Assessing foraging landscape quality in Quebec's commercial beekeeping through remote sensing, machine learning, and survival analysis.

Abstract

Honey bees (Apis mellifera) play an important role in our agricultural systems. In recent years, beekeepers have reported high colony mortality rates in several parts of the world. Inadequate foraging landscapes are often cited as a major factor deterring honey bee colony health. Few studies, if any, have yet used large-scale datasets to assess the quality of landscapes encountered in commercial pollination activities. Here, we coupled a unique dataset comprising georeferenced reports on 17,743 colonies in the province of Quebec, Canada, with data derived from satellite remote sensing, to compute landscape metrics at each visited location. We ran a Cox and a random survival forests (RSF) model with time-weighted features to predict the lifespan of colonies in various landscape scenarios. Survival estimates from our RSF model indicate that colonies foraging primarily in forested areas exhibit higher survival rates, whereas those in cranberry- and maize-dominated landscapes may face lower survival probabilities. Our findings suggest that vegetation abundance could play a significant role in shaping outcomes. Additionally, landscape diversity within a 1 km radius seems to have a positive effect, with potentially greater benefits in areas where vegetation is sparse. While topography contributes valuable predictive insights, its effects are complex and challenging to fully interpret.