Fragile coexistence of a global chytrid pathogen with amphibian populations is mediated by environment and demography

Abstract

Unravelling the multiple interacting drivers of host-pathogen coexistence is crucial in understanding how an apparently stable state of endemism may shift towards an epidemic and lead to biodiversity loss. Here, we investigate the apparent coexistence of the global amphibian pathogen Batrachochytrium dendrobatidis (Bd) with Bombina variegata populations in The Netherlands over a 7-year period. We used a multi-season mark-recapture dataset and assessed potential drivers of coexistence (individual condition, environmental mediation and demographic compensation) at the individual and population levels. We show that even in a situation with a clear cost incurred by endemic Bd, population sizes remain largely stable. Current environmental conditions and an over-dispersed pathogen load probably stabilize disease dynamics, but as higher temperatures increase infection probability, changing environmental conditions, for example a climate-change-driven rise in temperature, could unbalance the current fragile host-pathogen equilibrium. Understanding the proximate mechanisms of such environmental mediation and of site-specific differences in infection dynamics can provide vital information for mitigation actions.

Spatiotemporal heterogeneity decouples infection parameters of amphibian chytridiomycosis

“…This corroborates predictions that Bd should be more abundant in wetter areas (Kriger, Pereoglou, & Hero, ; Ron, ), and field studies indicating that Bd outbreaks might be more likely under wet conditions (Bosch et al, ; Lips et al, ). Yet, no published study has linked infection intensity with a reduction in either (a) the size of water basin (hydroshed area) or (b) the density of river networks (however, see Kärvemo et al, ; Spitzen‐van der Sluijs, Canessa, Martel, & Pasmans, ). We found that as hydroshed area increased, infection intensity decreased slightly, suggesting that R. pipiens populations concentrated within a small drainage basin may facilitate the proliferation and transmission of zoospores, as increasing host population density increases transmission rates (Briggs et al, ).…”

Disease and climate effects on individuals drive post‐reintroduction population dynamics of an endangered amphibian

“…) and that evaluated effects of Bd load at the individual level in wild populations (Spitzen‐van der Sluijs et al. ). Our approach is unique in part because we treat unobserved infection status and intensity as parameters, rather than using imputation to backfill missing intensity values.…”

“…Previous efforts to understand disease impacts in wild populations as a function of load at the individual level have randomly imputed missing infection load data with the observed distribution of load values (Spitzen‐van der Sluijs et al. ). However, random imputation could bias inference if load data are not missing at random, for example, if heavily infected individuals are easier or more difficult to detect than individuals with lower loads.…”

Disease and climate effects on individuals jointly drive post-reintroduction population dynamics of an endangered amphibian

“…The modeling approach developed here provides a quantitative means to assess how climate and disease affect demographic rates and population dynamics in hard-to-sample host populations. This builds on previous research that incorporated individual-level covariates in mark-recapture models (Pledger et al 2003, Royle 2008, Gimenez and Choquet 2010, Ford et al 2012), and that evaluated effects of Bd load at the individual level in wild populations (Spitzen-van der Sluijs et al 2017). Our approach is unique in part because we treat unobserved infection status and intensity as parameters, rather than using imputation to backfill missing intensity values.…”

“…This presents a major challenge: infection intensity is hard to measure, but may be critical for understanding population dynamics. Previous efforts to understand disease impacts in wild populations as a function of load at the individual level have randomly imputed missing infection load data with the observed distribution of load values (Spitzen-van der Sluijs et al 2017). However, random imputation could bias inference if load data are not missing at random, for example, if load-dependent disease affects detection.…”