Snow and Wildlife Detections from Remote Camera Stations on Moscow Mountain in Latah County, ID, USA (10/20/20-6/30/21)
Dates
Publication Date
2022-10-05
Start Date
2020-10-20
End Date
2021-07-31
Citation
Kaitlyn Strickfaden and Timothy Link, 2022, Snow and Wildlife Detections from Remote Camera Stations on Moscow Mountain in Latah County, ID, USA (10/20/20-6/30/21): U.S. Geological Survey data release, https://doi.org/10.21429/bma6-xn17.
Summary
Remote camera data on snow presence, snow depth, and wildlife detections on Moscow Mountain in Latah County, ID, USA. Reconyx Hyperfire I and Hyperfire II cameras were set to take hourly timelapse images and motion-triggered images from October 2020 - May 2021 at 5 elevation categories (800-925m, 925-1050m, 1050-1175m, 1775-1300m, and > 1300m), 4 aspects (N, S, E, and W), and 3 canopy densities (Sparse [0-35%], Moderate [35-75%], and Dense [75-100%]), in duplicate, plus 17 selected microclimates (137 locations total), on Moscow Mountain in Latah County, ID. Images from 27 other locations were part of a pilot experiment during January to May 2020. Data in the CSVs include image metadata, camera site characteristics, temperature (degrees [...]
Summary
Remote camera data on snow presence, snow depth, and wildlife detections on Moscow Mountain in Latah County, ID, USA. Reconyx Hyperfire I and Hyperfire II cameras were set to take hourly timelapse images and motion-triggered images from October 2020 - May 2021 at 5 elevation categories (800-925m, 925-1050m, 1050-1175m, 1775-1300m, and > 1300m), 4 aspects (N, S, E, and W), and 3 canopy densities (Sparse [0-35%], Moderate [35-75%], and Dense [75-100%]), in duplicate, plus 17 selected microclimates (137 locations total), on Moscow Mountain in Latah County, ID. Images from 27 other locations were part of a pilot experiment during January to May 2020. Data in the CSVs include image metadata, camera site characteristics, temperature (degrees Celsius), precipitation events (T/F), snow presence (T/F), manual measurements of snow depth (cm), and wildlife detections. Snow presence was assessed up to 15 m from the camera. Snow depth was measured using virtual snow stakes created with the edger R package created by the author. Wildlife were marked as present in all photos in which they appear, and new individuals were counted.
Camera sites were chosen by stratified non-random sampling. Cameras were never closer than 25m to other cameras, nor were they placed facing trails. Branches and vegetation which could impede the FOV of the camera or cause false-positive triggers were removed. Cameras were deployed approximately 2m from the ground on trees and tilted slightly downward to prevent snow from accumulating on the lens. Cameras were programmed to take hourly photographs as well as motion-triggered photographs at high sensitivity. Cameras were programmed to take three images per trigger with a 1-second delay between images and no quiet period between triggers. Cameras were checked approximately monthly to ensure proper function. After collection of cameras, images were pre-processed to superimpose a "virtual" snow stake (VSS) onto images for snow depth estimations. The VSS method was developed by the MS student in Program R and allows the user to superimpose a snow stake onto images based on reference images taken during camera deployment or retrieval which contained a snow stake. Pre-processed images were then manually processed by technicians. Technicians measured snow depth using VSS's at 5m, 10m, and 15m from the camera, dependent on the camera viewshed. A subset of cameras (20) also had a permanent PVC snow stake installed in the camera viewshed. Technicians also reported snow presence, precipitation events (either happening [True] or not happening [False]), and wildlife detections. Wildlife detections were recorded such that every image in which a particular wildlife species appeared was recorded as "Present." Then, each individual was counted a single time in the proper demographic category (female/antlerless male, male, fawn, and unknown for ungulate species and adult or young-of-year for predator species). Other data recorded included camera operating state (normal or otherwise), human detections, and unique markings on wildlife.
Snow conditions and dynamics are changing due to climate change. Changes to snow impact snow-dependent species through loss of snow cover needed for survival and fitness, while changes to snow impact snow-inhibited species through changes in energy expenditure, access to food, and predation risk. These data were used for two purposes: 1) to explore variability in snow disappearance dates in a complex forested terrain, and 2) to examine relationships between white-tailed deer (Odocoileus virginianus) and mule deer (O. hemionus) and snow properties including snow depth, density, and hardness. These data were used to create a model predicting snow disappearance dates (SDD) at our camera sites, which we could then use to map SDDs across our entire study area and identify priority areas of conservation for snow-dependent wildlife. We found that 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. We found that deer respond negatively to increased snow density and respond slightly positively to increased snow hardness.
