Look for complete geospatial metadata in this layer's associated xml document available from the download link * Metric Name: Potential Climate Refugia - Combined Modeled Climate Change (MIROC model - (Hotter and drier) and CNRM-CM5 (wetter and warmer) * Tier: 3 * Data Vintage: 2016 * Unit Of Measure: 0, 1, 2, 3, 8, 9 Low values indicate higher resilience to threats. High values indicate significant exposure to climate change. -1 represents ‘non analog’ areas, i.e. locations that are outside the historic climate envelope of a given vegetation type. * Metric Definition and Relevance: This raster dataset represents habitat types (Macro Veg Type, largely equivalent to CWHR habitat classes) and their predicted exposure to climate stress across the array of predicted climate conditions (separate layers for early (2010 - 2039), mid (2040-2069), and late century (2070-2099)) for all habitat types in comparison to the baseline climate conditions. This serves as the foundation from which habitat types will be exposed to predicted changes in climate. Data are arrayed across 0 to 1 in terms of their exposure to current climate conditions. These three data layers can be used to help land managers allocate limited resources for climate-adaptive field work by providing a view of climate risk that varies across the lands they manage. This analysis uses both the Miroc Earth System Model and the CNRM-CM5. CNRM- CM5 is an Earth system model designed to run climate simulations. It consists of several existing models designed independently and coupled through the [OASIS](http://pantar.cerfacs.fr/3-26568-OASIS.php) software. Both were used under the RCP 8.5 emission scenario given that this is more likely under current emission levels. This data layer is provided as a summary of likely exposure results. Exposure Scores: · 1 = Refugia: CNRM-CM5 only (CNRM exposure values < 80%) · 2 = Refugia: MIROC-ESM only (MIROC exposure values < 80%) · 3 = Refugia Consensus (both models agree exposure values < 80%) · **8 = High Exposure (both models agree exposure values >95%)** · **9 = Very High Exposure (both models agree exposure values >99%)** * Creation Method: Each dominant species is scored for its sensitivity to, and ability to adapt (adaptive capacity) to climate change. Sensitivity refers to the degree to which changes in climate are thought to directly impact different species. Adaptive capacity refers to estimates of the degree to which different species can use their life history characteristics to moderate impacts from changing climate. These two sets of scores represent the biological attributes of the dominant species in each macrogroup. We scored each of the dominant species comprising each macrogroup, according to life history characteristics defined in attribute tables of the California Manual of Vegetation, and supplemented by information found in the USDA plants database and the Jepson Interchange, a web portal for California plant taxonomy. The scores were combined to generate a single sensitivity and adaptive capacity (S&A) score. Climate exposure is the level of climate change expected in the areas where each macrogroup is dominating. This report uses the term “vegetation climate exposure analysis” to describe the following analysis which was conducted on each macrogroup. The vegetation climate exposure analysis is calculated using the mapped extent of each macrogroup. Every grid cell of each macrogroup was ranked as to its level of exposure, relative to the entire area of that macrogroup. This was done for the current time, and used to define the common climate found for each macrogroup. Once each type’s “climate envelope” was defined, we then assessed how much every grid cell changed under various future climate projections. This allowed a measure of the vegetation stress, or climate exposure. The area extent of each macrogroup that will be lost from the most commonly occurring climate conditions (≤80%) and the area that will fall into current marginal, or stressed, climate conditions (>95%) or outside the current climate conditions was calculated. This approach is particularly useful for resource managers, who often are constrained to work in specified areas, and need estimates of what areas within their jurisdiction are likely to be highly stressed, and what areas are likely to be less stressed, in effect climate refuge areas. To consider how refugial conditions from a range of stressors can inform conservation planning and management, the authors integrated metrics of refugial capacity across different domains, which are defined as social, ecological, or physical drivers, processes, or cycles that influence landscape structure, function, or composition. To persist in the California landscape, species and ecosystems may need refugia from shifting climatic conditions, including extremely hot summers and prolonged droughts, but non-climate stressors can also affect conservation outcomes. In this landscape, changes in fire frequency can be a significant stressor affecting plant community structure and persistence. Anthropogenic features that modify hydrologic flows alter the ability of watersheds to sustain functional habitats. And finally, protected areas are often designed to mitigate the impacts of anthropogenic activities; however, recreational activities may alter the refugial capacity of the protected land, affecting the ability of the landscape to sustain species and their habitats. We combined these individual metrics to assess landscape level refugial capacity. Sites with high refugial capacity (super-refugia sites) have, on average, 30% fewer extremely warm summers, 20% fewer fire events, 10% less exposure to altered river channels and riparian areas, and 50% fewer recreational trails than the surrounding landscape. Our results suggest that super-refugia sites (∼8,200 km2) for some natural communities are underrepresented in the existing protected area network, a finding that can inform efforts to expand protected areas. For more information on methods for the development of these climate refugia data see: Thorne et al. 2015 Thorne et al. 2016 Thorne et al. 2017 Thorne et al. 2020 * Credits: Information Center for the Environment, UC Davis \--Jim Thorne