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The value of reducing uncertainties to support the management of a high‐elevation endemic salamander
The value of reducing uncertainties to support the management of a high‐elevation endemic salamander
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The value of reducing uncertainties to support the management of a high‐elevation endemic salamander
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The value of reducing uncertainties to support the management of a high‐elevation endemic salamander
The value of reducing uncertainties to support the management of a high‐elevation endemic salamander

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The value of reducing uncertainties to support the management of a high‐elevation endemic salamander
The value of reducing uncertainties to support the management of a high‐elevation endemic salamander
Journal Article

The value of reducing uncertainties to support the management of a high‐elevation endemic salamander

2025
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Overview
Many salamander populations are declining, and methods to determine how best to allocate limited resources to slow or reverse these declines could support land managers in their decision‐making processes. Multiple types of uncertainty may delay management decisions, including when (1) knowledge of a species' ecology is incomplete, (2) climate change effects on environmental covariates are uncertain, and (3) the efficacy of management alternatives is unknown. For management decisions, a value‐of‐information analysis can identify which uncertainties are critical to reduce in order to identify an optimal strategy from a set of possible management actions. If the same management action is optimal across the full range of uncertainties, then resources for research can be redirected toward active management. Using value‐of‐information analyses, we examine the effect of uncertainty on identifying optimal management to maximize the future expected occupancy of Plethodon shenandoah, a Federally Endangered high‐elevation endemic salamander that is threatened by climate change. Out of 11 management actions proposed by National Park Service managers, those that increase environmental moisture are expected to maximize occupancy, and we find that the selection of this action is robust to all the identified uncertainties. We show that, even in systems with multiple sources of large uncertainty, value of information analyses discriminate among investments in species management.