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,California supports one of the greatest displays of biodiversity in the nation and the world. The challenge posed by the 30x30 initiative, is to plan and implement conservation strategies which allow all Californians to continue to flourish and succeed, while also ensuring that we safeguard the great abundance of species which reside in this state, and in many cases, exist nowhere else on Earth.,Maximizing the benefits of 30x30 for everyone requires, among many other factors, deliberate consideration of the landscape and the ways in which biodiversity is distributed within it. This Explorer introduces several types of biodiversity data for stakeholders to consider when engaged in conservation planning.,The Biodiversity Explorer includes dashboards for the Areas of Conservation Emphasis (ACE) and Habitat and Land Cover datasets. These allow deeper explorations of the state’s exceptional biodiversity and the current state of conservation by land cover.,The Areas of Conservation Emphasis (ACE) dashboard presents summaries of species data collected and analyzed by the California Department of Fish and Wildlife (CDFW) as part of its ongoing ACE project. ACE rolls multiple types of Species Richness into a Biodiversity Index, and also considers Connectivity, Climate Resilience, and Significant Habitats, all important factors to species and ecological health.,The Habitat and Land Cover dashboard presents maps and summaries of land cover according to categories defined by the California Wildlife Habitat Relationship System (CWHR) maintained by CDFW. Conserving connected networks of all land cover types is key to conserving the species which depend upon them. The Habitat and Land Cover dashboard shows the percentage that each land cover type comprises within a county or ecoregion, and the degree to which it falls within already conserved areas.,

This data release provides 270-m resolution maps of hotspots of vulnerability to projected changes in land-use, water shortages, and climate from 2001-2061 for agricultural, domestic, and ecological communities in the Central Coast of California, USA, under five management scenarios. This data covers the counties of Santa Cruz, San Benito, Monterey, San Luis Obispo, and Santa Barbara counties, but only cover those areas overlying a groundwater basin (because these contain the overwhelming majority of regional anthropogenic land-uses). Data are provided as .zip compressed file packages containing geospatial raster surfaces (.tif format). Each map is the product of one of three types of exposure to change (land, water, or climate) and one of three types of sensitivity to that change (agricultural, domestic, ecological). The resulting vulnerability measures map hotspots of nine vulnerabilities, plus a tenth map that is the sum of all nine measures to identify hotspots of overall vulnerability. See Van Schmidt et al. (2023) in Ecology & Society (doi: TBD) for full methodological details. Briefly, exposure to future land-use change and water shortages were jointly forecast from 2001 to 2061 with the Land Use and Carbon + Water Simulator (LUCAS-W) based on historical empirical rates. Exposure to climate change was calculated from five model-averaged RCP 8.5 forecasts of the Basin Characterization Model (BCM), which estimated change in runoff as surface water, potential recharge to groundwater aquifers, and climatic water deficit (CWD), among other variables. Lastly, sensitivity for communities was obtained from diverse datasets including LUCAS-W cropland projections, crop water demand data, farmland importance rankings, 2017 census data, range maps for imperiled species and subspecies, and wildlife agency reports. Sensitivity and exposure layers were rescaled 0-1 to allow for comparison, and the final vulnerability measures therefore have a possible range from 0 (no vulnerability) up to a maximum of 1 (maximum exposure and maximum sensitivity). The nine measures are as follows: (1) Land-Agricultural: Loss of important farmland; (2) Land-Domestic: Lack of new development in areas with housing needs; (3) Land-Ecological: Loss of critical habitats for endangered species; (4) Water-Agricultural: Increased water demand that cannot be fallowed (orchards/vineyards); (5) Water-Domestic: Household vulnerability to increased water inaffordability; (6) Water-Ecological: Drying of groundwater-dependent habitats for endangered species; (7) Climate-Agricultural: Increased irrigation water needs of crops; (8) Climate-Domestic: Household vulnerability to heat-related health impacts; (9) Climate-Ecological: Loss of runoff & recharge that keeps streams, ponds, and vernal pools wet. Each .zip file is a compressed file package containing maps of each measure under five scenarios, which have different sets of management assumptions along two axes, Water management Low/Moderate/High intensity and Land use management Low/Moderate/High intensity: - MM (Moderate / Moderate management intensity): a scenario where water demand caps under the Sustainable Groundwater Management Act (SGMA) reduce development in overdrafted groundwater basins based on current total water supplies, and where prime farmland and groundwater recharge areas will be protected from urban sprawl (i.e., land use projections assuming development stabilizes at a level sustainable with current water supplies, and urban sprawl limits). The other four scenarios differ from the MM scenario by altering one of these management strategies, while keeping the second strategy at the "Moderate" level. -- WL (Water management Low intensity): a pre-SGMA "business-as-usual" scenario where water demand is uncoupled from land-use change and does not need to stabilize at sustainable levels. -- WH (Water management High intensity): a scenario that assumes that water demand caps, but with increased caps due

This data release provides 270-m resolution maps of hotspots of vulnerability to projected changes in land-use, water shortages, and climate from 2001-2061 for agricultural, domestic, and ecological communities in the Central Coast of California, USA, under five management scenarios. This data covers the counties of Santa Cruz, San Benito, Monterey, San Luis Obispo, and Santa Barbara counties, but only cover those areas overlying a groundwater basin (because these contain the overwhelming majority of regional anthropogenic land-uses). Data are provided as .zip compressed file packages containing geospatial raster surfaces (.tif format). Each map is the product of one of three types of exposure to change (land, water, or climate) and one of three types of sensitivity to that change (agricultural, domestic, ecological). The resulting vulnerability measures map hotspots of nine vulnerabilities, plus a tenth map that is the sum of all nine measures to identify hotspots of overall vulnerability. See Van Schmidt et al. (2023) in Ecology & Society (doi: TBD) for full methodological details. Briefly, exposure to future land-use change and water shortages were jointly forecast from 2001 to 2061 with the Land Use and Carbon + Water Simulator (LUCAS-W) based on historical empirical rates. Exposure to climate change was calculated from five model-averaged RCP 8.5 forecasts of the Basin Characterization Model (BCM), which estimated change in runoff as surface water, potential recharge to groundwater aquifers, and climatic water deficit (CWD), among other variables. Lastly, sensitivity for communities was obtained from diverse datasets including LUCAS-W cropland projections, crop water demand data, farmland importance rankings, 2017 census data, range maps for imperiled species and subspecies, and wildlife agency reports. Sensitivity and exposure layers were rescaled 0-1 to allow for comparison, and the final vulnerability measures therefore have a possible range from 0 (no vulnerability) up to a maximum of 1 (maximum exposure and maximum sensitivity). The nine measures are as follows: (1) Land-Agricultural: Loss of important farmland; (2) Land-Domestic: Lack of new development in areas with housing needs; (3) Land-Ecological: Loss of critical habitats for endangered species; (4) Water-Agricultural: Increased water demand that cannot be fallowed (orchards/vineyards); (5) Water-Domestic: Household vulnerability to increased water inaffordability; (6) Water-Ecological: Drying of groundwater-dependent habitats for endangered species; (7) Climate-Agricultural: Increased irrigation water needs of crops; (8) Climate-Domestic: Household vulnerability to heat-related health impacts; (9) Climate-Ecological: Loss of runoff & recharge that keeps streams, ponds, and vernal pools wet. Each .zip file is a compressed file package containing maps of each measure under five scenarios, which have different sets of management assumptions along two axes, Water management Low/Moderate/High intensity and Land use management Low/Moderate/High intensity: - MM (Moderate / Moderate management intensity): a scenario where water demand caps under the Sustainable Groundwater Management Act (SGMA) reduce development in overdrafted groundwater basins based on current total water supplies, and where prime farmland and groundwater recharge areas will be protected from urban sprawl (i.e., land use projections assuming development stabilizes at a level sustainable with current water supplies, and urban sprawl limits). The other four scenarios differ from the MM scenario by altering one of these management strategies, while keeping the second strategy at the "Moderate" level. -- WL (Water management Low intensity): a pre-SGMA "business-as-usual" scenario where water demand is uncoupled from land-use change and does not need to stabilize at sustainable levels. -- WH (Water management High intensity): a scenario that assumes that water demand caps, but with increased caps due

Regional boundaries for use by CA Nature to support activities related to Executive Order N-82-20. These include California's 30x30 effort, Climate Smart Land Strategies, and equitable access to open space. This layer is derived from the 4th California Climate Assessment regions, and enhanced using the California County Boundaries dataset (version 19.1) maintained by the California Department of Forestry and Fire Protection's Fire Resource Assessment Program, and the 3 Nautical Mile marine boundary for California sourced from the California Department of Fish and Wildlife.

Regional boundaries for use by CA Nature to support activities related to Executive Order N-82-20. These include California's 30x30 effort, Climate Smart Land Strategies, and equitable access to open space. This layer is derived from the 4th California Climate Assessment regions, and enhanced using the California County Boundaries dataset (version 19.1) maintained by the California Department of Forestry and Fire Protection's Fire Resource Assessment Program, and the 3 Nautical Mile marine boundary for California sourced from the California Department of Fish and Wildlife.

Regional boundaries for use by CA Nature to support activities related to Executive Order N-82-20. These include California's 30x30 effort, Climate Smart Land Strategies, and equitable access to open space. This layer is derived from the 4th California Climate Assessment regions, and enhanced using the California County Boundaries dataset (version 19.1) maintained by the California Department of Forestry and Fire Protection's Fire Resource Assessment Program, and the 3 Nautical Mile marine boundary for California sourced from the California Department of Fish and Wildlife.

This dataset contains projections of shoreline change and uncertainty bands across California for future scenarios of sea-level rise (SLR). Projections were made using the Coastal Storm Modeling System - Coastal One-line Assimilated Simulation Tool (CoSMoS-COAST), a numerical model run in an ensemble forced with global-to-local nested wave models and assimilated with satellite-derived shoreline (SDS) observations across the state. Scenarios include 25, 50, 75, 100, 125, 150, 175, 200, 250, 300 and 500 centimeters (cm) of SLR by the year 2100. Output for SLR of 0 cm is also included, reflective of conditions in 2000. This model shows change in shoreline positions along pre-determined cross-shore transects, considering sea level, wave conditions, along-shore/cross-shore sediment transport, long-term trends due to sediment supply, and estimated variability due to unresolved processes (as described in Vitousek and others, 2021). Variability associated with complex coastal processes (for example, beach cusps/undulations and shore-attached sandbars) are included via a noise parameter in a model, which is tuned using observations of shoreline change at each transect and run in an ensemble of 200 simulations; this approach allows for a representation of statistical variability in a model that is assimilated with sequences of noisy observations. The model synthesizes and improves upon numerous, well-established shoreline models in the scientific literature; processes and methods are described in this metadata (see lineage and process steps), but also described in more detail in Vitousek and others 2017, 2021, and 2023. Output includes different cases covering important model behaviors (cases are described in process steps of this metadata). KMZ data are readily viewable in Google Earth. For best display of results, it is recommended to turn off any 3D features or terrain. For technical users and researchers, shapefile and KMZ data can be ingested into geographic information system (GIS) software such as Global Mapper or QGIS.

This dataset contains projections of shoreline change and uncertainty bands across California for future scenarios of sea-level rise (SLR). Projections were made using the Coastal Storm Modeling System - Coastal One-line Assimilated Simulation Tool (CoSMoS-COAST), a numerical model run in an ensemble forced with global-to-local nested wave models and assimilated with satellite-derived shoreline (SDS) observations across the state. Scenarios include 25, 50, 75, 100, 125, 150, 175, 200, 250, 300 and 500 centimeters (cm) of SLR by the year 2100. Output for SLR of 0 cm is also included, reflective of conditions in 2000. This model shows change in shoreline positions along pre-determined cross-shore transects, considering sea level, wave conditions, along-shore/cross-shore sediment transport, long-term trends due to sediment supply, and estimated variability due to unresolved processes (as described in Vitousek and others, 2021). Variability associated with complex coastal processes (for example, beach cusps/undulations and shore-attached sandbars) are included via a noise parameter in a model, which is tuned using observations of shoreline change at each transect and run in an ensemble of 200 simulations; this approach allows for a representation of statistical variability in a model that is assimilated with sequences of noisy observations. The model synthesizes and improves upon numerous, well-established shoreline models in the scientific literature; processes and methods are described in this metadata (see lineage and process steps), but also described in more detail in Vitousek and others 2017, 2021, and 2023. Output includes different cases covering important model behaviors (cases are described in process steps of this metadata). KMZ data are readily viewable in Google Earth. For best display of results, it is recommended to turn off any 3D features or terrain. For technical users and researchers, shapefile and KMZ data can be ingested into geographic information system (GIS) software such as Global Mapper or QGIS.

This dataset contains California shoreline change rates derived from mean high water (MHW) shorelines from 1998 (in Central and Southern California) and 2002 (in Northern California) to 2016. The MHW elevation in each analysis region (Northern, Central, and Southern California) maintained consistency with that of the National Assessment of Shoreline Change. The operational MHW line was extracted from Light Detection and Ranging (LiDAR) digital elevation models (DEMs) using the ArcGIS smoothed contour method. Within the Digital Shoreline Analysis System (DSAS), end-point rates (EPR) of shoreline change were calculated between the 1998/2002 and the 2016 shorelines at a transect spacing of 50 meters to provide a long-term perspective of sandy shoreline behavior along the coast of California.