Advertisement

Mammalian Biology

, Volume 98, Issue 1, pp 188–200 | Cite as

The relative influence of abiotic and biotic factors on suitable habitat of Old World fruit bats under current and future climate scenarios

  • Nikhail Arumoogum
  • M. Corrie SchoemanEmail author
  • Syd Ramdhani
Original investigation

Abstract

There is growing evidence that biotic factors such as predator-prey interactions play significant roles in driving species distribution across large spatial scales. The relative influence of abiotic and biotic factors on species distribution, however, may change under climate change. We investigated the relative influence of abiotic and biotic variables on the potential current and future distributions of three fruit bat species, Epomophorus angolensis (Gray, 1870), E. wahlbergi (Sundevall, 1846) and Rousettus aegyptiacus (E. Geoffroy St.-Hilaire, 1810), in southern Africa. We tested three hypotheses, namely that bat species’ distribution is primarily driven by (1) productivity; (2) physiological tolerance to climate; and (3) biotic interactions, specifically fig distribution. We adopted an ensemble niche modelling approach to project the suitable habitat of fruit bat species for current and future climate scenarios, and assessed variable importance in the models using a randomised variable shuffle procedure. We predicted that both biotic and abiotic factors influence suitable habitat of fruit bats, the relative influence of factors on habitat suitability of bat species are taxon specific, and the relative influence of abiotic and biotic factors will change from current to future climate scenarios. Abiotic variables associated with productivity were the primary determinants of habitat suitability for E. wahlbergi and E. angolensis under both current and future conditions. By contrast, suitable habitat of R. aegyptiacus was primarily mediated by temperature under current climatic conditions yet by freestanding fig distribution under both moderate and extreme future climate change scenarios. Freestanding fig distribution was also the most significant factor of habitat suitability for E. angolensis under the extreme future climate change scenario. Our results were congruent with our predictions and suggest that biotic variables play important roles in determining habitat suitability of species at relatively large spatial scales, contrary to the conventional assumptions of the Grinnellian niche.

Keywords

Climate Productivity Biotic interactions Ecological niche model Pteropodidae 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Allouche, O., Tsoar, A., Kadmon, R., 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232  https://doi.org/10.1111/j.1365-2664.2006.01214.x.CrossRefGoogle Scholar
  2. Anderson, R.P., 2017. When and how should biotic interactions be considered in models of species niches and distributions? J. Biogeogr. 44, 8–17  https://doi.org/10.1111/jbi.12825.CrossRefGoogle Scholar
  3. Anderson, R.P., Raza, A., 2010. The effect of the extent of the study region on GIS models of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela. J. Biogeogr. 37, 1378–1393  https://doi.org/10.1111/j.1365-2699.2010.02290.x.CrossRefGoogle Scholar
  4. Andrews, P., O’Brien, E.M., 2000. Climate, vegetation, and predictable gradients in mammal species richness in southern Africa. J. Zool. 251, 205–231  https://doi.org/10.1111/j.1469-7998.2000.tb00605.x.CrossRefGoogle Scholar
  5. Aragón, P., Sánchez-Fernández, D., 2013. Can we disentangle predator - prey interactions from species distributions at a macro-scale? A case study with a raptor species. Oikos 122, 64–72  https://doi.org/10.2307/41937642.CrossRefGoogle Scholar
  6. Araújo, M.B., Luoto, M., 2007. The importance of biotic interactions for modelling species distributions under climate change. Glob. Ecol. Biogeogr. 16, 743–753  https://doi.org/10.1111/j.1466-8238.2007.00359.x.CrossRefGoogle Scholar
  7. Araújo, M.B., New, M., 2007. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22, 42–47  https://doi.org/10.1016/j.tree.2006.09.010.PubMedCrossRefPubMedCentralGoogle Scholar
  8. Barclay, R.M.R., Jacobs, D.S., 2011. Differences in the foraging behaviour of male and female Egyptian fruit bats (Rousettus aegyptiacus). Can. J. Zool. 89, 466–473  https://doi.org/10.1139/z11-013.CrossRefGoogle Scholar
  9. Barve, N., Barve, V., Available at 2013. ENMGadgets: Tools for Pre and Post Processing in ENM Workflows. https://doi.org/github.com/narayanibarve/ENMGadgets.Google Scholar
  10. Bellamy, C., Scott, C., Altringham, J., 2013. Multiscale, presence-only habitat suitability models: fine-resolution maps for eight bat species. J. Appl. Ecol. 50, 892–901  https://doi.org/10.1111/1365-2664.12117.CrossRefGoogle Scholar
  11. Boria, R.A., Olson, L.E., Goodman, S.M., Anderson, R.P., 2014. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecol. Modell. 275, 73–77  https://doi.org/10.1016/j.ecolmodel.2013.12.012.CrossRefGoogle Scholar
  12. Bosso, L., Luchi, N., Maresi, G., Cristinzio, G., Smeraldo, S., Russo, D., 2017. Predicting current and future disease outbreaks ofDiplodia sapinea shoot blight in Italy: species distribution models as a tool for forest management planning. For. Ecol. Manage. 400, 655–664  https://doi.org/10.1016/j.foreco.2017.06.044.CrossRefGoogle Scholar
  13. Boulangeat, I., Gravel, D., Thuiller, W., 2012. Accounting for dispersal and biotic interactions to disentangle the drivers of species distributions and their abundances. Ecol. Lett. 15, 584–593  https://doi.org/10.1111/j.1461-0248.2012.01772.x.PubMedPubMedCentralCrossRefGoogle Scholar
  14. Breiman, L., 2001. Random forests. Mach. Learn. 45, 5–32.CrossRefGoogle Scholar
  15. Breiman, L., 1996. Bagging predictors. Mach. Learn. 24, 123–140.Google Scholar
  16. Brown, J., 2014. SDMtoolbox: a python-based GIS toolkit for landscape, genetic, biogeographic, and species distribution model analyses. Methods Ecol. Evol. 5, 694–700.CrossRefGoogle Scholar
  17. Burrows, J., Burrows, S., 2003. Figs of Southern and South-Central Africa. Umdaus Press, Hatfield, South Africa.Google Scholar
  18. Busby, J.R., 1991. BIOCLIM - A Bioclimate Analysis and Prediction System. CSIRO Publishing, Australia.Google Scholar
  19. Chase, J.M., Myers, J.A., 2011. Disentangling the importance of ecological niches from stochastic processes across scales. Philos. Trans. R. Soc. B Biol. Sci. 366, 2351–2363  https://doi.org/10.1098/rstb.2011.0063.CrossRefGoogle Scholar
  20. Christensen, J.H., Hewitson, B., Busuioc, A., Chen, A., Gao, X., Held, I., Jones, R., Kolli, R.K., Kwon, W.T., Laprise, R., Magaña Rueda, V., Mearns, L., Menéndez, C.G., Raïsänen, J., Rinke, A., Sarr, A., Whetton, P., 2007. Regional climate projections. In: IPCC 2007. Climate Change 2007-Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the IPCC. Cambridge University Press.Google Scholar
  21. Cooper-Bohannon, R., Rebelo, H., Jones, G., Cotterill, F.P.D.W., Monadjem, A., Schoeman, M.C., Taylor, P.J., Park, K., 2016. Predicting bat distributions and diversity hotspots in southern Africa. Hystrix. Ital. J. Mammal. 27, 1–11  https://doi.org/10.4404/hystrix-27.1-11722.Google Scholar
  22. Cumming, S., Bernard, F., 1997. Rainfall, food abundance and timing of parturition in African bats. Oecologia 111, 309–317.PubMedCrossRefGoogle Scholar
  23. de Araújo, C.B., Marcondes-Machado, L.O., Costa, G.C., 2014. The importance of biotic interactions in species distribution models: a test of the Eltonian noise hypothesis using parrots. J. Biogeogr. 41, 513–523  https://doi.org/10.1111/jbi.12234.CrossRefGoogle Scholar
  24. Downs, C.T., Zungu, M.M., Brown, M., 2012. Seasonal effects on thermoregulatory abilities of the Wahlberg’s epauletted fruit bat (Epomophorus wahlbergi) in KwaZulu-Natal, South Africa. J. Therm. Biol. 37, 144–150  https://doi.org/10.1016/j.jtherbio.2011.12.003.CrossRefGoogle Scholar
  25. Elith, J., Graham, C.H., Anderson, R.P., Dudik, M., Ferrier, S., Guisan, A., Hijmans, R.J., Huettmann, F., Leathwick, J.R., Lehmann, A., Li, J., Lohmann, L.G., Loiselle, B.A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J.M., Peterson, A.T., Phillips, S.J., Richardson, K., Scachetti-Pereira, R., Schapire, R.E., Soberon, J., Williams, S., Wisz, M.S., Zimmermann, N.E., 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151  https://doi.org/10.1111/j.2006.0906-7590.04596.x.CrossRefGoogle Scholar
  26. Elton, C., 1927. Animal Ecology. Sedgwick and London, London, United Kingdom.Google Scholar
  27. Environmental Systems Research Institute (ESRI), 2011. ArcGIS Desktop.Google Scholar
  28. Fauchereau, N., Trzaska, S., Rouault, M., Richard, Y., 2003. Rainfall variability and changes in southern Africa during the 20th century in the global warming context. Nat. Hazards 29, 139–154 10.1023/A.CrossRefGoogle Scholar
  29. Felton, A.M., Felton, A., Rumiz, D.I., Villaroel, N., Chapman, C.A., Lindenmayer, D.B., 2013. Commercial harvesting of Ficus timber - an emerging threat to frugivorous wildlife and sustainable forestry. Biol. Conserv. 159, 96–100  https://doi.org/10.1016/j.biocon.2012.10.025.CrossRefGoogle Scholar
  30. Fielding, A.H., Bell, J.F., 1997. A review of methods forthe assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 24, 38–49.CrossRefGoogle Scholar
  31. Franklin, J., 2013. Species distribution models in conservation biogeography: developments and challenges. Divers. Distrib. 19, 1217–1223  https://doi.org/10.1111/ddi.12125.CrossRefGoogle Scholar
  32. Friedman, J.H., 1991. Rejoinder: multivariate adaptive regression splines. Ann. Stat. 19, 123–141.CrossRefGoogle Scholar
  33. Giannini, T.C., Chapman, D.S., Saraiva, A.M., Alves-dos-Santos, I., Biesmeijer, J.C., 2013. Improving species distribution models using biotic interactiosn: a case study of parasites, pollinators and plants. Ecography 36, 649–656.CrossRefGoogle Scholar
  34. Godsoe, W., Harmon, L.J., 2012. How do species interactions affect species distribution models? Ecography 35, 1–10  https://doi.org/10.2307/23258164.CrossRefGoogle Scholar
  35. González-Salazar, C., Stephens, C.R., Marquet, P.A., 2013. Comparing the relative contributions of biotic and abiotic factors as mediators of species’ distributions. Ecol. Modell. 248, 57–70  https://doi.org/10.1016/j.ecolmodel.2012.10.007.CrossRefGoogle Scholar
  36. Graham, M.H., 2003. Confronting multicollinearity in ecological multiple regression. Ecology 84, 2809–2815.CrossRefGoogle Scholar
  37. Greer, B.T., Still, C., Howe, G.T., Tague, C., Roberts, D.A., 2016. Populations of aspen (Populus tremuloides Michx.) with different evolutionary histories differ in their climate occupancy. Ecol. Evol. 6, 3032–3039  https://doi.org/10.1002/ece3.2102.PubMedPubMedCentralCrossRefGoogle Scholar
  38. Grinnell, J., 1917. The niche-relationships of the California thrasher. Auk 34, 427–433.CrossRefGoogle Scholar
  39. Guisan, A., Thuiller, W., 2005. Predicting species distribution: offering more than simple habitat models. Ecol. Lett. 8, 993–1009  https://doi.org/10.1111/j.1461-0248.2005.00792.x.CrossRefGoogle Scholar
  40. Guisan, A., Zimmermann, N.E., 2000. Predictive habitat distribution models in ecology. Ecol. Modell. 135, 147–186.CrossRefGoogle Scholar
  41. Hall, M., Scholte, P., Al-Khulaidi, A.W., Miller, A.G., Al-Qadasi, A.H., Al-Farhan, A., Al-Abbasi, T.M., 2009. Arabia’s last forests under threat II: remaining fragments of unique valley forest in southwest Arabia. Edinburgh J. Bot. 66, 263–281  https://doi.org/10.1017/S0960428609005460.CrossRefGoogle Scholar
  42. Happold, M., Happold, D.C.D., 2013. Mammals of Africa. Volume IV. Hedgehogs, Shrews, and Bats. Bloomsbury Publishing, London, United Kingdom.Google Scholar
  43. Hastie, T., Tibshirani, R.J., 1986. Generalized additive models. Stat. Sci. 1, 297–318.CrossRefGoogle Scholar
  44. Hastie, T., Tibshirani, R.J., Buja, A., 1994. Flexible discriminant analysis by optimal scoring. J. Am. Stat. Assoc. 89, 1255–1270.CrossRefGoogle Scholar
  45. Hawkins, B.A., Field, R., Cornell, H.V., Currie, D.J., Guégan, J.F., Kaufman, D.M., Kerr, J.T., Mittelbach, G.G., Oberdorff, T., O’Brien, E.M., Porter, E.E., Turner, J.R.G., 2003. Energy, water, and broad-scale geographic patterns of species richness. Ecology 84, 3105–3117.CrossRefGoogle Scholar
  46. Heikkinen, R.K., Luoto, M., Virkkala, R., Pearson, R.G., Körber, J.H., 2007. Biotic interactions improve prediction of boreal bird distributions at macro-scales. Glob. Ecol. Biogeogr. 16, 754–763  https://doi.org/10.1111/j.1466-8238.2007.00345.x.CrossRefGoogle Scholar
  47. Hepburn, H.R., Radloff, S.E., 1995. First approximation to a phenology of the honeybees (Apis mellifera) and flora of Africa. Oecologia 101, 265–273.PubMedCrossRefPubMedCentralGoogle Scholar
  48. Herre, E.A., 1996. An overview of studies on a community of Panamanian figs. J. Biogeogr. 23, 593–607.CrossRefGoogle Scholar
  49. Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, G., Jarvis, A., 2005. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978  https://doi.org/10.1002/joc.1276.CrossRefGoogle Scholar
  50. Hulme, M., Doherty, R., Ngara, T., New, M., Lister, D., 2001. African climate change: 1900–2100. Clim. Res. 17, 145–168  https://doi.org/10.3354/cr017145.CrossRefGoogle Scholar
  51. Hutchinson, G.E., 1959. Homage to Santa Rosalia or why are there so many kinds of animals? Am. Nat. 93, 145–159.CrossRefGoogle Scholar
  52. IUCN, URL https://doi.org/www.iucnredlist.org/ (Accessed 26 August 2019) 2019. The IUCN Red List of Threatened Species. Version 2019.2 [WWW Document].
  53. Jury, M.R., Nkosi, S.E., 2000. Easterly flow in the tropical Indian Ocean and climate variability over south-east Africa. Water SA 26, 147–152.Google Scholar
  54. Kerr, J.T., Packer, L., 1997. Habitat heterogeneity as a determinant of mammal species richness in high-energy regions. Nature 385, 252–254.CrossRefGoogle Scholar
  55. Kingston, T., 2013. Response of bat diversity to forest disturbance in Southeast Asia: insights from long-term research in Malaysia. In: Pedersen, S.C., Adams, R.A. (Eds.), Bat Evolution, Ecology, and Conservation. Springer, New York, pp. 169–185.CrossRefGoogle Scholar
  56. Korine, C., Arad, Z., 1993. Effect of water restriction on temperature regulation of the fruit-bat Rousettus aegyptiacus. J. Therm. Biol. 18, 61–69.CrossRefGoogle Scholar
  57. Kunz, T.H., Diaz, C.A., 1995. Folivory in fruit-eating bats, with new evidence from Artibeus jamaicensis (Chiroptera: Phyllostomidae). Biotropica 27, 106–120.CrossRefGoogle Scholar
  58. Lobo, J.M., Jiménez-valverde, A., Real, R., 2008. AUC: a misleading measure of the performance of predictive distribution models. Glob. Ecol. Biogeogr. 17, 145–151  https://doi.org/10.1111/j.1466-8238.2007.00358.x.CrossRefGoogle Scholar
  59. McCullagh, P., Nelder, J.A., 1989. Generalized Linear Models. Chapman and Hall, Washington.CrossRefGoogle Scholar
  60. Monadjem, A., Reside, A., 2008. The influence of riparian vegetation on the distribution and abundance of bats in an African savanna. Acta Chiropt. 10, 339–348  https://doi.org/10.3161/150811008X414917.CrossRefGoogle Scholar
  61. Monadjem, A., Taylor, P.J., Cotterill, F.P.D.W., Schoeman, M.C., 2010. Bats of Southern and Central Africa: A Biogeographic and Taxonomic Synthesis. Wits University Press, Johannesburg.Google Scholar
  62. Monserud, R.A., Leemans, R., 1992. Comparing global vegetation maps with the Kappa-Statistic. Ecol. Modell. 62, 275–293  https://doi.org/10.1016/0304-3800(92)90003-W.CrossRefGoogle Scholar
  63. Morán-Ordóñez, A., Roces-Diaz, J.V., Otsu, K., Ameztegui, A., Coll, L., Lefevre, F., Reatan, J., Brotons, L., 2018. The use of scenarios and models to evaluate the future of nature values and ecosystem services in Mediterranean forests. Reg. Environ. Change 19, 415–428.CrossRefGoogle Scholar
  64. O’Brien, E.M., 1993. Climatic gradients in woody plant species richness: towards an explanation based on an analysis of southern Africa’s woody flora. J. Biogeogr. 20, 181–198.CrossRefGoogle Scholar
  65. Pearson, R.G., Dawson, T.P., 2003. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Glob. Ecol. Biogeogr. 12, 361–371.CrossRefGoogle Scholar
  66. Pearson, R.G., Raxworthy, C.J., Nakamura, M., Peterson, A.T., 2007. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J. Biogeogr. 34, 102–117  https://doi.org/10.1111/j.1365-2699.2006.01594.x.CrossRefGoogle Scholar
  67. Persechino, A., Mignot, J., Swingedouw, D., Labetoulle, S., Guilyardi, E., 2013. Decadal predictability of the Atlantic meridional overturning circulation and climate in the IPSL-CM5A-LR model. Clim. Dyn. 40, 2359–2380  https://doi.org/10.1007/s00382-012-1466-1.CrossRefGoogle Scholar
  68. Peterson, A.T., 2006. Uses and requirements of ecological niche models and related distributional models. Biodivers. Informatics 3, 59–72.CrossRefGoogle Scholar
  69. Peterson, A.T., Soberon, J., 2012. Species distribution modeling and ecological niche modeling: getting the concepts right. Nat. Conserv. 10, 102–107  https://doi.org/10.4322/natcon.2012.019.CrossRefGoogle Scholar
  70. Phillips, S.J., Anderson, R.P., Schapire, R.E., 2006. Maximum entropy modelling of species geographic distributions. Ecol. Modell. 190, 231–259.CrossRefGoogle Scholar
  71. Qian, H., Kissling, W.D., Wang, X., Andrews, P., 2009. Effects of woody plant species richness on mammal species richness in southern Africa. J. Biogeogr. 36, 1685–1697  https://doi.org/10.1111/j.1365-2699.2009.02128.x.CrossRefGoogle Scholar
  72. R Development Core Team, Available at 2014. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria https://doi.org/www.r-project.org.Google Scholar
  73. Rautenbach, I., Whiting, M., Fenton, M., 1996. Bats in riverine forests and woodlands: a latitudinal transect in southern Africa. Can. J. Zool. 74, 312–322.CrossRefGoogle Scholar
  74. Reason, C.J.C., Landman, W., Tennant, W., 2006. Seasonal to decadal prediction of southern African climate and its links with variability of the Atlantic Ocean. Bull. Am. Meteorol. Soc. 87, 941–955  https://doi.org/10.1175/BAMS-87-7-941.CrossRefGoogle Scholar
  75. Ridgeway, G., 1999. The state of boosting. Comput. Sci. Stat., 172–181.Google Scholar
  76. Ripley, B.D., 1996. Pattern Recognition and Neural Networks. Cambridge University Press.CrossRefGoogle Scholar
  77. Rodríguez, J.P., Brotons, L., Bustamante, J., Seoane, J., 2007. The application of predictive modelling of species distribution to biodiversity conservation. Divers. Distrib. 13, 243–251  https://doi.org/10.1111/j.1472-4642.2007.00356.x.CrossRefGoogle Scholar
  78. Sanchez, M.S., Giannini, N.P., 2018. Trophic structure of frugivorous bats in the Neotropics: emergent patterns in evolutionary history. Mammal Rev. 48, 90–107  https://doi.org/10.1111/mam.12116.CrossRefGoogle Scholar
  79. Scheel, A.D., Vincent, T.L.S., Cameron, G.N., 1996. Global warming and the species richness of bats in Texas. Conserv. Biol. 10, 452–464.CrossRefGoogle Scholar
  80. Schoeman, M.C., Cotterill, F.P.D.W., Taylor, P.J., Monadjem, A., 2013. Using potential distributions to explore environmental correlates of bat species richness in southern Africa: effects of model selection and taxonomy. Curr. Zool. 59, 279–293  https://doi.org/10.1093/czoolo/59.3.279.CrossRefGoogle Scholar
  81. Schwartz, M.W., 2012. Using niche models with climate projections to inform conservation management decisions. Biol. Conserv. 155, 149–156  https://doi.org/10.1016/j.biocon.2012.06.011.CrossRefGoogle Scholar
  82. Schweiger, O., Settele, J., Kudrna, O., Klotz, S., Kühn, I., 2008. Climate change can cause spatial mismatch of trophically interacting species. Ecology 89, 3472–3479.CrossRefGoogle Scholar
  83. Shanahan, M., So, S., Gomptom, S.G., Gorlett, R., 2001. Fig-eating by vertebrate frugivores: aglobal review. Biol. Rev. 76, 529–572  https://doi.org/10.1017/S1464793101005760.PubMedCrossRefPubMedCentralGoogle Scholar
  84. Sherwin, H.A., Montgomery, I., Lundy, M., 2012. The impact and implications of climate change for bats. Mamm. Rev. 43, 171–182  https://doi.org/10.1111/j.1365-2907.2012.00214.x.CrossRefGoogle Scholar
  85. Smith, A., Schoeman, M.C., Keith, M., Erasmus, B.F.N., Monadjem, A., Moilanen, A., Di Minin, E., 2016. Synergistic effects of climate and land-use change on representation of African bats in priority conservation areas. Ecol. Indic. 69, 276–283  https://doi.org/10.1016/j.ecolind.2016.04.039.CrossRefGoogle Scholar
  86. Soberón, J., 2007. Grinnellian and Eltonian niches and geographic distributions of species. Ecol. Lett. 10, 1115–1123  https://doi.org/10.1111/j.1461-0248.2007.01107.x.PubMedCrossRefPubMedCentralGoogle Scholar
  87. Soberón, J., Peterson, A.T., 2005. Interpretation of models of fundamental ecological niches and species’ distributional axis. Biodivers. Informatics 2, 1–10.CrossRefGoogle Scholar
  88. Stringer, L.C., Dyer, J.C., Reed, M.S., Dougill, A.J., Twyman, C., Mkwambisi, D., 2009. Adaptations to climate change, drought and desertification: local insights to enhance policy in southern Africa. Environ. Sci. Policy 12, 748–765  https://doi.org/10.1016/j.envsci.2009.04.002.CrossRefGoogle Scholar
  89. Thuiller, W., Lafourcade, B., Engler, R., Araújo, M.B., 2009. BIOMOD - A platform for ensemble forecasting of species distributions. Ecography 32, 369–373  https://doi.org/10.1111/j.1600-0587.2008.05742.x.CrossRefGoogle Scholar
  90. Turner, J.R.G., Gatehouse, C.M., Corey, C.A., 1987. Does solar energy control organic diversity? Butterflies, moths and the British climate. Oikos 48, 195–205.Google Scholar
  91. van Vuuren, D.P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G.C., Kram, T., Krey, V., Lamarque, J.F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S.J., Rose, S.K., 2011. The representative concentration pathways: An overview. Clim. Change 109, 5–31  https://doi.org/10.1007/s10584-011-0148-z.CrossRefGoogle Scholar
  92. Veloz, S.D., 2009. Spatially autocorrelated sampling falsely inflates measures of accuracy for presence-only niche models. J. Biogeogr. 36, 2290–2299  https://doi.org/10.1111/j.1365-2699.2009.02174.x.CrossRefGoogle Scholar
  93. Waltari, E., Guralnick, R.P., Manne, L., 2009. Ecological niche modelling of montane mammals in the Great Basin, North America: examining past and present connectivity of species across basins and ranges. J. Biogeogr. 36, 148–161.CrossRefGoogle Scholar
  94. Wendeln, M.C., Runkle, J.R., Kalko, E.K.V., 2000. Nutritional values of 14 fig species and bat feeding preferences in Panama. Biotropica 32, 489–501.CrossRefGoogle Scholar
  95. Wenger, S.J., Isaak, D.J., Luce, C.H., Neville, H.M., Fausch, K.D., Dunham, J.B., Dauwalter, D.C., Young, M.K., Elsner, M.M., Rieman, B.E., Hamlet, A.F., Williams, J.E., 2011. Flow regime, temperature, and biotic interactions drive differential declines of trout species under climate change. Proc. Natl. Acad. Sci. 108, 14175–14180  https://doi.org/10.1073/pnas.1103097108.PubMedCrossRefGoogle Scholar
  96. Wuebbles, D.J., Fahey, D.W., Hibbard, K.A., Dokken, D.J., Stewart, B.C., Maycock, T.K., 2017. Climate Science Special Report: Fourth National Climate Assessment, Vol. 1. US Global Change Research Program, Washington, DC.CrossRefGoogle Scholar
  97. Zhang, J., Nielsen, S.E., Chen, Y., Georges, D., Qin, Y., Wang, S.S., Svenning, J.-C., Thuiller, W., 2017. Extinction risk of North American seed plants elevated by climate and land-use change. J. Appl. Ecol. 54, 303–312  https://doi.org/10.1111/1365-2664.12701.CrossRefGoogle Scholar

Copyright information

© Deutsche Gesellschaft für Säugetierkunde 2019

Authors and Affiliations

  • Nikhail Arumoogum
    • 1
  • M. Corrie Schoeman
    • 1
    Email author
  • Syd Ramdhani
    • 1
  1. 1.School of Life Sciences, Westville CampusUniversity of KwaZulu-NatalDurbanSouth Africa

Personalised recommendations