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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.

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.

The spatial heterogeneity of vegetation types on a landscape has been linked to multiple ecosystem functions, including habitat for wildlife and pollinators, water cycling, human aesthetic values, and nutrient cycling. Although agricultural land uses are sometimes combined into a single unit when quantifying landscape heterogeneity, diverse cropping systems are a valuable alternative to near-monocultural croplands and contribute more strongly to ecosystem service provision, including services such as pest regulation and carbon sequestration that are of direct interest for agriculture. The USDA Cropland Data Layer was used to characterize crop diversity across the contiguous US for 2008–2018. Percentage of each crop type, along with non-crop uses such as forest and development, were calculated for each 4 km PRISM climate data grid cell. To better understand the drivers of crop diversity, Random Forest modeling was used to assess the importance of climate, soils, and irrigation for patterns of crop effective richness for the contiguous United States, stratified by USDA Land Resource Region. The models explained 57–89% of the variation in maximum crop diversity, with irrigation being by far the most important explanatory variable in regions where it was employed. The drivers of change from 2008 to 2018 were less clear. Random Forest models explained only 20–60% of the change in agricultural diversity over the 11-year period; both soil and climate properties were important, with no clear dominant drivers. Potential crop effective richness was greater than actual across the entire region studied, but substantial increases would require irrigation. Major changes in agricultural systems and infrastructure may be necessary to increase agricultural diversity at large spatial extents, and declining availability of water for irrigation could threaten the agricultural systems that are now most diverse.

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.

Distance-based methods have been a valuable tool for ecologists for decades. Indirectly, distance-based ordination and cluster analysis, in particular, have been widely practiced as they allow the visualization of a multivariate data set in a few dimensions. The explicitly distance-based Mantel test and multiple regression on distance matrices (MRM) add hypothesis testing to the toolbox. One concern for ecologists wishing to use these methods lies in deciding whether to combine data vectors into a compound multivariate dissimilarity to analyze them individually. For Euclidean distances on scaled data, the correlation of a pair of multivariate distance matrices can be calculated from the correlations between the two sets of individual distance matrices if one set is orthogonal, demonstrating a clear link between individual and compound distances. The choice between Mantel and MRM should be driven by ecological hypotheses rather than mathematical concerns. The relationship between individual and compound distance matrices also provides a means for calculating the maximum possible value of the Mantel statistic, which can be considerably less than 1 for a given analysis. These relationships are demonstrated with simulated data. Although these mathematical relationships are only strictly true for Euclidean distances when one set of variables is orthogonal, simulations show that they are approximately true for weakly correlated variables and Bray-Curtis dissimilarities.

Introduction: Climate change is expected to alter environmental suitability for crops. In the United States, the Northern Great Plains (NGP), a primary production area for many crops including oilseeds, is at particular risk for decreasing cropland suitability under climate change. While canola (Brassica napus L.) has historically dominated oilseed production in the NGP, camelina (Camelinasativa L.) has been suggested as a potential climate-smart oilseed crop for the future due to agronomic attributes including drought tolerance, low input requirements, and cold hardiness.Methods: In this study, we examine the viability of both camelina and canola under future climate scenarios by first defining theircurrent environmental niche and then projecting their potential distribution under two different carbon emission scenarios.Results: While both crops are currently grown primarily in the NGP, we found that they have distinct spatial and environmental ranges, and that these ranges are largely consistent with what is described in the literature about their differing biological niches. We also find that under future climate change scenarios, environmental suitability for both crops is projected to decrease, though more so for canola.Discussion: This study helps to identify specific regions that may experience shifts (positive or negative) in environmental suitability for growing canola and camelina, but more broadly sheds light on the nonstationary and shifting range suitability that might be expected for crops under climate change.

Context Agricultural landscapes must produce food, fodder, fuel, or fiber while simultaneously maintaining non-market ecosystem services. Conservation practices such as planting perennial wildflower strips can improve multifunctionality, but their effectiveness may depend on the surrounding landscape. Objectives Our goals were to (1) model the effect of adding perennial wildflower strips along crop field margins on ecosystem services, and (2) assess the role of natural land cover and landscape complexity (landscape composition, connectivity, fragmentation, and heterogeneity) in the magnitude of ecosystem services. Methods We modeled pollinators, groundwater recharge, sediment runoff, and nutrient runoff in 437 US subwatersheds across a gradient of agricultural to natural land cover. We compared baseline ecosystem service values to a scenario adding wildflower strips along all crop field margins. We assessed the magnitude of the response in relation to natural land cover and landscape complexity. Results Planting perennial strips improved all four ecosystem services, but in distinct ways. Pollinators (wild bees) increased with percent natural area. Landscape-level increase peaked between 20 and 50%, reflecting annual agriculture edge density changes. Annual agriculture cohesion (a connectivity metric) and edge density (a fragmentation metric) were positively but nonlinearly associated with groundwater recharge and sediment runoff, respectively. Nutrient runoff decreased with increasing natural area and edge density. Conclusion The effects of landscape composition and configuration on ecosystem services varied with the service considered, and most relationships were nonlinear or showed threshold responses. Management interventions such as wildflower strips must be placed in their landscape context to ensure that investments in conservation practices are as effective as possible.

Question: Species distribution modelling has most commonly been applied to presence-only data and to woody species. Can similar methods be used to create detailed predicted abundance maps for forage species? These predictions would be of great value for agricultural management and land-use planning. Location: Northeastern USA. Methods: We used field data from 31 grazed farms to model abundances for six forage species with three statistical methods: GLM, GAM and Random Forest models. A hierarchical ecological framework encompassing climatic, edaphic and topographic variables related to the plant species requirements for water, light and temperature was used to guide variable selection. Results: Although many species distribution modelling studies have used only climatic variables, the inclusion of topography greatly improved explanatory power. Edaphic variables contributed little more beyond the information already provided by climate and topography. Random Forest models had higher overall predictive capability, and were used to produce the final potential abundance maps for the six forage species. Conclusions: Climate-only predictions may be suitable for state or regional planning, but topographic variables must be included in species distribution models used to support decision-making at the farm and field scales.

Vegetated filter strips (VFSs) are a best management practice (BMP) commonly implemented adjacent to row-cropped fields to trap overland transport of sediment and other constituents present in agricultural runoff. Although they have been widely adopted, insufficient data exist to understand their short and long-term effectiveness. High inter-event variability in performance has been observed, yet the majority of studies report average removal efficiencies over observed or simulated events, ignoring the disproportional effects of loads into and out of VFSs over longer periods of time. We argue that due to positively correlated sediment concentration-discharge relationships, disproportional contribution of runoff events transporting sediment over the course of a year (i.e., temporal inequality), decreased performance with increasing flow rates, and effects of antecedent moisture condition, VFS removal efficiencies over annual time scales may be significantly lower than reported per-event averages. By applying a stochastic approach, we investigated the extent of disparity between reporting average efficiencies from each runoff event over the course of 1 year versus the total annual load reduction. Additionally, we examined the effects of soil texture, concentration-discharge relationship, and VFS slope in contributing to this disparity, with the goal of revealing potential errors that may be incurred by ignoring the effects of temporal inequality in quantifying VFS performance. Simulation results suggest that ignoring temporal inequality can lead to overestimation of annual performance by as little as < 2% and to as much as > 20%, with the greatest disparities observed for soils with high clay content.