Conservation priority maps based on combined bird species current and projected abundance and distribution, updated with new model with improved inputs.
Point Blue Conservation Science is currently assessing the effects of sea-level rise (SLR) and salinity changes on San Francisco Bay tidal marsh ecosystems. Tidal marshes are naturally resilient to SLR, in that they can build up elevation through the capture of suspended sediment and deposition of organic material (vegetation). Thus, a “bathtub” model approach is not appropriate for assessing impacts to this dynamic habitat. Rather, dynamic accretion potential can be modeled annually based on tidal inundation, sediment availability, and the rate of organic accumulation (related to salinity).Working with researchers at Philip Williams and Associates (http://www.pwa-ltd.com) University of San Francisco, University of California Berkeley, and San Francisco State University, we have developed a set of geographically based climate change scenarios based on a dynamic marsh accretion model. We have developed preliminary projections for potential changes in tidal marsh elevation and extent over five time frames (20, 40, 60, 80 and 100 years from now) and under eight scenarios representing different assumptions about sea-level rise, salinity, and sediment supply. Our goal is to provide an overview of potential future tidal marsh extent and location in San Francisco Bay, as well as information on priorities for restoration and conservation efforts.Due to the additional complexity of open-bay hydrodynamics, our analysis does not include bay-edge mudflats</purpose>See the following website for additional information http://data.prbo.org/apps/sfbslr/
Current (2010) and future (2030-2110) conservation prioritization of existing and potential tidal marsh habitat in California’s San Francisco Bay estuary. The prioritization is based upon distribution and abundance models for five tidal marsh bird species which utilized avian observation data (2000 - 2009), a marsh accretion model, and physical variables (e.g. salinity, distance to nearest channel, slope, etc). Values represent the rank in which pixels were removed from the landscape using Zonation Conservation Planning software with core area zonation algorithm. Higher values indicate pixels that were removed last and are of the highest conservation value. See http://data.prbo.org/apps/sfbslr/sfbslrapp for interactive maps and additional details.
