Abstract
Predicting which species will occur together in the future, and where, remains one of the greatest challenges in ecology, and requires a sound understanding of how the abiotic and biotic environments interact with dispersal processes and history across scales. Biotic interactions and their dynamics influence species' relationships to climate, and this also has important implications for predicting future distributions of species. It is already well accepted that biotic interactions shape species' spatial distributions at local spatial extents, but the role of these interactions beyond local extents (e.g. 10 km2 to global extents) are usually dismissed as unimportant. In this review we consolidate evidence for how biotic interactions shape species distributions beyond local extents and review methods for integrating biotic interactions into species distribution modelling tools. Drawing upon evidence from contemporary and palaeoecological studies of individual species ranges, functional groups, and species richness patterns, we show that biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents. We demonstrate this with examples from within and across trophic groups. A range of species distribution modelling tools is available to quantify species environmental relationships and predict species occurrence, such as: (i) integrating pairwise dependencies, (ii) using integrative predictors, and (iii) hybridising species distribution models (SDMs) with dynamic models. These methods have typically only been applied to interacting pairs of species at a single time, require a priori ecological knowledge about which species interact, and due to data paucity must assume that biotic interactions are constant in space and time. To better inform the future development of these models across spatial scales, we call for accelerated collection of spatially and temporally explicit species data. Ideally, these data should be sampled to reflect variation in the underlying environment across large spatial extents, and at fine spatial resolution. Simplified ecosystems where there are relatively few interacting species and sometimes a wealth of existing ecosystem monitoring data (e.g. arctic, alpine or island habitats) offer settings where the development of modelling tools that account for biotic interactions may be less difficult than elsewhere.
Highlights
Drawing upon evidence from contemporary and palaeoecological studies of individual species ranges, functional groups, and species richness patterns, we show that biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents
Pearson & Dawson (2003) proposed a conceptual framework in which biotic interactions are expected to play a role in shaping species distributions only over local extents while other factors, such as climate, play a role at broader spatial extents, but not more locally
We call for observational data at high spatial resolution with of high accuracy (GPS coordinates) compiled across large spatial extents. These data combined with experimental and long-term monitoring data will provide a better mechanistic understanding of how biotic interactions affect broad-scale species distributions, which in turn will make our predictions for future changes in biodiversity more accurate
Summary
In the context of spatial ecology, biotic interactions have been generally dismissed as unimportant beyond the local scale (Fig. 2). Concerns that correlations can be influenced by how the absences are drawn to fit presence-absence models led Schweiger et al (2008, 2011) to use only the range of the host plant species to select absences for calibrating their models of dependent butterfly species All these pairwise approaches need some prior knowledge on the ecology of the species under study to parameterise the model—or a system of multiple models—including the appropriate environmental predictors at appropriate resolution, in order to avoid a high risk of type I error (i.e. concluding there is competitive exclusion when this is not the case). Methods do not exist to integrate such an approach into an SDM predictive framework
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