BackgroundAmphibians are experiencing substantial declines attributed to emerging pathogens. Efforts to understand what drives patterns of pathogen prevalence and differential responses among species are challenging because numerous factors related to the host, pathogen, and their shared environment can influence infection dynamics. Furthermore, sampling across broad taxonomic and geographic scales to evaluate these factors poses logistical challenges, and interpreting the roles of multiple potentially correlated variables is difficult with traditional statistical approaches. In this study, we leverage frozen tissues stored in natural history collections and machine learning techniques to characterize infection dynamics of three generalist pathogens known to cause mortality in frogs. MethodsWe selected 12 widespread and abundant focal taxa within three ecologically distinct, co-distributed host families (Bufonidae, Hylidae, and Ranidae) and sampled them across the eastern two-thirds of the United States of America. We screened and quantified infection loadsviaquantitative PCR for three major pathogens: the fungal pathogenBatrachochytrium dendrobatidis(Bd), double-stranded viruses in the lineageRanavirus(Rv), and the alveolate parasite currently referred to as Amphibian Perkinsea (Pr). We then built balanced random forests (RF) models to predict infection status and intensity based on host taxonomy, age, sex, geography, and environmental variables and to assess relative variable importance across pathogens. Lastly, we used one-way analyses to determine directional relationships and significance of identified predictors. ResultsWe found approximately 20% of individuals were infected with at least one pathogen (231 single infections and 25 coinfections). The most prevalent pathogen across all taxonomic groups was Bd (16.9%; 95% CI [14.9–19%]), followed by Rv (4.38%; 95% CI [3.35–5.7%]) and Pr (1.06%; 95% CI [0.618–1.82%]). The highest prevalence and intensity were found in the family Ranidae, which represented 74.3% of all infections, including the majority of Rv infection points, and had significantly higher Bd intensities compared to Bufonidae and Hylidae. Host species and environmental variables related to temperature were key predictors identified in RF models, with differences in importance among pathogens and host families. For Bd and Rv, infected individuals were associated with higher latitudes and cooler, more stable temperatures, while Pr showed trends in the opposite direction. We found no significant differences between sexes, but juvenile frogs had higher Rv prevalence and Bd infection intensity compared to adults. Overall, our study highlights the use of machine learning techniques and a broad sampling strategy for identifying important factors related to infection in multi-host, multi-pathogen systems.
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Modelling Batrachochytrium dendrobatidis Presence and Intensity Using High Spatial and Temporal Resolution Weather Data
ABSTRACT AimSpecies distribution models (SDMs) are an important tool for conservation efforts, and when constructed using high‐quality predictor and response data could be useful for identifying portions of a species' range that could serve as refugia from risks that are influenced by environmental conditions, such as pathogens.Batrachochytrium dendrobatidis(Bd) is a fungal pathogen that causes lethal disease in amphibians but is also sensitive to environmental factors including temperature and humidity. SDMs built using high spatial and temporal resolution environmental data may be able to help identify environmental refugia fromBdfor amphibians. In this study, we create our own high‐resolution remotely sensed environmental dataset and use high‐quality presence/absence survey data to build newBdSDMs of Panama. LocationPanama. TaxaThe amphibian chytrid fungusBdand amphibian communities. MethodsWe used environmental values from various time periods prior toBdtesting (lag of 2, 7, 15, 30 days) and levels ofBdinfection intensity from survey swabs (present, medium intensity, high intensity) to run multiple Boosted Regression Trees to model environmental suitability forBdand evaluated their performance. ResultsOur results indicated that the 15‐day period prior to testing was the most predictive time‐period forBdenvironmental suitability in Panama. Additionally, we found that we could not only model the distribution of the pathogen itself but discern unique spatial and temporal patterns of high infection intensities often associated with disease outbreaks. We created daily predictive maps of the probability ofBdoccurrence and of medium and high infection intensities in Panama from 2005 to 2018 and averaged them by season. Main ConclusionsHigher environmental suitability forBdtended to be found at higher elevations, a pattern observed in previousBdfield studies and consistent withBd's physiological requirements. The high temporal and spatial resolution of these maps gives a unique insight into exploring temporal patterns ofBdintensity in specific locations and to identify environmental refugia fromBdfor amphibian reintroduction efforts and surveys for relict populations.
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- PAR ID:
- 10709295
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Diversity and Distributions
- Volume:
- 31
- Issue:
- 7
- ISSN:
- 1366-9516
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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