Geology topics

Charles R. Anderson

Publications and source records attributed to Charles R. Anderson.

4 recordsLinked to original sources

USGS2023

Influence of camera model and alignment on the performance of paired camera stations

The probability of obtaining images of target species may vary across camera models or relative position of cameras at survey locations. Alignment of cameras within paired camera stations (hereafter, stations) could affect species detection due to issues with image exposure. We quantified effects of 3 camera models and alignment (staggered, offset by a perpendicular distance of 4.6 m, and aligned, directly facing one another) on camera performance in a station design. Mean exposure events (flash from one camera overexposes or underexposes pictures) at aligned stations was 3.93 (SE = 1.01; n = 40), whereas no exposure events were documented at staggered ( n = 36) stations. Overall frequency of exposure events of mammal images at aligned cameras was 44% (68 exposure events/153 images). On average, 8% (range 0−35%) of mammal images from aligned stations were exposure events. We detected no difference ( P = 0.88) in exposure events among paired camera models. Further, we detected no overall differences ( P ≥ 0.07) in paired camera performance (i.e., number of mammal images over survey interval) between aligned or staggered stations, though reliability (i.e., percentage of camera stations that lasted entire survey interval) varied ( P ≤ 0.001) between model types. Research deploying 2 cameras within a camera station framework can eliminate exposure events by using a staggered camera alignment without affecting the number of usable mammal photos. Rigorous field testing prior to deployment of stations is warranted to optimize reliability. One of our low-cost models performed as well as a more expensive model within our paired camera stations at collecting mammal images, and thus could be incorporated into study designs without compromising quality of camera photo data. We suggest a pilot study before large-scale deployment to evaluate reliability and performance of cameras, particularly when deploying multiple models.

Illinois↗

USGS2019

Estimating density and detection of bobcats in fragmented Midwestern landscapes using spatial capture-recapture data from camera traps

Camera-trapping data analyzed with spatially explicit capture–recapture (SCR) models can provide a rigorous method for estimating density of small populations of elusive carnivore species. We sought to develop and evaluate the efficacy of SCR models for estimating density of a presumed low-density bobcat ( Lynx rufus ) population in fragmented landscapes of west-central Illinois, USA. We analyzed camera-trapping data from 49 camera stations in a 1,458-km 2 area deployed over a 77-day period from 1 February to 18 April 2017. Mean operational time of cameras was 52 days (range = 32–67 days). We captured 23 uniquely identifiable bobcats 113 times and recaptured these same individuals 90 times; 15 of 23 (65.2%) individuals were recaptured at ≥2 camera traps. Total number of bobcat capture events was 139, of which 26 (18.7%) were discarded from analyses because of poor image quality or capture of only a part of an animal in photographs. Of 113 capture events used in analyses, 106 (93.8%) and 7 (6.2%) were classified as positive and tentative identifications, respectively; agreement on tentative identifications of bobcats was high (71.4%) among 3 observers. We photographed bobcats at 36 of 49 (73.5%) camera stations, of which 34 stations were used in analyses. We estimated bobcat density at 1.40 individuals (range = 1.00–2.02)/100 km 2 . Our modeled bobcat density estimates are considerably below previously reported densities (30.5 individuals/100 km 2 ) within the state, and among the lowest yet recorded for the species. Nevertheless, use of remote cameras and SCR models was a viable technique for reliably estimating bobcat density across west-central Illinois. Our research establishes ecological benchmarks for understanding potential effects of colonization, habitat fragmentation, and exploitation on future assessments of bobcat density using standardized methodologies that can be compared directly over time. Further application of SCR models that quantify specific costs of animal movements (i.e., least-cost path models) while accounting for landscape connectivity has great utility and relevance for conservation and management of bobcat populations across fragmented Midwestern landscapes.

Illinois↗

USGS2016

Movement reveals scale dependence in habitat selection of a large ungulate

Ecological processes operate across temporal and spatial scales. Anthropogenic disturbances impact these processes, but examinations of scale dependence in impacts are infrequent. Such examinations can provide important insight to wildlife–human interactions and guide management efforts to reduce impacts. We assessed spatiotemporal scale dependence in habitat selection of mule deer ( Odocoileus hemionus ) in the Piceance Basin of Colorado, USA, an area of ongoing natural gas development. We employed a newly developed animal movement method to assess habitat selection across scales defined using animal-centric spatiotemporal definitions ranging from the local (defined from five hour movements) to the broad (defined from weekly movements). We extended our analysis to examine variation in scale dependence between night and day and assess functional responses in habitat selection patterns relative to the density of anthropogenic features. Mule deer displayed scale invariance in the direction of their response to energy development features, avoiding well pads and the areas closest to roads at all scales, though with increasing strength of avoidance at coarser scales. Deer displayed scale-dependent responses to most other habitat features, including land cover type and habitat edges. Selection differed between night and day at the finest scales, but homogenized as scale increased. Deer displayed functional responses to development, with deer inhabiting the least developed ranges more strongly avoiding development relative to those with more development in their ranges. Energy development was a primary driver of habitat selection patterns in mule deer, structuring their behaviors across all scales examined. Stronger avoidance at coarser scales suggests that deer behaviorally mediated their interaction with development, but only to a degree. At higher development densities than seen in this area, such mediation may not be possible and thus maintenance of sufficient habitat with lower development densities will be a critical best management practice as development expands globally.

Colorado↗

USGS2013

Practical guidance on characterizing availability in resource selection functions under a use-availability design

Habitat selection is a fundamental aspect of animal ecology, the understanding of which is critical to management and conservation. Global positioning system data from animals allow fine-scale assessments of habitat selection and typically are analyzed in a use-availability framework, whereby animal locations are contrasted with random locations (the availability sample). Although most use-availability methods are in fact spatial point process models, they often are fit using logistic regression. This framework offers numerous methodological challenges, for which the literature provides little guidance. Specifically, the size and spatial extent of the availability sample influences coefficient estimates potentially causing interpretational bias. We examined the influence of availability on statistical inference through simulations and analysis of serially correlated mule deer GPS data. Bias in estimates arose from incorrectly assessing and sampling the spatial extent of availability. Spatial autocorrelation in covariates, which is common for landscape characteristics, exacerbated the error in availability sampling leading to increased bias. These results have strong implications for habitat selection analyses using GPS data, which are increasingly prevalent in the literature. We recommend researchers assess the sensitivity of their results to their availability sample and, where bias is likely, take care with interpretations and use cross validation to assess robustness.

Ecology↗