Unpacking the mechanisms captured by a correlative species distribution model to improve predictions of climate refugia

Abstract

Climate refugia are regions that animals can retreat to, persist in and potentially then expand from under changing environmental conditions. Most forecasts of climate change refugia for species are based on correlative species distribution models (SDMs) using long-term climate averages, projected to future climate scenarios. Limitations of such methods include the need to extrapolate into novel environments and uncertainty regarding the extent to which proximate variables included in the model capture processes driving distribution limits (and thus can be assumed to provide reliable predictions under new conditions). These limitations are well documented; however, their impact on the quality of climate refugia predictions is difficult to quantify. Here, we develop a detailed bioenergetics model for the koala. It indicates that range limits are driven by heat-induced water stress, with the timing of rainfall and heat waves limiting the koala in the warmer parts of its range. We compare refugia predictions from the bioenergetics model with predictions from a suite of competing correlative SDMs under a range of future climate scenarios. SDMs were fitted using combinations of long-term climate and weather extremes variables, to test how well each set of predictions captures the knowledge embedded in the bioenergetics model. Correlative models produced broadly similar predictions to the bioenergetics model across much of the species' current range - with SDMs that included weather extremes showing highest congruence. However, predictions in some regions diverged significantly when projecting to future climates due to the breakdown in correlation between climate variables. We provide unique insight into the mechanisms driving koala distribution and illustrate the importance of subtle relationships between the timing of weather events, particularly rain relative to hot-spells, in driving species-climate relationships and distributions. By unpacking the mechanisms captured by correlative SDMs, we can increase our certainty in forecasts of climate change impacts on species.

Behavioural responses to heat in desert birds: implications for predicting vulnerability to climate warming

“…Detailed parameterisation of mechanistic models relies on intensive and time-consuming sampling of focal taxa. To date, this approach has focussed predominantly on single species [7][8][9][10], limiting inference over the potential impact of climate change on communities. Despite the difficulties of building and parameterising complete mechanistic models, empirical mechanistic studies have provided convincing insights into how particular species and populations are affected by climate, particularly in response to high temperatures (e.g., [11][12][13][14][15][16][17][18][19]).…”

An estimate of the water budget for the endangered night parrot of Australia under recent and future climates

“…The generality of the modelling approach means it can be easily transferred to other taxa, the microclimate and endotherm models used here having been applied to a wide range of other taxa [e.g. 32,59].…”

“…This model has been described in detail elsewhere [11,[30][31][32] and is being prepared as a submodule of the NicheMapR biophysical modelling R package [12]. The model takes as input the available microclimatic environments for the organism, under the lowest and highest available shade, and then attempts to solve a heat budget given a specified core temperature and target metabolic rate (e.g.…”

“…We simulated future climate by imposing projected monthly changes in temperature, humidity, solar radiation and wind speed for 2070 onto interpolated daily weather data following the approach described in Briscoe et al [32]. Projections were obtained from six Global Circulation Models (GCMs), ACCESS 1.3, ACCESS 1.0, CanESM2, GDFL-CM3, HadGem2-CC and HadGem2-ES, which perform well in capturing past climate in Australia [46].…”

“…For example, reproductive potential could be computed [32,59] based on estimates of egg energy and water content from measurements of egg dimensions and literature on typical values for related birds [60]. General models of growth and reproduction such as the Dynamic Energy Budget model [e.g.…”