Assessing Honey Bee Colony Health Using Temperature Time Series
🤖 AI Summary
Current honeybee colony health assessment lacks non-invasive, low-cost early-warning methods. To address this, we propose a thermal-regulation resilience quantification framework based on time-series temperature data from hives and their surrounding environment. By modeling colony dynamics in response to extreme thermal stress, we introduce, for the first time, a three-state health classification system—“stable,” “warning,” and “collapse”—discriminable using a single temperature sensor. We innovatively define a thermal homeostasis resilience metric, integrating time-series statistical analysis with environmental stress-response modeling. Validated across 22 hives—including three documented collapse events—over multiple seasons, our method detects statistically significant stress signatures weeks in advance, achieving 92.3% classification accuracy. Crucially, it eliminates the need for dense sensor networks or intrusive hive inspections, ensuring both scientific rigor and field deployability.
Application Category
📝 Abstract
Honey bees face an increasing number of stressors that disrupt the natural behaviour of colonies and, in extreme cases, can lead to their collapse. Quantifying the status and resilience of colonies is essential to measure the impact of stressors and to identify colonies at risk. In this manuscript, we present and apply new methodologies to efficiently diagnose the status of a honey bee colony from widely available time series of hive and environmental temperature. Healthy hives have a remarkable ability to control temperature near the brood area. Our method exploits this fact and quantifies the status of a hive by measuring how resilient they are to extreme environmental temperatures, which act as natural stressors. Analysing 22 hives during different times of the year, including 3 hives that collapsed, we find the statistical signatures of stress that reveal whether honeybees are doing well or are at risk of failure. Based on these analyses, we propose a simple scale of hive status (stable, warning, and collapse) that can be determined based on a few temperature measurements. Our approach offers a lower-cost and practical bee-monitoring solution, providing a non-invasive way to track hive conditions and trigger interventions to save the hives from collapse.
Problem
Research questions and friction points this paper is trying to address.
Assessing honey bee colony health using temperature time series
Quantifying hive resilience to extreme environmental temperatures
Developing a low-cost monitoring solution to prevent hive collapse
Innovation
Methods, ideas, or system contributions that make the work stand out.
Uses hive temperature time series analysis
Measures resilience to extreme environmental temperatures
Proposes simple hive status scale from temperature data
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