Effective pest management approaches can mitigate honey bee (Apis mellifera) colony winter loss across a range of weather conditions in small-scale, stationary apiaries.

Managed honey bee (Apis mellifera L.) colonies in North America and Europe have experienced high losses in recent years, which have been linked to weather conditions, lack of quality forage, and high parasite loads, particularly the obligate brood parasite, Varroa destructor. These factors may interact at various scales to have compounding effects on honey bee health, but few studies have been able to simultaneously investigate the effects of weather conditions, landscape factors, and management of parasites. We analyzed a dataset of 3,210 survey responses from beekeepers in Pennsylvania from 2017 to 2022 and combined these with remotely sensed weather variables and novel datasets about seasonal forage availability into a Random Forest model to investigate drivers of winter loss. We found that beekeepers who used treatment against Varroa had higher colony survival than those who did not treat. Moreover, beekeepers who used multiple types of Varroa treatment had higher colony survival rates than those who used 1 type of treatment. Our models found weather conditions are strongly associated with survival, but multiple-treatment type colonies had higher survival across a broader range of climate conditions. These findings suggest that the integrated pest management approach of combining treatment types can potentially buffer managed honey bee colonies from adverse weather conditions.

Illuminating patterns of firefly abundance using citizen science data and machine learning models.

As insect populations decline in many regions, conservation biologists are increasingly tasked with identifying factors that threaten insect species and developing effective strategies for their conservation. One insect group of global conservation concern are fireflies (Coleoptera: Lampyridae). Although quantitative data on firefly populations are lacking for most species, anecdotal reports suggest that some firefly populations have declined in recent decades. Researchers have hypothesized that North American firefly populations are most threatened by habitat loss, pesticide use, and light pollution, but the importance of these factors in shaping firefly populations has not been rigorously examined at broad spatial scales. Using data from >24,000 surveys (spanning 2008-16) from the citizen science program Firefly Watch, we trained machine learning models to evaluate the relative importance of a variety of factors on bioluminescent firefly populations: pesticides, artificial lights at night, land cover, soil/topography, short-term weather, and long-term climate. Our analyses revealed that firefly abundance was driven by complex interactions among soil conditions (e.g., percent sand composition), climate/weather (e.g., growing degree days), and land cover characteristics (e.g., percent agriculture and impervious cover). Given the significant impact that climactic and weather conditions have on firefly abundance, there is a strong likelihood that firefly populations will be influenced by climate change, with some regions becoming higher quality and supporting larger firefly populations, and others potentially losing populations altogether. Collectively, our results support hypotheses related to factors threatening firefly populations, especially habitat loss, and suggest that climate change may pose a greater threat than appreciated in previous assessments. Thus, future conservation of North American firefly populations will depend upon 1) consistent and continued monitoring of populations via programs like Firefly Watch, 2) efforts to mitigate the impacts of climate change, and 3) insect-friendly conservation practices.

Not just crop or forest: an integrated land cover map for agricultural and natural areas.

Due to the key role surrounding landscape plays in ecological processes, a detailed characterization of land cover is critical for researchers and conservation practitioners. Unfortunately, in the United States, land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this gap, we merged two datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce integrated 'Spatial Products for Agriculture and Nature' (SPAN). Our workflow leveraged strengths of the NVC and the CDL to create detailed rasters comprising both agricultural and natural land-cover classes. We generated SPAN annually from 2012-2021 for the conterminous United States, quantified agreement and accuracy of SPAN, and published the complete computational workflow. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved most conflicts, leaving only 0.6% of agricultural pixels unresolved in SPAN. These ready-to-use rasters characterizing both agricultural and natural land cover will be widely useful in environmental research and management.

Climate-based variability in the essential fatty acid composition of soybean oil.

BACKGROUND: Soybean oil is a major dietary source of the essential fatty acids linoleic acid (LA) and α-linolenic acid (ALA); however, high-daytime temperatures during seed development reduce desaturase activity in soybeans. The resultant reduction in LA and ALA levels is a phenomenon well-known to soybean breeders, although the impact of this interaction between plants and environment on human nutrition is poorly understood. OBJECTIVES: Using data from the literature, we developed a model for soybean essential fatty acid composition. Combining this model with contemporary agricultural and meteorological data sets, we determined whether insufficiency of essential fatty acids could result from geographic, intrayear, or interyear variability. METHODS: We modeled this change using 233 data points from 16 studies that provided fatty acid composition data from plants grown under daytime high temperatures ranging from 15°C to 40°C. RESULTS: As temperature increased, LA and ALA concentrations decreased from 55% to 30% and 13% to 3.5%, respectively. Application of the model to daytime high temperatures from 2 growth periods over 6 y showed significant regional, interyear, and intrayear variation in essential fatty acid content (P < 0.05). Using county yield data, we developed oil fatty acid models for the 3 top-producing regions of the United States. From this work, it was determined that soybean oil manufactured from soybeans in the southern United States may contain insufficient ALA to meet human nutritional needs because of high-daytime temperatures. CONCLUSIONS: This work suggests that climate-based variation may result in many human populations not achieving an adequate daily intake of ALA.

Opportunities to implement manureshed management in the Iowa, North Carolina, and Pennsylvania swine industry.

The U.S. swine industry is diverse, but opportunities exist to strategically improve manure management, especially given much of the industry's vertical integration. We investigate opportunities for improving manureshed management, using swine production examples in Iowa, North Carolina, and Pennsylvania as a lens into historical trends and the current range of management conditions. Manure management reflects regional differences and the specialized nature of hog farms, resulting in a large range of land bases required to assimilate manure generated by these operations. Selected representative farm scenarios were evaluated on an annual basis; farm-level manuresheds were largest for Pennsylvania sow farms and smallest for North Carolina nursery farms. Compared with nitrogen-based manuresheds, phosphorus-based manuresheds were up to 12.5 times larger. Technology advancements are needed to promote export of concentrated nutrients, especially phosphorus, from existing "source" manuresheds to suitable croplands. The industry is dynamic, as revealed by historical analysis of the siting of hog barns in Pennsylvania, which are currently trending toward the north and west where there is greater isolation to prevent the spread of disease and a larger land base to assimilate manure. Industry expansion should focus on locating animals in nutrient "sink" areas.

Summer weather conditions influence winter survival of honey bees (Apis mellifera) in the northeastern United States.

Honey bees are crucial pollinators for agricultural and natural ecosystems, but are experiencing heavy mortality in North America and Europe due to a complex suite of factors. Understanding the relative importance of each factor would enable beekeepers to make more informed decisions and improve assessment of local and regional habitat suitability. We used 3 years of Pennsylvania beekeepers' survey data to assess the importance of weather, topography, land use, and management factors on overwintering mortality at both apiary and colony levels, and to predict survival given current weather conditions and projected climate changes. Random Forest, a tree-based machine learning approach suited to describing complex nonlinear relationships among factors, was used. A Random Forest model predicted overwintering survival with 73.3% accuracy for colonies and 65.7% for apiaries where Varroa mite populations were managed. Growing degree days and precipitation of the warmest quarter of the preceding year were the most important predictors at both levels. A weather-only model was used to predict colony survival probability, and to create a composite map of survival for 1981-2019. Although 3 years data were likely not enough to adequately capture the range of possible climatic conditions, the model performed well within its constraints.

Wild bees as winners and losers: Relative impacts of landscape composition, quality, and climate.

Wild bees, like many other taxa, are threatened by land-use and climate change, which, in turn, jeopardizes pollination of crops and wild plants. Understanding how land-use and climate factors interact is critical to predicting and managing pollinator populations and ensuring adequate pollination services, but most studies have evaluated either land-use or climate effects, not both. Furthermore, bee species are incredibly variable, spanning an array of behavioral, physiological, and life-history traits that can increase or decrease resilience to land-use or climate change. Thus, there are likely bee species that benefit, while others suffer, from changing climate and land use, but few studies have documented taxon-specific trends. To address these critical knowledge gaps, we analyzed a long-term dataset of wild bee occurrences from Maryland, Delaware, and Washington DC, USA, examining how different bee genera and functional groups respond to landscape composition, quality, and climate factors. Despite a large body of literature documenting land-use effects on wild bees, in this study, climate factors emerged as the main drivers of wild-bee abundance and richness. For wild-bee communities in spring and summer/fall, temperature and precipitation were more important predictors than landscape composition, landscape quality, or topography. However, relationships varied substantially between wild-bee genera and functional groups. In the Northeast USA, past trends and future predictions show a changing climate with warmer winters, more intense precipitation in winter and spring, and longer growing seasons with higher maximum temperatures. In almost all of our analyses, these conditions were associated with lower abundance of wild bees. Wild-bee richness results were more mixed, including neutral and positive relationships with predicted temperature and precipitation patterns. Thus, in this region and undoubtedly more broadly, changing climate poses a significant threat to wild-bee communities.

Bumble bees in landscapes with abundant floral resources have lower pathogen loads.

The pollination services provided by bees are essential for supporting natural and agricultural ecosystems. However, bee population declines have been documented across the world. Many of the factors known to undermine bee health (e.g., poor nutrition) can decrease immunocompetence and, thereby, increase bees' susceptibility to diseases. Given the myriad of stressors that can exacerbate disease in wild bee populations, assessments of the relative impact of landscape habitat conditions on bee pathogen prevalence are needed to effectively conserve pollinator populations. Herein, we assess how landscape-level conditions, including various metrics of floral/nesting resources, insecticides, weather, and honey bee (Apis mellifera) abundance, drive variation in wild bumble bee (Bombus impatiens) pathogen loads. Specifically, we screened 890 bumble bee workers from varied habitats in Pennsylvania, USA for three pathogens (deformed wing virus, black queen cell virus, and Vairimorpha (= Nosema) bombi), Defensin expression, and body size. Bumble bees collected within low-quality landscapes exhibited the highest pathogen loads, with spring floral resources and nesting habitat availability serving as the main drivers. We also found higher loads of pathogens where honey bee apiaries are more abundant, a positive relationship between Vairimorpha loads and rainfall, and differences in pathogens by geographic region. Collectively, our results highlight the need to support high-quality landscapes (i.e., those with abundant floral/nesting resources) to maintain healthy wild bee populations.

Analyzing Within-County Hydrogeomorphological Characteristics as a Precursor to Phosphorus Index Modifications.

Phosphorus (P) site assessment is used nationally and internationally to assess the vulnerability of agricultural fields to P loss and identify high-risk areas controlling watershed P export. Current efforts to update P site assessment tools must ensure that these tools are representative of the range of conditions to which they will be applied. We sought to identify key parameters available in public GIS data that are descriptive of potential source areas in Pennsylvania and that ensure that modifications of the P Index span all feasible parameter combinations. Relevant soil and topographic variables were compiled for Pennsylvania at 30-m resolution, and areas within 90 m of permanent streams were extracted. Within each county, -means and classification trees were used to identify and create classification rules for topoedaphic groups. Within counties, two to five groups adequately represented near-stream complexity, with available water capacity, hydraulic conductivity, and organic matter being the most important environmental variables. Discontinuities across soil survey boundaries made it impossible to develop clusterings beyond the county level. For county-scale research and management efforts, these groupings provide a manageable approach to identifying representative sites for near-stream agricultural lands. The full set of representative sites across the state enables evaluation of the P Index throughout the full hydrogeomorphic diversity of Pennsylvania. In future work, we can then combine a set of reasonable management practices with each of the main hydrogeomorphological regions resulting from this study and verify the revised P Index against expert knowledge and simulation results.

Plant community associations of two invasive thistles.

In order to combat the growing problems associated with biological invasions, many researchers have focused on identifying which communities are most vulnerable to invasion by exotic species. However, once established, invasive species can significantly change the composition of the communities that they invade. The first step to disentangling the direction of causality is to discern whether a relationship with other vegetation exists at all. Carduus nutans and C. acanthoides are similar invasive thistles, which have caused substantial economic damage worldwide. We assessed the associations between the thistles and the standing flora in four sites in central Pennsylvania in which they co-occur. After sampling nearly 2000 plots of 1 m(2), we used partial Mantel tests to assess the differences in vegetation between thistle and non-thistle plots after accounting for location, and non-metric multidimensional scaling to visualize differences among plots and sites. We found significant differences in community composition in plots with and without Carduus thistles. The non-native species Sisymbrium officinale and Coronilla varia were consistently associated with the presence of Carduus thistles. Several species were associated with areas that were free of Carduus thistles, including an important non-native pasture species (Trifolium repens). We found no evidence for differences in composition between plots with C. nutans versus C. acanthoides, suggesting that they have similar associations with the vegetation community. We conclude that even at the within-field scale, areas invaded by Carduus thistles have different vegetation associations than uninvaded areas, allowing us to target future research about the role of vegetation structure in resisting and responding to invasion.