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The Amphibians of Louisiana was made possible by a grant from the Barataria-Terrebonne National Estuary Program (BTNEP). The 84 page booklet includes species accounts that describe with pictures every species of amphibian in Louisiana, along with some front and back matter concerning general amphibian information, how to understand the species accounts, and how you can help amphibians.

The biodiversity crisis necessitates spatially extensive methods to monitor multiple taxonomic groups for evidence of change in response to evolving environmental conditions. Programs that combine passive acoustic monitoring and machine learning are increasingly used to meet this need. These methods require large, annotated datasets, which are time-consuming and expensive to produce, creating potential barriers to adoption in data- and funding-poor regions. Recently released pre-trained avian acoustic classification models provide opportunities to reduce the need for manual labelling and accelerate the development of new acoustic classification algorithms through transfer learning. Transfer learning is a strategy for developing algorithms under data scarcity that uses pre-trained models from related tasks to adapt to new tasks.

Our primary objective was to develop a transfer learning strategy using the feature embeddings of a pre-trained avian classification model to train custom acoustic classification models in data-scarce contexts. We used three annotated avian acoustic datasets to test whether transfer learning and soundscape simulation-based data augmentation could substantially reduce the annotated training data necessary to develop performant custom acoustic classifiers. We also conducted a sensitivity analysis for hyperparameter choice and model architecture. We then assessed the generalizability of our strategy to increasingly novel non-avian classification tasks.

With as few as two training examples per class, our soundscape simulation data augmentation approach consistently yielded new classifiers with improved performance relative to the pre-trained classification model and transfer learning classifiers trained with other augmentation approaches. Performance increases were evident for three avian test datasets, including single-class and multi-label contexts. We observed that the relative performance among our data augmentation approaches varied for the avian datasets and nearly converged for one dataset when we included more training examples.

We demonstrate an efficient approach to developing new acoustic classifiers leveraging open-source sound repositories and pre-trained networks to reduce manual labelling. With very few examples, our soundscape simulation approach to data augmentation yielded classifiers with performance equivalent to those trained with many more examples, showing it is possible to reduce manual labelling while still achieving high-performance classifiers and, in turn, expanding the potential for passive acoustic monitoring to address rising biodiversity monitoring needs.

Western palearctic salamander susceptibility to the skin disease caused by the amphibian chytrid fungus Batrachochytrium salamandrivorans (Bsal) was recognized in 2014, eliciting concerns for a potential novel wave of amphibian declines following the B. dendrobatidis (Bd) chytridiomycosis global pandemic. Although Bsal had not been detected in North America, initial experimental trials supported the heightened susceptibility of caudate amphibians to Bsal chytridiomycosis, recognizing the critical threat this pathogen poses to the North American salamander biodiversity hotspot. Here, we take stock of 10 years of research, collaboration, engagement, and outreach by the North American Bsal Task Force. We summarize main knowledge and conservation actions to both forestall and respond to Bsal invasion into North America. We address the questions: what have we learned; what are current challenges; and are we ready for a more effective reaction to Bsal’s eventual detection? We expect that the many contributions to preemptive planning accrued over the past decade will pay dividends in amphibian conservation effectiveness and can inform future responses to other novel wildlife diseases and extreme threats.

Species with extensive geographical ranges pose special challenges to assessing drivers of wildlife disease, necessitating collaborative and large-scale analyses. The imperilled foothill yellow-legged frog (Rana boylii) inhabits a wide geographical range and variable conditions in rivers of California and Oregon (USA), and is considered threatened by the pathogen Batrachochytrium dendrobatidis (Bd). To assess drivers of Bd infections over time and space, we compiled over 2000 datapoints from R. boylii museum specimens (collected 1897–2005) and field samples (2005–2021) spanning 9° of latitude. We observed a south-to-north spread of Bd detections beginning in the 1940s and increase in prevalence from the 1940s to 1970s, coinciding with extirpation from southern latitudes. We detected eight high-prevalence geographical clusters through time that span the species' geographical range. Field-sampled male R. boylii exhibited the highest prevalence, and juveniles sampled in autumn exhibited the highest loads. Bd infection risk was highest in lower elevation rain-dominated watersheds, and with cool temperatures and low stream-flow conditions at the end of the dry season. Through a holistic assessment of relationships between infection risk, geographical context and time, we identify the locations and time periods where Bd mitigation and monitoring will be critical for conservation of this imperilled species.

This is a brief response to an article about using amphibians as part of rewilding programs, that points out some flaws in the presentation of ideas in that article.

Managers often rely on predictions of species distributions and habitat suitability to inform conservation and management decisions. Although numerous approaches are available to develop models to make these predictions, few approaches exist to update existing models as new data accumulate. There is a need for updatable models to ensure good modeling practices in an aim to keep pace with change in the environment and change in data availability to continue to use the best-available science to inform decisions. We demonstrated a workflow to deliver predictive models to user groups within Bayesian networks, allowing models to be used to make predictions across new sites and to be easily updated with new data. To demonstrate this workflow, we focus on species distribution and habitat suitability models given their importance to informing conservation strategies across the globe. In particular, we followed a standard process of collating species encounter data available in online databases and ancillary covariate data to develop a habitat suitability model. We then used this model to parameterize a Bayesian network and updated the model with new data to predict species presence in a new focal ecoregion. We found the network updated relatively quickly as new data were incorporated, and the overall error rate generally decreased with each model update. Our approach allows for the formal incorporation of new data into predictions to help ensure model predictions are based on all relevant data available, regardless of whether they were collected after initial model development. Although our focus is on species distribution and habitat suitability models to inform conservation efforts, the workflow we describe herein can easily be applied to any use case where model uncertainty reduction and increased model prediction accuracy are desired via model updating as new data become available. Thus, our paper describes a generalizable workflow to implement model updating, which is widely recognized as a good modeling practice but is also underutilized in applied ecology.

Rapid warming in polar regions is causing large changes to ecosystems, including altering environmentally available mercury (Hg). Though subarctic freshwater systems have simple vertebrate communities, Hg in amphibians remains unexplored. We measured total Hg (THg) in wetland sediments and methylmercury (MeHg) in multiple life stages (eggs to adults) of Wood Frogs (Rana sylvatica) and larval Boreal Chorus Frogs (Pseudacris maculata) from up to 25 wetlands near Churchill, Manitoba (Canada), during summers 2018?2019. We used egg mass counts for Wood Frogs from 24 wetlands (2015–2019) and per-ovum MeHg concentrations to estimate site-level MeHg flux by metamorphs from wetlands to the terrestrial environment. Total Hg in wetland sediment was unrelated to MeHg concentrations of amphibian larvae, but sediment THg increased with from coastal tundra vegetation to inland boreal forests. Methylmercury concentrations of Wood Frog eggs (geometric mean = 35.9; range: 6.7–77.9 ng/g dry weight [dw]) exceeded previous reports for amphibians, including from sites contaminated by industrial sources of Hg. Methylmercury concentrations of adult Wood Frogs (298.9 ng/g dw) was also higher than that for frogs included in a recent assessment of MeHg in amphibians across the contiguous United States. Within wetlands, MeHg concentrations of Wood Frog larvae were strongly correlated with MeHg concentrations in eggs earlier in the summer and concentrations increased with each life stage. We estimate there would have been 1971.8?3286.4 ng MeHg exported from wetlands by Wood Frog metamorphs, which is 3.4?5.6 times more MeHg than inputted by eggs. Collectively, these data provide an initial assessment of Hg concentrations, body burdens, and dynamics in subarctic food webs that are expected to experience large changes from climate warming.

Climate change is a primary threat to biodiversity, but for many species, we still lack information required to assess their relative vulnerability to changes. Climate change vulnerability assessment (CCVA) is a widely used technique to rank relative vulnerability to climate change based on species characteristics, such as their distributions, habitat associations, environmental tolerances, and life-history traits. However, for species that we expect are vulnerable to climate change yet are understudied, like many amphibians, we often lack information required to construct CCVAs using existing methods. We used the CCVA framework to construct trait-based models based on life history theory, using empirical evidence of traits and distributions that reflected sensitivity of amphibians to environmental perturbation. We performed CCVAs for amphibians in 7 states in the north-central USA, focusing on 31 aquatic-breeding species listed as species of greatest conservation need by at last 1 state. Because detailed information on habitat requirements is unavailable for most amphibian species, we used species distributions and information on traits expected to influence vulnerability to a drying climate (e.g., clutch size and habitat breadth). We scored species vulnerability based on changes projected for mid-century (2040?2069) from 2 climate models representing “least-dry” and “most-dry” scenarios for the region. Species characteristics useful for discriminating vulnerability in our models included small range size, small clutch size, inflexible diel activity patterns, and smaller habitat breadth. When projected climate scenarios included a mix of drier and wetter conditions in the future, the exposure of a species to drying conditions was most important to relative rankings. When the scenario was universally drier, species characteristics were more important to relative rankings. Using information typically available even for understudied species and a range of climate projections, our results highlight the potential of using life history traits as indicators of relative climate vulnerability. The commonalities we identified provide a framework that can be used to assess other understudied species threatened by climate change.

We assessed amphibian diversity, rarity, and threats across the U.S. National Park System, which covers 3.5% of the U.S. and 12% of federal lands. At least 230 of 354 (65%) amphibian species native to the U.S. occur in parks. Of the species documented in parks, 17% are considered at-risk globally and 20% are uncategorized, reflecting still-widespread data deficiencies. Parks in the Northwest and Northeast accumulated species most quickly (i.e., steepest species?area relationships). Non-native crayfishes and amphibians occur within 50 km of 60% and 25% of parks, respectively, illustrating the broad threat of non-native predators. Projected mid-century (2040–2069) changes in climatic water deficit, based on 25 climate futures, produced an expected 34% increase in dryness across all parks in the contiguous U.S. territory. Our analyses highlight the extent and regional differences in current and future threats and reveal gaps in species protection, but also reveal opportunities for targeted expansion and active management.

Optimal egg size theory predicts females must balance investment per offspring to maximize fitness. This balance can change based on resources or habitat quality. In wetlands, common aspects of habitat quality are duration of water (hydroperiod) and predator presence. Ectotherms using habitats that dry or contain predators are likely under selection to optimize offspring production. We measured ovum and clutch sizes from wood frogs (Rana sylvatica) in 30 wetlands (2014?2019) in Subarctic Canada, where rapid changes in climate are accelerating wetland drying. We predicted wetlands with short hydroperiods would have larger ova, smaller clutch sizes, and larger ovum-to-clutch-sizes than wetlands with long hydroperiods or with fish predators. We found partial support for our predictions, with larger ova in habitats with short hydroperiods and no fish. We did not, however, find evidence of larger clutch sizes in wetlands with fish or a relationship with ovum-to-clutch size. Given the large environmental changes that are already occurring, our study is novel as one of the first to implicate these rapid changes as potential selective agents on reproductive output in an ectotherm.