Geology topics

Laura Christine Gigliotti

Publications and source records attributed to Laura Christine Gigliotti.

5 recordsLinked to original sources

USGS2026

Individual encounter data of six African carnivore species optimized for multi-species density estimation

The ability to estimate abundances of multiple wildlife species within an area is valuable for both conservation and ecological inquiry. Spatially explicit capture–recapture (SCR) methods are commonly used to obtain reliable population size estimates, particularly for low-density and individually identifiable carnivore species. However, estimating abundance within multi-species communities poses a methodological challenge as survey designs and analytical tools are primarily tailored for single target species. Here, we present a dataset of spatially referenced individual encounter histories of six carnivore species with varying space requirements (lion, Panthera leo ; leopard, Panthera pardus ; spotted hyena, Crocuta crocuta ; cheetah, Acinonyx jubatus ; serval, Leptailurus serval ; large-spotted genet, Genetta tigrina ). These data were collected in a South African game reserve using a camera trap array optimized for multi-species density estimation using SCR methods. This dataset will be a valuable resource for studying spatial processes among potentially interacting carnivores without the common pitfalls that come with by-catch data of non-target species, and will provide a much-needed case study for the further development of multi-species statistical method development.

Munywana Conservancy↗

USGS2026

Optimizing camera-trap survey designs for multi-species density estimation using spatial capture–recapture models

Conservation efforts are increasingly required to move beyond single-species perspectives and towards community-level inferences. Obtaining reliable multispecies population size estimates poses practical challenges as analytical tools and design recommendations are primarily focused on single species. Density estimation using spatial capture–recapture methods requires deploying detectors (e.g. camera-traps) with spacing proportional to the space use of the focal species. Given that the design itself is species-specific, sampling can be inefficient for species with larger ranges than the focal species due to restricted spatial coverage and insufficient for species with smaller ranges because fewer recaptures are generated. To address this practical issue, we developed a two-stage optimization approach to generate camera-trap survey designs that are appropriate for estimating density of a suite of individually identifiable species that vary in home range sizes. Our approach applies an algorithm to first optimize placement of a subset of detectors for large and vagile species based on maximizing spatial coverage, followed by a second optimization for the remaining cameras based on maximizing spatial recaptures for smaller and less mobile species. We empirically tested our approach using six individually identifiable carnivore species with varying home range sizes in the Munywana Conservancy, South Africa. Our design included 60 camera locations optimized for leopards ( Panthera pardus ) and 40 cameras optimized for small-bodied, less-mobile carnivores. Our design optimization procedure generated designs characterized by a distribution of inter-trap distances, based on ecological parameters, and resulted in plausible density estimates for all species. The two-stage approach resulted in moderate precision gains for larger ranging species and, importantly, substantial gains for smaller ranging species. Simulations demonstrated improved precision of spatially explicit capture–recapture (SCR) parameter estimates for all species compared to standard grid-based designs, driven not solely by increased sampling but also by the optimized spatial configuration. Synthesis and applications . We developed and tested a new camera-trap survey design method for estimating population densities for multiple co-occurring species with differing spatial ecologies. By streamlining multispecies population monitoring, our approach reduces costs associated with species-specific programmes and broadens opportunities for community ecology and conservation research based on explicit demographic parameters.

Munyawana Conservancy↗

USGS2025

A framework for analyzing wild turkey summer sighting data.

Wildlife agencies collect data on productivity (e.g., proportion of hens with poults and number of poults per hen) of wild turkey ( Meleagris gallopavo ) to monitor population status and trends. However, sampling protocols to collect productivity data rely on opportunistic observations reported by wildlife agency personnel and the public and have changed over time and differed among agencies. A protocol to standardize data collection was adopted by most state wildlife agencies in 2019, but long-term historical datasets exist that cannot be analyzed readily to make inferences about spatial and temporal patterns in wild turkey productivity. We developed statistical models to allow comparisons and model trends in productivity among and within states even though data collection protocols changed over time and differed among states. We found greater spatial variation in the proportion of hens with poults than the number of poults per brood, which may reflect how environmental factors influence wild turkey productivity. Our models can also provide inferences about productivity when data are limited or temporally discontinuous for some spatial units. Additionally, we found that temporal and spatial variation in data collection, even under the new protocol, can affect inferences about trends in productivity. The statistical models we developed address the uncontrolled nature of when and where data are collected and offer the ability to investigate long-term patterns of productivity in relation to factors such as changing climate or habitat conditions.

Maryland, New Jersey, New York, Ohio, Pennsylvania↗

USGS2025

Atypical winter coat coloration of snowshoe hares near the southern extent of their range

Many species have a variety of adaptations to winter weather, but these adaptations could become maladaptive if winter snowfall and temperatures are more variable. Snowshoe hares ( Lepus americanus ) molt from a brown summer coat to a white winter coat, but reductions in snow cover could result in phenotypic mismatch, which in turn could reduce survival. Hare populations near the southern extent of their range might be especially sensitive to phenotypic mismatch because of variable winter weather, but variation in winter coat coloration could allow for these populations to persist in inconsistent snow cover conditions. Using capture data ( n = 59 individual hares) spanning 8 years, we document the prevalence of three atypical winter coat color phenotypes (brown bodies, brown-ringed eyes, and brown ears) in a snowshoe hare population in Pennsylvania. The majority of hares in our study (84.7%) exhibited at least one of these atypical winter phenotypes, with a high probability of hares having brown-ringed eyes or brown ears, and four hares remaining brown during the winter. The presence and high prevalence of non-white winter phenotypes could be beneficial for hares in this population if winters are mild with low snow cover. If these phenotypes have a genetic basis, there may be evolutionary potential for hares to persist near the southern extent of their range, even in the face of changing winters.

Pennsylvania↗

USGS2023

Multi-level thresholds of residential and agricultural land use for elk avoidance across the Greater Yellowstone Ecosystem

1. Conversion of land for settlements and agriculture is increasing globally and can influence wildlife space use. However, there is limited research to identify the thresholds of land-use change that incur wildlife avoidance and how these thresh-olds might vary across levels of selection. 2. We evaluated multi-level avoidance thresholds of elk Cervus canadensis impacted by residential development and irrigated agriculture across the Greater Yellowstone Ecosystem in Idaho, Montana and Wyoming. Using GPS data from765 elk in 21 herds, we estimated habitat selection in relation to development and agriculture at three levels (home range selection, within home range selection and movement path selection). Next, using individual selection covariates and as-sociated measures of land-use availability, we used functional-response models to evaluate how selection varied based on availability, and in turn, to estimate avoidance thresholds. 3. We found individual and level-specific variation in elk responses to environmental factors. Elk exhibited stronger responses (either selection or avoidance) when selecting home range locations (i.e. second-order selection) than when selecting areas within home ranges (i.e. third-order selection) or selecting movement paths (i.e. fourth-order selection). Importantly, elk avoidance of development and agriculture changed as the amount of land in these categories changed. Across all levels of selection elk exhibited neutral selection for human development at low levels of availability (<1.1%–2.2% developed) but avoided areas that were >1.1%–2.2% developed. Conversely, elk selected positively for irrigated agriculture at low to moderate levels of availability (<52.0%–66.2% agriculture) but exhibited neutral selection in areas that were >52.0%–66.2% agriculture. 4. Synthesis and applications . Elk avoidance of low levels of human development suggests conservation efforts such as restrictions on future development or conservation easements could focus on areas that are still below 2% developed. Additionally, because elk selection was strongest at the landscape scale, conservation actions that are based on information about the overall landscape structure may be most impactful. Our results highlight the importance of under-standing variability in wildlife habitat selection at multiple levels, particularly in relation to land-use change, and highlight how functional response modelling can help inform landscape conservation.

Montana, Wyoming↗