Assessing Honey Bee Colony Health Using Temperature Time Series

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.

💡 Research Summary

This paper introduces a low‑cost, data‑light methodology for assessing the health of honey‑bee colonies by exploiting the well‑known thermoregulatory behavior of bees. The authors argue that a healthy colony can maintain its brood nest temperature within a narrow optimal range (33 °C–36 °C) even when external ambient temperature (T_E) deviates substantially from that range. By quantifying how strongly the internal hive temperature (T_H) follows or resists changes in T_E, they derive two scalar indicators of colony performance:

1. Π = −log m, where m is the slope of the linear regression of T_H on T_E after a short lag. Small m (low susceptibility) yields a large Π, indicating efficient thermoregulation.
2. ΔT, the offset between the ideal ambient temperature and the temperature that would be observed if the colony performed no active heating or cooling. Larger ΔT reflects a stronger intrinsic heat‑generation capacity.

Two complementary estimation procedures are presented:

Method 1 (extreme‑value approach) extracts daily minima and maxima of T_E, then fits the linear model to T_H data within a two‑hour window after each extreme. This yields direct estimates of m and ΔT for each day.

Method 2 (cross‑correlation approach) computes the full cross‑correlation ρ(τ) between T_H(t + τ) and T_E(t) over a moving seven‑day window. The lag τ* at which ρ peaks is taken as the effective response delay; the slope is then recovered as m = ρ(τ*) · σ_T_H / σ_T_E. ΔT is derived from the same window’s mean values.

The authors applied both methods to temperature recordings from 22 hives, split into two data sets (six hives observed from 2016‑2017 and sixteen hives from early 2020). Three of the hives experienced collapse during the monitoring period. Results show a clear separation of colonies into three zones:

• Stable – Π ≈ 3–4, ΔT ≈ 10 °C. Internal temperature remains tightly bounded within the optimal band despite large swings in T_E.
• Warning – Π ≈ 2–3, ΔT ≈ 6–8 °C. The colony’s thermoregulatory response weakens; temperature fluctuations increase but the hive has not yet failed.
• Collapse – Π < 1.5, ΔT < 5 °C. The hive can no longer buffer external changes; T_H tracks T_E closely, and the standard deviation of T_H becomes proportional to that of T_E.

Empirically, the authors propose thresholds Π < 3.5 and ΔT < 8 °C to trigger a warning, and Π < 1.5 as an imminent‑collapse signal. Cross‑correlation analysis revealed typical response lags of ~1.5 h for the 2016‑2017 set and ~0.5 h for the 2020 set, suggesting seasonal or climatic influences on the speed of thermoregulatory adjustments.

The study’s strengths lie in its simplicity and practicality: only inexpensive temperature sensors are required, and the analysis can be performed with modest computational resources, enabling real‑time monitoring and early‑warning alerts for beekeepers. Compared with weight‑based, image‑processing, or complex population‑dynamics models, this approach dramatically reduces data acquisition and processing burdens while still delivering actionable insights.

Limitations include sensitivity to sensor placement (internal vs. peripheral readings), the fact that Π and ΔT capture only temperature‑related stress and may not directly reflect other stressors such as parasites, pesticides, or nutritional deficits, and the need for additional criteria to distinguish seasonal low‑Π periods (e.g., winter) from genuine health decline.

In conclusion, the paper demonstrates that temperature time‑series, when analyzed through the proposed susceptibility metrics, provide a robust, low‑cost diagnostic tool for honey‑bee colony health. The three‑tier status scale (stable, warning, collapse) can be implemented with minimal hardware and offers a promising avenue for scalable, non‑invasive hive monitoring. Future work should integrate these temperature metrics with other sensor modalities (e.g., acoustic, weight, humidity) and validate the approach across diverse climatic regions and management practices.