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Description: <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>We initially analyzed relationships between refugia datasets (n = 8, all listed below) through a principal components analysis where each component helps define a refugia class. As with principal components, datasets were assigned to a class based on the sign and size of the eigenvector. However, to avoid a tradeoff in refugia identification within a single class, all datasets within the class were required to load together and in the same direction on a principal component. In addition to presenting three separate classes, we weighted datasets based on their principal component loadings and combined them in a single dataset so that no one refugia class has a greater weight in identifying climate refugia locations. All datasets were normalized to a scale of 0 to 1 prior to being combined. We extracted refugia values for each ecoregion separately (EPA level II; EPA 2006), classifying the locations that fell into the top 20% of each distribution as areas of interest. This dataset represents the combination of the top 20% of each ecoregion's distribution (a value of 0 indicates the bottom 80%, a value of 1 indicates the top 20%), which can be used to map ecoregion-specific refugia, ensuring equal representation of all ecoregions relative to size. </SPAN></P><P><SPAN STYLE="font-weight:bold;">Refugia datasets:</SPAN></P><P><SPAN STYLE="text-decoration:underline;">Current Climate Diversity:</SPAN><SPAN> Based on 11 bioclimatic variables using climate data for a 30-year climate normal period (1981-2010) (Carroll et al. 2017)</SPAN></P><P><SPAN STYLE="text-decoration:underline;">Ecotypic Diversity:</SPAN><SPAN> Derived from edaphic, climatic, and landcover data (Carroll et al. 2017)</SPAN></P><P><SPAN STYLE="text-decoration:underline;">Land Facet Diversity:</SPAN><SPAN> Incorporated elevation, latitude-adjusted elevation, topographic position index, slope, modified heat load index, and soil (Carroll et al. 2017)</SPAN></P><P><SPAN STYLE="text-decoration:underline;">Landscape Diversity:</SPAN><SPAN> Described the diversity of microhabitats and climatic gradients. Microclimates were measured by quantifying elevation range, the variety of small-scale landforms, and the density and configuration of wetlands in a 100-acre neighborhood (The Nature Conservancy 2020)</SPAN></P><P><SPAN STYLE="text-decoration:underline;">Bird Macrorefugia:</SPAN><SPAN> Focused on regions where the current and projected future species ranges overlap. Input based on current species niches for 268 songbird species; climate velocity based on 4 representative GCMs, RCP 4.5, 2080s (Stralberg et al. 2018)</SPAN></P><P><SPAN STYLE="text-decoration:underline;">Climatic Dissimilarity:</SPAN><SPAN> Described how different the future climate at a location will be from its current climate. Measured in terms of multivariate climate characteristics, via a principal components analysis (PCA) of 11 biologically-relevant temperature and precipitation variables, RCP 4.5, 2080s (Belote et al. 2018)</SPAN></P><P><SPAN STYLE="text-decoration:underline;">Climate Velocity:</SPAN><SPAN> Velocity was calculated by dividing the rate of climate change by the rate of spatial climate variability to focus on regions where climatic conditions move more slowly across the landscape. Input based on A2 emissions scenarios implemented by seven GCMs of the CMIP3 multimodel dataset, RCP 4.5, 2080s (AdaptWest Project 2015)</SPAN></P><P><SPAN STYLE="text-decoration:underline;">Tree Macrorefugia:</SPAN><SPAN> Focused on regions where the current and projected future species ranges overlapped. Input based on current species niches for 324 tree species; climate velocity based on 4 representative GCMs, RCP 4.5, 2080s (Stralberg et al. 2018)</SPAN></P><P /><P><SPAN STYLE="font-weight:bold;">Sources:</SPAN></P><P><SPAN>AdaptWest Project. (2015). Gridded climatic velocity data for North America at 1km resolution. https://adaptwest.databasin.org/pages/adaptwest-velocitywna/ </SPAN></P><P><SPAN>Belote, R. T., Carroll, C., Martinuzzi, S., Michalak, J., Williams, J. W., Williamson, M. A., & Aplet, G. H. (2018). Assessing agreement among alternative climate change projections to inform conservation recommendations in the contiguous United States</SPAN><SPAN STYLE="font-style:italic;">. Scientific Reports, 8</SPAN><SPAN>(1), 9441. https://doi.org/10.1038/s41598-018-27721-6 </SPAN></P><P><SPAN>Carroll, C., Roberts, D.R., Michalak, J.L. et al. (2017). Scale-dependent complementarity of climatic velocity and environmental diversity for identifying priority areas for conservation under climate change. Global Change Biology Early View. https://doi.org/10.1111/gcb.13679 </SPAN></P><P><SPAN>Environmental Protection Agency (EPA). (2006). Ecoregions of North America. https://www.epa.gov/eco-research/ecoregions-north-america </SPAN></P><P><SPAN>Nature Conservancy, The. (2020). Resilient Land Mapping tool. https://maps.tnc.org/resilientland/ </SPAN></P><P><SPAN>Stralberg, D., Carroll, C., Pedlar, J. H., Wilsey, C. B., McKenney, D. W., & Nielsen, S. E. (2018). Macrorefugia for North American trees and songbirds: Climatic limiting factors and multi-scale topographic influences. </SPAN><SPAN STYLE="font-style:italic;">Global Ecology and Biogeography, 27</SPAN><SPAN>(6), 690–703. https://doi.org/10.1111/geb.12731 </SPAN></P><P><SPAN /></P></DIV></DIV></DIV>
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Copyright Text: CC-BY Defenders of Wildlife 2021
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