Habitat and climatic associations of climate-sensitive species along a southern range boundary ...
Climate change and habitat loss are recognized as important drivers of shifts in wildlife species' geographic distributions. While often considered independently, there is considerable overlap between these drivers, and understanding how they contribute to range shifts can predict future specie...
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| Format: | Software |
| Language: | unknown |
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Zenodo
2023
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| Online Access: | https://dx.doi.org/10.5281/zenodo.7908522 https://zenodo.org/record/7908522 |
| Summary: | Climate change and habitat loss are recognized as important drivers of shifts in wildlife species' geographic distributions. While often considered independently, there is considerable overlap between these drivers, and understanding how they contribute to range shifts can predict future species assemblages and inform effective management. Our objective was to evaluate the impacts of habitat, climatic, and anthropogenic effects on the distributions of climate‐sensitive vertebrates along a southern range boundary in Northern Michigan, USA. We combined multiple sources of occurrence data, including harvest and citizen‐science data, then used hierarchical Bayesian spatial models to determine habitat and climatic associations for four climate‐sensitive vertebrate species (American marten [ Martes americana ], snowshoe hare [ Lepus americanus ], ruffed grouse [ Bonasa umbellus ], and moose [ Alces alces ]). We used total basal area of at‐risk forest types to represent habitat, and temperature and winter habitat ... : This data comprises 3 .csv files, containing spatial locations for moose, American marten, and snowshoe hare (*Note that location data for ruffed grouse was entirely composed of eBird data, which is publically available elsewhere (eBird Basic Dataset). Hence no .csv file is included here). . Data has been compiled from various sources (as indicated in files) and identifying information has been removed. Column 1: Name = "ID" An integer type unique identifier for each row of the data. Column 2: Name = "Spp" A character type label indicating the species name. Column 3: Name = "OBS" An integer type value of 1 (one) or 0 (zero) indicating presence (1) or absence (0). Column 4: Name = "Year" An integer type value indicating the year of data collection (i.e., "1997", "2017", etc.). Column 5: Name = "Long" The longitude as an unprojected geographic coordinate of the location (decimal). Column 6: Name = "Lat" The latitude as an unprojected geographic coordinate of the location (decimal). Column 7: Name = "Source" A ... |
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