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

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 presenceabsence 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.

Examining the link between competition and negative co‐occurrence patterns

“…2.11.12). These two pairwise co‐occurrence tests performed better than other available pairwise co‐occurrence tests in a recent analysis (Lavender et al ). For the first two years of community data (third year co‐occurrence would be linked to subsequent turnover), we independently subjected control and herbivore exclusion plots to these pairwise co‐occurrence tests and collected the identities of pairs identified as significantly negatively co‐occurring (alpha = 0.1) as well as those identified as positively co‐occurring (alpha = 0.1).…”

Measuring continuous compositional change using decline and decay in zeta diversity

“…, D'Amen et al. , Hui and Richardson ). The drivers of differences in bird community turnover shown between catchments, for example, clearly warrants further investigation.…”

Geospatial variation in co‐occurrence networks of nitrifying microbial guilds

“…We observed that nitrifier communities were also more segregated than expected by chance and that AOA and Nitrospira were even more segregated across the farm than AOB and Nitrobacter . Significant patterns of segregation are commonly attributed to deterministic forces, such as competition, nonoverlapping niches or historical effects such dispersal limitation or evolutionary processes (D'Amen, Mod, Gotelli, & Guisan, ; Horner‐Devine et al., ). Both Nitrospira and AOA are known to be highly diverse functional groups that occur in a wide range of environments (Daims et al., ; Hatzenpichler, ; Pester et al., , ), and niche partitioning across or even within lineages has been demonstrated for both groups (Gruber‐Dorninger et al., ; Gubry‐Rangin et al., ).…”