Geology topics

John E. DePue

Publications and source records attributed to John E. DePue.

3 recordsLinked to original sources

USGS2023

Sex-biased infections scale to population impacts for an emerging wildlife disease

Demographic factors are fundamental in shaping infectious disease dynamics. Aspects of populations that create structure, like age and sex, can affect patterns of transmission, infection intensity and population outcomes. However, studies rarely link these processes from individual to population-scale effects. Moreover, the mechanisms underlying demographic differences in disease are frequently unclear. Here, we explore sex-biased infections for a multi-host fungal disease of bats, white-nose syndrome, and link disease-associated mortality between sexes, the distortion of sex ratios and the potential mechanisms underlying sex differences in infection. We collected data on host traits, infection intensity and survival of five bat species at 42 sites across seven years. We found females were more infected than males for all five species. Females also had lower apparent survival over winter and accounted for a smaller proportion of populations over time. Notably, female-biased infections were evident by early hibernation and likely driven by sex-based differences in autumn mating behaviour. Male bats were more active during autumn which likely reduced replication of the cool-growing fungus. Higher disease impacts in female bats may have cascading effects on bat populations beyond the hibernation season by limiting recruitment and increasing the risk of Allee effects.

Proceedings of the Royal Society B: Biological Sci↗

USGS2019

Satellite-detected forest disturbance forecasts American marten population decline: The case for supportive space-based monitoring

Limited monitoring resources often constrain rigorous monitoring practices to species or populations of conservation concern. Insufficient monitoring can induce a tautology as lack of monitoring resources makes it difficult to determine whether a species or population deserves additional monitoring resources. When in-situ monitoring resources are limited, remote habitat monitoring could be a useful supplementary tool, as linking parameterized species distribution models to spatially explicit time-series of environmental correlates allows iterative prediction of population change. Yet the performance of predictive forecasts or hindcasts has been difficult to evaluate. We paired contemporary field data, historic population estimates, and a remotely-sensed archive of landscape change to evaluate predictions of American marten ( Martes americana ) population decline owing to habitat loss in Maine, USA. We estimated contemporary spatial patterns in marten density relative to landscape disturbance with spatial capture-recapture models. We compared current density estimates to historical density calculations to evaluate population decline, and compared historical calculations to habitat-based model predictions to evaluate the efficacy of habitat monitoring as a proxy for direct monitoring. Marten density was negatively associated with the proportion of surrounding regenerating forest, and point estimates within focal townships were 50–80% lower than historical calculations. Habitat-based hindcasts of marten density across our entire focal area interest suggested a smaller population decline (roughly 50%) within our focal area. Thus, although habitat-based predictions underpredicted marten decline, they provided correct directional inference. Habitat monitoring and predictions from species distribution models may provide useful inference about population changes given trends in habitat at limited expense when in-situ information is lacking.

Maine↗

USGS2017

Pairing field methods to improve inference in wildlife surveys while accommodating detection covariance

It is common to use multiple field sampling methods when implementing wildlife surveys to compare method efficacy or cost efficiency, integrate distinct pieces of information provided by separate methods, or evaluate method-specific biases and misclassification error. Existing models that combine information from multiple field methods or sampling devices permit rigorous comparison of method-specific detection parameters, enable estimation of additional parameters such as false-positive detection probability, and improve occurrence or abundance estimates, but with the assumption that the separate sampling methods produce detections independently of one another. This assumption is tenuous if methods are paired or deployed in close proximity simultaneously, a common practice that reduces the additional effort required to implement multiple methods and reduces the risk that differences between method-specific detection parameters are confounded by other environmental factors. We develop occupancy and spatial capture–recapture models that permit covariance between the detections produced by different methods, use simulation to compare estimator performance of the new models to models assuming independence, and provide an empirical application based on American marten ( Martes americana ) surveys using paired remote cameras, hair catches, and snow tracking. Simulation results indicate existing models that assume that methods independently detect organisms produce biased parameter estimates and substantially understate estimate uncertainty when this assumption is violated, while our reformulated models are robust to either methodological independence or covariance. Empirical results suggested that remote cameras and snow tracking had comparable probability of detecting present martens, but that snow tracking also produced false-positive marten detections that could potentially substantially bias distribution estimates if not corrected for. Remote cameras detected marten individuals more readily than passive hair catches. Inability to photographically distinguish individual sex did not appear to induce negative bias in camera density estimates; instead, hair catches appeared to produce detection competition between individuals that may have been a source of negative bias. Our model reformulations broaden the range of circumstances in which analyses incorporating multiple sources of information can be robustly used, and our empirical results demonstrate that using multiple field-methods can enhance inferences regarding ecological parameters of interest and improve understanding of how reliably survey methods sample these parameters.

Maine↗