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Environmental Data at Remote Camera Stations on Moscow Mountain in Latah County, ID, USA (10/20/20-5/30/21)
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 used and set to take hourly timelapse images and motion-triggered images. The cameras were deployed from October 2020 - May 2021. 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. Snow density was collected using a federal or prairie snow sampler. Snow hardness was collected using a ram penetrometer. Solar radiation was calculated using hemispherical...
Snow and Wildlife Detections from Remote Camera Stations on Moscow Mountain in Latah County, ID, USA (10/20/20-6/30/21)
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,...
Remote cameras (“trail cameras”) are a popular tool for non-invasive, continuous wildlife monitoring, and as they become more prevalent in wildlife research, machine learning (ML) is increasingly used to automate or accelerate the labor-intensive process of labelling (i.e., tagging) photos. Human-machine hybrid tagging approaches have been shown to greatly increase tagging efficiency (i.e., time to tag a single image). However, those potential increases hinge on the extent to which an ML model makes correct vs. incorrect predictions. We performed an experiment using a ML model that produces bounding boxes around animals, people, and vehicles in remote camera imagery (MegaDetector), to consider the impact of a ML...