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

By M. Wisz, J. Pottier, Werner Kissling, Loïc Pellissier, Jonathan Lenoir, Christian Damgaard, Carsten F. Dormann, M. Forchhammer, John‐Arvid Grytnes, Antoine Guisan and 14 more. Biological Reviews of The Cambridge Philosophical Society, 2012

1,574 citations recorded by the source corpus.

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.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/c00d5dcf07d998866f75234f81eb3883005a89ec

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Citations in the corpus

The importance of biotic interactions in species distribution models: a test of the Eltonian noise hypothesis using parrots (2014, background): “Recent studies have explored the conceptual and methodological issues regarding the role of biotic interactions in SDM (Boulangeat et al., 2012; Godsoe & Harmon, 2012; Wisz et al., 2013).”
Risk Analysis and Bioeconomics of Invasive Species to Inform Policy and Management (2016, background): “Current research is focusing on improved algorithms and applications (135, 136), as well as incorporating the effects of species interactions (137).”
Causes of warm‐edge range limits: systematic review, proximate factors and implications for climate change (2014, background): “For example, species distribution models can often successfully predict species ranges (and related large-scale patterns) with climatic data alone (e.g. Pearson & Dawson, 2003; Ara ujo et al., 2005; but see Hampe, 2004; Wisz et al., 2013).”
Climatic niche shifts between species' native and naturalized ranges raise concern for ecological forecasts during invasions and climate change (2014, background): “Current techniques and data can rarely detect these effects at coarse spatial resolutions (Wisz et al., 2013).”
How to make more out of community data? A conceptual framework and its implementation as models and software. (2017, background): “This understanding can hardly be achieved by studying species one by one since their abundances and distributions depend not only on their individual responses to the abiotic environment, but also on their interactions (Wisz et al. 2013).”
Using joint species distribution models for evaluating how species‐to‐species associations depend on the environmental context (2017, background): “…literature on how to predict species’ distributions based on abiotic variation (Elith & Leathwick 2009), the inclusion of interspecific interactions into species distribution modelling frameworks has been rather recent (Boulangeat, Gravel & Thuiller 2012; Kissling et al. 2012; Wisz et al. 2013).”
Seven Shortfalls that Beset Large-Scale Knowledge of Biodiversity (2015, background): “The effectiveness of SDMs is also hindered by our lack of knowledge about species interactions, as this limits our ability to incorporate them into the latest generation of models (e.g., Wisz et al. 2013).”
Biotic interactions in species distribution modelling: 10 questions to guide interpretation and avoid false conclusions (2018, neutral): “The methods for this task are in substantial flux (Gonz alez-Salazar, Stephens, & Marquet, 2013; Louthan, Doak, & Angert, 2006; Morales-Castilla, Matias, Gravel, & Ara ujo, 2015; NietoLugilde, Maguire, Blois, Williams, & Fitzpatrick, 2018; Ovaskainen et al., 2017; Staniczenko, Sivasubramaniam, Suttl”
Co-occurrence is not evidence of ecological interactions. (2020, background): “Indeed SDMs, developed in the 1990s and the early 2000s were criticised for neglecting biotic interactions (Wisz et al., 2013) whereas it has been repeatedly shown that biotic interactions improve the accuracy of predictions (Leathwick & Austin, 2001; Heikkinen et al.”