Abstract
Predictive species distribution models are mostly based on statistical dependence between environmental and distributional data and therefore may fail to account for physiological limits and biological interactions that are fundamental when modelling species distributions under future climate conditions. Here, we developed a state-of-the-art method integrating biological theory with survey and experimental data in a way that allows us to explicitly model both physical tolerance limits of species and inherent natural variability in regional conditions and thereby improve the reliability of species distribution predictions under future climate conditions. By using a macroalga-herbivore association (Fucus vesiculosus - Idotea balthica) as a case study, we illustrated how salinity reduction and temperature increase under future climate conditions may significantly reduce the occurrence and biomass of these important coastal species. Moreover, we showed that the reduction of herbivore occurrence is linked to reduction of their host macroalgae. Spatial predictive modelling and experimental biology have been traditionally seen as separate fields but stronger interlinkages between these disciplines can improve species distribution projections under climate change. Experiments enable qualitative prior knowledge to be defined and identify cause-effect relationships, and thereby better foresee alterations in ecosystem structure and functioning under future climate conditions that are not necessarily seen in projections based on non-causal statistical relationships alone.
Highlights
Species distribution responses to climate change are commonly studied using predictive species distribution models (SDMs)
One potentially important aspect that may affect range shifts driven by climate change is the presence of locally adapted populations[22] with varying potential to respond to shifts in environmental conditions[23]
It is widely acknowledged that communities are more than the sum of the parts, most SDMs neglect biological mechanisms despite the fact that species interactions often explain unexpected responses to climate change[13,20,30,31], and most extinctions attributed to climate change to date have involved altered species interactions[32]
Summary
Species distribution responses to climate change are commonly studied using predictive species distribution models (SDMs). Various physiological thresholds are known for a number of taxa and can be experimentally tested under elevated stress conditions Information from such tolerance experiments, especially when including relevant aspects and levels of possible futures, should be combined with traditional SDM approaches in order to achieve more robust projections of biotic patterns under known climate change scenarios[19,29]. In order to improve the realism of statistical SDMs under climate change projections, we developed a novel semiparametric methodology that can combine qualitative prior information and experimentally defined tolerance levels on species’ response to selected environmental gradients with information obtained from surveyed non-causal distribution data. This assessment is supported by a novel developed state-of-the-art modelling method that can be applied beyond coastal habitats and aquatic ecosystems and used for climate change and other purposes
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