Geology topics

Travis Mcdevitt-Galles

Publications and source records attributed to Travis Mcdevitt-Galles.

4 recordsLinked to original sources

USGS2025

Hosts, pathogens and hot ponds: Thermal mean and variability contribute to spatial patterns of chytrid infection

Temperature is a primary driver of heterogeneity in host–pathogen dynamics and understanding how patch-scale temperature affects landscape-scale patterns of pathogen infection is key to effective monitoring and management. In field studies, both temperature variability and mean temperature are often related to infection of ectothermic animals by fungal pathogens, and although these factors vary spatiotemporally, their contributions to infection outcomes are rarely decomposed into spatial and temporal components. We studied how patch-scale thermal conditions (mean and variability) affect infection of eastern newts Notophthalmus viridescens by Batrachochytrium dendrobatidis ( Bd ), with a special focus on disentangling spatial versus temporal contributions of thermal conditions to infection outcomes. We measured in situtemperature and Bd infection across 20 ponds in two years in southeastern Wisconsin, USA to 1) understand thermal mediation of infection and 2) quantify whether seasonal and/or among-site variation in thermal conditions drive heterogeneity in host–pathogen interactions. In our system, thermal mean and variability covaried tightly, necessitating the creation of a single index to capture both components. We found that 1) this index of thermal mean and variability was strongly and nonlinearly related to Bd infection and 2) differences among patches in thermal conditions drove this relationship, highlighting that variation in patch-level conditions can drive heterogenous host–pathogen outcomes across landscapes. Our research collectively reveals insights about the importance of local, patch-level conditions for mediating disease risk at broader scales.

Wisconsin↗

USGS2025

Rapid risk assessment framework to estimate potential for spillback at human-wildlife interfaces

More than 60% of emerging infectious diseases of humans have a wildlife origin, and when these diseases spread through human populations to new geographical areas, there is a considerable risk of spillback from humans to wildlife species. Spillback events can have severe consequences for wildlife populations, where the disease may cause morbidity and mortality, and human populations, where the establishment in wildlife may lead to prolonged transmission or new exposures in humans. Mitigating these consequences requires identifying the key risk factors that lead to human–wildlife transmission events and implementing risk-reducing actions, a challenge given that cross-species transmission events are rare and often data deficient. To identify potential species and locations that are most likely to lead to these rare events, we developed a spatially explicit, rapid risk assessment framework that incorporates three components of the spillback process: wildlife susceptibility, wildlife exposure, and pathogen introduction pressure. To demonstrate the broad applicability of our framework, we conducted a rapid risk assessment on two recent emerging zoonotic pathogens in humans, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and mpox, to determine the relative spillback risk to wild mammalian species in the continental United States. The rapid risk assessment identified both species and locations with higher than expected spillback risk, providing managers and researchers with valuable information to prioritize surveillance and risk-mitigation actions. Our framework represents a rapid and flexible approach to assess the risks of spillback to wildlife populations during rapidly evolving zoonotic disease outbreaks.

Transboundary and Emerging Diseases↗

USGS2024

Partly cloudy with a chance of mosquitoes: Developing a flexible approach to forecasting mosquito populations

Climate-induced shifts in mosquito phenology and population structure have important implications for the health of humans and wildlife. The timing and intensity of mosquito interactions with infected and susceptible hosts are a primary determinant of vector-borne disease dynamics. Like most ectotherms, rates of mosquito development and corresponding phenological patterns are expected to change under shifting climates. However, developing accurate forecast of mosquito phenology under climate change that can be used to inform management programs remains challenging despite an abundance of available data. As climate change will have variable effects on mosquito demography and phenology across species it is vital that we identify associated traits which may explain the observed variation. Here, we review a suite of modeling approaches that could be applied to generate forecasts of mosquito activity under climate change and evaluate the strengths and weaknesses of the different approaches. We describe four primary life-history and physiological traits that can be used to constrain models and demonstrate how this prior information can be harnessed to develop a more general understanding of how mosquito activity will shift under changing climates. Combining a trait-based approach with appropriate modeling techniques can allow for the development of actionable, flexible, and multi-scale forecasts of mosquito population dynamics and phenology for diverse stakeholders.

Michigan, Wisconsin↗

USGS2023

Dynamic population models with temporal preferential sampling to infer phenology

To study population dynamics, ecologists and wildlife biologists typically use relative abundance data, which may be subject to temporal preferential sampling. Temporal preferential sampling occurs when the times at which observations are made and the latent process of interest are conditionally dependent. To account for preferential sampling, we specify a Bayesian hierarchical abundance model that considers the dependence between observation times and the ecological process of interest. The proposed model improves relative abundance estimates during periods of infrequent observation and accounts for temporal preferential sampling in discrete time. Additionally, our model facilitates posterior inference for population growth rates and mechanistic phenometrics. We apply our model to analyze both simulated data and mosquito count data collected by the National Ecological Observatory Network. In the second case study, we characterize the population growth rate and relative abundance of several mosquito species in the Aedes genus. Supplementary materials accompanying this paper appear on-line.

Journal of Agricultural, Biological, and Environme↗