Geology topics

David Choate

Publications and source records attributed to David Choate.

3 recordsLinked to original sources

USGS2018

Cougar dispersal and natal homing in a desert environment

We present a review of cougar dispersal literature and the first evidence of natural (i.e., unmanipulated) homing behavior by a dispersing male cougar (Puma concolor) that sustained severe injuries crossing the northern Mojave Desert. Based on Global Positioning System and ground tracking data, the male traveled a total distance of 981.1 km at 5.03 km/d, including 170.31 km from the Desert National Wildlife Refuge to the northwestern Grand Canyon, where he sustained severe injuries. The interkill interval increased from 7.1 ± 2.7 d while he was in his natal range to 17.5 ± 4.9 d during dispersal. While homing, the male appeared to consume only reptiles until he died, 33.7 km from his capture site. In desert environments where prey availability is low, homing behavior may be an important strategy for dispersing cougars, providing a mechanism for persistence when the best quality habitats they encounter are already occupied by adult residents. Therefore, managing for habitat connectivity can ensure successful homing as well as dispersal on a greater scale than has been previously suggested. Elucidating the mechanisms that trigger homing during dispersal may provide critical insight into animal movements often overlooked as mundane behavior.

Arizona, California, Nevada, Utah↗

USGS2017

Variable terrestrial GPS telemetry detection rates: Addressing the probability of successful acquisitions

Studies using global positioning system (GPS) telemetry rarely result in 100% fix success rates (FSR), which may bias datasets because data loss is systematic rather than a random process. Previous spatially explicit models developed to correct for sampling bias have been limited to small study areas, a small range of data loss, or were study-area specific. We modeled environmental effects on FSR from desert to alpine biomes, investigated the full range of potential data loss (0–100% FSR), and evaluated whether animal body position can contribute to lower FSR because of changes in antenna orientation based on GPS detection rates for 4 focal species: cougars ( Puma concolor ), desert bighorn sheep ( Ovis canadensis nelsoni ), Rocky Mountain elk ( Cervus elaphus nelsoni ), and mule deer ( Odocoileus hemionus ). Terrain exposure and height of over story vegetation were the most influential factors affecting FSR. Model evaluation showed a strong correlation (0.88) between observed and predicted FSR and no significant differences between predicted and observed FSRs using 2 independent validation datasets. We found that cougars and canyon-dwelling bighorn sheep may select for environmental features that influence their detectability by GPS technology, mule deer may select against these features, and elk appear to be nonselective. We observed temporal patterns in missed fixes only for cougars. We provide a model for cougars, predicting fix success by time of day that is likely due to circadian changes in collar orientation and selection of daybed sites. We also provide a model predicting the probability of GPS fix acquisitions given environmental conditions, which had a strong relationship ( r 2 = 0.82) with deployed collar FSRs across species.

Wildlife Society Bulletin↗

USGS2015

Linking resource selection and mortality modeling for population estimation of mountain lions in Montana

To be most effective, the scale of wildlife management practices should match the range of a particular species’ movements. For this reason, combined with our inability to rigorously or regularly census mountain lion populations, several authors have suggested that mountain lions be managed in a source-sink or metapopulation framework. We used a combination of resource selection functions, mortality estimation, and dispersal modeling to estimate cougar population levels in Montana statewide and potential population level effects of planned harvest levels. Between 1980 and 2012, 236 independent mountain lions were collared and monitored for research in Montana. From these data we used 18,695 GPS locations collected during winter from 85 animals to develop a resource selection function (RSF), and 11,726 VHF and GPS locations from 142 animals along with the locations of 6343 mountain lions harvested from 1988–2011 to validate the RSF model. Our RSF model validated well in all portions of the State, although it appeared to perform better in Montana Fish, Wildlife and Parks (MFWP) Regions 1, 2, 4 and 6, than in Regions 3, 5, and 7. Our mean RSF based population estimate for the total population (kittens, juveniles, and adults) of mountain lions in Montana in 2005 was 3926, with almost 25% of the entire population in MFWP Region 1. Estimates based on a high and low reference population estimates produce a possible range of 2784 to 5156 mountain lions statewide. Based on a range of possible survival rates we estimated the mountain lion population in Montana to be stable to slightly increasing between 2005 and 2010 with lambda ranging from 0.999 (SD = 0.05) to 1.02 (SD = 0.03). We believe these population growth rates to be a conservative estimate of true population growth. Our model suggests that proposed changes to female harvest quotas for 2013–2015 will result in an annual statewide population decline of 3% and shows that, due to reduced dispersal, changes to harvest in one management unit may affect population growth in neighboring units where smaller or even no changes were made. Uncertainty regarding dispersal levels and initial population density may have a significant effect on predictions at a management unit scale (i.e. 2000 km 2 ), while at a regional scale (i.e. 50,000 km 2 ) large differences in initial population density result in relatively small changes in population growth rate, and uncertainty about dispersal may not be as influential. Doubling the presumed initial density from a low estimation of 2.19 total animals per 100 km 2 resulted in a difference in annual population growth rate of only 2.6% statewide when compared to high density of 4.04 total animals per 100 km 2 (low initial population estimate λ = 0.99, while high initial population estimate λ = 1.03). We suggest modeling tools such as this may be useful in harvest planning at a regional and statewide level.

Ecological Modelling↗