Spatial predictions at the community level: from current approaches to future frameworks

Research output: Contribution to journalReviewResearchpeer-review

Manuela D'Amen, Carsten Rahbek, Niklaus E. Zimmermann, Antoine Guisan

A fundamental goal of ecological research is to understand and model how processes generate patterns so that if conditions change, changes in the patterns can be predicted. Different approaches have been proposed for modelling species assemblage, but their use to predict spatial patterns of species richness and other community attributes over a range of spatial and temporal scales remains challenging. Different methods emphasize different processes of structuring communities and different goals. In this review, we focus on models that were developed for generating spatially explicit predictions of communities, with a particular focus on species richness, composition, relative abundance and related attributes. We first briefly describe the concepts and theories that span the different drivers of species assembly. A combination of abiotic processes and biotic mechanisms are thought to influence the community assembly process. In this review, we describe four categories of drivers: (i) historical and evolutionary, (ii) environmental, (iii) biotic, and (iv) stochastic. We discuss the different modelling approaches proposed or applied at the community level and examine them from different standpoints, i.e. the theoretical bases, the drivers included, the source data, and the expected outputs, with special emphasis on conservation needs under climate change. We also highlight the most promising novelties, possible shortcomings, and potential extensions of existing methods. Finally, we present new approaches to model and predict species assemblages by reviewing promising 'integrative frameworks' and views that seek to incorporate all drivers of community assembly into a unique modelling workflow. We discuss the strengths and weaknesses of these new solutions and how they may hasten progress in community-level modelling.

Original languageEnglish
JournalBiological Reviews
Volume92
Issue number1
Pages (from-to)169-187
Number of pages19
ISSN1464-7931
DOIs
Publication statusPublished - 2017

    Research areas

  • Biotic interactions, Dispersal, Environmental filter, Evolutionary forces, Modelling framework, Species pool, Stochasticity

ID: 154752568