DOI: 10.5061/dryad.rm382
title: Data from: Large-scale recovery of an endangered amphibian despite ongoing exposure to multiple stressors

description: Amphibians are one of the most threatened animal groups, with 32% of
 species at risk for extinction. Given this imperiled status, is the
 disappearance of a large fraction of the Earth’s amphibians inevitable, or
 are some declining species more resilient than is generally assumed? We
 address this question in a species that is emblematic of many declining
 amphibians, the endangered Sierra Nevada yellow-legged frog (Rana
 sierrae). Based on &gt;7,000 frog surveys conducted across Yosemite
 National Park over a 20-y period, we show that, after decades of decline
 and despite ongoing exposure to multiple stressors, including introduced
 fish, the recently emerged disease chytridiomycosis, and pesticides, R.
 sierrae abundance increased sevenfold during the study and at a rate of
 11% per year. These increases occurred in hundreds of populations
 throughout Yosemite, providing a rare example of amphibian recovery at an
 ecologically relevant spatial scale. Results from a laboratory experiment
 indicate that these increases may be in part because of reduced frog
 susceptibility to chytridiomycosis. The disappearance of nonnative fish
 from numerous water bodies after cessation of stocking also contributed to
 the recovery. The large-scale increases in R. sierrae abundance that we
 document suggest that, when habitats are relatively intact and stressors
 are reduced in their importance by active management or species’ adaptive
 responses, declines of some amphibians may be partially reversible, at
 least at a regional scale. Other studies conducted over similarly large
 temporal and spatial scales are critically needed to provide insight and
 generality about the reversibility of amphibian declines at a global
 scale.
description: Frog resurvey
 datasetFor each of the 7678
 surveys conducted during the study, abundance of Rana sierrae adults,
 subadults, and tadpoles, as well as survey/site characteristics. Data
 fields are described in the associated ReadMe file.Data.csvFrog resurvey: R code used in analysis of frog population trendsR code to run primary trend analyses in Knapp et al. (2016). Models were fit using the package 'INLA' version 0.0-1420281647. An adjacency file (W.dat) is needed to model the spatial random effects, and is provided as part of this data package.RCode.RFrog resurvey: Adjacency fileAdjacency file needed to model the spatial random effectsW.datFrog susceptibility datasetResults of the laboratory frog susceptibility experiment described in Knapp et al. (2016). In this experiment, we collected Rana sierrae adults from each of three persistent populations (in which frogs have persisted with the amphibian chytrid fungs (Batrachochytrium dendrobatidis; Bd) for at least 10 years) and three Bd-naïve populations (that have never been exposed to Bd). In the laboratory, we exposed pairs of frogs (one from a persistent population and one from a naïve population) to one of four strains of Bd. Bd was cultured from two persistent frog populations and two populations in which frogs had recently experienced Bd-caused mass die offs (“die-off” populations). After Bd exposure, we measured Bd infection intensities (“loads”) on frogs over a 15-wk period. Additional details are provided in Knapp et al. (2016) and in the associated ReadMe file.Knapp_lab_experiment_data.csvFrog susceptibility: SAS code used to analyze results from laboratory experimentThis SAS code was used to analyze the results obtained from the laboratory Rana sierrae susceptibility experiment. We used a model selection strategy to examine the effects of frog type (collected from a persistent or naïve population), frog source (one of six populations), Bd type (collected from a persistent or die-off population), Bd source (one of four populations), and interactions of these effects on Bd load [log10(zoospore equivalent [ZE] + 1)]. Additional details are provided in Knapp et al. (2016).Knapp_lab_experiment_analysis.sas