Clayton G. Pollock | USGS Science

USGS2023

The potential for coral reef restoration to mitigate coastal flooding as sea levels rise

The ability of reefs to protect coastlines from storm-driven flooding hinges on their capacity to keep pace with sea-level rise. Here, we show how and whether coral restoration could achieve the often-cited goal of reversing the impacts of coral-reef degradation to preserve this essential function. We combined coral-growth measurements and carbonate-budget assessments of reef-accretion potential at Buck Island Reef, U.S. Virgin Islands, with hydrodynamic modeling to quantify future coastal flooding under various coral-restoration, sea-level rise, and storm scenarios. Our results provide guidance on how restoration of Acropora palmata , if successful, could mitigate the most extreme impacts of coastal flooding by reversing projected trajectories of reef erosion and allowing reefs to keep pace with the ~0.5 m of sea-level rise expected by 2100 with moderate carbon-emissions reductions. This highlights the potential long-term benefits of pursuing coral-reef restoration alongside climate-change mitigation to support the persistence of essential coral-reef ecosystem services.

U.S. Virgin Islands, Buck Island Reef National Mon↗

USGS2021

A novel framework to predict relative habitat selection in aquatic systems: Applying machine learning and resource selection functions to acoustic telemetry data from multiple shark species

Resource selection functions (RSFs) have been widely applied to animal tracking data to examine relative habitat selection and to help guide management and conservation strategies. While readily used in terrestrial ecology, RSFs have yet to be extensively used within marine systems. As acoustic telemetry continues to be a pervasive approach within marine environments, incorporation of RSFs can provide new insights to help prioritize habitat protection and restoration to meet conservation goals. To overcome statistical hurdles and achieve high prediction accuracy, machine learning algorithms could be paired with RSFs to predict relative habitat selection for a species within and even outside the monitoring range of acoustic receiver arrays, making this a valuable tool for marine ecologists and resource managers. Here, we apply RSFs using machine learning to an acoustic telemetry dataset of four shark species to explore and predict species-specific habitat selection within a marine protected area. In addition, we also apply this RSF-machine learning approach to investigate predator-prey relationships by comparing and averaging tiger shark relative selection values with the relative selection values derived for eight potential prey-species. We provide methodological considerations along with a framework and flexible approach to apply RSFs with machine learning algorithms to acoustic telemetry data and suggest marine ecologists and resource managers consider adopting such tools to help guide both conservation and management strategies.

Buck Island Reef National Monument, St Croix↗