AimUnderstanding how ecological processes shape species assemblages in dynamic environments is central to community ecology. Estuarine waterbirds represent a useful system for metacommunity analysis due to their high mobility, functional diversity, and sensitivity to environmental variation. We investigate the internal structure of a waterbird metacommunity and assess the relative roles of environmental filtering, spatial processes, and species co-distribution.Location Paranaguá Estuarine Complex, southern Brazil, as a model system for dynamic subtropical estuarine systems.TaxonEstuarine waterbirdsMethodsUsing joint species distribution models (JSDMs), we partitioned variation in species occurrence among environmental predictors, spatial structure, and species co-distribution. Our analysis included 32 waterbird species grouped into functional foraging guilds. Models included environmental field data, land-cover variables, and Moran’s eigenvector spatial predictors derived from 36 transects surveyed monthly between March 2020 to February 2021.ResultsEnvironmental filtering and species co-distribution explained most of the variation in community composition, whereas spatial effects were comparatively weak. Environmental variables, particularly salinity and air temperature, showed contrasting effects across foraging guilds. Migratory and resident ground predators exhibited different seasonal tendencies, suggesting potential temporal niche partitioning. In contrast to our expectations, aerial predators showed positive intraguild co-distributions, possibly reflecting heterospecific attraction or shared responses to unmeasured factors. Landscape context also influenced community structure as assemblages in bays subject to higher anthropogenic pressure exhibited greater contribution from species co-distribution, potentially indicating weaker species–environment relationships.Main ConclusionsOur results emphasize the importance of environmental gradients and species associations in shaping waterbird metacommunity structure in a dynamic estuarine system. By integrating species-level and guild-level responses across landscape contexts, this study illustrates how the internal structure framework can help disentangle multiple assembly processes in highly mobile taxa.

Ecological communities, and especially metacommunities, are complex and dynamic entities. Resolving the processes and mechanisms that shape these systems remains a central challenge in ecology. This challenge is compounded by the increasing entanglement of mechanisms, processes, and emergent patterns of biodiversity as scales of space, time, and biological organization expand. Here, we define and contextualize key issues, describe recent progress, and identify remaining challenges in interpreting basic metacommunity data and using predictive models to link processes to patterns. We identify two contrasting modeling strategies for complex metacommunities – top-down and bottom-up – and consider how they guide different approaches to pattern-to-process inference. We find substantial progress in connecting pattern and process through improved data repeatability and scaling, enhanced analytical tools to quantify patterns, and increasingly sophisticated theoretical models that address ecological complexity. However, accurately matching observable patterns with process-oriented theory remains a persistent challenge. Finally, we identify potential pipelines connecting process and pattern and highlight areas for future progress.

Ecological communities, and especially metacommunities, are complex and dynamic entities. Resolving the processes and mechanisms that shape these systems remains a central challenge in ecology. This challenge is compounded by the increasing entanglement of mechanisms, processes, and emergent patterns of biodiversity as scales of space, time, and biological organization expand. Here, we define and contextualize key issues, recent progress, and remaining challenges in interpreting basic metacommunity data and using predictive models to link processes to patterns. We find substantial progress in connecting pattern and process through improved data repeatability and scaling, enhanced analytical tools to quantify patterns, and increasingly sophisticated theoretical models that address ecological complexity. However, accurately matching observable patterns with process-oriented theory remains a persistent challenge. We identify potential pipelines connecting process and pattern and highlight areas for future progress.