Weather and leaf age separately contribute to temporal shifts in phyllosphere community structure and composition.

Microbial communities living on plant leaves can positively or negatively influence plant health and, by extension, can impact whole ecosystems. Most research into the leaf microbiome consists of snapshots, and little is known about how microbial communities change over time. Weather and host physiological characteristics change over time and are often collinear with other time-varying factors, such as substrate availability, making it difficult to separate the factors driving microbial community change. We leveraged repeated measures over the course of an entire year to isolate the relative importance of environmental, host physiological, and substrate age-related factors on the assembly, structure, and composition of leaf-associated fungal communities. We applied both culturing and sequencing approaches to investigate these communities, focusing on a foundational, widely-distributed plant of conservation concern: basin big sagebrush ( Artemisia tridentata subsp. tridentata ). We found that changes in alpha diversity were independently affected by the age of a community and the air temperature. Surprisingly, total fungal abundance and species richness were not positively correlated and responded differently, sometimes oppositely, to weather. With regard to beta diversity, communities were more similar to each other across similar leaf ages, air temperatures, leaf types, and δ 13 C stable isotope ratios. Nine different genera were differentially abundant with air temperature, δ 13 C, leaf type, and leaf age, and a set of 20 genera were continuously present across the year. Our findings highlight the necessity for longer-term, repeated sampling to parse drivers of temporal change in leaf microbial communities. Open Research Statement: All ITS DNA amplicon sequence raw data are deposited in the NCBI Sequence Read Archive (SRA), BioProject number PRJNA1107252, data will be released upon publication. All community data, metadata, taxonomic data, and R code necessary to reproduce these results are deposited in the GitHub repository archived on Zenodo: 10.5281/zenodo.11106439.

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.