The role of biotic interactions in shaping distributions and realised assemblages of species: Implications for species distribution modelling

TY - JOUR

T1 - The role of biotic interactions in shaping distributions and realised assemblages of species

T2 - Implications for species distribution modelling

AU - Wisz, Mary Susanne

AU - Pottier, Julien

AU - Kissling, W. Daniel

AU - Pellissier, Loïc

AU - Lenoir, Jonathan

AU - Damgaard, Christian Frølund

AU - Dormann, Carsten F.

AU - Forchhammer, Mads C.

AU - Grytnes, John Arvid

AU - Guisan, Antoine

AU - Høye, Toke T.

AU - Heikkinen, Risto K.

AU - Kühn, Ingolf

AU - Luoto, Miska

AU - Maiorano, Luigi

AU - Nilsson, Marie Charlotte

AU - Normand, Signe

AU - Öckinger, Erik

AU - Schmidt, Niels M.

AU - Termansen, Mette

AU - Timmermann, Allan

AU - Wardle, David A.

AU - Aastrup, Peter

AU - Svenning, Jens Christian

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - Biotic interaction

KW - Climate

KW - Macroecology

KW - Prediction

KW - Sampling

KW - Scale

KW - Spatial extent

KW - Species assemblage

KW - Species distribution model

U2 - 10.1111/j.1469-185X.2012.00235.x

DO - 10.1111/j.1469-185X.2012.00235.x

M3 - Journal article

C2 - 22686347

AN - SCOPUS:84871770547

SN - 1464-7931

VL - 88

SP - 15

EP - 30

JO - Biological Reviews

JF - Biological Reviews

IS - 1

ER -