DOI: 10.5061/dryad.08kprr55p
title: Habitat and climatic associations of climate-sensitive species along a southern range boundary

description: 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 indices to represent climate. Marten associated with
 upland spruce‐fir and lowland riparian forest types, hares with lowland
 conifer and aspen‐birch, grouse with lowland riparian hardwoods, and moose
 with upland spruce‐fir. Species differed in climatic drivers with hares
 positively associated with cooler annual temperatures, moose with cooler
 summer temperatures, and grouse with colder winter temperatures. Contrary
 to expectations, temperature variables outperformed winter habitat
 indices. Model performance varied greatly among species, as did predicted
 distributions along the southern edge of the Northwoods region. As
 multiple species were associated with lowland riparian and upland
 spruce‐fir habitats, these results provide potential for efficient
 prioritization of habitat management. Both direct and indirect effects
 from climate change are likely to impact the distribution of
 climate‐sensitive species in the future and the use of multiple data types
 and sources in the modelling of species distributions can result in more
 accurate predictions resulting in improved management at policy‐relevant
 scales.
description: 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). <br>. 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 character type label
 indicating the person/organization providing the data.  
 R code is available on Zenodo at https://doi.org/10.5281/zenodo.7908523