Species composition is constrained by two upper‐level processes in ecological contexts where the dispersion of organisms is not severely limited, namely selection and ecological drift. This intuitive framework has motivated a constant flow of empirical models for linking the species matrix to the local environmental descriptors, in which the environment rarely explains more than 30–40% of the variation in species composition. In most cases, researchers only approximate the environmental axes that drive fitness differences between species, as the list of measured descriptors reflect both logistical constraints and hypothesis‐driven questions. Moreover, contextual factors, such as the species pool size (SPS) and the spatial extent of the sampled area, could moderate species–environment associations through sampling effects and dispersal limitations. This study's objective was to quantify the influence of contextual factors (i.e., related to the circumstances in which the study was conducted) on the species–environment association strength on the basis of a synthesis of 156 models of forest bird communities. Our results reveal that factors related to the SPS and the number of independent environmental axes studied affect our capacity to detect selection, whereas spatial factors such as the study's spatial extent and latitude are less important determinants. The study context explains almost a third of the observed variation in the strength of the species–environment association. We conclude that strong species–environment associations can be found for properly designed studies of forest bird communities, which raises the question of whether ecologists have underestimated the importance of selection in community assembly processes.
The alteration of environmental conditions has two major outcomes on the demographics of living organisms: population decline of the common species and extinction of the rarest ones. Halting the decline of abundant species as well as the erosion of biodiversity require solutions that may be mismatched, despite being rooted in similar causes. In this study, we demonstrate how rank abundance distribution (RAD) models are mathematical representations of a dominance-diversity dilemma. Across 4,375 animal communities from a range of taxonomic groups, we found that a reversed RAD model correctly predicts species richness, based solely on the relative dominance of the most abundant species in a community and the total number of individuals. Overall, predictions from this RAD model explained 69% of the variance in species richness, compared to 20% explained by simply regressing species richness on the relative dominance of the most abundant species. Using the reversed RAD model, we illustrate how species richness is co-limited by the total abundance of a community and the relative dominance of the most common species. Our results highlight an intrinsic trade-off between species richness and dominance that is present in the structure of RAD models and real-world animal community data. This dominance-diversity dilemma suggests that withdrawing individuals from abundant populations might contribute to the conservation of species richness. However, we posit that the positive effect of harvesting on biodiversity is often offset by exploitation practices with negative collateral consequences, such as habitat destruction or species bycatches.
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