Management

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.

Ecologists can learn a lot about species by studying the precise locations in which they do (and do not) occur, but the location information associated with many species records is imprecise. A prominent example of this is the North American Breeding Bird Survey (BBS), in which volunteer observers have surveyed birds at points along consistent routes across the United States for over fifty-five years. As the BBS was designed for large-scale analyses, detailed location information for each bird count is not recorded. We estimate location uncertainty, and the resulting uncertainty in land cover covariates, for the BBS data and present a modeling method that accounts for this uncertainty in a way that opens new possibilities for fine-scale uses of this extensive dataset, unlocking its potential to advance the study of the relationships between birds and their immediate habitat. More broadly, our methods and modeling framework could be used in a variety of situations in which covariate or location uncertainty is a challenge.

A book chapter highlighting uncertainty and inaction in conservation, decision analysis steps and challenges and decision analysis for amphibians and reptiles