Geology topics

Jennifer Mullinax

Publications and source records attributed to Jennifer Mullinax.

4 recordsLinked to original sources

USGS2026

Improved estimates of relative occurrence and abundance using opportunistic surveys and presence-only observations: A zero-inflated integrated species distribution model

Modeling tools for estimating and forecasting shifts in species distributions are becoming increasingly valuable for conservation planning and response. This is especially true for wild bird populations, which have been declining across habitats and regions. Species distribution models (SDM) represent a diverse set of tools with options for addressing various sources of bias. Complex spatial processes associated with rare or clustered species can be accounted for using zero-inflated SDMs, whereas biased survey data can be integrated with additional data sources to improve estimates. Each option addresses an important and common source of bias, but the two SDM frameworks have not been implemented together previously. We present a novel zero-inflated extension of an integrated SDM framework for a Poisson regression (ZI-iSDM), allowing for the estimation of independent occurrence and abundance processes by integrating opportunistic survey and presence-only data. We validated performance of this ZI-iSDM using simulated datasets under different degrees of species rarity and density on the landscape as well as sampling bias in opportunistic datasets. We additionally applied this model to real survey data for multiple wild bird species using publicly available observation data combined with open-access environmental information to describe habitat associations. We found that integrating presence-only data, such as banding or harvest events, can compensate for potential deficiencies in opportunistic surveys by expanding sampling to be more representative of available and used habitat. Additionally, models that first differentiated occurrence and abundance using a ZI term were better suited for approximating distributions of spatially clustered species.

Ecological Modeling↗

USGS2026

Feathers and flu: Identifying data gaps in avian influenza host dynamics to prioritize wildlife conservation

Highly pathogenic avian influenza viruses (HPAIV) have had disastrous, worldwide effects on wild birds and domestic poultry since the emergence of the A/goose/Guangdong/1/1996 (Gs/GD/96) lineage. The currently circulating H5N1 clade 2.3.4.4b has an expanded set of susceptible hosts, including many migratory wild birds, and is associated with higher transmission rates, increased susceptibility among wild bird hosts, and a greater number of wildlife reservoirs. Certain wild bird life-history strategies and behaviors have been suggested to explain avian hosts’ susceptibility and exposure to HPAIV. These biological traits include gregariousness, such as colonial nesting and mixed flock foraging, predation or scavenging on wild birds, and association with aquatic habitats. Variation in host infection responses (e.g., infectability, shedding rates and duration, mortality rate, antibody development) informs the overall infection risk across avian species, yet the specific role of biological traits is often inconsistent and unclear across taxa. Moreover, the interactions and potential compounding effects among these biological traits remain largely unknown. To develop a more holistic understanding of cumulative risk across bird species, we integrate existing information on infection risk factors (i.e., susceptibility, immunological response, and behavioral traits) into a qualitative multivariate analysis. This approach enabled us to examine how infection risk factors relate to biological traits (e.g., phylogeny, physiology, behavior, species range) and to begin disentangling their complex interactions. We quantified and summarized these risk factors across host species and qualitatively ranked species by their viral responses along a proposed HPAIV response continuum, guided by expectations of traits and metrics associated with competence or vulnerability to HPAIV. In doing so, we aimed to better understand how viral responses and biological traits synergistically interact to influence cumulative risk across wild bird species. This work broadly expands on the previous avian influenza literature, which has focused on Anseriformes and Charadriiformes as primary viral reservoirs. We tie our findings to effective disease management responses with links to risk components, including descriptions of potential surveillance strategies applied to research and One Health goals, as well as a fuller understanding of how resources may be better deployed for rapid response when spillovers do inevitably occur. Additionally, we identified numerous areas where vital epidemiological information is lacking to best characterize the spread of these viruses. Ultimately, this improved understanding will help identify and inform disease management needs and decision making.

Wildlife Monographs↗

USGS2025

Evaluation of SARS-CoV-2 antibody detection methods for wild Cervidae

Wildlife surveillance programs often use serological data to monitor exposure to pathogens. Diagnostic sensitivity and specificity of a serological assay quantify the true positive and negative rates of the diagnostic assay, respectively. However, an assay’s accuracy can be affected by wild animals’ pathogen exposure history and quality of the sample collected, requiring separate estimates of an assay’s detection ability for wild-sampled animals where an animal’s true disease status is unknown (referred to hereafter as sampling sensitivity and specificity). We assessed the sampling sensitivity and specificity of a Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) surrogate virus neutralization test (sVNT) and conventional virus neutralization tests (cVNT) to detect antibodies for ancestral and Omicron B.1.1.529 variants of SARS-CoV-2 in wild white-tailed deer ( Odocoileus virginianus ) and mule deer ( Odocoileus hemionus ). We studied the influence of sample collection method using paired blood samples collected in serum separator tubes and on Nobuto strips from the same animal. Mean estimates of sampling sensitivity and specificity ranged from 0.21–0.95 and 0.94–1.00, respectively, varying by sample collection method, host species, and SARS-CoV-2 variant targeted by the assay. Broadly, sampling sensitivity was estimated to be higher for 1) sera collected in tubes, 2) detecting pre-Omicron SARS-CoV-2 variants, and 3) sVNT relative to cVNT assays. Sampling specificity tended to be high for all tests. We augmented our study with SARS-CoV-2 spike protein sequences derived from sampling locations and times coincident with white-tailed deer captures, finding common amino acid mutations relative to the sVNT Omicron antigen variant. The mutations may indicate that the SARS-CoV-2 variants circulating in cervids from 2021 through 2024 may be better adapted to cervid hosts and more closely related to variants that circulated in humans prior to Omicron variants. We conclude our study with an inter-test comparison of sVNT results, revealing that 40 % inhibition is an optimal threshold for test positivity when testing deer sera for responses to Omicron variant B.1.1.529, compared to the 30 % inhibition recommended for ancestral variants.

Colorado, Illinois, Minnesota, New York, Pennsylva↗

USGS2025

Avian influenza spillover into poultry: Environmental influences and biosecurity protections

With the continued spread of highly pathogenic avian influenza (HPAI), understanding the complex dynamics of virus transfer at the wild – agriculture interface is paramount. Spillover events (i.e., virus transfer from wild birds into poultry) are related to proximity to infected wild bird populations and environmental conditions. By accounting for such dynamics, we can take a combined approach to assess the impacts of biosecurity measures implemented at poultry farms while simultaneously accounting for their local risk levels. We implemented a Bayesian joint-likelihood logistic regression for the Continental U.S. comparing models of spatiotemporal risk according to land use, weather, and predicted waterfowl distributions followed by integrating a farm-level case-control questionnaire dataset focused on identifying trends in HPAI spillover risk associated with a farm's biosecurity practices. We found that estimates of waterfowl abundance, along with mean precipitation and temperature during winter, were most correlated with spatiotemporal HPAI risk. Additionally, we identified multiple biosecurity practices associated with reduced risk to HPAI, where the strongest relationships were related to litter decontamination treatments, vehicle wash stations, and avoiding shared dead-bird disposal sites with other farms. This model broadly guides surveillance of HPAI in wild and domestic populations, identifying when and where we are most likely to see increased instances of the virus while also providing insights into how poultry farms can better protect themselves from risk.

contiguous United States↗