Geology topics

Vratika Chaudhary

Publications and source records attributed to Vratika Chaudhary.

3 recordsLinked to original sources

USGS2023

Does coat colour influence survival? A test in a cyclic population of snowshoe hares

Some mammal species inhabiting high-latitude biomes have evolved a seasonal moulting pattern that improves camouflage via white coats in winter and brown coats in summer. In many high-latitude and high-altitude areas, the duration and depth of snow cover has been substantially reduced in the last five decades. This reduction in depth and duration of snow cover may create a mismatch between coat colour and colour of the background environment, and potentially reduce the survival rate of species that depend on crypsis. We used long-term (1977–2020) field data and capture–mark–recapture models to test the hypothesis that whiteness of the coat influences winter apparent survival in a cyclic population of snowshoe hares ( Lepus americanus ) at Kluane, Yukon, Canada. Whiteness of the snowshoe hare coat in autumn declined during this study, and snowshoe hares with a greater proportion of whiteness in their coats in autumn survived better during winter. However, whiteness of the coat in spring did not affect subsequent summer survival. These results are consistent with the hypothesis that the timing of coat colour change in autumn can reduce overwinter survival. Because declines in cyclic snowshoe hare populations are strongly affected by low winter survival, the timing of coat colour change may adversely affect snowshoe hare population dynamics as climate change continues.

Proceedings of the Royal Society of London Ser B.↗

USGS2022

Estimating abundance, temporary emigration and the pattern of density dependence in a cyclic snowshoe hare population in Yukon, Canada

Estimates of demographic parameters based on capture-mark-recapture (CMR) methods may be biased when some individuals in the population are temporarily unavailable for capture (temporary emigration). We estimated snowshoe hare abundance, apparent survival, and probability of temporary emigration in a population of snowshoe hares (Lepus americanus Erxleben 1777) in the Yukon using Pollock’s robust design CMR model, and population density using spatially-explicit CMR models. Survival rates strongly varied among cyclic phases, seasons, and across five population cycles. We found strong evidence that temporary emigration was Markovian (i.e., non-random), suggesting that it varied among individuals that were temporary emigrant in the previous sampling period and those that were present in the sampled area. The probability of temporary emigration for individuals that were in the study area during the previous sampling occasion (γ´´) varied among cycles. Probability that individuals that were temporarily absent from the sampled area would remain temporary emigrants (γ´) showed strongly seasonal pattern, low in winter and high during summers. Snowshoe hare population density ranged from 0.017 (0.015–0.05) hares/ha to 4.43 (3.90–5.00) hares/ha and large-scale cyclical fluctuation. Autocorrelation functions and autoregressive analyses revealed that our study population exhibited statistically significant cyclic fluctuations, with a periodicity of 9-10 years.

Yukon↗

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↗