DOI: 10.21429/bma6-xn17
title: Estimating the Spatial and Temporal Extent of Snowpack Properties in Complex Terrain: Leveraging Novel Data to Adapt Wildlife and Habitat Management Practices to Climate Change

description: Snow conditions are changing dramatically in the mountains of the interior Pacific Northwest, including eastern Washington, northern Idaho, and western Montana. These changes can both benefit and hinder a variety of wildlife species. The timing and extent of seasonal snowpacks, in addition to snow depth, density, and hardness, can impact the ability of wildlife to access forage, their ability to move across the landscape, and their vulnerability to predators, to name a few. In order to respond effectively to changes in snow conditions, wildlife managers need tools to identify areas and promote conditions that maintain late spring and early summer snowpack for some sensitive species. Managers also require an index of winter severity that includes information on temperature, snow depth, and snow hardness at relevant spatial and temporal scales to adapt management strategies for seasonal conditions. 
 
This project seeks to advance the understanding of how snow conditions vary and how such variation affects both species of greatest conservation need (e.g., wolverine, hoary marmot, western bumble bee, and mountain goat) and species of economic and recreational importance (e.g., elk and moose) in forests spanning the rain-snow transition zone in the interior Pacific Northwest. To do this, researchers created new tools that managers can use to estimate snow depth, map areas of late season snow (known as “snow refugia”), and estimate winter severity for ungulate species such as elk and moose. Researchers used these novel datasets to predict winter range habitat use by deer and elk in Idaho and to identify linkages between ungulate survival and winter severity. These data were used to create a model predicting snow disappearance dates (SDD) at camera sites and across our entire study area to identify priority areas of conservation for snow-dependent wildlife. The model predicted high-elevation areas, north-facing aspects, and cold-air pools retained snow latest. These data were also used to model the probability of deer presence at camera sites dependent on snow conditions, and it was determined that deer respond negatively to increased snow density and respond slightly positively to increased snow hardness.
 
The results of this project will be directly applicable to federal (U.S. Fish and Wildlife Service), state (Idaho Department of Fish and Game), and tribal (Coeur D’Alene Tribe) managers in the region. Providing natural resource managers with tools to identify locations of snow retention for sensitive and listed species is critical for identifying habitats to conserve or modify in order to facilitate species recovery. Lastly, a winter severity model will provide wildlife managers with a much-needed tool for predicting future climate change effects on ungulates and adjusting management strategies accordingly.
description: This dataset contains the following: Attributes of Remote Camera Stations on Moscow Mountain in Latah County, ID, USA (10/20/20-5/30/21); edger: An R package facilitating distance and snow depth measurements at remote camera stations; Environmental Data at Remote Camera Stations on Moscow Mountain in Latah County, ID, USA (10/20/20-5/30/21);  Hemispherical Photographs at Remote Camera Stations on Moscow Mountain in Latah County, ID, USA (6/2/21-11/2/21); Map of predicted relative snow disappearance dates across Moscow Mountain in Latah County, ID; Snow and Wildlife Detections from Remote Camera Stations on Moscow Mountain in Latah County, ID, USA (10/20/20-6/30/21); Snow Density Measurements at Remote Camera Stations on Moscow Mountain in Latah County, ID (12/1/20-4/30/21); Snow Hardness Measurements at Remote Camera Stations on Moscow Mountain in Latah County, ID (12/1/20-4/30-21)