Orchard Management and Landscape Context Mediate the Pear Floral Microbiome.

Crop-associated microbiota are a key factor affecting host health and productivity. Most crops are grown within heterogeneous landscapes, and interactions between management practices and landscape context often affect plant and animal biodiversity in agroecosystems. However, whether these same factors typically affect crop-associated microbiota is less clear. Here, we assessed whether orchard management strategies and landscape context affected bacterial and fungal communities in pear (Pyrus communis) flowers. We found that bacteria and fungi responded differently to management schemes. Organically certified orchards had higher fungal diversity in flowers than conventional or bio-based integrated pest management (IPM) orchards, but organic orchards had the lowest bacterial diversity. Orchard management scheme also best predicted the distribution of several important bacterial and fungal genera that either cause or suppress disease; organic and bio-based IPM best explained the distributions of bacterial and fungal genera, respectively. Moreover, patterns of bacterial and fungal diversity were affected by interactions between management, landscape context, and climate. When examining the similarity of bacterial and fungal communities across sites, both abundance- and taxon-related turnovers were mediated primarily by orchard management scheme and landscape context and, specifically, the amount of land in cultivation. Our study reveals local- and landscape-level drivers of floral microbiome structure in a major fruit crop, providing insights that can inform microbiome management to promote host health and high-yielding quality fruit. IMPORTANCE Proper crop management during bloom is essential for producing disease-free tree fruit. Tree fruits are often grown in heterogeneous landscapes; however, few studies have assessed whether landscape context and crop management affect the floral microbiome, which plays a critical role in shaping plant health and disease tolerance. Such work is key for identification of tactics and/or contexts where beneficial microbes proliferate and pathogenic microbes are limited. Here, we characterize the floral microbiome of pear crops in Washington State, where major production occurs in intermountain valleys and basins with variable elevation and microclimates. Our results show that both local-level (crop management) and landscape-level (habitat types and climate) factors affect floral microbiota but in disparate ways for each kingdom. More broadly, these findings can potentially inform microbiome management in orchards for promotion of host health and high-quality yields.

National-scale remotely sensed lake trophic state from 1984 through 2020.

Lake trophic state is a key ecosystem property that integrates a lake's physical, chemical, and biological processes. Despite the importance of trophic state as a gauge of lake water quality, standardized and machine-readable observations are uncommon. Remote sensing presents an opportunity to detect and analyze lake trophic state with reproducible, robust methods across time and space. We used Landsat surface reflectance data to create the first compendium of annual lake trophic state for 55,662 lakes of at least 10 ha in area throughout the contiguous United States from 1984 through 2020. The dataset was constructed with FAIR data principles (Findable, Accessible, Interoperable, and Reproducible) in mind, where data are publicly available, relational keys from parent datasets are retained, and all data wrangling and modeling routines are scripted for future reuse. Together, this resource offers critical data to address basic and applied research questions about lake water quality at a suite of spatial and temporal scales.

Documenting pollinators, floral hosts, and plant-pollinator interactions in U.S. Pacific Northwest agroecosystems.

The abundance and diversity of pollinator populations are in global decline. Managed pollinator species, like honey bees, and wild species are key ecosystem service providers in both natural and managed agroecosystems. However, relatively few studies have exhaustively characterized pollinator populations in diverse agroecosystems over multiple years, while also thoroughly documenting plant-pollinator interactions. Yet, such studies are needed to fulfill the national pollinator protection plans that have been released by the United States and other nations. Our research is among the first studies to respond to these directives by systematically documenting bee and plant biodiversity, bee-plant interactions, and bee-mediated pollen movement in farming systems of the Pacific Northwest, USA. Our data provides insight into the processes mediating pollinator and plant community assembly, persistence, and resilience across landscapes with variable crop and landscape diversity and agroecosystem management practices. These data will also contribute to the development of a United States pollinator database, supporting the United States' plan to promote pollinators. With few publicly available data sets that systematically take account of agroecosystem practices, plant populations, and pollinators, our research will provide future users the means to conduct synesthetic studies of pollinators and ecosystem function in a period of rapid and global pollinator declines. There are no copyright or proprietary restrictions for research or teaching purposes. Usage of the data set must be cited.

The global lake area, climate, and population dataset.

An increasing population in conjunction with a changing climate necessitates a detailed understanding of water abundance at multiple spatial and temporal scales. Remote sensing has provided massive data volumes to track fluctuations in water quantity, yet contextualizing water abundance with other local, regional, and global trends remains challenging by often requiring large computational resources to combine multiple data sources into analytically-friendly formats. To bridge this gap and facilitate future freshwater research opportunities, we harmonized existing global datasets to create the Global Lake area, Climate, and Population (GLCP) dataset. The GLCP is a compilation of lake surface area for 1.42 + million lakes and reservoirs of at least 10 ha in size from 1995 to 2015 with co-located basin-level temperature, precipitation, and population data. The GLCP was created with FAIR (findable, accessible, interoperable, reusable) data principles in mind and retains unique identifiers from parent datasets to expedite interoperability. The GLCP offers critical data for basic and applied investigations of lake surface area and water quantity at local, regional, and global scales.