Disentangling biotic interactions, environmental filters, and dispersal limitation as drivers of species co‐occurrence

Heidi K. Mod,Manuela D'Amen,Antoine Guisan,Nicholas J. Gotelli

doi:10.1111/ecog.03148

Heidi K. Mod, Manuela D'Amen + Show 2 more

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https://doi.org/10.1111/ecog.03148

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Journal: Ecography	Publication Date: Dec 26, 2017
Citations: 150	License type: cc-by

Affiliation: University of Lausanne, University of Vermont

Abstract

A key focus in ecology is to search for community assembly rules. Here we compare two community modelling frameworks that integrate a combination of environmental and spatial data to identify positive and negative species associations from presence–absence matrices, and incorporate an additional comparison using joint species distribution models (JSDM).The frameworks use a dichotomous logic tree that distinguishes dispersal limitation, environmental requirements, and interspecific interactions as causes of segregated or aggregated species pairs. The first framework is based on a classical null model analysis complemented by tests of spatial arrangement and environmental characteristics of the sites occupied by the members of each species pair (Classic framework). The second framework, (SDM framework) implemented here for the first time, builds on the application of environmentally‐constrained null models (or JSDMs) to partial out the influence of the environment, and includes an analysis of the geographical configuration of species ranges to account for dispersal effects.We applied these approaches to examine plot‐level species co‐occurrence in plant communities sampled along a wide elevation gradient in the Swiss Alps. According to the frameworks, the majority of species pairs were randomly associated, and most of the non‐random positive and negative species associations could be attributed to environmental filtering and/or dispersal limitation. These patterns were partly detected also with JSDM. Biotic interactions were detected more frequently in the SDM framework, and by JSDM, than in the Classic framework. All approaches detected species aggregation more often than segregation, perhaps reflecting the important role of facilitation in stressful high‐elevation environments.Differences between the frameworks may reflect the explicit incorporation of elevational segregation in the SDM framework and the sensitivity of JSDM to the environmental data. Nevertheless, all methods have the potential to reveal general patterns of species co‐occurrence for different taxa, spatial scales, and environmental conditions.

Highlights

Understanding the causes of species co-occurrence patterns is a major research focus in community ecology. Diamond (1975) proposed that species coexistence is regulated by ‘community assembly rules’ based primarily on species interactions, and inferred these rules from the pattern of species co-occurrence in replicated island assemblages
The search for community assembly rules has dominated community ecology (Gotelli and Graves 1996, Ovaskainen et al 2010, Boulangeat et al 2012, HilleRisLambers et al 2012, Blois et al 2014), and null model analysis has become a standard tool to search for patterns that may reflect processes of community assembly (Gotelli and Ulrich 2012)
Hereafter we refer to these as the ‘species distribution models (SDMs) framework’ and the ‘Classic framework’, respectively. Because another emerging SDM tool – joint species distribution modelling (JSDM; Pollock et al 2014, Warton et al 2015) – is increasingly presented to infer community assembly rules, and could be integrated into our test, we present an analytical alternative in which we use JSDMs in modules 1) and 2) of the SDM framework

Summary

Introduction

Understanding the causes of species co-occurrence patterns is a major research focus in community ecology. Diamond (1975) proposed that species coexistence is regulated by ‘community assembly rules’ based primarily on species interactions, and inferred these rules from the pattern of species co-occurrence in replicated island assemblages. Connor and Simberloff (1979) used null model analysis to argue that co-occurrence structure may be no different than expected by chance. The search for community assembly rules has dominated community ecology (Gotelli and Graves 1996, Ovaskainen et al 2010, Boulangeat et al 2012, HilleRisLambers et al 2012, Blois et al 2014), and null model analysis has become a standard tool to search for patterns that may reflect processes of community assembly (Gotelli and Ulrich 2012). Null model analyses were often based on a single summary metric for an entire assemblage, such as the number of species pairs that form perfect checkerboards (Graves and Gotelli 1993), or the number of missing species combinations in an archipelago (Simberloff and Connor 1981). More recent approaches view individual species pairs as a more informative unit of co-occurrence, allowing for a classification of all of the unique species pairs in an assemblage as random, aggregated, or segregated (Boulangeat et al 2012, Gotelli and Ulrich 2012, Veech 2014)

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