Detecting the influence of rare stressors on rare species in Yosemite National Park using a novel stratified permutation test.

Statistical models often use observational data to predict phenomena; however, interpreting model terms to understand their influence can be problematic. This issue poses a challenge in species conservation where setting priorities requires estimating influences of potential stressors using observational data. We present a novel approach for inferring influence of a rare stressor on a rare species by blending predictive models with nonparametric permutation tests. We illustrate the approach with two case studies involving rare amphibians in Yosemite National Park, USA. The endangered frog, Rana sierrae, is known to be negatively impacted by non-native fish, while the threatened toad, Anaxyrus canorus, is potentially affected by packstock. Both stressors and amphibians are rare, occurring in ~10% of potential habitat patches. We first predict amphibian occupancy with a statistical model that includes all predictors but the stressor to stratify potential habitat by predicted suitability. A stratified permutation test then evaluates the association between stressor and amphibian, all else equal. Our approach confirms the known negative relationship between fish and R. sierrae, but finds no evidence of a negative relationship between current packstock use and A. canorus breeding. Our statistical approach has potential broad application for deriving understanding (not just prediction) from observational data.

Raising the bar for systematic conservation planning.

Systematic conservation planning (SCP) represents a significant step toward cost-effective, transparent allocation of resources for biodiversity conservation. However, research demonstrates important consequences of uncertainties in SCP and of basing methods on simplified circumstances involving few real-world complexities. Current research often relies on single case studies with unknown forms and amounts of uncertainty as well as low statistical power for generalizing results. Consequently, conservation managers have little evidence for the true performance of conservation planning methods in their own complex, uncertain applications. To build effective and reliable methods in SCP, there is a need for more challenging and integrated testing of their robustness to uncertainty and complexity, and much greater emphasis on generalization to real-world situations.