Despite the importance of active management and strong protection in driving marine protected areas (MPA) performance, coverage area remains the sole indicator for global targets. To assess whether conservation quality lags behind quantity, we conducted a global meta-analysis of 123 MPAs. We show that MPAs’ Levels of Protection and Stages of Establishment are reliable proxies for MPAs’ ecological outcomes; henc...
Biogeographic regions arise due to constraints on species ranges, fostering lineage divergence as a result. Yet, convergent evolution means that evolutionary distinct lineages can share similar characteristics when subjected to similar environmental conditions. The ecological convergence of distinct regions has been demonstrated in terrestrial communities, but it remains uncertain if marine systems exhibit simi...
Ocean currents are fundamental drivers of marine biodiversity distribution, mediating the exchange of genetic material and individuals between populations. Their effect ranges from creating barriers that foster isolation to facilitating long-distance dispersal, which is crucial for species expansion and resilience in the face of climate change. Despite the significance of oceanographic connectivity, comprehensi...
Despite the importance of active management and strong protection in driving marine protected areas (MPA) performance, coverage area remains the sole indicator for global targets. To assess whether conservation quality lags behind quantity, we conducted a global meta-analysis of 123 MPAs. We show that MPAs’ Levels of Protection and Stages of Establishment are reliable proxies for MPAs’ ecological outcomes; henc...
Global patterns of intraspecific genetic diversity are key to understanding evolutionary and ecological processes. However, insights into the distribution and drivers of genetic diversity remain limited, particularly for marine species. Here, we explain and predict the genetic diversity of cold and temperate brown macroalgae using genetic data from 29 species and a machine-learning algorithm that incorporates c...
Data on contemporary and future geographical distributions of marine species are crucial for guiding conservation and management policies in face of climate change. However, available distributional patterns have overlooked key ecosystem structuring species, despite their numerous ecological and socioeconomic services. Future range estimates are mostly available for few species at regional scales, and often rel...
Climate change is rapidly shifting marine species distributions, with more changes anticipated for the future. For species that structure essential habitats, climate change effects can be magnified into losses of ecosystem services and functioning. Despite the key role of marine ecosystem structuring species, baseline information on their global status and projections of their biodiversity patterns are incomple...
Motivation: Impacts of climate change on marine biodiversity are often projected with species distribution modelling using standardized data layers representing physical, chemical and biological conditions of the global ocean. Yet, the available data layers (1) have not been updated to incorporate data of the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), which comprise the Shared Socioeconom...
Biodiversity information in the form of species occurrence records is key for monitoring and predicting current and fu- ture biodiversity patterns, as well as for guiding conserva- tion and management strategies. However, the reliability and accuracy of this information are frequently undermined by taxonomic and spatial errors. Additionally, biodiversity in- formation facilities often share data in diverse inco...
Aim: Future climate change threatens marine forests across the world, potentially disrupting ecosystem function and services. Nonetheless, the direction and intensity of climate-induced changes in kelp forest biodiversity remain unknown, precluding well-informed conservation and management practices. Location: Global. Methods: We use machine-learning models to forecast global changes in species richness and com...