Mammalian Biology

, Volume 99, Issue 1, pp 88–96 | Cite as

Influence of microhabitat and landscape-scale factors on the richness and occupancy of small mammals in the northern Western Ghats: A multi-species occupancy modeling approach

  • Sameer B. BajaruEmail author
  • Amol R. Kulavmode
  • Ranjit Manakadan
Original investigation


Human-dominated ecosystems are characterized by three main processes, viz., habitat degradation, habitat loss and habitat fragmentation, these posing a great threat to biodiversity. However, the relationships between these processes are not clearly understood. Moreover, habitat loss and habitat fragmentation occur at landscape-scale and their effects depend on the spatial scale. We trapped small mammals in a human-dominated area in the northern Western Ghats, India, at 23 sites in three habitats, capturing 479 individuals of 17 species. We adopted the multi-species occupancy model (MSOM) approach within a Bayesian framework to assess species site occupancy using microhabitat-scale and landscape-scale variables measured at five spatial scales. We found that species richness had a hump-shaped relationship with landscape complexity, and it can be best explained at a spatial scale of 300 m. The findings suggest that both microhabitat-scale and landscape-scale variables influence small mammal occupancy. Overall, shrub density had a positive effect on species occurrence with high certainty, which could be related to the protection provided by shrubs from predators and harsh weather conditions. Shrub density was largely influenced small mammal richness and occupancy and thus should be managed appropriately for their conservation. Considering the negative impact of landscape complexity on endemic rodents, especially on the Critically Endangered Millardia kondana, further fragmentation of grasslands and forest habitats and their conversion into unsuitable habitats in the area should be minimized. All high-elevation grasslands important for M. kondana should be strictly protected and managed. We recommend adoption of the MSOM approach in similar studies, as it allows estimation of occupancy even for species with low detectability.


Human-dominated ecosystem Landscape complexity Landscape heterogeneity Bayesian inference Scale of effect 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Agrawal, V.C., 2000. Taxonomic Studies on Indian Muridae and Hystricidae (Mammalia: Rodentia). Records of the Zoological Survey of India, Occasional Paper No. 180. Director, Zoological Survey of India, Calcutta, pp. 108–115.Google Scholar
  2. Bajaru, S.B., 2015. Distribution and Assessment of the Population Status of Critically Endangered Kondana Soft-Furred Rat, With a Special Emphasis on Implementation of the Conservation Management Plan at Sinhgad. Report Submitted to Critical Ecosystem Partnership Fund (CEPF) - Ashoka Trust for Research in Ecology and Environment (ATREE) Western Ghats Small Grant Programme.Google Scholar
  3. Barrett, G.W., Peles, J.D., Harper, S.J., 1995. Reflections on the use of experimental landscapes in mammalian ecology. In: Lidicker, W.Z. (Ed.), Landscape Approaches in Mammalian Ecology and Conservation. University of Minnesota Press, Minneapolis, Minnesota, pp. 157–174.Google Scholar
  4. Bowers, M.A., Gregario, K., Brame, C.J., Matter, S.F., Dooley, J.L., 1996. Use of pace and habitats by meadow voles at the home range, patch, and landscape-scales. Oecologia 105, 107–115.PubMedCrossRefGoogle Scholar
  5. Broms, K.M., Hooten, M.B., Fitzpatrick, R.M., 2016. Model selection and assessment for multi-species occupancy models. Ecology 97 (7), 1759–1770.PubMedCrossRefGoogle Scholar
  6. Census India, Retrieved on 15 July 2019 2011. District Census Handbook- Pune. Directorate of census Operations Maharashtra.Google Scholar
  7. Chandrasekar-Rao, A., Sunquist, M.E., 1996. Ecology of small mammals in tropical forest habitats of southern India. J. Trop. Ecol. 12, 561–571.CrossRefGoogle Scholar
  8. Collins, R.J., Barrett, G.W., 1997. Effects of habitat fragmentation on meadow vole (Microtus pennsylvanicus) population dynamics in experimental landscape patches. Landsc. Ecol. 12, 63–76.CrossRefGoogle Scholar
  9. Connell, J.H., 1978. Diversity in tropical rain forests and coral reefs - high diversity of trees and corals is maintained only in a non-equilibrium state. Science 199, 1302–1310.PubMedCrossRefGoogle Scholar
  10. Cushman, S.A., McGarigal, K., 2004. Patterns in the species- environment relationship depend on both scale and choice of response variables. Oikos 105, 117–124.CrossRefGoogle Scholar
  11. Denwood, M.J., 2016. Runjags: an R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS. J. Stat. Softw. 71, 1–25.CrossRefGoogle Scholar
  12. Dorazio, R., Gotelli, N., Ellison, A., 2011. Modern methods of estimating biodiversity from presence-absence surveys. In: Grillo, O., Venora, G. (Eds.), Biodiversity Loss in a Changing Planet. IntechOpen Ltd., London, pp. 277–302.Google Scholar
  13. Ellis, E.C., Ramankutty, N., 2008. Putting people in the map: anthropogenic biomes of the world. Front. Ecol. Environ. 6, 439–447.CrossRefGoogle Scholar
  14. Enquist, B.J. Jordan, M.A., Brown, J.H., 1995. Connections between ecology, biogeography, and paleobiology: relationship between local abundance and geographic distribution in fossil and recent molluscs. Evol. Ecol. 9, 586–604.CrossRefGoogle Scholar
  15. Fahrig, L., Baudry, J., Brotons, L.L., Burel, F.G., Crist, T.O., Fuller, R.J., Sirami, C., Siriwardena, G.M., Martin, J.L., 2011. Functional landscape heterogeneity and animal biodiversity in agricultural landscapes. Ecol. Lett. 14,101–112.PubMedCrossRefGoogle Scholar
  16. Foley, J.A., DeFries, R., Asner, G.P., et al., 2005. Global consequences of land use. Science 309, 570–574.PubMedCrossRefPubMedCentralGoogle Scholar
  17. Gardiner, R., Bain, G., Hamer, R., Jones, M.E., Johnson, C.N., 2018. Habitat amount and quality, not patch size, determine persistence of a woodland-dependent mammal in an agricultural landscape. Lands. Ecol. 33, 1837–1849.CrossRefGoogle Scholar
  18. Gelman, A., Hill, J., 2006. Data Analysis Using Regression and Multilevel/hierarchical Models. Cambridge University Press, New York, pp. 648.CrossRefGoogle Scholar
  19. Gelman, A., Rubin, D.B., 1992. Inference from iterative simulation using multiple sequences. Stat. Sci. 4, 457–472.CrossRefGoogle Scholar
  20. Gelman, A., Carlin, J.B., Stern, H.S., Rubin, D.B., 2004. Bayesian Data Analysis. Chapman & Hall, London, pp. 675.Google Scholar
  21. Gomez, M.D., Goijman, A.P., Coda, J.A., Serafini, V.N., Priotto, J.W., 2018. Small mammal responses to farming practices in central Argentinian agroecosystems: The use of hierarchical occupancy models. Austral Ecol. 43, 828–838.CrossRefGoogle Scholar
  22. Gupta, S., Mondal, K., Sankar, K., Qureshi, Q., 2013. Diversity and abundance of rodents in the semi-arid landscape of sariska tiger reserve, Western India. J. Bombay Nat. Hist. Soc. 110, 122–128.Google Scholar
  23. Harper, S.J., Bollinger, E.K., Barrett, G.W., 1993. The effects of habitat patch shape on population dynamics of meadow voles (Microtus pennsylvanicus). J. Mammal. 74, 1045–1055.CrossRefGoogle Scholar
  24. Hesselbarth, M.H., Sciaini, M., With, K.A., Wiegand, K., Nowosad, J., 2019. landscapemetrics: an open-source R tool to calculate landscape metrics. Ecography 42, 1648–1657, Scholar
  25. Hooten, M.B., Hobbs, N.T., 2015. A guide to Bayesian model selection for ecologists. Ecol. Monogr. 85, 3–28.CrossRefGoogle Scholar
  26. Ieno, E.N., Zuur, A.F., 2015. A Beginner’s Guide to Data Exploration and Visualisation with R. Highland Statistics, Newburgh.Google Scholar
  27. Jackson, H.B., Fahrig, L., 2015. Are ecologists conducting research at the optimal scale? Glob. Ecol. Biogeogr. 24, 52–63.CrossRefGoogle Scholar
  28. Jha, C.S., Dutt, C.B.S., Bawa, K.S., 2000. Deforestation and land use changes in Western Ghats, India. Curr. Sci. 79, 231–238.Google Scholar
  29. Jorgensen, E.E., 2004. Small mammal use of microhabitat reviewed. J. Mammal. 85, 531–539.CrossRefGoogle Scholar
  30. Kalies, E.B., Dickson, B.G., Chambers, C.L., Covington, W.W., 2012. Community occupancy responses of small mammals to restoration treatments in ponderosa pine forests, northern Arizona, USA. Ecol. Appl. 22, 204–217.PubMedCrossRefGoogle Scholar
  31. Kellner, K.F., Swihart, R.K., 2014. Accounting for imperfect detection in ecology: a quantitative review. PLoS ONE 9 (10), e111436.CrossRefGoogle Scholar
  32. Kelt, D.A., Meserve, P.L., Lang, B.K., 1994. Quantitative habitat associations of small mammals in a temperate rainforest in southern Chile: empirical patterns and the importance of ecological scale. J. Mammal. 75, 890–904.CrossRefGoogle Scholar
  33. Kéry, M., Royle, J.A., 2015. Applied Hierarchical Modeling in Ecology, Analysis of Distribution, Abundance and Species Richness in R and BUGS. Academic Press & Elsevier, pp. 808.Google Scholar
  34. Lindenmayer, D.B., Fischer, J., 2007. Tackling the habitat fragmentation panchreston. Trends Ecol. Evol. 22, 127–132.PubMedCrossRefGoogle Scholar
  35. Lindsay, K.E., Kirk, DA, Bergin, T.M., Louis, B., Sifneos, J.C., Smith, J., Sifneos, J.C., 2013. Farmland heterogeneity benefits birds in American mid-west watersheds. Am. Midl. Nat. 170, 121–143.CrossRefGoogle Scholar
  36. Mackenzie, D.I., Nichols, J.D., Lachman, G.B., Droege, S., Royle, J.A., Langtimm, C.A., 2002. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83, 248–255.Google Scholar
  37. Matson, P.A., Parton, W.J., Power, A.G., Swift, M.J., 1997. Agricultural intensification and ecosystem properties. Science 277, 504–509.PubMedCrossRefGoogle Scholar
  38. Meena, V., Master’s thesis 1997. Community Ecology of Small Mammals in Mudumalai Wildlife Sanctuary, South India. Pondicherry University, Pondicherry, India.Google Scholar
  39. Miguet, P., Jackson, H.B., Jackson, N.D., Martin, A.E., Fahrig, L., 2016. What determines the spatial extent of landscape effects on species? Lands. Ecol. 31, 1–18.CrossRefGoogle Scholar
  40. Mitchell, M.G.E., Bennett, E.M., Gonzalez, A., 2014. Agricultural landscape structure affects arthropod diversity and arthropod-derived ecosystem services. Agric. Ecosyst. Environ. 192, 144–151.CrossRefGoogle Scholar
  41. Mohammadi, S., 2010. Microhabitat selection by small mammals. Adv. Biol. Res. 4Google Scholar
  42. Molur, S., Singh, M., 2009. Non-volant small mammals of the Western Ghats of Coorg District, southern India. JoTT 1, 589–608.Google Scholar
  43. Moore, J.E., Swihart, R.K., 2005. Modeling patch occupancy by forest rodents: incorporating detectability and spatial autocorrelation with hierarchically structured data. J. Wildl. Manag. 69, 933–949.CrossRefGoogle Scholar
  44. Mortelliti, A., 2013. Targeting habitat management in fragmented landscapes: a case study with forest vertebrates. Biodivers. Conserv. 22, 187–207.CrossRefGoogle Scholar
  45. Mortelliti, A., Amori, G., Boitani, L., 2010. The role of habitat quality in fragmented landscapes: a conceptual overview and prospectus for future research. Oecologia 163, 535–547.PubMedCrossRefGoogle Scholar
  46. Mudappa, D., Kumar, A., Chellam, R., 2001. Abundance and habitat selection of the Malabar spiny dormouse in the rainforests of the southern Western Ghats, India. Curr. Sci. India 80, 424–427.Google Scholar
  47. Naxara, L., Pinotti, B.T., Pardini, R., 2009. Seasonal microhabitat selection by terrestrial rodents in an old-growth Atlantic Forest. J. Mammal. 90, 404–415.CrossRefGoogle Scholar
  48. Newton, I., 2006. Links between abundance and distribution of birds. Ecography 20, 137–145.CrossRefGoogle Scholar
  49. Outhwaite, C.L., Chandler, R.E., Powney, G.D., Collen, B., Gregory, R.D., Isaac, J.B.N., 2018. Prior specification in Bayesian occupancy modelling improves analysis of species occurrence data. Ecol. Indic. 93, 333–343.CrossRefGoogle Scholar
  50. Prabhakar, A., Ph.D. Thesis 1998. Small Mammals of Fragmented Rainforests of the Western Ghats. Bharaliyar University, India.Google Scholar
  51. Pradhan, M.S., Molur, S., Nameer, P.O., Version 2018.1. <>. Accessed on 15 June 2018 2008. Millardia kondana. The IUCN Red List of Threatened Species.Google Scholar
  52. Prakash, I., Singh, H., 2000. Small mammal diversity and ecology of small mammals in the aravalli mountain ecosystem in Southern Rajasthan. Proc. Natl. Acad. Sci. India 70 (B), 211–227.Google Scholar
  53. Prakash, I., Singh, H., 2001. Composition and species diversity of small mammals in hilly tracts of Southeastern Rajasthan. Trop. Ecol. 42, 25–33.Google Scholar
  54. Prakash, I., Singh, P., 2005. Ecology of Small Mammals of Desert and Montane Ecosystems. Scientific Publishers, Jodhpur, India, pp. 17–80.Google Scholar
  55. Prakash, I., Singh, P., Saravanan, A., 1995. Small mammals ofthe Abu hill, Arvalli ranges, Rajasthan, India. A comprehensive taxonomical and ecological study. Zoology 5, 55–64.Google Scholar
  56. QGIS Development Team, 2016. QGIS Geographic Information System. Open Source Geospatial Foundation Project Scholar
  57. R Core Team, 2019. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria Scholar
  58. Ramchandran, V., Ph.D. Thesis 2013. Effect of Habitat Alteration on Canopy Bird and Small Mammal Communities in the Wet Evergreen Forests ofthe Western Ghats. Manipal University, India.Google Scholar
  59. Reddy, C.S., Dutta, K., Jha, C.S., 2013. Analysing the gross and net deforestation rates in India. Curr. Sci. 105, 1492–1500.Google Scholar
  60. Rickart, E.A., Balete, D.S., Rowe, R.J., Heaney, L.R., 2011. Mammals ofthe northern Philippines: tolerance for habitat disturbance and resistance to invasive species in an endemic fauna. Divers. Distrib. 17, 530–541.CrossRefGoogle Scholar
  61. Ricklefs, R.E., 1987. Community diversity: relative roles of local and regional processes. Science 235, 167–171.PubMedCrossRefGoogle Scholar
  62. Rosenzweig, M.L., 1989. Habitat selection, community organization and small mammal studies. In: Morris, D.W., Abramsky, Z., Fox, B.J., Willig, M.R. (Eds.), Patterns in the Structure of Mammalian Communities. Texas Tech University Press, Lubbock, pp. 5–21.Google Scholar
  63. Royle, J.A., Dorazio, R.M., 2008. Hierarchical Modeling and Inference in Ecology. Academic Press, London, U.K, pp. 464.Google Scholar
  64. Schweiger, E.W., Diffendorfer, J.E., Pierotti, R., Holt, R.D., 1999. The relative importance of small-scale and landscape-level heterogeneity in structuring small mammal distributions. In: Barrett, G.W., Peles, J.D. (Eds.), Landscape Ecology of Small Mammals. Spinger-Verlag, New York, pp. 175–207.CrossRefGoogle Scholar
  65. Serafini, V.N., Priotto, J.W., Gomez, M.D., 2019. Effects of agroecosystem landscape complexity on small mammals: a multi-species approach at different spatial scales. Lands. Ecol. 34, 1117–1129.CrossRefGoogle Scholar
  66. Seto, K.C., Guneralp, B., Hutyra, L.R., 2012. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc. Natl. Acad. Sci. U. S. A. 109, 16083–16088.PubMedPubMedCentralCrossRefGoogle Scholar
  67. Shanker, K., 2001. The role of competition and habitat in structuring small mammal communities in a tropical montane ecosystem in southern India. J. Zool. 253, 15–24.CrossRefGoogle Scholar
  68. Shanker, K., Sukumar, R., 1998. Community ecology and demography of small mammal communities in insular montane forests in southern India. Oecologia (Berl.) 116, 243–251.CrossRefGoogle Scholar
  69. Shenoy, K., Madhusudan, P.S., 2006. Small mammal communities in a rapidly developing southern Indian city. Zoos’ Print. J. 21, 2152–2159.CrossRefGoogle Scholar
  70. Sikes, R.S., Gannon, W.L., the Animal Care and Use Committee ofthe American Society of Mammalogists, 2011. Guidelines ofthe American society of mammalogists for the use of wild mammals in research. J. Mammal. 92, 235–253.CrossRefGoogle Scholar
  71. Simonetti, J.A., 1989. Microhabitat use by small mammals in central Chile. Oikos 56, 309–318.CrossRefGoogle Scholar
  72. Smith, A.C., Fahrig, L., Francis, CM., 2011. Landscape size affects the relative importance of habitat amount, habitat fragmentation, and matrix quality on forest birds. Ecography 34, 103–113.CrossRefGoogle Scholar
  73. Stephens, R.B., Anderson, E.M., 2014. Habitat associations and assemblages of small mammals in natural plant communities of Wisconsin. J. Mammal. 95, 404–420.CrossRefGoogle Scholar
  74. Suarez, O.V., Bonaventura, S.M., 2001. Habitat use and diet in sympatric species of rodents ofthe low Parana Delta, Argentina. Mammalia 65, 167–176.Google Scholar
  75. Swihart, R.K., Slade, N.A., 1990. Long-term dynamics of and early successional small mammal community. Am. Midl. Nat. 123, 372–382.CrossRefGoogle Scholar
  76. Thornton, D., Branch, L., Sunquist, M.E., 2011. The influence of landscape, patch, and within-patch factors on species presence and abundance: a review of focal patch studies. Lands Ecol. 26, 7–18.CrossRefGoogle Scholar
  77. Urban, N.A., Swihart, R.K., 2009. Multiscale perspectives on occupancy of meadow jumping mice in landscapes dominated by agriculture. J. Mammal. 90, 1431–1439.CrossRefGoogle Scholar
  78. Venkataraman, M., Shanker, K., Sukumar, R., 2005. Small mammal communities of tropical forest habitats in mudumalai wildlife sanctuary, southern India. Mammalia 69, 349–358.CrossRefGoogle Scholar
  79. Vitousek, P.M., Mooney, H.A., Lubchenco, J., Melillo, J.M., 1997. Human domination of earth’s ecosystems. Science 277, 494–499.CrossRefGoogle Scholar
  80. Watanabe, S., 2010. Asymptotic equivalence of Bayes cross-validation and widely applicable information criterion in singular learning theory. J. Mach. Learn. Res. 11, 3571–3594.Google Scholar
  81. Weibull, A., Östman, Ö., Granqvist, Å., 2003. Species richness in agroecosystems: the effect of landscape, habitat and farm management. Biodivers. Conserv. 12, 1335–1355.CrossRefGoogle Scholar
  82. Wiens, J.A., Stenseth, N.C., Van Horne, B., Ims, R.A., 1993. Ecological mechanisms and landscape ecology. Oikos 66, 369–380.CrossRefGoogle Scholar
  83. Wolff, J.O., Schauber, E.M., Edge, W.O., 1997. Effects of habitat fragmentation on the social dynamics ofthe gray-tailed vole. Conser. Biol. 11, 945–956.CrossRefGoogle Scholar
  84. Wood, S.N., 2011. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. B 73, 3–36.CrossRefGoogle Scholar
  85. Zipkin, E.F., Royle, A., Dawson, D.K., Bates, S., 2010. Multi-species occurrence models to evaluate the effects of conservation and management actions. Biol. Conser. 143, 479–484.CrossRefGoogle Scholar
  86. Zuur, A.F., 2012. A Beginner’s Guide to Generalized Additive Models with R. Highland Statistics Ltd, Newburgh.Google Scholar

Copyright information

© Deutsche Gesellschaft für Säugetierkunde 2019

Authors and Affiliations

  • Sameer B. Bajaru
    • 1
    Email author
  • Amol R. Kulavmode
    • 2
  • Ranjit Manakadan
    • 2
  1. 1.Natural History Collection DepartmentBombay Natural History SocietyMumbaiIndia
  2. 2.Bombay Natural History SocietyMumbaiIndia

Personalised recommendations