Effect of physiological tolerances and evolutionary adaptation on the distribution of species and communities under climate change

Alex Bush (1), Renee Catullo (2), Karel Mokany (3), Simon Ferrier (4)

1 CSIRO, Black Mountain, Canberra ACT 2601 alex.bush@csiro.au

2 CSIRO, Black Mountain, Canberra ACT 2601 renee.catullo@csiro.au

3 CSIRO, Black Mountain, Canberra ACT 2601 karel.mokany@csiro.au

4 CSIRO, Black Mountain, Canberra ACT 2601 simon.ferrier@csiro.au

Correlative spatial models of species or communities assume that occurrences reflect environmental preferences, but in many cases ranges may not be limited by physiological tolerances, and in addition, those physiological tolerances may change over time. Consequently species distribution models (SDM) may underestimate future habitat suitability, and approaches like Generalised Dissimilarity Modelling (GDM), may overestimate the rate of compositional turnover over time. Therefore the capacity for species to tolerate or adapt to climatic shifts has clear implications for our understanding of species potential responses to climate change, and consequently how we evaluate risk and prioritise conservation. We present results from new methods designed to incorporate estimates of physiological tolerances and adaptive capacity into models of species distributions. The modified-SDM uses detailed data of the genetics and thermal traits of Drosophila, and shows how species persistence in the landscape is likely to be affected by the rate of climatic change, the presence of extreme events, the rate of dispersal, and heritability of traits such as thermal tolerance. To infer adaptive capacity of large communities such as Australian reptiles and plants we used indirect estimates to predict their adaptive capacity and test a modified approach to GDM that highlights where lags in predicted turnover would occur in the future. Under severe climate scenarios change will be inevitable as most species’ capacity to resist and adapt is limited. These extended modelling approaches bridge the gap between our knowledge of ecology and physiology, and spatial predictions, to better inform our management actions in the future.