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Climate Adaptation Science Centers

From the wildfires to sea-level rise, climate change creates evolving challenges for ecosystems across the Nation. The USGS National and Regional Climate Adaptation Science Centers (CASCs) is a partnership-driven program that teams scientists with natural and cultural resource managers and local communities to help fish, wildlife, waters, and lands across the country adapt to changing conditions.

News

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CreelCat Database: Development, Applications, and Opportunities

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Winter Ticks, Moose, and Climate Change

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National CASC Chief Presents at the COP26 Conference

Publications

A new approach for representing agent-environment feedbacks: Coupled agent-based and state-and-transition simulation models

ContextAgent-based models (ABMs) and state-and-transition simulation models (STSMs) have proven useful for understanding processes underlying social-ecological systems and evaluating practical questions about how systems might respond to different scenarios. ABMs can simulate a variety of agents (autonomous units, such as wildlife or people); agent characteristics, decision-making, adaptive behavi

Aquatic foods to nourish nations

Despite contributing to healthy diets for billions of people, aquatic foods are often undervalued as a nutritional solution because their diversity is often reduced to the protein and energy value of a single food type (‘seafood’ or ‘fish’)1,2,3,4. Here we create a cohesive model that unites terrestrial foods with nearly 3,000 taxa of aquatic foods to understand the future impact of aquatic foods

A new approach to evaluate and reduce uncertainty of model-based biodiversity projections for conservation policy formulation

Biodiversity projections with uncertainty estimates under different climate, land-use, and policy scenarios are essential to setting and achieving international targets to mitigate biodiversity loss. Evaluating and improving biodiversity predictions to better inform policy decisions remains a central conservation goal and challenge. A comprehensive strategy to evaluate and reduce uncertainty of mo