Spatial scale, topography and thermoregulatory behaviour interact when modelling species’ thermal niches

3 June 2018

Barton, Madeleine; Clusella-Trullas, Susana; Terblanche, John

The spatial scale at which climate and species’ occupancy data are gathered, and the resolution at which ecological models are run, can strongly influence predictions of species performance and distributions. Running model simulations at coarse rather than fine spatial resolutions, for example, can determine if a model accurately predicted the distribution of a species. The impacts of spatial scale on a model’s accuracy are particularly pronounced across mountainous terrain. Understanding how these discrepancies arise requires a modelling approach in which the underlying processes that determine a species’ distribution are explicitly described. Here we use a process-based model to explore how spatial resolution, topography and behaviour alter predictions of species thermal niche, which in turn constrains survival and geographic distribution. The model incorporates biophysical equations to predict the operative temperature (Te), thermal-dependent performance and survival of a typical insect, with a complex life-cycle,in its microclimate. We run this model with geographic data from a mountainous terrain in South Africa using climate data at three spatial resolutions. We also explore how behavioural thermoregulation affects predictions of a species performance and survival by allowing the animal to select the optimum thermal location within each coarse-grid cell. At the regional level, coarse-resolution models predicted lower Te at low elevations and higher Te at high elevations than models run at fine-resolutions. These differences were more prominent on steep, north-facing slopes. The discrepancies in Te in turn affected estimates of the species thermal niche. The modelling framework revealed how spatial resolution and topography influence predictions of species distribution models, including the potential impacts of climate change. These systematic biases must be accounted for when interpreting the outputs of future modelling studies, particularly when species distributions are predicted to shift from uniform to topographically heterogeneous landscapes.