EOMF-05. Interactions between thresholds and spatial discretizations of snow: lessons-learned from a wolverine habitat assessment
Thresholds can be used to interpret environmental datasets in a way that is easily communicated and used for decision making purposes. However, thresholds are often developed for specific data products, influencing results when the same threshold is applied to datasets with different characteristics. Here, we tested the impact that different spatial discretizations of snow had on estimates of wolverine habitat in the Colorado Rocky Mountains, defined using a snow water equivalent (SWE) threshold (0.20 m) and threshold date (15 May) used by previous habitat assessments. Annual wolverine habitable area (WHA) was thresholded from a 36-year (1985 – 2020) snow reanalysis performed at three different spatial discretizations: 1) 480 m gridcells, 2) 90 m gridcells, and 3) 480 m gridcells with implicit representations of subgrid snow spatial heterogeneity. Relative to the 480 m gridcells, 90 m gridcells resolved shallower snow deposits on steep unvegetated slopes, decreasing WHA by 10%, on average. In years with warmer and/or drier winters, gridcells with subgrid representations of snow heterogeneity increased WHA by upwards of 30%, as compared to simulations without subgrid snow heterogeneity. Despite these sensitivities, annual WHA was primarily controlled by winter precipitation and temperature. However, small changes to the SWE threshold (± 0.07 m) and threshold date (± 2 weeks) also changed WHA by as much as 82%. Our results show that snow thresholds are useful but may not always provide a complete picture of the annual changes to snow volume, snow distribution, and the resulting snow-adapted wildlife habitat. Studies thresholding spatiotemporal datasets could be improved by including 1) information about the fidelity of thresholds across multiple spatial discretizations, and 2) sensitivities to ranges of realistic thresholds.