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Hosted/NationalRefugia__as_per_Fig_4c_Tile (MapServer)

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Map Name: Map

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Layers: Description: 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 analyzed locations in the 80th percentile (i.e., the top 20% of values) of the distribution of values for the combined data and for each refugia class separately. The combined data are represented here in quantiles (a value of 1 indicates the bottom 20%, a value of 5 indicates the top 20%).Refugia datasets:Current Climate Diversity: Based on 11 bioclimatic variables using climate data for a 30-year climate normal period (1981-2010) (Carroll et al. 2017)Ecotypic Diversity: Derived from edaphic, climatic, and landcover data (Carroll et al. 2017)Land Facet Diversity: Incorporated elevation, latitude-adjusted elevation, topographic position index, slope, modified heat load index, and soil (Carroll et al. 2017)Landscape Diversity: 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)Bird Macrorefugia: 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)Climatic Dissimilarity: 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)Climate Velocity: 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)Tree Macrorefugia: 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)Sources:AdaptWest Project. (2015). Gridded climatic velocity data for North America at 1km resolution. https://adaptwest.databasin.org/pages/adaptwest-velocitywna/ 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. Scientific Reports, 8(1), 9441. https://doi.org/10.1038/s41598-018-27721-6 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 Nature Conservancy, The. (2020). Resilient Land Mapping tool. https://maps.tnc.org/resilientland/ 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. Global Ecology and Biogeography, 27(6), 690–703. https://doi.org/10.1111/geb.12731

Service Item Id: 2871ece7b4c5446baf2a46c02ca8bb7b

Copyright Text: CC-BY Defenders of Wildlife 2021

Spatial Reference: 102100  (3857)  LatestVCSWkid(0)


Single Fused Map Cache: true

Tile Info: Storage Info: Initial Extent: Full Extent: Units: esriMeters

Supported Image Format Types: PNG32,PNG24,PNG,JPG,DIB,TIFF,EMF,PS,PDF,GIF,SVG,SVGZ,BMP

Document Info: Supports Dynamic Layers: false

MaxRecordCount: 1000

MaxImageHeight: 4096

MaxImageWidth: 4096

Supported Query Formats: JSON, geoJSON, PBF

Supports Query Data Elements: true

Min Scale: 7.3957190948944E7

Max Scale: 2311162.217155

Min LOD: 3

Max LOD: 8

Supports Datum Transformation: true



Child Resources:   Info

Supported Operations:   QueryLegends   Return Updates