Hostname: page-component-78c5997874-fbnjt Total loading time: 0 Render date: 2024-11-10T17:39:19.439Z Has data issue: false hasContentIssue false

Species distribution modelling using bioclimatic variables to determine the impacts of a changing climate on the western ringtail possum (Pseudocheirus occidentals; Pseudocheiridae)

Published online by Cambridge University Press:  08 October 2013

SHAUN W. MOLLOY*
Affiliation:
School of Natural Sciences, Centre for Ecosystem Management, Edith Cowan University, Joondalup WA 6027, Australia
ROBERT A. DAVIS
Affiliation:
School of Natural Sciences, Centre for Ecosystem Management, Edith Cowan University, Joondalup WA 6027, Australia
EDDIE J. B. VAN ETTEN
Affiliation:
School of Natural Sciences, Centre for Ecosystem Management, Edith Cowan University, Joondalup WA 6027, Australia
*
*Correspondence: Shaun Molloy e-mail: shaun.molloy@ecu.edu.au

Summary

The ngwayir (western ringtail possum Pseudocheirus occidentalis) is an arboreal species endemic to south-western Australia. The range and population of this species have been significantly reduced through multiple anthropogenic impacts. Classified as vulnerable, the ngwayir is highly susceptible to extremes of temperature and reduced water intake. Ngwayir distribution was determined using three different species distribution models using ngwayir presence records related to a set of 19 bioclimatic variables derived from historical climate data, overlaid with 2050 climate change scenarios. MaxEnt was used to identify core habitat and demonstrate how this habitat may be impacted. A supplementary modelling exercise was also conducted to ascertain potential impacts on the tree species that are core habitat for ngwayir. All models predicted a reduction of up to 60% in the range of the ngwayir and its habitat, as a result of global warming towards the south-west of the project area, with a mean potential distribution of 10.3% of the total modelled area of 561 059 km2. All three tree species modelled (jarrah, marri and peppermint) were predicted to experience similar contractions in range throughout most of the predicted ngwayir range, although their distributions differed. Populations of ngwayir persisting outside core habitat may indicate potential conservation opportunities.

Type
THEMATIC SECTION: Spatial Simulation Models in Planning for Resilience
Copyright
Copyright © Foundation for Environmental Conservation 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adams-Hosking, C., Grantham, H.S., Rhodes, J.R., McAlpine, C. & Moss, P.T. (2011) Modelling climate-change-induced shifts in the distribution of the koala. Wildlife Research 38 (2): 122130.Google Scholar
Allen, C.D., Hogg, E.H., Gonzalez, P., Fensham, R., Zhang, Z., Castro, J., Demidova, N., Lim, J.-H., Allard, G., Running, S.W., Semerci, A., Macalady, A.K., Cobb, N., Chenchouni, H., Bachelet, D., McDowell, N., Vennetier, M., Kitzberger, T., Rigling, A. & Breshears, D.D. (2010) A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecology and Management 259 (4): 660684.Google Scholar
Allison, I., Bindoff, N.L., Bindschadler, R.A., Cox, P.M., Noblet, N.D., England, M.H., Francis, J.E., Gruber, N., Haywood, A.M., Karoly, D.J., Kaser, G., Le Quéré, C., Lenton, T.M., Mann, M.E., McNeil, B.I., Pitman, A.J., Rahmstorf, S., Rignot, E., Schellnhuber, H.J., Schneider, S.H., Sherwood, S.C., Somerville, R.C.J., Steffen, K., Steig, E.J., Visbeck, M. & Weaver, A.J. (2009) The Copenhagen Diagnosis, 2009: Updating the World on the Latest Climate Science. Sydney, Australia: The University of New South Wales Climate Change Research Centre (CCRC).Google Scholar
Bateman, B.L., VanDerWal, J., Williams, S.E. & Johnson, C.N. (2012) Biotic interactions influence the projected distribution of a specialist mammal under climate change. Diversity and Distributions 18 (9): 861872.Google Scholar
Beaumont, L.J., Hughes, L. & Poulsen, M. (2005) Predicting species distributions: use of climatic parameters in BIOCLIM and its impact on predictions of species’ current and future distributions. Ecological Modelling 186 (2): 251270.Google Scholar
Beaumont, L.J., Pitman, A.J., Poulsen, M. & Hughes, L. (2007) Where will species go? Incorporating new advances in climate modelling into projections of species distributions. Global Change Biology 13 (7): 13681385.CrossRefGoogle Scholar
Burbidge, A. (2010) Global hotspot under stress: while the south-west corner of Western Australia is recognised as a global biodiversity hotspot, its unique ecosystems have suffered land clearing, introduced pests and weeds, a changed fire regime, loss of water and salinisation. climate change may tip the balance for some species, unless effective action is taken. Ecos 153: 1819.Google Scholar
Bystriakova, N., Peregrym, M., Erkens, R.H.J., Bezsmertna, O. & Schneider, H. (2012) Sampling bias in geographic and environmental space and its effect on the predictive power of species distribution models. Systematics and Biodiversity 10 (3): 305315.CrossRefGoogle Scholar
Carpenter, G., Gillison, A.N. & Winter, J. (1993) Domain: a flexible modeling procedure for mapping potential distributions of plants and animals. Biodiversity and Conservation 2 (6): 667680.CrossRefGoogle Scholar
CCAFS (2008) Downscaled GCM Data Portal [www document]. URL http://www.ccafs-climate.org/data/ Google Scholar
Chaturvedi, R.K., Gopalakrishnan, R., Jayaraman, M., Bala, G., Joshi, N., Sukumar, R. & Ravindranath, N.H. (2011) Impact of climate change on Indian forests: a dynamic vegetation modeling approach. Mitigation and Adaptation Strategies for Global Change 16 (2): 119142.Google Scholar
Cowled, B.D., Giannini, F., Beckett, S.D., Woolnough, A., Barry, S., Randall, L. & Garner, G. (2009) Feral pigs: predicting future distributions. Wildlife Research 36 (3): 242251.Google Scholar
Cristianini, N. & Scholkopf, B. (2002) Support vector machines and kernel methods: the new generation of learning machines. AI Magazine 23 (3): 3141.Google Scholar
Crossman, N.D., Bryan, B.A. & Summers, D.M. (2012) Identifying priority areas for reducing species vulnerability to climate change. Diversity and Distributions 18 (1): 6072.Google Scholar
CSIRO & BOM (2007) Climate change in Australia: technical report [www document]. URL http://www.climatechangeinaustralia.gov.au/technical_report.php Google Scholar
CSIRO & BOM (2012) State of the climate 2012 [www document]. URL http://www.csiro.au/Outcomes/Climate/Understanding/State-of-the-Climate-2012.aspx Google Scholar
de Tores, P.J. (2008) Western ringtail possum. In: The Mammals of Australia, 3rd Edition, ed. Dyck, S. Van & Strahan, R., pp. 253255. Sydney, Australia: Reed New Holland.Google Scholar
de Tores, P.J. (2009) A summary of research by Department of Environment and Conservation (DEC), Murdoch University and Curtin University on the western ringtail possum (Pseudocheirus occidentalis) on the southern Swan Coastal Plain and recommendations relevant to current research proposals. Report prepared for the Commonwealth Department of Environment, Water, Heritage and the Arts, Canberra, Australia.Google Scholar
de Tores, P.J., Hayward, M.W. & Rosier, S.M. (2004) The western ringtail possum, Pseudocheirus occidentalis and the quokka, Setonix brachyurus, case studies: Western Shield review February 2003. Conservation Science Western Australia 5 (2): 235257.Google Scholar
DEC (2007–2013) NatureMap: mapping Western Australia's biodiversity [www document]. URL http://naturemap.dec.wa.gov.au/ Google Scholar
DeGabriel, J.L., Wallis, I.R., Moore, B.D. & Foley, W.J. (2008) A simple, integrative assay to quantify nutritional quality of browses for herbivores. Oecologia 156 (1): 107116.Google Scholar
Dunlop, M. & Brown, P.R. (2008) Implications of climate change for Australia's national reserve system. In: A preliminary assessment. Report. Department of Climate Change, Canberra, Australia.Google Scholar
Ebbers, M.J.H., Wallis, I.R., Dury, S., Floyd, R. & Foley, W.J. (2002) Spectrometric prediction of secondary metabolites and nitrogen in fresh eucalyptus foliage: towards remote sensing of the nutritional quality of foliage for leaf-eating marsupials. Australian Journal of Botany 50 (6): 761768.CrossRefGoogle Scholar
Elith, J., Phillips, S.J., Hastie, T., Dudík, M., Chee, Y.E. & Yates, C.J. (2011) A statistical explanation of MaxEnt for ecologists. Diversity and Distributions 17 (1): 4357.Google Scholar
EPA (2007) State of the environment report: Western Australia 2007. Report. Western Australian Government Environmental Protection Authority, Perth, WA, Australia.Google Scholar
Foley, D.H., Weitzman, A.L., Miller, S.E., Faran, M.E., Rueda, L.M. & Wilkerson, R.C. (2008) The value of georeferenced collection records for predicting patterns of mosquito species richness and endemism in the Neotropics. Ecological Entomology 33 (1): 1223.Google Scholar
Fordham, D.A., Regan, T.J., Tozer, M., Watts, M.J., White, M., Wintle, B.A., Yates, C., Brook, B.W., Resit Akçakaya, H., Araújo, M.B., Elith, J., Keith, D.A., Pearson, R., Auld, T.D., Mellin, C. & Morgan, J.W. (2012) Plant extinction risk under climate change: are forecast range shifts alone a good indicator of species vulnerability to global warming? Global Change Biology 18 (4): 13571371.Google Scholar
Green, K., Stein, J.A. & Driessen, M.M. (2008) The projected distributions of Mastacomys fuscus and Rattus lutreolus in south-eastern Australia under a scenario of climate change: potential for increased competition? Wildlife Research 35 (2): 113119.Google Scholar
Gritti, E.S., Smith, B., Sykes, M.T. & Lund, U. (2006) Vulnerability of Mediterranean basin ecosystems to climate change and invasion by exotic plant species. Journal of Biogeography 33 (1): 145157.Google Scholar
Guerin, G.R. & Lowe, A.J. (2012) Multi-species distribution modelling highlights the Adelaide Geosyncline, South Australia, as an important continental-scale arid-zone refugium. Austral Ecology 38 (4): 427435.CrossRefGoogle Scholar
Guisan, A. & Thuiller, W. (2005) Predicting species distribution: offering more than simple habitat models. Ecology Letters 8 (9): 9931009.Google Scholar
Guo, Q. & Liu, Y. (2010) ModEco: an integrated software package for ecological niche modeling. Ecography 33 (4): 637642.Google Scholar
Hawkes, P.G. (2010) A new species of Asphinctopone (Hymenoptera: Formicidae: Ponerinae) from Tanzania. Zootaxa 2480: 2736.Google Scholar
Hijmans, R.J. & Graham, C.H. (2006) The ability of climate envelope models to predict the effect of climate change on species distributions. Global Change Biology 12 (12): 22722281.Google Scholar
Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. & Jarvis, A. (2005) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25 (15): 19651978.Google Scholar
Hijmans, R.J., Guarino, L. & Mathur, P. (2012) Diva-GIS Version 7.5 Manual [www document]. URL http://www.diva-gis.org/ Google Scholar
Hopper, S.D. & Gioia, P. (2004) The Southwest Australian floristic region: evolution and conservation of a global hot spot of biodiversity. Annual Review of Ecology, Evolution, and Systematics 35 (1): 623650.Google Scholar
Hughes, L. (2003) Climate change and Australia: trends, projections and impacts. Austral Ecology 28 (4): 423443.Google Scholar
Hughes, L. (2011) Climate change and Australia: key vulnerable regions. Regional Environmental Change 11 (1): 189195.Google Scholar
Hutchinson, G.E. (1957) Concluding remarks. Cold Spring Harbor Symposia on Quantitative Biology 22: 415427.Google Scholar
Indian Ocean Climate Initiative (2012) Western Australia's Weather and Cimate: A Synthesis of Indian Ocean Climate Initiative Stage 3 Research. Report, CSIRO and BOM, Australia.Google Scholar
IPCC (2007) Summary for policymakers. In: Climate Change 2007: the Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, ed. Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M. & Miller, H.L., pp. 433497. Cambridge, UK and New York, NY, USA: Cambridge University Press.Google Scholar
Jimenez-Valverde, A., Decae, A.E. & Arnedo, M.A. (2011) Environmental suitability of new reported localities of the funnelweb spider Macrothele calpeiana: an assessment using potential distribution modelling with presence-only techniques. Journal of Biogeography 38 (6): 12131223.Google Scholar
Jones, B. (2004) The possum fauna of Western Australia: decline persistence and status. In: The Biology of Australian Possums and Gliders, ed. Goldingay, R.L. & Jackson, S.M., pp. 149160. Chipping Norton, Australia: Surrey Beatty & Sons.Google Scholar
Jones, B., Henry, J. & Francesconi, B. (2007) An important local population of the western ringtail possum Pseudocheirus occidentalis: a 2006 survey study of the population and habitat in the Busselton localities of Siesta Park and Kealy. Report. Geocatch, Busselton, WA, Australia.Google Scholar
Jones, B. & Hillcox, S. (1995) A survey of the possums Trichosurus vulpecula and Pseudocheirus occidentalis and their habitats at Ludlow, Western Australia. Western Australian Naturalist 20 (3): 139150.Google Scholar
Jones, B., How, R. & Kitchener, D. (1994) A field-study of Pseudocheirus occidentalis (Marsupialia, Petauridae). 1. Distribution and habitat. Wildlife Research 21 (2): 175187.Google Scholar
Khatchikian, C., Sangermano, F., Kendell, D. & Livdahl, T. (2011) Evaluation of species distribution model algorithms for fine-scale container-breeding mosquito risk prediction. Medical and Veterinary Entomology 25 (3): 268275.Google Scholar
Klausmeyer, K.R. & Shaw, M.R. (2009) Climate change, habitat loss, protected areas and the climate adaptation potential of species in mediterranean ecosystems worldwide. PloS One 4 (7): e6392.Google Scholar
Lawes, R.A. & Dodd, M.B. (2009) Does re-vegetating poor performing patches in agricultural fields improve ecosystem function in the northern sandplain of the Western Australian wheatbelt? Crop and Pasture Science 60 (9): 912920.CrossRefGoogle Scholar
Littell, J.S., Oneil, E.E., McKenzie, D., Hicke, J.A., Lutz, J.A., Norheim, R.A. & Elsner, M.M. (2010) Forest ecosystems, disturbance, and climatic change in Washington State, USA. Climatic Change 102 (1): 129158.Google Scholar
Malenovský, Z., Mishra, K.B., Zemek, F., Rascher, U. & Nedbal, L. (2009) Scientific and technical challenges in remote sensing of plant canopy reflectance and fluorescence. Journal of Experimental Botany 60 (11): 29873004.Google Scholar
Marcial, L.H. & Hemminger, B.M. (2010) Scientific data repositories on the Web: An initial survey. Journal of the American Society for Information Science and Technology 61 (10): 20292048.Google Scholar
Milad, M., Schaich, H., Bürgi, M. & Konold, W. (2011) Climate change and nature conservation in Central European forests: a review of consequences, concepts and challenges. Forest Ecology and Management 261 (4): 829843.CrossRefGoogle Scholar
Molloy, S., O'Connor, T., Wood, J. & Wallrodt, S. (2007) Local Government Biodiversity Planning Guidelines for the Perth Metropolitan Region: Addendum for the South West Biodiversity Project Area. Perth, Australia: Western Australian Local Government Association.Google Scholar
Monk, J., Ierodiaconou, D., Versace, V.L., Bellgrove, A., Harvey, E., Rattray, A., Laurenson, L. & Quinn, G.P. (2010) Habitat suitability for marine fishes using presence-only modelling and multibeam sonar. Marine Ecology Progress Series 420: 157174.Google Scholar
Moore, B.D., Wallis, I.R., Pala-Paul, J., Brophy, J.J., Willis, R.H. & Foley, W.J. (2004) Antiherbivore chemistry of eucalyptus: cues and deterrents for marsupial folivores. Journal of Chemical Ecology 30 (9): 17431769.CrossRefGoogle ScholarPubMed
Morris, K., Burbidge, A. & Friend, T. (2008) Pseudocheirus occidentalis. IUCN Red List of Threatened Species. Version 2012.1 [www document]. URL http://www.iucnredlist.org/details/18492/0 Google Scholar
Myers, N., Mittermeier, R.A., Mittermeier, C.G., da Fonseca, G.A.B. & Kent, J. (2000) Biodiversity hotspots for conservation priorities. Nature 403 (6772): 853858.Google Scholar
Navarro-Cerrillo, R.M., Hernandez-Bermejo, J.E. & Hernandez-Clemente, R. (2011) Evaluating models to assess the distribution of Buxus balearica in southern Spain. Applied Vegetation Science 14 (2): 256267.Google Scholar
Opdam, P., Luque, S. & Jones, B.K. (2009) Changing landscapes to accommodate for climate change impacts: a call for landscape ecology. Landscape Ecology 24 (6): 715721.Google Scholar
Pearson, R.G. & Dawson, T.P. (2003) Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Global Ecology and Biogeography 12 (5): 361371.Google Scholar
Perkins, S.E., Pitman, A.J., Holbrook, N.J. & McAneney, J. (2007) Evaluation of the AR4 climate models’ simulated daily maximum temperature, minimum temperature, and precipitation over Australia using probability density functions. Journal of Climate 20 (17): 43564376.Google Scholar
Phillips, S.J., Anderson, R.P. & Schapire, R.E. (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling 190 (3): 231259.Google Scholar
Phillips, S.J. & Dudík, M. (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31 (2): 161175.Google Scholar
Prober, S.M., Lemson, K., Lyons, T., Macfarlane, C., O'Connor, M.H., Scott, J.K., Standish, R.J., Stock, W.D., van Etten, E.J.B., Wardell-Johnson, G.W., Watson, A., Thiele, K.R., Rundel, P.W., Yates, C.J., Berry, S.L., Byrne, M., Christidis, L., Gosper, C.R. & Grierson, P.F. (2012) Facilitating adaptation of biodiversity to climate change: a conceptual framework applied to the world's largest Mediterranean-climate woodland. Climatic Change 110 (1): 227248.Google Scholar
Ramirez, J. & Jarvis, A. (2008) High resolution statistically downscaled future climate surfaces [www document]. URL http://www.ccafs-climate.org/data/ Google Scholar
Richardson, K., Steffen, W.L. & Liverman, D. (2011) Climate Change: Global Risks, Challenges and Decisions. New York, NY, USA: Cambridge University Press.Google Scholar
Scrivener, N.J., Johnson, C.N., Wallis, I.R., Takasaki, M., Foley, W.J. & Krockenberger, A.K. (2004) Which trees do wild common brushtail possums (Trichosurus vulpecula) prefer? Problems and solutions in scaling laboratory findings to diet selection in the field. Evolutionary Ecology Research 6 (1): 7787.Google Scholar
Smith, F.P. (2008) Who's planting what, where and why, and who's paying? An analysis of farmland revegetation in the central wheatbelt of Western Australia. Landscape and Urban Planning 86 (1): 6678.Google Scholar
Tognelli, M.F., Roig-Junent, S.A., Marvaldi, A.E., Flores, G.E. & Lobo, J.M. (2009) An evaluation of methods for modelling distribution of Patagonian insects. Revista Chilena De Historia Natural 82 (3): 347360.Google Scholar
UCMERCED (2011) ModEco homepage [www document]. URL http://gis.ucmerced.edu/ModEco/ Google Scholar
Vapnik, V.N. (1995) The Nature of Statistical Learning Theory. New York, NY, USA: Springer.Google Scholar
Vasconcelos, T.S., Rodríguez, M.Á. & Hawkins, B.A. (2012) Species distribution modelling as a macroecological tool: a case study using New World amphibians. Ecography 35 (6): 539548.Google Scholar
Wallis, I.R., Watson, M.L. & Foley, W.J. (2002) Secondary metabolites in Eucalyptus melliodora: field distribution and laboratory feeding choices by a generalist herbivore, the common brushtail possum. Australian Journal of Zoology 50 (5): 507519.Google Scholar
Wayne, A. (2009) New conservation initiative to save the woylie from extinction in the wild. Pacific Conservation Biology 15 (4): 233.CrossRefGoogle Scholar
Wayne, A.F., Cowling, A., Lindenmayer, D.B., Ward, C.G., Vellios, C.V., Donnelly, C.F. & Calvey, M.C. (2006) The abundance of a threatened arboreal marsupial in relation to anthropogenic disturbances at local and landscape scales in Mediterranean-type forests in south-western Australia. Biological Conservation 127 (4): 463476.Google Scholar
Wayne, A.F., Rooney, J.F., Ward, C.G., Vellios, C.V. & Lindenmayer, D.B. (2005) The life history of Pseudocheirus occidentalis (Pseudocheiridae) in the jarrah forest of south-western Australia. Australian Journal of Zoology 53 (5): 325337.Google Scholar
Welsh, A.H., Lindenmayer, D.B. & Donnelly, C.F. (2013) Fitting and interpreting occupancy models. PLoS One 8 (1): e52015.Google Scholar
Williams, S.E., Shoo, L.P., Isaac, J.L., Hoffmann, A.A. & Langham, G. (2008) Towards an integrated framework for assessing the vulnerability of species to climate change. PLoS Biology 6 (12): 2621.Google Scholar
Wilson, K.J. (2009) Quantifying the genetic effects of habitat fragmentation on the western ringtail possums (Pseudocheirus occidentalis) in south-west Western Australia. Thesis. Murdoch University, Perth, WA, Australia.Google Scholar
WorldClim (2012) Global climate data base [www document]. URL http://worldclim.org/ Google Scholar
Yates, C.J., Elith, J., Latimer, A.M., Le Maitre, D., Midgley, G.F., Schurr, F.M. & West, A.G. (2010 a) Projecting climate change impacts on species distributions in megadiverse South African Cape and Southwest Australian Floristic Regions: opportunities and challenges. Austral Ecology 35 (4): 374–374.Google Scholar
Yates, C.J., McNeill, A., Elith, J. & Midgley, G.F. (2010 b) Assessing the impacts of climate change and land transformation on banksia in the South West Australian Floristic Region. Diversity and Distributions 16 (1): 187201.Google Scholar
Yin, H.K. (2006) The metabolic and hygric physiology of the western ringtail possum (Pseudocheirus occidentalis). Thesis. Department of Environmental Biology, Curtin University, Perth, Australia.Google Scholar
Ziska, L.H., Blumenthal, D.M., Runion, G.B., Hunt Jr, E.R. & Diaz-Soltero, H. (2011) Invasive species and climate change: an agronomic perspective. Climatic Change 105 (1): 1342.Google Scholar
Supplementary material: File

Molloy Supplementary Material

Appendix

Download Molloy Supplementary Material(File)
File 3 MB
Supplementary material: Image

Molloy Supplementary Material

Image

Download Molloy Supplementary Material(Image)
Image 2.2 MB
Supplementary material: Image

Molloy Supplementary Material

Image

Download Molloy Supplementary Material(Image)
Image 701.2 KB