Multiscale model of regional population decline in little brown bats due to white‐nose syndrome

Abstract

The introduced fungal pathogen Pseudogymnoascus destructans is causing decline of several species of bats in North America, with some even at risk of extinction or extirpation. The severity of the epidemic of white‐nose syndrome caused by P. destructans has prompted investigation of the transmission and virulence of infection at multiple scales, but linking these scales is necessary to quantify the mechanisms of transmission and assess population‐scale declines. We built a model connecting within‐hibernaculum disease dynamics of little brown bats to regional‐scale dispersal, reproduction, and disease spread, including multiple plausible mechanisms of transmission. We parameterized the model using the approach of plausible parameter sets, by comparing stochastic simulation results to statistical probes from empirical data on within‐hibernaculum prevalence and survival, as well as among‐hibernacula spread across a region. Our results are consistent with frequency‐dependent transmission between bats, support an important role of environmental transmission, and show very little effect of dispersal among colonies on metapopulation survival. The results help identify the influential parameters and largest sources of uncertainty. The model also offers a generalizable method to assess hypotheses about hibernaculum‐to‐hibernaculum transmission and to identify gaps in knowledge about key processes, and could be expanded to include additional mechanisms or bat species.

Phylogeographic analysis of Pseudogymnoascus destructans partitivirus-pa explains the spread dynamics of white-nose syndrome in North America

“…The authors found that the geographic heterogeneity associated with cave connectivity and winter length were the best predictors for their model. A multiscale process involving stochasticity in the spread of WNS was also emphasized in a recent study focusing on the data from 54 hibernacula of little brown bats in New York [ 14 ]. The patchy spread fits well with the stochasticity associated with multiscale processes because of the lack of deterministic directionality found in the phylogeny from a single location.…”

“…Understanding these variables that drive the spread is important for the epidemiology of WNS and planning conservation efforts to protect bats from WNS. Several models were published recently to explain the spread of WNS [11][12][13][14]. However, the models were based on theoretical assumptions and did not capture the dynamic characteristics of the spread over time.…”

Optimal Strategies for Managing Wildlife Harvest Under System Change

“…This work provides a foundation for understanding optimal time‐dependent harvest management policies under system change. The cost of ignoring system non‐stationarity was the greatest when there was a large and early decline in carrying capacity or population growth rate, which could occur as a result of habitat loss (Boult et al 2019), declining food availability (Woodworth‐Jefcoats et al 2017), emergent diseases (Kramer et al 2019), or novel competitors or predators (Lurgi et al 2012). Changes in population growth rate and carrying capacity have competing effects on the optimal policy, and therefore on the consequences of applying a stationary policy.…”

Management efficacy in a metapopulation model of white‐nose syndrome

“…Multiple modeling studies have explored various aspects of WNS disease dynamics via both continuous‐time and discrete‐time models (Erickson et al, 2014, 2016; Kramer et al, 2019; Langwig et al, 2017; Maslo et al, 2017; O'Regan et al, 2015; Reynolds et al, 2015; Russell et al, 2015; Thogmartin et al, 2013). Additionally, a few other modeling studies have considered the efficacy of several proposed control methods (Cornwell et al, 2019; Hallam & McCracken, 2011; Meyer et al, 2016).…”