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Decision support for climate change adaptation and fire management strategies for at risk species in southern California
This project integrates fire risk models, species distribution models (SDMs) and population models with scenarios of future climate and land cover to project how the effects of climate-induced changes to species distributions and land use change will impact threatened species in fire-prone ecosystems. This project also identifies and prioritizes potential management responses to climate change (e.g. assisted colonization, fire management, land protection, dispersal corridors). Anticipated products include: 1) maps (digital and hard copy) of habitat suitability under current and future climate change, current and future projected urban growth and combinations of climate change and future projected urban growth, under...
This project uses bottom-up modeling at a parcel scale to measure the effects of sea-level rise (SLR) on coastal ecosystems and tidal salt marshes. At selected tidal marshes, the project team will measure several parameters that will be incorporated into ArcGIS models creating comparable datasets across the Pacific coast tidal gradient with a focus on 2-4 sites in the California LCC (e.g. San Diego, San Francisco Bay Refuges). The ultimate goal is to provide science support tools for local adaptation planning from the bottom-up that may be implemented under a structured decision-making framework.Science Delivery Phase (2013): The objectives are to: (1) Disseminate site-specific baseline data and modeling results,...
The project objective is to transfer to California a previously developed prioritization framework that combines intraspecific genetic and morphological variation with traditionally used indices of biodiversity, and test its general utility for conservation prioritization. This project will integrate existing data on intraspecific variation of multiple species in the Santa Monica Mountains National Recreational Area with climate data and space-borne measurements of the environment to identify areas with high intraspecific variation.