Cougars throughout California are under threat due to habitat change, landscape fragmentation, and urban and exurban development. Understanding cougar population sizes and habitat associations within areas of their distribution that are least affected by people (e.g., National Parks, designated wilderness areas) provides a benchmark from which to compare other landscapes and future conditions, informs conservation and management actions, and contributes to cougar persistence. Yosemite National Park in the Sierra Nevada Mountains of California may act as an important refuge for cougars. Despite this, there is no previous estimate of the number of cougars living in Yosemite or an evaluation of habitat selection patterns within the Park. We collected data on cougar distribution in Yosemite detection dog team surveys and infrared trail cameras. We then used spatial capture-recapture modeling to estimate the population size of cougars living in Yosemite and determine habitat associations of cougars in this protected area. Determining cougar population size and habitat associations here may provide critical information for their conservation and management elsewhere in California. Our research represents a critical step in cougar conservation and has important implications for natural resource management.

The Oiled Wildlife Care Network often deploys trail cameras during oil spills to determine wildlife species present in the area, and if they are oiled. To our knowledge, no studies have reported the accuracy of trail camera-based wildlife oiling determination. The purpose of this pilot study was to determine if trail camera photos can be used to reliably assess the oiling status of animals. Crude oil was applied to dark skunk pelts, and diesel to light-grey rabbit pelts to cover 0%, 25%, 50%, 75%, and 100% of the pelts’ surface area. Photos of the pelts were taken with trail cameras at 2m, 4m, and 6m distance from the cameras. Ten individuals familiar with oiled wildlife reviewed 21 photos each and were instructed to identify if the animal was oiled and the percentage of oiling. Observers correctly identified about 50% of the photos as oiled/non-oiled. For oiling percentage, about 23% of the photos were correctly identified (mostly within 2-4m of the cameras and 0-75% oiling percentage). These initial results suggest that using trail cameras to identify oiled wildlife may have limited use. Future directions will explore the use of an artificial intelligence program to bolster oiled wildlife identification accuracy.

Camera traps have become a ubiquitous survey tool for many wildlife species. While there are many studies evaluating camera trap design parameters such as camera density or duration there has been far less evaluation of the influence of camera settings in study design. In particular there is little guidance on how two camera settings, trigger-delay interval and number of photos per trigger (burst), affect species detectability. As these two settings determine the number of photos a camera generates, they not only influence species detectability but also the time and labor cost associated with processing photos. Non-optimal trigger-delay or burst settings that are too low could result in inability to detect species but conversely, settings that are too high may generate extraneous photos that only serve to increase workloads for photo processing and data management. We analyzed camera trap data from California and Oregon to evaluate the influence of camera settings on detection probability and occupancy for an array of mammalian species ranging from common to rare. Our results provide guidance on how to optimize camera settings to maximize detectability of species while minimizing extraneous photos that can lead to unnecessary additional work in data management.

The Mohave ground squirrel (Xerospermophilus mohavensis) occupies a small area in the western Mojave Desert of California. It is listed as Threatened under the California Endangered Species Act and is extirpated from a significant portion of its historical range. Much remaining habitat is on public land managed by the U.S. Bureau of Land Management. An extensive trail camera study was carried out in 2011-2012 to document its distribution on these lands. The BLM has now sponsored a similar effort to re-sample most of the same sites 10 years later. We report on the results of the 2021 survey in the central portion of the Mohave ground squirrel range. The species was documented at 37 of the 55 sites sampled in 2021, similar to 2011-2012 results, thus confirming its continued persistence in this region. This result suggests reasonably good recovery from the severe 2012-2016 drought. However, no reproduction was observed in the extremely dry spring of 2021, winter annuals were absent and many shrubs were nearly leafless. Photos revealed many severely emaciated Mohave ground squirrels unlikely to gain enough body weight to survive dormancy. The long-term effects of climate change on this desert species are a serious continuing concern.

Studying small-animal space-use is challenging because most measures of habitat and animal locations are relatively coarse compared to the home ranges, movements or habitat patches of interest. The increasing prevalence of drones has provided a new platform for collecting centimeter-resolution remotely sensed habitat data, but this data has limited utility for studying small-animal space-use if it is not paired with fine-scale animal tracking. We tested a method of recording fine-scale location data in the field, which uses the same drone imagery that provides high-resolution habitat data, to create a centimeter-resolution map onto which locations are recorded within a smartphone app. We tested the accuracy by mapping targets in the field using this method, and a handheld GPS device for comparison, and measuring errors against a high-accuracy RTK GPS. We found that 82% of mapping locations had sub-meter errors compared to just 21% for the GPS device. Most errors were caused by misidentification of landmarks in feature-poor areas so the addition of reference markers in these locations should improve accuracy. This approach provides a new tool for studying fine-scale animal space-use using drone imagery for both habitat measurement and location mapping.

Between 2017 and 2021, the author monitored motion surveillance cameras at three sites in the West Mojave Desert intended to census the occurrence of the State-listed Mohave ground squirrel (Xerospermophilus mohavensis) (MGS). MGS, in addition to white-tailed antelope ground squirrel (Ammospermophilus leucurus) (AGS), are the focal squirrel species of this study. There are numerous factors affecting the function of motion cameras and the subsequent review and analyses of photographs. Variable factors considered in this presentation include 1. Hardware, 2. Bait Presentation, 3. Animal Identifications, 4. Weather Conditions, and 5. Collecting/Reporting Results. Camera quality may affect results, but even two cameras of the same make vary. Bait presentations include blocks, PVC tubes, and screened sandwich containers, each with pros and cons. Between the two squirrel species within the study area, there are difficulties discerning individuals of the species. Early indications suggest that winter precipitation, which affects annual plant germination at the time of studies, likely affect detectability and abundance of squirrel images. As camera trapping for these species is relatively new, since 2011, variable approaches are being implemented until acceptable methods are identified.