Diversity and Zoogeography of the Fairy Shrimps (Branchiopoda: Anostraca) on the Indian Subcontinent

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Diversity and Zoogeography of the Fairy Shrimps (Branchiopoda: Anostraca) on the Indian Subcontinent Hydrobiologia DOI 10.1007/s10750-017-3122-6 CHALLENGES IN ANOSTRACAN RESEARCH Diversity and zoogeography of the fairy shrimps (Branchiopoda: Anostraca) on the Indian subcontinent Sameer M. Padhye . Mihir R. Kulkarni . Henri J. Dumont Received: 21 December 2016 / Revised: 1 February 2017 / Accepted: 11 February 2017 Ó Springer International Publishing Switzerland 2017 Abstract The Indian subcontinent has a specific two zoogeographic ‘‘zones,’’ viz., a Northern (NZ) biogeographical history, but has remained understud- zone and the rest of the subcontinent (RS) comprising ied with respect to invertebrates like the Anostraca. In the Central (CZ) and South (SZ) zones by Unweighted this study, we discuss the anostracan diversity and Pair-Group Method using arithmetic averages cluster- zoogeography on the subcontinent. We collected all ing and Analysis of Similarity. Complementarity pertinent literature and considered nineteen biocli- index shows that no fauna is shared between NZ and matic variables along with altitude and its terrestrial RS, while CZ and SZ share 50% of the species. ecoregions. The study area was overlaid with Principal Component analysis shows that NZ and RS 10,000 km2 grids, and five hundred random GIS data differ somewhat from one another climatically. NZ points per grid were extracted for analysis besides the and RS have different ecoregions with montane and species locality data. Species richness estimators temperate grasslands commonly observed in NZ while predict at least 3–4 more species to the existing list the latter comprising tropical forests, implying differ- of 19 species. The beta diversity measure bsim reveals ences in soil geochemistry which is crucial for anostracan distribution. Guest editors: Federico Marrone, D. Cristopher Rogers, Paola Zarattini & Luigi Naselli-Flores / New Challenges in Keywords Artemia Á Biogeography Á Anostracan Research: a Tribute to Graziella Mura Chirocephalus Á Himalayas Á Streptocephalus Electronic supplementary material The online version of this article (doi:10.1007/s10750-017-3122-6) contains supple- mentary material, which is available to authorized users. S. M. Padhye H. J. Dumont Wildlife Information Liaison Development Society, Biology Department, Ghent University, Ghent, Belgium Coimbatore, Tamil Nadu 641035, India H. J. Dumont S. M. Padhye (&) Department of Ecology and Hydrobiology, Jinan Department of Biodiversity, Abasaheb Garware College, University, Guangzhou, China Karve Road, Pune 411004, Maharashtra, India e-mail: [email protected] M. R. Kulkarni Department of Zoology, Savitribai Phule Pune University, Pune 411007, Maharashtra, India 123 Hydrobiologia Introduction biogeographical patterns (Mani, 1974; Briggs, 2003), making it an interesting landmass to study. Recently, Anostraca (‘fairy shrimps’) are typical of temporary patterns in the branchiopod genus Daphnia were waterbodies, high mountain lakes, and salt water lakes studied, and altitude and latitude were shown to (Dumont & Negrea, 2002; Brendonck et al., 2008; determine its zoogeography (Padhye et al., 2016). Rogers, 2015) around the world. Even though local distribution records of fairy Zoogeographical studies of Anostraca on a large shrimps from the subcontinent are available, a com- scale are few (Rogers, 2015) in contrast to studies on prehensive zoogeographical analysis is still missing. specific countries: Botswana (Brendonck & Riddoch, Against this background, we put forth a first species ´ 1997); Morocco (Thiery, 1991); South Africa (Hamer richness estimate of fairy shrimps from the region to ´ & Brendonck, 1997); Mexico (Maeda-Martınez et al., approximate the number of missing species and assess 1997) Russia (partim) (Vekhov, 1993), Thailand (and the diversity and distribution patterns of Anostraca other South East Asian countries) (Rogers et al., using species occurrence along with environmental 2013). Distinct bioregions have been identified in variables. North America and Australia by analyzing species occurrence and species assemblage data, availability of habitats, soil geochemistry, and dispersal patterns Materials and methods (Rogers, 2014a, b, c; Rogers & Timms, 2014). Rogers (2015) proposed a conceptual model for Anostracan Study area biogeography with probable causes (such as resource monopolization and island biogeography dynamics) The region covers the countries bound by the for the extant distributional patterns of these animals. Himalayan and Hindu Kush mountains, viz., India, The Indian subcontinent currently represents a gap Pakistan, Afghanistan (partim), Nepal, Sri Lanka, in the knowledge of anostracan species diversity and Bangladesh, and Bhutan. There exists a broad spec- distribution. Bond (1934) provided the first data for all trum of physical variables like altitude (0–8000 ? m large branchiopods including fairy shrimps from the a.s.l.), temperature and precipitation within the study region in the form of a few maps. This was followed by area. Most of this region has a characteristic season, a review (rather a checklist) by Battish (1983) listing viz., ‘monsoon,’ where it rains on average for species per region with comments on their localities. 3–4 months of the year (more in some areas) (Mani, Belk & Esparza (1995) presented distribution records 1974). For more details, see Padhye et al. (2016). of 15 species of Anostraca from the Indian subconti- nent (barring Afghanistan), primarily discussing (1) Data collection authenticity of records of some species and (2) taxonomical identities of Indian streptocephalids and Locality data were gathered from published Anostraca thamnocephalids. Velu & Munuswamy (2005) taxo- literature from the region (see Supplementary Mate- nomically re-evaluated all Indian Streptocephalus rial—Appendix 1) and collections of authors from the briefly, presenting distributional records. Selvarajah western region of India. Articles from predatory & Costa (1979) presented locality data for strepto- journals were avoided as far as possible. We found cephalids of Sri Lanka while Kazmi & Sultana (2014) no information of Anostraca from Bangladesh and published a checklist of Anostraca of Pakistan with Bhutan, and only a single incidence of one species was distributional comments. Rogers & Padhye (2015) obtained from Nepal. GIS data on each locality were have recently presented a list of valid Anostraca then obtained from Google Earth portal. Grids of area species occurring in India, Pakistan, and Sri Lanka. nearly 10,000 sq. km. (http://earth-info.nga.mil/ Some reports focus on local habitats and/or single GandG/coordsys/index.html) were used for deter- species within the region (Mathur & Sidhu; Baid, mining the zoogeographic regions by data extraction 1958; Qadri & Baqai, 1956; Tiwari, 1958, 1965; of species locality data across these grids (names of the Manca & Mura, 1997; Rogers & Padhye, 2014; grids given in Fig. 1a). Standard grids of a uniform Padhye et al., 2015). The Indian subcontinent has a area were used to avoid sampling errors related to complex geography and geology resulting in different variable area sizes. Broader resolution (in area) was 123 Hydrobiologia Fig. 1 a Grids used for data analysis (The numbers in each grid indicate longitude while the alphabets indicate latitude); b altitudinal ranges of the anostracan species reported from the subcontinent (Pa Artemia: Parthenogenetic Artemia populations) selected due to the distribution pattern of the locality 1994). Species incidence data (coded as 1: presence; 0: data in the study area (as many parts of the study area absence) from all the localities across the subcontinent had no occurrence data). As a result, most of the grids were used, and each locality was considered as a (more than 90%) had at least one occurrence datum. distinct sample. We selected sample-based estimators To test the differences if/any in the environmental such as Chao2, Jackknife2, and Bootstrap indices for conditions across different grids (of our study area), our data to estimate the true species number. Five data of 19 climatic variables, with altitude and hundred randomizations per sample were carried out terrestrial ecoregions, were extracted from (1) BIO- to obtain standard deviations (SD) around the mean. CLIM dataset (http://www.worldclim.org; Hijmans Determination of the zoogeographical regions for et al., 2005) at ten-minute spatial resolution and (2) Anostraca of the Indian subcontinent was done using ‘Terrestrial Biomes’ shapefile (maps.tnc.org/gis_- the methods given by Kreft & Jetz (2010). It is based data.html) for a set of 500 random points (generated on a distance index called bsim which is calculated using a random number generator) across individual using the species incidence data extracted across grids, grid cell, respectively. Random points and non-local- and calculates the distinctness if/any between the grids ity data were used so that (1) any sample number bias from the study area. It is given by the formula: across the grids would be nullified and (2) each grid a b ¼ 1 À ; would be sufficiently represented for better determi- sim minðÞþb; c a nation of the inherent environment. where a = number of shared species; b and c are the Data analysis number of species unique to each grid cells. For calculation of this index, a data matrix was Species richness was estimated using nonparametric created using the grid cells and species occurrence per methods to evaluate true species richness from sam- grid in form of a presence–absence matrix where rows ple-based data (Chao, 1987; Colwell
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