Understanding how community assembly processes vary across spatial scales and environmental gradients is key to predicting species coexistence and informing conservation priorities. Beta-diversity represents the spatial variation in biodiversity and is intimately linked to processes ranging from fully niche-based to neutral assembly processes. Here we investigated the beta-diversity patterns of rocky subtidal macrobenthic assemblages in two environmentally contrasting fjords of Chilean Patagonia and across three spatial scales and dimensions within each fjord: vertical scale (subtidal depth; 0 – 21 m), fine horizontal (within fjord sections; 1 - 10 km) and broad horizontal (between fjord sections; 10 - 100 km). We applied generalized dissimilarity models (GDMs) to analyse three underlying processes that shape ecological communities: density-independent responses to abiotic conditions (salinity and temperature), dispersal (geographical distances and depth), and stochasticity (beta-null deviations) on beta-diversity expressed as Bray-Curtis dissimilarities. Stochastic and dispersal processes dominated beta-diversity along horizontal gradients, particularly at larger spatial scales, while environmental filters consistently contributed to beta-diversity along depth gradients. The southern fjord presented higher community variation in line with higher environmental heterogeneity. Stochastic processes dominate the northern fjord, suggesting a greater influence of habitat homogenization. These results emphasize the scale-dependent nature of assembly processes in fjord ecosystems and underscore the importance of incorporating multiple spatial dimensions into biodiversity assessments.

We present a conceptual framework positioning dispersal, a multivariate behavioral syndrome defined by emigration propensity, displacement capacity, and habitat selection, as a central life-history trait governing and linking the temporal variability and spatial viability of metapopulations. Using mechanistic simulations, we show that under weak habitat selection, greater dispersal reduces local variability, increases synchrony, and expands minimum viable range size (MVRS). Conversely, under strong habitat selection, dispersal increases variability, reduces synchrony, and decreases MVRS. To test predictions in a conservation context, we analyzed spatiotemporal data from 313 moth species exposed to varying levels of light pollution, an anthropogenic stressor known to disrupt habitat selection. Under light-polluted skies, increased dispersal reduced local variability and increased synchrony and MVRS, while in darker skies it produced the opposite pattern. These findings provide both theoretical and empirical evidence that temporal dynamics and spatial requirements of metapopulations are mechanistically coupled through their shared dependence on dispersal.

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.

Understanding how metacommunities respond to natural and anthropogenic disturbances is a key objective in ecology. In this study, we introduce a robust analytical framework to identify communities whose extirpation triggers stronger (hereafter keystone communities) or weaker (hereafter idle communities) cascading effects on extinction and colonization events that ultimately drive temporal changes in compositional patterns of the remaining communities. These cascading dynamics reflect the impact of extirpated communities on connectivity and subsequent dispersal dynamics. Since the framework uses spatial information on compositional similarities to infer changes that would unfold over time due to the extirpation of one or more communities, we describe it as a space-for-time approach. Through mechanistic simulation models that replicate removal experiments, we demonstrate that our framework accurately estimates ”keystoneness”, ranking local communities by their role in maintaining the metacommunity’s compositional patterns. As such, our models demonstrate that the relationship between patch characteristics and our keystoneness metric is closely linked to the structure and dynamics of their metacommunities. A key feature of our framework is its ability to generate community keystoneness estimates that are statistically independent of local diversity, providing a valuable tool for assessing the relevance and conservation value of local communities. This is particularly important in cases where high local diversity reflects an influx of individuals into demographic sinks, a common consequence of human activities near natural areas. To showcase the unique insights of this framework, we examined and contrasted the effects of artificial light at night on the diversity and keystoneness of a moth metacommunity sampled over two decades. We conclude with a discussion of the framework’s potential applications and underlying assumptions, emphasizing its relevance for addressing both conceptual and applied ecological questions, particularly its potential to assess the conservation value of local communities under ecological stress.