Impact of landscape predictors on climate change modelling of species distributions: a case study with Eucalyptus fastigata in southern New South Wales, Australia

Authors


Mike P. Austin, CSIRO Sustainable Ecosystems, GPO Box 284, Canberra, ACT 2601, Australia.
E-mail: mike.austin@csiro.au

Abstract

Aim  We consider three questions. (1) How different are the predicted distribution maps when climate-only and climate-plus-terrain models are developed from high-resolution data? (2) What are the implications of differences between the models when predicting future distributions under climate change scenarios, particularly for climate-only models at coarse resolution? (3) Does the use of high-resolution data and climate-plus-terrain models predict an increase in the number of local refugia?

Location  South-eastern New South Wales, Australia.

Methods  We developed two species distribution models for Eucalyptus fastigata under current climate conditions using generalized additive modelling. One used only climate variables as predictors (mean annual temperature, mean annual rainfall, mean summer rainfall); the other used both climate and landscape (June daily radiation, topographic position, lithology, nutrients) variables as predictors. Predictions of the distribution under current climate and climate change were then made for both models at a pixel resolution of 100 m.

Results  The model using climate and landscape variables as predictors explained a significantly greater proportion of the deviance than the climate-only model. Inclusion of landscape variables resulted in the prediction of much larger areas of existing optimal habitat. An overlay of predicted future climate on the current climate space indicated that extrapolation of the statistical models was not occurring and models were therefore more robust. Under climate change, landscape-defined refugia persisted in areas where the climate-only model predicted major declines. In areas where expansion was predicted, the increase in optimal habitat was always greater with landscape predictors. Recognition of extensive optimal habitat conditions and potential refugia was dependent on the use of high-resolution landscape data.

Main conclusions  Using only climate variables as predictors for assessing species responses to climate change ignores the accepted conceptual model of plant species distribution. Explicit statements justifying the selection of predictors based on ecological principles are needed. Models using only climate variables overestimate range reduction under climate change and fail to predict potential refugia. Fine-scale-resolution data are required to capture important climate/landscape interactions. Extrapolation of statistical models to regions in climate space outside the region where they were fitted is risky.

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