Statistical considerations of nonrandom treatment applications reveal region-wide benefits of widespread post-fire restoration action.

Accurate predictions of ecological restoration outcomes are needed across the increasingly large landscapes requiring treatment following disturbances. However, observational studies often fail to account for nonrandom treatment application, which can result in invalid inference. Examining a spatiotemporally extensive management treatment involving post-fire seeding of declining sagebrush shrubs across semiarid areas of the western USA over two decades, we quantify drivers and consequences of selection biases in restoration using remotely sensed data. From following more than 1,500 wildfires, we find treatments were disproportionately applied in more stressful, degraded ecological conditions. Failure to incorporate unmeasured drivers of treatment allocation led to the conclusion that costly, widespread seedings were unsuccessful; however, after considering sources of bias, restoration positively affected sagebrush recovery. Treatment effects varied with climate, indicating prioritization criteria for interventions. Our findings revise the perspective that post-fire sagebrush seedings have been broadly unsuccessful and demonstrate how selection biases can pose substantive inferential hazards in observational studies of restoration efficacy and the development of restoration theory.

Community-level prevalence of a forest pathogen, not individual-level disease risk, declines with tree diversity.

Understanding why diversity sometimes limits disease is essential for managing outbreaks; however, mechanisms underlying this 'dilution effect' remain poorly understood. Negative diversity-disease relationships have previously been detected in plant communities impacted by an emerging forest disease, sudden oak death. We used this focal system to empirically evaluate whether these relationships were driven by dilution mechanisms that reduce transmission risk for individuals or from the fact that disease was averaged across the host community. We integrated laboratory competence measurements with plant community and symptom data from a large forest monitoring network. Richness increased disease risk for bay laurel trees, dismissing possible dilution mechanisms. Nonetheless, richness was negatively associated with community-level disease prevalence because the disease was aggregated among hosts that vary in disease susceptibility. Aggregating observations (which is surprisingly common in other dilution effect studies) can lead to misinterpretations of dilution mechanisms and bias towards a negative diversity-disease relationship.