Look for complete geospatial metadata in this layer's associated xml document available from the download link * Metric Name: Potential Climate Refugia - under Modeled Climate Change (MIROC model - hotter and drier) * Tier: 3 * Data Vintage: 2016 * Unit Of Measure: 0- 1. 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 (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. The Climate Change Model used in this analysis is the Miroc Earth System Model. This ESM, named “MIROC-ESM”, is based on a global climate model MIROC (Model for Interdisciplinary Research on Climate) which has been cooperatively developed by researchers in Japan and others. This model suggests a hotter and drier future. The emission scenario used is the RCP 8.5, which represents a range of warming statewide from 1.99 to 4.56°C and between a 24.8% decrease in precipitation and a 22.9% increase, respectively. * Creation Method: The vegetation climate exposure analysis takes advantage of the 2015 vegetation map compiled for California, which is described above. The vegetation climate exposure model is implemented in the R programming language, and takes the vegetation and climate raster files as the primary input data. The values of the climate raster files were randomly sampled at 100,000 points on the landscape, which were used to fit a statistical model characterizing the relationship between the variables both in the current time and for the modeled future data. At each of these 100,000 points, 9 hydro-climatic variables were sampled to characterize the range and variation of conditions in the study region. These variables were: annual mean minimum temperature (Tmin), annual mean maximum temperature (Tmax), annual precipitation (PPT), actual evapotranspiration (AET), potential evapotranspiration (PET), climatic water deficit (CWD), snowpack depth on April 1st, runoff, and recharge. The variation between these variables was modeled using a principal component analysis21 (PCA) to identify the dominant components of variation. The top-two principal components axes, representing about 79% of the variability across the four climate projections, were extracted as a two-dimensional space, and are portrayed as the axes for the PCA plots shown in each macrogroup chapter below. This was done to simplify the representation of the climate space, while maintaining the most important information on the variables to be associated with the observed vegetation distributions. The climate space occupied by each distinct macrogroup from the current time period was identified. This was done by using the points for each type and applying a kernel density estimator on a 2-d surface composed of the first two principal components of the climate conditions. The result is a smoothed continuous point density surface, showing the prevalence of each vegetation type across the range of sampled climatic conditions. This surface was partitioned by fitting contour lines so that they enclose a proportion of the original points from the current time period. Contours were calculated at 5% increments. For example the innermost 5% contour line encloses the 5% of pixels for the given vegetation type which are at the core of the climate space for that type, as determined by its density in the climate space. Cells further away from the dense central core, are considered to be more marginal in the vegetation type’s distribution. The outer contours are fit to enclose the 95-99% of climatically marginal points, with the last 1% of cells (beyond the 99% contour) being the most marginal. In addition, if a cell lies outside the space defined by the 99% contour of any vegetation type, it is considered to be “non-analog,” which means that it experiences climatic conditions outside of the conditions where we have a good sample in the initial time period. As a result, the status of that point is uncertain. There are occasionally a few extreme points which appear to be far outside the general distribution for the type. These may be due to misclassified vegetation types in the source data, microclimatic conditions not captured by the climate data, historic anomalies in long-lived species, etc. 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