Using species distribution models to infer potential climate change-induced range shifts of freshwater fish in south-eastern Australia
Nick Bond A B C F , Jim Thomson A , Paul Reich A D and Janet Stein E
+ Author Affiliations
- Author Affiliations
A School of Biological Sciences, Monash University, Clayton, Vic. 3800, Australia.
B eWater Cooperative Research Centre, Monash University, Clayton, Vic. 3800, Australia.
C Present address: Australian Rivers Institute, Griffith University, Nathan, Qld 4111, Australia.
D Arthur Rylah Institute for Environmental Research, Department of Sustainability and Environment, Heidelberg, Vic. 3084, Australia.
E The Fenner School of Environment and Society, Australian National University, Canberra, ACT 0200, Australia.
F Corresponding author. Email: n.bond@griffith.edu.au
Marine and Freshwater Research 62(9) 1043-1061 https://doi.org/10.1071/MF10286
Submitted: 16 November 2010 Accepted: 11 June 2011 Published: 21 September 2011
Journal Compilation © CSIRO Publishing 2011 Open Access CC BY-NC-ND
Abstract
There are few quantitative predictions for the impacts of climate change on freshwater fish in Australia. We developed species distribution models (SDMs) linking historical fish distributions for 43 species from Victorian streams to a suite of hydro-climatic and catchment predictors, and applied these models to explore predicted range shifts under future climate-change scenarios. Here, we present summary results for the 43 species, together with a more detailed analysis for a subset of species with distinct distributions in relation to temperature and hydrology. Range shifts increased from the lower to upper climate-change scenarios, with most species predicted to undergo some degree of range shift. Changes in total occupancy ranged from –38% to +63% under the lower climate-change scenario to –47% to +182% under the upper climate-change scenario. We do, however, caution that range expansions are more putative than range contractions, because the effects of barriers, limited dispersal and potential life-history factors are likely to exclude some areas from being colonised. As well as potentially informing more mechanistic modelling approaches, quantitative predictions such as these should be seen as representing hypotheses to be tested and discussed, and should be valuable for informing long-term strategies to protect aquatic biota.
Additional keywords: bioclimatic model, conservation planning, environmental filters, hydrology, prediction, validation.
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