Samantha M Wisely | Search USGS

USGS2020

A multi-state occupancy modelling framework for robust estimation of disease prevalence in multi-tissue disease systems

Given the public health, economic and conservation implications of zoonotic diseases, their effective surveillance is of paramount importance. The traditional approach to estimating pathogen prevalence as the proportion of infected individuals in the population is biased because it fails to account for imperfect detection. A statistically robust way to reduce bias in prevalence estimates is to obtain repeated samples (or sample many tissues in multi‐tissue disease systems) and to apply statistical methods that account for imperfect detection and permit the interdependence of the infection process across multiple tissues. We developed a multi‐state occupancy modelling framework which considers two scenarios about the infection process, one where no assumptions about the dependencies among the tissues are made (general), and another where dependence among tissues is not permitted (constrained). We applied this framework to pseudorabies virus (PrV) DNA detection data obtained from whole blood; and oral, nasal and genital mucosa of 510 feral swine Sus scrofa during the years 2014–2016 in Florida, USA. The constrained model was better supported by data. PrV prevalence estimates varied among tissues and were higher than the naïve estimates, ranging from to 0.06 (CI: 0.02–0.14) in genital to 0.54 (CI: 0.14 ‐ 0.82) in nasal tissue. Probability of PrV detection ranged from 0.11 (CI: 0.06–0.18) in nasal to 0.51 (CI: 0.21–0.81) in genital tissue. PrV prevalence was not affected by the age or sex of the animal or the year of sampling, but prevalence increased as drought severity increased. The conditional probability of detecting PrV given infection in at least one tissue type within an individual was highest for nasal tissue, suggesting that nasal is the best tissue to sample for PrV surveillance if only one tissue can be sampled, at least for systems with tissue‐specific prevalence and detection probabilities similar to ours. Synthesis and applications . We focused on inferences about pathogen prevalence in multi‐tissue disease systems, dealing with both nondetection and potential dependencies among tissues in infection status. We found strong evidence of variation in both prevalence and detection probabilities among tissues. Our results emphasize the importance of sampling multiple tissues and of applying inference methods that account for imperfect detection in the surveillance of systemic diseases. The multi‐state modelling framework is broadly applicable to the surveillance of pathogens that infect multiple tissues and can be used even when the infection status of the pathogen in one tissue may depend on the infection status of the pathogen in other tissue(s).

Journal of Applied Ecology↗

USGS2017

Quantifying drivers of wild pig movement across multiple spatial and temporal scales

Background The movement behavior of an animal is determined by extrinsic and intrinsic factors that operate at multiple spatio-temporal scales, yet much of our knowledge of animal movement comes from studies that examine only one or two scales concurrently. Understanding the drivers of animal movement across multiple scales is crucial for understanding the fundamentals of movement ecology, predicting changes in distribution, describing disease dynamics, and identifying efficient methods of wildlife conservation and management. Methods We obtained over 400,000 GPS locations of wild pigs from 13 different studies spanning six states in southern U.S.A., and quantified movement rates and home range size within a single analytical framework. We used a generalized additive mixed model framework to quantify the effects of five broad predictor categories on movement: individual-level attributes, geographic factors, landscape attributes, meteorological conditions, and temporal variables. We examined effects of predictors across three temporal scales: daily, monthly, and using all data during the study period. We considered both local environmental factors such as daily weather data and distance to various resources on the landscape, as well as factors acting at a broader spatial scale such as ecoregion and season. Results We found meteorological variables (temperature and pressure), landscape features (distance to water sources), a broad-scale geographic factor (ecoregion), and individual-level characteristics (sex-age class), drove wild pig movement across all scales, but both the magnitude and shape of covariate relationships to movement differed across temporal scales. Conclusions The analytical framework we present can be used to assess movement patterns arising from multiple data sources for a range of species while accounting for spatio-temporal correlations. Our analyses show the magnitude by which reaction norms can change based on the temporal scale of response data, illustrating the importance of appropriately defining temporal scales of both the movement response and covariates depending on the intended implications of research (e.g., predicting effects of movement due to climate change versus planning local-scale management). We argue that consideration of multiple spatial scales within the same framework (rather than comparing across separate studies post-hoc) gives a more accurate quantification of cross-scale spatial effects by appropriately accounting for error correlation.

Movement Ecology↗