ABSTRACT
Aim Pesticides are widespread and may alter host-pathogen interactions, ultimately influencing pathogen distributions across landscapes. Previous laboratory research supports two hypotheses regarding the effects of pesticides on interactions between amphibians and the predominately aquatic fungal pathogen Batrachochytrium dendrobatidis (Bd): 1) pesticides can be directly toxic to Bd reducing infection risk of aquatic larval amphibians, and 2) exposure to pesticides at formative stages of amphibian development can have long-term consequences on defenses, increasing disease risk after metamorphosis. It remains equivocal whether these laboratory patterns are consistent across amphibian species and occur in the field across broad spatial scales. The aim of this research is to address this research gap on the impact of pesticides on Bd distributions.
Location Contiguous United States.
Time Period 1998-2009.
Major Taxa Studied Amphibian hosts and Bd.
Methods Our data included 3,946 individuals evaluated for Bd infection across 49 amphibian species, at 126 locations, which resulted in 199 estimates of Bd prevalence in populations. We used species distribution models and multimodel inference to assess the influence of 1) total pesticide use, 2) pesticide use by type (herbicide, insecticide, fungicide), and 3) the most commonly used pesticide compounds on Bd infection prevalence in amphibian populations across life stages, controlling for several factors previously documented to affect Bd's distribution.
Results Consistent with laboratory findings, our results indicate 36 that exposure to multiple herbicide compounds is associated with lowered infection risk in the aquatic larval stage but higher risk in the terrestrial post-metamorphic stage.
Main Conclusions Our study highlights the complex nature of the effects that pesticides can have on disease distributions and suggests that pesticides should be strongly considered at broad scales and across host species, especially in environments in which exposure is widespread. Accurate predictions of disease distributions may lead to more effective management strategies to limit disease spread.