Management

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.

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.