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

Abstract

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.

A biophysical approach to assess weather impacts on honey bee colony winter mortality

“…The indicator ranked third in model performance. It is quite interesting that weather conditions occurring several months before the start of wintering have an impact on colony mortality [23]. This connection was also found by Switanek et al [24], van Esch et al [22], who defined a similar measure for optimal flying conditions, and Beyer et al [21], who associated cooler and wetter conditions in July with higher mortality rates.…”

Robust Variable Selection with Optimality Guarantees for High-Dimensional Logistic Regression

“…Focusing on the state of Pennsylvania, honey bee winter survival was recently investigated in [18] based on winter loss survey data provided by the Pennsylvania State Beekeepers Association. The data cover three winter periods (2016-2017, 2017-2018, and 2018-2019), and the main goals of the analysis were to assess the importance of weather, topography, land use, and management factors on overwintering mortality, and to predict survival given current weather conditions and projected changes in climate.…”

“…We use our proposal to investigate the main drivers of honey bee (Apis mellifera) loss during winter (overwintering), which represents the most critical part of the year in several areas [15][16][17]. In particular, we use survey data collected by the Pennsylvania State Beekeepers Association, which include information related to honey bee survival, stressors and management practices, as well as bio-climatic indexes, topography and land use information [18]. Previous studies mainly focused on predictive performance and relied on statistical learning tools such as random forest, which capture relevance but not effect signs for each feature, and do not account for the possible impact of outlying cases-making results harder to interpret and potentially less robust.…”

Varroa destructor from the Laboratory to the Field: Control, Biocontrol and IPM Perspectives—A Review

“…Moreover, the use of models allows scientists to handle simulated experiments when conducting those experiments is realistically impossible. It is, thus a powerful tool to explore and understand the complexities of ecosystems [ 264 ], especially when associated with field data [ 265 ]. In the case of host-parasite dynamics, it is well-known that they are influenced by the exchange between parasite and host density.…”