Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents.

Aim: Biotic interactions – within guilds or across trophic levels – have widely

been ignored in species distribution models (SDMs). This synthesis outlines the

development of ‘species interaction distribution models’ (SIDMs), which aim to

incorporate multispecies interactions at large spatial extents using interaction

matrices.

Location: Local to global.

Methods: We review recent approaches for extending classical SDMs to

incorporate biotic interactions, and identify some methodological and

conceptual limitations. To illustrate possible directions for conceptual

advancement we explore three principal ways of modelling multispecies

interactions using interaction matrices: simple qualitative linkages between

species, quantitative interaction coefficients reflecting interaction strengths, and

interactions mediated by interaction currencies. We explain methodological

advancements for static interaction data and multispecies time series, and outline

methods to reduce complexity when modelling multispecies interactions.

Results: Classical SDMs ignore biotic interactions and recent SDM extensions

only include the unidirectional influence of one or a few species. However, novel

methods using error matrices in multivariate regression models allow interactions

between multiple species to be modelled explicitly with spatial co-occurrence

data. If time series are available, multivariate versions of population dynamic

models can be applied that account for the effects and relative importance of

species interactions and environmental drivers. These methods need to be

extended by incorporating the non-stationarity in interaction coefficients across

space and time, and are challenged by the limited empirical knowledge on spatiotemporal

variation in the existence and strength of species interactions. Model

complexity may be reduced by: (1) using prior ecological knowledge to set a

subset of interaction coefficients to zero, (2) modelling guilds and functional

groups rather than individual species, and (3) modelling interaction currencies

and species’ effect and response traits.

Main conclusions: There is great potential for developing novel approaches that

incorporate multispecies interactions into the projection of species distributions

and community structure at large spatial extents. Progress can be made by: (1)

developing statistical models with interaction matrices for multispecies cooccurrence

datasets across large-scale environmental gradients, (2) testing the

potential and limitations of methods for complexity reduction, and (3) sampling

and monitoring comprehensive spatio-temporal data on biotic interactions in

multispecies communities.

In  Journal of Biogeography

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