Reconstructing historical snow depth surfaces to evaluate changes in critical demographic rates and habitat components of snow‐dependent and snow‐restricted species

• Published in: Methods in Ecology and Evolution
• Main Authors: Manning, Jeffrey A., Garton, Edward O.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2011
• Subjects: Ecological Modeling; Ecology, Evolution, Behavior and Systematics; Gulo gulo
• Online Access: http://dx.doi.org/10.1111/j.2041-210x.2011.00144.x; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fj.2041-210X.2011.00144.x; https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/j.2041-210X.2011.00144.x

Summary 1. Climate change models consistently predict snow depth declines across the Northern Hemisphere. Snow depth has been linked to the demography of numerous species, and snow depth reduction is expected to affect the demography of some northern species. As many demographic studies depend on long‐term population data that extend back beyond available sources of spatially continuous snow depth data, reliable hindcasting of snow depth surface maps is needed. 2. We developed a two‐stage regression modelling approach to reconstructing historic snow depths using an existing and readily available spatiotemporal data set of daily meteorological variables across a large, heterogeneous landscape in North America from 1982 to 2003. The final model accounted for ecoregional differences and predicted snow depth as a function of elevation and total snowfall accumulation. 3. Model validations showed that the estimated snow depths were spatially and temporally accurate. 4. We demonstrate how to apply this model with ArcGIS to hindcast monthly and yearly sequences of high‐resolution, spatially continuous surfaces of historic snow depths. We also illustrate the utility of the modelled snow depth surfaces for informing predictions of how changes in snow depth may influence the demography and habitats of snow‐dependent and snow‐restricted species by assessing the spatiotemporal stochasticity of snow conditions that potentially benefit wolverine Gulo gulo and the winter ranges, net energy costs and population growth of mule deer Odocoileus hemionus . 5. This model can be used to hindcast snow depth surfaces across similar regions; our simple two‐stage modelling approach can be applied to other regions where equivalent climate data are available. The resulting spatiotemporal snow depth surfaces estimated from this model can be linked to existing long‐term wildlife population data sets to investigate the extent to which snow depth declines may regulate or limit populations.