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This data collection is the product of the CA LCC-funded project “Climate Change/Land Use Change Scenarios for Habitat Threat Assessments on California Rangelands”.The project aids conservation of California rangelands by identifying future integrated threats of climate change and land use change, and quantifying two main co-benefits of rangeland conservation – water supply and carbon sequestration. Through a multi-stakeholder partnership, the project proponents developed integrated climate change/land use change scenarios for the Central Valley and Chaparral and Oak Woodland eco-regions, and disseminated information about future potential threats to high priority conservation areas within the California Rangeland...
Water-Wildlife Hotspots: Areas where changes in water availability (recharge plus runoff) and loss of critical habitat coincide. These maps display percent change in water availability relative to the 1981-2010 climate period where 5% or more of watershed area has lost critical habitat. Water availability is defined as recharge plus runoff. Critical habitat is defined as critical priority conservation areas mapped in the California Rangeland Conservation Coalition’s focus area map (http://www.carangeland.org/focusarea.html) (TNC, 2007). Percent change in water availability is provided for two climate projections for each of the three IPCC-SRES scenarios – A1B, A2 and B1. Scenarios of critical habitat loss for years...
Marsh accretion was modeled by ESA PWA using the Marsh-98 model, described here. The model assumes that rates of marsh plain elevation change depend on the availability of suspended sediment and organic material, water depth, and duration of inundation periods. If enough suspended sediment is available, then tidal marsh elevations can keep pace with increased inundation. Model outputs were linearly interpolated in 10-cm increments for starting elevations ranging from -3.7 to 1.7 m (relative to mean higher high water, or MHHW), and applied to a composite 5-m elevation grid (see below) for SF Bay. Results for each possible combination of projected sea level rise, sediment and organic material availability, and target...
In California, the near-shore area where the ocean meets the land is a highly productive yet sensitive region that supports a wealth of wildlife, including several native bird species. These saltmarshes, mudflats, and shallow bays are not only critical for wildlife, but they also provide economic and recreational benefits to local communities. Today, sea-level rise, more frequent and stronger storms, saltwater intrusion, and warming water temperatures are among the threats that are altering these important habitats.To support future planning and conservation of California’s near-shore habitats, researchers examined current weather patterns, elevations, tides, and sediments at these sites to see how they affect plants...
Full Title: Environmental Change Network: Current and Projected VegetationThe current vegetation layer is derived from the vegetation map developed as part of the California Gap Analysis project. The derivation takes the California Wildlife Habitat Relationships (CWHR) habitat classification provided in the California Gap Analysis layer, generalizes the classes to a set of broader habitat types, and rasterizes it at 800 meter resolution.The future vegetation layers for both the GFDL and CCSM GCM models are derived using a random forest model of the vegetation classification. The original CWHR classification has been generalized to 12 classes for ease in modeling. Inputs to the model include eight bioclimatic variables...
A collection of spatial data resources assembled to support the CVLCP partnership efforts, including scenario planning, assessing vulnerabilities, and developing adaptation strategies.
The U.S. Geological Survey (USGS) developed future scenarios of land use-land cover (LULC) change in the United States as part of a national carbon sequestration assessment required by the U.S. Congress (Energy Independence and Security Act of 2007). Future potential demand, or the area of land required for each LULC class, was based on a set of scenarios from three Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES) (Nakicenovic et al. 2000): A2 (emphasizes economic development with a regional focus), A1B (emphasizes economic development with a global orientation), and B1 (emphasizes environmental sustainability with a global orientation). To develop LULC change scenarios...
The development of sophisticated species distribution modeling techniques provides an opportunity to examine the potential effects of climate change on bird communities. Using these modeling approaches, we are relating bird data to environmental layers to generate robust predictions of current (1971–2000) and projected future species occurrence. Future bird distributions are based on regional climate model projections for the periods 2038–2070 (IPCC Scenario A2). Bird species distributions were created using the Maxent modeling technique: Maxent (Phillips et al. 2006), which is able to model non-linear responses to environmental variables. Map values represent the predicted habitat suitability; the higher the values,...
Densities for five key tidal marsh-dependent bird species were modeled using boosted regression trees (Elith et al. 2008). The models are able to fit non-linear functions between environmental variables and the presence/absence or density of a species. Map values represent the probability of occurrence of a species or the density (birds/ha). Higher values in a map indicate a higher likelihood that a species will be present at a site. Bird species modeled: Common yellowthroat, black rail, clapper rail, marsh wren, song sparrow. Model outputs: Probably of occurrence, density (birds per hectare)
Current and projected bird distribution and abundance layers, updated with new model that has better inputs. Point Blue Conservation Science assessed 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...
Maps of the probability of occurrence of tidal marsh plant species were created using generalized additive models (Hastie and Tibshirani 1990). Species modeled: Saltgrass, alkali-heath, SF Bay gumplant, jaumea, wirerush, pepperweed, giant reed, pickleweed, hard-stem tule, three-square bullrush, smooth cordgrass, California cordgrass, cattail.
Understanding San Francisco Bay’s vulnerabilities to sea level rise is important for both biodiversity conservation and for management of public infrastructure. Coastal marshes provide essential ecosystem services such as water filtration and flood abatement while also providing important habitat for species of conservation concern. Improving our understanding of how tidal marsh habitats will be affected by sea level rise is important so that we maximize ecosystem services that coastal marshes provide and ensure that endemic populations of plants and animals persist into the future. For this project, marsh accretion was modeled by ESA PWA (http://www.pwa-ltd.com/index.html) using the Marsh-98 model, described here:...
Data layers of current and projected suitable habitat for five species: big-eared woodrat (Neotoma macrotis), California gnatcatcher, Ceanothus greggii, Ceanothus verrucosus, and Tecate cypress in the South Coast Ecoregion in California, USA. Data set includes scenarios with and without projected urban growth over a 50 year period, and with and without projected climate change over a 50 year period. The potential distribution of California gnatcatcher was modeled using a MaxEnt species distribution model using recent and future climate data with presence records from the San Diego Natural History Museum. Species distributions were modeled only for the South Coast Ecoregion in California, USA as this is where management...
Species richness indicates the number of different species predicted to be able to occur at a location. Maps show the projected species richness under current climate and two models of future climate conditions. Species richness is calculated by converting the predictions from maxent models into binary maps of presence and absence and summing the maps across all species. Higher values in the maps indicate where more bird species are projected to be able to occur.