Geology topics

Frances E. Buderman

Publications and source records attributed to Frances E. Buderman.

10 recordsLinked to original sources

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

Decadal stability in stream fish communities and contemporary ecological drivers of species occupancy in two Appalachian U.S. National Parks

Objective Although conserving fish biodiversity in lotic systems is challenging, protected areas can provide refuge from certain environmental stressors. In the Appalachian region, USA, the National Park Service manages Delaware Water Gap National Recreation Area (DEWA) and New River Gorge National Park & Preserve (NERI), which contain abundant and diverse freshwater resources. To assess the effectiveness of these protected areas in conserving stream fishes, we evaluated decadal changes and ecological drivers of species occupancy and detection. Methods Using fish assemblage data from backpack electrofishing surveys conducted in both parks during 2013–2014 and 2022–2023, we quantified temporal differences in species occupancy and detection probabilities using a Bayesian hierarchical multispecies occupancy modeling approach. For the 2022–2023 survey, we included habitat variables as predictors of occupancy and detection. Results Community composition and occupancy probabilities for species in both parks remained similar through time, with the most recent occupancy estimates ranging from 0.07 (90% CI = 0.02, 0.14) for Variegate Darter Etheostoma variatum and Rainbow Darter E. caeruleum to 0.73 (90% credible interval = 0.59, 0.85) for Blacknose Dace Rhinichthys atratulus . Changes in occupancy were more prominent at Delaware Water Gap National Recreation Area than New River Gorge National Park & Preserve, with Yellow Perch Perca flavescens having a posterior mean difference of −0.17 [90% credible interval = −0.35, −0.01] and American Eel Anguilla rostrata having a high posterior probability (>80%) of occupancy increasing by at least 1%. Habitat variables were related to community structure, but effects varied in significance, magnitude, and direction among species and parks. Conversely, species-specific detection probabilities were comparatively less affected by environmental and sampling effort predictors. Conclusions Between 2013 and 2023, occupancy estimates for 44 fish species across two protected, ecologically diverse landscapes remained relatively stable. Furthermore, we highlight the efficacy of national parks in maintaining freshwater fish biodiversity amidst rapid global change.

New Jersey, Pennsylvania, West Virginia↗

USGS2024

Multistage time-to-event models improve survival inference by partitioning mortality processes of tracked organisms

Advances in tagging technologies are expanding opportunities to estimate survival of fish and wildlife populations. Yet, capture and handling effects could impact survival outcomes and bias inference about natural mortality processes. We developed a multistage time-to-event model that can partition the survival process into sequential phases that reflect the tagged animal experience, including handling and release mortality, post-release recovery mortality, and subsequently, natural mortality. We demonstrate performance of multistage survival models through simulation testing and through fish and bird telemetry case studies. Models are implemented in a Bayesian framework and can accommodate left, right, and interval censorship events. Our results indicate that accurate survival estimates can be achieved with reasonable sample sizes ( n ≈ 100 + ) "> 𝑛≈100+) and that multimodel inference can inform hypotheses about the configuration and length of survival stages needed to adequately describe mortality processes for tracked specimens. While we focus on survival estimation for tagged fish and wildlife populations, multistage time-to-event models could be used to understand other phenomena of interest such as migration, reproduction, or disease events across a range of taxa including plants and insects.

Scientific Reports↗

USGS2023

A multi-level modeling approach to guide management of female feral hogs in Great Smoky Mountains National Park

We trapped, anesthetized, and fit 16 female feral swine ( Sus scrofa ) with Global Positioning System (GPS) collars in Great Smoky Mountains National Park (GRSM) to develop predictive summer and winter models for more effective population control efforts. Given the highly diverse habitat and topography in GRSM and the spatial extent of our dataset, we employed Step Selection Function (SSF) to evaluate resource selection at the 3 rd -order level and Resource Selection Function (RSF) models at the 2 nd -order level for both summer and winter seasons. The summer SSF and RSF models suggested relatively similar levels of selection, whereas the winter models differed by method. We created a straightforward consensus model to better visualize the agreement and constraints of each set of models. In summer, feral swine used lower slopes regardless of elevation, especially those closer to human-dominated spaces such as along paved and gravel roadways. In winter, feral swine maintained preference for lower slopes but preferred oak-dominated forest areas and selection for human development was less than in summer. Wildlife managers can use these models to better focus feral swine surveillance and management in GRSM. Managers can identify areas of high use by season and plan control activities that are both accessible and highly efficient. The combination and consensus framework presented here can be applied to other systems where species’ habitat selection may result in incongruous results across different levels of selection or seasons of interest.

Tennessee↗

USGS2021

Caution is warranted when using animal space-use and movement to infer behavioral states

Background Identifying the behavioral state for wild animals that can’t be directly observed is of growing interest to the ecological community. Advances in telemetry technology and statistical methodologies allow researchers to use space-use and movement metrics to infer the underlying, latent, behavioral state of an animal without direct observations. For example, researchers studying ungulate ecology have started using these methods to quantify behaviors related to mating strategies. However, little work has been done to determine if assumed behaviors inferred from movement and space-use patterns correspond to actual behaviors of individuals. Methods Using a dataset with male and female white-tailed deer location data, we evaluated the ability of these two methods to correctly identify male-female interaction events (MFIEs). We identified MFIEs using the proximity of their locations in space as indicators of when mating could have occurred. We then tested the ability of utilization distributions (UDs) and hidden Markov models (HMMs) rendered with single sex location data to identify these events. Results For white-tailed deer, male and female space-use and movement behavior did not vary consistently when with a potential mate. There was no evidence that a probability contour threshold based on UD volume applied to an individual’s UD could be used to identify MFIEs. Additionally, HMMs were unable to identify MFIEs, as single MFIEs were often split across multiple states and the primary state of each MFIE was not consistent across events. Conclusions Caution is warranted when interpreting behavioral insights rendered from statistical models applied to location data, particularly when there is no form of validation data. For these models to detect latent behaviors, the individual needs to exhibit a consistently different type of space-use and movement when engaged in the behavior. Unvalidated assumptions about that relationship may lead to incorrect inference about mating strategies or other behaviors.

Movement Ecology↗

USGS2018

Time-varying predatory behavior is primary predictor of fine-scale movement of wildland-urban cougars

Background While many species have suffered from the detrimental impacts of increasing human population growth, some species, such as cougars ( Puma concolor ), have been observed using human-modified landscapes. However, human-modified habitat can be a source of both increased risk and increased food availability, particularly for large carnivores. Assessing preferential use of the landscape is important for managing wildlife and can be particularly useful in transitional habitats, such as at the wildland-urban interface. Preferential use is often evaluated using resource selection functions (RSFs), which are focused on quantifying habitat preference using either a temporally static framework or researcher-defined temporal delineations. Many applications of RSFs do not incorporate time-varying landscape availability or temporally-varying behavior, which may mask conflict and avoidance behavior. Methods Contemporary approaches to incorporate landscape availability into the assessment of habitat selection include spatio-temporal point process models, step selection functions, and continuous-time Markov chain (CTMC) models; in contrast with the other methods, the CTMC model allows for explicit inference on animal movement in continuous-time. We used a hierarchical version of the CTMC framework to model speed and directionality of fine-scale movement by a population of cougars inhabiting the Front Range of Colorado, U.S.A., an area exhibiting rapid population growth and increased recreational use, as a function of individual variation and time-varying responses to landscape covariates. Results We found evidence for individual- and daily temporal-variability in cougar response to landscape characteristics. Distance to nearest kill site emerged as the most important driver of movement at a population-level. We also detected seasonal differences in average response to elevation, heat loading, and distance to roads. Motility was also a function of amount of development, with cougars moving faster in developed areas than in undeveloped areas. Conclusions The time-varying framework allowed us to detect temporal variability that would be masked in a generalized linear model, and improved the within-sample predictive ability of the model. The high degree of individual variation suggests that, if agencies want to minimize human-wildlife conflict management options should be varied and flexible. However, due to the effect of recursive behavior on cougar movement, likely related to the location and timing of potential kill-sites, kill-site identification tools may be useful for identifying areas of potential conflict.

Colorado↗

USGS2017

Spatial ecology and movement of reintroduced Canada lynx

Understanding movement behavior and identifying areas of landscape connectivity is critical for the conservation of many species. However, collecting fine‐scale movement data can be prohibitively time consuming and costly, especially for rare or endangered species, whereas existing data sets may provide the best available information on animal movement. Contemporary movement models may not be an option for modeling existing data due to low temporal resolution and large or unusual error structures, but inference can still be obtained using a functional movement modeling approach. We use a functional movement model to perform a population‐level analysis of telemetry data collected during the reintroduction of Canada lynx to Colorado. Little is known about southern lynx populations compared to those in Canada and Alaska, and inference is often limited to a few individuals due to their low densities. Our analysis of a population of Canada lynx fills significant gaps in the knowledge of Canada lynx behavior at the southern edge of its historical range. We analyzed functions of individual‐level movement paths, such as speed, residence time, and tortuosity, and identified a region of connectivity that extended north from the San Juan Mountains, along the continental divide, and terminated in Wyoming at the northern edge of the Southern Rocky Mountains. Individuals were able to traverse large distances across non‐boreal habitat, including exploratory movements to the Greater Yellowstone area and beyond. We found evidence for an effect of seasonality and breeding status on many of the movement quantities and documented a potential reintroduction effect. Our findings provide the first analysis of Canada lynx movement in Colorado and substantially augment the information available for conservation and management decisions. The functional movement framework can be extended to other species and demonstrates that information on movement behavior can be obtained using existing data sets.

Wyoming↗

USGS2016

Hierarchical animal movement models for population-level inference

New methods for modeling animal movement based on telemetry data are developed regularly. With advances in telemetry capabilities, animal movement models are becoming increasingly sophisticated. Despite a need for population-level inference, animal movement models are still predominantly developed for individual-level inference. Most efforts to upscale the inference to the population level are either post hoc or complicated enough that only the developer can implement the model. Hierarchical Bayesian models provide an ideal platform for the development of population-level animal movement models but can be challenging to fit due to computational limitations or extensive tuning required. We propose a two-stage procedure for fitting hierarchical animal movement models to telemetry data. The two-stage approach is statistically rigorous and allows one to fit individual-level movement models separately, then resample them using a secondary MCMC algorithm. The primary advantages of the two-stage approach are that the first stage is easily parallelizable and the second stage is completely unsupervised, allowing for an automated fitting procedure in many cases. We demonstrate the two-stage procedure with two applications of animal movement models. The first application involves a spatial point process approach to modeling telemetry data, and the second involves a more complicated continuous-time discrete-space animal movement model. We fit these models to simulated data and real telemetry data arising from a population of monitored Canada lynx in Colorado, USA.

Environmetrics↗

USGS2015

A functional model for characterizing long-distance movement behaviour

Advancements in wildlife telemetry techniques have made it possible to collect large data sets of highly accurate animal locations at a fine temporal resolution. These data sets have prompted the development of a number of statistical methodologies for modelling animal movement. Telemetry data sets are often collected for purposes other than fine-scale movement analysis. These data sets may differ substantially from those that are collected with technologies suitable for fine-scale movement modelling and may consist of locations that are irregular in time, are temporally coarse or have large measurement error. These data sets are time-consuming and costly to collect but may still provide valuable information about movement behaviour. We developed a Bayesian movement model that accounts for error from multiple data sources as well as movement behaviour at different temporal scales. The Bayesian framework allows us to calculate derived quantities that describe temporally varying movement behaviour, such as residence time, speed and persistence in direction. The model is flexible, easy to implement and computationally efficient. We apply this model to data from Colorado Canada lynx ( Lynx canadensis ) and use derived quantities to identify changes in movement behaviour.

Methods in Ecology and Evolution↗

USGS2014

Effect of hunter selectivity on harvest rates of radio-collared white-tailed deer in Pennsylvania

Radio transmitters are a commonly used tool for monitoring the fates of harvested species, although little research has been devoted to whether a visible radio transmitter changes a hunters' willingness to harvest that animal. We initially surveyed deer hunters to assess their willingness to harvest radio-collared deer and predicted radio collars were unlikely to affect the harvest of antlerless deer, but hunters may be less willing to harvest small-antlered males with radio collars compared to large-antlered males. We fitted white-tailed deer ( Odocoileus virginianus ) with radio collars that were visible to hunters or with ear-tag transmitters or ear-tags that were difficult to detect visually and estimated if harvest rates differed among marking methods. For females, the best model failed to detect an effect of radio collars on harvest rates. Also, we failed to detect a difference between male deer fitted with radio collars and ear-tag transmitters. When we compared males fitted with radio collars versus ear tags, we found harvest rate patterns were opposite to our predictions, with lower harvest rates for adult males fitted with radio collars and higher harvest rates for yearling males fitted with radio collars. Our study suggests that harvest rate estimates generated from a sample of deer fitted with visible radio collars can be representative of the population of inference.

Pennsylvania↗