 3.1.2, Ronguist & Huelsenbeck, 2003). To identify an

8 appropriate nucleotide substitution model, the minimum Bayesian Information Criterion (BIC) in

MODELFINDER was employed (Kalyaanamoorthy et al., 2017). The best partitioning scheme for the phylogenetic analysis was determined by applying PARTITION MODELS (Chernomor et al.,

2016). Branch support was determined by ultrafast bootstrap values (UFBOOT2; Hoang et al.,

2018), derived using 1000 iterations, with values ≥95% indicating strongly supported nodes.

Additionally, branch support was assessed using the SH-aLRT test (Shimodaira-Hasegawa approximate Likelihood Ratio Test), with values ≥80% indicating strongly supported nodes.

For the BI analysis, the best partitioning scheme was determined by applying Partitions

Models (Chernomor et al., 2016). Nodal support was assessed using Bayesian posterior probabilities (PP) with values ≥95% indicating strong support. Additionally, all parameters were examined for congruence post-analysis, including sample size (TRACER v1.7.1; Rambaut &

Drummond, 2009).

Kishino-Hasegawa (1989) and Shimodaira-Hasegawa (1999) tests based on 1000 replicates were carried out to determine the best tree topology as derived from ML and BI analyses. Final trees were visualized using FIGTREE v1.4.4 (Rambaut & Drummond, 2009).

MEGA version X (MEGA-X; Kumar et al., 2018) was employed to obtain a timetree by implementing the RELTIME method (Tamura et al., 2012) and applying the Tamura-Nei model of nucleotide substitution (Tamura et al., 2012). One fossil calibration was employed in the analysis (at 39 Ma), based on the fossil record of Myxocyprinus asiaticus (Chen et al., 2019).

Barcode gap analysis was performed using the Automatic Barcode Gap Discovery (ABGD) webserver (Puillandre et al., 2012), employing the K2P distance with a transition/transversion ratio of two and with a prior maximal intraspecific divergence threshold of ≥3% (Smith et al.,

2005). Furthermore, a range of intraspecific divergences was used to explore the possibility of greater resolution existing within the sequence data. The ML tree for delimitations based on

9 Poisson Tree Process (MPTP) (Zhang et al., 2013), was employed using 500,000 Markov chain

Monte Carlo (MCMC) generations, thinning parameters of 500, and burn-in of 0.1. Additionally, the ultrametric tree derived from MEGA-X was used for species delimitation using the

Generalized Mixed Yule Coalescent (GMYC) method with single threshold (Fujisawa &

Barraclough, 2013). Individual were coalesced within species, then pair-wise sequence divergences calculated (as p-distances corrected for within group variance) using MEGA-X.

Results

Sequencing provided 707 bp of mtDNA COI gene that were subsequently used for downstream analyses. The appropriate model of sequence evolution was identified as TPM2+F+I+G4 (i.e., equal base frequencies, proportion of invariant sites, and a 4-rate category discrete Gamma model). Model scores were as follows: BIC = 9526; InL = -4356.2164. Thirteen (out of the 17 ML nodes) were strongly supported on the basis of SH-aLRT and UFBOOT2 values (Fig. 2).

Although both ML and BI analyses supported the monophyly of Bhutanese nemacheilids with strong statistical support (UFBOOT2: 95% and PP: 100%) (Figs. 2 and 3), the ML tree is employed when taxa are discussed, because it scored higher than the BI tree in comparative evaluations (KH test: Kishino-Hasegawa, 1989; SH test: Shimodaira-Hasegawa, 1999) (Table

2). Bhutanese nemacheilids were broadly divided into two monophyletic clades: Clade A

(Schistura) and clade B (Acanthocobitis and Aborichthys) (UFBOOT2: 84% and 89%, respectively). ABGD (blue division; Fig. 2) and GMYC (black) each supported 11 distinct lineages across both clades, with a high prior intraspecific divergence (at 5.9%), whereas mPTP

(red) and bPTP (green) supported 13 and 12, respectively. Six delineations (three per clade) grouped specimens from Bhutan versus presumed conspecifics from other areas.

10 Within clade A, Schistura tirapensis was monophyletic (clade L; Fig. 2) with strong support (UFBOOT2: 100%; SH-ALRT ≥ 80%). Monophyly was recognized by all four species delimitation methods. Bhutanese S. fasciata (clade N) formed a distinct clade (UFBOOT2: 100%,

SH-ALRT ≥ 80%), that clustered separately from five Indian conspecifics (clade P), as well as from S. scaturigina and S. rupecula (Clade O). Again, all four delimitation methods corroborated clade N as being distinct. Presumed conspecific S. fasciata from India also formed a distinct cluster (Clade P; UFBOOT2: 92%, SH-ALRT ≥ 80%), as well as reflected an internal substructure that was not substantiated by the species-delineation methods, yet likely reflects regional diversity.

Schistura scaturigina (clade R) also showed substructure, but only one delimitation method (mPTP) identified two groups. The first group contains three samples from Bhutan, while the second group includes an individual from People’s Republic of China as well as one from Bhutan. Clade R and its internal subdivisions are also supported by significant UFBOOT2 values (100% and 99%). Within S. rupecula (clade Q), all four delimitation methods recognized two distinct lineages: One contains 11 samples from Bhutan (clade S; UFBOOT2: 100%; SH-

ALRT ≥ 80%), whereas the second (clade T; N=1) consists only of a conspecific from Nepal.

Within clade B, N=18 individuals of Acanthocobitis botia from India, Bhutan, Nepal and

Bangladesh clustered as a distinct clade with strong support (clade E: SH-aLRT >80%). One conspecific from India (MK610312; clade F) formed a distinct lineage that was supported by all four delimitation methods.

Clade B also included the genus Aborichthys (Clade D), as represented by two clusters:

One is taxonomically recognized (clade G=Ab. elongatus) while the other is undescribed (clade

H=Ab. spp.). Aborichthys elongatus (clade G) was split into two subclades: One contained ten samples from Bhutan (clade I), whereas the second (clade J) comprised two samples from India

11 with strong support (UFBOOT2: 100%; SH-aLRT >80%). One of these was identified as a conspecific (AP011304) whereas the other is undescribed (MK962530). All four delimitation methods supported the distinctiveness of the two clade G lineages, with mPTP and bPTP further splitting Clade J. Clade H (the undescribed Aborichthys spp.) is represented by N=6 samples from Bhutan and is recognized as distinct with strong support (UFBOOT2: 100%; SH- aLRT >80%), and by all four species-delimitation methods.

Sequence divergences between putative groups/ species ranged from 1.8-17.2% for intraspecific groups, and 5.1-20.5% for interspecific groups (Table 3). Groups are defined as distinct linages (between or within a species), as identified by the species delimitation methods.

Timeline divergences

Time-calibrated divergences among linages, as derived using RELTIME, are depicted in the ML- based tree (Fig. 4). The two major clades (A and B) separated in late Paleocene (green vertical bar; ~49.5 Ma, CI:61.2-38.4). Within Schistura (clade A), S. tirapensis separated at mid-Eocene

(~40.3 Ma, CI: 52.9-28.3). Schistura fasciata (Bhutan) then diverged late Eocene (~29.7 Ma), followed by S. fasciata (India) in late Oligocene (18.3 Ma, CI:27.2-9.8). Schistura scaturigina then separated in early-Miocene (14.3 Ma, CI:22.4-6.5), followed by S. rupecula in mid-Miocene

(11.8 Ma, CI:19.7-4.3).

Within clade B, the two sister-taxa (Aborichthys and Acanthocobitis) separated late

Eocene (~34.8 Ma, CI:45.1-25), with Acanthocobitis subdividing at mid-Oligocene (23.2 Ma).

Within Aborichthys, two species (Ab. elongatus and Ab. spp.) diverged late Miocene (18.6 Ma,

CI:26.2-11.4), with Ab. elongatus branching in mid Miocene (13.7 Ma, CI:20.5-7.2).

12 Discussion

Nemacheilidae has been defined as a monophyletic group (Chen et al., 2019), and my analyses involving an ML approach and samples from a region not included in previous phylogenetic evaluations, are consistent with this hypothesis. Relationships within and among samples from

Bhutan, however, were somewhat surprising, with cryptic diversity suggesting the potential for unrecognized species and distinct biogeographic patterns that may impact our understanding of nemacheilid evolution in the trans-Himalaya.

For Bhutan, five nemacheilid genera have been reported to date (Aborichthys,

Acanthocobitis, Paracanthocobitis, Schistura and Triplophysa) involving ten species (National

Research Centre for Riverine & Lake Fisheries, 2017; Gurung & Thoni, 2015). Although my results add no new genera to this list, they do suggest the potential for five additional species, four of which are identified by all species-delimitation methods employed. These are distributed as: Schistura (i.e., S. rupecula-like, S. fasciata-like), Acanthocobitis (i.e., Ac. botia-like), and

Aborichthys (i.e., Ab. elongatus-like). A potential fifth species (S. scaturigina-like) is only recognized as such by the mPTP method, which clustered three Bhutanese samples separately from a discrete clade that group a single sample from Bhutan with one from the People’s

Republic of China and Bhutan.

Schistura is the most species-rich genus in the family Nemacheilidae, and while it contains many superficially similar species-groups (Bohlen et al., 2020), a consensus of opinion suggests it is non-monophyletic. My study involved four Schistura species, and only two are well defined (per UFBOOT2 values; Fig. 2). This result may be due, in part, to the fact that each is subdivided into subclades that could potentially represent undescribed species, thus contributing to uncertainty about sister-taxon relationships amongst subclades. Both of these can act to depress single species UFBOOT2 values.

13 The timeline estimate indicates that S. rupecula, S. scatturigina, and S. fasciata separated from S. tirapensis at 29.7 Ma (Fig. 4), which is deemed rather early for an intraspecific divergence. Samples from Bhutan were identified using the DNA barcoding method, so it is possible that reference species in GenBank© were misidentified, or the samples collected from India were incorrectly identified. Schistura fasciata was described as a new species from Manipur (India), and is very similar to S. khugae, S trigrinum and S. multifasciatus

(Lokeshwor & Vishwanath, 2011). It is possible that S. fasciata from India (GenBank©; clade P in Fig. 2) could instead belong to one of those species. Alternatively, the S. fasciata-like individuals in Bhutan (clade N) could belong to either of those as well. Because none of these species were available for inclusion in my analyses, the above interpretation remains inconclusive.

Schistura scaturigina (clade R) formed a monophyletic group, with three out of four delimitation methods supporting single-lineage status (with only mPTP identifying two such lineages). These inconsistencies should be interpreted with caution, despite the fact that mPTP is generally considered more accurate than other methods in inferring putative species boundaries (Zhang et al., 2013). It also significantly outperforms GMYC when few species are involved (as herein) (Luo et al., 2018). Interestingly, this two-lineage status also emerged when a slightly reduced prior intraspecific divergence value (=0.7%) was employed in the ABGD approach, supporting its potential validity.

In Clade R, Bhutanese S. scaturigina (i.e., S. scaturigina Rind_01) was also more closely related to a conspecific from China (NC031378.1) than to conspecifics from Bhutan. In addition, two S. scaturigina-like individuals from the same Bhutanese region [i.e., S. scaturigina

Bibi_01 and 02 (from Bibigang, Bhutan)] were well differentiated from a third Bhutanese sample

[S. scaturigina Rind_02 (Rindigang, Bhutan)], suggesting the presence of additional cryptic

14 variation. This again suggests the potential presence of either S. khugae, S trigrinum or S. multifasciatus in Bhutan.

Given these inconsistencies, I cannot comment with confidence on the putative species status of undescribed biodiversity. This is particularly true given that mtDNA does not reflect clear-cut benchmarks to delineate species-status itself, or to define subspecies from species.

Additional sampling in the Himalaya might help clarify intraspecific divergences, especially if samples from different drainages are compared. Schistura scaturigina is reported from

Northeast India, Nepal, Bhutan and Bangladesh (Menon, 1999). Future investigations should acquire samples from Nepal and the Ganges River Basin of India, the Brahmaputra drainage in

Bangladesh, Bhutan and Northeast India, and the Indus river basin in Pakistan.

All three species within clade B (i.e., Ab. elongatus, Ab. spp., and Ac. botia) formed distinct clades with good support. The delimitation methods detected at least one additional lineage within both Ab. elongatus and Ac. botia. One (Ac. Botia - MK610312 from India) formed a separate lineage from all remaining Indian, Nepali, Bhutanese and Bangladeshi conspecifics.

The timeline estimate suggests the two separated ~23.2 Ma (mid-Oligocene). Again, this is an elevated estimate with regard to the separation of intraspecific lineages, and given this, might suggest the two are potentially misidentified and belong instead to an entirely different species.

Within the Ab. elongatus species-complex (clade G), Ab. elongatus (AP011304) and Ab. spp. (MK962530) from India formed a lineage separate from Bhutanese Ab. elongatus, with a timeline divergence estimated at mid-Miocene (13 Ma). This could again suggest a misidentified sample such that the two represent an entirely different species. Aborichthys was described by

Chaudhuri (1913) and represented four species: Ab. kempi (Chaudhuri, 1913), Ab. elongatus

(Hora, 1921), Ab. garoensis (Hora, 1925) and Ab. tikaderi (Barman, 1984). All are restricted to northeast India, but with Ab. kempi reported as far as Myanmar (Zoological Survey of India,

15 2009). Two new Ab. species (i.e., Ab. cataracta and Ab. verticuada) were recently identified from northeast India (Muthukumarasamy et al., 2014). The analysis herein is limited by the fact that COI GenBank© sequences exist for only Ab. elongatus and Ab. denisonii (as of 25th October

2020). Adding more sequences, especially those newly described, would help clarify relationships and intraspecific divergences among individuals of the species.

Intraspecific sequence divergence for many species (i.e., Ab. elongatus, Ac. botia, S. fasciata and S. rupecula) was ≥3.5% (some as high as 17.2 %; Table 3). The 3.5% is a threshold value for a provisional species, a recommendation (Hebert et al., 2003) derived as being 10x the average intraspecific COI sequence divergence recorded for many typical marine fishes (Ward et al., 2005; Ward et al., 2008). If applied herein, it would corroborate the potential involvement of entirely different (congeneric) species. However, my concern is that the value greatly elevates divergences among conspecifics (>3.5%), and is based on sequences acquired from GenBank© that may, in turn, be erroneous.

Bhutanese Nemacheilidae

I used COI to identify individuals, delimit boundaries, and recognize putative species in five (of seven) nemacheilid species in Bhutan. Four of these instances are corroborated by all four delimitation methods. My analyses also added a new species record (i.e., S. fasciata) to the

Bhutanese fish database. Additionally, I speculate that Ab. spp. from Bhutan could either be a new species or simply one not yet been submitted to GenBank© (such as Ab. garoensis, Ab. kempi, or Ab. tikaderi). In either case, it would also represent a new species-record for Bhutan.

Bhutanese nemacheilids first diverged into two clades during the late Eocene (~49.5 Ma)

(Fig. 4). However, further diversification (and different species) appeared mid-Eocene (~40.3

Ma). This coincides with the initial uplift of the QTP (~40 Ma), an event that gave rise to many

16 mountains and reshaped the original paleo-drainage into modern-day river networks (Chaung et al., 1998; Rowley and Currie, 2006). The most recent divergence occurred mid-Miocene (~14.3

Ma), suggesting that Bhutanese nemacheilids diversified during the initial uplift of QTP, well before the most recent orogeny at ~3.6 Ma (Zhou et al., 2016). This event also played a significant role in the diversification of a specialized catfish lineage, the glyptosternoids

(Sisoridae), lending credence to the argument that orogeny and tectonism played a large part in the diversification of aquatic taxa on the QTP and within the Himalaya.

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22 Tables

Table 1: Fish biodiversity evaluated in this study. Listed are: Species Name = Genus/ species; Accession # = UA-sequence number (begins with 95) or GenBank© accession number (begins with 2 capital letters); Location = Country, river basin and locality or drainage (indicated by ‘Chhu’ and ‘Khola’).

Species Name Accession # Location Schistura tirapensis 95dor02LCH Bhutan: Amo Chhu (Dorti Khola) Schistura tirapensis 95dort06LCH Bhutan: Amo Chhu (Dorti Khola) Schistura tirapensis 95dort10LCH Bhutan: Amo Chhu (Dorti Khola) Schistura tirapensis 95dort13LCH Bhutan: Amo Chhu (Dorti Khola) Schistura tirapensis 95sing21LCH Bhutan: Amo Chhu (Singye Khola) Schistura tirapensis 95kali19LCH Bhutan: Punatsang Chhu (Kali Khola) Schistura tirapensis 95labr03LCH Bhutan: Punatsang Chhu (Labrang Khola) Schistura tirapensis 95labr05LCH Bhutan: Punatsang Chhu (Labrang Khola) Schistura tirapensis KY853033 India: Brahmaputra (Assam) Schistura rupecula 95burk02LCH Bhutan: Brahmaputra (Bhur Khola) Schistura rupecula 95burk03LCH Bhutan: Brahmaputra (Bhur Khola) Schistura rupecula 95Kali07LCH Bhutan: Punatsang Chhu (Kali Khola) Schistura rupecula 95pipp10LCH Bhutan: Wang Chhu (Pipping Chhu) Schistura rupecula 95takl06LCH Bhutan: Brahmaputra (Taklai Khola) Schistura rupecula 95sarp05LCH Bhutan: Brahmaputra (Sarpang Khola) Schistura rupecula 95sarp06LCH Bhutan: Brahmaputra (Sarpang Khola) Schistura rupecula 95sing01LCH Bhutan: Amo Chhu (Singye Khola) Schistura rupecula 95sing05LCH Bhutan: Amo Chhu (Singye Khola) Schistura rupecula 95kali06LCH Bhutan: Punatsang Chhu (Kali Khola) Schistura rupecula MN172328 Nepal: Ganges (Karnali) Schistura scaturigina 95bibi01LCH Bhutan: Mangde Chhu (Bibigang Chhu) Schistura scaturigina 95bibi02LCH Bhutan: Mangde Chhu (Bibigang Chhu) Schistura scaturigina 95rind01LCH Bhutan: Mangde Chhu (Rindigang Chhu) schistura scaturigina 95rind02LCH Bhutan: Mangde Chhu (Rindigang Chhu) Schistura scaturigina NC031378 China Schistura fasciata 95dort07LCH Bhutan: Amo Chhu (Dorti Khola) Schistura fasciata 95dort08LCH Bhutan: Amo Chhu (Dorti Khola) Schistura fasciata MT269753 India: Brahmaputra Schistura fasciata KX951823 India: Brahmaputra Schistura fasciata KX399160 India: Brahmaputra (Meghalaya) Schistura fasciata KY810453 India: Brahmaputra (Mizoram) Schistura fasciata KJ936803 India: Brahmaputra (Mizoram)

23 Cont. Table 1: Fish biodiversity evaluated in this study. Listed are: Species Name = Genus/ species; Accession # = UA-sequence number (begins with 95) or GenBank© accession number (begins with 2 capital letters); Location = Country, river basin and locality or drainage (indicated by ‘Chhu’ and ‘Khola’).

Species Name Accession # Location Aborichthys spp 95kiri11LCH Bhutan: Mangde Chhu (Kirigang Chhu) Aborichthys spp 95kiri12LCH Bhutan: Mangde Chhu (Kirigang Chhu) Aborichthys spp 95klat01LCH Bhutan: Mangde Chhu (Klatang) Aborichthys spp 95klat02LCH Bhutan: Mangde Chhu (Klatang) Aborichthys spp 95klat03LCH Bhutan: Mangde Chhu (Klatang) Aborichthys spp 94klat04LCH Bhutan: Mangde Chhu (Klatang) Aborichthys spp MK962530 India: Brahmaputra (Arunachal Pradesh) Aborichthys elongatus AP011304 India Aborichthys elongatus 95kali09LCH Bhutan: Wang Chhu (Kali Khola) Aborichthys elongatus 95kali17LCH Bhutan: Wang Chhu (Kali Khola) Aborichthys elongatus 95kali18LCH Bhutan: Wang Chhu (Kali Khola) Aborichthys elongatus 95pipp01LCH Bhutan: Brahmaputra (Pipping Chhu) Aborichthys elongatus 95pipp02LCH Bhutan: Wang Chhu (Pipping Chhu) Aborichthys elongatus 95pipp07LCH Bhutan: Wang Chhu (Pipping Chhu) Aborichthys elongatus 95pipp08LCH Bhutan: Wang Chhu (Pipping Chhu) Aborichthys elongatus 95pipp09LCH Bhutan: Wang Chhu (Pipping Chhu) Aborichthys elongatus 95labr01LCH Bhutan: Wang Chhu (Labrang Khola) Aborichthys elongatus 95labr02LCH Bhutan: Wang Chhu (Labrang Khola) Acanthocobitis botia MN259190 Bangladesh Acanthocobitis botia MK388802 India: Brahmaputra (NE India) Acanthocobitis botia MK804134 India: Ganges Acanthocobitis botia MN172304 Nepal: Ganges (Karnali) Acanthocobitis botia MN172294 Nepal: Ganges (Durgali) Acanthocobitis botia MK993520 Nepal Acanthocobitis botia MK610312 India: Brahmaputra Acanthocobitis botia 95kalg01LCH Bhutan: Brahmaputra (Kali Khola - Gelephu) Acanthocobitis botia 95kalg02LCH Bhutan: Brahmaputra (Kali Khola - Gelephu) Acanthocobitis botia 95kalg03LCH Bhutan: Brahmaputra (Kali Khola - Gelephu) Acanthocobitis botia 95kalg04LCH Bhutan: Brahmaputra (Kali Khola - Gelephu) Acanthocobitis botia 95kalg05LCH Bhutan: Brahmaputra (Kali Khola - Gelephu) Acanthocobitis botia 95kalg06LCH Bhutan: Brahmaputra (Kali Khola - Gelephu) Acanthocobitis botia 95kalg07LCH Bhutan: Brahmaputra (Kali Khola - Gelephu) Acanthocobitis botia 95kalg08LCH Bhutan: Brahmaputra (Kali Khola - Gelephu) Acanthocobitis botia 95kalg09LCH Bhutan: Brahmaputra (Kali Khola - Gelephu) Acanthocobitis botia 95kalg10LCH Bhutan: Brahmaputra (Kali Khola - Gelephu)

24 Table 2: Statistical tests comparing topology of Bayesian (BI) and maximum likelihood (ML) phylogenetic trees derived in this study. LogL=Log Likelihood values; deltaL=logL difference from maximal logL in the set; Bp-RELL=Bootstrap proportions using RELL methods; p-KH=p- value of one-sided Kishino-Hasegawa test; p-SH=p-value of Shimodaira-Hasegawa test; c- ELW=Expected Likelihood Weight (weights sum to 1 across trees); and p-AU=p-value of Approximately Unbiased test. A plus sign (+) next to a p-value denotes 95% confidence sets, whereas a minus sign (-) denotes a significant exclusion (i.e., the tree is rejected). All tests based on 1000 resamplings using the RELL method.

Tree logL deltaL bp-RELL p-KH p-SH c-ELW p-AU BI -4157.64 3.20 0.094 + 0.11 + 0.11 + 0.146 + 0.055 + ML -4154.43 0 0.906 + 1 + 0.89 + 0.854 + 0.945

25 Table 3: Net pairwise sequence divergences between 13 species or species-groups of nemacheilid loach (lower triangle), with standard errors (upper triangle). Sequence divergences reflect p-distances as derived from 707 base pairs of the (mt)DNA COI gene and calculated using the Tamura-Nei model. Color codes correspond to respective groups coalesced as per linages identified by the species delimitation methods (Figure 2)

S_rupecula S_rupecula S_scat_ S_scat_ S_fasc_ S_fasc_ S_tirap_ Ac_botia Ac_botia Ab_elong Ab_elong Ab_spp_ S_scat_ _1 _2 1 2 1 2 1 _1 _2 _1 _3 1 1

S_rupecula_1 0.012 0.011 0.008 0.011 0.016 0.013 0.021 0.023 0.018 0.022 0.019 0.012

S_rupecula_2 0.078 0.010 0.008 0.010 0.016 0.014 0.022 0.021 0.022 0.023 0.022 0.010

S_scat_1 0.079 0.054 0.005 0.009 0.014 0.012 0.020 0.021 0.019 0.023 0.019 0.003

S_scat_2 0.055 0.048 0.018 0.008 0.012 0.009 0.016 0.018 0.009 0.016 0.014 0.005

S_fasc_1 0.077 0.060 0.056 0.044 0.014 0.013 0.020 0.023 0.020 0.022 0.020 0.010

S_fasc_2 0.132 0.125 0.115 0.092 0.102 0.014 0.019 0.024 0.021 0.022 0.019 0.015

S_tirap_1 0.111 0.119 0.099 0.069 0.107 0.134 0.014 0.021 0.016 0.016 0.015 0.013

Ac_botia_1 0.205 0.203 0.191 0.151 0.183 0.183 0.138 0.021 0.019 0.022 0.018 0.020

Ac_botia_2 0.205 0.190 0.188 0.152 0.205 0.217 0.179 0.172 0.024 0.024 0.023 0.021

Ab_elong_1 0.177 0.207 0.190 0.093 0.187 0.214 0.160 0.195 0.220 0.017 0.016 0.019

Ab_elong_3 0.213 0.212 0.211 0.148 0.197 0.204 0.161 0.214 0.213 0.136 0.015 0.023

Ab_spp_1 0.183 0.209 0.188 0.127 0.190 0.183 0.146 0.180 0.207 0.142 0.105 0.020

S_scat_1 0.082 0.051 0.009 0.020 0.059 0.122 0.107 0.196 0.193 0.192 0.211 0.197

2 6

26 Figures

Figure 1: Geographic location of Bhutan (top) and localities within Bhutan (bottom) where samples of nemacheilid loaches were acquired. Colored topography (bottom) indicates elevational ranges (in meters) whereas colored symbols indicate sampling locations. Details in Table 1.

27 S_rupecula_Burk02 S_rupecula_Sarp11 S_rupecula_Takl01 S_rupecula_Sarp06 S_rupecula_Sing06 S_rupecula_Sarp05 Schistura rupecula S_rupecula_Kali06 S S_rupecula_Pipp06 Q S_rupecula_Sing01 60 * S_rupecula_Burk03 O * T S_rupecula_NP_02_MN172328MN172326 (Nepal) 64 S_scatturigina_RD_01 100 S_scaturigina Rind01 R 99 S_scatturigina_CH_01S_scaturigina NC031378.1 (China) S_scatturigina_Bibi_01S_scaturigina Schistura scaturigina M 100 Bibi01 66 S_scaturiginaS_scatturigina_Bibi_02Bibi02 100 S_scatturigina_RD_02S_scaturigina Rind02 S_fasciata_IN_01_KX951823.1_KX951823.1 (India) K P S_fasciata_IN_02_KX399160.1_KX399160.1 (India) 61 S_fasciata_IN_03_KY810453.1_KY810453.1 (India) 92* Schistura fasciata * S_fasciata_IN_04_KJ936803.1_KJ936803.1 (India) S_fasciata_IN_05_MT269753.1_MT269753.1 (India) N 100 S_fasciata_Dort07 S_fasciata_Dort08 A * S_tirapensis_Dort06 84 S_tirapensis_Labr3 S_tirapensis_Sing08 S_tirapensis_Kali19 S_tirapensis_Labr05 Schistura tirapensis S_tirapensis_Sing03 L S_tirapensis_IN_01_KY853033_KY853033 (India) 100 S_tirapensis_Dor13 * S_tirapensis_Sing21 A_botia_Kalg01Ac_botia_Kalg01 A_botia_Kalg08Ac_botia_Kalg08 S_tirapensis_Dort13Ac_botia_Dort13A_botia_Dort13 Ac_botiaA_botia_NP_03_MN172294_MN172294_ MN172294 (Nepal) (Nepal) Ac_botiaA_botia_NP_04_MK993520_MK993520_ MK993520 (Nepal) (Nepal) Ac_botiaA_botia_IN_02_MK388802_MK388802_ MK388802 (India) (India) Ac_botiaA_botia_NP_02_MN172304_MN172304_ MN172304 (Nepal) (Nepal) 95 A_botia_Kalg02Ac_botia_Kalg02 Ac_botia_Kalg07 * A_botia_Kalg07 Acanthocobitis botia A_botia_Kalg06Ac_botia_Kalg06 Ac_botia_MN259190A_botia_BD_01_MN259190_MN259190 (Bangladesh) (Bangladesh ) Ac_botia_MK804134A_botia_IN_03_MK804134_MK804134 (India) (India ) E Ac_botia_Kalg03A_botia_Kalg03 Ac_botia_Kalg10A_botia_Kalg10 C * Ac_botia_Kalg05A_botia_Kalg05 93 Ac_botia_Kalg09A_botia_Kalg09 * F A_botia_Kalg04Ac_botia_Kalg04 A_botia_IN_04_MK610312Ac_botia_MK610312_MK610312 (India) (India) Ab_elongatus_Kali09A_elongatus_Kali09 Ab_elongatus_Labr02A_elongatus_Labr02 Ab_elongatus_Labr01A_elongatus_Labr01 B Ab_elongatus_Kali017A_elongatus_Kali17 Aborichthys elongatus 89 I A_elongatus_Kali18Ab_elongatus_Kali18 * * A_elongatus_Pipp01Ab_elongatus_Pipp01 A_elongatus_Pipp08Ab_elongatus_Pipp08 G A_elongatus_Pipp09Ab_elongatus_Pipp09 72 A_elongatus_Pipp07Ab_elongatus_Pipp07 Ab_elongatus_Pipp02 J A_elongatus_Pipp02 D 100 A_elongatus_IN_01_AP011304Ab_elongatus_AP011304_AP011304 (India) (India) 100 * Aborichthys_spp_IN_01_MK962530Ab_spp_MK962530_MK962530 (India) (India) * Aborichthys_spp_Klat01Ab_spp_Klat01 Aborichthys_spp_Klat02Ab_spp_Klat02 H Aborichthys_spp_Klat04Ab_spp_Klat04 Aborichthys spp. Aborichthys_spp_Klat03Ab_spp_Klat03 * Aborichthys_spp_Kiri11Ab_spp_Kiri11 Aborichthys_spp_Kiri12Ab_spp_Kiri12 Carpiodes_carpio Myxocyprinus_asiaticus Anguilla_bengalensis

0.04 ABGD (COI) mPTP (COI) bPTP (COI) GMYC (COI)

Figure 2: Maximum Likelihood phylogenetic analysis of Loach (Nemacheilidae) based on 707 base pairs of the mtDNA COI gene. Values at nodes represent UFBOOT2 (ultrafast bootstrap) values. Red asterisk = values for SH-aLRT (Shimodaira-Hasegawa likelihood ratio test) ≥80%. Four delimitation methods are designated by colored bars: Blue= Automated Barcode Gap Discovery (ABGD); Red=Poisson Tree Process using maximum likelihood (mPTP); Green=Bayesian (bPTP) approach; Black=Generalized Mixed Yule Coalescent (GYMC).

28 S_rupecula_Burk02 S_rupecula_Burk03 S_rupecula_Pipp06 S_rupecula_Sing01 S_rupecula_Kali06 S_rupecula_Sarp05 S_fasciata_MT269753.1_India S_rupecula_Sarp11 S_rupecula_Sing06 S_rupecula_Takl01 S_fasciata_KX951823.1_India S_fasciata_KX399160.1_India S_fasciata_KY810453.1_India S_fasciata_KJ936803.1_India S_fasciata_IN_05_MT269753.1 98 S_fasciata_Dort07 A_botia_MK993520_Nepal 66 S.S_scatturigina_RD_01 scaturigina S.S_scatturigina_NC031378.1_China scaturigina S_scatturigina_Bibi_01S. scaturigina S_scatturigina_Bibi_02S. scaturigina S_scatturigina_RD_02S. scaturigina 88 S_rupecula_NP_02_MN172328 S_tirapensis_Dort06 S_tirapensis_Kali19 S_tirapensis_Labr3 S_tirapensis_Labr05 S_tirapensis_Sing03 S_tirapensis_Sing08 S_tirapensis_Dor13 S_tirapensis_Sing21 S_tirapensis_KY853033_India A_botia_Kalg01Ac_botia _Kalg01 A_botia_Kalg08Ac_botia _Kalg08 S_tirapensis_Dort13Ac_botia _Dort08 A_botia_MN172294_Nepal Ac_botia _MN172294 (Nepal) A_botia_NP_04_MK993520Ac_botia _MK993520 (Nepal) A_botia_MK388802_IndiaAc_botia _MK388802 (India) 95 A_botia_MN172304_NepalAc_botia _MN172304 (Nepal) A_botia_Kalg02Ac_botia _Kalg02 A_botia_Kalg06Ac_botia _Kalg06 A_botia_Kalg07Ac_botia _Kalg07 A_botia_MN259190_BangladeshAc_botia _MN259190 (Bangladesh) A_botia_MK804134_IndiaAc_botia _MK804143 (India) A_botia_Kalg03Ac_botia _Kalg03 A_botia_Kalg10Ac_botia _Kalg10 A_botia_Kalg04Ac_botia _Kalg04 99 A_botia_Kalg05Ac_botia _Kalg05 A_botia_Kalg09Ac_botia _Kalg09 A_botia_MK610312_IndiaAc_botia _MK610312 (India) A_elongatus_Kali09Ab_elongatus _Kali09 A_elongatus_Kali17Ab_elongatus _Kali17 A_elongatus_Kali18Ab_elongatus _Kali18 A_elongatus_Labr01Ab_elongatus _Labr01 100 96 A_elongatus_Labr02Ab_elongatus_Labr02 A_elongatus_Pipp01Ab_elongatus_Pipp01 A_elongatus_Pipp02Ab_elongatus_Pipp02 A_elongatus_Pipp07Ab_elongatus_Pipp07 A_elongatus_Pipp08Ab_elongatus_Pipp08 A_elongatus_Pipp09Ab_elongatus_Pipp09 Ab_spp_Klat01Aborichthys_spp_Klat01 100 Aborichthys_spp_Klat02Ab_spp_Klat02 Aborichthys_spp_Klat03Ab_spp_Klat03 Aborichthys_spp_Klat04Ab_spp_Klat04 Aborichthys_spp_Kiri11Ab_spp_Kiri11 Aborichthys_spp_Kiri12Ab_spp_Kiri12 86 Aborichthys_elongatus_AP011304_IndiaAb_elongatus_AP011304 (India) Aborichthys_spp_IN_01_MK962530Ab_spp_MK962430 (India) Carpiodes_carpio Myxocyprinus_asiaticus Anguilla_bengalensis

0.09

Figure 3: Phylogenetic analysis using Bayesian Inference for Nemacheilidae based on 707 base pairs of mtDNA COI gene. Numbers at nodes are Bayesian posterior probabilities (PP).

Figure 4: Timetree for Nemacheilidae based on 707 base pairs of the mtDNA COI gene and derived using Reltime (Tamura et al., 2012). Numbers designate mean divergence times (in Million years). C1 = temporal constraint as a calibrations point. A-F represented by colored vertical bars indicate geologic periods (as designated below figure).

30 CHAPTER 2

Tectonism and Orogeny Drives Evolution and Diversification of Himalayan Catfish

(Sisoridae)

Introduction

The distribution of organisms in a region can reveal much about geological history and mode of speciation (Simon, 2008). The mode is commonly interpreted through two biogeographic lenses: dispersal and vicariance. The former describes the patterns that result from movements away from a central population, with subsequent isolation occurring over time (i.e., isolation by distance; Jensen et al., 2005), with significant diversification as a potential (Orsini et al., 2013).

The latter describes the development of barriers that physically separate biodiversity into isolated units that may eventually develop into new species (He et al., 2001). Both promote evolutionary divergence by reducing or eliminating gene flow among populations, (Slatkin,

1987).

Vicariance is a dominant explanation for the distribution of many plants and animals

(Bourguignon et al., 2018). One such result can be seen in the distribution of biodiversity within and across the Himalaya Mountains. The uplift of the Qinghai-Tibetan Plateau (QTP), a result of the collision between Indian and Eurasian plates (Spicer et al., 2003), not only impacted biodiversity distribution (Liu et al., 2012; Rüber et al., 2004; Zhang et al., 2006), but also rearranged connectivity within and among rivers (Clark et al., 2004). Paleo-drainages of East

Asia were once the major tributaries of a single river that drained southeast across the QTP and into the South China Sea. These were subsequently reorganized by captures and flow reversals to form contemporary drainages (Clark et al., 2004). Such alterations in basin morphology had clear impacts on the biogeographic patterns of resident ichthyofauna (Kottelat, 1989; Rüber et al., 2004). Thus, by studying the molecular phylogeny of biodiversity endemic to the QTP, it is

31 possible to understand how geologic processes have driven their evolutionary history. One potential result is the manner by which geomorphic events have driven diversification and speciation in Himalaya fishes (He et al., 2001).

One such group of fishes is the rheophilic (i.e., current loving) catfish of the family

Sisoridae (Siluriformes) primarily endemic to torrential riverscapes such as those found in the

Himalaya. Here I juxtapose the biogeographic distribution of the sisorids with the geologic history of the region so as to interpret the phylogeny of the family, and to add new samples gathered from a region of the Himalaya that has yet to be adequately sampled.

Methods and Materials

Study taxa

The sisorids possess unique anatomical structures that adapt them for existence in extreme habitats (Lujan & Conway, 2015). These include: A dorsoventrally flattened phenotype; Inferior mouth; and paired fins that are both enlarged and equipped with adhesive structures for maintenance and orientation within torrential currents. Many are primarily endemic to the

Himalaya and the QTP (de Pinna, 1996; Zhou et al., 2016), and their distribution and diversification are seemingly associated with the three geomorphic events (van Hinsberg et al.,

2012) that promoted the uplift of the QTP (He et al., 2001; Ma et al., 2015). In this regard, the

‘glyptosternoids’, a specialized group of catfish within Sisoridae, have been employed in specific reference to the geologic history of the region (He et al., 2001; Zhou et al., 2016), to include relationships among contemporary drainages (Guo et al., 2005).

Sisorid catfish are divided into 13 genera, and all except Bagarius, Gagata,

Pseudecheneis and Glyptothorax, are termed ‘glyptosternoids’ (Hora & Silas, 1952; Zhou et al.,

2016), a term that is colloquial rather than taxonomic. The results of most taxonomic

32 investigations, however, are inconsistent with regard to relationships within Pseudecheneis,

Euchiloglanis, Exostoma, Glaridoglanis and Glyptosternon (Guo et al., 2005; Hora & Silas,

1952; Zhou et al., 2016), an apparent result of limited taxon and character sampling (Chen et al., 2008; Mayden et al., 2008).

Earlier studies of sisorid phylogeny were primarily based on specimens from the

People’s Republic of China (PRC: Guo et al., 2005; He et al., 2001; Yu & He, 2012; Zhang et al., 2006; Zhou et al., 2016). Taxon sampling, however, is an important requirement for phylogenetic analysis (Hillis et al., 2003), and a reliable taxonomic hypothesis must not only include newly described individuals, but also those from regions previously unsampled (Sgouros et al., 2019).

Although Bhutan (Eastern Himalaya; Fig.1) is within the distributional range of many rheophilic fishes, including the Sisoridae, data regarding occurrence and distribution of fishes from this region are rather sparse, due largely to limited access, difficult terrain, and a relatively non-existent historical database (Thoni & Gurung, 2018). In this study, I generate mitochondrial

(mt)DNA sequences for 25 sisorid individuals across four genera and five species, in an attempt to expand the species records within the study region. I then add a series of GenBank© sequences so as to achieve two main objectives: 1) Reconstruct the phylogeny of Sisoridae by incorporating new specimens from Bhutan; and 2) Test if divergence events within Bhutanese sisorids are associated with the uplift of the QTP.

Sample collection, DNA extraction and amplification

Fin clips were acquired non-lethally from 35 specimens across nine localities in various drainages of Bhutan during 2016-18 (Table 1, Fig. 1). Sampling and tissue collection were done with prior approval from the Department of Forests & Parks Services, Ministry of Agriculture &

Forests, Royal Government of Bhutan as well as the University of Arkansas Institutional Animal

33 Care and Use Committee (IACUC# 17064). Voucher specimens are housed at the National

Research & Development Centre for Riverine & Lake Fisheries, (NRDCR&LF; Haa, Bhutan).

DNA was extracted using Qiagen DNeasy kits (following manufacturer’s protocols) and the mtDNA cytochrome oxidase subunit I (COI) gene was amplified using published primers (Ali et al., 2013) and sequenced using BIGDYE© [ver.3.1; Applied Biosystems Inc. (ABI), Forest

City, CA, USA]. Sequences were generated on an ABI Prism 3700 Gene Analyzer (W. M. Keck

Center, University of Illinois, Champaign), manually edited using SEQUENCHER v.5.4.6. and aligned using MUSCLE v.3.8 (Edgar, 2004).

Additionally, Bhutanese samples were compared with 31 GenBank© sequences (Table

2), to include those from neighboring countries [i.e., India (N=6); Nepal (N=3); China (N=14);

Pakistan (N=1); Myanmar (N=1); and N=6 without locality information]. I used two species representing the family Cyprinidae as outgroup taxa for phylogenetic analyses: Cyprinus carpio and Danio rerio. The use of taxonomically more appropriate species (i.e., catfish) as outgroup was not possible in that GenBank© sequences for Akysis manipuransis (Akysidae) and

Pylodictis olivaries (Ictaluridae) did not yield an expected basal sister group to Sisoridae.

Phylogenetic analyses

I used Bayesian Inference (BI: MRBAYES 3.1.2, Ronguist & Huelsenbeck, 2003) and Maximum

Likelihood (ML: IQ-TREE v.2; Nguyen et al., 2015) to infer relationships among Sisoridae. For

ML, I employed MODELFINDER (Kalyaanamoorthy et al., 2017) to determine the proper model of nucleotide substitution, as accessed in IQ-TREE v.2. Two independent runs of five million generations were employed, with trees sampled every 2000th generation, yielding 2,500 sampled trees with the first 25% discarded as burn-in. Convergence was estimated using the average standard deviation of split frequencies < 0.01, with a potential scale reduction factor

(PSRF) = 1.0 for all parameters. The latter ensures that splits (or “clades”) in the tree eventually

34 converge towards a similar value across all runs, and that the standard deviation of the split across all runs does not differ by >0.01.

Branch support for ML analyses was calculated as ulftrafast bootstrap values derived from 1000 iterations (UFBOOT2; Hoang et al., 2018), with ≥95% denoting a strongly supported clade. I also assessed branch support using SH-aLRT (Shimodaira-Hasegawa approximate

Likelihood Ratio Test) with values ≥80% indicating strongly supported clades. The final tree was visualized using FIGTREE v1.4.4 (Rambaut & Drummond, 2009).

For the BI analysis, I determined the best partitioning scheme for the phylogenetic analysis by applying Partition Models (Chernomor et al., 2016). Nodal support was assessed using the Bayesian posterior probabilities (PP) with values ≥95% indicating strong support.

Additionally, I examined all parameters for congruence post-analysis, including sample size

(TRACER v1.7.1; Rambaut & Drummond, 2009). I then determined the best tree topology (ML versus BI) by employing Kishino-Hasegawa (1989) and Shimodaira-Hasegawa (1999) tests based on 1000 replicates.

To derive a timetree for the ML phylogeny, I employed the RELTIME method (MEGA version X; Kumar et al., 2018) with an independent GTR model of nucleotide substitution and a gamma distributed rate variation (Tamura et al., 2012). To do so, I utilized a refined dataset that included only one taxon per genus. A single calibration point was employed, corresponding to the estimated divergence between Bagarius and Glyptothorax, per the fossil record of Bagarius yarreli from the Pliocene Siwalik Hills of India (5.3-1.8 Ma; Lydekker, 1886).

Finally, I coalesced individuals within Pseudecheneis sulcata, based on biogeographic groupings. I then calculated pair-wise sequence divergences among groups (as p-distances corrected for within group variance) using MEGA-X.

35 Results

Sequence alignment and verification

The total number of mtDNA base pairs obtained from sequence was 456. Only 25 (of 35) study sequences from Bhutan were identified as Sisoridae. The remaining 10 were excluded from the analysis as they belonged to other catfish families [Claridae (N=1); Amblicipitidae (N=3);

Bagridae (N=6)]. Six off the 25 were not assigned to species in GenBank© but were retained as

Sisoridae based on morphology of voucher specimens.

Phylogenetic analyses

A GTR+I+G model of sequence evolution (General Time Reversible plus proportion invariant plus gamma-distributed heterogeneity) was derived for ML analyses, based on 1,000 nonparametric bootstrap replicates. Both ML and BI trees confirmed the monophyly of the

Sisoridae (PP: 100% and UFBOOT2: 99.9%). However, the ML tree is employed for discussion as it scored higher than the BI tree in comparative evaluations employing the KH (Kishino-

Hasegawa, 1989) and SH tests (Shimodaira & Hasegawa, 1999) (Table 3).

The genus Pseudecheneis has been placed in varying positions within the sisorid phylogeny. Guo et al. (2005) and de Pinna (1996) placed it as sister to the ‘glyptosternoids’, whereas Peng et al. (2006) and Zhou et al. (2016) viewed it as basal to all sisorids. My analysis using P. sulcata from Bhutan, India and Nepal was consistent with Peng et al. (2006) and Zhou et al. (2016) in placing the genus as basal and sister to the remaining sisorids [ML tree, Fig. 2

(UFBOOT2= 100%, SH-aLRT ≥80%) and BI tree, Fig. 3 (PP=100%)].

The other controversial group within the Sisoridae is Exostoma labiatum (Guo et al.,

2005; Peng et al., 2006; Zhou et al., 2016), and both BI and ML analyses were unable to clarify its taxonomy. The BI analysis placed Exostoma in a polytomy with other sisorids (Fig. 3, PP:

36 67%), whereas it was basal to all ‘glyptosternoids’ in the ML tree (Green highlight in Fig. 4,

UFBOOT2= 68%). My analyses also clarified the identity of six individuals sampled from Eastern

(green highlight, UFBOOT2= 92%), as well as in the BI tree (Fig. 3, but with further subdivision;

PP=100%).

Two Glyptothorax spp. (Fig. 5–yellow highlight) clustered significantly with G. trilineatus from Nepal (UFBOOT2: >95%). Interestingly, two G. dakpathari individuals from Bhutan (Fig. 5 – upper green highlight) clustered with G. annandalei from China rather than with conspecifics from Nepal and India (Fig. 5–lower green highlight). Both nodes had strong support (UFBOOT2:

>95%).

Results also indicated genetic structure within the Pseudecheneis group (Fig. 6).

Pseudecheneis sulcata from Bhutan (N=14) and India (N=2) formed one distinct clade (Fig. 6– green highlight), while two conspecifics (Nepal and India) formed a separate clade (Fig. 6– yellow highlight). However, neither clade was significantly supported in the ML tree, but both did reflect elevated sequence divergences one from another (Table 4).

Discussion

The Sisoridae were previously divided into two lineages (Guo et al. 2005), and I present these below in newick format (a method of representing graphical trees via parentheses and commas; see https://evolution.genetics.washington.edu/phylip/newicktree.html). One lineage represents

[Gagata (Bagarius, Glyptothorax)] (Fig. 2–blue highlight), and the second is (Pseudecheneis and ‘glyptosternoids’) (Fig. 2–yellow and green highlights, respectively). Given these previous results, my expectation was also to identify two major clades in my study. While both BI and ML methods did so (Figs. 2 and 3), results varied with regards to the placement of several taxa.

37 In this regard, de Pinna (1996) suggested Glyptothorax did not associate with Bagarius

(as above), and attributed this to inadequate taxon sampling, specifically relating to the absence of Sisor and Nangra (Guo et al., 2005). I, however, included Sisor in my analysis, and those results corroborated results from Guo et al. (2005). Glyptothorax is indeed included within the same lineage as sister to Bagarius and Sisor.

A second unresolved relationship within the sisorid phylogeny is that of Pseudecheneis, and earlier studies (Hora & Silas, 1952) were inconclusive with regard to the exact placement of this clade (Guo et al., 2005). My results (Fig. 2–lower yellow highlight) are congruent with that of

Peng et al. (2004) and Zhou et al. (2016) in placing Pseudecheneis as basal to both

‘glyptosternoid’ and ‘non-glyptosternoid‘ sisorids. In addition, I interpret Pseudecheneis sulcata as basal and sister to the remaining sisorids, rather than as sister to the ‘glyptosternoids’ [per

Guo et al. (2005) and de Pinna (1996)]. This is based on the relatively higher support values derived for this relationship (i.e., BI; PP=100%; ML; UFBOOT2=100%).

Finally, the third controversy regarding the phylogeny of the Sisoridae is placement of

Exostoma labiatum (Fig. 2). Earlier studies (Guo et al., 2005; He et al., 2001; Peng et al., 2004;

Zhou et al., 2016) placed it within Glyptosternon, but with weak support (Peng et al., 2004). My

ML analysis also placed E. labiatum within the ‘glyptosternoids,’ but basal to all others, including

G. maculatum. This differs from previous studies (as above). However, support is weak at this node (UFBOOT2: 68%).

BI analyses, on the other hand, offered little clarification as E. labiatum was placed in a polytomy with (Glyptothorax + Bagarius) and ‘glyptosternoids.’ Given these confounded results and weak statistical support, I cannot comment with confidence on the exact placement of

Exostoma. Interestingly, my BI and ML results differ from those of previous authors (Guo et al.,

38 2005; Peng et al., 2006; Zhou et al., 2016). who placed G. maculatum as an occasional sister group basal to ‘glyptosternoids,’ with Exostoma internal to G. maculatum.

Bhutanese samples

My phylogenetic analyses also shed light on the disposition of samples from Bhutan unidentified as to taxon. Strong statistical support in both BI and ML analyses indicated that six unassigned individuals (Fig. 4) are either conspecifics with, or a direct relative of Myersglanis blythi. Thoni &

Gurung (2018) emphasized the interchangeable use of Parachiloglanis and Myersglanis as generic names for the same species when they redescribed Parachiloglanis hodgarti. I acquired a GenBank© sequence of P. hodgarti (from India) and my resulting reaffirm the claim that M. blythii and P. hodgarti are indeed the same species (results not shown). This leads to an hypothesis. While individuals from Bhutan are closely related, they often do not represent the same species. Thoni & Gurung (2018) described five new sisorid species, including a novel species (Parachiloglanis drukyulensis) from Sarpang, Bhutan [the same locality as two unidentified individuals in Fig. 4 (i.e., Unknown_Bh_Sarp03 and 04). Using Thoni & Gurung

(2018), and the resulting strong support values produced in Fig. 4 (UFBOOT2= 100%, SH-aLRT

≥80%), I hypothesize that (given the synonymity between Parachiloglanis and Myersglanis), my two unidentified Bhutanese individuals should be allocated instead to P. drukyulensis.

Two Glyptothorax spp. from Bhutan (Bh_Sarp02 and Bh_Sing01; Fig. 5—yellow highlight) are both sister to G. trilineatus MN172316 from Nepal. These data could potentially suggest that the Bhutanese samples are a cryptic species closely related to G. trilineatus.

Another intriguing observation is the close association of two G. dakpathari from Bhutan

(Bh_Kali02 and Bh_Lung01) with G. annandalei NC045214 (China) (upper green highlight), rather than with conspecifics from Nepal and India (lower green highlight). Another option is that the reference species from Nepal and India were misidentified when submitted to GenBank©.

39 A second consideration is that G. dakpathari from Bhutan (Bh_Kali02 and Bh_Lung01) and G. annandalei NC045214 (Yarlung, China) are from Brahmaputra drainages, whereas G. dakpathari from Nepal (MN178264) and India (MK993299) are from the Ganges drainage. It is therefore possible that G. dakpathari shows intraspecific divergence due to drainage. The third and least plausible explanation is that G. dakpathari is non-monophyletic with at least two lineages in existence. In this regard, Glyptothorax (as with most sisorids) inhabits rapidly flowing hill-streams, or similar reaches within larger rivers (Jiang et al., 2011). It is possible that increasingly harsh environments have driven the evolution of similar morphological adaptations in hill stream versus large river habitats, resulting the consolidation of separate lineages broadly defined as G. dakpathari. Convergent evolution is a common phenomenon in species-rich communities (Barluenga et al., 2006; Feng et al., 2019; Scheffer & Van Nes, 2006), and

Glyptothorax is recognized as such (Jiang et al., 2011). Convergent evolution due to harsh environments has also been documented in a second rheophilic taxon from the QTP, the loach

Triplophysa stoliczkae (Nemacheilidae) (Feng et al., 2019).

Further, respective clusters within P. sulcata (Fig. 6) coincide with geographic distribution. Those from the Brahmaputra basin (northeast India and Bhutan) form a cluster

(green highlight) separate from those from the Ganges (northern India and Nepal; yellow cluster). Within the Brahmaputra group, members seemingly group, apparently indicating genetic structure based on geographic distance. Those from the Toeb Rong Chhu (Punatsang

Chhu Basin) are separate from those in the Rindigang Chhu (Mangde Chhu Basin). This is corroborated by sequence divergence estimates, as higher values (≥3.5%; Table 4) that primarily identify groups from separate drainages. I used a sequence divergence of 3.5% as a screening threshold for highly diverged groups based on Hebert et al. (2003). This value is equivalent to 10x the average intraspecific COI sequence divergence for many typical marine

40 fishes (Ward et al., 2005; Ward et al., 2008). By that approximation, the use of 3.5% is a liberal estimate, given that within-species divergence is being evaluated.

Biogeography of sisorids and the uplift of Tibetan Plateau

My molecular timeline suggests sisorids first diverged 9.5 Ma (CI:5.4-4.3) during Miocene and diversified into Pseudecheneis, Bagarius, Sisor and Glyptothorax. The second divergence occurred at 1.9 Ma (CI:3.8-0.65) during early Pleistocene whereby more specialized

‘glyptosternoid’ lineages emerged (i.e., Creteuchiloglanis, Euchiloglanis, Oreoglanis, and

Pareuchiloglanis) (Fig. 7).

Harrison et al. (1992) hypothesized that rapid uplift and un-roofing of the southern QTP began ~20 Ma (early Miocene), with the QTP achieving its present elevation by ~8 Ma (late

Miocene). Cui et al. (1996) and Li et al. (1996) provided an alternative hypothesis by suggesting the QTP acquired maximum elevation well before 8 Ma, but temporarily receded due to extensional faulting, such that the final uplift occurred more recently (and rapidly) at ~3.6 Ma

(mid-Pliocene). My estimates agree with the Cui/ Li hypothesis indicating specialized glyptosternoids evolved at 1.9 Ma (early Pleistocene) subsequent to the rapid uplift at 3.6 Ma.

This argument is also consistent with those of previous authors (Peng et al., 2006; Zhou et al.,

2016).

He (1995) suggested that sisorid divergence is directly correlated with the uplift of the

QTP, in that the collision of the Indian and Eurasian plates altered the habitat for ancestral, large-river sisorids (He et al., 2001). Higher gradients and more rapid stream flows in QTP drainages generated strong selective pressure, driving the evolution of adaptive features that reduced water resistance and enhanced anatomical structures that promoted adherence to smooth stones (Zhou et al., 2016). Subsequent uplifts exacerbated these extreme riverscapes, driving the evolution of additional specialized traits.

41 I hypothesize a two-phase divergence in Sisoridae, concurrent with this adaptive radiation. The first occurred during late Miocene (~9.5 Ma) with the evolution of ancestral lineages: Pseudecheneis, Bagarius, Glyptothorax, Exostoma, and Glyptosternum. The second occurred late Pliocene (~1.9 Ma), following the third stage of the QTP uplift (~3.6 Ma) (Zhou et al., 2016). During this phase, specialized sisorid lineages evolved, to include Creteuchiloglanis,

Euchiloglanis, Oreoglanis, and Pareuchiloglanis.

Timeline for Bhutanese sisorids

Sisorids from Bhutan have been identified as: E. labiatum, G. dakpathari, a G. trilineatus-like species, and a close relative of M. blythii and P. sulcata. Those related to M. blythii diverged at approximately 2.08 Ma (CI:3.9-0.7) during early Pleistocene, and again at 1.3 Ma (CI:2.6-0.3) during early-mid Pleistocene) (Fig. 7). Glyptothorax dakpathari separated from its conspecific at

~0.08 Ma (CI:0.5-0.0) during late Pleistocene. The G. trilineatus-like form diverged from a

Nepali conspecific at ~0.04 Ma (CI:0.2-0.0) during late Pleistocene. Bhutanese divergence estimates post-date the hypothesized rapid uplift event at 3.6 Ma, suggesting diversification and speciation in Bhutanese sisorids is relatively recent, seemingly in response to orogeny in the

Eastern Himalaya.

The timeline estimate also reflects an earlier divergence among conspecifics of P. sulcata (Fig. 7). Shortly after the initial sisorid divergence, those from the Ganges drainage

(Uttarakhand, India) diverged in late Miocene [6.8 Ma (CI:10-2.5)] from conspecifics in the

Brahmaputra drainage. Further splits between members in different Brahmaputra basin drainages [i.e., Sikkim (India), Bhutan, and Arunachal Pradesh (India)] occurred Pleistocene-to-

Holocene. I deem it unlikely that elevated intraspecific divergence in P. sulcata is due to fragmented, patchy populations in isolation. Rather, I speculatively suggest that P. sulcata

(Uttarakhand, India) could represent a misidentification and may represent instead a different

42 species (potentially a new genus). Sequence divergence estimates also suggest some groups

(12 out of the 21) are highly diverged and far exceed the standard threshold (≥3.5%), lending support to the claim that these could potentially represent different (congeneric) species.

Conclusion

Plate tectonics and geologic events played a crucial role in the evolution of sisorid catfish on the

QTP, with subsequent river capture and reversal events reorganizing drainages into contemporary configurations (Clark et al., 2004). This also altered river topology in elevated regions, creating extremely torrential riverscapes. The origin and diversification of the sisorids, represented herein by species from China, India, Nepal and Bhutan, was influenced by the late

Miocene geomorphic evolution of the QTP. Further, these processes often shape fine-scale structure within geographically circumscribed species (including but not limited to the sisorids), creating species ‘complex’ of many morphologically similar (potentially cryptic) species. Bhutan, along with parts of Eastern Himalaya (northeast India) has produced scant inventory work in terms of fishes, and given this, the ichthyofaunal diversity is still largely unexplored (Barman et al., 2018; Thoni & Gurung, 2018). It is, therefore, possible that Bhutan harbors several potentially cryptic (or new) species, with additional molecular research instrumental in verifying this argument.

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48 Tables

Table 1: Metadata for 25 individuals in three sisorid catfish species, and eight unidentified to taxon based on the mtDNA COI gene. Listed are: Species Name=Genus/ species; Sequence ID=UA-sequence number (Begins with 96); Site=Local stream; and Drainage=Major river (indicated by ‘Chhu’ and ‘Khola’).

Sl Sequence Species Name Site Drainage No ID 1 Exostoma labiatum 96kiri03 Kirigang Chhu Mangde Chhu 2 Glyptothorax dakpathari 96kali02 Kalikhola Punatsang Chhu 3 Glyptothorax dakpathari 96lung01 Lunjong Mangde Chhu 4 Glyptothorax spp. 96sarp02 Sarpang Khola Brahmaputra 4 Glyptothorax spp. 96sing01 Singye Khola Brahmaputra 5 Pseudecheneis sulcata 96toeb01 Toeb Rong Chhu Punatsang Chhu 6 Pseudecheneis sulcata 96toeb02 Toeb Rong Chhu Punatsang Chhu 7 Pseudecheneis sulcata 96toeb03 Toeb Rong Chhu Punatsang Chhu 8 Pseudecheneis sulcata 96toeb04 Toeb Rong Chhu Punatsang Chhu 9 Pseudecheneis sulcata 96toeb05 Toeb Rong Chhu Punatsang Chhu 10 Pseudecheneis sulcata 96toeb06 Toeb Rong Chhu Punatsang Chhu 11 Pseudecheneis sulcata 96toeb07 Toeb Rong Chhu Punatsang Chhu 12 Pseudecheneis sulcata 96toeb08 Toeb Rong Chhu Punatsang Chhu 13 Pseudecheneis sulcata 96toeb09 Toeb Rong Chhu Punatsang Chhu 14 Pseudecheneis sulcata 96toeb10 Toeb Rong Chhu Punatsang Chhu 15 Pseudecheneis sulcata 96toeb11 Toeb Rong Chhu Punatsang Chhu 16 Pseudecheneis sulcata 96rind04 Rindigang Chhu Mangde Chhu 17 Pseudecheneis sulcata 96rind05 Rindigang Chhu Mangde Chhu 18 Pseudecheneis sulcata 96rind06 Rindigang Chhu Mangde Chhu 19 Pseudecheneis sulcata 96rind07 Rindigang Chhu Mangde Chhu 20 Unassignable 96rind01 Rindigang Chhu Mangde Chhu 21 Unassignable 96rind02 Rindigang Chhu Mangde Chhu 22 Unassignable 96rind03 Rindigang Chhu Mangde Chhu 23 Unassignable 96rind04 Rindigang Chhu Mangde Chhu 24 Unassignable 96sarp03 Sarpang Khola Brahmaputra 25 Unassignable 96sarp04 Sarpang Khola Brahmaputra

49 Table 2: Metadata for 31 sisorid catfish in 27 species and 12 genera, as acquired from GenBank©. Species Name=Genus/ species; Accession Number= GenBank© number; Country/Region=Location.

Sl Species Name Accession# Country/Region No 1 Bagarius bagarius MT670297 Pakistan 2 Bagarius yarrelli KP342264 China 3 Cretuchiloglanis macropterus NC028509 N/A 4 Euchiloglanis davidi NC042210 N/A 5 Exostoma berdmorei DQ846699.1 N/A 6 Exostoma labiatum MH156943 India 7 Gagata cenia MK480352 NE India 8 Glypothorax dakpathari MN178264 Nepal 9 Glyptothorax dakpathari MK993299 India 10 Glyptothorax fokiensis NC018769 China 11 Glyptothorax granosus LC190340 Myanmar 12 Glyptosternon maculatum NC021597 China 13 Glyptothorax annandalei NC045214 China 14 Glyptothorax cavia NC034921 China 15 Glyptothorax lanceatus JQ086569 China 16 Glyptothorax macromaculata NC039561 China 17 Glyptothorax sinensis HQ593580 China 18 Glyptothorax zanaensis NC029709 China 19 Glyptothorax laosensis NC0349702 China 20 Glytothorax longinema HQ593582 China 21 Glyptothorax pallozonum HQ593586 China 22 Glytothorax trilineatus MN172316 Nepal 23 Myersglanis blythii DQ846709 N/A 24 Oreoglanis macropterus NC021607 China 25 Pareuchiloglanis anteanalis DQ508085 N/A 26 Pareuchiloglanis sinensis MF122630 China 27 Pseudecheneis sulcata KR809748 India 28 Pseudecheneis sulcata MN178259 Nepal 29 Pseudecheneis sulcata MK785007 India 30 Pseudecheneis sulcata KF511527 India 31 Sisor rabdophorus KJ909363 India

50 Table 3: Statistical tests comparing topology of maximum likelihood (ML) and Bayesian (BI) phylogenetic trees derived in this study. LogL=Log Likelihood values; deltaL=logL difference from maximal logL in the set; Bp-RELL=Bootstrap proportions using RELL methods; p-KH=p- value of one-sided Kishino-Hasegawa test; p-SH=p-value of Shimodaira-Hasegawa test; c- ELW=Expected Likelihood Weight (weights sum to 1 across trees); and p-AU=p-value of Approximately Unbiased test. A plus sign (+) next to a p-value denotes 95% confidence sets, whereas a minus sign (-) denotes a significant exclusion (i.e., the tree is rejected). All tests based on 1000 resamplings using the RELL method.

Tree logL deltaL bp-RELL p-KH p-SH c-ELW p-AU BA -6227.36 0.18 + 0.522 + 0.478 + 0.478 + 0.524 + 0.428 + ML -6227.17 0 0.478 + 0.522 + 1 + 0.476 + 0.572

51 Table 4: Net pairwise sequence divergences between seven biogeographic groups of Pseudecheneis sulcata (lower triangle) with standard errors (upper triangle). Sequence divergences were derived from 456 base pair of the mtDNA COI gene and calculated using the Tamura-Nei model. Taxa label (= taxon genus/ species and initials of the locality or drainage, followed by Country in parenthesis). Color code correspond to basin: Green=Brahmaputra; yellow=Ganges (See Fig. 6)

P. sulcata Rind P. sulcata Toeb P. sulcata AP P. sulcata SK P. sulcata UT P. sulcate NP

(Bhutan) (Bhutan) (India) (India) (India) (Nepal) P. sulcata Rind (Bhutan) 0.006 0.002 0.005 0.011 0.011 P. sulcata Toeb (Bhutan) 0.014 0.007 0.005 0.012 0.013 P. sulcata_AP (India) 0.002 0.017 0.005 0.012 0.012 P. sulcata_SK (India) 0.013 0.018 0.016 0.010 0.011 P. sulcata_UT (India) 0.039 0.044 0.041 0.038 0.005 P. sulcata_NP (Nepal) 0.041 0.046 0.044 0.042 0.012

5 2

52 Figures

Figure 1: Top: Geographic location of Bhutan (top), and localities within Bhutan where samples of sisorid catfish were acquired (bottom). Colored topography (bottom) indicates elevational ranges (in meters), whereas colored symbols indicate sampling locations. Details in Table 1.

Figure 2: Maximum Likelihood phylogenetic tree of Sisoridae based on 456 base pairs of the (mt)DNA COI gene. Numbers at selected nodes represent UFBOOT2 (ultrafast bootstrap) values. Red asterisk = SH-aLRT (Shimodaira-Hasegawa likelihood ratio test) values ≥80%. Highlights: Green=Glyptosternoid; Blue=Non-glyptosternoids; Yellow=Pseudecheneis sulcata.

Figure 3: Phylogenetic analysis using Bayesian Inference for Sisoridae based on 456 base pairs of the (mt)DNA COI gene. Numbers at selected nodes represent Bayesian posterior probabilities (PP).

Figure 4: Maximum Likelihood phylogenetic analysis of glyptosternoid catfish (Sisoridae) based on 456 base pairs of the (mt)DNA COI gene. Numbers at selected nodes represent UFBOOT2 (ultrafast bootstrap) support values. Red asterisk = SH-aLRT (Shimodaira-Hasegawa likelihood ratio test) values ≥80%.

Figure 5: Maximum Likelihood phylogenetic analysis of non-glyptosternoid catfish (Sisoridae) based on 456 base pairs of the (mt)DNA COI gene. Numbers at selected nodes represent UFBOOT2 (ultrafast bootstrap) support values. Red asterisk = SH-aLRT (Shimodaira-Hasegawa likelihood ratio test) values ≥80%. Highlights: Green=Glyptothorax dakpathari; Yellow=G. trilineatus.

Figure 6: Maximum Likelihood phylogenetic tree based on 456 base pairs of the (mt)DNA COI gene that depict individuals of Pseudecheneis sulcata based on drainage system. Values at nodes represent UFBOOT2 (ultrafast bootstrap) support values. Red asterisk = SH-aLRT (Shimodaira-Hasegawa likelihood ratio test) values ≥80%. Shaded regions: Green=P. sulcata from Brahmaputra basin; Yellow: P. sulcata from Ganges basin.

Figure 7: Time tree for Sisoridae based on 456 base pairs of the (mt)DNA COI gene, and derived using Reltime (Tamura et al., 2012). Numbers indicate mean divergence times (in Million years). C1 represents calibration point for divergence times between designated taxa as derived from the fossil record. A-D, represented by colored vertical bars, indicate geologic periods (designated at bottom of figure).

59 CONCLUSION

The geologic uplift of QTP played a crucial role in the evolution and diversification of rheophilic fishes of the family Nemacheilidae and Sisoridae. Bhutanese nemacheilids first diverged during the late Eocene (~49.5 Ma), with further diversification (and different species) occurring at mid-

Eocene (~40.3 Ma). This coincides with the initial uplift of the QTP (~40 Ma), an event that gave rise to many mountains and reshaped the original paleo-drainage into contemporary river networks (Chaung et al., 1998; Rowley and Currie, 2006). The most recent divergence occurred mid-Miocene (~14.3 Ma), suggesting that Bhutanese nemacheilids diversified during the initial uplift of QTP, well before the most recent orogeny at ~3.6 Ma (Zhou et al., 2016). Diversification in Sisoridae occurred relatively later and did so in two-phases. The first occurred during late

Miocene (~9.5 Ma) while the second ensued in late Pliocene (~1.9 Ma), following the third stage of the QTP uplift (~3.6 Ma) (Zhou et al., 2016).

The underlying impact of the geomorphic event on the evolutionary histories of these fishes include the evolution of a group of nemacheilid loach so similar in appearance that the boundaries between them are blurred, and which have been compiled instead into ‘species complexes’ that require further segregation and separation. In sisorids, a specialized group, the

‘glyptosternoids’, has instead evolved on a more recent trajectory than the Nemacheilidae.

These fishes and their evolutionary histories provide a suitable model as an historic baseline from which to interpret the fates of these specialized freshwater fishes in a region particularly prone to rapid climate change.

This study is an attempt to benchmark those taxonomic gaps that exist in Himalaya biodiversity by contributing novel genetic data for N=76 individuals (51 Nemacheilidae + 25

Sisoridae) within 12 species and eight genera. These data represent the initiation of a database that has been sorely neglected, within a region relatively unexplored by conservation biologists.

60 Besides the addition of putative new species, my study also catalogued genetic and taxonomic diversity in Bhutan, thus filling an important geographic and distributional gap. It also contributes to an expanding database regarding the evolutionary history of these poorly understood Asian freshwater fishes.

My research also pushes Himalayan conservation one step closer to a much-needed trans-boundary program of pro-active management and policy development. It initiates an historic baseline for specialized freshwater fishes in the region, as represented by multiple rheophilic lineages, and hopefully promotes an extension of this research across national boundaries, particularly given the vulnerability of the trans-Himalaya and its impending response to future change.

61 Reference

Chung, S.-L., Lo, C.-H., Lee, T.-Y., Zhang, Y., Xie, Y., Li, X., Wang, K.-L., & Wang, P.-L. (1998). Diachronous uplift of the Tibetan plateau starting 40?Myr ago. Nature, 394, 769-773. https://doi.org/10.1038/29511

Rowley, D. B., & Currie, B. S. (2006). Paleo-altimetry of the late Eocene to Miocene Lunpola basin basin, central Tibet. Nature, 439, 677. https:// doi.org/10.1038/nature04506