It is often suggested that habitat attributes and interspecific interactions can cause non-random species co-occurrence patterns, but quantifying their contributions can be difficult. Null models that systematically exclude and include habitat effects can give information on the contribution of these factors to community assembly. In the boreal forest, saproxylic beetles are known to be attracted to recently burned forests where they breed in dead and dying trees. We examined whether species co-occurrences of saproxylic beetles that develop in, and emerge from, boles of recently burned trees show non-random patterns. We also estimated the extent to which both the post-fire habitat attributes and interspecific interactions among beetles contribute to such patterns. We sampled tree boles encompassing key attributes (tree species, tree size/dbh and burn severity) that are thought to characterize species-habitat associations of saproxylic beetles, a proposition that we tested using indicator species analysis. Two null models with no habitat constraints ("unconstrained") indicated that a total of 29.4% of the species pairs tested had significant co-occurrence patterns. Habitat-constrained null models indicated that most of the detected species aggregations (72%) and segregations (59%) can be explained by shared and distinct species-habitat relationships, respectively. The assembly pattern was also driven by interspecific interactions, of which some were modulated by habitat; for example, predator and prey species tended to co-occur in large-sized trees (a proxy of available bark/wood food resource primarily for the prey). In addition, some species segregation suggesting antagonistic, competitive, or prey-predator interactions were evident after accounting for the species' affinities for the same tree species. Overall, our results suggest that an intimate link between habitat and interspecific interactions can have important roles for community assembly of saproxylic assemblages even following disturbance by fire. We also show that a systematic application of null models can offer insight into the mechanisms behind the assembly of ecological communities.
Aim Using total species richness to characterize biodiversity may mask multiple response patterns of species. We propose a null model analysis of species cooccurrence-based classification to identify sets of species that may have similar (within-groups) and distinct (between groups) response patterns to their environment. The classification should also provide an explicit framework for selecting indicator species with characteristic co-occurrence patterns to predict overall species richness.Location Cô te-Nord, Québec, Canada. MethodsWe combined null-model of species co-occurrence and cluster analysis to identify species groups within diverse assemblages of ground-dwelling and flying beetles of stands in a boreal forest mosaic; we then examined their cooccurrence and response patterns to habitat characteristics. Best subset regressions were used to select indicator species of richness within each group, from which indicators of total species richness were selected. ResultsThe identified species groups appeared to display contrasting cooccurrence and response patterns to at least one of the stand-level habitat characteristics. Among flying beetles, for example, richness increased with standlevel heterogeneity for two groups and decreased for two other groups, but the relationship was non-significant for the total richness. We identified 28 indicator species that explained > 80% (validated by bootstrap analysis) of the variation in total species richness. Predictive performance of indicators was higher than when their co-occurrence were reshuffled, even under a highly constrained null model, indicating that co-occurrence patterns contributed to their predictive performance.Main conclusions Co-occurrence-based classification appears as a promising and effective tool for deconstructing biodiversity into species groups which reflect their ecological commonalities and differences, thus reducing the risk of making faulty inferences about the causes underlying overall diversity patterns. The method provides an explicit framework for selecting indicator species representing different species groups that may reflect the multiple responses of species co-occurring with them. Indicator species can be effective for predicting overall species richness.
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