Integrating Genomics, Physiology, and Ecology to Predict Intertidal Ecosystem Resilience

Rocky intertidal ecosystems along the Pacific Coast support fisheries, tourism, and coastal economies that are important to communities across the United States. As changing environmental conditions increasingly threaten these ecosystems, there is an urgent need to better predict which species and ecological communities are most vulnerable and which may be resilient. This project combines field studies, genomics, ecological modeling, and physiological measurements to develop new tools for forecasting how coastal ecosystems respond to environmental stress. This project uses purple sea urchins and other intertidal species to study how well they can adapt to environmental change and how those changes may affect the health and resilience of coastal ecosystems that support fisheries, recreation, and biodiversity. Broader impacts include hands-on training and workforce development for undergraduate, graduate, and postdoctoral researchers, K-12 outreach activities, and public dissemination through conferences and applications, helping inform conservation and management of coastal ecosystems. Rocky intertidal ecosystems are increasingly exposed to ocean warming, altered upwelling dynamics, and shifting species distributions, yet predicting ecosystem responses remains challenging because adaptive processes operate across multiple scales. This project develops a cross-scale predictive framework integrating genomics, physiology, ecological network modeling, and simulations to evaluate ecosystem resilience under environmental change using the genomic offset (GO) framework. The project addresses three primary objectives. First, it empirically validates relationships between GO predictions and realized fitness outcomes using the purple sea urchin as a model species by integrating genomic data with physiological performance assays across environmental gradients. Second, it integrates multi-species (GO) estimates with ecological interaction networks and long-term field observations to assess adaptive capacity and ecosystem resilience across latitudinally structured rocky intertidal communities. Third, simulation approaches evaluate how species-specific traits, including dispersal and standing genetic diversity, influence single-species and community level GO predictions of maladaptation and resilience. The work combines newly generated genomic and physiological datasets with decades of ecological observation, shared biodiversity resources, and machine learning assisted simulation approaches. By explicitly linking genomic predictions with physiological performance and species interaction networks, the project tests whether GO provides robust predictions of maladaptation, realized evolutionary change, and ecosystem function under future environmental conditions. Results advance theory about adaptation, community ecology, and resilience while generating transferable predictive tools applicable to conservation and management of marine ecosystems facing rapid environmental change. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.