Geology topics

Kristin J. Bondo

Publications and source records attributed to Kristin J. Bondo.

6 recordsLinked to original sources

USGS2026

Intraspecific contact among white-tailed deer: A literature review and chronic wasting disease case study

White-tailed deer ( Odocoileus virginianus ) are a valuable game mammal in the eastern United States necessitating detailed understanding of disease transmission. We conducted a literature review on intraspecific contact (i.e., interactions wherein disease transmission may occur) among deer. From 69 studies, we identified five themes underlying research on intraspecific deer contact: physical touch, social groups, spatial overlap, association rates, and social networks. Visual observations determined physical touch to be infrequent (< 2 touches/h) and indicated deer social groups were dependent on spatial dynamics of parturition and dispersal; most females remained with matriarchal family groups while males dispersed and formed bachelor groups. Assessed using global positioning system (GPS) monitoring, spatial overlap and association rates (i.e., instances of deer in close spatial–temporal proximity) were higher in correspondence to within-group social dynamics, and between-group scores were correspondingly low. Social network analyses indicated between-group transmission may be driven by socially dominant males, often termed super-spreaders (i.e., hosts infecting disproportionately high numbers of healthy individuals). We investigated these themes via a case study of deer infected with chronic wasting disease (CWD) in southcentral Pennsylvania, United States. We assessed spatial overlap and association rates using GPS monitoring data from 180 deer. Our results supported findings in the literature, showing strong correlations among spatial overlap, association rates, and correlated movements. Further, CWD-infected deer exhibited similar association rates to deer in which CWD was not detected. Our literature review and case study indicate direct transmission of CWD and other diseases is likely greatest within social groups following seasonal behavioral dynamics and that between-group transmission is likely driven by males via dispersal and mating interactions. Our results may be used to inform population management models with future work focused on high resolution spatial assessments of transmission in localized areas.

Pennsylvania↗

USGS2026

Generating geochemical and mineralogy distributions of soil in the conterminous United States using Bayesian hierarchical spatial models

Characterizing geochemical and mineralogical soil distributions across large spatial extents is essential for understanding mineral resources, ecosystem processes, and environmental risks. Rasters of soil geochemical distributions for the conterminous United States, however, are limited. We present a Bayesian modeling workflow and tool for generating predictive geochemical and mineralogy distribution maps for the conterminous United States using integrated nested Laplace approximation (INLA) with the stochastic partial differential equation approach. By modeling soil geostatistical data with environmental covariates (soil properties, topography, climate, and land cover), we generate predictive distributions of soil geochemistry that can be mapped or extracted for further analyses. As an example, we model the spatial distribution of trace elements in soil relevant to vertebrate health (cobalt, copper, iron, manganese, selenium, and zinc) and provide a workflow that can be used to generate and visualize predictive distributions of 39 other major and trace elements and 21 minerals of the soil survey, supporting a variety of ecological, environmental, and agricultural applications.

MethodsX↗

USGS2024

Discordance between taxonomy and population genomic data: An avian example relevant to the United States Endangered Species Act

Population genomics can reveal cryptic biological diversity that may impact fitness while simultaneously serving to delineate relevant conservation units. Here, we leverage the power of whole-genome resequencing for conservation by studying 433 individual lesser prairie-chicken ( Tympanuchus pallidicinctus ; LEPC, a federally endangered species of conservation concern in the United States) and greater prairie-chicken ( Tympanuchus cupido ; GRPC, a legally huntable species throughout much of its range). The genomic diversity of two formally recognized distinct population segments (DPSs) of LEPCs is similar, but they are genetically distinct. Neither DPS is depleted of its genomic diversity, neither is especially inbred, and temporal diversity is relatively stable in both conservation units. Interspecific differentiation between the two species was only slightly higher than that observed between LEPC DPSs, due largely to bidirectional introgression. The high resolution provided by our dataset identified a genomic continuum between the two species such that individuals sampled from the hybrid zone were imperfectly assigned to their presumptive species when considering only their physical characteristics. The admixture between the two species is reflected in the spectrum of individual ancestry coefficients, which has legal implications for the “take” of individuals under the Endangered Species Act. Overall, our data highlight the recurring dissonance between static policies and dynamic species boundaries that are increasingly obvious in the population genomic era.

Colorado, Kansas, Nebraska, New Mexico, Oklahoma, ↗

USGS2024

Comparing risk of chronic wasting disease occurrence using Bayesian hierarchical spatial models and different surveillance types

Spatial modeling of wildlife diseases can be used to describe patterns of disease risk, understand biological mechanisms of disease occurrence, and for spatial prediction. Risk of wildlife disease occurrence in relation to environmental variables is often modeled and predicted using Markov chain Monte Carlo (MCMC) methods, which are unsuitable for large datasets and those covering large spatial extents. Integrated nested Laplace approximation (INLA) and INLA using the stochastic partial differential equation (INLA-SPDE) approach have become popular alternatives to MCMC for Bayesian inference because of their fast computational time and ability to process large datasets. Studies investigating risk of disease occurrence in wildlife, to our knowledge, have not yet compared Bayesian hierarchical spatial models over large spatial extents using real world data. Using chronic wasting disease (CWD) surveillance data from white-tailed deer ( Odocoileus virginianus ) collected in Pennsylvania, United States, as a case study, we first demonstrate how parameter estimates compare among MCMC, INLA, and INLA-SPDE modeling frameworks. We then model CWD (detected/non-detected) using INLA-SPDE over a much larger spatial extent than has been conducted previously for this disease to determine how surveillance type (e.g., hunter harvest, roadkill, or all surveillance) influences model parameters and predicted risk of CWD occurrence at locations not sampled. Fixed effects considered in the models included deer age and sex, elevation, slope, distance to streams, percent clay, and proportion of two habitat classes (forest and open) known to influence deer movements. We found INLA to produce comparable estimates to MCMC and permit modeling large datasets covering expansive spatial extents much faster and more efficiently than MCMC. We identified potential biases in surveillance types, indicating the value of including all surveillance in models rather than only a single type. Comparing modeling tools available for mapping diseases of wildlife in relation to ecological variables at large spatial extents will guide future modeling efforts for CWD and other wildlife diseases. Understanding spatial patterns of CWD using different surveillance types can help improve understanding of CWD disease outbreaks, assist with control of CWD through geographical targeting, and inform future CWD surveillance efforts.

Ecological Modeling↗

USGS2023

A highly-contiguous and annotated genome assembly of the Lesser Prairie-Chicken (Tympanuchus pallidicinctus).

The Lesser Prairie-Chicken ( Tympanuchus pallidicinctus ; LEPC) is an iconic North American prairie grouse, renowned for ornate and spectacular breeding season displays. Unfortunately, the species has disappeared across much of its historical range, with corresponding precipitous declines in contemporary population abundance, largely due to climatic and anthropogenic factors. These declines led to a 2022 US Fish and Wildlife decision to identify and list two distinct population segments (DPSs; i.e., northern and southern DPSs) as threatened or endangered under the 1973 Endangered Species Act. Herein, we describe an annotated reference genome that was generated from a LEPC sample collected from the southern DPS. We chose a representative from the southern DPS because of the potential for introgression in the northern DPS, where some populations hybridize with the Greater Prairie-Chicken ( Tympanuchus cupido ). This new LEPC reference assembly consists of 206 scaffolds, an N50 of 45 Mb, and 15,563 predicted protein-coding genes. We demonstrate the utility of this new genome assembly by estimating genome-wide heterozygosity in a representative LEPC and in related species. Heterozygosity in a LEPC sample was 0.0024, near the middle of the range (0.0003–0.0050) of related species. Overall, this new assembly provides a valuable resource that will enhance evolutionary and conservation genetic research in prairie grouse.

Genome Biology and Evolution↗

USGS2023

Spatial modeling of two mosquito vectors of West Nile virus using integrated nested Laplace approximations

The abundance of Culex restuans and Culex pipiens in relation to ecological predictors is poorly understood in regions of the United States where their ranges overlap. It is suspected that these species play different roles in spreading West Nile virus (WNV) in these regions, but few studies have modeled these species separately or accounted for spatial correlation using Bayesian methods. We used mosquito surveillance data collected by the Pennsylvania Department of Environmental Protection from 2002 to 2016 and integrated nested Laplace approximations with the stochastic partial differential equation approach to predict C. restuans and C. pipiens abundance in relation to several ecological predictors. We then made a predictive risk surface of abundance for each species at locations that were not sampled. Explanatory variables in the models included ecological variables previously described to be important predictors of the abundance of these mosquito species. Developed habitat, temperature, and precipitation were important predictor variables for the abundance of C. restuans , whereas developed habitat, snow water equivalent, and normalized difference water index were important predictor variables for the abundance of C. pipiens . The abundance of C. restuans had a negative relationship with developed habitat in contrast to C. pipiens abundance, which had a positive relationship with developed habitat. Julian date was modeled as a temporal trend for both species and showed C. restuans to be more abundant from late April through late June and C. pipiens to be more abundant from July through September. A seasonal crossover was observed between these two species on Julian day 185, 4 July. We observed different spatial patterns of abundance in the predictive risk maps of each of the species. Our results indicate that modeling the abundance of these species spatially and separately in regions where these two mosquito vectors coexist can help gain further insight into understanding the epidemiology of WNV in human and susceptible animal populations.

Pennsylvania↗