Management

In the late 1980s, the scientific community became concerned about severe, enigmatic amphibian declines. These dramatic declines triggered a wave of research focused on quantifying the extent and drivers of declines. We use text-analysis techniques, including topic modelling and geoparsing, to examine the evolution of research focused on amphibian declines. We delineated 15 research topics and tracked spatiotemporal trends from 1985 to 2024, and extracted the number of publications per topic. We uncover a fascinating example of scientific inquiry in action, from the initial recognition and quantification of the phenomenon to identifying drivers and understanding mechanisms of amphibian decline. Early research focused on evaluating the veracity of declines, followed by investigating potential drivers (e.g., UVB radiation, pollution, and habitat fragmentation and loss). After the amphibian chytrid fungus was identified in the late 1990s, research shifted toward disease as a core focus. Subsequently, disease-focused research has become increasingly specialized, with topics focused on susceptibility, resistance/tolerance and mitigation. Most recently, extinction risk and climate change have become increasingly prominent topics, reflecting emerging threats to amphibians. Regions with high amphibian biodiversity and observed declines (e.g., Central and South America) are under-represented in the published literature, with research strongly biased toward Australia, North America, and Europe. We uncovered a clear disconnect between the amphibian decline literature and translation into effective management and conservation actions. To address this gap, we recommend the application of existing knowledge to drive meaningful conservation outcomes, alongside prioritising new research on ongoing and emerging threats.

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.