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Author: Venugopal, Parvathy Title: An integrated approach to the taxonomy of hipposiderid bats in South Asia

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An integrated approach to the taxonomy of hipposiderid bats in South Asia

Parvathy Venugopal

A dissertation submitted to the University of Bristol in accordance with the requirements for award of the degree of Doctor of Philosophy (PhD) in the Faculty of Life Sciences

School of Biological Sciences January 2020

39,380 words

Abstract

Cryptic diversity has been well documented in several bat families and particularly in the Old-World families such as the Hipposideridae and Rhinolophidae which exhibit high levels of acoustic divergence. The genus Hipposideros is the most speciose in the family Hipposideridae and is well known for its taxonomic complexity due to the presence of several morphologically cryptic lineages. This study aims to unravel the taxonomic uncertainty in two hipposiderids, Hipposideros pomona Andersen, 1918 and Hipposideros lankadiva Kelaart, 1850 from South Asia. H. pomona from southern India has recently been identified as a distinct species. Meanwhile, all specimens from northeast India and Southeast Asia have been assigned to H. gentilis Andersen, 1918. Currently, three subspecies are recognised in H. lankadiva: H. l. lankadiva (Sri Lanka), H. l. indus (peninsular India) and H. l. gyi (northeast India and Myanmar). To date, no study has reassessed the taxonomic status of these taxa using an integrated taxonomic approach throughout their geographic extent. Therefore, an integrated taxonomic approach was applied using multiple lines of evidence, namely: morphometrics, bioacoustics and molecular phylogenetics. In addition, a presence- only modelling approach (MaxEnt) was used to better understand the geographic distribution of the targeted taxa. Results showed that H. pomona is distinct from H. gentilis sensu lato based on morphometrics, bacular and molecular data and its distribution is confined to the south of peninsular India. Hence, the recent species status of H. pomona is valid. Although there is a significant variation in the size and echolocation call frequency of H. lankadiva from Sri Lanka and northeast India- Myanmar compared with bats from mainland India, the taxon exhibited moderate to low genetic divergence in both mitochondrial and nuclear datasets. Therefore, the current subspecies status is appropriate in H. lankadiva. Species distribution models predicted that H. pomona is restricted to southern areas in peninsular India, though suitable conditions exist for its presence in Sri Lanka. The range of H. gentilis s.l. potentially overlaps with that of H. pomona in some areas of peninsular India. Models predicted that the three subspecies of H. lankadiva do not overlap in range. In conclusion, the present results reiterate the importance of using integrated approaches in bat taxonomy.

Dedication

പ്രിയപ്പെട്ട വല്ല്യ, വവപ്പെയമ്മ, സൂര്യ, നീെിക്കാട്ട ....

ഞാൻ ഈ രി. എ്. ഡി. തീസിസ് നിങ്ങൾക്കായി സമർെിക്കുന്നു. ഞാൻ ഈ യാപ്ത തുടങ്ങിയവൊ എൻ്്പ്പെ കൂപ്പട ഉണ്ടായിര്ുന്നവര്ാണ് നിങ്ങൾ. എന്നാൽ ഈ അഞ്ച് വർഷങ്ങൾക്ക് ഇടക്ക്, ഒര്ു യാപ്ത വരാലുԂ വ ാദിക്കാപ്പത, ഒര്ുരിടി സ്വനഹവയാർമകൾ തന്ന് നിങ്ങൾ ഓവര്ാര്ുത്തര്ായി എപ്പന്ന വിട്ട് വരായി. എങ്കിലുԂ എനിക്കെിയാԂ അകപ്പല എവിപ്പടവയാ ഇര്ുന്ന് നിങ്ങളുԂ എനിക്കായി സവതാഷിക്കുന്നുണ്ടാവുԂ. നന്ദി വല്ല്യ! സവതԂ മകപ്പള വരാപ്പല നിെപ്പയ സ്്‌വനഹി് നിെമുള്ള കുട്ടിക്കാല ഓർമ്മകൾ തന്നതിന്. നന്ദി വവപ്പെയമ്മ! കകരിടി് കളര്ിയിൽ പ്പകാണ്ട് വിട്ടതിന്, രിപ്പന്ന പ്പരവേ എന്ന് വിളി് സ്വനഹിു കലഹിതിന്. നന്ദി സൂര്യ! കാടുകയെി ഇവൊളുԂ രഠിവാണ്ടിര്ിക്കുന്ന നിവന്നാപ്പടനിക്ക് പ്പരര്ുത്ത അസൂയ ആണ് രാെു എന്ന് നിെപ്പയ സ്വനഹവത്താപ്പട രെഞ്ഞു എൻ്്പ്പെ ആത്മാഭിമാനപ്പത്ത ഉണർത്തിയതിന്. നന്ദി നീെിക്കാട്ട! നന്നായി രഠി് സവതԂ കാലിൽ നിൽക്കാൻ എപ്പന്നന്നുԂ ഓർമിെിതിന്, മുെത് വയസ് കഴിഞ്ഞിട്ടുԂ നാട്ടുകാര്ുԂ വീട്ടുകാര്ുԂ കൂട്ടുകാര്ുԂ കലയാണപ്പത്ത രറ്റി വ ാദിവൊ 'കുവഞ്ഞ ഈ രഠിത്തԂ കഴിഞ്ഞാ നല്ല് വ ാലി കിട്ടിവല്ല്' എന്ന് മുവന്നാട്ടുള്ള എൻ്്പ്പെ ീവിതപ്പത്ത രറ്റി ഒര്ുരടി മുവന്ന ആശങ്കപ്പെട്ടതിന്.

(I would like to dedicate my PhD wholeheartedly to few wonderful persons of my life who had been there with me from the very outset of my journey, supporting me and applauding me throughout my thick and thin but too early bid goodbye to this world. Today when I am taking the final step in this journey my heart sinks mourning on your absence, but I wish to believe that you all are proudly embracing this achievement. I can't express enough of my gratitude towards you uncle, my godfather, for loving me as a daughter of yours and adding more colours to my childhood. I fall short of words when expressing my love and affection towards you Grandma, the strongest lady I have witnessed in my life. Those silly fights and warm hugs of yours are still missed!! I can't find enough words for you Grandpa. You were the one who has always motivated me to look beyond the stereotypes of the society and engage in the fruits of success. Last but not the least, dear Surya you are always remembered. My long journey towards this stage of PhD has always been intertwined with your generous and inspiring words which has always boosted and uplifted my morale).

Acknowledgements

My five-year journey, from a tiny village in southern part of India, to a doctorate degree from one of the most prestigious universities in the world is almost complete. A million thanks to my supervisor Prof. Gareth Jones for lending a hand when I stumbled, for picking me up when I struggled and for his words of wisdom when I thought I could not move anymore. I am proud to be a member of the Gareth Jones's Bat Lab. I am grateful to Prof. Paul Racey, whose kindness and timely intervention, brought me in touch with Prof. Gareth.

I am deeply indebted to Dr. Paul Bates, who was involved in the project from the beginning. His constant presence, advice and encouragement were invaluable to me. Words fail me to express my sincere gratitude to him for his support and concern. Much love to Paul and Beatrix for looking after me whenever I came to the Bowerwood House.

I am grateful to my main sponsor, the 'Commonwealth Scholarship Commission (CSC)' for the studentship and research grant that made this herculean undertaking possible. Special thanks to Dr. P. O. Nameer who introduced me to the world of bats. Thanks to Dr. Animon Illias, Dr. Praveen Karanth, Dr. Janhvi Joshi, and Dr. Felix Francis for helping me piece together a successful project proposal and CSC application.

The project would have been incomplete without my collaborators Dr. Adora Thabah, Dr. Manuel Ruedi, Dr. Paul Bates, Tharaka Kusuminda and Dr. Wipula Yapa. The best parts of my work have the footprints of their wholehearted effort. I feel proud and honoured in working with them across borders and I look forward to the same in future.

I cannot imagine finishing my project without the excellent training I received in lab work, data processing and analysis at the expert hands of Dr. Angelica Menchaca, Edmund Moody, Dr. Matt Zeale, Dr. Orly Razgour, Dr. Rachael, C. B., Dr. Rupert Collins, Dr. Stephen Cross, Dr. Tom Davis and Dr. Tom Williams. Their time, patience and willingness to share knowledge were extremely precious to me.

I would like to express my sincere thanks and appreciation to Dr. Kailash Chandra, Dr. Gourav Sharma, Dr. Venkatraman C., Dr. Kamalakannan, M. (ZSI, Kolkata), Dr. Uttam Saikia (Northeastern Regional Centre, ZSI), Rahul Khot (BNHS, Mumbai), Roberto Portela Miguez (NHM, London) and Dr. Görföl Tamás (HNHM, Budapest) for permitting me to work on the bat collections in the respective museums.

I thank Dr. Adora Thabah, Kiran Thomas, Nilantha Vishvanath, Tharaka Kusuminda Shyam Bhayya, Venkidesh, D. and Vishnu Satheesan for their immense support and dedication during the tiring field-work days in India and Sri Lanka. I also thank

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everyone who took care of me by providing a roof over my head and food on my plate, during my research travels throughout India. Harpreet Kaur, Jasmin Purushothaman, Kavitha Subin, Kumari, Mini Anil, Smrithu Mohan and Tariq Ahmed.

My entire journey would have fallen apart without the unflinching support of my parents, Rema Devi and Venugopal, R. They gave their all in every choice and decision made by me, often sacrificing themselves. Furthermore, thanks to Bhaskar Nandagopal (Nandi), my brother, for all your love and care you provided me during this journey. Thanks to Shyam, G (uncle) for believing in me and constantly encouraging me to follow my heart. No words could fully justify the support and care I received from them - loads of love to all!

I was lucky enough to be a part of such a vibrant, fun-loving ‘Batlab’ research group. Thanks to Andy, C for the motivation and funny ‘egg’ talks. Words fail me to thank Angelica for being my ever so patient ‘molecular lab guru’, ‘academic-survival trainer’ and an amazing friend. Thanks to Emily, C for constantly checking on me even when you were away. Thanks to Jack and Luke for the countless badminton games and empty teacups. Thanks to Jeremy for being a lovely friend and inspiring colleague. Thanks to Jo and Jeff for the care, support and big hugs! A huge thanks to Lia for being a very supportive, understanding, loving and caring friend to whom I could always turn to when in need. Thanks to Liz, a hardworking colleague, who I looked up to during hard times. Thanks to Matt for being a wonderful teacher and guide who opened the doors to the world of ‘R’ and MaxEnt. Thanks to Penelope for being a straightforward, honest and supportive friend. Thanks to Raphael, for the brief but evergreen funny moments (spicy food, Malayalam learning and movie!). Thanks to Sarah - ‘grandpa-kitchen fan friend- for all the little fun moments. Thanks to James, G., Lizy, T., and Roky!

I remember all my friends in LSB with utmost love and a big smile on my face. Special thanks to Jenny, Kate, Edith, Dora, Gerardo, Tom (ICC worldcup’19!), Mike, Hind, Shatha, Kelly, James C, Ashutosh and Bhavana for the sky lounge meals, pub nights, constant love and support throughout my PhD life. Thanks to Suki and Swaid for being a part of our own teasing Indian trio. Thanks to Mark for being such an emotionally supportive and caring friend who made me enjoy gig nights. Thanks to Armin for listening to my worries and boosting my confidence whenever I felt down. Thanks to Nick for being such an easy-going, reliable and supportive person on whom I could always count on. Moreover, I really appreciate him for joining and letting me go through with my crazy spontaneous plans. Thanks to Hend (running partner!) for your friendship, love and care.

The past five years were quite challenging, filled with memories and experiences, failures and learnings, bereavement and loss. I was lucky to be surrounded by a lovely

bunch of people who acted as my strong pillars - Parvathy Venugopal (not myself but my soul-sis!), Vinu Jacob, Vishnu Satheesan, Vishnu H Das, and Paul Roby. They had patiently listened to all my sorrows, frustrations, weeping, and of course to the same stories, day after day, even when being in different corners of the world. I express my heartfelt gratitude for all the love and care given by my unforgettable friends- Yesoda Bai, Raneesh, Remya, Lakshmi, Syamily, Sooraj, Venkidesh and Anoob.

Writing my thesis proved to be unimaginably stressful and exhausting, both emotionally and physically. This period unveiled a handful of kind-hearted people who offered their unconditional love and support. I don’t know how to express my gratitude and love to Jo and Jeff! My deep love and thanks to Hara who was my partner in crime! No words can describe how thankful I am to Charlotte, Sarah, Hara and Felix. I was lucky to have such amazing housemates - big hugs and love you all! I really appreciate Mark Olenik, Armin Elsler, Matt Tarnowski, Penelope Fialas and Stefano Gallini for their unconditional support when I was in need.

Last but not least, big thanks and love to those who literally created a home for me in Bristol. Big hugs and love to Thomas Ambadan and Marlyn Seby for all the good times we shared during my first year! Rahul, Varghese and Dixy (‘Duranthamzz’ family) have stuck with me through every thick and thin for the past three years. I can’t simply describe how important every one of you are to me. Thanks to all my Malayali family friends in the UK who made me feel at home.

Author’s declaration

I declare that the work in this dissertation was carried out in accordance with the requirements of the University's Regulations and Code of Practice for Research Degree Programmes and that it has not been submitted for any other academic award. Except where indicated by specific reference in the text, the work is the candidate's own work. Work done in collaboration with, or with the assistance of, others, is indicated as such. Any views expressed in the dissertation are those of the author.

Parvathy Venugopal

Signed: Date:

Table of Contents

Abstract ...... i Dedication ……………………………………………………………………………………...... ii Acknowledgments ...... iii Author’s declaration ...... vi Table of contents …………………………………………………………………………...... …….. vii List of Tables ………………………………………………………………………………...... ……… xi List of Figures ………………………………………………………………………………...... xii CHAPTER 1 General Introduction ...... 1 1.1 Biodiversity and the importance of taxonomy ...... 2 1.2 Cryptic species – hidden challenges for taxonomists and conservationists ..... 5 1.3 Cryptic diversity in bats & importance of integrative taxonomy ...... 6 1.3.1 Traditional and geometric morphometric analysis in bat taxonomy ...... 8 1.3.2 Bioacoustics in bat taxonomy ...... 9 1.3.3 Molecular techniques in bat taxonomy ...... 10 1.4 Species distribution modelling in bat research ...... 13 1.5 Bat diversity in India ...... 14 1.6 Thesis overview ………………………………………………………………...... 15 1.6.1 Objectives of the project ………………………………………………………………...... 15 1.6.2 Targeted species ………………………………………………………………...... 15 1.6.3 Thesis outline ………………………………………………………………...... 17 CHAPTER 2 Taxonomic reassessment of Hipposideros pomona Andersen, 1918 with a focus on the south Indian population ………………………………………………………………. 18 Abstract ……………………………………………………………………………………………………………… 19 2.1 Introduction ………………………………………………………………...... 20 2.1.1 Taxonomic history of H. pomona (sensu Corbet & Hill, 1992) ……………………. 21 2.1.2 Distribution ………………………………………………………………...... 22 2.1.3 Background and objectives of the study ……………………………………………………. 23 2.2 Materials and methods …………………………….…………………………………...... 24 2.2.1 Details of study specimens ……………..……………………………….….……...... 24 2.2.2 Measurements and morphometric analysis ……………………….…………………….. 24 2.2.2.1 External and skull measurements ………………………………………………………. 24 2.2.2.2 Morphometric analysis ………………………………………………………………………. 25 2.2.3 Bacular morphology …………………………………………………………...... 27 2.2.4 Echolocation call analysis ……………………………………………….…………...... 28 2.2.5 Molecular data collection and analysis ………………………………………………………. 29

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2.2.5.1 Taxon sampling …………………………………………………………………………………. 29 2.2.5.2 DNA extraction, amplification and sequencing ……………..……………………. 29 2.2.5.3 Sequence alignment and editing ……………………………………..…………...... 33 2.2.5.4 Haplotype network and genetic divergence analysis ………….……………… 34 2.2.5.5 Phylogenetic analysis …………………………………………………………...... 34 2.3 Results …………………………………………………….……………………………………………………. 35 2.3.1 External morphology ………………………………………………………………………………. 35 2.3.1.1 Test for normality and sexual dimorphism across geographic regions …. 35 2.3.1.2 Correlation among variables and Principal Component Analysis (PCA)……………………………………………………………………………………………………………………. 39 2.3.2 Craniodental analysis ………………………………………………………………………………. 41 2.3.2.1 Tests for normality and sexual dimorphism across regions ………………… 41 2.3.2.2 Correlation among variables and Principal Component Analysis (PCA) .. 44 2.3.3 Comparative analysis of H. pomona and H. gentilis s.l. ………………………………. 48 2.3.4 Bacular morphology …………………………………………………………………………………. 50 2.3.5 Echolocation call comparisons ………………………………………………………………….. 50 2.3.6 Molecular data analysis ………………………………………………………………..………….. 53 2.3.6.1 Haplotype diversity pattern and network analysis……………………………….. 53 2.3.6.2 Phylogenetic analysis ………………………………………………………………………….. 56 2.3.6.3 Concatenated mitochondrial and nuclear tree ……………………………………… 56 2.4 Discussion …………………………………………………………………………………………………….. 60 2.4.1 Variation in morphology ……………………………………………………………………………. 60 2.4.2 Variation in bacular morphology ………………………………………………………...... 61 2.4.3 Variation echolocation call frequencies …………………………………………………….. 62 2.4.4 Variation in molecular data ……………………………………………………………………….. 63 2.5 Conclusion ……………………………………………………………………………………………………. 64

CHAPTER 3 An integrated approach to the taxonomy and evolutionary history of Hipposideros lankadiva Kelaart, 1850 from the Indian subcontinent ……………………. 65

Abstract ……………………………………………………………………………………………………………… 66 3.1 Introduction ………………………………………………………………………………………………….. 67 3.1.1 Taxonomic history of H. lankadiva ……………………………………………………………. 67 3.1.2 Distribution of H. lankadiva ……………………………………………………………………… 70 3.1.3 Island rule and Insular bats ………………………………………………………………………. 71 3.1.4 Background and objectives of the study …………………………………………………… 71 3.2 Materials and methods …………………………………………………………………………………. 72 3.2.1 Details of study specimens ……………………………………………………………………….. 72 3.2.2 Study sites and sampling …………………………………………………………………………… 72 3.2.3 Measurements and morphometric analysis ………………………………………………. 74 3.2.3.1 External and skull measurements ………………………………………………………… 74 3.2.3.2 Morphometric analysis ……………………………………………………………………….. 75

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3.2.4 Bacular morphology ………………………………………………………………………………….. 76 3.2.5 Echolocation call recording and analysis …………………………………………………… 76 3.2.5.1 Statistical analysis ………………………………………………………………………………… 77 3.2.6 Molecular data collection and analysis ……………………………………………………… 78 3.2.6.1 Taxon sampling ……………………………………………………………………………………. 78 3.2.6.2 DNA extraction, amplification and sequencing …………………………………….. 78 3.2.6.3 Sequence alignment and editing …………………………………………………………… 78 3.2.6.4 Genetic divergence analysis ………………………………………………………………….. 79 3.2.6.5 Haplotype network ………………………………………………………………………………. 79 3.2.6.6 Phylogenetic analysis ……………………………………………………………………………. 79 3.3 Results ………………………………………………………………………………………………………….. 80 3.3.1 External morphology analysis ……………………………………………………………………. 80 3.3.1.1 Correlation among variables and Principal Component Analysis (PCA) …………………………………………………………………………………………………………………… 84 3.3.2 Craniodental analysis ……………………………………………………………………………….. 86 3.3.2.1 Tests for normality and sexual dimorphism across regions ………………….. 89 3.3.2.3 Correlation among variables and Principal Component Analysis (PCA) .. 89 3.3.3 Bacular morphology ………………………………………………………………………………….. 95 3.3.4 Ecolocation call analysis ……………………………………………………………………………. 96 3.3.5 Molecular data analysis …………………………………………………………………………… 100 3.3.5.1 Genetic distances ………………………………………………………………………………. 100 3.3.5.2 Haplotype diversity pattern and network analysis ………………………………. 102 3.3.5.3 Phylogenetic analysis ………………………………………………………………………… 106 3.3.5.4 Concatenated mitochondrial tree ………………………………………………………. 106 3.3.5.5 Concatenated nuclear tree ………………………………………………………………... 106 3.4 Discussion …………………………………………………………………………………………………… 109 3.4.1 Differences in morphology ……………………………………………………………………... 110 3.4.2 Differences in bacular morphology …………………………………………………………. 111 3.4.3 Differences in echolocation frequency ……………………………………………………. 111 3.4.4. Genetic structure among geographic regions …………………………………………. 113 3.5 Conclusion ………………………………………………………………………………………………….. 116

CHAPTER 4 Assessing the geographic distributions of H. pomona, H. gentilis s.l. and subspecies of H. lanakdiva using MaxEnt …………………………………………………………… 118

Abstract ……………………………………………………………………………………………………………. 119 4.1 Introduction ………………………………………………………………………………………………… 120 4.1.1 Species distribution models and their importance in bat research ………….. 120 4.1.2 Background and aims of the study ………………………………………………………….. 122 4.2 Materials and methods ………………………………………………………………………………. 122 4.2.1 species records data ……………………………………………………………………………….. 122 4.2.2 Study area and environmental variable selection …………………………………… 123

4.2.3 Modelling procedure and parameter selection ……………………………………….. 124 4.3 Results ………………………………………………………………………………………………………… 125 4.3.1 H. pomona and H. gentilis s.l. …………………………………………………………………… 125 4.3.2 H. lankadiva s.l. …………………………………………………………………………………...... 126 4.4 Discussion ………………………………………………………………………………………………….. 131 4.4.1 H. pomona and H. gentilis s.l. …………………………………………………………………. 132 4.4.2 H. lankadiva s.l. …………………………………………………………………………………...... 132 4.5 Implications and future line of work ………………………………………………………….. 134

CHAPTER 5 General Discussion …………………………………………………...... 136

5.1 Thesis overview …………………………………………………………………………………………… 137 5.2 Importance of integrated taxonomy in bat research ………………………………….. 138 5.3 Suggestions for further lines of study …………………………………………………………. 140 5.4 Scope and limitations of species distribution models for bats in developing countries …………………………………………………………………………………………………………… 142

References ……………………………………………………………………………………………………….. 144 Supplementary Material ……………………………………………………………………………………………… 170 Appendix I ……………………………………………………………………………………………………………………. 174 Appendix II ………………………………………………………………………………………………………… 178 Appendix III ……………………………………………………………………………………………………….. 179

List of Tables

Table 2.1. Details of the primers, introns targeted, and gene names used in the study. ………………………………………………………………………………………………………………….. 31 Table 2.2. Details of the bat-specific cocktail 2 (C_VF1LFt1/C_VR1LRt1) primer for the CO1 gene used to amplify a sequence length of 658 bp. This is a modified version of the CO1 cocktail 1 (Ivanova, deWaard & Hebert, 2006) including M13-tailed versions of the primers developed by Clare et al. (2007). ………………………………………………….. 32 Table 2.3. External measurements (in mm) of H. pomona from south India and H. gentilis s.l. from northeast India, Myanmar, Vietnam, Cambodia, Andaman Islands, Thailand and China from the present and previous studies. The sample size is given in brackets. …………………………………………………………………………………………………………. 37 Table 2.4. Variable loadings for the three principal components (PC1, PC2, PC3) from an analysis of external morphological characters of H. pomona and H. gentilis s.l. from different study regions. Total sample size is 45. Values in bold indicate high loadings on that particular component. Measurement acronyms are defined in the ‘Materials and methods section 2.2.2.1’. …………………………………………………………… 40 Table 2.5. Craniodental measurements (in mm) of H. pomona from south India and H. gentilis s.l. from from northeast India, Myanmar, Vietnam, Cambodia, Thailand, China and Andaman Islands from the present and previous studies. The sample size is given in brackets. ……………………………………………………………………………………………… 42 Table 2.6. Variable loadings for the three principal components (PC1, PC2, PC3) from an analysis of craniodental characters of H. pomona and H. gentilis s.l. from different study regions. Values in bold indicate high loadings on that particular component. Measurement acronyms are defined in the ‘Materials and methods section 2.2.2.1’. ………………………………………………………………………………………………………………. 46 Table 2.7. The gene name, total number of base pairs sequenced, and total number of samples sequenced for each gene ………………………………………………………………….. 53 Table 2.8. The uncorrected group mean p-distance between each region of H. pomona and H. gentilis s.l. Below the diagonal: the uncorrected group mean genetic divergence for CO1. Above the diagonal: the uncorrected group mean genetic divergence for 16s. ……………………………………………………………………………………………... 58 Table 3.1. External measurements (in mm) of H. lankadiva from different study regions. Means and standard deviations are given. The range values are in parentheses. The sample size is given in brackets of ‘sex’ column. ………………………. 82 Table 3.2. Variable loadings for the two principal components (PC1 and PC2,) from an analysis of external morphological characters of 100 individuals of H. lankadiva from different study regions. Values in bold indicate high loadings on that particular

component. Measurement acronyms are defined in ‘section 2.2.2.1’ of Chapter 2. ……………………………………………………………………………………………………………………….. 85 Table 3.3. Cranial measurements (in mm) of H. lankadiva from different study regions. Means and standard deviations are given. The range values are in parentheses. The sample size is given in brackets of ‘sex’ column. ……………………… 87 Table 3.4. Variable loadings for the three principal components (PC1, PC2) from an analysis of craniodental characters of 95 individuals of H. lankadiva from different study regions. Values in bold indicate high loadings on that particular component. The ‘-’ sign indicates the negative correlation between the variables and the components. Measurement acronyms are defined in ‘section 2.2.2.1’ of Chapter 2. ………………………………………………………………………………………………………………………… 90 Table 3.5. Standardized canonical discriminant function coefficients and eigenvalues for a stepwise DFA comparing skull morphology of 92 individuals of H. lankadiva from south India, central India, northeast India-Myanmar and Sri Lanka. …………………….. 93 Table 3.6. Stepwise DFA classification of skull morphology of H. lankadiva from south India, central India, northeast India – Myanmar and Sri Lanka with predicted group membership of original and cross-validated grouped samples. Percentage values of prediction for each group is given in brackets. …………………………………………………….. 94 Table 3.7. The forearm length (FA) and frequency of most energy (FMAXE) measurements for H. lankadiva in India, Sri Lanka (from present study) and Myanmar (Bates et al., 2015). ……………………………………………………………………………………………… 97 Table 3.8. The gene name, total number of base pairs sequenced, and total number of samples sequenced for each gene. ………………………………………………………………… 100 Table 3.9. The uncorrected group mean p -distance between each region of H. lankadiva. Below the diagonal: the uncorrected group mean genetic divergence for ND2. Above the diagonal: the uncorrected group mean genetic divergence for 16s. …………………………………………………………………………………………………………………… 102 Table 4.1. The variables included in the final model and their relative contributions (in percentage) for each taxon is given. The feature type and regularization multiplier chosen for the final model is given in the brackets for each taxon. ……………………………………………………………………………………………………………….. 128

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List of Figures

Figure 2.1. The current known distribution of H. pomona sensu stricto (green circles) and H. gentilis sensu lato (blue circles). ………………………………………………………………… 23

Figure 2.2. Correlation matrices of 12 external characters of H. pomona and H. gentilis s.l. Total sample size is 45. The correlation coefficients (r) are displayed below the diagonal. The blue and red circles on the top of the diagonal represent the positive and negative correlations respectively. The colour intensity and the size of the circles are proportional to the correlation coefficients. 1ph3mt (with a star)– abbreviation of the variable (1ph3mt/3mt X 100). …………………………..…………………… 39

Figure 2.3. Scatter plot of Principal Component Analysis based on PC1 and PC2 for (a) external measurements (n = 45) of H. pomona and H. gentilis s.l. without type specimens (b) including type specimens (n = 47) of H. pomona and H. gentilis s.l. 41

Figure 2.4. Correlation matrices of 14 cranial characters of H. pomona and H. gentilis s.l. The sample size is 109. The correlation coefficients (r) are displayed on the bottom of the diagonal. The blue and red circles on the top of the diagonal represent the positive and negative correlations respectively. The colour intensity and the size of the circles are proportional to the magnitude of the correlation coefficients. … 45

Figure 2.5. Scatter plot of Principal Component Analysis based on (A) PC1 and PC2 and (B) PC1 and PC3 for 14 craniodental measurements of H. pomona and H. gentilis s.l. The sample size is 109. ……………………………………………………………………………..……. 47

Figure 2.6. Scatter plot of Principal Component Analysis based on PC1 and PC2 for nine craniodental measurements of H. pomona and H. gentilis s.l. including type specimens. The sample size is 111. …………………………………….………………………………… 48

Figure 2.7. Photographs of H. pomona (A - BM.2003.397) and H. gentilis s.l (B. ZSIVM/ERS/348 from northeast India; C. ZSI.31ee from Myanmar) showing the variation in noseleaf structure. The white arrows indicate the anterior noseleaf. In H. pomona noseleaf is wider than H. gentilis s.l. Photographs are not to scale. ©Parvathy Venugopal. ……………………………………………………………………………………….………………… 49

Figure 2.8. Bacular morphology and size of (A) H. pomona (HZM 53.40201 from Tamil Nadu, south India) and H. gentilis s.l. [ (B) MEHHP001 from northeast India; (C) HZM 50.36836 from Myanmar; (D) HZM 14.34185 from Cambodia]. …………………………………………………………………………………………………………. 50

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2017); Andaman Islands 2, n =4 (Srinivasulu et al., 2017); Yunnan, China, n = 14 (Zhang et al., 2009); Guangdong, China, n = 34 (Zhang et al., 2009); Hainan, China, n = 40 (Zhang et al., 2009); Hong Kong (Shek & Lau, 2006), Myanmar 1, n = 22 (Struebig et al., 2005); Myanmar 2, n = 5 (Sisook, P.) pers. comm.; Lao PDR (Francis, 2008); central Thailand (Douangboubpha et al., 2010); Thailand 1, n = 33 (Hughes et al., 2010); Thailand 2, n = 38 (Douangboubpha et al., 2010); northern Vietnam, n = 4 (Abramov & Kruskop, 2012); Malaysia, n = 3 (Murray et al., 2012). ‘n’ refers to the number of bats recorded in the studies cited. …………………………………………………………………………………………………………………… 52

Figure 2.10. The median -joining haplotype network of (A) CO1 (B) 16s (C) THY (D) PRKC1 for H. pomona from south India and H. gentilis s.l. from northeast India, China, Laos and Cambodia. Circle size is proportional to haplotype frequency; the colour of the circles shows the study region and connective lines show the number of mutational steps as hatch marks. ………………………………………………………………………… 54

Figure 2.11. The concatenated mtDNA and nuDNA phylogenetic tree displaying the relationship between H. pomona from south India and H. gentilis s.l. from China (CHIHP), Laos (LaosHP), Andaman Islands (MG) and northeast India (MEHHP and LAMHP). The posterior probabilities from the Bayesian analysis and bootstrap values from the Maximum Likelihood analysis is given on each node with a ‘/’ sign. ……………………………………………………………………………………………………………….…… 59

Figure 3.1. The current known distribution of currently recognised subspecies of H. lankadiva: H. l. gyi (blue circles), H. l. indus (green circles) and H. l. lankadiva (red circles). The type specimen localities (approx.) are also given: H. l. gyi (green triangle), H. l. lankadiva (blue cross), H. indus (red square), H. i. mixtus (blue triangle), H. schistaceus (blue circle) and H. i. unitus (red triangle). The red query indicates the taxonomic uncertainty of H. lankadiva material from West Bengal and Bangladesh as in Bates et al. (2015). The locations based on specimen data, either from the literature, online databases such as GBIF and BOLD or collected personally. ………………………………………………………………………………………………………….. 70

Figure 3.2. Map showing the selected study regions from where either acoustic samples and/or molecular samples collected during the study. The study region names abbreviated as folows: SL – Sri Lanka, WI – west India, CI – central India, SI – south India and NEI-Mya – northeast India and Myanmar. …………………………………… 74

Figure 3.3. Photographs of H. lankadiva from (A) Sri Lanka, (B) west India and (c) central India showing overall similarity in appearance, variations in fur color, noseleaf and supplementary leaflets. The black arrow indicates the fourth supplementary leaflet. Photos by Tharaka Kusuminda (Sri Lanka) and Parvathy Venugopal (west and central India). Photographs are not to scale. ………………………………………..………………. 81

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Figure 3.4. Scatter plot of Principal Component Analysis based on PC1 and PC2 for external measurements of 100 individuals of H. lankadiva from different study regions. ……………………………………………………………………………………………………………… 85

Figure 3.5. Skulls of five Hipposideros lankadiva from different study regions. A: Gampaha, Sri Lanka, HZM.8.30232, ♂; B: Kachin State, Myanmar, HZM.10.40222 (Holotype of H. l. gyi - OMT110105.1, ♂); C: Meghalaya, northeast India, PV2017.05.09.1, ♂; D: Raisen Fort, Madhya Pradesh, central India, PV2017.05.25.1, ♂; E: Lamgao Buddhist Caves, Goa, west India, PV2017.02.27.1, ♀. …………………….86

Figure 3.6. Scatter plot of Principal Component Analysis based on PC1 and PC2 for 14 craniodental measurements of 95 individuals of H. lankadiva. The red ellipse is for indicative purposes only which encloses the larger samples of H. lanakdiva from Sri Lanka, Myanmar, northeast India (NE India) and West Bengal. …………………………… 91

Figure 3.7. Scatter plot of Principal Component Analysis based on PC1 and PC2 for seven craniodental measurements of 101 individuals of H. lankadiva including type specimens. The red circles indicate the type specimens. ……………………………………… 92

Figure 3.8. Scatter plot of discriminant function analysis (DFA) based on Function 1 and 2 on 10 skull characters of H. lankadiva from different study regions. The numbers in red indicate each region as follows: 1 - south India, 2 - central India, 3 - northeast India – Myanmar (NEI-Myanmar,) and 4 - Sri Lanka (4). The group centroids for each region are marked as yellow squares. ………………….…………………………………. 94

Figure 3.9. Bacular morphology and size of H. lankadiva from (A) Sri Lanka [HZM.8.30232, Bates et al., 2015], (B) Myanmar [HZM.10.40222, Bates et al., 2015] (C) Northeast India [ZSI 20031] (D) West India (E) South India [ZSI 20196] (F) Central India [ZSI 25807]. Scale 1mm. ……………………………………………………………………………… 95

Figure 3.10. Frequency distributions of FMAXE of H. lankadiva across different study regions. ………………………………………………………………………………………………………………. 96

Figure 3.11. Spectrograms of representative echolocation calls of H. lankadiva from different study regions. ………………………………………………………………………..……………… 97

Figure 3.12. Boxplot showing the frequency variation in adults and juvenile bats of H. lankadiva from Sri Lanka. The bold line shows the median frequency with the minimum, first quartile (Q1), third quartile (Q3), and maximum values. ………………. 98

Figure 3.13. Distribution of echolocation call frequencies (FMAXE) and lengths of forearm for all H. lankadiva bats from different study regions for which echolocation call data were available. …………………………………………………………………………….………… 99

Figure 3.14. Boxplot showing the echolocation call frequency variation in female (F) and male (M) bats of H. lankadiva. The bold line shows the median frequency with

the minimum, first quartile (Q1), third quartile (Q3), and maximum values. ……………………………………………………………………………………………………..…………. 99

Figure 3.15. Median-joining haplotype networks of H. lankadiva from different study regions based on mitochondrial (A) ND2 (B) 16s and phased nuclear (C) STAT5A (D) PRKC1 and (E) THY. Circle size is proportional to haplotype frequency and the circle colour denotes each study region; connective lines show the number of mutational steps as hatch marks. ……………………………………………………………………………………..…. 104

Figure 3.16. The concatenated mtDNA phylogenetic tree displaying the relationship between H. lankadiva from different geographic regions. The bootstrap values >50 are presented on corresponding nodes. NEI – Myanmar = northeast India and Myanmar. …………………………………………………………………………………………….…………… 107

Figure 3.17. The Maximum Likelihood tree of concatenated nulcear intron displaying the relationship between H. lankadiva from different geographic regions. The bootstrap values >50 are presented on corresponding nodes. …………….…………….. 108

Figure 4.1. Habitat suitability areas predicted using MaxEnt for H. pomona. Suitable areas are shaded in red and unsuitable areas are in grey. The golden circles indicate the current known species occurrence records. …………………………………………………. 129

Figure 4.2. Habitat suitability areas predicted using MaxEnt for H. gentilis s.l. Suitable areas are shaded in red and unsuitable areas are in grey. The golden circles indicate the current known species occurrence records. ……………………………………………….... 129

Figure 4.3. Habitat suitability areas predicted using MaxEnt for H. l. lankadiva. Suitable areas are shaded in red and unsuitable areas are in grey. The golden circles indicate the current known species occurrence records. ……………………………………. 130

Figure 4.4. Habitat suitability areas predicted using MaxEnt for H. l. indus. Suitable areas are shaded in red and unsuitable areas are in grey. The golden circles indicate the current known species occurrence records. ………………………………………..………. 130

Figure 4.5. Habitat suitability areas predicted using MaxEnt for H. lankdadiva gyi. Suitable areas are shaded in red and unsuitable areas are in grey. The golden circles indicate the current known species occurrence records. ……………………………………. 131

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CHAPTER 1

General Introduction

CHAPTER 1

1.1 Biodiversity and the importance of taxonomy

One of the most distinctive aspects of our planet is the diversity of life present. Biodiversity is not only a list of the diversity or distributions of organisms, but also includes its short and long-term temporal variability, including diversity at the genetic level (Sigwart et al., 2018). A fundamental problem in biology is to determine the number of distinct life forms and/or to what extend we can identify them correctly. In order to understand the extent of biological diversity and how organisms are related to one another, we need to determine their evolutionary histories and classify them. This work is woven into many disciplines including ecology, environmental science, conservation, palaeontology, phylogenetics, evolutionary and developmental biology; but, the basics of how we measure species diversity depends on taxonomy and systematics (Sigwart et al., 2018).

Taxonomy is the ‘science of naming (nomenclature), describing and classifying organisms including all plants, animals and microorganisms of the world’ (Secretariat of Convention on Biological Diversity, 2007). This helps us to determine whether different individuals belong to the same taxa or not. Thus, the correct and accurate delineation of species boundaries and identification of species are crucial to the description of biodiversity (Dayrat, 2005). Moreover, we can use information on biodiversity for our own use e.g. to produce new varieties or hybrids, and for the conservation and management of the natural heritage. Since taxonomy is a key tool for understanding biodiversity, the information that it provides is essential in framing and developing conservation policies and management. This would also help us to draw stronger public support towards conservation efforts. According to the ‘Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) Global Assessment Report’ on Biodiversity and Ecosystem Services (2019) around one million species are under the threat of extinction. Therefore, a robust taxonomic model is inevitable in mitigating biodiversity loss otherwise the impacts on global biodiversity will be irrevocable (Thomson et al., 2018).

The need and importance of taxonomy for global conservation is unequivocal even after >250 years of Linnaean taxonomic classification system and more than 1.2

CHAPTER 1 million species having been catalogued (Mora et al., 2011). Some 86% of species on earth and 91% of species in the ocean still await description (Mora et al., 2011). Knowing the number of species on earth is one of the fundamental but elusive questions in biology because of a range of constraints. One of the constraints that has challenged the growth of taxonomy is the presence of cryptic species (two or more distinct species that are morphologically indistinguishable - detailed in 1.2). The earlier conventional biodiversity assessments were based only on morphological traits, and species were usually described according to human visual perception. Therefore, cryptic species almost always represented undiscovered biodiversity.

Here, a short introduction of the main species concepts is given before defining cryptic species. One of the fundamental units in almost all fields of biology is a species (Mayr, 1982; de Queiroz, 2007) and hence defining a species is always important in biodiversity science (Mayden, 1997; Fiser, Robinson & Malard, 2018). Defining and delimiting a species has been a controversial issue for over 70 years among different subgroups of biologists. As a result, many species concepts have been proposed to determine the variation within and the delimitation between, species. A number of these concepts are incompatible leading to different conclusions regarding the boundaries and numbers of species (de Queiroz, 2007; Ereshefsky, 2007; Aldhebiani, 2018). Therefore, four prominent species concepts in biology are considered here:

(a) Biological Species Concept (BSC) proposed by Mayr (1942) is the most widely applied and accepted species concept in biology. This defines species as a group of interbreeding natural populations that is reproductively isolated from other populations. Even though, the BSC is very simple and apparent, it continues to be criticised. The two main disadvantages of the BSC are its inapplicability to asexual organisms (Mishler & Theriot, 2000; Wheeler & Platnick, 2000) and the difficulty of applying it to allopatric (geographically isolated) populations (Cronquist, 1978; Stace, 1989; Mallet, 1995). Another criticism of the BSC is the practical impossibility of ascertaining reproductive isolation between large numbers of populations in the wild (Balakrishnan, 2005).

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(b) Morphological Species Concept (MSC) is based on shared morphological characters that can differentiate individuals of same species from others. According to this concept, “species are the smallest groups that are consistently and persistently distinct, and distinguishable by ordinary means” (Cronquist, 1978; Wilkins, 2009). Under the MSC concept, morphological differences are used as a criterion to assign individuals to specific species. Therefore, this is more useful in describing fossil taxa and can be applied to both sexual and asexual organisms (Aldhebiani, 2018). However, the MSC is inadequate for a study of species because (i) of its failure in delimiting morphologically similar, genetically distinct cryptic species, (ii) it is inapplicable in species that have different morphological forms due to individual genetic variation or life histories such as females, males or immatures (Wheeler & Meier, 2000).

(c) The Ecological Species Concept is based on the niche occupation of a species. A species is defined as “a lineage (or a closely related set of lineages) which occupies an adaptive zone minimally different from that of any other lineage in its range and which evolves separately from all lineages outside its range’’ (Van Valen, 1976). A lineage is a clone or an ancestral-descendent sequence of populations. An adaptive zone is some part of the resource space together with whatever predation and parasitism occurs on the group considered (Van Valen, 1971). The ESC is not workable, mainly because of two reasons (Wheeler & Meier, 2000). Firstly, it cannot be applicable in all most all widespread species whose local populations can exist in different niches. Secondly, sympatric species that share the same ecological niche are unable to be defined under this concept.

(d) The Phylogenetic Species Concept (PSC) simply defines species as a basal monophyletic lineage (Mishler & Brandon, 1987) of a small group of (sexual) populations or (asexual) lineages diagnosable by unique combination of characters (Wheeler & Platnick, 2000). A monophyletic lineage only contains all individuals that share a common ancestor. Although the PSC overcomes the drawbacks of the BSC with its applicability on both asexual taxa and allotropic populations, it has also been criticised. One of the major disadvantages with PSC is that it fails to provide precise species boundaries as monophyly can exist at any

CHAPTER 1

level within the phylogeny. Another problem with the PSC is the rare existence of a single, true phylogeny for a taxon (Aldhebiani, 2018). Different phylogenies are produced by different genes (e.g. mitochondrial versus nuclear). Therefore, the ability of mitochondrial DNA sequences to accurately reflect species boundaries and species histories is being increasingly questioned (Shaw, 2002; Balakrishnan, 2005; Dool et al., 2017). Moreover, it would be time-consuming and expensive to delimit all species in this fashion in species-rich but financially constrained areas such as many the tropical regions (Balakrishnan, 2005).

1.2 Cryptic species – hidden challenges for taxonomists and conservationists

Cryptic species are species, the diagnostic features of which are not easily perceived (Mayr, 1970) but are biologically and phylogenetically different and are erroneously classified and or hidden under one species name (Bickford et al., 2007). Cryptic species, like non-cryptic species are reproductively isolated. Researchers may be blind towards cryptic species and fail to recognize them as different species. Thus, these hidden species have become a taxonomic and evolutionary enigma in biodiversity science (Struck et al., 2018). It is now apparent that cryptic species are a common evolutionary phenomenon, occur in most faunal groups from parasites to giraffes and thereby contribute significantly to global species richness (Fennessy et al., 2016; Karanovic, Djurakic & Eberhard, 2016; Perez-Ponce de Leon & Poulin, 2016; Delić et al., 2017; Winter, Fennessy & Janke, 2018).

Cryptic species have been recognised for over 300 years with an early report in 1718 by William Derham from an avian genus called Phylloscopus (Winker, 2005; Bickford et al., 2007; Sun et al., 2009; Struck et al., 2018). Pseudo-cryptic, semicryptic, hyper- cryptic and sibling have also been proposed and used in different context based on the small differences detected between morphologically identical species. This created debate among several researchers regarding the definition as well as the biological relevance of cryptic species. Among these terms, ‘sibling’ was considered and used synonymously with cryptic species for a long time by many researchers in their literature (Jones & Parijs, 1993; Saez & Lozano, 2005). Later, in a review of

CHAPTER 1 cryptic species, Bickford et al. (2007) followed the view of Steyskal, (1972) and Knowlton (1986, 1993) and proposed the separate use of sibling species from cryptic species. The authors differentiated sibling species from cryptic species by specifying that ‘sibling’ indicates genetic closeness with a more recent common ancestry than cryptic. Studies have shown that cryptic species are not necessarily closely related (e.g. Brandt's bat (Myotis brandtii) and the whiskered bat (M. mystacinus) - Ruedi & Mayer, 2001). Since it is very difficult to identify such species using morphological characters (Chattopadhyay et al., 2012), molecular techniques have been increasingly used over the past two decades to identify cryptic species (Avise, 2004; Bickford et al., 2007; Nygren, 2014) in various taxa from different habitats (e.g. tropical butterflies - Hebert et al., 2004; fresh water fish - Feulner et al., 2006; Arctic plants - Grundt et al., 2006). Even though molecular techniques have accelerated the discovery of cryptic species, taxonomic incompleteness is still an on-going problem in biodiversity research. Cryptic species may also occupy different ecological niches and hence need different conservation management methods. For example, the bat Pipistrellus pygmaeus is much more specialised for feeding in riparian habitats than the more generalist but morphologically similar P. pipistrellus (Davidson-Watts, Walls & Jones, 2006).

Since cryptic species represent undiscovered biodiversity, their identification increases our knowledge of species diversity and conservation (Bickford et al., 2007; Sun et al., 2009). The majority of known cryptic species are undescribed or un-named and unavailable to biodiversity management or conservation practices. Moreover, ambiguity on the distribution of cryptic species exists across the biogeographical regions as well as across taxa (Pfenninger & Schwenk, 2007). To help formulate more efficient conservation management policies, it is essential to understand true diversity, including the prevalence of cryptic species (Chattopadhyay et al., 2012; Fiser, Robinson & Malard, 2018).

1.3 Cryptic diversity in bats & Importance of integrative taxonomy

The order Chiroptera comprises the world’s second - highest species-rich order of mammals. Bats are the only mammals capable of powered flight and show several

CHAPTER 1 other specialised traits such as echolocation (Russo, Ancillotto & Jones, 2017), extreme longevity (Foley et al., 2018) and hibernation in temperate regions (Besler & Broders, 2019). Having a cosmopolitan distribution, bats account for nearly a fifth of total mammal diversity with 1406 currently recognised species in 21 families (Frick, Kingston & Flanders, 2019; Simmons & Cirranello, 2019). Bats play major ecological roles as pollinators, seed dispersers and insect predators and thereby undertake a range of ecosystem services including enhancing soil fertility and nutrient distribution, especially in tropical ecosystems (Kunz et al., 2011; Williams- Guillén et al., 2016; Sheherazade, Ober & Tsang, 2019; Tremlett et al., 2019). These flying mammals are increasingly used as bioindicators to assess the biodiversity potential of areas and to monitor environmental changes (Jones et al., 2009; Pedersen et al., 2012; Wordley et al., 2014; Russo & Jones, 2015). Thus, bats contribute to the ecosystem health in terms of both ecological and economic values (Boyles et al., 2011; Wanger et al., 2014; Tremlett et al., 2019).

In insectivorous bats audition is a dominant sense and echolocating bats typically emit ultrasonic sounds to which humans are deaf (Metzner & Müller, 2016). Acoustic differences among bats species may result in reproductive isolation yet may have long been overlooked by humans prior to the development of affordable ultrasound detectors and analysis methods. Echolocation, together with flight, makes bats stand out from other mammals and these traits are important in determining niche utilization. Therefore, differences in flight and echolocation characteristics lead to differences in foraging strategies and habitat selection (Norberg & Rayner, 1987; Schnitzler & Kalko, 2001; Denzinger & Schnitzler, 2013). Species that use the same ecological niches, echolocation and flight can evolve via parallel and convergent evolution (Jones & Teeling, 2006; Jones & Holderied, 2007). This can make taxonomic assessment difficult, especially among species who are evolutionarily related and ecologically similar. Therefore, cryptic diversity in echolocating bats has been a topic of great interest (Mayer & von Helversen, 2001; Jones & Barlow, 2004; Mayer, Dietz, & Kiefer, 2007). Many cryptic species of bats from different regions have been identified especially with the advent of molecular techniques (Thabah et al., 2006; Bates et al., 2007; Sun et al., 2008; Thong et al., 2012; Koubinova et al., 2013; Filippi-

CHAPTER 1

Codaccioni et al., 2018). However, either the phylogenetic relationships and genus and/or species level delimitation within newly described taxa (e.g. phyllostomids) are still much debated among researchers (Solari, Sotero-Caio & Baker, 2019). As a result, the status of many taxa is not yet well established (e.g. genus Molossus, Gager et al., 2016). Therefore, the modern taxonomic approach has been started to investigate how to incorporate the molecular data into other multidisciplinary approaches in order to best define the status of a taxon. Thus, the idea of ‘integrative taxonomy’ (Padial et al., 2010) has been accepted and advocated as a holistic approach towards stable classifications and understanding the underlying evolutionary processes (Fiser, Robinson & Malard, 2018; Solari, Sotero-Caio & Baker, 2019).

With reference to bats, integrative taxonomy uses multiple lines of evidence including molecular, morphological, morphometric, acoustic, karyotype, behavioural, ecological and any other dataset which can facilitate discrimination among taxa (Clare et al., 2013; Csorba et al., 2014; Tu et al., 2017; Görföl & Csorba, 2018; Taylor et al., 2018; Srinivasulu et al., 2019). The use of integrative taxonomy has boosted the discovery of cryptic species in bats for almost two decades and thus the total number of bat species. For instance, in the year 2000 Vaughan et al. reported a total of 925 bats in the world whereas according to a very recent taxonomic and geographic database by Simmons & Cirranello (2019) the number is 1406. Between 2005 and 2014, nearly 200 bat species were either newly described or raised from synonymy from every part of the world (Simmons, 2005; Fenton & Simmons, 2014; Tsang et al., 2016). Thus, the age of discovery is still ongoing for bats since the year 2000, with an increase of almost 34.2% in the total number of bats in the world.

1.3.1 Traditional and geometric morphometric analysis in bat taxonomy

Bats have been identified and described based on their external and cranial morphology, but research has shown that in many cases this evidence is inadequate to discriminate between cryptic species. The size and shape of the baculum (os penis) differ in many cryptic taxa of bats (Rakotondramanana & Goodman, 2017), perhaps

CHAPTER 1 because differences in genital morphology assist in reproductive isolation (Hill & Harrison, 1987). Nevertheless, it is difficult to find morphologically distinctive characters in cryptic species. Therefore, cryptic species can easily be overlooked in the field leaving the practical value of such diagnoses in question. Several studies have shown how important and fundamental it is to incorporate multiple sets of information along with the traditional morphological data (Pavan & Marroig, 2016).

The use of geometric morphometrics (GM) has become popular in bat taxonomic studies. This method is effective in identifying morphological overlap between individuals when compared to the traditional morphological methods (Evin et al., 2008; Schweiger, 2017; Shi, Westeen & Rabosky, 2018). GM is a multivariate shape analysis technique which allows the separation of both size and shape components of morphometric variation. The key advantage of GM over traditional methods is that since the data preserve the original biological shape of the specimen as landmark coordinates, outline curves and surfaces, researchers can easily analyse and compare the global shape trends. In addition to this, the variation in the position of anatomical structures relative to one another can be quantified using GM analysis which might not be captured using linear morphometric techniques (Cooke & Terhune, 2015). The GM approach has proved to be a powerful tool in discriminating closely related species in many bat families such as Molossidae (Richards et al., 2012), Vespertilionidae (Evin et al., 2008; Sztencel-Jabłonka, Jones & Bogdanowicz, 2009), Rhinolophidae (Taylor et al., 2012; Schmieder et al., 2015), Hipposideridae (Wilson et al., 2016), Phyllostomidae (Hedrick & Dumont, 2018) and Emballonuridae (Vivas- Toro & Murillo-García, 2019) and by detecting shape changes.

1.3.2 Bioacoustics in bat taxonomy

Laryngeal echolocation is one of the unique characteristics shared by most bats, which makes them an unusual mammalian group (Fenton, 1984). Bats use echolocation to orientate, forage and communicate socially (Fenton, 2013). The remarkable acoustic diversity in their echolocation calls, ranging between 8 to >200 kHz, is expressed at both interspecific and intraspecific levels (Altringham, 2011). However, echolocation calls were not used as a species identification criterion until

CHAPTER 1 close to the end of the 20st century (López-Baucells et al., 2018). This was mainly due to the unawareness of bat bioacoustics as these flying mammals operate in the dark in an acoustic world which uses frequencies often far beyond the human hearing range (i.e. >20 kHz). Thanks to the new tools and digital technology, in the last two decades, acoustic identification of bats has advanced at an unprecedented speed (López-Baucells et al., 2018). Research has had expanded our knowledge of the echolocation calls of many species, both within and between species (Russo, Ancillotto & Jones, 2017). This has again been helping scientists discover new species worldwide.

Acoustic divergence has been identified as one of the important triggers for ecological speciation in cryptic vertebrates (Wilkins, Seddon & Safran, 2013). Both adaptive (ecological or sexual selection) and non-adaptive (genetic drift) processes can produce divergence in acoustic signals. Cryptic speciation driven by acoustic differentiation is therefore widespread in echolocating insectivorous bats. Several studies have shown acoustic divergence in both sympatric and allopatric bat species as a response to environmental or climate-driven selection pressures, coupled with geographic or genetic isolation (Russo, Ancillotto & Jones, 2017; Tu et al., 2017; López-Baucells et al., 2018).

1.3.3 Molecular techniques in bat taxonomy

The invention, development and use of molecular techniques and genetic screening approaches such as DNA barcoding has boosted the detection of cryptic species in all major groups of terrestrial (e.g. plants – Shneyer & Kotseruba, 2015; rodents – Rivera et al., 2018; butterflies – Rosser et al., 2019; birds – Taylor et al., 2019) and aquatic organisms (e.g. amphipods - Fiser, Robinson & Malard, 2018) across different biogeographic regions (Pfenninger & Schwenk, 2007). The same trend is also apparent in bats. For instance, Mayer, Dietz & Kiefer (2007) reported a 50% increase (from 37 to 54) in the preliminary total of bats from the Western Palaearctic region mainly using genetic data combined with phenotypic information. Similarly, 35 new species were described from several families of bats in the Afro-Malagasy region between 2009 and 2017 (Taylor et al., 2018). This highlights the importance of

CHAPTER 1 incorporating molecular information into taxonomic studies for the fast and accurate identification and differentiation of species which have minimal morphological differences (Clare et al., 2007; Clare et al., 2011; Dool et al., 2016; Mota et al., 2018). Indeed, molecular methods can also replace the unnecessary collection of organisms (Corthals et al., 2015; Raupach et al., 2016) and thus avoid the myriad of problems and delay related to environmental licensing and ethical concerns (Wilson et al., 2014; Russo et al., 2017).

From the beginning, uniparentally inherited mitochondrial DNA (mtDNA) markers, such as mitochondrial cytochrome oxidase subunit I gene (COI - the DNA Barcode region in animals), cytochrome oxidase b (cytb), and NADH dehydrogenase subunit 2 (ND2), have been selected and widely used in taxonomic or phylogenetic studies (Tahir & Akhtar, 2016; Riesle‐Sbarbaro et al., 2018). This is because the relatively rapid mutation rate of these mitochondrial genes can reflect the divergence of reproductively isolated populations (Morgan-Richards et al., 2017). The use of these mitochondrial markers has also been facilitated by the availability of ‘universal’ primers that amplify DNA across a wide range of taxa, their ease of amplification, maternal inheritance and presumed neutral status (Dool et al., 2016). Furthermore, the wide and extensive use of mtDNA markers and publicly accessible databases like GenBank and the Barcode of Life (BoLD) have opened new opportunities for comparative studies.

Many studies have illustrated that although cryptic species can be identified among similar or identical looking individuals from mtDNA difference, the reproductive isolation in those individuals remains undetected or unknown (Miller et al., 2012; Dool et al., 2016). For example, in Western Europe, the European Kuhl's Pipistrelles (Pipistrellus kuhlii) exhibited two deeply divergent mitochondrial lineages that were thought to be a representative of cryptic species. However, a study by Andriollo, Naciri & Ruedi (2015) using both mitochondrial and nuclear genes revealed that the divergent mtDNA lineages could have evolved in allopatry and are part of single biological species as they share similar nuclear genotypes. There are several reasons for not considering the deeply divergent mtDNA only as a criterion for the existence of cryptic species. Different genetic markers and genomes evolve at different rates,

CHAPTER 1 reflecting distinct parts of the evolutionary history of an organism (Hinojosa et al., 2019). Several biological processes like hybridisation followed by introgression of mitochondrial genome, incomplete lineage sorting, direct or indirect selection acting on mitochondrial genes, can generate discordance between mitochondrial and nuclear phylogenies (Thielsch et al., 2017; Hinojosa et al., 2019). Therefore, care is needed when using mtDNA divergence as evidence for reproductive isolation since it does not tell us necessarily the true species status of individuals. In order to overcome the limitations of mtDNA markers in taxonomic and phylogenetic studies, biparentally inherited nuclear markers such as microsatellites and nuclear introns, have been used as viable genetic material in combination with mtDNA over the last decade (Filippi-Codaccioni et al., 2018). A study by Dool et al. (2016) warned against the indiscriminate use of mtDNA in taxonomic or phylogenetic studies of recently diverged taxa, especially in bats, and recommended the use of two or more nuclear loci in addition to mtDNA markers to recover a reliable species tree. Thus, the combination of different markers from both mitochondrial and nuclear genomes may help to assess the gene flow between putative species as well as to understand about the underlying biological processes.

With the recent advent of high-throughput Next Generation Sequencing (NGS) technologies, the accessibility to genomic information has transformed dramatically (Thorell et al., 2019). Nowadays, researchers can sequence and assemble genomes of their interested species and generate genome-wide DNA sequence data from many individuals simultaneously (Larsen & Matocq, 2019). This has helped species delimitation using molecular data more active and accurate (Flot, 2015; Wu et al., 2018). Though sequencing costs are rapidly declining, for many projects and/or laboratories, it is still unaffordable to sequence the whole genome of large number of organisms (Luca et al., 2011; Larsen & Matocq, 2019). Reduced-representation sequencing (RRS) approaches have developed as a cost-effective alternative to whole-genome sequencing and they focus on sequencing a subset of genome which may informative for specific research questions (Baird et al., 2008; Wright et al., 2019). Restriction site-associated DNA sequencing (RADseq, Baird et al., 2008) is an RRS approach which uses for individual identification, population structure, and

CHAPTER 1 phylogenetic studies in recently diverged taxa (Peterson et al., 2012; Lemmon & Lemmon, 2013; Ree & Hipp, 2015; Wu et al., 2018). Thus, phylogenetic analyses of genome-wide data generated via RADseq approach revealed its greater resolution power over mtDNA markers and its suitability to resolve cryptic species complexes (e.g. cichlids – Wagner et al., 2013; swordtails – Jones et al., 2013; octopuses – Amor et al., 2019). Although, higher-order evolutionary relationships in the order Chiroptera have been determined using datasets of bat genomes (Tsagkogeorga et al., 2013; Hawkins et al., 2019), techniques such as RADseq are yet to be explored for species-level taxonomy in bats. A novel initiative called “Bat1K” has started with the aim of sequencing the genomes of all living bats (Teeling et al., 2018) and hopefully, this will bring step changes to the future of bat taxonomy in coming years.

1.4 Species distribution modelling in bat research

Mapping the spatial distribution of species is an important aspect of conservation biology contributing to both management practices as well as ecosystem analysis (Rebelo & Jones, 2010; Razgour, Hanmer & Jones, 2011; Bellamy, Scott & Altringham, 2013). Species distribution models (SDMs) have been mainly used to study broad scale patterns of distribution (Razgour, Hanmer & Jones, 2011) by relating presence or abundance of species to eco-geographic variables or environmental predictors (Elith et al., 2006). The eco-geographic variables that were deemed to be ecologically relevant are often based on prior knowledge of bat biology and ecology. Many bat SDMs have stressed the importance of water bodies, land cover and carbonate rock deposition as affecting distribution patterns (Hahn et al., 2014). Presence-only modelling seems to be promising and relevant in bat studies because of the difficulty in conducting intensive surveys, identification problems, and the nocturnal and elusive behaviour of bats, that often occupy expansive home ranges (Greaves, Mathieu & Seddon, 2006; Rebelo & Jones, 2010; Razgour, Hanmer & Jones, 2011). MaxEnt (Phillips, Dudík & Schapire, 2004) has emerged as a robust modelling tool whose predictability has also been tested using limited presence data, as is the case for many cryptic species (Sattler et al., 2007; Razgour, Hanmer & Jones, 2011; Rutishauser et al., 2012; Santos et al., 2014).

CHAPTER 1

1.5 Bat diversity in India

Bats are been well documented and researched in temperate zones (Kingston et al., 2001; Sun et al., 2009; Raghuram, Jain & Balakrishnan, 2014). Bats from many tropical areas are still awaiting research on vital topics such as species diversity, distribution and habitat use. One classical example concerns Indian bats. Bats account for about 25% of the known mammals in India (Srinivasulu & Srinivasulu, 2001). Despite this, bats in India have received little study in terms of their distribution, taxonomy and ecology. Specifically, there has been little work to integrate behavioural, morphological and molecular data to better understand their phylogenetic relationships.

To accurately assess the number of species both locally and globally, and to set conservation priorities, it is essential to identify and describe cryptic diversity (Murray et al., 2012), especially since cryptic species often show distinct patterns of habitat use (Davidson-Watts, Walls & Jones, 2006; Nicholls & Racey, 2006; Russo, Jones & Arlettaz, 2007). Recent studies of bats in some parts of India (Chattopadhyay et al., 2012; Raghuram, Jain, & Balakrishnan, 2014; Wordley et al., 2014) have emphasised the lack of accurate distributional data, as well as insufficient taxonomic knowledge of Indian bats, especially species in the families Rhinolophidae and Hipposideridae. Chattopadhyay et al. (2012) highlighted the importance and value of combining genetics with behavioural (mainly acoustic) and morphological measurements in the discovery of cryptic bats, as cryptic species of rhinolophid and hipposiderid bats typically use different frequencies of echolocation call.

Globally, a total of 90 species in seven extant genera (Anthops, Asellia, Aselliscus, Coelops, Doryrhina, Hipposideros, Macronycteris – Simmons & Cirranello, 2019) have been reported from the family Hipposideridae. Among that, India harbours 13 species (Bates & Harrison, 1997; Srinivasulu & Srinivasulu, 2001) and they are: Coelops frithii, Hipposideros ater, H. cineraceus, H. durgadasi, H. fulvus, H. pomona, H. hypophyllus, H. galeritus, H. speoris, H. larvatus, H. armiger, H. lankadiva, and H. diadema. Since these species are rarely studied in India it is possible that many cryptic species are present, and that our estimation of taxonomic biodiversity is

CHAPTER 1 currently lower than it should be. Therefore, studies are urgently needed to assess the distribution and diversity of bats in India. In this study, the presence of cryptic diversity in two hipposiderid taxa is investigated using an integrative taxonomic approach.

1.6 Thesis overview

1.6.1 Objectives of the project

This study aims to:

1. Investigate and re-evaluate the species status of Hipposideros pomona Andersen, 1918 from south India through an integrated taxonomic approach using traditional morphometrics, bioacoustics and molecular techniques.

2. Review the taxonomy of currently recognised subspecies of Hipposideros lankadiva Kelaart, 1850 including evidence from morphometrics, acoustic and genetic data collected from throughout its range. The study will evaluate whether cryptic species are present.

3. Apply MaxEnt modelling to understand the geographic distribution of H. pomona and H. lankadiva as well as to determine if there is any overlap between the species and/or subspecies ranges.

1.6.2 Targeted species

A brief background about the targeted species in this study is given here:

(a) Hipposideros pomona Andersen, 1918

Until very recently, the H. pomona was considered as a species complex with three subspecies: Hipposideros pomona pomona, Hipposideros pomona gentilis and Hipposideros pomona sinensis. However, Srinivasulu and Srinivasulu (2018) assigned distinct species status for populations from south India as H. pomona and northeast Indian and Southeast Asian populations as Hiposideros gentilis as suggested by Douangboubpha et al. (2010b). Currently, H. gentilis sensu lato comprised of two taxa: H. p. gentilis and H. p. sinensis Douangboubpha et al. (2010b). Srinivasulu and

CHAPTER 1

Srinivasulu (2018) separated H. pomona from H. gentilis s.l. mainly based on morphometric and bacular characters by examining 10 specimens. The study was published during the progress of my research and it did not provide any molecular evidence in support of the findings. Therefore, this study tries to validate the species status of H. pomona from south India through an integrated taxonomic approach.

(b) Hipposideros lankadiva Kelaart, 1850

H. lankadiva is confined only to a few localities in India, Sri Lanka, Bangladesh and Myanmar (Bates et al., 2015). According to Bates and Harrison (1997), the specimens from India are referred to Hipposideros lankadiva indus and are relatively small when compared to the specimens described from Sri Lanka which are referred to Hipposideros lankadiva lankadiva. A new subspecies, Hipposideros lankadiva gyi has also been described recently by Bates et al. (2015) from Myanmar, with individuals comparable in morphometrics to the Sri Lankan specimens.

H. lankadiva exhibits considerable variation in body size and skull size. Taylor et al. (2012) described four new bat species in the Rhinolophus hildebrandtii complex whose evolution has entailed adaptive shifts in body size. They also proposed an “Allometric Speciation Hypothesis”, which attributes the evolution of the R. hildebrandtii species complex to divergence in constant frequency (CF) echolocation calls associated with adaptive shifts of body size. Taylor et al. (2012) proposed that the Allometric Species Hypothesis has implications for the divergence of CF echolocation calls in Rhinolophus and Hipposideros taxa across Southeast Asia. Given that H. l. lankadiva from Sri Lanka is considerably larger in body size than H. l. indus, and similar in body size to H. l. gyi from Myanmar, two interesting question arise: 1) Are the Sri Lankan bats larger than mainland Indian bats because of island gigantism and 2) have they evolved large body size independently of H. l. gyi, or are H. l. gyi and H. l. lankadiva sister taxa that share large body size via recent common ancestry?. Hence, this study reassesses the subspecies status of H. l. lankadiva, H. l. indus and H. l. gyi.

CHAPTER 1

1.6.3 Thesis outline

This thesis has three data chapters followed by a general discussion.

In Chapter two, an integrated taxonomic approach is used to validate the distinct taxonomic status of the south Indian population of H. pomona. The chapter tests the hypothesis that this isolated population of H. pomona is a distinct species from the H. gentilis s.l. occurring throughout northeast India and Southeast Asia. This chapter uses multiple lines of evidence from morphometrics (external morphology, cranial characters, baculum), acoustics and genetics in order to resolve its taxonomic position.

In Chapter three, the variation in H. lankadiva throughout its range is tested. This chapter re-evaluates the taxonomic status of subspecies of H. lankadiva using morphometric, acoustic and genetic data. Chapter three also discusses the variation between the Island and mainland populations of H. lankadiva and try to understand the possible reasons underpinning variation. The hypotheses tested here are that H. l. indus, H. l. gyi and H. l. lankadiva are cryptic species, and whether the large body size seen in H. l. gyi and H. l. lankadiva evolved by convergent evolution or by recent shared ancestry.

In Chapter four, Species Distribution Models (MaxEnt) is used to understand the distribution of H. pomona and H. lankadiva taxa. The chapter tests whether the ranges of putative species and subspecies are likely to be overlapping or not. Chapter four also discusses how the information about the geographic distributions can be used as supporting evidence in drawing species boundaries.

CHAPTER 2

Taxonomic reassessment of Hipposideros pomona Andersen, 1918 with a focus on the south Indian population

1860

CHAPTER 2

Abstract

Hipposideros pomona (sensu Corbet & Hill, 1992) has always been a problem to taxonomists with regards to its ambiguous taxonomic status and its uncertain geographic range. Very recently, the H. pomona sensu stricto from southern India reported to be distinct from H. gentlis sensu lato from northeast India and Southeast Asia. An integrative approach was applied with a large dataset to validate the species status of H. pomona s.s (now H. pomona). The molecular and bacular analysis showed a substantial difference between the two taxa. However, the morphometric and echolocation dataset could not resolve the corresponding distinction. Therefore, the present study validates the current species status of H. pomona s.s. (now H. pomona) whose distribution is confined to south India. Potential cryptic diversity was also documented in H. gentilis s.l. Therefore, a detailed study of H. gentilis s.l. is recommended throughout from its distribution to resolve the complexity.

CHAPTER 2

2.1 Introduction

The family Hipposideridae is composed of a group of insectivorous bats, widely distributed in the tropical and subtropical areas of Old World. They are commonly known as ‘roundleaf or Old-World leaf-nosed’ bats (Monadjem et al., 2019). The geographic range of bats in the family Hipposideridae extends from Palearctic, Afrotropical, Indo-Malayan to Australasian regions (Koopman, 1994; Nowak & Paradiso, 1999; Foley et al., 2017; Monadjem et al., 2019; Simmons & Cirranello, 2019). The family Hipposideridae contains 90 species in seven extant genera (Anthops, Asellia, Aselliscus, Coelops, Doryrhina, Hipposideros, Macronycteris – Simmons & Cirranello, 2019). However, taxonomic and phylogenetic studies have suggested that the diversity in hipposiderid bats is yet to be explored fully, especially from species-rich areas such as Asia and Africa (e.g. in bats currently described as Hipposideros cineraceus from Southeast Asia (Francis et al., 2010; Murray et al., 2012) and in Hipposideros aff. ruber from West Africa (Vallo et al., 2011). One of the reasons why current taxonomy has underestimated species richness in the Hipposideridae is the presence of cryptic species in the family (Murray et al., 2012; Murray et al., 2018). Cryptic diversity is notably high in Hipposideridae (and their sister taxon, Rhinolophidae) compared with other echolocating bats, in part due to their highly specialized echolocation systems which may promote speciation via ‘harmonic hopping’ (Kingston & Rossiter, 2004) and which certainly makes cryptic species easier to detect as calls focus energy into one dominant frequency (Kingston et al., 2001).

Hipposideros, the most species rich genus of Hipposideridae, is currently known to contain 74 species (Simmons & Cirranello, 2019). There are nine morphologically recognised species groups reported in this genus (Simmons, 2005) but disagreements exist among researchers concerning this arrangement (Bogdanowicz & Owen, 1998; Hand & Kirsch, 1998; Murray et al., 2012; Foley et al., 2015; Foley et al., 2017; Amador et al., 2018). Therefore, the current species group classification needs to be re-evaluated based on molecular data (Murray et al., 2012; Foley et al., 2017). Since the morphological species groups are familiar and still widely used

CHAPTER 2 among taxonomists (Murray et al., 2018), the same arrangement follow here. Overall, the bicolor species group accounts for at least half of all named species in Hipposideros. The bicolor group is taxonomically complex because many species have similar morphological traits or overlapping external body measurements which make them difficult to identify accurately in the field (e.g. Guillén-Servent & Francis, 2006; Thabah et al., 2006; Murray et al., 2018). In addition, many species in this group lack detailed species descriptions and/or their type specimens are either damaged, lack diagnosable characters or are not traceable (Murray et al., 2018). One such species in this group is Hipposideros pomona Andersen, 1918.

2.1.1 Taxonomic history of H. pomona (sensu Corbet & Hill, 1992)

Hipposideros pomona (sensu Corbet & Hill, 1992) has always been a problem to taxonomists with regards to its ambiguous taxonomic status and its uncertain geographic range (Douangboubpha et al., 2010; Zhao et al., 2015; Srinivasulu & Srinivasulu, 2018). In a review of the H. bicolor group, Andersen described and named two species (Andersen, 1918): Hipposideros pomona from southern India and Hipposideros gentilis from Myanmar. He separated pomona from the latter only based on it having a slightly wider noseleaf. H. pomona was described using a single male specimen from Haleri, north Coorg in southern India whereas the description of H. gentilis was based on specimens from Masuri, Bago (Pegu) in Myanmar (Burma). In the same paper, Andersen also named three new subspecies of H. gentilis: H. g. sinensis, H. g. atrox and H. g. major. This taxonomic review was also accepted and followed by Tate (1941). The holotype specimen for H. pomona from south India is a damaged skull (BMNH No. 18.8.3.4) housed in The Natural History Museum, London. Srinivasulu & Srinivasulu (2018) reported that while conducting a museum study at Zoological Survey of India, Kolkata they discovered a cotype specimen of south Indian H. pomona (skin - ZSI No. 21529) and thus helped to resolve its status.

Hill (1963) did not accept the taxonomic view put forward by Andersen or Tate, and he considered H. pomona, H. gentilis, H. sinensis, H. atrox and H. major all as subspecies of H. bicolor. Later, Hill, Zubaid & Davison (1986) revised the classification of H. bicolor (sensu Hill, 1963) and considered H. pomona and H. bicolor as two

CHAPTER 2 distinct species of H. bicolor. They treated H. pomona, H. gentilis and H. sinensis as conspecifics and assigned H. major and H. atrox to H. bicolor. This view was followed by several authors until 2005 (Yenbutra & Felten, 1986; Zubaid & Davison, 1987; Corbet & Hill, 1992; Simmons, 2005).

Douangboubpha et al. (2010), while reviewing the H. bicolor complex and H. pomona in Thailand, suggested that the H. pomona sensu lato might consist of two species: H. pomona (sensu stricto) restricted to peninsular India and H. gentilis (sensu lato) ranging from northeast India and Southeast Asia. The authors included all specimens of H. p. gentilis and H. p. sinensis in H. gentilis s.l. They suggested the second species name as H. gentilis because gentilis is a senior synonym to sinensis as per the line order on page 380 of Andersen (1918). Douangboubpha et al. (2010) recommended a comprehensive review of H. pomona s.l. from through out its range to validate the suggested taxonomic revision. However, Zhao et al. (2015) retained the subspecies status of gentilis and sinensis while studying the subspecies differences and differentiation in H. pomona s.l. from three different regions of south China. Based on morphometric and molecular evidence, the authors assigned the populations from South Yunnan (SY) and Min-Guang coastal (SM) subregion to H. p. gentilis and H. p. sinensis respectively. According to the authors, a third population of H. pomona s.l. from Hainan Island (SH), separated from mainland China, may be evolving into a unique subspecies to adapt to the ecological and geographical conditions there. However, Zhao et al. (2015) mentioned that both the SY and SH populations require further study, along with detailed research from the full geographic range of H. pomona s.l.

2.1.2 Distribution

Currently, H. pomona s.l. is known to have a geographic range which extends from India through to Southeast Asia. H. pomona s.s. is restricted to south India in isolated populations. The distribution of H. gentilis s.l. includes northeast India, Bangladesh, Nepal, Myanmar, Southern China, Thailand, Lao PDR, Cambodia, Vietnam and western Malaysia (Fig. 2.1).

CHAPTER 2

Figure 2.1. The current known distribution of H. pomona sensu stricto (green circles) and H. gentilis sensu lato (blue circles). The locations based on specimen data, either from the literature, online databases such as GBIF and BOLD or collected personally.

2.1.3 Background and objectives of the study

At the beginning of this study H. pomona s.s. (= recently moved from the bicolor species group and placed in the new ater species group – Monadjem et al., 2019) was considered as a subspecies of H. pomona. A very recent study by Srinivasulu & Srinivasulu (2018) supported the distinct species status of H. pomona s.s. as suggested by Douangboubpha et al. (2010). The authors distinguished H. pomona s.s. from H. gentilis s.l. mainly based on morphometric and bacular characters. The study was published during the progress of this research and it did not provide any molecular evidence in support of the findings. Also, the authors only examined a total of 10 specimens from the Zoological Survey of India, Kolkata: six vouchers of H. gentilis from Myanmar, three vouchers of H. pomona s.l. and the holotype specimen (skin only) of H. pomona s.s. In this study, we tried to validate the species status of H. pomona s.s. from south India through an integrated taxonomic approach. The present research includes a wide-ranging and substantial representation of samples

CHAPTER 2 from throughout the geographic range of H. pomona s.s. and H. gentilis s.l. Conventional morphometrics along with echolocation and molecular evidences were used to determine how different the south Indian population of H. pomona s.s. from H. gentilis s.l. to resolve whether H. pomona s.s. merits being recognised as a distinct species, especially in the light of its geographic isolation.

2.2 Materials and methods

According to the latest taxonomic revision (Srinivasulu & Srinivasulu, 2018), from hereafter, all the specimens from south India would be treated as H. pomona and those from northeast India, Andaman Islands, Myanmar, Vietnam, Laos and Cambodia as H. gentilis s.l.

2.2.1 Details of study specimens

The museum collections of both skin and skulls of H. pomona and H. gentilis s.l. were examined from The Natural History Museum, London (BMNH), the Harrison Institute, Sevenoaks, UK (HZM), the Hungarian Natural History Museum, Budapest (HNHM), the Zoological Survey of India, Kolkata (ZSI), the North Eastern Regional Centre of ZSI, Shillong, (NERC), and The Bombay Natural History Society, Mumbai, India (BNHS) (See Appendix I for details of the specimens examined). The type specimens for H. pomona (BM.18.8.3.4; holotype) from south India and H. gentilis s.s. (BM.93.11.15.2; holotype) from Myanmar were studied.

2.2.2 Measurements and morphometric analysis

2.2.2.1 External and skull measurements

The external and craniodental measurements (see Appendix II) were taken using analogue callipers accurate to 0.1 mm following Bates & Harrison (1997). The fourteen external measurements used in the study were as follows: FA - forearm length, from the extremity of the elbow to the extremity of the carpus with the wings folded; E - ear length, from the lower border of the external auditory meatus to the tip of the pinna; T - tail length, from the tip of the tail to its base adjacent to the anus; TIB - tibia length, from the knee joint to the ankle; HF - hindfoot length, from the

CHAPTER 2 extremity of the heel behind the os calcis to the extremity of the longest digit, not including the claws; 3mt - third metacarpal, from the extremity of the carpus to the distal extremity of the metacarpal; 4mt, 5mt; as above as 3mt but for the fourth and fifth metacarpals respectively; 1ph3mt - first phalanx of third metacarpal, taken from the proximal to the distal extremity of the phalanx; 2ph3mt - second phalanx of third metacarpal, taken from the proximal to the distal extremity of the phalanx; 1ph4mt, 2ph4mt – for the first and second phalanx of fourth metacarpal as same as for the third metacarpal; (1ph3mt/3mt)*100 – percentage length of the first phalanx of the third digit relative to its metacarpal.

The fourteen craniodental measurements included were: GTL - greatest length of skull, taken from the tip of the premaxillae to the lambda; SL - skull length, taken from the occiput to the anterior part of the canine; CBL - condylobasal length, from the exoccipital condyle to the alveolus of the incisor; CCL - condylocanine length, from an exoccipital condyle to the anterior alveolus of a canine; ZB - zygomatic breadth, the greatest width of the skull across the zygomatic arches; BB - breadth of braincase, taken at the posterior roots of the zygomatic arches; MW - mastoid width, the greatest distance across the mastoid region; PC - post orbital constriction, taken at the narrowest point; RW - greatest rostral width, taken across the anterior lateral swellings (chambers), in dorsal view; CM3 - maxillary toothrow length, from the most anterior part of the upper canine to the back of the crown of the third upper molar; C1–C1 - anterior palatal width, taken across the outer border of the upper canines; M3–M3 - posterior palatal width, taken across the outer border of the posterior upper molars; ML - mandible length, from the most posterior part of the condyle to the most anterior part of the first lower incisors; cm3 - mandibular toothrow length, from the most anterior part of the lower canine to the back of the crown of the third lower molar.

2.2.2.2 Morphometric analysis

All the measurements collected during the study were compared with data published by Douangboubpha et al. (2010), Zhao et al. (2015) and Srinivasulu & Srinivasulu (2018). The external morphological characters were collected for the specimens of

CHAPTER 2

H. pomona from south India and for H. gentilis s.l. from northeast India, Andaman Islands, Myanmar, Vietnam and Cambodia. Most of the museum specimens were old preservations either as dry skins and/or specimens in formalin or alcohol. Therefore, they were too brittle to handle for taking certain measurements. Due to this, only approximate measures were taken for the characters, HB and T, from most of the museum specimens. Also, the skins dry and shrink making it very inaccurate to take measurements. So, these two characters (HB and T) were used only for calculating mean and SD, and did not include in any other statistical analyses. For the craniodental measurements, the specimens of H. pomona and H. gentilis s.l. from different geographic regions such as south India, northeast India, Myanmar, Vietnam and Cambodia were examined.

All statistical analyses were carried out in either R version 3.5.1 ( R Core Team, 2018) and/or in SPSS for windows version 24.0.0.1 (IBM Corp, 2016). Prior to comparing the morphometric data across species the outliers were checked and removed (if any) from the dataset before checking the normality using either the Shapiro-Wilk normality test or Q-Q plots. Sexual dimorphism was then tested for, either by using student t-tests or Mann-Whitney U tests with a Bonferroni correction for multiple tests depending on results from normality testing. Sexual dimorphism was tested across each region except for the Little Andaman and Laos specimens due to their small sample sizes.

In the combined analysis of H. pomona and H. gentilis s.l., the sample sizes for H. pomona were too small to treat sexes separately. Therefore, pooled dataset of both males and females used in all further analysis for both external and craniodental measurements. Principal Component Analysis (PCA) was used in a multi-dimensional space to visualise any variation among species. Before performing PCA, the Spearman correlation between the 12 external and 14 skull variables were checked using the package Corrplot v0.84 (Wei & Simko, 2017). The sampling adequacy for the PCA analysis was also varified from the Kaiser–Meyer–Olkin (KMO) measure calculated using the R add-in package psych version1.8.12 (Revelle, 2019). PCA was rerun on ten external and nine craniodental characters to include type specimens of H. pomona and H. gentilis s.l.

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2.2.3 Bacular morphology

The penis of the male bats contains a supporting bone called the os penis or baculum. The structure and shape of the baculum has been used as a taxonomic character for species level identification especially in cryptic taxa (Herdina et al., 2014; Rakotondramanana & Goodman, 2017). In this study, two methods – conventional bacular extraction and CT scan imaging- were used to study the bacular morphology of the target species.

The conventional bacular extraction was carried out following the method of Topal (1975) with some modifications: The penis was cut from the base of the selected specimen using a sharp scissors/blade and boiled in a prelabelled (Specimen number, Species, Location) test tube containing a small amount of distilled water for two minutes and 15 to 30 seconds depending on the size of the penis. A spirit lamp was used to heat the test tube. The penis was carefully transferred using forceps to a prelabelled 2ml vial which was half-filled with a solution of 5 percent KOH and 2-3 drops of Alizarin dye. This was kept overnight for maceration. The penis was then transferred to a petri dish which was placed under a Leica MZ8 Stereomicroscope (Leica Microsystems, Germany). The digested tissue was gently removed using a fine pair of needles to extract the stained reddish – purple baculum, which generally lies in the distal part of the shaft. The extracted baculum was then transferred to a new petri dish which contained a one or two drops of glycerol. This was done so that the baculum remained attached to the dish and did not get lost during transfer. The ventral, dorsal and lateral side views of the baculum were drawn under a Leica MZ8 Stereomicroscope.

A Nikon XTH 225 ST CT Scanner (Nikon, UK) and Nikon CT Pro 3D software were used to capture detailed images of the baculum and to reconstruct the data for further analysis. Either the whole specimen or the excised penis was scanned. The captured images were reconstructed to generate a 3-D volumetric representation of the baculum in Nikon CT Pro 3D software using default settings.

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A total of eight bacula were prepared or scanned for this study. The total length of the baculum was measured from the drawings and scanned images. The bacular length measured from the scanned images using the ‘Linear measurement’ feature of the Avizo v9.2 software programme (Konrad-Zuse-Zentrum f¨ur Informationstechnik Berlin (ZIB), Germany) .

2.2.4 Echolocation call analysis

During the study (2016 – 2017), only a single individual of H. gentilis s.l. from northeast India was captured and recorded. The ultrasonic sounds produced by the hand-held bat were recorded using a Pettersson Ultrasound Detector D980 (http://www.batsound.com) with a sampling rate of 350 kHz and a frequency range of 10 - 200 kHz (frequency division and heterodyne) 10 - 150 kHz (time expansion) recorded on to an Edirol R-09 (www.roland.com) digital recorder sampling at 44.1 kHz. The detector was manually triggered to capture 12s in 10x time expansion as WAV files.

BatSound version 4.1.4.309 (Pettersson Electronics and Acoustics AB, Uppsala, Sweden) was used to analyse calls (with an FFT size of 1024 in a Hanning window). Since bats in the family Hipposideridae (also Rhinolophidae) emit calls with a dominant constant frequency (CF) component, only FMAXE (frequency of most energy) was measured. The CF calls of the hipposiderids have a frequency-modulated component which involves a downward frequency sweep at the end of the CF component. Power spectra were used to derive FMAXE (with an FFT size of 8192 in a Hanning window) . Up to 10 clear calls with the highest signal to noise ratios were selected and the mean from this was used for description.

The survey for H. pomona in south India was not successful during this study and only a single individual of H. gentilis s.l. was captured Wahlakya Cave, Meghalaya, northeast India. Therefore a review analysis of echolocation call variation was carried out for the two taxa using previously published studies.

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2.2.5 Molecular data collection and analysis

2.2.5.1 Taxon sampling

The tissue samples used for the genetic analysis were mostly collected from museum specimens or during field work either by myself or collaborators. Bats were captured using varying lengths of mistnets or harptraps set on the flyways, at the entrance of caves or in old forts. Hand-held nets were also used sometimes to catch the bats from their roosts wherever it was possible. The initial species identification was made using keys in Bates & Harrison (1997). The captured bats were kept inside cotton cloth bags prior to data collection. The unharmed bats were then released. Wing tissue was collected using 3mm biopsy punches(kai Europe GmbH, Germany). Soon after, tissue was stored in one of the following media at room temperature - molecular grade ethanol, silica beads and nucleic acid preservation buffer (NAP) - until transported to the lab. Later, the samples were stored at -20oC until DNA extraction. The samples from museum specimens included wing punches, liver, kidney or muscle tissue.

During the entire study period (2016-2017), field surveys were conducted in areas including the type locality of H. pomona (Haleri, Karnataka) and other reported areas in south India (Madhavan, 2000). All of them were unsuccessful in detecting any individuals of H. pomona. Therefore samples of H. pomona were collected from the specimens deposited at the Harrison Zoological Institute, UK by Prof. Juliet Vanitharani. For H. gentilis s.l., samples collected from the single individual from northeast India captured during this study and rest of the samples from the same region were provided by Dr. Adora Thabah and Dr. Manuel Rudei.

2.2.5.2 DNA extraction, amplification and sequencing

The total genomic DNA was isolated using the QIAGEN DNeasy Blood & Tissue Kit (Qiagen GmbH, Hilden, Germany) following the manufacturer’s instructions with additional modifications. The final elution volume of each sample was 100 µl. The DNA quantity in every sample was checked using a Qubit 3.0® Fluorometer (Thermo

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Fisher Scientific). The extracted DNA samples were stored at -20o c until polymerase chain reactions (PCRs) were performed.

Total of five genes, two mitochondrial (mtDNA) and three nuclear introns (ncDNA) were sequenced for this study. The mitochondrial genes were the 5’ fragment of the cytochrome oxidase 1 (CO1) gene and the 5’ fragment of 16s rRNA. Recent taxonomic and phylogenetic studies of families Hipposideridae and Rhinolophidae showed that mtDNA does not always depict the true phylogeny of a species. Therefore, fast evolving nuclear introns were used in order to better resolve phylogenetic relationships (Dool et al., 2016; Foley et al., 2017). So, the following introns from nuclear genes were amplified and sequenced for this study: protein kinase C, iota 1 (PRKC1), signal transducer and activator of transcription 5A (STAT5A) and thyrotropin beta chain precursor (THY). Apart from the three introns, a fourth one – SPTBN (B-Spectrin nonerythrocytic 1) was selected and amplified using the following primers: CCAGGCAGAGCGGGTGAGAGG (forward) and CCACTCGGTCTCGGATCACCTGG (reverse) (Eick, Jacobs & Mathee, 2005; Lack et al., 2010). Previously designed bat-specific primers were used to target the selected genes (Eick, Jacobs & Mathee, 2005; Clare et al., 2007; Lack et al., 2010). The details of the primers are given in Table 2.1. For CO1, cocktail 2 (C_VF1LFt1/C_VR1LRt1), used by Clare et al. (2007) was chosen. The primer was an improved version of CO1 cocktail 1 (Ivanova, deWaard & Hebert, 2006) including M13-tailed versions of the primers and an additional primer pair, LepF1_t1 and LepRI_t1 in the following ratio; 10 pmol/ µl, VF1_t1: VF1d_t1: LepF1_t1: VF1i_t1 (1:1:1:3) or VR1_t1: VR1d_t1: LepRI_t1: VR1i_t1 (1:1:1:3) (Table 2.2).

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Table 2.1. Details of the primers, introns targeted, and gene names used in the study

Gene Targeted bp length Intron Forward primer (5’ → 3’) Reverse primer (5’ → 3’) References

16s cp 294 bp CGAGGGCTTTACTGTCTCTT CCTATTGTCGATATGGACTCT Pomilla et al., 2009

PRKC1 ~419 bp 9-10 CTTGTCAATGATGATGAGG CCTATTTTAAAATATGAAAGAAATC

Eick, Jacobs & Mathee, 2005 & STAT5A ~460-480 bp 16-17 CTGCTCATCAACAAGCCCGA GGCTTCAGGTTCCACAGGTTGC Lack et al., 2010

THY ~521bp 2-3 GGGTATGTAGTTCATCTTACTTC GGCATCCTGGTATTTCTACAGTCTTG

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Table 2.2. Details of the bat-specific cocktail 2 (C_VF1LFt1/C_VR1LRt1) primer for the CO1 gene used to amplify a sequence length of 658 bp. This is a modified version of the CO1 cocktail 1 (Ivanova, deWaard & Hebert, 2006) including M13-tailed versions of the primers developed by Clare et al. (2007).

CO1 Cocktail 2 [C_VF1LFt1/C_VR1LRt1]

Forward Primers (ratio in brackets) Reverse Primers (ratio in brackets) C_VF1di (Forward cocktail name) C_VR1di (reverse cocktail name)

LepF1_t1 (1) LepRI_t1 (1) 5′-TGTAAAACGACGGCCAGTATTCAACCAATCATAAAGATATTGG-3′ 5′-CAGGAAACAGCTATGACTAAACTTCTGGATGTCCAAAAAATCA-3′

VF1_t1 (1) VR1_t1 (1) 5′-TGTAAAACGACGGCCAGTTCTCAACCAACCACAAAGACATTGG-3′ 5′-CAGGAAACAGCTATGACTAGACTTCTGGGTGGCCAAAGAATCA-3′

VF1d_t1 (1) VR1d_t1 (1) 5′-TGTAAAACGACGGCCAGTTCTCAACCAACCACAARGAYATYGG-3′ 5′-CAGGAAACAGCTATGACTAGACTTCTGGGTGGCCRAARAAYCA-3′

VF1i_t1 (3) VR1i_t1 (3) 5′-TGTAAAACGACGGCCAGTTCTCAACCAACCAIAAIGAIATIGG-3′ 5′-CAGGAAACAGCTATGACTAGACTTCTGGGTGICCIAAIAAICA-3′

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The PCR amplifications were performed using the Qiagen HotStartTaq Plus Master Mix (Qiagen GmbH, Hilden, Germany). Each reaction was carried out in 20 µl reaction volumes as follows: 3 µl of template DNA (approx. 25 – 250 ng), 1 unit of Taq, 1x PCR buffer, 200 µM dNTP, 1.5 mM Mgcl2, 0.25 µM of forward and reverse primers. The thermocycling conditions for all genes were the same as recommended in the Qiagen HotStartTaq Plus Master Mix. This consisted of an initial denaturation at 95°C for 5 min followed by 35 cycles of denaturation at 95°C for 30s, annealing at 55°C for 30s, elongation at 72°C for 30s and a final elongation of 72°C for 10 min. PCR amplifications were carried out in an Eppendorf Mastercycler nexus Thermal Cycler. The amplified products were resolved by electrophoresis in 3% agarose gels stained with SYBR Safe and visualized under UV light. The successful PCR plates were cleaned with ExoSap-IT (Life Technologies Europe BV). Sanger sequencing was carried out in both forward and reverse directions on an ABI 3730 thermocycler (Applied Biosystems, CA, USA) at DBS Genomics, Department of Biosciences at Durham University, UK.

2.2.5.3 Sequence alignment and editing

The sequences were assembled and edited using Geneious Prime v2019.2.1 (Biomatters Ltd.) after checking the chromatographs manually. The heterozygosity positions (double peaks) were scored and corrected using the IUPAC (The International Union of Pure and Applied Chemistry) nucleotide ambiguity codes. The consensus region was then selected for each sample. The sequences for CO1 were translated into amino acids and inspected for indels and premature stop codons to exclude paralogous sequences. In order to confirm the species identity, a BLAST search was carried out either using the inbuilt algorithm in Geneious or the NCBI webpage (https://blast.ncbi.nlm.nih.gov/Blast.cgi) for all the consensus sequences.

The sequences from all the selected loci were aligned separately using the MUSCLE algorithm (Edgar, 2004) with default settings in Geneious. All alignments were visually checked for any inconsistencies. Each intron dataset was statistically resolved for the nuclear alleles using PHASE 2.1.1 (Stephens, Smith & Donnelly, 2001) except for the haplotype acceptance threshold (0.70). The chosen value for

CHAPTER 2 haplotype acceptance threshold can reduce the number of unresolved genotypes (Garrick, Sunnucks & Dyer, 2010). The input ﬁles for PHASE were generated using the SEQPHASE web server (Flot, 2010) using 1,000 iterations for PHASE run with a burn- in of 500 and a thinning interval of 1.

2.2.5.4 Haplotype network and genetic divergence analysis

Haplotype networks were used to analyse and visualise the relationships among DNA sequences within populations and/or species. The haplotype diversity within each dataset of targeted genes was computed with DnaSP v6.12.03 (Rozas et al., 2017). The Nexus file of haplotypes was then imported from DnaSP to PopART v1.7 (http://popart.otago.ac.nz) to visualise the networks. The median-joining haplotype networks (Bandelt, Forster & Röhl, 1999) were constructed in PopART.

The pairwise genetic distance within and among different populations of H. gentilis s.l. and H. pomona was determined by calculating the uncorrected p-distances in MEGA v10.0.5 (Kumar et al., 2018).

2.2.5.5 Phylogenetic analysis

To better resolve the relationships between the individuals from different regions and to validate the taxonomic status of targeted taxa, representative lineages from three closely related taxa were used as ingroups: Hipposideros ater, Hipposideros cineraceus, Hipposideros dyacorum. Hipposideros lankadiva and Hipposideros larvatus were used as outgroups. Both the ingroup and outgroup taxa were chosen based on their positions in the phylogenetic tree from previously published studies (Francis et al., 2010; Murray et al., 2012; Foley et al., 2017). Hipposideros genus phylogenetic trees based on both mtDNA (CO1 and 16s) and nuclear intron (STAT5A, PRKC1 and THY) datasets were built in order to confirm the position and relationships of chosen ingroup and outgroup taxa.

An appropriate nucleotide substitution model for each marker was chosen using the AIC criteria for model selection in jModeltest 2.1.10 v20160303 (Darriba et al., 2012).

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In further analysis, when a model specified by jModeltest was not available in the software then the next best model was used.

The Maximum Likelihood (ML) and Bayesian Analysis (BA) approaches were used for phylogenetics analyses. The maximum-likelihood estimates of all individual gene trees and concatenated gene trees were built using PhyML v2.2.4 (Guindon et al., 2010) installed in Geneious. Mitochondrial and nuclear genes were concatenated and analysed separately for both ML and BA. The ML analysis of the concatenated genes was carried out on unpartitioned data and with 100 bootstrap replicates.

Bayesian analyses for the concatenated datasets were done using MRBAYES v.3.2.6 (Ronquist et al., 2012). Two replicates were run after unlinking the nucleotide substitution models across partitions which then allowed to evolve at individual rates for each chosen gene in the concatenated alignment. Four MCMC runs with default heating values were conducted for two million generations and sampled every 1,000th generation. Each run was assessed using TRACER v.1.6 (Rambaut et al., 2014). A burn-in percentage of 25 was used. The remaining samples comprised the posterior probability (PP) distributions. Majority rule consensus tree was then generated for each analysis.

2.3 Results

2.3.1 External morphology

2.3.1.1 Test for normality and sexual dimorphism across geographic regions

Sexual dimorphism was tested in 12 external morphometric characters across different regions. The external morphometric data were available only for eight individuals of H. pomona from south India. All variables except hidefoot (HF) and second phalanx of fourth metacarpal (2ph4mt), were normally distributed. There was no significant sexual size dimorphism observed in any external characters for the south Indian population (p >0.05). In H. gentilis s.l. population from northeast India, a total of five out of 12 variables showed deviation from normality. Two variables - forearm length (FA) and fourth metacarpal (4mt) – showed significant differences

CHAPTER 2 between males and females of H. gentilis s.l. from northeast India. The females from northeast India had longer FA (U = 627.5, p = .004) and 4mt (t(18) = 2.29, p = 0.034) than the males. Only one variable, HF, was non-normal (p = 0.010) among all external measurements data from Myanmar region. In the Myanmar population of H. gentilis s.l. sexual dimorphism was not observed in any external characters except FA, first phalanges of third and fourth metacarpal (1ph3mt and 1ph4mt) and the percentage length of first phalanx of third digit relative to its metacarpal (1ph3mt/3mt X 100).

Males had shorter FA lengths (t(56) = -3.01, p = 0.005), 1ph3mt (t(31) = -2.87, p = 0.007),

1ph4mt (t(30) = -3.15, p = 0.004) and 1ph3mt/3mt X 100 (t(31) = -2.93, p = 0.006). The sample sizes were too small to test for sexual dimorphism in H. gentilis s.l. populations from Vietnam (n=6), Cambodia (n=2) and the Andaman Islands (n=2). The means and standard deviations for the 14 external characters of H. pomona and H. gentilis s.l. are given in Table 2.3.

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Table 2.3. External measurements (in mm) of H. pomona from south India and H. gentilis s.l. from northeast India, Myanmar, Vietnam, Cambodia, Andaman Islands, Thailand and China from the present and previous studies. The sample size is given in brackets. Region Sex FA HB (approx.) TL (approx.) E TIB HF 3mt

♂ (6) 39.70 ± 1.12 44.10 ± 5.22 (4) 25.68 ± 4.90 (4) 17.42 ± 0.78 17.40 ± 0.58 6.07 ± 0.20 30.02 ± 1.15 South India ♀ (2) 41.00 ± 0.85 47.25 ± 1.20 28.50 ± 0.71 18.65 ± 0.49 18.30 ± 0.14 7.50 ± 0.71 31.55 ± 0.35

♂ 40.67 ± 0.76 (25) 46.85 ± 4.98 (13) 29.48 ± 5.07 (16) 21.33 ± 2.88 (18) 18.24 ± 0.68 (21) 7.60 ± 0.78 (24) 29.64 ± 0.68 (8) Northeast India ♀ 41.39 ± 0.93 (35) 43.76 ± 5.62 (17) 26.4 ± 5.53 (26) 21.20 ± 2.50 (22) 18.25 ± 0.58 (31) 7.75 ± 0.54 (33) 30.47 ± 0.89 (12)

♂ 40.16 ± 0.80 (33) 40.63 ± 1.90 (9) 28.77 ± 1.45 (11) 19.03 ± 1.65 (11) 17.73 ± 0.76 (32) 7.38 ± 0.62 (32) 30.01 ± 1.11 (23) Myanmar ♀ 41.01 ± 1.22 (25) 43.88 ± 2.60 (9) 28.29 ± 2.01 (9) 20.29 ± 1.71 (9) 17.76 ± 0.93 (23) 7.17 ± 0.70 (25) 30.23 ± 0.70 (11)

♂ (2) 41.45 ± 0.35 40.95 ± 0.07 31.10 ± 0.28 21.45 ± 0.21 19.15 ± 0.78 5.65 ± 0.07 30.30 ± 1.70 Vietnam ♀ (3) 43.23 ± 1.33 43.90 ± 3.34 33.53 ± 3.48 22.60 ± 0.36 19.33 ± 0.47 7.13 ± 0.38 32.40 ± 1.56

Cambodia ♂ (2) 42.80 ± 0.57 43.75 ± 3.18 28.10 ± 0.14 19.40 ± 0.14 19.40 ± 0.00 6.55 ± 0.21 32.00 ± 0.71

Andaman Islands ♂ (2) 43.10 ± 0.42 45.90 ± 0.14 26.50 ± 3.54 21.20 ± 2.82 18.85 ± 0.07 7.00 ± 0.14 32.85 ± 0.07

Thailand ♂ (20) 41.70 ± 1.20 47.70 ± 2.50 29.70 ± 2.30 21.00 ± 1.30 19.00 ± 0.70 6.80 ± 0.50 30.90 ± 0.90 (Douangboubpha et al. 2010) ♀ (16) 42.20 ± 1.20 48.10 ± 2.50 30.60 ± 1.60 21.10 ± 1.40 18.60 ± 0.80 6.50 ± 0.50 31.60 ± 1.00

South Yunnan, China ♂ (19) 43.26 ± 1.07 46.48 ± 1.97 30.87 ± 1.45 23.54 ± 0.82 18.97 ± 0.88 6.67 ± 0.57 29.54 ± 0.84 (Zhao et al., 2015) ♀ (18) 43.43 ± 1.47 47.35 ± 2.47 30.99 ± 1.56 23.88 ± 1.10 19.02 ± 0.65 6.89 ± 0.39 29.70 ± 0.88

Min-Guang coastal, China ♂ (15) 42.52 ± 1.13 45.30 ± 1.92 31.61 ± 1.47 22.51 ± 1.05 18.86 ± 1.02 6.26 ± 0.38 29.52 ± 0.70 (Zhao et al., 2015) ♀ (16) 43.53 ± 0.86 46.75 ± 2.63 33.26 ± 2.07 23.16 ± 1.19 19.23 ± 0.91 6.49 ± 0.38 29.87 ± 0.88

Hainan Island, China ♂ (10) 39.76 ± 0.55 44.83 ± 2.16 30.08 ± 1.71 20.38 ± 1.20 16.98 ± 0.59 6.04 ± 0.43 27.80 ± 0.55 (Zhao et al., 2015) ♀ (22) 40.74 ± 0.75 45.29 ± 2.07 31.54 ± 1.63 21.03 ± 0.90 17.44 ± 0.52 6.23 ± 0.40 28.26 ± 0.77 Andaman Islands 41.80 ± 1.26 42.51 ± 4.08 29.13 ± 1.75 18.21 ± 2.42 6.25 ± 0.89 31.27 ± 1.26 (Srinivasulu et al., 2018) Havelock Isl., South Andaman 40.86 ± 1.00 44.42 ± 0.47 29.65 ± 0.65 18.84 ± 0.76 5.40 ± 0.93 30.64 ± 0.78 (Srinivasulu et al., 2018)

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Table 2.3. continued.

Region Sex 4mt 5mt 1ph3mt 2ph3mt 1ph4mt 2ph4mt 1ph3mt/3mt*100

South India ♂ (6) 31.90 ± 1.00 30.32 ± 0.92 16.68 ± 0.29 15.35 ± 0.46 9.58 ± 0.31 8.30 ± 0.46 55.63 ± 1.60 ♀ (2) 33.60 ± 0.71 31.60 ± 0.28 17.70 ± 0.28 15.60 ± 0.42 10.20 ± 0.28 8.25 ± 0.50 56.10 ± 0.27 Northeast India ♂ 31.69 ± 0.97 (8) 30.64 ± 1.21 (8) 17.34 ± 0.97 (8) 15.08 ± 0.85 (8) 10.63 ± 0.36 (8) 8.05 ± 0.32 (8) 58.84 ± 3.55 (8) ♀ 32.60 ± 0.80 (12) 31.59 ± 0.89 (12) 17.38 ± 0.68 (12) 14.80 ± 1.22 (12) 10.75 ± 0.89 (12) 8.46 ± 0.84 (12) 57.06 ± 2.50 (12) Myanmar ♂ 31.74 ± 1.01 (23) 30.55 ± 1.05 (23) 16.60 ± 0.61 (23) 15.70 ± 0.70 (20) 10.22 ± 0.48 (22) 7.96 ± 0.29 (22) 55.33 ± 1.59 (23) ♀ 32.25 ± 0.92 (11) 31.06 ± 0.71 (11) 17.29 ± 0.69 (10) 16.19 ± 0.90 (10) 10.62 ± 0.23 (10) 8.23 ± 0.46 (10) 57.05 ± 1.45 (10) Vietnam ♂ (2) 32.50 ± 1.41 32.00 ± 1.41 17.70 ± 0.57 16.40 ± 0.42 9.95 ± 0.78 8.30 ± 0.57 58.46 ± 1.41 ♀ (3) 34.03 ± 1.33 33.43 ± 2.02 18.47 ± 0.85 16.87 ± 0.23 11.07 ± 0.93 9.17 ± 0.93 57.00 ± 0.48 Cambodia ♂ (2) 33.15 ± 1.06 32.25 ± 0.92 17.65 ± 0.21 16.80 ± 0.28 10.80 ± 0.00 8.55 ± 0.07 55.17 ± 0.56 Andaman Islands ♂ (2) 34.05 ± 0.21 33.20 ± 0.42 18.40 ± 0.28 16.40 ± 2.26 10.60 ± 0.14 9.10 ± 0.14 56.01 ± 0.74 Thailand ♂ (20) 33.20 ± 0.90 31.60 ± 0.90 17.90 ± 0.80 16.70 ± 0.80 10.90 ± 0.50 8.60 ± 0.40 57.70 ± 1.80 (Douangboubpha et al. 2010) ♀ (16) 33.70 ± 1.20 32.90 ± 2.90 17.90 ± 0.80 16.70 ± 1.30 11.10 ± 0.40 8.70 ± 0.50 57.71 ± 2.40 South Yunnan, China ♂ (19) 31.12 ± 0.90 30.26 ± 0.78 17.67 ± 0.65 16.55 ± 0.71 10.88 ± 0.33 8.57 ± 0.60 NA (Zhao et al., 2015) ♀ (18) 31.53 ± 0.79 30.50 ± 0.77 17.92 ± 0.56 16.63 ± 0.79 11.03 ± 0.46 8.80 ± 0.49 NA Min-Guang coastal, China ♂ (15) 31.01 ± 0.79 30.03 ± 0.92 16.95 ± 0.50 16.30 ± 0.57 10.01 ± 0.37 8.61 ± 0.39 NA (Zhao et al., 2015) ♀ (16) 31.41 ± 1.04 30.83 ± 1.21 17.51 ± 0.66 16.48 ± 0.49 10.50 ± 0.44 8.86 ± 0.36 NA Hainan Island, China ♂ (10) 29.45 ± 0.79 28.40 ± 0.83 16.45 ± 0.56 15.44 ± 0.52 10.21 ± 0.23 7.93 ± 0.37 NA (Zhao et al., 2015) ♀ (22) 29.99 ± 0.94 29.03 ± 1.03 16.83 ± 0.42 15.94 ± 0.57 10.42 ± 0.30 8.14 ± 0.43 NA Andaman Islands 32.94 ± 1.30 31.27 ± 3.09 17.23 ± 0.85 16.45 ± 1.01 10.48 ± 1.11 7.66 ± 0.92 NA (Srinivasulu et al., 2018) Havelock Isl., South Andaman 32.23 ± 0.76 31.04 ± 0.46 16.12 ± 0.22 15.49 ± 1.57 9.96 ± 0.65 7.17 ± 0.57 NA (Srinivasulu et al., 2018)

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2.3.1.2 Correlations among variables and Principal Component Analysis (PCA)

The raw data pooled from both males and females of H. pomona and H. gentilis s.l. were together tested for normality using Shapiro – Wilk tests. Four external morphometric variables out of 12 in total showed non-normal distributions .The Spearman correlation matrices for 12 external measurements showed both positive and negative associations between each of them (Fig. 2.2). The highest correlations (r >0.80, p< 0.001) were observed between the following variables: metacarpals 3 and 4 (3mt – 4mt), metacarpals 3 and 5 (3mt – 5mt) and metacarpals 4 and 5 (4mt – 5mt).

All 12 variables were log-transformed before conducting PCA, in order to achieve an approximate standard normal distribution. The Kaiser–Meyer–Olkin (KMO) measure verified the sampling adequacy for the analysis, with KMO = 0.75 (‘good’ according to Field, 2009), and all KMO values for individual items were above the acceptable limit of 0.5 (Field, 2009).

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Table 2.4. Variable loadings for the three principal components (PC1, PC2, PC3) from an analysis of external morphological characters of H. pomona and H. gentilis s.l. from different study regions. Total sample size is 45. Values in bold indicate high loadings on that particular component. Measurement acronyms are defined in the ‘Materials and methods section 2.2.2.1’.

Factor loadings External morphometric charachers PC1 PC2 PC3 FA 0.366 0.080 0.115 E 0.239 0.378 -0.158 TIB 0.296 0.087 -0.009 HF 0.052 -0.357 0.642 3mt 0.365 -0.275 -0.127 4mt 0.350 -0.252 -0.179 5mt 0.100 -0.163 0.018 1ph3mt -0.067 -0.009 -0.185 2ph3mt -0.506 0.226 -0.209 1ph4mt 0.622 -0.085 0.028 2ph4mt -0.193 0.264 -0.120 1ph3mt/3mt X 100 -0.137 -0.221 -0.031 Eigenvalue 5.65 1.90 1.20 Variance explained (%) 47.11 15.79 9.97

The PCA of 12 external variables extracted three components (eigenvalue > 1), which accounted for 72.87% of the total variation (Table 2.4). A scree plot was also used to check the validity of the selected principal components. In the PCA analysis, 62.9% of the total variation in the data was explained by the first two principal components (PC1 – 47.11% & PC2 – 15.79%). The third component (PC3) had an eigenvalue of 1.20 but only contributed 9.97% of the total variation (Table 2.4). The first component (PC1) included six of 12 variables with high positive loading values (0.296 – 0.622; FA, TIB, 3mt, 4mt and 1ph4mt) except for the second phalanx of the third metacarpal (2ph3mt; Table 2.4) which loaded negatively. PC2 was largely composed of four variables (E, 2ph4mt, 5mt and 1ph3mt/3mt X 100) with positive and negative values. The third component revealed two variables with positive (HF = 0.642) and negative values (1ph3mt = -0.185). No obvious clusters were identified in the PCA as most of the variables had overlapping values for both H. pomona and H. gentilis s.l. (Fig. 2.3a). The PCA was rerun after including the type specimens of H. pomona and H. gentilis s.l. (Fig. 2.3b)

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(a)

(b)

2.3.2 Craniodental analysis

2.3.2.1 Tests for normality and sexual dimorphism across regions

The sexual size dimorphism in the 14 skull characters of H. gentilis s.l. was analysed across different study regions. Only one sample was available from Laos, therefore that did not include that in this analysis. Only one female specimen was available for H. pomona; therefore the size differences of skull characters among sexes was not checked for the south Indian population. The normality of the raw data was checked using Shapiro-Wilk tests for each region. The means and standard deviations for 14 cranial characters of H. pomona and H. gentilis s.l. are given in Table 2.5.

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Table 2.5. Craniodental measurements (in mm) of H. pomona from south India and H. gentilis s.l. from from northeast India, Myanmar, Vietnam, Cambodia, Thailand, China and Andaman Islands from the present and previous studies. The sample size is given in brackets.

Region Sex GTL SL CCL CBL ZB BB MB

♂ (4) 17.23 ± 0.47 17.08 ± 0.52 14.73 ± 0.68 14.97 ± 0.60 8.20 ± 0.35 7.70 ± 0.30 8.90 ± 0.20 South India ♀ (1) 17.35 17.18 14.95 15.32 8.45 7.45 9.05

♂ (18) 18.05 ± 0.31 16.90 ± 0.91 15.53 ± 0.45 16.82 ± 0.87 8.75 ± 0.23 7.94 ± 0.37 9.16 ± 0.34 Northeast India ♀ (18) 18.24 ± 0.29 16.63 ± 0.77 15.91 ± 0.28 17.52 ± 0.80 8.77 ± 0.23 7.97 ± 0.19 9.27 ± 0.15

♂ (9) 17.81 ± 0.34 17.56 ± 0.33 15.26 ± 0.19 15.73 ± 0.27 8.89 ± 0.11 7.87 ± 0.23 9.02 ± 0.13 Myanmar ♀ (17) 17.69 ± 0.34 17.44 ± 0.35 15.42 ± 0.24 15.80 ± 0.23 8.70 ± 0.10 7.86 ± 0.21 9.01 ± 0.13

♂ (11) 17.90 ± 0.41 17.53 ± 0.36 15.42 ± 0.24 16.00 ± 0.33 8.67 ± 0.11 7.77 ± 0.17 9.00 ± 0.09 Vietnam ♀ (12) 17.91 ± 0.23 17.55 ± 0.34 15.49 ± 0.29 16.06 ± 0.37 8.74 ± 0.24 7.78 ± 0.24 9.01 ± 0.17

♂ (4) 17.97 ± 0.49 17.73 ± 0.42 15.51 ± 0.40 15.88 ± 0.47 8.99 ± 0.22 7.86 ± 0.17 9.09 ± 0.15 Cambodia ♀ (3) 17.87 ± 0.42 17.75 ± 0.41 15.43 ± 0.29 15.73 ± 0.31 8.93 ± 0.31 7.60 ± 0.10 9.05 ± 0.23

Thailand ♂ 18.00 ± 0.50 15.60 ± 0.50 16.70 ± 0.30 9.00 ± 0.20 8.20 ± 0.10 9.00 ± 0.20 (Douangboubpha et al. 2010) ♀ 17.60 ± 0.70 15.50 ± 0.40 15.70 ± 0.40 8.80 ± 0.40 8.10 ± 0.30 9.00 ± 0.20

South Yunnan, China ♂ (4) 17.20 ± 0.20 14.02 ± 0.60 15.42 ± 0.26 8.71 ± 0.06 7.82 ± 0.17 (Zhao et al., 2015) ♀ (4) 17.23 ± 0.32 13.99 ± 0.48 15.32 ± 0.32 8.76 ± 0.17 7.86 ± 0.06

Min-Guang coastal, China ♂ (6) 17.45 ± 0.26 13.83 ± 0.51 15.42 ± 0.24 8.55 ± 0.13 7.82 ± 0.15 (Zhao et al., 2015) ♀ (8) 17.53 ± 0.30 13.98 ± 0.11 15.48 ± 0.29 8.39 ± 0.47 7.69 ± 0.31

Hainan Island, China ♂ (1) 16.99 13.69 14.97 7.78 7.36

(Zhao et al., 2015) ♀ (7) 17.11 ± 0.33 13.77 ± 0.31 15.26 ± 0.33 8.22 ± 0.38 7.62 ± 0.17 Andaman Islands (10) 18.31 ± 0.52 15.74 ± 0.36 16.18 ± 0.38 8.87 ± 0.29 8.60 ± 0.59 (Srinivasulu et al., 2018) Havelock Islands, South Andaman (4) 18.02 ± 0.11 15.57 ± 0.11 16.02 ± 0.16 8.83 ± 0.06 8.33 ± 0.60 (Srinivasulu et al., 2018)

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Table 2.5. continued

Region Sex PC RW CM3 C1 – C1 M3 – M3 M cm3

♂ (4) 2.63 ± 0.03 4.48 ± 0.20 5.70 ± 0.20 3.27 ± 0.25 5.53 ± 0.12 10.27 ± 0.32 6.07 ± 0.06 South India ♀ (1) 2.75 4.65 5.78 3.53 5.55 10.45 5.80

♂ (18) 2.93 ± 0.18 4.91 ± 0.33 6.17 ± 0.17 3.71 ± 0.14 6.01 ± 0.14 10.78 ± 0.28 6.40 ± 0.20 NE India ♀ (18) 3.04 ± 0.23 5.09 ± 0.37 6.30 ± 0.11 3.75 ±0.14 6.06 ± 0.15 10.97 ± 0.29 6.45 ± 0.17

♂ (9) 2.92 ± 0.09 4.78 ± 0.29 6.07 ± 0.07 3.73 ± 0.12 6.05 ± 0.14 10.69 ± 0.15 6.10 ± 0.14 Myanmar ♀ (17) 2.94 ± 0.11 4.77 ± 0.29 6.11 ± 0.21 3.68 ± 0.11 5.92 ± 0.14 10.69 ± 0.26 6.16 ± 0.31

♂ (11) 2.75 ± 0.14 4.62 ± 0.09 6.07 ± 0.13 3.65 ± 0.10 5.84 ± 0.14 10.63 ± 0.23 6.18 ± 0.18 Vietnam ♀ (12) 2.77 ± 0.14 4.63 ± 0.09 6.11 ± 0.12 3.69 ± 0.12 5.92 ± 0.11 10.68 ± 0.24 6.20 ± 0.17

♂ (4) 2.85 ± 0.52 4.78 ± 0.22 6.24 ±0.23 3.77 ± 0.25 6.18 ± 0.14 10.96 ± 0.41 6.51 ± 0.39 Cambodia (7) ♀ (3) 2.88 ± 0.10 4.73 ± 0.21 6.27 ± 0.15 3.70 ± 0.17 6.17 ± 0.15 10.83 ± 0.29 6.40 ± 0.10

Thailand ♂ 2.60 ± 0.10 4.60 ± 0.10 6.20 ± 0.20 3.50 ± 0.10 6.1 ± 0.20 11.20 ± 0.40 6.50 ± 0.20 (Douangboubpha ♀ 2.60 ± 0.30 4.60 ± 0.1 6.10 ± 0.20 3.40 ± 0.20 6.00 ± 0.20 11.10 ± 0.40 6.40 ± 0.30 et al. 2010) ♂ 4.56 ± 0.09 6.34 ± 0.12 3.55 ± 0.07 10.67 ± 0.15 6.93 ± 0.03 South Yunnan, China (4) (Zhao et al., 2015) ♀ (4) 4.50 ± 0.08 6.55 ± 0.14 3.62 ± 0.12 10.59 ± 0.09 7.02 ± 0.04

Min-Guang coastal, China ♂ (6) 4.61 ± 0.15 6.69 ± 0.21 3.62 ± 0.04 10.66 ± 0.16 6.92 ± 0.07

(Zhao et al., 2015) ♀ (8) 4.61 ± 0.16 6.70 ± 0.13 3.56 ± 0.12 10.65 ± 0.19 6.92 ± 0.13

Hainan Island, China ♂ (1) 4.68 6.07 3.37 10.25 6.46

(Zhao et al., 2015) ♀ (8) 4.64 ± 0.12 6.45 ± 0.26 3.48 ± 0.06 10.30 ± 0.18 6.68 ± 0.09 Andaman Islands (10) 6.22 ± 0.15 3.61 ± 0.18 6.13 ± 0.13 10.96 ± 0.53 6.76 ± 0.25 (Srinivasulu et al., 2018) Havelock Islands, South Andaman (4) 6.08 ± 0.09 3.46 ± 0.11 6.06 ± 0.09 10.73 ± 0.46 6.86 ± 0.63 (Srinivasulu et al., 2018)

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2.3.2.2 Correlations among variables and Principal Component Analysis (PCA)

All skull variables in the pooled dataset of males and females of H. pomona and H. gentilis s.l. showed a deviation from normality in the the Shapiro-Wilk test. Different transformations were tried but none was able to transform the data to normality. The Spearman’s correlation test revealed that most of the variables had a correlation higher than r = 0.50 (Fig. 2.4). The highest correlation (r >0.75) observed was between greatest skull length (GTL) and condylobasal length (CBL), greatest skull length (GTL) and condylocanine length (CCL), condylobasal (CBL) and condylocanine (CCL) length and condylocanine length (CCL) and maxillary toothrow length (CM3).

All the 14 skull variables were scaled before doing PCA in order to achieve an approximate equal variance. The sampling adequacy for the analysis was KMO = 0.86 (‘superb’ according to Field (2009), and all KMO values for individual items were above the acceptable limit of 0.5 (Field, 2009). PCA based on 14 craniodental measurements showed that 73.78% of the total variance in the dataset could be explained by the first three principal components (eigenvalue >1). A scree plot was also used to check the validity of the selected principal components. PC1, PC2 and PC3 accounted for 53.48%, 13.06% and 7.24 % of the total variance respectively (Table 2.6).

CHAPTER 2

PC1 reflected the skull size of the bats, with small-sized bats characterised by larger PC1 scores. All the measurements were negatively loaded on to the first component (PC1) and most of them had similar loading levels. Therefore, all specimens of the two taxa, except the two samples from Andaman, were evenly distributed along this axis (Table 2.6). Samples of H. pomona from Tamil Nadu, south India (n = 5) showed a narrow separation from the H. gentilis s.l. The samples of H. pomona from Kerala, south India (n = 3) formed a separate cluster far from the other samples. Samples of H. pomona and H. gentilis s.l. showed a much better separation in a morphospace between PC1 and PC3 compared to PC1 and PC2 (Fig. 2.5a & b) and also in a 3- dimentional space of PCA components (Supplementary Material Fig. S2.1).

CHAPTER 2

Table 2.6. Variable loadings for the three principal components (PC1, PC2, PC3) from an analysis of craniodental characters of H. pomona and H. gentilis s.l. from different study regions. Values in bold indicate high loadings on that particular component. Measurement acronyms are defined in the ‘Materials and methods section 2.2.2.1’.

Factor loadings Craniodental charachers PC1 PC2 PC3

GTL -0.322 0.109 -0.142

SL -0.063 0.680 0.169

CBL -0.278 -0.390 -0.182

CCL -0.330 0.011 -0.011

ZB -0.277 0.281 0.149

BB -0.228 0.058 0.602

MB -0.291 -0.031 0.332

PC -0.208 -0.309 0.297

RW -0.235 -0.355 0.150

C-M3 -0.309 0.020 -0.190

C1-C1 -0.234 0.200 -0.411

M3-M3 -0.289 0.060 -0.031

M -0.316 0.133 -0.194

cm3 -0.246 -0.081 -0.263

Eigenvalue 7.54 1.83 1.01

Variance explained (%) 53.88 13.05 7.23

CHAPTER 2

(A)

(B)

Figure 2.5. Scatter plot of Principal Component Analysis based on (A) PC1 and PC2 and (B) PC1 and PC3 for 14 craniodental measurements of H. pomona and H. gentilis s.l. The sample size is 109.

The PCA with nine craniodental measurements including the type specimens of H. pomona and H. gentilis s.l. did not show any distinct grouping (Fig. 2.6).

CHAPTER 2

Figure 2.6. Scatter plot of Principal Component Analysis based on PC1 and PC2 for nine craniodental measurements of H. pomona and H. gentilis s.l. including type specimens. The sample size is 111.

2.3.3 Comparative analysis of H. pomona and H. gentilis s.l.

The structure of the noseleaf is different in H. pomona and H. gentilis s.l. In H. pomona, the noseleaf is slightly broader than in H. gentilis s.l. (Fig. 2.7). H. pomona is in general smaller than H. gentilis s.l. The zygoma width is broader in H. gentilis s.l. than in H. pomona. The mandible is shorter in H. pomona in comparison with H. gentilis s.l. (Table 2.6). The sagittal crest in H. gentilis s.l. is well developed, and extends upto the parietal region of the skull, wheras in H. pomona it is weakly developed.

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2.3.4 Bacular morphology

Four bacula of H. pomona from two different populations in south India were extracted and prepared. To compare the bacular morphology between H. pomona and H. gentilis s.l., four male specimens of H. gentilis s.l. from northeast India, Myanmar and Cambodia were scanned and images were taken.

In agreement with the observations of Srinivasulu & Srinivasulu (2018), the present study found differences in the bacular structure and size of H. pomona and H. gentilis s.l. The bacula of H. pomona is large (1.40 mm – 1.88 mm) whereas that of H. gentilis s.l. is very small (0.4 mm – 0.6 mm). In H. pomona, the shaft of the baculum is long, the base has a slightly bilobate base and the tip is characteristically bifid, with a short and narrow apical processes. The bacular structure in H. gentilis s.l. is very simple. It has a straight shaft ending with a bluntly rounded tip and a slightly expanded round base (Fig. 2.8).

(A) (B) (C) (D)

2.3.5 Echolocation call comparisons

H. gentilis s.l. from Wahlakya Cave, Meghalaya, northeast India emitted calls with an average FMAXE of 115 kHz but this is based on the single male specimen captured from the study location. The sampling of H. pomona and H. gentilis s.l. was not

CHAPTER 2 successful in south India and in other sites in northeast India respectively. Therefore, a review analysis based on the available literature published so far on the echolocation call variation of H. pomona and H. gentilis s.l. across its geographic range was carried out.

Wordley et al. (2014) recorded H. pomona from Valparai, Southern India with an average FMAXE of 126.34 kHz (range 123.7 - 128.2 kHz). The forearm length (FA) of adult H. pomona from their study ranged from 40 – 42mm. Raman, S. (pers. comm.) recorded an average FMAXE of 121.7 kHz from Wayanad, Southern India in 2017 for H. pomona and the FA values were 38.78 - 40.11mm. The average FMAXE of the echolocation calls of H. pomona from Mankulam, Kerala, South India was 126. 61 kHz and the FA values ranged from 39-40 mm (pers. comm. from Joy, T. K.).

All specimens from northeast India, Andaman Islands, northern Myanmar, Thailand, the South Yunnan subregion in China, Vietnam, Lao PDR, Cambodia and Western Malaysia are referred to H. gentilis s.l. There is a certain amount of variability in both echolocation call frequency and forearm length of H. gentilis s.l. across its geographic range (Fig. 2.9). The echolocation calls across these regions were 115.78 – 141.4 kHz (Fig. 2.9). The FA values were ranged from 38.7 – 47.1mm. (Srinivasulu et al., 2017) reported a new phonic type of H. gentilis s.l. (provisionally H. cf. gentilis = previously H. cf. pomona) from Havlock, South Andaman Islands, with an average frequency of 137.45 kHz (range 133.3–140.3). In their study, all the other individuals of H. gentilis were recorded on average with FMAXE at 126.5 kHz (range 121.9–131.7 kHz) from other parts of the Andaman Islands which is similar to H. pomona from south India. The FA values of the new phonic type (40.86 mm) and the other individuals of H. gentilis s.l. (41.80 mm) from the Andaman Islands, were also comparable to those from bats in south India.

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Figure 2.9. Biogeographic variation in the FMAXE of H. pomona: south India 1, n = 6 (Wordley et al., 2014); south India 2, n = 3 (Ramas, S., pers. comm.); H. gentilis s.l.: Northeast India, n = 1 (present study); Andaman Islands 1, n = 10 (Srinivasulu et al., 2017); Andaman Islands 2, n =4 (Srinivasulu et al., 2017); Yunnan, China, n = 14 (Zhang et al., 2009); Guangdong, China, n = 34 (Zhang et al., 2009); Hainan, China, n = 40 (Zhang et al., 2009); Hong Kong (Shek & Lau, 2006), Myanmar 1, n = 22 (Struebig et al., 2005); Myanmar 2, n = 5 (Sisook, P.) pers. comm.; Lao PDR (Francis, 2008); central Thailand (Douangboubpha et al., 2010); Thailand 1, n = 33 (Hughes et al., 2010); Thailand 2, n = 38 (Douangboubpha et al., 2010); northern Vietnam, n = 4 (Abramov & Kruskop, 2012); Malaysia, n = 3 (Murray et al., 2012). ‘n’ refers to the number of bats recorded in the studies cited.

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2.3.6 Molecular data analysis

A total of 123 sequences were generated for the five markers (CO1, 16s, STAT5A, PRKC1 and THY) chosen. The same genes were not necessarily sequenced for all the bats. Sequencing of samples from each study region was successful except for H. pomona samples for STAT5A. The total length of the sequences varied from 296 bp (16s) to 710 bp (CO1) for different markers. The number of samples, total base pairs, variable sites and parsimony informative sites for each gene are given in Table 2.7.

Table 2.7. The gene name, number of individuals sequenced, total sites, variable sites and parsimony informative sites for each sequenced gene

Number of Parsimony Gene individuals Total sites Variable sites informative sites sequenced CO1 24 710 120 63 16s 21 296 35 17 STAT5A 37 545 17 13 PRKC1 13 401 5 0 THY 27 502 8 1

Apart from the five markers studied, a sixth marker – SPTBN – was also selected and amplified in this study however, the sequencing was not successful for the same. Therefore, the sequences generated for SPTBN were discarded from further analysis due to their low quality.

2.3.6.1 Haplotype diversity pattern and network analysis

The haplotype diversity analysis of the two mtDNA genes revealed that CO1 has more haplogroups than 16s. The 11 unique haplotypes (h) for CO1 were defined by 120 variable sites with a haplotype diversity (Hd) of 0.924. For 16s, the haplotype diversity was 0.844 with 53 variable sites. Eight haplotypes were identified for 16s. Among the three nuclear introns, the highest number of haplotypes existed in the STAT5A dataset (h = 11) with a haplotype diversity of 0.740. The smallest number of haplotypes occurred in the PRKC1 dataset (h = 3; Hd = 0.295). These were however the largest and smallest numbers of bats sequenced succesfully respectively. Five haplotypes were recognised in THY (Hd = 0.578). Compared to the mtDNA datasets, the number of variable sites identified for nuclear introns was very low (STAT5A – 16; THY – 8 ; PRKCI – 5).

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Figure 2.10. The median -joining haplotype network of (A) CO1 (B) 16s for H. pomona from south India and H. gentilis s.l. from northeast India, China, Laos and Cambodia. Circle size is proportional to haplotype frequency; the colour of the circles shows the study region and connective lines show the number of mutational steps as hatch marks.

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Figure 2.10 conti. The median -joining haplotype network of (C) THY (D) PRKC1 for H. pomona from south India and H. gentilis s.l. from northeast India, China, Laos and Cambodia. Circle size is proportional to haplotype frequency; the colour of the circles shows the study region and connective lines show the number of mutational steps as hatch marks.

CHAPTER 2

In all mtDNA and nDNA median-joining haplotype network (Fig. 2.10a-d), the H. pomona haplotype was separated from the haplotypes of H. gentilis s.l. by several mutation steps. The mutations between the two taxa were higher in mitochondrial genes than in nuclear introns. There were >50 and 25 steps observed connecting between the H. pomona haplotype and the nearest H. gentilis s.l. haplotype for CO1 and 16s respectively. However, the mutation rate was lower for nuclear introns. The H. pomona haplotype was connected by 4 and 2 steps in THY and PRKC1 respectively. No H. pomona sequences were available for STAT5A.

No haplotypes were shared among any populations of H. gentilis s.l in both mtDNA haplotype networks However, the networks from nuclear introns suggested haplotype sharing among populations.

2.3.6.2 Phylogenetic analysis

The concatenation of mtDNA markers CO1 and 16s resulted in a supermatrix of 1001 aligned positions for 63 sequences whereas that of three nuclear introns (STAT5A, PRKC1 and THY) had a total length of 1439 bp from 55 sequences. The following models were selected under the AIC criteria in jModelTest for each gene fragment: CO1 – TrN + G; 16s – GTR + G; STAT5A – GTR + G; PRKC1 – HKY; THY – HKY.

2.3.6.3 Concatenated mitochondrial and nuclear tree

Both the Maximum Likelihood and Bayesian approaches yielded higly congruent topologes with high support values for most of the nodes for both mtDNA and nuDNA. In both analysis, H. pomona and H. gentilis s.l. were not sister taxa (Fig. 2.11). H. gentilis s.l. individuals were more closely related to the ingroup taxa H. cineraceus from Malaysia than H. pomona. There was 9.4 – 10.8 % and 7.8 – 8.2 % uncorrected sequence divergence between H. pomona and H. gentilis s.l. for CO1 and 16s respectively (Table 2.8).

The two major clades comprised of all the H. gentilis s.l. individuals from China, Laos, Vietnam, Andaman and northeast India and their placement was well supported (posterior probability [pp] = 1). The first major clade within H. gentilis s.l containing

CHAPTER 2 all the individuals from China, Laos and Vietnam had weak support (posterior probability [pp] = 0.74). Two different clusters were identified for H. gentilis individuals from Laos in this clade. The second major clade consisted of all the individuals from northeast India and the Andaman Islands. The Andaman group was basal to the northeast Indian individuals and the grouping had a support value of 0.87 (pp). The uncorrected sequence divergence between all the individuals of H. gentilis s.l. ragned from 2.9 – 9.3 % for CO1.

The position of H. cineraceus is supported with very low bootstrap (41) and/or posterior probability (pp = 0.57) values. Therefore it should be interpreted with caution.

CHAPTER 2

Table 2.8. The uncorrected group mean p-distance between each region of H. pomona and H. gentilis s.l. Below the diagonal : the uncorrected group mean genetic divergence for CO1. Above the diagonal: the uncorrecetd group mean genetic divergence for 16s.

H. pomona H. gentilis s.l. Population South India NE India Myanmar China Vietnam Laos Cambodia Andaman H. cineraceus

South India 8.2 7.8 8.1 6.3

NE India 9.4 5.0 3.8 8.6

Myanmar 10.4 4.9

China 10.7 4.3 6.2 3.2 8.6

Vietnam 10.3 6.0 7.1 4.9

Laos 10.2 5.3 7.0 5.4 6.1 8.5

Cambodia 10.7 8.5 9.3 9.3 9.0 8.8

Andaman 10.8 2.9 5.0 5.0 6.7 6.3 8.8

H. cineraceus 10.9 9.3 9.9 9.3 9.7 9.5 9.1 9.8

CHAPTER 2

China

Laos 2

Laos 1

Andaman

NE India

2.4 Discussion

The taxonomy of H. pomona Andersen, 1918 had been confusing until very recently. While reviewing the H. bicolor group, Andersen (1918) described two new taxa: H. pomona from south India and H. gentilis from Myanmar. Later, the latter taxon was refered as a subspecies to the former (Hill et al., 1986). However, the isolated geographic distribution of these two taxa had always demanded a taxonomic reassessment of their status (Douangboubpha et al., 2010). Subsequently, Srinivaulu & Srinivasulu (2018) provided evidence based on morphological, craniodental and bacular characters that H. pomona from south India and H. gentilis from northeast India to Southeast Asia are distinct. In this Chapter, with a wide ranging and substantial representation of samples from throughout the geographic range of H. pomona and H. gentilis s.l, it has shown that the H. pomona population from south India is distict from H. gentilis s.l. from northeast India and Southeast Asia based on morphometrics, bacular and genetic data. The below discussions focus on the major morphometric, bacular and molecular variation between H. pomona and H. gentilis s.l along with their echolocation call similarities. In light of results from the present and previous studies, the potential cryptic diversity in some populations of H. gentilis s.l. has also highlighted here.

2.4.1 Variation in morphology

Although H. pomona and H. gentilis s.l. morphologically look similar, the present analysis showed that there are some quantifiable differences in morphology. H. pomona is comparitively smaller in size than H. gentilis s.l. (Table 2.3 & 2.5). However, individuals of H. gentilis s.l. from China and Andaman Islands show an overlap in both external and craniodental measurements. Compared to H. gentilis s.l., the length of maxillary toothrow (CM3), anterior palatal width (C1-C1) and mandibular toothrow (cm3) are smaller in H. pomona (Table 2.5). The size differences in dentary apparatus has been reported from other cryptic bat species and is therefore not surprising (e.g. Pipistrellus pipistrellus and P. pygmaeus - Barlow, 1997). This could be an indication of diet differences in H. pomona and H. gentilis s.l. but further study on the diet and ecology of the spcies is needed to understand this variation. With in H. pomona, the

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CHAPTER 2 individuals from different populations showed considerable size difference (Fig. 2.5a & 2.5b). More samples needed to undestand this intraspecific variation in size among H. pomona population.

2.4.2 Variation in bacular morphology

A considerable variation in the bacular morphology, including shape and size, has observed between H. pomona population from south India and H. gentilis s.l. (Fig. 2.8). The baculum is larger in H. pomona (1.40 mm – 1.88 mm) having a long shaft with a slightly bilobate base and bifid tip (Fig. 2.8A) whereas the baculum of H. gentlis s.l. is very small (0.4 mm – 0.6 mm) and simple bearing a shaft ending with a bluntly rounded tip and a slightly expanded round base (Fig. 2.8B-D). These findings corroborate with the previous studies by Douangboubpha et al. (2010) and Srinivasulu & Srinivasulu (2018). According to Patterson & Thaeler (1982) the variation in bacular morphology acts as a ‘lock and key’ during copulation promoting reproductive isolation and as a mechanical barrier between closely related or sympatric species preventing interbreeding among them. Therefore the distinct bacular stuctrure of H. pomona from H. gentilis s.l. supports the recent split of the two cryptic taxa. The species- specific bacular morphology has been observed in other cryptic species such as Pipistrellus pipistrellus, P. pygmaeus (Herdina et al., 2014) and Rhinolophus andamanensis (Srinivasulu et al., 2019).

The bacular size shows some variations among the H. gentilis s.l. individuals. The baculum of H. gentilis s.l. from northeast India is comparatively larger (Fig. 2.8B) when compared to the individuals from Myanmar and Cambodia. However, the shape of the bacula are similar among these populations and they all have a straight shaft with slightly expanded base and either a pointed or bluntly rounded tip (Fig. 2.8B-D). Douangboubpha et al. (2010) reported a similar bacular length (0.5 – 0.8 mm) and shape in H. gentilis s.l. individulas from Thailand. Douangboubpha et al. (2010) mentioned that the bacular illustrations of H. gentilis s.l. from Vietnam and Malaysia in Topal (1975) and Zubaid & Davison (1987) respectively are similar to those from Thailand. However, Srinivasulu & Srinivasulu (2018) observed that both ends of the bacula of some of the H. gentilis s.l. from northeast India, Andaman

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Islands and northern Myanmar are the same instead of having a slightly expanded base or rounded/pointed tip. These variations could either be: (i) a case of intraspecific variation which is reported from other species of bats (e.g. Chaerephon astinanana – Rakotondramanana & Goodman, 2017; Rhinolophus andamanensis – Srinivasulu et al., 2019) or (ii) be an indication of potential cryptic species within H. gentilis s.l. particularly in the light of considerable variation in echolocation calls (Fig. 2.9) and molecular analysis (Fig. 2.10 & Fig. 2.11; Francis et al., 2010; Murray et al., 2010; Zhao et al., 2015; Yuzefovich, Kruskop & Artyushin, 2019) which has been reported from different regions in south Asia and southeast Asia. Further integrated taxonomic studies are needed to understand these variations in the bacular morphology considering the sampling of H. gentilis s.l. throughout from its geographic distribution.

2.4.3 Variation in echolocation call frequencies

The echolocation call frequency of H. pomona from three different populations in south India ranged from 121.7 kHz – 128. 2 kHz. The average FMAXE recorded for H. pomona individulas from Tamil Nadu, Wayanad and Mankulam (Kerala,) in south India are 126.34 kHz, 121.61 kHz and 126.61 kHz respectively. However, a wider range in echolocation call frequency of H. gentilis s.l. (115.78 – 141.4 kHz) has been reported across different regions from northeast India to South and Southeast Asia (Fig. 2.9). Amongst the H. gentilis s.l., one population in Andaman Islands (range 121 – 131.7 kHz, Srinivasulu & Srinivasulu, 2017), Laos (125 kHz, Francis & Habersetzer, 1998; 120 – 126 kHz, Francis, 2008), central Thailand (125.6 kHz – 128. 2 kHz, Douangboubpha et al., 2010) show considerable overlap with the call frequency of H. pomona (Fig. 2.9). Although H. pomona is comparitively smaller in size than H. gentilis s.l. the forearm length (FA) values of H. gentilis s.l. from some regions also show considerable overlap with that of H. pomona. Therefore, one of the possible explanations for the overlapping echolocation call frequency ranges in these two taxa could be allometric scaling. Smaller bats typically calling at higher frequencies than larger bats and this has been reported from hipposiderids (e.g. Hipposideros lankadiva, see Chapter 3), rhinolophids, vespertilionids, molossids (Jung, Kalko & Helversen, 2014) and emballonurids (Jung, Kalko & Helversen, 2007). In Chapter 4, it

CHAPTER 2 has shown that the distriution of H. pomona is confined to mainland India (particularly south India) and is not overlapping with that of H. gentilis s.l. Therefore, the convergence in echolocation calls might be attributed to environmental influences or local adaptations.

There is a certain amount of variability in both echolocation call frequency and forearm length of H. gentilis s.l. across its range. For example, A recent survey from Andaman Islands identified a new phonic type from Havelock Island (south Andaman) which could be potentially a new species (Srinivasulu et al., 2017). The regional variation in echolocation calls needs to be addressed in order to improve the localized knowledge of the species and to identify cryptic populations (Hughes et al., 2010; Wordley et al., 2014). Therefore, more work is needed to see whether these phonic types correlates with cryptic diversity in H. gentilis s.l.

2.4.4 Variation in molecular data

Results showed that H. pomona from south India is genetically distinct from H. gentilis s.l. both on the mtDNA and nuDNA. The mtDNA divergence between the two taxa >8 % which is expected for species level differentiation (Baker & Bradley, 2006) in mammals. Similar level of interspecific genetic divergence has also been reported in other cryptic species of bats (e.g. Hipposideros bicolor and Hipposideros kunzi sp. nov. – Murray et al., 2018; Rhinolophus gorongosae sp. nov and R. swinnyi s.l. – Taylor et al., 2018). No haplotypes of H. pomona from south India are shared with H. gentilis s.l. Similarly, in the phylogenetic analysis individuals of H. pomona do not fall into the H. gentilis s.l. clade instead the two taxa showed a non-sister relationship. Considering the disjunct distribution of H. pomona and H. gentilis s.l., the high genetic divergence among them is not surprising. High level of genetic divergence was observed within H. gentilis s.l. (Table 2.8; Fig. 2.11). This corroborates with the findings from previous studies. Francis et al. (2010) and Murray et al. (2012) suggested the presence of possibly two distinct species of H. gentilis s.l. from Laos which are morphologically similar but highly divergent on mtDNA (12% at ND2). Similarly, Zhao et al. (2015) identified three populations from China including a unique population from Hainan Island. A recent study by Yuzefovich, Kruskop &

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Artyushin (2019) based on two mtDNA genes and seven nuclear genes revealed that at least four mitochondrial lineages are present in H. gentilis s.l. from Indochina. Therefore, a detailed integrated taxonomic study is needed to unravel the cryptic diversity exist in H. gentilis s.l.

2.5 Conclusion

In conclusion, the molecular and bacular analysis showed a substantial difference between H. pomona from south India and H. gentilis s.l. from northeast India to Southeast Asia. However, the morphometric (except cranial data) and echolocation dataset could not resolve the corresponding distinction. Therefore, the present study validates the current species status of H. pomona whose distribution is confined to south India. The study also documented potential cryptic diversity in H. gentilis s.l. therefore a detailed study of H. gentilis s.l. is recommended throughout from its distribution to resolve the complexity. The revised taxonomy for the H. pomona s.l. is given below:

(a) Hipposideros pomona Andersen, 1918 Pomona leaf-nosed bat Hipposideros pomona Andersen, 1918; 380; 381; Haleri, North Coorg, India (a few miles north of Mercara, Coorg district, Karnataka).

(b) Hipposideros gentilis Andersen, 1918 Andersen’s roundleaf bat Hipposideros gentilis Andersen, 1918; 380; 381; Thayetmyo, Myanmar. Hipposideros gentilis sinensis Andersen, 1918; 380; 381; Foochow, Fujian, China.

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An integrated approach to the taxonomy and evolutionary history of Hipposideros lankadiva Kelaart, 1850 from

South Asia

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Abstract

Hipposideros lankadiva Kelaart, 1850 has always caused confusion among bat taxonomists due to its uncertain subspecies status and lack of knowledge regarding its geographic range. An integrative taxonomic approach was used to understand whether the currently recognised subspecies of H. lankadiva require a re-evaluation of their status. The results showed that H. lankadiva from Sri Lanka and northeast India-Myanmar are substantially larger in body size and call at lower frequency than those from the rest of mainland India. However, similarity in baculum morphology among populations, generally low sequence divergence in mtDNA and a lack of differentiation in nuDNA provide little support for hypotheses proposing that cryptic species are present. The extensive variation in the morphology and call frequency of H. lankadiva, could be a consequence of local adaptation. The large size of H. lankadiva in Sri Lanka can perhaps be explained by the ‘Island rule’. In conclusion, these findings suggest that the current subspecies status is appropriate.

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3.1 Introduction

Hipposideros lankadiva Kelaart, 1850 is a member of the diadema species group in the bat family Hipposideridae. The taxonomy including the subspecies status and synonyms of H. lankadiva have long been uncertain among bat taxonomists. Considering the importance of integrated taxonomy in the cryptic species-rich families Hipposideridae and Rhinolophidae, it is timely to reassess the taxonomy of H. lankadiva. In this chapter, an integrated approach includes conventional morphometry, bacular, acoustics and molecular methods will be used to understand whether the subspecies merit species status.

3.1.1 Taxonomic history of H. lankadiva

H. lankadiva is commonly known as the ‘Indian leaf-nosed bat’ or ‘Indian roundleaf bat’ and was first described from Kandy in the central Hills of Sri Lanka (Kelaart, 1850). Kelaart, in his description used the common name, ‘Ceylon Gigantic Horse-shoe Bat’ for H. lankadiva and he considered it as a new species from Sri Lanka as it was not found in Mr. Blythe's Monograph of Indian Bats. Andersen (1905) also considered that the range of H. lankadiva was restricted to Sri Lanka. However, Andersen (1907) reported a range extension of H. lankadiva to Myanmar based on two immature female specimens from Bhamo (Banmaw) in southern Kachin State of upper Myanmar. The two immature specimens were collected by Leonardo Fea (1888 & 1886) and they had originally been referred to Hipposideros diadema by Thomas (1892). Andersen (1907) wrote ‘H. lankadiva is now known to occur not only in Ceylon (Sri Lanka) but also in Burma (Myanmar), and therefore, no doubt, also inhabits the Indian Peninsula and parts of Himalaya’.

Later, Andersen described four new hipposiderid taxa from the Indian peninsular region (Andersen, 1918). This included two species and two subspecies. The two species were: H. schistaceus from Vijayanagar, Karnataka and H. indus from Gersoppa, Karnataka. Andersen mentioned that the H. indus was smaller in size when compared to H. lankadiva from Sri Lanka. The two new subspecies were: H. indus mixtus from E. Mysore, Karnataka and H. indus unitus from Hoshamgabad, Saugor, Madhya Pradesh. Minor differences in the body size and/or hair colour were

CHAPTER 3 the basis for the distinction between these new four taxa. In contradiction to Andersen’s previous conclusion (Andersen, 1905), he restricted H. lankadiva to Sri Lanka only and omitted any reference about the Myanmar specimens. This added confusion to the geographic range of H. lankadiva.

Kemp (1924) reported an interesting case of a ‘discontinuous distribution’ of H. lankadiva from Siju Cave in the Garo Hills of northeast India. In his report, he quoted Mr. Martin Hinton’s notes who identified the species and according to that Mr. Hinton could not find any features in the skull or skin of these bats to distinguish them from H. lankadiva Kel., a species reported only in Ceylon (Sri Lanka). Hinton also mentioned that the H. indus and its subspecies (considerably smaller forms) are supposed to be taking the place of H. lankadiva in mainland India. It was predicted that H. lankadiva may in future be discovered in peninsular India (Kemp, 1924).

H. lankadiva, H. unitus, H. indus, H. mixtus and H. schistaceus were included in a ‘mainland offshoot of the diadema group’ in the Hipposideridae family by Tate (1941). However, Tate (1941) did not specify the species, subspecies and/or synonym status of these taxa. Ellerman & Morrison-Scott (1951) referred H. indus, H. mixtus and H. unitus to H. lankadiva and suggested the H. schistaceus might be a synonym of H. lankadiva. Brosset (1962) considered all the subspecies created by Andersen in 1918 (H. indus, H. mixtus, H. unitus and H. schistaceus) as ‘being without real existence’. Brosset erroneously mentioned H. schistaceus also as a subspecies (Das et al., 1995) described by Andersen. Brosset (1962) did not recognise any of these subspecies because the principal distinction between them was the fur colour which is an extremely variable feature among individuals.

In a revision of the genus Hipposideros, Hill (1963) treated H. indus and H. unitus from peninsular India as subspecies of H. lankadiva: H. lankadiva indus (H. l. indus) and H. lankadiva unitus (H. l. unitus). In the absence of observable differences between H. l. indus and H. lankadiva mixtus, Hill referred H. l. mixtus as a synonym of H. l. indus. H. schistaceus remained as a separate species in his revision. This view was followed by Corbet & Hill (1992) and they also considered that the species is confined to central and south India and Sri Lanka.

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The occurrence of H. lankadiva was reported by Mandal & Nandi (1989) from the Sundarbans of West Bengal, India but was lacking any details. Later, Agrawal et al. (1992) collected new material from West Bengal and suggested for a taxonomic revaluation of subspecies of H. lankadiva based on freshly collected materials from its entire range. Das, Lal & Agrawal (1993) and Mandal, Poddar & Bhattcharyya (1993) agreed with this view. Das et al. (1995) examined specimens of H. lankadiva from Sri Lanka, peninsular India and northeast India including Meghalaya, Tripura. They noted that the peninsular Indian population was smaller than the other two and body size in Sri Lankan and northeast Indian specimens was relatively large and similar. Subsequently, Mandal, Poddar & Bhattcharyya (1997) reported new occurrence from Mizoram, northeast India and following the view of Brosset (1962), they suggested that the species was monotypic.

In a detailed systematic review of bats of the Indian subcontinent, Bates & Harrison (1997) considered only two valid subspecies of H. lankadiva: H. l. lankadiva, which included all specimens from Sri Lanka and H. l. indus included all specimens from India. Since they did not notice any major differences in size and colour in H. indus, H. mixtus and H. unitus, all these taxa were treated as synonyms of H. l. indus. Bates & Harrison (1997) reported that a number of topotypes of H. schistaceus were juveniles and hence the average smaller size recorded for that taxa. Therefore, H. schistaceus was also referred as a synonym of H. l. indus. Srinivasulu & Srinivasulu (2001, 2012) followed the same arrangement. The comparison of external and cranial measurements of H. lankadiva, H. indus, H. mixtus and H. unitus by Sinha (1999a) revealed no major differences among these taxa.

When Sinha (1999b) examined specimens from Meghalaya, he did not mention its subspecific status. The occurrence of H. lankadiva in Myanmar was also omitted in Francis (2008). A recent morphometric and echolocation study by Bates et al. (2015) revealed an interesting discovery of a large colony of H. lankadiva in eastern Kachin Province from Myanmar. Bates et al. (2015) reported it as the first record of H. lankadiva from Myanmar after Andersen (1907) and first confirmed occurrence from Southeast Asia. Based on the larger size of the specimens from Myanmar in comparison to the ones from peninsular India and their geographic separation from

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Sri Lanka, Bates et al. (2015) described the material from Myanmar as a new subspecies of H. lankadiva named H. l. gyi. They also referred the materials from northeast India to this new taxon based on the measurements from available literature.

3.1.2 Distribution of H. lankadiva

The occurrence of H. lankadiva was also reported from Bangladesh by Khan (2001) but later he excluded it (Khan, 2015). Therefore, the presence of the species was doubtful in Bangladesh until the discovery of a roosting colony of 450-500 individuals of H. lankadiva in the northern part of Bangladesh, very close to the Meghalaya hill range of India (Saha, Feeroz & Hasan, 2015). Thus, the known distribution of H. lankadiva extends from Myanmar, northeast India, Bangladesh, peninsular India and Sri Lanka (Fig. 3.1; Bates et al., 2015).

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3.1.3 Island rule and Insular bats

According to van Valens’ ‘Island rule’ (van Valen, 1973) the body size variation in insular mammal population seemed to be a general phenomenon. The insular environment adaptations result in dwarfism in larger mammals (e.g. carnivores) and gigantism in smaller mammals (e.g. rodents). The Island rule is an emergent pattern resulting from a combination of selective forces such as resource limitations, intra- and interspecific interactions and immigration filters (Lomolino, 2005). Although the body size of bats is similar to smaller mammals, several studies of bats have shown that the island species and subspecies exhibit smaller size than their continental relatives (Krzanowski, 1967; Juste et al., 2007; Taylor et al., 2012). However, Krzanowski (1967) reported at least 35 cases of gigantism in insular bats. Insular selection pressures and advantages of being large which include high vagility, greater survival abilities during short-lived periods of hunger and cool climates of island environment (Krzanowski, 1967; Lomolino, 2005).

3.1.4 Background and objectives of the study

The currently recognised subspecies in H. lankadiva – H. l. lankadiva, H. l. indus, and H. l. gyi – are described based on variations in either morphological, bacular and/or echolocation characters along with their geographical isolation. So far, no molecular studies have been performed on this taxon to see whether these subspecies are genetically distinct or not, or even whether they merit elevation to species status. All the previous studies of H. lankadiva were undertaken at different times without sampling from its geographical range and collecting information using multiple taxonomic methods. With this background, it is planned to undertake an integrated taxonomic study throughout the geographical range of H. lankadiva using conventional morphometrics, acoustic and molecular approaches. Through this, the present study aims to answer the following questions:

1) Do any of the three currently recognised subspecies require a re-evaluation of their current status especially based on an integrated taxonomic approach? 2) Is Island gigantism a potential reason for the bats in Sri Lanka being substantially larger than the mainland Indian bats?

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3) Is the similarity in large body size and echolocation calls in H. l. gyi (Myanmar) and H. l. lankadiva (Sri Lanka) a result of independent evolution or are they sister taxa that share these characters via recent common ancestry?

3.2 Materials and methods

3.2.1 Details of study specimens

The museum collections of both skin and skulls of H. l. lankadiva, H. l. indus and H. l. gyi were examined from The Natural History Museum, London (BMNH), the Harrison Institute, Sevenoaks, UK (HZM), the Hungarian Natural History Museum, Budapest (HNHM), National Museum of Natural History, Sri Lanka (NMNH), the Zoological Survey of India, Kolkata (ZSI), the North Eastern Regional Centre of ZSI, Shillong, (NERC), and The Bombay Natural History Society, Mumbai, India (BNHS) (See Appendix III for the details of the specimens examined). The type specimens were also studied for H. l. lankadiva (BM 7.1.1.311, lectotype), H. indus (BM 12.11.28.20; holotype), H. i. unitus (12.11.29.20; holotype), H. i. mixtus (BM.13.4.11.19; holotype), H. schistaceus (BM.13.4.10.3, holotype) and H. l. gyi [HZM.10.40222 (OMT 110105.1; holotype)].

3.2.2 Study sites and sampling

Bat surveys were conducted in different locations representing the current known distribution of H. lankadiva except in Myanmar and Bangladesh (Fig. 3.2). Bureaucratic and financial constraints restricted me from carrying out field work in those two areas. Bat surveys were conducted at caves, mine tunnels, old forts or forest sites in India and Sri Lanka. The surveys were carried out in January - June 2016 and February - June 2017. Bats were captured using varying length of mist nets and/or by setting custom-made two bank harp traps (Bat Conservation and Management, Carlisle, PA, USA) along flyways, at the entrance of caves/tunnels or in the old forts. Hand-held nets were also used to catch the bats from their roosts wherever it was possible. The captured bats were kept inside cotton cloth bags prior to data collection and unharmed bats were later released.

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The initial species identification was made using keys in Bates & Harrison (1997). The sex, age (juveniles/ adults) and body mass were noted for every individual captured. Sex of the bats was determined after inspecting the genitalia (Racey, 2009). The wings of each bat were trans-illuminated and examined visually in order to recognise juveniles by the presence of cartilaginous epiphyseal growth-plates in the phalanges or finger bones and more tapered finger joints than in adults (Anthony, 1988; Brunet- Rossinni & Wilkinson, 2009). Individuals were weighed with Pesola spring scales (Pesola AG, Switzerland) to the nearest 0.1 g, lengths of forearm and tibia were measured using dial callipers to the nearest 0.1 mm. Wing tissues were collected using 3mm biopsy punches (kai Europe GmbH, Germany). Soon after, tissue was stored in one of the following media at room temperature -, molecular grade ethanol; silica beads; and nucleic acid preservation buffer (NAP) -, until transported to the laboratory. Later, the samples were stored at -20oC until DNA extraction.

Bats were captured, and the tissue samples were imported to the UK under the guidelines of National Biodiversity Authority, India (Permit No. NBA/Tech Appl/9/Form B/11/16/16-17/361 and NBA/Tech Appl/9/2241/18/19-20/1498), Animal and Plant Health Agency, UK (Import license authorisation No. ITIMP17.1427). The study regions were divided into six as: south India, west India, central India, northeast India (NE India), Myanmar and Sri Lanka. These regions were defined arbitrarily to facilitate identification of geographic variation (Fig. 3.2). Moreover, either acoustic and/or molecular samples were collected from these regions during the study.

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3.2.3 Measurements and morphometric analysis

3.2.3.1 External and skull measurements

Thirteen external [excluding (1ph3mt/3mt)*100] and 14 cranial measurements used in the study (for details, see section 2.2.2.1 Measurements and morphometric analysis). The variable, (1ph3mt/3mt)*100, was not calculated because it did not appear in any previous publications on H. lankadiva and therefore comparison across studies was not possible. Most of the museum specimens were old preservations either as dry skins and/or specimens in formalin or alcohol. Therefore, they were too brittle to handle for taking certain measurements. Due to this, it was only possible to take the approximate measures for the characters, Head to body length (HB), Ear (E) and Tail (T). So, these three characters were only used for calculating mean and SD only, and did not not include these measurements in any other statistical analyses. Sexual dimorphism in cranial characters was tested for, either by using student t-

CHAPTER 3 tests or Mann-Whitney U tests with a Bonferroni correction for multiple tests depending on results from normality testing.

3.2.3.2 Morphometric analysis

The samples size was limited making it problematic to treat both sexes separately. Therefore, the Principal Component Analysis (PCA) for external and cranial measurements was carried out using a pooled dataset from both males and females across the different study regions. For PCA of 10 external measurements, a sample of 100 specimens was used. The variation in a sample of 95 skulls was analysed using 14 craniodental characters. Before performing PCA, the Spearman correlation within external and skull variables were checked using the package Corrplot v0.84 (Wei & Simko, 2017). The sampling adequacy for the PCA analysis was also verified from the Kaiser–Meyer–Olkin (KMO) measure calculated using the R add-in package psych v1.8.12 (Revelle, 2019). The PCA was rerun to include type specimens for which only seven craniodental characters (CCL, ZB, BB, PC, CM3, M and cm3) were available resulting in a total sample of 101 skulls.

A forward stepwise Discriminant Function Analyses (DFA) was used to determine whether the individuals of H. lankadiva from different regions could be differentiated using these characters, and to find characters that could be used for field identification. As DFA is an a priori test, individuals were first assigned to each study region based on the results from PCA. Leave-one-out classification was used to assess the performance of the selected characters in predicting group membership. For datasets with sufficiently large sample sizes, 70% of the individuals were randomly selected and used to calculate the DFA, and the remaining 30% of the samples were used as an independent test of the DFA. In the initial DFA analysis, all 14 craniodental characters and samples from all regions were used. The samples from northeast India and Myanmar were combined as they showed no difference in the genetic data (Table 3.8). In this initial analysis, the standardised canonical discriminant function coefficient (beta values) for C1-C1 was -1.18 which exceeded the normal range (-1 to 1). According to Deegan (1978) if there are two or more predictors that are correlated, positively or negatively, then the beta values may

CHAPTER 3 exceed those bounds. Considering this, the highly correlated craniodental variables (GTL, CCL, CM3 and cm3; r ≥0.80) were removed from the final analysis. Since the sample size from west India was very low (n = 3), it excluded from the final DFA analysis.

3.2.4 Bacular morphology

Both conventional bacular extraction and CT scan imaging was used to study the bacular morphology of the target species (see section 2.2.3 in Chapter 2).

3.2.5 Echolocation call recording and analysis

H. lankadiva was sampled at nine locations across the Indian subcontinent. In India, bats were studied in south, west, central and northeast Indian regions at the following locations: Kadem project, Adilabad District, Telangana, south India (n = 16), Dhabelwada, Bicholim, Goa, west Inidia (n = 3); Lamgao Buddhist Caves, Bicholim, Goa, west India (n = 4); Raisen Fort, Bhopal, central India (n = 8) and Phlang karuh cave, Meghalaya, northeast India (n = 10). In Sri Lanka, bats were sampled from the following sites : Ingiria-Dumbara Pumbigo Mine (n = 8), Bogala Mine (n = 7), Kanhelia Mine (n = 12), and Yetideria Cave (n = 1).

All bats were recorded handheld approximately 30cm from the microphone. The ultrasonic sound produced by bats were recorded using a Pettersson Ultrasound Detector D980 (Pettersson Electronik AB, Uppsala, Sweden; frequency response 8 and 160 kHz ± 3.5 dB ) except in two locations in India. It has a sampling rate of 350 kHz. The detector was manually triggered to capture 12s in 10x time expansion. The output was recorded as 24-bit .wav files on to an Edirol R-09 (Roland Ltd., UK) digital recorder sampling at 44.1 kHz.

The echolocation calls of bats captured from Kadem Reservoir, Telengana, south India were recorded using a Batbox Griffin (Batbox Ltd, West Sussex, UK) with a sampling rate of 705.6 kHz and a frequency range of 16 - 190 kHz and the files were saved at 16x time expansion as 16-bit .wav files. The bats from Phlang karuh cave, Meghalaya, northeast India were recorded using an S-25 bat detector (Ultra Sound

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Advice, London, UK), which was attached to a Portable Ultrasound Processor (PUSP – Ultra Sound Advice) and a Sony professional Walkman (Sony Corporation, Tokyo, Japan). The ultrasound detected by the bat detector was time expanded 10 times by the PUSP and the output recorded onto a metal tape in the Walkman. The samples from the former location was collected by Prof. G. Jones and from the latter by Dr. Adora Thabah. Similarly, the sample from Yetideria cave in Sri Lanka was collected by Mr. Tharaka Kusuminda.

BatSound version 4.1.4.309 (Pettersson Electronics and Acoustics AB, Uppsala, Sweden) was used to visualise calls (with an FFT size of 1024 in a Hanning window). Since bats in the family Hipposideridae (also Rhinolophidae) emit calls with a strong constant frequency (CF) component, only FMAXE (Peak frequency, or frequency of maximum energy) was measured. The CF calls of the hipposiderids include a downward frequency modulated sweep at the end of the CF component. Power spectra were used to derive peak frequency (with an FFT size of 8192 in a Hanning window). Up to 10 clear calls with the highest signal to noise ratio were selected from each individual recording and the mean value was used for each bat in analyses.

3.2.5.1 Statistical analysis

Since juveniles were captured only from one region (Sri Lanka), the effect of age on FMAXE was tested separately, using a t-test to compare adults and juveniles. A Shapiro-Wilk test was used to check the normality of FMAXE for each group of samples. The differences in FMAXE among region and between sexes were tested using a General Linear Models (GLM) to assess the effect of geographic region and sex on echolocation calls. In the GLM, both independent variables were treated as fixed factors. In this analysis, both the inadequate sample size and unbalanced data across region and sex restricted me from testing for an ‘interaction effect’ of fixed factors on FMAXE. Tukey’s HSD post-hoc test was used to identify where differences occurred in echolocation call frequency across the five study regions.

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3.2.6 Molecular data collection and analysis

3.2.6.1 Taxon sampling

The tissue samples from different locations were collected in the selected study regions except south India. Dr. Adora Thabah and Dr. Manuel Ruedi provided samples from northeast India and Dr. Paul Bates took samples in Myanmar. The procedures for tissue collection, storage and export were as detailed in section 2.2.5 in Chapter 2.

3.2.6.2 DNA extraction, amplification and sequencing

DNA extraction, amplification and sequencing proceedures were same as in Chapter 2 (see section 2.2.5.2 for details). A total of six genes, three mitochondrial (mtDNA) and three nuclear introns (nuDNA) were sequenced for H. lankadiva. Five genes were the same as in Chapter 2 (CO1, 16s, STAT5A, PRKC1 and THY). An additional mtDNA gene, NADH dehydrogenase subunit 2 (ND2), was sequenced because only eight samples produced high quality sequences for CO1. Different primers were tried for CO1 (LCO1490 and HCO2198 – Folmer et al., 1994; FishF1 and FishR1 – Ward et al., 2005; VF1di and VR1di – Ivanova, deWaard & Hebert, 2006) but none was successful. ND2 locus was amplified using previously designed bat-specific primers (Murray et al., 2012): L5758.M (5′-GGH TGA GGN GGM CTN AAY CAR AC-3′) and H6305.M (5′-GGC TTT GAA GGC YCT TGG TC-3′). Apart from the three introns (STAT5A, PRKC1 and THY), a fourth one – SPTBN (B-Spectrin nonerythrocytic 1) was selected and amplified using the following primers: CCAGGCAGAGCGGGTGAGAGG (forward) and CCACTCGGTCTCGGATCACCTGG (reverse) (Eick, Jacobs & Mathee, 2005; Lack et al., 2010).

3.2.6.3 Sequence alignment and editing

The same procedures in ‘sequence alignment and editing’ in section 2.2.5.3 of Chapter 2 was followed in order to assemble and edit the sequences and to confirm the species identity.

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3.2.6.4 Genetic divergence analysis

The uncorrected p-distances were calculated in MEGA v10.0.5 (Kumar et al., 2018) for all the six genes (ND2, 16s, CO1, STAT5A, PRKC1 and THY). No data for CO1 were included in any other molecular analysis due to its limited sample size. The following sequences of H. lankadiva from GenBank were used to calculate genetic distance and/or in phylogenetic analysis: CO1 (HM540536 – Sri Lanka) and 16s (KF059983 – south India; KY113125 – central India).

3.2.6.5 Haplotype network

Haplotype networks were constructed for all genes except CO1 using the same procedure as in section 2.2.5.4 of Chapter 2.

3.2.6.6 Phylogenetic analysis

The same procedures for phylogenetic analysis were followed as described in Chapter 2 (see ‘Phylogenetic analysis’ in section 2.2.5.5). To better resolve the relationships between the individuals from different regions and to validate the current subspecies status of targeted taxa, representative lineages from three closely related taxa were used as ingroups: Hipposideros diadema, Hipposideros armiger and Hipposideros larvatus. Hipposideros pomona and Hipposideros cineraceus were used as outgroups. Both the ingroup and outgroup taxa were chosen based on their positions in the phylogenetic tree from previously published studies (Francis et al., 2010; Murray et al., 2012; Foley et al., 2017). The Hipposideros genus phylogenetic trees based on both mtDNA (ND2 and 16s) and nuclear intron (STAT5A, PRKC1 and THY) datasets were built in order to confirm the position and relationships of chosen ingroup and outgroup taxa.

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3.3 Results

3.3.1 External morphology analysis

The external measurements were considerably larger in H. lankadiva individuals from Sri Lanka, northeast India and Myanmar than those from peninsular India. The west Indian population was the smallest among the other two (south and central) populations of H. lankadiva from peninsular India. The mean, standard deviation and range of 13 external characters of H. lankadiva from different study regions are given in Table 3.1. Females were smaller in all regions than males (Table 3.1). Apart from size and fur colour, no other visible external character could discriminate between specimens from different study regions. The noseleaf of H. lankadiva from Sri Lanka, west India, central India (Fig. 3.3) and northeast India has three or four supplementary leaflets but the fourth one is always very small. Bates et al. (2015) reported only three supplementary leaflets in all the five specimens studied from Myanmar. The fifth metacarpal of the wing was shorter than the third and fourth (Table 3.1)

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Table 3.1. External measurements (in mm) of H. lankadiva from different study regions. Means and standard deviations are given. The range values are in parentheseis. The sample size is given in brackets of ‘sex’ column.

Sex FA HB (approx.) TL (approx.) E TIB HF 3mt

79.80 ± 0.99 87.15 ± 4.31 35.50 ± 0.99 22.55 ± 3.18 30.70 ± 1.27 14.33 ± 2.93 56.08 ± 1.31 ♂ (2) (79.10 – 80.50) (84.10 – 90.20) (34.80 – 36.20) (20.30 – 24.80) (29.80 – 31.60) (12.25 – 16.40) (55.15 – 57.00) West India 78.87 ± 2.15 78.32 ± 4.98 35.30 ± 2.74 19.29 ± 2.35 28.48 ± 0.61 12.14 ± 1.98 60.24 ± 2.77 ♀ (5) (75.50 – 80.85) (73.00 – 85.70) (31.70 – 38.70) (15.50 – 22.00) (27.50 – 29.10) (9.80 – 15.30) (56.20 – 63)

83.03 ± 4.31 (44) 91.47 ± 5.25 (17) 43.75 ± 3.81 (35) 25.24 ± 1.49 (36) 32 ± 2.74 (16) 15.59 ± 1.28 (44) 61.01 ± 3.40 (26) ♂ (73.10 – 90.60) (82.00 – 99.00) (35.00 – 50.90) (20.50 – 27.40) (27.30 – 36.50) (11.90 – 18) (55.40 – 68.20) South India 80.79 ± 3.60 (16) 90.50 ± 4.04 (8) 42.32 ± 2.83 (10) 25.54 ± 1.27 (10) 30.11 ± 2.02 (44) 15.06 ± 1.08 (16) 60.37 ± 3.39 (11) ♀ (75.00 – 88.70) (82.00 – 95.00) (38.00 – 47.40) (23.70 – 28.50) (28.30 – 34.20) (13.50 – 17.20) (54.50 – 66.00)

86.86 ± 1.89 (16) 88.94 ± 2.15 (10) 40.97 ± 2.53 (11) 23.37 ± 1.56 (11) 33.09 ± 1.75 (15) 15.62 ± 1.29 (15) 62.69 ± 1.31 (13) ♂ (83.30 – 89.50) (86.10 – 93.00) (37.00 – 45.00) (22.00 – 27.00) (29.00 – 35.20) (12.70 – 17.30) (60.83 – 65.10) Central India 85.50 ± 2.20 (15) 88.20 ± 1.70 (7) 43.30 ± 2.72 (7) 22.47 ± 2.63 (13) 32.09 ± 1.60 (13) 14.99 ± 0.92 (13) 62.91 ± 2.05 (11) ♀ (82.50 – 88.20) (86.00 – 91.00) (40.00 – 47.00) (19.00 – 27.00) (30.20 – 35.00) (13.20 – 16.10) (59.10 – 65.80)

90.24 ± 2.94 55.65 ± 5.44 (2) 26.75 ± 3.18 (2) 34.96 ± 2.07 16.98 ± 1.52 63.96 ± 2.47 ♂ (5) NA (87.40 – 94.40) (51.80 – 59.50) (24.50 – 29.00) (32.30 – 37.30) (15.60 – 18.80) (59.60 – 65.60) Northeast India 87.20 ± 0.84 23.30 ± 0.99 (2) 34.78 ± 1.22 16.53 ± 1.26 65.25 ± 1.06 (2) ♀ (4) NA 50.00 (1) (86.50 – 88.20) (22.60 – 24.00) (33.10 – 36.00) (14.70 – 17.60) (64.50 – 66.00)

91.89 ± 2.04 97 ± 6.51 (3) 50.31 ± 4.51 26.06 ± 1.21 35.81 ± 1.30 14.25 ± 0.82 67.67 ± 2.28 Myanmar ♂ (4) (89.20 – 93.97) (89.50 – 101.20) (45.82 – 54.20) (24.71 – 27.60) (34.00 – 36.82) (13.20 – 15.20) (64.40 – 69.66)

♀ (1) 91.59 88.20 43.83 24.18 33.90 12.78 67.30

91.80 ± 1.58 (13) 97.04 ± 7.63 (8) 44.95 ± 3.04 (9) 26.33 ± 2.28 (9) 37.86 ± 1.10 (9) 13.99 ± 1.40 (10) 63.61 ± 1.76 (8) Sri Lanka ♂ (88.40 – 94.60) (83.10 – 106.00) (41.50 – 50.00) (22.00 – 29.50) (36.24 – 39.60) (11.70 – 16.10) (59.10 – 65.90)

90.13 ± 2.79 (18) 95.33 ± 4.55 (12) 48.88 ± 4.26 (17) 25.58 ± 1.67 (16) 36.28 ± 1.36 (16) 14.61 ± 2.05 (17) 63.44 ± 1.51 (17) ♀ (86.90 – 99.00) (85.50 – 101.40) (42.20 – 58.00) (23.20 – 28.20) (33.40 – 38.00) (10.60 – 20.00) (62.20 – 66.50)

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Table 3.1. Continued.

Sex 4mt 5mt 1ph3mt 2ph3mt 1ph4mt 2ph4mt

54.15 ± 1.63 50.35 ± 0.64 26.08 ± 0.39 25.23 ± 0.18 19.55 ± 0.28 12 ± 0.14 ♂ (2) (53.00 – 55.30) (49.90 – 50.80) (25.80 – 26.35) (25.10 – 25.35) (19.35 – 19.75) (11.90 – 12.10) West India 58.17 ± 2.71 53.27 ± 1.42 26.66 ± 0.50 25.37 ± 0.94 19.67 ± 0.50 12.47 ± 0.38 ♀ (5) (54.35 – 61.40) (51.10 – 54.55) (25.90 – 27.10) (24.50 – 27.00) (19.20 – 20.50) (11.90 – 12.90)

59.46 ± 2.78 (26) 55.08 ± 3.11 (25) 27.33 ± 1.57 (26) 26.60 ± 1.93 (25) 20.37 ± 1.14 (25) 13.10 ± 0.67 (25) ♂ (53.50 – 65.00) (49.70 – 59.70) (24.80 – 31.00) (21.00 – 29.50) (18.20 – 22.40) (11.70 – 14.10) South India 58.38 ± 3.20 (11) 53.56 ± 3.13 (11) 27.43 ± 1.32 (11) 26.62 ± 0.96 (11) 19.90 ± 0.81 (11) 13 ± 0.61 (10) ♀ (53.90 – 64.50) (49.20 – 59.70) (25.70 - 29.50) (24.50 – 28.50) (18.30 – 21.10) (11.70 – 13.90)

60.44 ± 2.02 (13) 56.19 ± 1.88 (13) 28.38 ± 0.91 (13) 27.22 ± 1.23 (13) 20.91 ± 0.71 (13) 13.56 ± 0.76 (13) ♂ (57.00 – 63.80) (52.50 – 59.20) (27.00 – 29.70) (24.80 – 28.90) (19.20 – 22.00) (12.10 – 14.70) Central India 61.13 ± 1.94 (12) 56.23 ± 1.68 (12) 27.85 ± 1.39 (11) 27.26 ± 1.32 (10) 20.56 ± 0.87 (10) 13.51 ± 0.77 (9) ♀ (57.30 – 64.10) (58.60 – 53.50) (25.50 – 30.10) (24.80 – 28.90) (19.70 – 22.40) (12.40 – 14.50)

62.56 ± 1.90 (2) 58.96 ± 2.04 31.14 ± 0.43 29.30 ± 2.48 22.74 ± 0.26 14.94 ± 0.62 ♂ (5) (59.50 – 64.00) (55.70 – 61.00) (30.60 – 31.60) (25.30 – 31.50) (22.40 – 23.10) (14.00 – 15.70) Northeast India 63.70 ± 0.42 (2) 58.70 ± 0.99 (2) 29.95 ± 1.34 (2) 22.85 ± 0.50 (2) 14.25 ± 0.21 (2) ♀ (3) 30.50 (63.40 – 64.00) (58.00 – 59.40) (29.00 – 30.90) (22.50 – 23.20) (14.10 – 14.40)

66.54 ± 1.91 60.91 ± 2.62 31.66 ± 0.54 31.65 ± 1.52 22.90 ± 0.51 14.77 ± 0.74 Myanmar ♂ (4) (64.00 – 68.59) (57.20 –63.33) (30.89 – 32.05) (30.14 – 33.76) (22.47 – 23.63) (13.68 – 15.30)

♀ (1) 67.00 61.43 31.43 30.29 24.55 15.16

62.30 ± 1.50 (8) 56.70 ± 2.26 (8) 30.29 ± 0.89 (8) 31.10 ± 1.07 (8) 23.06 ± 0.66 (8) 16.24 ± 2.73 (8) Sri Lanka ♂ (2) (60.50 – 65.00) (53.30 – 60.40) (28.60– 31.50) (29.70– 32.30) (22.20– 23.90) (14.50– 22.80)

61.79 ± 1.93 (17) 57.83 ± 1.91 (17) 30.44 ± 2.63 (17) 30.38 ± 1.31 (17) 22.09 ± 0.91 (17) 14.53 ± 0.71 (17) ♀ (1) (57.00 – 64.70) (52.55– 60.20) (28.50– 39.90) (27.70– 32.10) (21.20– 24.90) (13.60– 16.30)

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3.3.1.1 Correlations among variables and Principal Component Analysis (PCA)

The raw data pooled from both males and females of H. lankadiva were tested for normality using Shapiro – Wilk tests. Five out of total 10 external morphometric variables showed non-normal distributions. None of the data transformations were able to transform those variables to normality. The Spearman correlation matrices for 10 external measurements showed positive associations between each of them (Supplementary Material Fig. S3.1). The highest correlations (r >0.85, p< 0.001) were observed between the following variables: forearm length (FA) and tibia (TIB), metacarpals 3 and 4 (3mt – 4mt), metacarpals 3 and 5 (3mt – 5mt) and metacarpals 4 and 5 (4mt – 5mt).

All 10 variables were scaled before conducting PCA, in order to achieve an approximate equal variance. The Kaiser–Meyer–Olkin (KMO) measure verified the sampling adequacy for the analysis, with KMO = 0.92 (‘superb’ according Hutcheson & Sofroniou, 1999), and all KMO values for individual items were above the acceptable limit of 0.5 (Field, 2009).

In the PCA analysis, 77.33% of the total variation in the data was explained by the first two principal components (PC1 – 66.89% & PC2 – 10.44%) with eigen values >1 (Table 3.2). A scree plot was also used to check the validity of the selected principal components. All 10 variables loaded positively on to PC1 meaning larger bats have higher PC scores (Table 3.2; Fig. 3.4). The PC1 was represented by eight variables except hindfoot (HF) and length of second phalanx of the fourth digit (2ph4mt). PC2 was composed of only two variables (HF and 2ph4mt) with both positive and negative values respectively.

Two clusters were identified in the PCA plot (Fig. 3.4): one comprised larger individuals mainly from Sri Lanka and Myanmar and a second one mainly includes small to medium sized bats from west India, south India and central India. The samples from northeast India and West Bengal showed representation in both clusters but only limited samples were available from these regions. This restricts me from drawing any conclusion about the grouping of these samples.

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Table 3.2. Variable loadings for the two principal components (PC1 and PC2,) from an analysis of external morphological characters of 100 individuals of H. lankadiva from different study regions. Values in bold indicate high loadings on that particular component. Measurement acronyms are defined in ‘section 2.2.2.1’ of Chapter 2.

Factor loadings External morphometric characters PC1 PC2 FA 0.356 0.031 TIB 0.339 -0.090 HF 0.088 0.751 3mt 0.332 0.271 4mt 0.341 -0.235 5mt 0.341 0.271 1ph3mt 0.323 -0.134 2ph3mt 0.328 -0.285 1ph4mt 0.343 -0.197 2ph4mt 0.279 -0.296 Eigenvalue 6.69 1.04 Variance explained (%) 66.89 10.44

Figure 3.4. Scatter plot of Principal Component Analysis based on PC1 and PC2 for external measurements of 100 individuals of H. lankadiva from different study regions.

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3.3.2 Craniodental analysis

The cranial and dental measurements of were considerably larger in H. lankadiva individuals from Sri Lanka, northeast India and Myanmar than those from peninsular India (Fig. 3.5). The west Indian population was the smallest among the other two (south and central) populations of H. lankadiva from peninsular India. The mean, standard deviation and range of 14 craniodental characters of H. lankadiva from different study regions are given in Table 3.3. Females were smaller in all regions than males (Table 3.3).

A B C D E

1mm

Figure 3.5. Skulls of five Hipposideros lankadiva from different study regions. A: Gampaha , Sri Lanka, HZM.8.30232, ♂; B: Kachin State, Myanmar, HZM.10.40222 (Holotype – OMT110105.1, ♂); C: Meghalaya, northeast India, PV2017.05.09.1, ♂; D: Raisen Fort, Madhya Pradesh, central India, PV2017.05.25.1, ♂; E: Lamgao Buddhist Caves, Goa, west India, PV2017.02.27.1, ♀.

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Table 3.3. Cranial measurements (in mm) of H. lankadiva from different study regions. Means and standard deviations are given. The range values are in parentheseis. The sample size is given in brackets of ‘sex’ column.

West India South India Central India Myanmar NE India Sri Lanka Skull Variables Sex ♂ (4) ♀ (1) ♂ (31) ♀ (11) ♂ (9) ♀ (9) ♂ (4) ♀ (1) ♂ (6) ♀ (1) ♂ (6) ♀ (12)

♂ 30.95 ± 0.07 32.16 ± 0.61 32.05 ± 0.35 34.11 ± 1.16 34.31 ± 1.82 35.76 ± 0.30 (30.90 – 31.00) (30.60 – 33.40) (31.70 – 32.70) (32.76 – 35.12) (31.25 – 36.10) (35.40 – 36.10) GTL ♀ 31.35 ± 0.83 31.05 ± 0.66 34.70 ± 0.71 29.80 33.35 (30.00 – 33.00) (30.20 – 32.30) 33.86 (33.60 – 36.00) ♂ 31.28 ± 1.17 30.62± 1.48 32.98 ± 1.53 32.43 ± 2.10 35.34 ± 0.44 30.60 SL (28.70 – 32.80) (28.50 – 32.00) (31.18 – 34.63) (30.10 – 35.10) (34.85 – 35.80) ♀ 30.85 ± 0.82 30.40 ± 0.67 34.29 ± 0.68 29.30 32.34 33.20 (29.70 – 32.50) (29.60 – 31.70) (33.15 – 35.80) ♂ 27.40 ± 0.28 29.57 ± 1.09 30.06 ± 1.17 30.72 ± 0.90 32.35 ± 2.13 32.10 ± 0.25 (27.20 – 27.60) (27.80 – 32.00) (29.00 – 31.70) (29.83 – 31.70) (30.60 – 35.10) (31.90 – 32.50) CBL ♀ 28.21 ± 1.01 28.49 ± 0.48 30.96 ± 0.62 26.80 30.31 29.90 (26.10 – 30.00) (28.00 – 29.60) (29.80 – 32.20) ♂ 26.90 ± 0.14 28.30 ± 0.0.68 28.51 ± 0.27 30.23 ± 0.91 30.29 ± 0.71 31.40 ± 0.16 (26.80 – 27.00) (27.10 – 29.70) (28.15 – 28.90) (29.10 – 31.20) (29.20 – 31.10) (31.10 – 31.50) CCL ♀ 27.49 ± 0.99 27.48 ± 0.61 30.52 ± 0.56 26.20 30.11 29.50 (25.80 – 29.20) (26.60 – 28.60) (29.90 – 32.10) ♂ 17.45 ± 0.07 18.41 ± 0.42 18.41 ± 0.18 19.87 ± 0.39 19.97 ± 1.02 20.31 ± 0.35 ZB (17.40 – 17.50) (17.60 – 19.20) (18.20 – 18.80) (19.36– 20.29) (18.00 – 20.80) (19.75– 20.80) ♀ 17.86 ± 0.59 17.86 ± 0.17 19.59 ± 0.53 16.80 18.87 19.20 (17.00 – 18.90) (17.50 – 18.10) (18.65– 20.50) ♂ 11.70 ± 0.28 12.41 ± 0.36 12.37 ± 0.36 13.20 ± 0.11 12.97 ± 0.76 13.35 ± 0.53 BB (11.50 – 11.90) (11.60 – 13.20) (11.90 – 13.00) (13.11 – 13.34) (11.62 – 13.80) (12.70 – 14.00) ♀ 11.93 ± 0.35 11.93± 0.32 13.19 ± 0.46 11.30 12.95 13.20 (11.30 – 12.40) (11.50 – 12.40) (12.50 – 13.90) ♂ 13.85 ± 0.35 14.60 ± 0.44 14.86 ± 0.31 14.96 ± 0.40 15.45 ± 0.59 15.93 ± 0.17 (13.60 – 14.10) (13.40 – 15.30) (14.30 – 15.30) (14.66 – 15.53) (14.50 – 16.00) (15.70 – 16.10) MB ♀ 14.17 ± 0.41 14.22± 0.33 15.37 ± 0.59 13.40 14.05 15.30 (13.70 – 14.70) (13.60 – 14.60) (14.00 – 16.10)

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Table 3.3. Continued.

Skull Variables Sex West India South India Central India Myanmar NE India Sri Lanka

3.80 ± 0.27 3.89 ± 0.22 3.54 ± 0.17 3.72 ± 0.16 3.91 ± 0.27 ♂ 3.20 PC (3.20 – 4.50) (3.60 – 4.20) (3.31 – 3.70) (3.60 – 4.00) (3.90 – 3.83) 3.64 ± 0.20 3.79 ± 0.27 3.92 ± 0.33 ♀ 3.20 3.45 3.40 (3.40 – 4.10) (3.40 – 4.30) (3.40 – 4.40) 8.85± 0.07 9.26 ± 0.28 9.45 ± 0.26 9.31 ± 0.21 10.10 ± 0.53 10.20 ± 0.26 ♂ RW (8.80 – 8.90) (8.90 – 10.10) (9.10 – 9.90) (9.15 – 9.62) (9.30 – 10.50) (9.80 – 10.50) 9.07 ± 0.35 9.19 ± 0.26 10.04 ± 0.37 ♀ 8.60 8.94 9.00 (8.50 – 9.50) (8.70 – 9.50) (9.40 – 10.50) 12.70 ± 0.14 13.19 ± 0.46 13.38 ± 0.23 14.09 ± 0.46 14.28 ± 0.30 14.45 ± 0.15 ♂ CM3 (12.60 – 12.80) (12.50 – 14.00) (12.90 – 13.60) (13.45 – 14.55) (14 – 14.80) (14.30 – 14.65) 12.68 ± 0.50 12.71 ± 0.40 14.12 ± 0.44 ♀ 12.00 14.29 13.60 (12.10 – 13.80) (12.40 – 13.50) (13.50 – 15.35) 7.78 ± 0.04 8.44 ± 0.44 8.62 ± 0.21 8.74 ± 0.34 8.64 ± 0.47 9.07 ± 0.14 ♂ C1-C1 (7.75 – 7.80) (7.60 – 9.20) (8.30 – 9.00) (8.41 – 9.18) (8 – 9.10) (8.80 – 9.20) 7.92 ± 0.55 8.23 ± 0.18 8.61 ± 0.38 ♀ 7.00 7.80 7.90 (7.40 – 9.00) (8.00 – 8.60) (7.90 – 9.50) ♂ 11.65 ± 0.21 12.46 ± 0.49 12.78 ± 0.24 12.94 ± 0.42 13.16 ± 0.35 13.20 ± 0.13 M3-M3 (11.50 – 11.80) (11.80 – 13.30) (12.50 – 13.10) (12.53 – 13.51) (12.60– (13 – 13.55) 13.60) 12.00 ± 0.57 12.33 ± 0.24 13.03 ± 0.31 ♀ 11.20 13.03 12.60 (11.30 – 13.00) (12.00 – 12.70) (12.50– 13.80) 21.78 ± 0.04 23.02 ± 0.64 23.13 ± 0.56 24.63 ± 0.54 25.05 ± 0.81 25.79 ± 0.23 ♂ M (21.75 – 21.80) (21.40 – 24.30) (21.80 – 23.50) (23.90 – 25.16) (24.00 – (25.60 – 26.15) 26.10) 22.38 ± 0.77 22.18 ± 0.72 25.17 ± 0.75 ♀ 21.20 24.30 23.60 (21.50 – 23.70) (21.20 – 23.40) (24.30 – 26.70) 13.70 ± 0.14 14.31 ± 0.55 14.51 ± 0.31 15.83± 0.34 15.40 ± 0.43 15.93± 0.35 ♂ cm3 (13.60 – 13.80) (13.30 – 15.30) (14.10 – 14.90) (15.59 – 16.31) (14.60 – (15.60 – 16.50) 15.70) 13.86 ± 0.64 14.03 ± 0.46 15.45± 0.42 ♀ 13.00 15.90 14.20 (13.10 – 14.95) (13.50 – 14.80) (14.50 – 16.30)

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3.3.2.1 Tests for normality and sexual dimorphism across regions

The sexual size dimorphism in the 14 skull characters of H. lankadiva from south India, central India, northeast India-Myanmar and Sri Lanka was analysed. Sexual dimorphism was not tested in samples from west India due to the limited sample size (n = 3). Within the central Indian samples, all 11 characters except SL, PC and RW showed sexual dimorphism whereas in the south Indian population four skull characters were same across sexes (SL, RW, M and CM3). In the northeast India – Myanmar population, only three characters (ZB, C1-C1 and M) were significantly different between the sexes. H. lankadiva from Sri Lanka showed sexual dimorphism in eight characters except BB, PC, RW, CM3, M3-M3 and M. In all cases, males were larger than females (Table 3.3)

3.3.2.2 Correlations among variables, Principal Component Analysis (PCA) and Discriminant Function Analysis (DFA)

In the pooled dataset of males and females of H. lankadiva, seven out of 14 craniodental characters showed deviation from normality. Different transformations were tried but none was able to transform the data to normality. The Spearman’s correlation test revealed that most of the variables had a correlation higher than r = 0.60 (Supplementary Material Fig. S3.2). The highest correlation (r >0.90) observed was between greatest skull length (GTL) and condylocanine length (CCL). The condylocanine length (CCL) was higly correlated (r >0.90) with zygomatic breadth (ZB), maxillary toothrow length (C-M3) and mandibular length (M).

All the 14 skull variables were scaled before undertaking PCA in order to achieve an approximate equal variance. The sampling adequacy for the analysis was KMO = 0.92 (‘superb’ according to Hutcheson & Sofroniou, 1999) and all KMO values for individual items were above the acceptable limit of 0.5 (Field, 2009). PCA based on 14 craniodental measurements showed that 81.40% of the total variance in the dataset could be explained by the first two principal components (Eigenvalue >1). A scree plot was also used to check the validity of the selected principal components.

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PC1 and PC2 accounted for 73.88% and 7.52% of the total variance respectively (Table 3.4).

PC1 reflected the skull size of the bats. The negative sign of the variable loadings indicate that the variables are negatively correlated with the axis. Therefore all the variables were loaded negatively on to PC1 meaning larger sized bats have smaller PC1 scores. Two distinct clusters were identified in the PCA plot (Fig. 3.6): one comprised the large bodied individuals from Sri Lanka, Myanmar, northeast India and west Bengal; the second cluster comprised the small bodied individuals from west India, south India and central India. Available samples from northeast India were examined and measured. In the PCA analysis, these samples fell in the Sri Lanka/Myanmar cluster. In this analysis, most of the samples from West Bengal clusted with the individuals from Sri Lanka/Myanmar. In the second cluster, the bats from south and central India showed overlap with each other, whereas the individuals from west India were separate from the other two (i.e. south and central India).

Table 3.4. Variable loadings for the three principal components (PC1, PC2) from an analysis of craniodental characters of 95 individuals of H. lankadiva from different study regions. Values in bold indicate high loadings on that particular component. The ‘-’ sign indicates the negative correlation between the variables and the components. Measurement acronyms are defined in ‘section 2.2.2.1’ of Chapter 2.

Craniodental charachers Factor loadings PC1 PC2 GTL -0.295 0.205 SL -0.256 0.280 CBL -0.260 -0.046 CCL -0.301 0.131 ZB -0.293 0.081 BB -0.259 0.070 MB -0.275 -0.032 PC -0.125 -0.814 RW -0.258 -0.112 C-M3 -0.290 0.095 C1-C1 -0.241 0.287 M3-M3 -0.264 0.214 M -0.294 0.119 cm3 -0.282 -0.087 Eigenvalue 10.34 1.05 Variance explained (%) 73.88 7.52

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The PCA including the type specimens was rerun using seven craniodental characters (CCL, ZB, BB, PC, CM3, M, cm3). Two components (eigenvalue>1) extracted from the analysis explained a total variation of 90.19% (PC1 – 77.64%; PC2 – 12.55%; Fig. 3.7). A scree plot was also used to check the validity of the selected principal components. Except for the type specimen of H. indus (south India) and the holotype of H. schistaceus (south India), all others clustered along with the individuals from respective type locality regions. The species status of H. schistaceus was discounted by Bates et al. (1997) as the specimen used to describe the taxon was juvenile. Here, in this analysis also the H. schistaceus specimen was the smallest among all with a larger score on PC1 and this correlates with the age of the specimen as mentioned in the literature.

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A stepwise DFA was performed using the 10 skull characters available for H. lankadiva from all the selected study regions. In order to avoid multicollinearity, the following variables did not include in the DFA: GTL, CCL, CM3 and cm3. These variables were strongly correlated with others (Supplementary Material Fig. S3.2). The individuals from northeast India and Myanmar were combined and considered as one region (NE India-Myanmar) as bats from these regions showed no genetic differences (see Table 3.9). A total of five characters were retained with three functions (Table 3.5). The best separation among groups was with functions one and two (Fig. 3.8) and they explained 99.10% of the total variance. Function one explained 89.80% of the variance and had a high positive loading for mandible length (M) and high negative loading for canine-canine length (C1-C1) (Table 3.5). Function two explained 9.30% of the variance and had high positive loadings for skull length (SL) and post orbital constriction (PC), and high negative loadings for zygomatic breadth (ZB). The remaining three functions in the DFA accounted for only 0.90% of the variance and therefore were not considered further. In the leave-one-out analysis, only 71.70% of all individuals were correctly classified in the initial analysis (Table 3.6). Thirteen individuals from south India were misidentified as individuals

CHAPTER 3 from central India and seven individuals from central India were grouped as south India. Similarly, three members of northeast India/ Myanmar group misidentified as Sri Lanka and two samples from Sri Lanka classified northeast India/Myanmar (Table 3.6). There was lower accuracy in the cross-validation (64.10%), with similar number of misidentified cases in each group. In cross validation 16 individuals from south India were misidentified as individuals from central India and nine individuals from central India were grouped as south India. Similarly, three members of each from northeast India/ Myanmar and Sri Lanka misidentified as Sri Lanka and northeast India/Myanmar respectively. This shows the considerable overlap in the craniodental measurements between south and central India and between Sri Lanka and northeast India-Myanmar but also clear separation between bats from south and central India compared with the larger bats from Myanmar and Sri Lanka (Table 3.6).

Table 3.5. Standardized canonical discriminant function coefficients and eigenvalues for a stepwise DFA comparing skull morphology of 92 individuals of H. lankadiva from south India, central India, northeast India – Myanmar and Sri Lanka.

Measurement Function 1 Function 2 Function 3 ZB .699 -.993 .097 PC -.197 .692 .499 M .765 .050 .272 SL .324 .945 -.592 C1-C1 -.956 .133 .529 Eigenvalue 4.745 0.492 0.049 % of Variance 89.80 9.30 0.90

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Table 3.6. Stepwise DFA classification of skull morphology of H. lankadiva from south India, central India, northeast India – Myanmar and Sri Lanka with predicted group membership of original and cross-validated grouped samples. Percentage values of prediction for each group is given in brackets. Predicted Group Membership Total NEI- number South India Central India Sri Lanka Region Myanmar of (1) (2) (4) (3) samples 1 31 (70.50) 13 (29.50) 0 0 44 2 7 (38.90) 11 (61.10) 0 0 18 Originala 3 1 (8.30) 0 8 (66.70) 3 (25.00) 12 4 0 0 2(11.10) 16 (88.90) 18 1 27 (61.40) 16 (36.40) 1 (2.30) 0 44 Cross- 2 9 (50.00) 9 (50.00) 0 0 18 validatedb 3 1 (8.30) 0 8 (66.70) 3 (25.00) 12 4 0 0 3 (16.70) 15 (83.30) 18 a 71.70% of original grouped cases correctly classified. b Cross validation is done only for those cases in the analysis. In cross validation, each case is classified by the functions derived from all cases other than that case. 64.10% of cross-validated grouped cases correctly classified.

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3.3.3 Bacular morphology

Six bacula of H. lankadiva from west India (n = 1), central India (n = 2), south India (n = 2) and northeast India (West Bengal; n = 1) were either extracted and prepared, or scanned. The materials from Myanmar and Sri Lanka were not available for baculum extraction as permissions to transport samples from these localities were restricted. Therefore, Bates et al. (2015) was used to compare the bacular structure of specimens from those two locations with the ones from the present study.

In all cases, the general structure of the baculum consists of a bilobate base and two long ventrally curved distal processes or rami (Fig. 3.9). The two distal processes arising from the base are parallel with a pointed but slightly notched tips. The total length of the bacula across different regions varied from 2.29 mm to 2.77 mm. Significant variation in the size of bacula was not tested because of the limited sample size. The bacula of Sri Lankan and Myanmar specimens were slightly longer (2.77 mm) than those from different regions in mainland India (2.29 mm – 2.41 mm).

(A) (B) (C)

1 mm

(D) (E) (F)

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3.3.4 Echolocation call analysis

A total of 670 calls from 67 individuals comprising 28 adult females, 21 adult males, 10 adults (sex unknown) and seven juveniles of both sexes were analysed. The frequency distribution of echolocation call frequency from different study regions is shown in Fig. 3.10. Echolocation calls of H. lankadiva from different study regions (west India, south India, central India, northeast India) showed substantial interindividual variation in call frequency (FMAXE) (Fig. 3.11). Due to bureaucratic difficulties, no samples were collected from Myanmar during this study. Therefore, the FMAXE data for Myanmar was used from Bates et al. (2015) in order to compare the call frequency with those from this study. The individuals from Sri Lanka (69.44 ± 1.20 kHz) and Myanmar (69.65 kHz) were calling at a similar frequency, which was considerably lower than the echolocations calls from mainland India. FMAXE ranged from 72.50 kHz to 87.37 kHz across different regions in mainland India. Among that, the highest and lowest average frequency were recorded in west Indian (85.18 ± 1.71 kHz) and northeast Indian (73.95 ± 0.65 kHz) populations respectively (Table 3.7).

Figure 3.10. Frequency distributions of FMAXE of H. lankadiva across different study regions.

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Table 3.7. The forearm length (FA) and frequency of most energy (FMAXE) measurements for H. lankadiva in India, Sri Lanka (from present study) and Myanmar (Bates et al., 2015).

FA (mm) FMAXE (kHz) Number of Location individuals x ̅ ± SD Min - Max x ̅ ± SD Min - Max

West India 79.14 ± 1.86 75.50 – 80.85 85.18 ± 1.71 82.53 – 87.37 7

South India 87.05 ± 2.83 79.40 – 90.60 79.87 ± 0.85 77.98 – 80.98 16

Central India 87.31 ± 2.29 82.80 – 89.50 81.26 ± 1.03 79.29 – 82.91 8

NE India 89.70 ± 2.70 86.90 – 93.00 73.95 ± 0.65 72.50 – 74.54 10

Sri Lanka 89.86 ± 3.19 82.00 – 94.60 69.44 ± 1.20 67.10 – 71.45 26

Myanmar 91.80 89.20 – 94.00 69.65 68.80 – 70.70 4

Figure 3.11. Spectrograms of representative echolocation calls of H. lankadiva from different study regions.

A t-test showed significant differences between echolocation call (FMAXE) mean of adult and juvenile bats from Sri Lanka. FMAXE was significantly lower in juvenile bats (68.10 ± 0.88 kHz) than adults (69.93 ± 0.88 kHz), t (24) = 4.722, p < 0.001) (Fig. 3.12). The small female sample size (n = 1) in juveniles did not allow me to further test the sexual dimorphism in this age group. A Shapiro-Wilk test showed that the data for FMAXE for juveniles (W = 0.93, p = 0.52) and adults (W = 0.93, p = 0.18) from Sri Lanka were normally distributed.

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Spearman correlation showed a highly significant negative relationship between FMAXE and forearm length (FA) (rho =- 0.782, p<0.001; n = 67; Fig. 3.13). FMAXE of

H. lankadiva varied significantly across regions (F (3, 45) = 589.097, p = 0.000) and for sex (F (1, 45) = 6.611, p = 0.014). No data for sex were available for samples from northeast India therefore, the model excluded the samples from that region in the analysis. An interaction between sex and region on FMAXE cannot be tested and therefore, the main effects are interpreted with caution. The post-hoc test revealed that the males were calling at a lower frequency than females (Fig. 3.14). The echolocation frequency was significantly different across all possible pairs of regions. The individuals from Sri Lanka were calling at a lower mean frequency than other regions. The difference in mean FMAXE of H. lankadiva from Sri Lanka was 9.94 kHz, 11.33 kHz, 15.25 kHz compared with mean FMAXE values for south India, central and west India respectively. Within the Indian mainland, the difference between south and central India was only 1.39 kHz (p = 0.01). FMAXE values from west India were 5.31 kHz higher and 3.92 kHz higher than those from south and central India respectively. However, this significant variation in call frequencies across different regions in mainland India can be invalid with a large sample size. The adjusted R2 value was 0.97 meaning 97 percent of the variation in FMAXE can be explained by

CHAPTER 3 variation in forearm length. The data met the assumptions of homogeneity of variance and linearity and the residuals were normally distributed.

Figure 3.13. Distribution of echolocation call frequencies (FMAXE) and lengths of forearm for all H. lankadiva bats from different study regions for which echolocation call data were available.

Figure 3.14. Boxplot showing the echolocation call frequency variation in female (F) and male (M) bats of H. lankadiva. The bold line shows the median frequency with the minimum, first quartile (Q1), third quartile (Q3), and maximum values.

The analyses showed that the echolocation calls of H. lankadiva is varied across different study regions and is influenced by age, sex and forearm length (FA) of the bats.

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3.3.5 Molecular data analysis

A total of 184 sequences were generated from 41 bats for the five markers (ND2, 16s, STAT5A, PRKC1 and THY) chosen. The same genes were not necessarily sequenced for all the bats. Sequencing of samples from each study region was successful except for H. lankadiva from Myanmar for ND2. The total length of the sequences were varied from 294 bp (16s) to 545 bp (ND2) for different markers. The number of samples, total base pairs, variable sites and parsimony informative sites for each gene are given in Table 3.8.

Table 3.8. The gene name, number of individuals sequenced, total sites, variable sites and parsimony informative sites for each sequenced gene

Number of Parsimony Gene individuals Total sites Variable sites informative sites sequenced

ND2 22 455 41 34 16s 32 294 10 7 STAT5A 36 545 5 1 PRKC1 43 400 4 0 THY 51 502 5 1

Only eight sequences were successfully sequenced for CO1 for the following regions: west India (n = 1), central India (n = 4), northeast India (n = 1), and Sri Lanka (n = 2). Apart from the five markers studied, a sixth marker – SPTBN – was also selected and amplified in this study however, the sequencing was not successful for the same. Therefore, the sequences generated for SPTBN were discarded from further analysis due to their low quality.

3.3.5.1 Genetic distances

The pairwise uncorrected p-distance for ND2, between the outgroup taxa (H. gentilis and H. cineraceus) and H. lankadiva ranged from 15.88% to 18.54%. There was 10% - 14.32% divergence observed between the ingroup Hipposideros taxa (H. diadema, H. larvatus, and H. armiger) and H. lankadiva. Moderate levels of divergence were observed (3.30% - 5.43%) at ND2 among individuals of H. lankadiva from west India, central India, northeast India and Sri Lanka (Table 3.9). Within mainland India, the

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CHAPTER 3 divergence among different regions varied between 3.30% - 5.20%. The divergence between Sri Lanka vs. central India was lower (3.80%) than Sri Lanka vs. other study regions. The highest mean divergence for ND2 was 5.20% (northeast India/Sri Lanka) and the lowest was 3.51% (west India/central India). No samples from Myanmar sequenced successfully for ND2 and none were available in Genbank for both south India and Myanmar. Therefore, the genetic divergence for these two regions cannot be compared with others.

The overall genetic divergence for 16s was low when compared to that for ND2. The pairwise uncorrected p-distance for 16s, between the outgroup taxa (H. gentilis and H. cineraceus) and H. lankadiva ranged from 9.60% to 12%. The ingroup Hipposideros taxa (H. diadema, H. larvatus, and H. armiger) and H. lankadiva exhibited a divergence range of 5.50% - 10.50% for 16s. The genetic distance between single individual from south India and those from central India was lower (1.7%) than south India vs. west India (3.40% - 3.70%). The individuals from central India had equal distance with south India and west India (1.70%). No divergence was observed between the sequences from northeast India and Myanmar. Contrary to ND2, the highest and lowest group mean divergence for 16s were observed between west India/Sri Lanka (2.22%) and central India/Sri Lanka (0.50%) respectively (Table 3.9).

The sequencing for CO1 was successful only for eight individuals of H. lankadiva from all regions except Myanmar. Therefore, uncorrected p-distance was calculated only for these eight individuals along with one sequence deposited in Genbank (HM540536) from Sri Lanka. These results were used only to add more information and to compare with the distances observed for ND2 and 16s. Among the individuals of H. lankadiva from west India, central India, northeast India and Sri Lanka, the divergence observed at CO1 was also moderate (2% - 6.70%) as in the other mtDNA genes (See Supplementary Material Table S.3.1).

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Table 3.9. The uncorrected group mean p -distance between each region of H. lankadiva. Below the diagonal : the uncorrected group mean genetic divergence for ND2. Above the diagonal: the uncorrected group mean genetic divergence for 16s.

West India Central India Northeast Myanmar Sri Lanka West India 1.81 India 1.81 1.81 2.22 Central India 3.51 1.36 1.36 0.50 Northeast 4.03 4.78 0.00 1.81 IndiaMyanmar - - - 1.81 Sri Lanka 4.54 3.80 5.20 -

3.3.5.2 Haplotype diversity pattern and network analysis

The haplotype diversity analysis of the two mtDNA genes revealed that ND2 has more haplogroups than 16s. In the dataset for ND2, 41 variable sites corresponding to 10 unique haplotypes (h) were identified. For 16s, seven haplotypes were found containing 10 variable sites. H. lankadiva was accordingly found to have high overall haplotype diversity (ND2: Hd = 0.840; 16s = 0.833). Among the three nuclear introns, the highest number of haplotypes existed in the THY dataset (h = 6) followed by PRKC1 (h = 5) and STAT5A (h = 4). The haplotype diversity among the nuclear intron dataset was moderate (PRKC1 – 0.460 < STAT5A – 0.630 < THY – 0.646). Compared to the mtDNA datasets, the number of variable sites identified for nuclear introns was very low (STAT5A – 5; THY – 5; PRKCI – 4).

All the median-joining haplotype networks for both mtDNA and nDNA presented a star-like pattern (Fig. 3.15). No haplotype was shared in both mtDNA networks except for northeast India which shared a haplotype with Myanmar for 16s.

The median-joining network for ND2 (Fig. 3.15a) suggested two haplogroups – one comprising unique haplotypes from west India and northeast India and the other comprising unique haplotypes from central India and Sri Lanka. Only three mutation steps were separating these two haplogroups. There were four haplotypes identified from five individuals of same population in west India and they separated by only one mutation step from each other. Two haplotypes were existed in northeast India, central India and Sri Lanka and they were also separated by one or two mutation steps.

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In the haplotype network for 16s (Fig. 3.15b), two haplotypes from west India (Hap_6 & 7) and three from Sri Lanka (Hap_2 to 4) were identified. They separated by one mutation step within each region. Haplotype sharing was observed among populations in Sri Lanka and among populations in west India. However private haplotypes were also existed in these populations. One haplotype, comprising seven sequences from same population, in central India was separated only by a single mutation step from haplotypes from Sri Lanka.

Among the nuclear intron haplotype networks, both PRKC1 and THY comprised one main haplotype and could be separated from other haplotypes (four for PRKC1; five for THY) by only one mutation. One main haplotype in the PRKC1 haplotype network (Hap_1) comprised of 72% of all samples with 31 individuals (eight from central India, six from west India, 17 from Sri Lanka). In the haplotype network of THY, 29 individuals representing 67.44% of total samples constituted the one main haplotype (eight for central India, two for northeast India, one for Myanmar, 18 for Sri Lanka). Both private and shared haplotypes separated by only one or two mutations, existed in the haplotype network analysis for STAT5A (Fig. 3.15c-d).

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A) B)

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C) D)

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3.3.5.3 Phylogenetic analysis

The concatenation of mtDNA markers ND2 and 16s resulted in a supermatrix of 749 aligned positions for 45 sequences whereas that of three nuclear introns (STAT5A, PRKC1 and THY) had a total length of 1447 bp from 81 sequences. The following models were selected under the AIC criteria in jModelTest for each gene fragment: ND2 – TrN + G; 16s – GTR + G; STAT5A and THY – TPM1uf+G; PRKC1 – HKY. When a model specified by jModeltest was not available in the software then the next best model was used.

3.3.5.4 Concatenated mitochondrial tree

Both the Maximum Likelihood (ML) and Bayesian interference (BI) methods yielded highly congruent topologies with moderate to high support values for most of the nodes. H. lankadiva from different geographic regions showed monophyly of the species and it is closely related to H. diadema. The tree includes four well supported clades: northeast India-Myanmar, west India, central India and Sri Lanka (Fig. 3.16). The northeast India-Myanmar individuals grouped together and formed the basal clade in the tree. This clade connected to the major clade comprising west India, central India and Sri Lanka with a high bootstrap support (BP = 95). The individuals from Sri Lanka and central India are closely related and represented sister-taxa in the tree. The individual gene trees for both ND2 and 16s showed the same topology in both ML and BI analysis. In the 16s tree topology, the only available sequence from south India (KF059983) grouped together with the individuals from central India.

3.3.5.5 Concatenated nuclear tree

The individual nuclear intron trees and the supermatrix nuclear intron tree demonstrated insufficient phylogenetic signals. Therefore, neither of them resolved the relationships among H. lankadiva individuals from the selected study regions. This is not surprising given the small number of variable characters observed (Table 3.8) and slow mutation rate (Fig. 3.15). In this tree also the H. lankadiva was close to H. diadema but insuffient bootstrap support was available to support this node (Fig. 3.17). The concatenated tree showed monophyly of H. lankadiva samples as seen in the mtDNA tree but with a low bootstrap value (BP = 81).

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3.4 Discussion

In this Chapter, the results have shown that H. lankadiva from Sri Lanka together with northeast India and Myanmar are larger in size and call at lower frequency than those from most of peninsular India. The differences are substantial (Fig. 3.5 & Fig. 3.11 respectively). Moderate level genetic divergence (< 7%) was observed at mtDNA among the bats from all study regions and four distinct clades from Sri Lanka, central India, south India and northeast India-Myanmar identified in the mtDNA tree. The nuclear intron tree failed to resolve the phylogeny of H. lankadiva and a high level of haplotype sharing has observed in nuDNA. Therefore, in this section the following hypotheses are tested to understand whether any of the currently recognised subspecies of H. lankadiva demands species status or not:

Hypothesis A - could it be that these populations are cryptic species, as found in increasing numbers of hipposiderid taxa (Thabah et al., 2006; Murray et al., 2012; Murray et al., 2018). Such putative cryptic species could be sister taxa with the large body size found in bats from Sri Lanka and northeast India-Myanmar being a shared trait that evolved from a recent common ancestor. This predicts that bats from northeast India-Myanmar should be sister taxa to Sri Lanka bats in a phylogenetic tree.

Hypothesis B - as bats from Sri Lanka and northeast India-Myanmar are geographically isolated, they are likely to be reproductively isolated, and hence potentially cryptic species. Alternatively, large size could have evolved independently in Sri Lanka and Myanmar, and the bats in these populations might be considered sufficiently distinctive to warrant specific status. This predicts that these clades should not be sister taxa but should nonetheless be genetically distinct from most other populations on the Indian peninsula and show sufficient differences in other traits to merit specific status.

Hypothesis C - local adaptation could be driving morphological differences, and the substantial morphological differences documented could just reflect substantial intraspecific variation. Hypothesis C predicts that genetic differences should be

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CHAPTER 3 modest among all populations, and that factors such as latitude and island biogeography can explain variation in morphology and acoustics.

The key findings from the morphology and acoustics analyses will be reviewed below before interpreting these findings in the light of molecular phylogenetic analyses.

3.4.1 Differences in morphology

The PCA analyses on external (Fig. 3.4) and craniodental measurements (Fig. 3.6). show two morphologically distinct clusters: (1) The large-sized bats from Sri Lanka and northeast India-Myanmar (2) small sized bats from west India, central India and south India. Subsequent DFA analysis on craniodental characters reveals considerable misidentification between the individuals from Sri Lanka and northeast India-Myanmar and between those from central and south India (Table 3.6). These results corroborate with the findings of Bates et al. (2015). While describing a new subspecies of H. lankadiva from Myanmar (H. l. gyi), Bates et al (2015) reported that the size of Myanmar specimens is comparable with those from Sri Lanka. In this analysis, the specimens from northeast India and West Bengal grouped with the Myanmar specimens (Fig. 3.4 & Fig. 3.6). Therefore, the morphometric (and molecular data, see section 3.4.4) data in this this study strongly indicate that the H. lankadiva from West Bengal and northeast India is a southern extension of H. lankadiva from Myanmar. Although, no specimens from northeast India or West Bengal were available for Bates et al. (2015), the authors made similar observations considering the geographic affinity between northeast India and Myanmar as well as based on the descriptions and materials from literature as for them. Saha, Feeroz & Hasan (2015) reported H. lankadiva from Bangladesh and the external measurements of that specimen (FA = 87.64 – 91.26 mm) are comparable to the specimens from northeast India-Myanmar (86.50 - 94.40 mm). No samples were available from Bangladesh for the present study; however, the geographic closeness of Bangladesh with northeast India and the size similarities between the specimens strongly suggest that H. lankadiva from Bangladesh belong to the northeast India- Myanmar cluster.

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3.4.2 Differences in bacular morphology

The bacular analysis of H. lankadiva shows that the bacula of Sri Lankan and Myanmar specimens are slightly longer (2.77 mm) than those from different regions in mainland India (2.29 mm – 2.41 mm). Although the sample size was small, no obvious differences in bacular morphology were found across different study regions (Fig. 3.9). Relative to the size of the base, Bates et al. (2015) noticed a proportionately longer distal processes in Myanmar specimens than those from Sri Lanka. However, this needs to be tested with a large sample size as the intraspecific and interindividual variation can also found in many species (Petrie, 1988; Herdina et al., 2014). Many cryptic species of bats show obvious differences in bacular morphology (e.g. Pipistrellus pipistrellus and P. pygmaeus -Herdina et al., 2014; Rhinolophus andamanensis – Srinivasulu et al., 2019; H. pomona and H. gentilis s.l. – see Fig. 2.8 in Chapter 2). Differences in bacular morphology may promote prezygotic reproductive isolation between cryptic taxa (Patterson & Thaeler, 1982) and as none were apparent in this study, these findings do not support the hypothesis that cryptic species are present.

3.4.3 Differences in echolocation frequency

The analysis showed that echolocation call frequency of H. lankadiva varies with age, sex, and body size and across regions. Juveniles were using low frequency calls than adults. Echolocation call may vary between juvenile and adult bats and change over their lifetime (Jones & Ransome, 1993; Chen, Jones & Rossiter, 2009; Russo, Ancilloto & Jones, 2017). Juvenile bats emit lower frequency calls than adults in a wide range of species (e.g. Asellia tridens, Rhinolophus hipposideros, Myotis daubentonii, M. lucifugus – Jones et al., 1993; Rhinolophus ferrumequinum – Jones & Ransome, 1993; R. mehelyi – Russo, Jones & Mucedda, 2001; Eptesicus fuscus – Kazial, Burnett & Masters, 2001).

On average, females were calling at a higher frequency than males. This can be a consequence of body size differences as females were smaller than males. Similar trends have been observed in H. armiger terasensis (Heller & von Helversen, 1989),

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Asellia tridens (Jones et al., 1993) and H. bicolor (Murray et al., 2018). This runs counter to the general trend in bats where generally females are larger and more over sometimes (but not always) produce lower frequency calls (Russo, Ancilloto & Jones, 2017; e.g. H. kunzi sp. nov – Murray et al., 2018). Echolocation call variation among sexes have been reported in other hipposiderids (Guillén, Juste & Ibáñez, 2000) and rhinolophids (Jones, 1995; Russo, Jones & Mucedda, 2001) but this is not always the case. In some species males call at higher frequencies than females (e.g. Hipposideros speoris and H. ruber – Guillén, Juste & Ibáñez, 2000) , in others vice- versa (Rhinolophus blasii – Siemers et al., 2005), whereas in many taxa no differences in call frequency are apparent between sexes (e.g. Rhinolophus ferrumequinum and R. mehelyi – Schuchmann & Siemers, 2010).

There is a significant variation in echolocation frequencies across the study regions which could be related to variation in body size. The forearm length (proxy of body size) of bats increases in the following order west India< south India< central India< northeast India

Hence variation in call frequency could be related to variation in body size, with larger individuals calling at lower frequencies. However, the variation in body size is not due to latitudinal clines in body size, as the largest bats were in southern (Sri Lanka) and northern populations. Overall, findings in this study show strong divergence in morphology, with bats from Sri Lanka and northeast India – Myanmar being morphologically similar and substantially larger in size compared with bats

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CHAPTER 3 from the rest of the India mainland. Differences in call frequency track these morphological differences, with call frequency probably being a consequence of variation in body size.

3.4.4 Genetic structure among geographic regions

The phylogenetic tree topologies obtained for separate analysis of mtDNA (ND2 + 16s) and nuDNA (STAT5A + PRKC1 + THY) were highly incongruent, though they both confirmed the monophyly of H. lankadiva. Phylogenetic analysis of mtDNA markers (ND2 and 16s) identified four mitochondrial lineages in H. lankadiva. They correspond to the following geographic regions (subspecies): west India (H. l. indus), central India (H. l. indus), northeast India – Myanmar (H. l. gyi) and Sri Lanka (H. l. lankadiva). In contrast to the mtDNA tree, the phylogenetic relationship between H. lankadiva individuals was poorly resolved on the nuDNA tree. This is not surprising when considering the slow evolutionary rates of nuclear intron compared to the mitochondrial genes (Baird et al., 2017; Dool et al., 2016). The very low variable sites (≤5) in each nuclear data set thus fail to detect variation among closely related species (Table 3.8) and highly shared haplotype groups (Fig. 3.15) give an indication of insufficient phylogenetic signals of selected nuclear genes. Similar results were reported from a study on ‘H. bicolor’ and ‘H. larvatus’ species groups from Southeast Asia (Yuzefovich, Kruskop & Artyushin, 2019). They used seven nuclear genes including six introns and reported that only a concatenated data set of nuclear genes fully supported all species clades and higher-level groupings. The results in this study recognise clades that correspond to geographic regions from fast-evolving mtDNA gene sequences, supporting the hypothesis that recent divergence has occurred. However, the lack of a clear signal in the slowly evolving nuDNA sequences suggests that divergence has probably been relatively recent.

The highest genetic divergences observed across different regional populations of H. lankadiva were 5.20 % (ND2, Table 3.9) and 6.70% (CO1, Supplementary Material Table S3.1). In mammals, this is not typical of the amounts expected for species level differentiation (Baker & Bradley, 2006), although using mtDNA sequence divergence alone as a marker of species identity can be highly misleading (some taxa for example

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CHAPTER 3 are clearly divergent species, yet have low sequence divergence in mt genes (Eptesicus species -Artyushin et al., 2009). Similar level of divergence at ND2 was reported for other hipposiderids inhabiting in different geographical regions. H. bicolor – 131 from two sites in peninsular Malaysia differed by 4.7-5.1% over approximately 200 km distance (Murray et al., 2018). Among two island populations of H. cineraceus from Indonesia and Sulawesi, 5.1% genetic divergence was observed (Murray et al., 2012). Although it is arguable that the values of 5.20 % or 6.70% are above the intraspecific divergence of 2% proposed by Baker & Bradley (2006), the present study suggests treating the four geographic clades as mitochondrial lineages. Two reasons to support this are (1) clear haplotype sharing among geographical regions of H. lankadiva at nuclear introns and (2) only two to 12 mutation steps among mtDNA haplogroups.

In this analysis, the two populations from mainland India (west and central) and the population from Sri Lanka showed distinct genetic structure based on mtDNA sequences (Fig. 3.16) with a sequence divergence >3.5% (Table 3.9). However, they are less differentiated on nuclear introns. The low sample size for some locations and limited nuclear markers limit me from drawing strong conclusions here. Possible explanations for contrasting genetic structure between mtDNA and nuDNA are: (1) different mutation rates among maternally and bi-parentally inherited markers (Chesser & Baker, 1996) (2) limited female dispersal or male-mediated gene flow among populations as reported in other bats [e.g. Myotis myotis (Castella et al., 2001); Eptesicus fuscus (Turmelle et al., 2011); Hipposideros armiger (Lin et al., 2013)]. These hypotheses need to be tested using a large sample size with biparentally-inherited microsatellites, single nucleotide polymorphisms (SNPs) or whole genome sequencing.

It is interesting that H. lankadiva from Sri Lanka showed a sister relationship with the central Indian population from mtDNA sequence data, which contradicts the morphology and echolocation findings. Sri Lanka and India have been separated and reconnected multiple times in its geological history with a most recent separation - 10,000 years ago (Jacob, 1949; Cooray, 1967; Sahni & Mitra, 1980; Gunatilaka, 2000). These intermittent connections between Sri Lanka and India enabled biotic exchange

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CHAPTER 3 between the two landmasses (Biswas & Pawar, 2006). It is reported that most of the Sri Lankan mammals especially in the northern arid to middle sub montane climatic zones shows affinity with those of peninsular India (Dittus, 2017). The shallow Palk Strait isthmus provided land bridges to India and the opportunity for faunal exchanges during frequent sea-level low stands in the Pleistocene. All bat species including H. lankadiva in Sri Lanka are distributed around the northern arid to submontane zones and gene exchange between the mainland India and Sri Lanka is likely. It is suggested that the high vagility in bats enable them to fly easily over long distances over the landscape would promote gene flow among northern Sri Lanka and peninsular India (MacKay, 1984; Dittus, 2017). This is again supported by the fact that bats in Sri Lanka show the highest proportion of non-endemics when compare to fauna. High degrees of faunal similarity between the dry zones in India and Sri Lanka have been documented (e.g. tiger beetles - Pearson & Ghorpade, 1989; birds - Ripley & Bheehler, 1990; Hemidactylus geckos - Lajmi et al., 2019). These lines of evidence support the sister relationship of H. lankadiva from the dry central Indian zone and Sri Lanka.

As expected, the individuals from the nearby areas of northeast India and Myanmar grouped together in the concatenated mtDNA analysis. Shared haplotypes between these two regions and zero divergence between individuals for 16s confirm their genetic similarity. Thus, these findings support the observation of Bates et al. (2015) who referred the H. lankadiva material from northeast India to H. l. gyi from Myanmar based on measurements and descriptions from literature. The genetic similarity between individuals of northeast India and Myanmar over a geographic distance of approximately 1800 km suggests frequent gene flow among the populations there. Northeast India is considered as a ‘gateway’ (Mani, 1995; Kamei et al., 2012) lying between the Himalaya and Indo-Myanmar hotspots and provides a continuity of wet evergreen forest zone from Southeast Asia (Karanth, 2003). Therefore, the two regions share fauna and flora [e.g. birds – frogmouths (Karanth, 2003); reptiles and amphibians (Daniel, 2002); bats (Simmons & Cirranello, 2019)]. More comprehensive surveys in these regions are needed to understand how continuous the distribution H. lankadiva is. Unfortunately, no samples were available

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CHAPTER 3 from Bangladesh in this study, though a very first report of H. lankadiva from Bangladesh (Saha, Feeroz & Hasan, 2015) indicates a range extension of the species away from northeast India/Myanmar. The authors mentioned that the location in northern Bangladesh is very close to the reported H. lankadiva location in Meghalaya (15 km), northeast India. The concatenated mtDNA tree supports the hypothesis that ancestral populations of H. lankadiva existed in northeast India and Myanmar. The species then spread into western India, and then to the south where close affinities lie between the southern Indian bats and those from Sri Lanka, despite the considerable morphological differences between these two populations.

3.5 Conclusion

H. lankadiva from Sri Lanka and northeast India-Myanmar are substantially larger in body size than those from the rest of mainland India. Large body size in these populations was documented by Bates et al. (2015) who suggested that these populations are treated as distinct subspecies (H. l. lankadiva and H. l. gyi respectively). The present study suggests that differences in echolocation call frequency among the populations are the consequence of variation in body size, with larger individuals calling at lower frequencies. The present findings reject Hypothesis A as the large-bodied populations in Sri Lanka and northeast India-Myanmar are not sister taxa, suggesting that large size in not due to the recent common ancestry of these populations. The findings showing similarity in baculum morphology among populations, generally low sequence divergence in mtDNA and a lack of differentiation in nuDNA provide little support for hypotheses proposing that cryptic species are present. Although the results support the hypothesis that large size evolved independently in Sri Lanka and northeast India-Myanmar (see hypothesis B), there is insufficient evidence to suggest that these populations are cryptic species. Rather, it appears that H. lankadiva shows extensive variation in body size and echolocation call frequency over an extensive geographic range. The large size of H. lankadiva in Sri Lanka can perhaps be explained by the ‘Island rule’. According to this, larger mammals like carnivores and ungulates show dwarfism and smaller mammals like rodents show gigantism when they restricted to islands (van Valen, 1973).

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However, several studies on Chiroptera have showed dwarfism on islands rather than the gigantism expected under van Valen’s (1973) hypothesis (Krzanowski, 1967; Palmeirim, 1991; Jacobs, 1996; Lomolino, 2005; Juste et al., 2007). Some cases of gigantism also have been reported (Krzanowski, 1967). The ratio of dwarfs to giants was 15: 6 for pteropodids, and 52: 29 for laryngeal echolocating taxa (Krzanowski, 1967). The large body size in the northeast India-Myanmar populations could be the result of latitudinal effects, with larger animals favoured to be in cool, dry areas as predicted by James’ rule (James, 1970; Jacobs & Bastian, 2018). In conclusion, the results best support hypothesis C – that extensive variation occurs in the morphology and call frequency of H. lankadiva, that this variation is largely the consequence of local adaptation, and that the current designation into three subspecies is appropriate.

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CHAPTER 4

Assessing the geographic distributions of H. pomona, H. gentilis s.l. and subspecies of H. lankadiva using MaxEnt

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Abstract

Cryptic species and/ or subspecies in a species complex may have distinct ecological niche requirements and therefore their geographic distribution may also be different. Considering this, taxonomic revaluation of a taxa followed by the reassessment of its distribution would be ideal for their conservation and planning. Here, MaxEnt – a presence-only species distribution modelling approach- was used in order to explore whether the predicted distributions overlap among H. pomona and H. gentilis s.l. as well as among the subspecies of H. lankadiva. The models were also used to identify any new habitat suitability areas outside the current known distribution of these species or subspecies. The models for H. gentilis s.l. identified suitable habitats in south India that potentially overlap with the predicted distribution of H. pomona. The geographic distribution of subspecies of H. lankadiva was distinct and H. l. lankadiva, H. l. indus and H. l. gyi were confined to Sri Lanka, mainland India and northeast India-Myanmar respectively. In conclusion, presence only SDMs using MaxEnt can be used as an effective tool in identifying suitable habitats for subspecies and/or species of bats and thereby define their geographic distribution.

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4.1 Introduction

The advances in molecular techniques and geometric morphometrics have accelerated taxonomic reassessment of many species worldwide and is continuing (e.g. Loxodonta – Roca et al., 2001; Giraffa – Winter, Fennessy & Janke, 2018; bats – Taylor et al., 2018, Demos et al., 2019). The taxonomic revisions result in the splitting or grouping of animal taxa and, therefore, require us to revise our understanding of the geographic distributions of them (Puechmaille et al., 2012; Hernández‐Roldán et al., 2016). Different subspecies and/or cryptic species within the same species complex occupy distinct environmental niches and so may have different distributions (Cardador et al., 2016; Menchetti, Mori & Angelici, 2016). Mapping the distributions of such taxa is therefore important in identifying conservation priority areas for them and thus helping to formulate suitable policy and management decisions (Jackson & Robertson, 2011; Helliwell & Chapman, 2013).

4.1.1 Species distribution models and their importance in bat research

Species distribution models (SDMs) are spatial representations of occurrence probability or abundance of a species (Borzée et al., 2019). SDMs are built using different statistical methods that combine species distribution records (presence or presence – absence at locations) with environmental and/or spatial characteristics of those locations (Franklin, 2010). SDMs can either use to infer or predict the suitable habitat for a species across landscapes (Elith & Leathwick, 2009; Franklin, 2010) under present conditions or different future climate scenarios (Soberón & Nakamura, 2009; Elith et al., 2010). The application of species distribution models for a vast variety of taxa in various disciples such as ecology, biogeography, evolutionary biology, conservation biology has been growing over the past two decades (Franklin, 2010; Liu, Newell & White, 2016; Razgour et al., 2016; Mori et al., 2018). SDMs have been using as a powerful tool to estimate the distribution shifts under climate change (Borzée et al., 2019), areas susceptible for alien species invasion (Taucare‐Ríos, Bizama & Bustamante, 2016; Buchadas et al., 2017; Mori et al., 2019), to assess species specific conservation priority areas (Guisan et al., 2013; Araújo et al., 2019) and to identify future survey sites.

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SDMs approach have picked up momentum in bat research only in the last decade though bats represent taxonomically diverse group of mammals with wide range of distributions (Razgour et al., 2016). SDMs are very relevant and suitable for bats because of the difficulty in detecting these nocturnally active mammals and identifying them in flight. Though bat surveys using echolocation call recording (acoustic monitoring) are increasing in these years, some species cannot be detected due to their low intensity, high frequency calls or not differentiated because of overlapping frequencies among different species. Moreover, systematic bat surveys are still not a feasible practice in many species rich developing countries due to limited funds and lack of skilled resource persons (El-Gabbas & Dormann, 2018). Therefore, the available presence records for bat species are generally small or under-represented and the true absence data for a species are hard to separate from false absences. SDMs using presence-only methods may therefore applicable in bat research as these methods do not need absence data. Studies have shown that the presence-only methods can give robust species distribution predictions for bat species with limited occurrence data.

MaxEnt is one of the modelling programmes which relies on presence-only species occurrence records to identify suitable habitat areas for species (Phillips, Anderson & Schapire, 2006; Phillips & Dudik, 2008). MaxEnt has ranked as the most popular modelling algorithm for species distribution modelling across taxa and studies (Elith et al., 2006). MaxEnt was the only algorithm which showed a significant linear increase in its application in bat SDM studies over time (see review by Razgour et al., 2006) and is continuing to be used widely (Scherrer, Christe & Guisan, 2019). The preference for MaxEnt over other modelling techniques is because of its highly consistent predictive performance and ease of use compared to other presence-only and/or presence-absence modelling approaches (Elith et al., 2006; Elith et al., 2010; Merow, Smith & Silander, 2013). Moreover, MaxEnt performs best with high predictive accuracy even with a small/limited sample size of occurrence records (Hernandez et al., 2006). Thus, MaxEnt is highly advantageous in the case of many cryptic or rare species (Wisz et al., 2008). This presence-only modelling tool, MaxEnt, has been successfully applied and ground-validated in cryptic and rare bat species to

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CHAPTER 4 identify suitable habitat ranges and conservation priority areas (e.g. Barbastella barbastellus in Portugal – Rebelo & Jones, 2010; Plecotus austriacus in the UK – Razgour, Hanmer & Jones, 2011; Lasiurus cinereus in North America – Hayes, Cryan & Wunder, 2015).

4.1.2 Background and aims of the study

In chapter two and three, the taxonomic status of H. pomona, H. gentilis s.l. and H. lankadiva was re-assessed using an integrated taxonomic approach. The distribution of these taxa was also validated including the currently recognised subspecies of H. lankadiva (H. l. lankadiva, H. l. indus and H. l. gyi) coupling locality information from this study and from previously published studies. Therefore, a MaxEnt modelling approach is used in this chapter:

1. to predict the geographic distributions of H. pomona, H. gentilis s.l. and three subspecies of H. lankadiva s.l. (H. l. lankadiva, H. l. indus and H. l. gyi) in order to explore whether the distributions overlap among H. pomona and H. gentilis s.l. as well as among the subspecies of H. lankadiva. 2. to identify environmentally suitable areas for these taxa outside of the current known distribution. 3. to discuss whether the model results support the taxonomic status recognised for the target taxa as seen in Chapter 2 and Chapter 3.

4.2 Materials and methods

4.2.1 Species records data

The occurrence record data for the targeted taxa were collected from literature (journal articles, books), museum catalogues, Global Biodiversity Information Facility

(GBIF, www.gbif.org), Barcode of Life Data system (BOLD, www.boldsystems.org), field surveys conducted by myself during the study and from personal communication with Dr. Adora Thabah. In order to avoid any uncertain historic species records, this study used the species occurrence data since 1997. The dataset was checked for pseudo-replication and any duplicate occurrences were then

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CHAPTER 4 removed. Therefore, to avoid spatial autocorrelation between species occurrence records, the locality data were filtered at 1 km2 using the “spatially rarefy occurrence data tool” in SDMtoolbox 2.0 (Brown, Bennett & French, 2017) in ArcGIS 10.6 (ESRI Inc. Redlands, CA, USA). Thus, the resulting sample size of species occurrence records were as follows: H. pomona (n = 7), H. gentilis s.l. (n = 126), H. l. lankadiva (n = 22), H. l. indus (n = 32) and H. l. gyi (n = 8).

4.2.2 Study area and environmental variable selection

The environmental variables were chosen based on available knowledge on the species ecology and habitat (Bates & Harrison, 1997; Douangboubpha et al., 2010; Bates et al., 2015; Srinivasulu & Srinivasulu, 2019). These included the following continuous variables:- altitude, 19 bioclimatic variables (WorldClim , www.worldclim.org), distance to karst (generated from http://web.env.auckland.ac.nz/our_research/karst/) , distance to water (generated from GLWD-3 data set, http://www.wwfus.org/science/data.cfm; Lehner & Döll, 2004), human population density ( population/km2; NASA's SEDAC: Gridded Population of the World, Version 4 (GPWv4), http://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density), night light pollution (2013) 'Stable Lights' option (F182013_v4c_web.stable_lights.avg_vis.tif (2013); https://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html), human footprint (http://sedac.ciesin.columbia.edu/data/set/wildareas-v2-human- footprint-geographic) and the categorical variable:- land cover (ESA's Globcover dataset from 2009, http://due.esrin.esa.int/page_globcover.php; reclassified into 14 classes, see Supplementary Material Table S4.1). All the environmental variable layers had a resolution of 30 arc seconds (approximately 1 km at the equator). All layers were converted to .asc file type with the same resolution and extent in in ArcGIS 10.6 (ESRI Inc. Redlands, CA, USA). The following extent of environmental layers was used: (i) South Asia (India, Sri Lanka and Bangladesh) and upper Myanmar for all three subspecies of H. lankadiva and H. pomona (ii) South and Southeast Asia for H. gentilis s.l.

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Multicollinearity among the environmental variables was tested using SDMtoolbox 2.0 (Brown, Bennett & French, 2017) in ArcGIS 10.6 (ESRI Inc. Redlands, CA, USA). Highly correlated variables (r > 0.80) were removed. Two criteria were used to decide which layer should be retained among the correlated ones:- (i) layers which are ecologically important for a species (ii) layers which contributed more to the model gain when using them in isolation and/or decreasing the gain when omitted in a jackknife analysis. A stepdown modelling procedure with selected taxa specific uncorrelated layers was then used and separate models were run for each taxon using default settings in MaxEnt but with 1500 iterations instead of 500. The layers contributed less than 1% to the model were then removed from the final model. The variables selected for the final models of H. pomona, H. gentilis s.l. and three subspecies of H. lankadiva were given in Table 4.1.

4.2.3 Modelling procedure and parameter selection

SDMs were generated using MaxEnt v.3.4.1. (Phillips & Dudik, 2008) and modelling methods were followed the recommendations of Merow, Smith & Silander (2013). For each taxon, separate kernel density raster bias layer surrounding presence records was created in R v. 3.5.1 using ‘raster’, ‘rgdal’, ‘dismo’, ‘rJava’ packages to reflect uneven sampling efforts across the potential range (Fourcade et al., 2014).

Choosing appropriate feature types and adjusting regularization settings in MaxEnt is very important as these can influence model predictions and performance (Elith et al., 2010). In appropriate parameter settings can add complexity to the model output and thereby reduce its ability to infer relative variable importance (Warren & Seifert, 2011). Therefore, in this study the optimal model parameters were tested by comparing different combinations of regularization value and feature classes, and then compared model performance with corrected Akaike Information Criterion (AICc; Akaike, 1974) for small sample sizes. Model with a lower AICc score was selected for the final model. The following regularization multiplier values were used to test their effects on model performance: 0.5, 1 (default), 2, 5. Changing this value affects how a model fitted to the data and therefore it is important to try with both higher and lower values than default. A lower value gives a less spread out and more

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CHAPTER 4 focused distribution (but can result in over-fitting) and vice versa (Phillips, Anderson & Schapire, 2006). The different combinations of feature classes were based on the occurrence records (n) and they were: ‘Linear (L)’ if n < 15; ‘Linear (L)’, ‘Linear- Quadratic (LQ)’, ‘Hinge (H)’ and ‘Hinge-Quadratic (HQ)’ if n < 80 and ‘Linear (L)’, ‘Linear-Quadratic (LQ)’, ‘Linear-Quadratic-Product (LQP)’, ‘Hinge (H)’ and ‘Hinge- Quadratic (HQ)’ and ‘Hinge-Quadratic-Product (HQP)’ if n > 80.

In the final model of each taxon, a five-fold cross-validation was used with 10,000 background points and 1500 iterations. The model performance was determined using the Area Under the Curve (AUC) of the Receiver Operating Characteristics (ROC) plots (Fielding & Bell, 1997; Merow, Smith & Silander, 2013). AUC scores above 0.75 indicate a good model performance (Elith et al., 2006). MaxEnt probability of presence maps were then converted to binary predictions using the threshold value that maximises the sum of sensitivity and specificity (Liu, White & Newell, 2013; Razgour, 2015).

4.3 Results

The environmental variables included in the final model, regularization multiplier and feature types used in the model building were different for each taxon (Table 4.1).

4.3.1 H. pomona and H. gentilis s.l.

Only seven occurrence records were available for H. pomona. Therefore, the present study takes caution while interpreting the model predictions irrespective of model’s high predictive ability (AUCcrossvalidation = 0.927 ± 0.062). The model predicted suitable areas in south India including parts of Western Ghats, Kerala, Tamil Nadu, Karnataka. Suitable areas were also predicted in Sri Lanka although H. pomona is known to be absent from there (Fig. 4.1). Therefore, H. pomona is not predicted to occur outside the peninsular India except for Sri Lanka. One temperature variable (Bio 4- temperature seasonality) contributed 90.3% to the model. Other variables that contributed to model predictions included: Bio 18 (precipitation of the warmest

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CHAPTER 4 quarter), Bio 11 (annual precipitation), Bio 15 (precipitation seasonality), night light pollution and distance to water (Table 4.1).

The model for H. gentilis s.l. showed high predictive ability (AUCcrossvalidation = 0.909 ± 0.023). The majority of suitable areas identified for H. gentilis s.l were in and around the current distribution (Fig. 4.2). In addition to that, highly suitable habitat areas were identified along the east coast of India, areas including the Western Ghats in South India and Sri Lanka (Fig. 4.2). A total of eight ecogeographic variables contributed to the model (Table 4.1), among which the temperature variable ‘Bio 7 (temperature annual range)’ contributed 70.3% to the model. Other informative variables contributed to the model included four precipitation variables (Bio 15 - precipitation seasonality; Bio 16 - precipitation of wettest quarter; Bio 17 - precipitation of driest quarter and Bio 18 - precipitation of the warmest quarter), distance to karst, land cover and distance to water (Table 4.1).

4.3.2 Subspecies of H. lankadiva

For H. l. lankadiva, the model predicted suitable habitat areas in an around the known localities (Fig. 4.3). This included the southern dry and wet zone regions in Sri

Lanka. The model showed a high predictive ability (AUCcrossvalidation = 0.996 ± 0.002). Landcover, precipitation variable Bio 17, and temperature variable Bio 4 were contributed 94.3% to the final model. Other variables – distance to karst, human footprint, Bio 19 (precipitation of the coldest quarter) and Bio 15 (precipitation seasonality). The high probability of H. l. lankadiva presence was predicted in broadleaved evergreen and/or semi deciduous forests followed by evergreen and/or deciduous shrublands (See Supplementary Material Table S4.1).

For H. l. indus, the model predicted suitable areas confined to the peninsular India. The majority of the predicted suitable areas were in and around the known locality areas which included the Western Ghats and central India. The model also identified suitable areas outside the current extent (e.g. western Gujarat and Rajasthan) (Fig.

4.4). The predictive ability of the model was very high (AUCcrossvalidation = 0.889 ± 0.029). Four variables contributed to the final model: distance to karst, night light

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CHAPTER 4 pollution, land cover and Bio 15 (Table 4.1). The high probability of presence of H. l. indus was identified mainly in urban areas (artificial surfaces and associated areas). Rainfed croplands including mosaic cropland (50-70%) or grassland/shrubland/forest (20-50%) were also identified as suitable habitat. The taxon surprisingly showed an increased likelihood of occurrence with nightlight pollution.

For H. l. gyi, the model predicted suitable areas in parts of northeast India (Meghalaya) and upper Myanmar (Fig. 4.5). Only eight occurrence records were available for H. l. gyi. Therefore, the present study takes caution while interpreting the model predictions irrespective of model’s high predictive ability (AUCcrossvalidation = 0.985± 0.011). Only three environmental variables contributed to the final model and among that the precipitation variable Bio 18 contributed 96.8 % (Table 4.1).

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Table 4.1. The variables included in the final model and their relative contributions (in percentage) for each taxon is given. The feature type and regularization multiplier chosen for the final model is given in the brackets for each taxon.

Number of AUC score and Taxon name occurrence Model parameters Variables & percent contribution (%) standard deviation records (n) Bio 4 (90.3), Bio 11 (3.1), Bio 15 (3), Bio 18 (2.1), Distance to water (1.1) & H. pomona 0.927 ± 0.062 7 Linear & 2 Night light pollution (0.3) Bio 7 (70.3), Bio 16 (14.2), Bio 15 (7.4), Bio 18 (4.1), Bio 17 (1.6), H. gentilis s.l. 126 0.909 ± 0.023 Hinge & 5 Distance to karst (1.4), Landcover (0.9) & Distance to water (0.1)

Land cover (45.3), Bio 17 (29.4), Bio 4 (19.6), Distance to karst (2.7), Human footprint H. l. lankadiva 22 0.996 ± 0.002 Linear & 0.5 (2), Bio 19 (0.6), Bio 15 (0.5)

H. l. indus 32 Hinge & 5 Distance to karst (34.5), Night light pollution (32.8), Landcover (26.4) & Bio 15 (6.3) 0.889 ± 0.029

H. l. gyi 8 Linear & 2 Bio 18 (96.8), Bio 4 (1.7) & Bio 19 (1.5) 0.985± 0.011

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Figure 4.5. Habitat suitability areas predicted using MaxEnt for H. l. gyi. Suitable areas are shaded in red and unsuitable areas are in grey. The golden circles indicate the current known species occurrence records.

4.4 Discussion

Presence-only modelling technique, MaxEnt was used to understand whether the geographic distribution overlap was predicted between the species H. pomona and H. gentilis s.l. and among the subspecies of H. lankadiva. All the models had high predictive ability. Suitable habitats were identified in and around as well as outside the current known distribution of H. pomona and H. gentilis s.l. The areas predicted outside the current distribution of H. gentilis s.l. (e.g. south India including Western Ghats) showed overlap with the predicted suitable habitats for H. pomona. In the case of subspecies of H. lankadiva, no overlapping areas were identified in the predicted model distributions. For H. l. lankadiva and H. l. gyi, the models identified suitable habitats confined to the known localities of the taxa whereas for H. l. indus, the model identified suitable areas extending further than the known range.

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4.4.1 H. pomona and H. gentilis s.l.

The predicted suitable range for H. gentilis s.l. overlapped with that of H. pomona (Fig. 4.1 & 4.2). Moreover, the model identified suitable areas for H. gentilis s.l. along the east coast of India. However, the known localities of H. gentilis s.l. extends only from northeast India (including one location from West Bengal) to Southeast Asia (See Fig. 4.2). In Chapter 2, the evidences from molecular analysis and bacular morphology showed that H. pomona is distinct from H. gentilis s.l. and is confined to south India. Therefore, the predicted range overlap between the two taxa could be an indication of similar habitat requirements of the species. A landscape scale habitat suitability modelling study from Western Ghats showed that distance to wood edge or habitat richness is important for H. pomona, with species showing declines away from tree cover (Wordley et al., 2015). However, in this analysis the temperature seasonality was the single highest contributing variable to the model (90.3%). The effect of low sample size on the model prediction could be a reason for the increased influence of single variable on the model. More research is needed to understand the ecology and habitat requirements of H. pomona and H. gentilis s.l. in order to understand relevant environmental predictors influencing their distribution.

4.4.2 Subspecies of H. lankadiva

In Chapter 3, the results have shown that the presence of cryptic species in H. lankadiva can be ruled out because of low genetic divergence at both mtDNA and nuDNA levels, and the high morphological and echolocation variation in the taxa can be attributed to local adaptation. Hence the current subspecies status of H. l. lankadiva, H. l. indus and H. l. gyi is appropriate and valid. The MaxEnt results show that geographic distribution of each subspecies is confined to the currently known areas as: H. l. lanakadiva in Sri Lanka, H. l. indus in mainland India and H. l. gyi in northeast India and Myanmar. In Chapter 3, the individuals of H. lankadiva from West Bengal showed high morphological similarity with that of northeast India and Myanmar and thus strongly indicate that they belong to H. l. gyi. However, in this analysis, the SDM for H. l. gyi identified isolated patches of suitable areas in northeast India and Myanmar only and not in West Bengal or its neighbouring areas.

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This could be because of the small available sample size (n = 8) of H. l. gyi which used in the model. Moreover, no recent records were available from West Bengal.

For H. l. indus, the suitable areas identified were confined to peninsular India. The model identified the suitable habitats in urban areas (artificial surfaces and associated areas), rainfed croplands including mosaic cropland (50-70%) or grassland/shrubland/forest (20-50%). This is in line with the known habitat ecology of H. l. indus. The taxon is known to occur in non-aquatic subterranean habitats, shrubland, forests, caves, ruins, mines, old forts and temples (Bates et al., 2015). The positive response of the species to the nightlight could be explained by two reasons: (i) The available species records for H. l. indus are mainly from the urban areas in peninsular India. This is very common with the data from developing countries where species sightings are particularly spatially biased (i.e. records from more accessible locations) (Razgour et al., 2015; El‐Gabbas & Dormann, 2018); (ii) The habitats like old forts, temples or some even caves in the peninsular India which are important roost sites are mostly lit up during night as they either serve as touristic attractions or use for religious purpose. It would be interesting to determine if roost occupancy is less likely in lit versus unlit caves. Most rhinolophid bats studied to date are light averse (Stone, Harris & Jones, 2015), and so it is somewhat surprising that the likelihood of presence is higher for the morphologically similar H. l. indus in lit areas. It could be that lighting is correlated with another, more ecologically relevant variable in this case.

The suitable areas identified for H. l. lankadiva is confined to Sri Lanka and the most important predictor was landcover which includes broadleaved evergreen and/or semi deciduous forests followed by evergreen and/or deciduous shrublands. The subspecies is known to occur in the southern dry zone (semi deciduous forests/shrublands/grasslands), lower foothills and wet zone hills of Sri Lanka (evergreen forests) (Phillips, 1980). Unfortunately, these areas are subjected to highest rate of annual loss due to timber extraction particularly in the dry zone (FAO, 2005; Mattsson et al., 2012; Dittus, 2017).The results indicate the importance of conserving the landcover for better quality habitat for the H. l. lankadiva especially

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CHAPTER 4 ecosystem degradation mainly due to logging and wood harvesting, has identified as a major threat to the species survival (Srinivasulu & Srinivasulu, 2019).

In conclusion, the present study shows that presence only SDMs using MaxEnt can be used as an effective tool in identifying suitable habitats for subspecies and/or species of bats and thereby define their geographic distribution. Though the species records were limited for H. pomona and H. l. gyi, patterns of potential geographic distributions can indicate site to survey in the future and thus help to identify new populations.

4.5 Implications and future line of work

This study attempted to reduce the overfitting of the model using regularization modelling technique and by spatial cross‐validation which balances the number of presence locations and environmental variability between cross‐validation folds.

Sampling bias was also corrected using removing spatially autocorrelated records and introducing a bias layer to the models. However, it is not possible to quantify the efficiency of bias correction without bias‐free data for comparison (Phillips et al.,

2009; Warton, Renner & Ramp, 2013). Therefore, it is not clear how much model complexity optimization is affected by the limited number and quality of records (including sampling bias) (El‐Gabbas & Dormann, 2018) especially for H. pomona and

H. l. gyi. Availability of unbiased occurrence data from developing countries is always challenging particularly for bats due to their nocturnal and elusive behaviour, high manoeuverability and the need for specialized bat detectors for effective recording (Razgour et al., 2016; El‐Gabbas & Dormann, 2018). Therefore, enhancing sampling efforts from data-poor but biodiversity-rich regions such as the Indian subcontinent and Southeast Asia are much needed for better quality bat SDMs.

Choosing the proper species-specific environmental variables is also important for better model performance (Phillips, Anderson & Schapire, 2006; Merow et al., 2014). Therefore, it is recommended to use proximal predictors such as suitable roosting and foraging sites, proximity to water, food sources for bats than indirect distal predictors such as altitude (Austin, 2007; Merow et al., 2014). This is not possible

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CHAPTER 4 without having a detailed knowledge about the ecology and physiology of each species. In developing countries where research has not been fully established as in developed regions, the understanding of a species ecology or physiology is either lacking for most of the bat species or not yet available for large scale (Herkt et al., 2016; Petitpierre et al., 2017) and India is not an exception. Since the use of SDMs in bat research is under-represented from the Indian Subcontinent and nearby areas, future research in bat taxonomy, ecology and physiology is needed along with extensive systematic surveys.

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General Discussion

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5.1 Thesis overview

Cryptic diversity is well known in bats and many new species are being discovered across geographic regions and taxa through an integrative taxonomic approach using conventional and geometric morphometrics, modern echolocation recording, and molecular techniques. Biodiversity-rich areas in the tropics always harbour great potential for containing large numbers of cryptic species, especially in the Old-World families Hipposideridae and Rhinolophidae, as they possess high species richness and acoustic diversity. In the Indian subcontinent, species complexity exists especially within Rhinolophus, Hipposideros, Myotis and Pipistrellus genera and the taxonomic uncertainties within them are seldom evaluated using an integrated approach (Chattopadhyay et al., 2012). Hence, this thesis aimed to reappraise the current taxonomy of two hipposiderids from South Asia: Hipposideros pomona and Hipposideros lankadiva. An integrated taxonomic approach was used in this study incorporating multiple and independent lines of evidence from conventional morphometrics, bacular, acoustic and molecular phylogenetic methods. In this thesis, based on the molecular and bacular data, the H. pomona from south India is distinct from H. gentilis s.l from northeast India and Southeast Asia and thus the recent split into species status for the two taxa is valid. In contrast to this, both mtDNA and nuDNA and bacular results did not support the specific species status for the currently known subspecies of H. lankadiva despite having significant morphological and echolocation difference among them. Presence-only modelling technique using MaxEnt was also applied in this study in order to understand the geographic distribution of the targeted taxa. Therefore, the following sections focus on the importance and need of integrated taxonomy of bats taking H. pomona and H. lanakdiva as examples. This discussion incorporates the recommendations for bat taxonomy and suggestions for future lines of work in the Indian Subcontinent and Southeast Asia. The scope and limitations of species distribution models for bats in developing countries are also briefly outlined here.

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5.2 Importance of integrated taxonomy in bat research

In recent years, integrated taxonomy has been identified and recommended as a powerful tool to describe and delimit new species in many taxa particularly in bats. Cryptic diversity is high in bats and therefore using multiple lines of evidence from morphological, morphometric, echolocation calls, karyotypes, molecular and any other dataset can reinforce discrimination among taxa (Solari & Baker 2006; Khan et al., 2010; Velazco, Gardner & Patterson, 2010; Galimberti et al., 2012; Clare et al., 2013; Volleth et al., 2015; Pavan & Marroig, 2016; Górfól & Csorba, 2018). The present study has shown that how integrated taxonomy is important in taxonomic revisions of cryptic taxa. Therefore here, it is trying to reiterate the idea behind this approach using examples from this thesis and other studies.

Several studies have shown that a multisource approach in bat taxonomy will work better in resolving the complexity or they provide valid robust results (Solari, Sotero- Caio & Baker., 2019). One example is a study on Kerivoula (Vespertilionidae, Khan et al., 2010) which provided a clear framework for species taxonomy and phylogeny using different datasets including molecular sequences, morphological traits and karyotypes with a high congruence in their results. However, this is not often the case. For example, molecular phylogenies recovered clear distinction among some taxa of Platyrrhinus (Phyllostomidae), but morphometric analysis failed to retrieve the same distinctions (Velazco, Gardner & Patterson, 2010). In Chapter 2, a low morphometric resolution was demonstrated between H. pomona and H. gentilis s.l. but the two taxa are distinct on bacular morphology and molecular dataset. A similar trend was observed in Uroderma (Phyllostomidae, Mantilla-Meluk, 2014).

Nonetheless a robust distinction in morphometry or echolocation calls does not necessarily indicate a new taxon. As seen in Chapter 3, a substantial difference in the size and echolocation call frequency of H. l. lankadiva (Sri Lanka) and H. l. gyi (northeast India-Myanmar) from H. l. indus (peninsular India) is not supported by molecular (both mtDNA and nuDNA) and bacular morphology to a cryptic species level divergence. Instead the extensive variation in size and echolocation calls could be explained by island gigantism and/or local adaptations and hence the current

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CHAPTER 5 subspecies status is valid. Comparably, a study by Artyushin et al. (2012) reassessed the species status of Eptesicus bobrinskoi (Vespertilionidae) using multiple lines of evidence and recommended to treat the bat as a subspecies of E. gobiensis. They showed the morphological differences between E. gobiensis and E. bobrinskoi can accounted by size difference correction and the two taxa are closely related both on mtDNA (cyt b) and nuDNA (THY) datasets.

Echolocation call variation alone cannot be used as a discriminative character between cryptic bat species because various environmental (e.g. humidity, latitude, habitat) and non-environmental factors (e.g. age, sex, size) can influence the call frequency. Chapter 3 demonstrated how echolocation calls in H. lankadiva influenced by age, sex, size and geographic locations. However, echolocation calls can be used as character which can identify ‘potential cryptic’ or ‘candidate’ species especially in constant frequency bats such as hipposideros and rhinolophids. For example, Kingston et al. (2001) identified two phonic types of H. bicolor from Malaysia which later became distinguished as two different species based on morphological, echolocation and molecular dataset (Murray et al., 2018). A similar finding was reported by Thabah et al. (2006) from a sympatric population of H. larvatus. They reported two phonic types (85 kHz and 98 kHz) from northeast India and later named the 85 kHz as Hipposideros khasiana. Species status was supported both by echolocation call differences, but also by differences in mtDNA. Chapter 2 showed that there is considerable variation in echolocation calls of H. gentilis s.l. from different parts in Southeast Asia. Moreover, the molecular analysis also revealed distinct lineages in H. gentilis s.l. in the present and previous studies (Francis et al., 2010; Murray et al., 2012; Yuzefovich, Kruskop & Atryushin, 2019).

Delineating species boundaries is fundamental to biodiversity assessments and crucial to conservation and planning. Integrated taxonomy has shown that cryptic diversity exists in many widespread species of bats in biodiversity rich regions such as South and Southeast Asia. Many of these cryptic species have restricted range and therefore they require targeted conservation efforts (Francis, 2019). Therefore, recognising the importance of integrated taxonomy is urgently needed for better

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CHAPTER 5 understanding of under-represented bat diversity and their distribution as well as for effective conservation planning in South and Southeast Asia.

5.3 Suggestions for further lines of study

In recent years, cryptic diversity in bats have been increasingly documented from areas in Southeast Asia and the Indian subcontinent (e.g. Rhinolophus - Chattopadhyay et al., 2012, Srinivasulu et al., 2019; Hipposideros -Thabah et al., 2006, Murray et al., 2012, Murray et al., 2018; Murina – Yu, Csorba & Wu, 2019). Many species complexes still await taxonomic appraisal from these regions (Francis et al., 2010; Murray et al., 2012) as they are rarely subjected to studies which use an integrated approach. Moreover, species richness, and acoustic diversity are prevalent in tropical bat faunas (Kingston et al., 2001) therefore, cryptic taxa may be considerable in Southeast Asia and the Indian subcontinent where areas of high endemism (Andaman Islands, Sri Lanka) and biodiversity hotspots [Western Ghats – Sri Lanka (WGSL); Indo-Burma] are present (Deshpande & Kelkar, 2019; Srinivasulu et al., 2019).

In Chapter 2, it was shown that H. gentilis s.l. from northeast India and Southeast Asia could comprise cryptic species. The acoustic and genetic variation observed in the individuals from the Andaman Islands, Laos and Cambodia (Fig. 2.9) can be considered as an indication of presence of cryptic lineages within H. gentilis s.l. These findings are in line with previous studies those identified cryptic lineages in H. gentilis s.l. which could be potentially new species (from Thailand - Douangboubpha et al., 2010; Laos - Murray et al., 2012; China - Zhao et al., 2015; Andaman Islands - Srinivasulu et al., 2017 and Vietnam - Yuzefovich, Kruskop & Atryushin, 2019). Moreover, the geographic distribution of H. gentilis s.l. including the subspecies limit (H. g. sinensis) is uncertain. Therefore, a comprehensive study of H. gentilis s.l throughout its range is suggested. This should include an integrated taxonomic approach and the species/subspecies ranges needs to be redefined using SDMs.

In Chapter 3, it was shown that the echolocation call varies among different population of H. l. indus in Indian peninsula (West India vs central India; see Fig. 3.11).

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The results also showed that the H. l. indus population from west India (fall in the WGSL region) are smaller in size than those from central/south India (Fig. 3.5). A recent study has shown an interactive effect of environmental and phylogenetic factors on acoustic divergence in four species of Rhinolophus bats of the WGSL (Deshpande & Kelkar, 2019). Therefore, a future study testing for the acoustic variation in H. l. indus is suggested in mainland India due to environmental influences with the support of a larger sample size.

Bat research in the Indian subcontinent has still not picked up the momentum and therefore the species diversity and richness are not clear even from the biodiversity hotspots. Moreover, systematic bat surveys or acoustic call libraries are lacking. Therefore, there is a huge potential for unveiling the cryptic diversity in this region especially within the Rhinolophus, Hipposideros, Myotis and Pipistrellus genera (Thabah et al., 2006; Chattopadhyay et al., 2012; Deshpande & Kelkar, 2019).

The faunal affinity between mainland India and Sri Lanka was discussed in Chapter 3. The two hypotheses that explain the faunal and floral assemblage in Sri Lanka compared with the mainland India, faunal exchange vs. in situ diversification has tested in taxa such as freshwater crabs and shrimps, tree frogs, caecilians, blind snakes (Bossuyt et al., 2004), certain agamids (Grismer et al., 2016) and Hemidactylus geckos (Lajmi et al., 2019) but not yet in bats. The separation between Sri Lanka and Indian peninsula is only 35-40 km, so considering the high mobility of bats over long distances, gene flow might have occurred historically and through the Quaternary period (Bose et al., 2015). Sri Lanka harbours 20 species of bats with nine endemics from seven bat families (Dittus, 2017) and 11 of them are found in mainland India too. Therefore, bats would be an ideal group for studying the factors shaping biotic assembly of an island (Sri Lanka) compared with the mainland (peninsular India) through phylogeographic analysis including molecular phylogenetics, divergence date estimations and ancestral area reconstructions.

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5.4 Scope and limitations of species distribution models for bats in developing countries

Species distribution models (SDMs) use known occurrence records of a taxa and information on the environmental conditions to identify suitable habitat areas. SDMs have been widely used in an array of disciplines including ecology, control invasion, potential range shift under climate change conservation and planning (Newell & White, 2016; Razgour et al., 2016; Mori et al., 2018). Presence only modelling algorithms such as MaxEnt have identified as a potential tool for identifying bat distribution, biogeography and answering questions on species responses to past and future environmental changes (Razgour et al., 2016). In Chapter 4, it was shown that SDMs using MaxEnt can effectively use to identify suitable habitat areas and defining geographical distributions of species or subspecies particularly when taxonomic reassessment has done. However, the model performance could be limited by quality of species records (sampling bias).

In developing countries, the species records are mostly opportunistic, sporadic and spatially biased (Razgour et al., 2016). Moreover, for many species including bats most of the occurrence data be from museum catalogues, personal collections and literature (El-Gabbas & Dormann, 2018). Due to the limited funds dedicated to wildlife conservation and political instability, a systematic nation-wide sampling is lacking in these countries (Razgour et al., 2016; El-Gabbas & Dormann, 2018). Additionally, bats are rarely subjected to ecological and behavioural studies in countries like Egypt (El-Gabbas & Dormann, 2018), Indian subcontinent (Chattopadhyay et al., 2012) which again make it harder to decide the environmentally relevant variables for a species in SDMs. Therefore, more research and efforts needed from data-poor countries in order to enhance the use of SDMs to model either to discover new population or identifying threats for the species survival and to draft species specific conservation policies. The recent availability of inexpensive ultrasound data loggers such as Audiomoths (Hill et al., 2018) will increase survey effort in future, although their effectiveness with recording species that echolocate using high frequencies may be limited.

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In conclusion, this study has emphasised the value of using integrated approaches in understanding bat biodiversity and has contributed to understanding bat diversity in a little studied area of the world. We hope that the approach that we have used can be applied more widely in the future.

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Supplementary material

Figure S2.1. The 3D scatter plot of Principal Component Analysis based on PC1, PC2 and PC3 for 14 craniodental measurements of H. pomona (golden circles) and H. gentilis s.l. (blue triangles). The sample size is 109.

Figure S3.1. Correlation matrices of 10 external characters of 100 individuals of H. lankadiva from different study regions. The correlation coefficients (r) are displayed below the diagonal. The diagonal shows each external character (See section 2.2.2.1 in Chapter 2 for the external character descriptions). The blue and red circles on the top of the diagonal represent the positive and negative correlations respectively. The colour intensity and the size of the circles are proportional to the correlation coefficients.

Figure S3.2. Correlation matrices of 14 cranial characters of 95 individuals of H. lankadiva from different study regions. The correlation coefficients (r) are displayed on the bottom of the diagonal. The diagonal shows each cranial character (See section 2.2.2.1 in Chapter 2 for the cranial character descriptions). The blue circles on the top of the diagonal represent the positive correlations. The colour intensity and the size of the circles are proportional to the magnitude of the correlation coefficients.

Table S3.1. The uncorrected p distance between the individuals of H. lankadiva from different study regions.The study regions are indeicated by the last two letters in the sample ID as follows: SL – Sri Lanka; CI – Central India; NEI – Northeast India and SI – South India.

Table S4.1 The reclassified land cover categories used in the final models of H. pomona, H. gentilis s.l. and subspecies of H. lankadiva.

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Sl. Uncorrected p distance Sample ID No. 1 2 3 4 5 6 7 8 9 1 HM540536_SL 2 RFHL001b_CI 0.029 3 RFHL007_CI 0.032 0.003 4 RFHL002_CI 0.033 0.003 0.000 5 RFHL006_CI 0.025 0.003 0.000 0.000 6 MEHHL001_NEI 0.059 0.055 0.052 0.053 0.047 7 SLBMHL005_SL 0.009 0.036 0.033 0.033 0.023 0.058 8 SLBMHL006_SL 0.003 0.033 0.028 0.028 0.020 0.053 0.003 9 LBCHL002_SI 0.060 0.067 0.064 0.065 0.059 0.066 0.053 0.051

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Table S4.1 The reclassified land cover categories used in the final models of H. pomona, H. gentilis s.l. and subspecies of H. lankadiva.

Value Label Reclassified New value 11 Post-flooding or irrigated croplands (or aquatic) Flooded cropland 1 14 Rainfed croplands Rainfed cropland 2 20 Mosaic cropland (50-70%) / vegetation (grassland/shrubland/forest) (20-50%) Rainfed cropland 2 30 Mosaic vegetation (grassland/shrubland/forest) (50-70%) / cropland (20-50%) Mixed vegetation/cropland 3 40 Closed to open (>15%) broadleaved evergreen or semi-deciduous forest (>5m) Deciduousmosaic forest 4 50 Closed (>40%) broadleaved deciduous forest (>5m) Deciduous forest 4 60 Open (15-40%) broadleaved deciduous forest/woodland (>5m) Deciduous forest 4 70 Closed (>40%) needleleaved evergreen forest (>5m) Coniferous forest 5 90 Open (15-40%) needleleaved deciduous or evergreen forest (>5m) Coniferous forest 5 100 Closed to open (>15%) mixed broadleaved and needleleaved forest (>5m) Mixed forest 6 110 Mosaic forest or shrubland (50-70%) / grassland (20-50%) Mixed vegetation 7 120 Mosaic grassland (50-70%) / forest or shrubland (20-50%) Mixed(forest/shrubland/grassland vegetation 7 130 Closed to open (>15%) (broadleaved or needleleaved, evergreen or deciduous) shrubland (<5m) Shrubland(forest/shrubland/grasslandmosaic) 8 140 Closed to open (>15%) herbaceous vegetation (grassland, savannas or lichens/mosses) Grasslandmosaic) 9 150 Sparse (<15%) vegetation Bare ground 10 160 Closed to open (>15%) broadleaved forest regularly flooded (semi-permanently or temporarily) - Fresh or brackish Flooded mixed vegetation 11 water 170 Closed (>40%) broadleaved forest or shrubland permanently flooded - Saline or brackish water Flooded mixed vegetation 11 180 Closed to open (>15%) grassland or woody vegetation on regularly flooded or waterlogged soil - Fresh, brackish Flooded mixed vegetation 11 or saline water 190 Artificial surfaces and associated areas (Urban areas >50%) Urban 12 200 Bare areas Bare ground 10 210 Water bodies Water bodies 13 220 Permanent snow and ice Snow and ice 14 230 No data (burnt areas, clouds) No data

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Appendix I

Specimens details for H. pomona and H. gentilis s.l. with museum or field number, locality of collection and those used for different analysis in this study. The museum acronyms are as follows: Natural History Museum, London (BMNH), the Harrison Institute, Sevenoaks, UK (HZM), the Hungarian Natural History Museum, Budapest (HNHM), the Zoological Survey of India, Kolkata (ZSI), the North Eastern Regional Centre of ZSI, Shillong, (NERC), and The Bombay Natural History Society, Mumbai, India (BNHS).

Taxon Museum/Field Locality Skull External Baculum H. pomona HZMNumber 45.36546 South India: KMTR, Tamil Nadu X H. pomona HZM 46.36623 South India: KMTR, Tamil Nadu X X H. pomona HZM 53.40201 South India: Sengal Hari, Tamil Nadu X X X (3D) H. pomona HZM 54.40266 South India: Sengaltheri, Tamil Nadu X X H. pomona HZMJV-BRL 55.40267-120523.1 South India: Sengaltheri, Tamil Nadu X X H. pomona JV-BRL-140329.1120521.6 X(2D) H. pomona HZM35.40250 South India: Tamil Nadu X X(2D) H. pomona BM.2003.397JV-BRL-140217.1 South India: Trissur, Kerala X X X (3D) H. pomona BM.2003.399 South India: Trissur, Kerala X X H. pomona BM.2003.398 South India: Trissur, Kerala X X H. gentilis s.l. HZM 16.34710 Andaman Islands: Little Andaman X X H. gentilis s.l. HZM 17.34727 Andaman Islands: Little Andaman X X H. gentilis s.l. BNHS 3489 NE India: Laitkysao,Khasi Hills, , Meghalaya X H. gentilis s.l. BNHS 3490 NE India: Laitkysao,Khasi Hills, , Meghalaya X H. gentilis s.l. BNHS 3491 NE India: Laitkysao,Khasi Hills, , Meghalaya X H. gentilis s.l. BNHS 3492 NE India: Laitkysao,Khasi Hills, , Meghalaya X X H. gentilis s.l. BNHS 3493 NE India: Laitkysao,Khasi Hills, , Meghalaya X X H. gentilis s.l. BNHS 3496 NE India: Cherrapunji, Meghalaya X X H. gentilis s.l. BNHS 3497 NE India: Cherrapunji, Meghalaya X H. gentilis s.l. BNHS 3499 NE India: Cherrapunji, Meghalaya X H. gentilis s.l. BNHS 3501 NE India: Cherrapunji, Meghalaya X H. gentilis s.l. BNHS 3502 NE India: Cherrapunji, Meghalaya X H. gentilis s.l. BNHS 3503 NE India: Cherrapunji, Meghalaya X H. gentilis s.l. BNHS 3509 NE India: Pashok, Darjeeling, West Bengal X X

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H. gentilis s.l. BNHS 3510 NE India: Pashok, Darjeeling, West Bengal X H. gentilis s.l. BNHS 3511 NE India: Pashok, Darjeeling, West Bengal X X H. gentilis s.l. BNHS 3515 NE India: Pashok, Darjeeling, West Bengal X H. gentilis s.l. BNHS 3517 NE India: Pashok, Darjeeling, West Bengal X H. gentilis s.l. BNHS 3527 Hasimara, Bhutan Duars X H. gentilis s.l. ZSI 16898 NE India: Laitkynsew, Khasi Hills, X X H. gentilis s.l. ZSI 10657 NEMeghalaya India: Cherrapunji, Meghalaya X H. gentilis s.l. ZSI 21510 NE India: Nazira, Assam X H. gentilis s.l. ZSI 22325 NE India: Pashok, Darjeeling, West Bengal X H. gentilis s.l. ZSI 22326 NE India: Pashok, Darjeeling, West Bengal X H. gentilis s.l. ZSI 22327 NE India: Pashok, Darjeeling, West Bengal X H. gentilis s.l. ZSI 22329 NE India: Narbong, Darjeeling, West Bengal X H. gentilis s.l. ZSI 20844 NE India: 7 km south of Jatinga village, X H. gentilis s.l. BM.21.1.17.78 NECachar India: Dis, Samgser, Assam Darjeeling X H. gentilis s.l. BM.79.11.21.158 NE India: Masuri X H. gentilis s.l. BM.21.1.17.87 NE India: Nimbongs X H. gentilis s.l. BM.21.1.17.79 NE India: Sangser,Darjeeling X H. gentilis s.l. BM.20.6.7.4 NE India: Margherita, Assam X H. gentilis s.l. BM.20.11.1.19 NE India: Cherrapungi, Meghalaya X H. gentilis s.l. BM.20.11.1.15 NE India: Laitkensao, Khasi Hills, X H. gentilis s.l. BM.20.11.1.14 NEMeghalaya India: Khasi Hills, Meghalaya X H. gentilis s.l. BM.20.11.1.16 NE India: Cherrapungi, Meghalaya X H. gentilis s.l. BM.20.11.1.17 NE India: Cherrapungi, Meghalaya X H. gentilis s.l. BM.23.1.7.1 NE India: Mishmi Hills, Assam X H. gentilis s.l. No number Upper Myanmar: Mandalay X H. gentilis s.l. BM.75.11.3.6 Upper Myanmar: Toagiue X H. gentilis s.l. No number Upper Myanmar: Mandalay X H. gentilis s.l. BM.21.1.17.92 Upper Myanmar: Thonywa, S. Chindwin X H. gentilis s.l. BM.21.1.17.90 Upper Myanmar: mt. popa X H. gentilis s.l. BM.21.1.17.89 Upper Myanmar: Upper Chindurin X H. gentilis s.l. BM.21.1.17.81 Upper Myanmar: Mandalay X

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H. gentilis s.l. BNHS 3528 Upper Myanmar: Pagan X H. gentilis s.l. BNHS 3532 Upper Myanmar: Pagan X H. gentilis s.l. BNHS 3546 Upper Myanmar: Mingun, Sagaing X H. gentilis s.l. BNHS 3549 Upper Myanmar: Mingun, Sagaing, X H. gentilis s.l. BM.93.11.15.2 Upper Myanmar: Thayetmyo X H. gentilis s.l. HZM 41.35964 Upper Myanmar: Nanti Hill Forest X X H. gentilis s.l. HZM 43.36077 Upper Myanmar: Holin Village cave X X H. gentilis s.l. HZM 51.39888 Upper Myanmar: Nanti Hill Forest X X H. gentilis s.l. HZM 8.32584 Upper Myanmar: Nyaung-Oo, Mandalay X X H. gentilis s.l. HZM 19.35121 Upper Myanmar: Nyaung-Oo, Mandalay X X H. gentilis s.l. BM.87.3.4.12 Lower Myanmar: Pegu X X H. gentilis s.l. HZM 15.34201 Lower Myanmar: Gu Gyi Cave X X H. gentilis s.l. HZM 50.36837 Lower Myanmar: Gyo Reservoir X X X (3D) H. gentilis s.l. HZM 49.36836 Lower Myanmar: Gyo Reservoir X X H. gentilis s.l. HZM 12.33356 Lower Myanmar: Gu-gyi cave X X H. gentilis s.l. HZM 18.35013 Lower Myanmar: Kyauk Basat Cave X X H. gentilis s.l. HZM 10.33354 Lower Myanmar: Kyauk Basat Cave X X H. gentilis s.l. HZM 11.33355 Lower Myanmar: Gu-gyi cave X X H. gentilis s.l. HZM 13.33882 Lower Myanmar: Gu-gyi cave X X H. gentilis s.l. HZM 48.36835 Lower Myanmar: Gyo Reservoir X X H. gentilis s.l. HZM 52.39993 Lower Myanmar: Nagamauk Cave X X H. gentilis s.l. HZM 47.36834 Lower Myanmar: Gyo Reservoir X X H. gentilis s.l. HZM 7.32585 Lower Myanmar: Naga-mauk cave X X H. gentilis s.l. BM. 1997.387 Vietnam: Muong nhe Nature Reserve X X H. gentilis s.l. BM. 1997.383 Vietnam: Tat Ke Sector X X H. gentilis s.l. HZM 5.32357 Vietnam: Kon Cha rang Nature reserve X X H. gentilis s.l. HZM 2.32354 Vietnam: Kon Cha rang Nature reserve X X H. gentilis s.l. HZM 3.32355 Vietnam: Ruc Ma Rinh X X X (3D) H. gentilis s.l. HZM 4.32356 Vietnam: Toong Ching X X H. gentilis s.l. HZM 6.32358 Vietnam: Cao Dang cv. Buoi River Valley X X H. gentilis s.l. HZM 1.30542 Vietnam: Cuc- Phuong Nat. Park X X

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H. gentilis s.l. HNHM 88.24.1 Vietnam: Pagoda X X H. gentilis s.l. HNHM 88.24.2 Vietnam: Pagoda X X H. gentilis s.l. HNHM 88.24.3 Vietnam: Pagoda X H. gentilis s.l. HNHM 88.25.1 Vietnam: Cuc Phuong National Park X H. gentilis s.l. HNHM Vietnam: Cuc Phuong National Park X H. gentilis s.l. HNHM 88.27.1 Vietnam: Si Cave (no.2) X H. gentilis s.l. HNHM 88.27.2 Vietnam: Si Cave (no.2) X H. gentilis s.l. HNHM 88.28.1 Vietnam: Si Cave (no.2) X H. gentilis s.l. HNHM 88.28.2 Vietnam: Si Cave (no.2) X H. gentilis s.l. HNHM 88.28.3 Vietnam: Si Cave (no.2) X H. gentilis s.l. HNHM 88.28.4 Vietnam: Si Cave (no.2) X H. gentilis s.l. HNHM 88.28.5 Vietnam: Si Cave (no.2) X H. gentilis s.l. HNHM 88.29.1 Vietnam: Bat Cave (no.1) X H. gentilis s.l. HNHM 88.29.2 Vietnam: Bat Cave (no.1) X H. gentilis s.l. HNHM 88.29.3 Vietnam: Bat Cave (no.1) X H. gentilis s.l. HNHM 88.29.4 Vietnam: Bat Cave (no.1) X H. gentilis s.l. HNHM 98.90.3 Vietnam: Catba X H. gentilis s.l. HNHM 2005.81.11 Cambodia: Kbal Klar Cave X H. gentilis s.l. HNHM 2005.81.12 Cambodia: Kbal Klar Cave X H. gentilis s.l. HNHM 2005.81.13 Cambodia: Kbal Klar Cave X H. gentilis s.l. HNHM 2006.34.14 X H. gentilis s.l. HNHM 2007.49.2 Cambodia: Phnom Samkos X H. gentilis s.l. HZM 14.34185 Cambodia: Kirirom National Park X X (2D) H. gentilis s.l. HZM 44.36202 Cambodia: Kirirom National Park X H. gentilis s.l. HNHM 2005.82.46 Laos: Ban Phonsong, Khammouane X

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Appendix II

(a)

(b)

Schematic diagram of a bat with the (a) external and (b) craniodental measurements. The diagrams taken from Bates & Harrison, 1997.

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Appendix III

Specimens details for H. lankadiva with museum or field number, locality of collection and those used for different analysis in this study. The museum acronyms are as follows: Natural History Museum, London (BMNH), the Harrison Institute, Sevenoaks, UK (HZM), the Hungarian Natural History Museum, Budapest (HNHM), the Zoological Survey of India, Kolkata (ZSI), the North Eastern Regional Centre of ZSI, Shillong, (NERC), and The Bombay Natural History Society, Mumbai, India (BNHS). The acronyms for regions: SI – south India; WI – west India; CI – central India; WB – West Bengal; NEI – northeast India and SL – Sri Lanka.

Taxon Museum/Field Locality Skull External Baculum H. lankadiva ZSI18039Number SI: Gersoppa, North Kanara, Karnataka X X H. lankadiva ZSI18041 -do- X H. lankadiva ZSI18042 -do- X H. lankadiva ZSI20122 -do- X X H. lankadiva ZSI20123 -do- X X H. lankadiva ZSI20196 SI: Muroor, Karnataka X H. lankadiva ZSI21369 SI: Lankapakalu, Vishakhapattanam dist, Andhra Pradesh X X H. lankadiva ZSI21370 -do- X X H. lankadiva ZSI17147 CI: X H. lankadiva ZSI17156 SI:Mundra, Gersoppa, Saugor, Kanara Madhya Pradesh X H. lankadiva ZSI17157 - do- X H. lankadiva ZSI17160 Palkonda Hills, Kurnool, Andhra Pradesh X H. lankadiva ZSI17161 - do- X H. lankadiva ZSI26380 CI: Janamara, kanha forest range, Mandla dist. X H. lankadiva ZSI26381 -do- X H. lankadiva ZSI26382 -do- X H. lankadiva ZSI25806 CI: Guru Ghasidas NP, Rawgarh Forest Rest House, X X H. lankadiva ZSI25807 Chattisgarh-do- X X H. lankadiva ZSI20036 CI: Pullandur, Madhya pradesh X X H. lankadiva ZSI15302 CI: Bhopal, Madhya Pradesh X H. lankadiva ZSI21118 SI: Borraguhalu, Vishakhapatanam, Andhra Pradesh X X H. lankadiva ZSI21121 -do- X X H. lankadiva ZSI21119 -do- X X

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H. lankadiva ZSI21120 -do- X X H. lankadiva ZSI21129 -do- X X H. lankadiva ZSI21123 -do- X X H. lankadiva ZSI21127 -do- X X H. lankadiva ZSI21125 -do- X X H. lankadiva ZSI21126 -do- X X H. lankadiva ZSI21130 -do- X X H. lankadiva ZSI21122 -do- X H. lankadiva ZSI21128 -do- X H. lankadiva ZSI21124 -do- X H. lankadiva ZSI21131 -do- X H. lankadiva ZSI20031 WB: Khumtimari, Moraghat range, Jalpaiguri dis X X X H. lankadiva ZSI20032 -do- X H. lankadiva ZSI20033 -do- X H. lankadiva ZSI22047 -do- X X H. lankadiva ZSI22048 -do- X H. lankadiva ZSI22049 -do- X H. lankadiva ZSI20032 -do- X H. lankadiva ZSI20201 SI: Muroor, Kumta Taluk, North Kanara dist., Karnataka X H. lankadiva ZSI20203 -do- X H. lankadiva ZSI20204 -do- X X H. lankadiva ZSI20205 -do- X H. lankadiva ZSI20207 -do- X H. lankadiva ZSI20209 -do- X X H. lankadiva ZSI20210 -do- X H. lankadiva ZSI20211 -do- X X H. lankadiva ZSI20197 -do- X X H. lankadiva ZSI20199 -do- X X H. lankadiva ZSI4932 CI: X

H. lankadiva ZSI4933 Satpura-do- Hills, Maharashtra X H. lankadiva ZSI4934 -do- X

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H. lankadiva ZSI9193 CI: Khandagiri, Puri district, Orissa X X H. lankadiva ZSI9194 -do- X X H. lankadiva ZSI9195 -do- X X H. lankadiva ZSI23902 NEI: Manipur X H. lankadiva ZSIVM/ERS/133 NEI: Siju Caves X H. lankadiva ZSI132 NEI: Meghalaya X H. lankadiva ZSIKM17 CI: Madhya Pradesh X H. lankadiva ZSIKM18 CI: Madhya Pradesh X H. lankadiva ZSIKM24 CI: Madhya Pradesh X H. lankadiva BNHS3343 SI: Gersoppa, Kanara X H. lankadiva BNHS3346 SI: Gersoppa, Kanara X H. lankadiva BNHS3347 SI: Gersoppa, Kanara X H. lankadiva BNHS3349 SI: Gersoppa, Kanara X H. lankadiva BNHS3353 SI: Gersoppa, Kanara X H. lankadiva BNHS3364 SI: Gersoppa, Kanara X H. lankadiva OMT 110105.1 MY: Pawtawmu Cave, Karmine Township, Kachin State X X H. lankadiva OMT 110105.2 -do- X X H. lankadiva OMT 110105.4 -do- X X H. lankadiva OMT 110105.5 -do- X X H. lankadiva OMT 110105.3 -do- X X H. lankadiva HZM.3.25664 CI: Mandu, MP X H. lankadiva HZM.IN44 -do- X H. lankadiva HZM.2.25663 -do- X X H. lankadiva IN45 -do- X H. lankadiva HZM. 4.27326 SL: Bogala Tunnel, Ruwanwella X X H. lankadiva HZM.5.27327 -do- X X H. lankadiva HZM.6.27328 SL: Wavulpane, Pallebedda X X H. lankadiva HZM.7.30231 SL: Gampaha, nr Hewelkandura, Uva Province X H. lankadiva HZM.8.30232 -do- X H. lankadiva HZM.9.30233 -do- X H. lankadiva BM.12.11.28.17 SI: Gersoppa, Kanara X

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H. lankadiva BM.12.11.28.18 -do- X H. lankadiva BM.12.11.28.19 SI: Gersoppa, Kanara X H. lankadiva BM 12.11.28.20 (type) -do- X H. lankadiva BM.12.11.29.22 -do- X H. lankadiva BM.12.11.28.23 -do- X X H. lankadiva BM.12.11.29.21 CI: Sohagpur, MP X H. lankadiva BM13.4.11.19 (mixtus Kolar, E. Mysor X

H. lankadiva BM.13.2.10.9type) SL: Peradeniya X H. lankadiva BM.13.2.10.10 -do- X H. lankadiva BM.13.2.10.11 -do- X H. lankadiva BM.13.2.10.12 -do- X H. lankadiva BM.2.10.7.20 SL: Kandy X X H. lankadiva BM.2.10.7.21 SL: Kandy X H. lankadiva BM.52.5.9.11? Cotype -do- X H. lankadiva BM.53.7.19.3 -do- X X H. lankadiva BM.30.5.24.78 SI: Palakkad (Balaghat) X H. lankadiva BM.36.11.26.4 SL: Sinharaja Forest, cave at Pitakele X H. lankadiva BM.36.11.26.5 X X H. lankadiva NMNH13g SL: Kandy X H. lankadiva NMNH 13e -do- X H. lankadiva NMNH 13l -do- X H. lankadiva NMNH 13c -do- X H. lankadiva NMNH 3k -do- X H. lankadiva NMNH 3b -do- X H. lankadiva 2017.SLK.HL.IDPM2 SL: Ingiria-Dumbara Pumbigo Mine X H. lankadiva 2017.SLK.HL.IDPM7 -do- X H. lankadiva 2017.SLK.HL.BogM1 SL: Bogala Mine X H. lankadiva 2017.SLK.HL.BogM2 -do- X H. lankadiva 2017.SLK.HL.BogM3 -do- X H. lankadiva 2017.SLK.HL.BogM4 -do- X H. lankadiva 2017.SLK.HL.BogM5 -do- X

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H. lankadiva 2017.SLK.HL.BogM6 -do- X H. lankadiva 2017.SLK.HL.BogM7 -do- X H. lankadiva 2017.SLK.HL.KanhM1 SL: Kanhelia Mine X H. lankadiva 2017.SLK.HL.KanhM2 -do- X H. lankadiva 2017.SLK.HL.KanhM3 -do- X H. lankadiva 2017.SLK.HL.KanhM4 -do- X H. lankadiva 2017.SLK.HL.KanhM5 -do- X H. lankadiva 2017.SLK.HL.KanhM7 -do- X H. lankadiva 2017.SLK.HL.KanhM10 -do- X H. lankadiva 2017.IND.HL.RF1 CI: Raisen Fort, Bhopal X X X H. lankadiva 2017.IND.HL.RF2 -do- X X H. lankadiva 2017.IND.HL.RF3 -do- X H. lankadiva 2017.IND.HL.RF4 -do- X H. lankadiva 2017.IND.HL.RF5 -do- X H. lankadiva 2017.IND.HL.RF6 -do- X H. lankadiva 2017.IND.HL.RF7 -do- X H. lankadiva 2017.IND.HL.RF8 -do- X H. lankadiva 2017.IND.HL.LBC1 WI: Lamgao Buddhist Caves, Goa X X X H. lankadiva 2017.IND.HL.LBC2 -do- X X H. lankadiva 2017.IND.HL.LBC3 -do- X H. lankadiva 2017.IND.HL.LBC4 -do- X H. lankadiva 2017.IND.HL.DW1 WI: Dhabelwada, arvalam, Goa X X H. lankadiva 2017.IND.HL.DW2 -do- X H. lankadiva 2017.IND.HL.DW3 -do- X H. lankadiva C NEI: Jaintia X

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