Coupled beta diversity patterns among coral reef benthic taxa

Cross-taxa assessment of species diversity and phylogenetic structure of benthic communities in a dam-impacted river undergoing habitat restoration

Exploring diversity and community composition patterns across evolutionary and functionally diverse organisms is critical for understanding the general processes that shape biodiversity in response to environmental changes. Knowledge of multi-trophic relationships offers valuable insights to support the effective assessment and management of freshwater ecosystems. In this study, we conducted a cross-taxa assessment of benthic macroinvertebrates and microorganisms using metabarcoding-based surveys to evaluate habitat restoration in a dam-impacted river. We found no correlation between the α-diversity of the benthic macroinvertebrate and microbial communities. This suggests that factors influencing the α-diversity of different trophic groups might operate independently or through different mechanisms, even within the same habitat. In contrast, we observed positively correlated β-diversity patterns between the two benthic communities influenced by dam fragmentation and gravel bar restoration. This suggests that environmental heterogeneity between sites may have a common influence on the patterns of pairwise dissimilarities in the benthic communities, even though they have significant differences in key traits, e.g., species composition, functional roles, or trophic level. Additionally, phylogenetic structure analysis revealed a greater dam impact on benthic macroinvertebrates than microbial communities. Benthic microorganisms consistently formed phylogenetically clustered communities regardless of dam impact, while the macroinvertebrates shifted from competitive exclusion to environmental filtering in response to dam fragmentation. Our cross-taxa assessment further explained the relationships among benthic communities and their associations with environmental factors in a river ecosystem undergoing habitat restoration. Our study highlights the significant implications of evaluating different biological communities across trophic levels for river restoration strategies and ecosystem assessment.

Linking key human-environment theories to inform the sustainability of coral reefs

Effective solutions to the ongoing "coral reef crisis" will remain limited until the underlying drivers of coral reef degradation are better understood. Here, we conduct a global-scale study of how four key metrics of ecosystem states and processes on coral reefs (top predator presence, reef fish biomass, trait diversity, and parrotfish scraping potential) are explained by 11 indicators based on key human-environment theories from the social sciences. Our global analysis of >1,500 reefs reveals three key findings. First, the proximity of the nearest market has the strongest and most consistent relationships with these ecosystem metrics. This finding is in keeping with a body of terrestrial research on how market accessibility shapes agricultural practices, but the integration of these concepts in marine systems is nascent. Second, our global study shows that resource conditions tend to display a n-shaped relationship with socioeconomic development. Specifically, the probabilities of encountering a top predator, fish biomass, and fish trait diversity were highest where human development was moderate but lower where development was either high or low. This finding contrasts with previous regional-scale research demonstrating an environmental Kuznets curve hypothesis (which predicts a U-shaped relationship between socioeconomic development and resource conditions). Third, together, our ecosystem metrics are best explained by the integration of different human-environment theories. Our best model includes the interactions between indicators from different theoretical perspectives, revealing how marine reserves can have different outcomes depending on how far they are from markets and human settlements, as well as the size of the surrounding human population.