# Temperature as an early warning signal of honeybee colony failure

Theotime Colin, Vasilis Dakos, Andrew B Barron, William G Meikle, Eduardo

G Altmann, Tanya Latty

► To cite this version:

Theotime Colin, Vasilis Dakos, Andrew B Barron, William G Meikle, Eduardo G Altmann, et al. Temperature as an early warning signal of honeybee colony failure. Ecological Informatics, 2025, 92, 10.1016/j.ecoinf.2025.103445. hal-05312308

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# Temperature as an early warning signal of honeybee colony failure

Theotime Colin<sup>a,d,*</sup>, Vasilis Dakos<sup>b</sup>, Andrew B. Barron<sup>d</sup>, William G. Meikle<sup>e</sup>,
Eduardo G. Altmann<sup>c,1</sup>, Tanya Latty<sup>a,1</sup>

## ARTICLE INFO

### Keywords:

- Early warning signals

- Colony failure

- Colony death

- Honeybee mortality

- Indicators of colony failure

- *Apis mellifera*

- Western honeybees

## ABSTRACT

How to identify ecological systems at risk of failure is a central question of modern biology and agriculture. Due to human impacts and global change, there is a growing need for early warning signals that identify when a system is at risk of a state change before changes become irreversible or extremely complex and costly to remediate. Honeybees (*Apis mellifera*) are an urgent case because our food crops heavily rely on them for pollination and annual bee colony losses reported by beekeepers across the globe are unsustainable. Given enough warning, beekeepers can rescue dying colonies. Here we aim to develop an early warning system for the death of honeybee colonies. We used early warning signals to investigate whether fluctuations and dynamical patterns in internal hive temperature can be used as an early indicator of impending colony failure. Across three distinct datasets, we found that temperature regulation of failing colonies was different enough to distinguish them from healthy colonies before they died. We used a Receiver Operating Characteristic (ROC) approach to compare different predictors and determine the trade-off between sensitivity and specificity in our system. Our study shows that early warning theory can help to identify practical signals of risk of state change even in complex systems that change state relatively rapidly, such as a dying bee colony.

## 1. Introduction

Global change is exposing a wide range of complex systems such as ecosystems, agroecosystems or social insect colonies to new stressors and new magnitudes of stress. These stressors include landscape degradation (*Eswaran et al., 2019*), the spread of introduced pests (*Neumann and Elzen, 2004*; *Rosenkranz et al., 2010*) and climate change (*Araújo and Rahbek, 2006*). In response to chronic stress, complex systems can often manifest a “tipping point” response: complex systems are resilient to stress up to a point, beyond which they show rapid and dramatic changes of state (*Dakos et al., 2024*). These dramatic changes could be collapses of whale populations (*Clements et al., 2017*), bee colonies (*Bryden et al., 2013*; *Perry et al., 2015*), eutrophication of lakes (*Wang et al., 2012*), tree diebacks during droughts (*Camarero et al., 2015*), tree deaths in forests (*Cailleret et al., 2019*) and transitions from forests to savannah (*Hoffmann et al., 2012*). Practical methods to detect systems at risk of passing a tipping point are urgently needed to employ

protective management.

Early warning theory predicts that changes of states may often be detectable before a system passes a tipping point that leads to a new state (*Scheffer et al., 2009*). As a system approaches a tipping point, external perturbations of the system should result in variability and higher correlation in the system dynamics (*Dakos et al., 2024*; *Wang et al., 2012*). Early warning signals are receiving increasing attention in ecological and agricultural systems but practical examples outside of laboratories are still scarce (*Dakos et al., 2024*; *Wang et al., 2012*). The few examples of early warning signals applied to field environmental or biological systems are based on multi-year studies and strongly rely on modelling, which has perhaps slowed down practical applications in ecology. Non-experimental examples include the transition between savannah and forests (*Hoffmann et al., 2012*), the eutrophication of lakes (*Wang et al., 2012*), and the death of trees (*Dai et al., 2012*). But if the theoretical framework behind early warning signals is correct, indicators of vulnerability to state change should be common and detailed

<footnote><sup>a</sup> School of Life and Environmental Sciences, The University of Sydney, Sydney, Australia</footnote>

<footnote><sup>b</sup> ISEM, CNRS, Université de Montpellier, IRD, EPHE, Montpellier, France</footnote>

<footnote><sup>c</sup> School of Mathematics and Statistics, The University of Sydney, Sydney, Australia</footnote>

<footnote><sup>d</sup> School of Natural Sciences, Macquarie University, Sydney, Australia</footnote>

<footnote><sup>e</sup> Carl Hayden Bee Research Center, United States Department of Agriculture, Tucson, AZ, USA</footnote>

<footnote>* Corresponding author at: School of Life and Environmental Sciences, The University of Sydney, Sydney, Australia.
*E-mail address: theotime.colin@mq.edu.au (T. Colin).</footnote>

<footnote n="1">¹ These authors contributed equally to this work.</footnote>
knowledge of a system should not be needed to detect them.

Here we examined whether we could identify a practical and measurable early indicator of a Western honeybee (Apis mellifera) colony at risk of failure. Honeybee colony losses are an urgent case where early warning signals may help effective intervention. The death of bee colonies happens on a much shorter time-scale (Perry et al., 2015) than the transition of trophic states in lakes, the death of trees or plant community changes at the landscape level. An apparently healthy and well-functioning bee colony can decline in weeks, which has caused several authors to label it as a “collapse” (vanEngelsdorp et al., 2009). Globally, honey bee colony failure rates have increased in industrialised nations in the last decade (Goulson et al., 2015). At the same time, many industrialised nations have increased their reliance on honey bees for pollination services for food crops (Aizen and Harder, 2009). Hence there is concern that high honey bee colony failure rates could pose a risk to food chain security (Klein et al., 2007). Traditionally, an assessment of colony condition requires opening the hive every few weeks for a manual inspection of the frames, to detect weak colonies that are the most at risk of dying (Döke et al., 2019; Harbo, 1986). This has become too labour-intensive to be sustainable at the scale at which colony failure now occurs. At the same time, electronic sensors specifically designed for hive monitoring are becoming more precise and available (McMinn-Sauder et al., 2024; Meikle et al., 2016). If there were a practical early indicator of a colony at risk of failure, colony failures could be reduced through targeted management using the wide range of already existing beekeeping techniques (Brasil et al., 2013; Çakmak et al., 2023).

Temperature regulation is considered an indicator of bee colony health in ecotoxicology (Colin et al., 2019; Meikle et al., 2022) because it directly reflects the capacity of the colony to maintain optimal brood development conditions (Jones et al., 2005; Jones and Oldroyd, 2006). In warmer months, honeybee colonies keep the temperature of the brood between 33 °C and 36 °C. In Winter, the amount of brood is reduced (Meikle and Holst, 2015) and colonies maintain the temperature of the hive at a lower average, with higher fluctuations. The presence of equilibria in this easily measurable and actively controlled variable of the honeybee colony makes temperature an ideal candidate to explore early warning signals of colony failure. We do not imply that failing temperature regulation is the cause of colony failure, but predict that failing temperature regulation is a useful predictor of colony failure independent of the ultimate cause (Arias-Calluari et al., 2025; Minaud et al., 2024). Colony failure is likely to lead to higher temperature fluctuations and higher minima and maxima. We also expect the average temperature to decrease in stressed colonies, which may have less brood and thus require less heat, or a smaller bee population unable to warm up the hive.

Here, we use three independent datasets containing temperature time-series, brood population estimates and census data for a total of 96 bee colonies to determine how dying, weak and healthy bee colonies controlled temperature. We compare the performance of simple summary statistics, used as early warning signals, as classifiers of the survival and health status of colonies using a Receiver Operating Characteristic (ROC) approach. Next, we simplified our approach by setting a classification threshold relevant to real-world beekeeping practices, to test the practicality of our method. Our results confirm that early warning signals based on hive temperature are useful predictors of colony failure.

## 2. Methods

### 2.1. Overview of the datasets used

We used temperature measurements from the centre of 96 bee colonies from three field experiments at two locations: 1) the Sydney 2018 dataset (Colin et al., 2021); 2) the Sydney 2020 dataset; and 3) the California 2016–17 dataset (Meikle et al., 2020). Colonies were exposed to different conditions between and within these experiments which

reflects the diversity of real environmental conditions bees are often exposed to. No significant effects of treatments on hive survival or brood levels were detected.

The Sydney 2018 dataset consisted of hive temperatures measured over one year, from March 2018 to March 2019 from 21 hives including seven treated with the miticide thymol, seven treated with the miticide tau-fluvalinate and seven not treated with miticides. These miticides were applied according to the label and are commonly used in hives to control *Varroa destructor*, a parasite of the honeybee (Colin et al., 2021). Initially twenty-five hives were established from bee packages of equal strength with new queens obtained from a single local breeder and fed with 6 L of 50:50 water:sucrose syrup and 1 kg pollen patties for establishment (Colin et al., 2021). At the time of the experiment, there was no *Varroa destructor* in Australia, and no other noticeable pests or disease were found in the hives prior to the beginning of the experiment. Prior to the beginning of the experiment, 21 healthy colonies of similar sizes and condition were selected. By the end of the experiment, 7 died, 4 were considered to have low brood and 10 were considered healthy.

In the second experimental dataset from 2020, 16 healthy colonies of similar sizes and with queens obtained from a local breeder were selected. These contained no noticeable pests or disease (V. destructor mites were not present in Australia at the time). This dataset consisted of 2 months of hive temperature data obtained from a field experiment using established colonies with at least 5 frames covered in bees housed in standard 8-frames Australian Langstroth hives, in which all frames containing only food stores (nectar and pollen) were removed fortnightly from 8 hives while stores were left in another 8 colonies. One colony absconded and was removed from the dataset. One colony in the food removal group and two in the control group died. A sensor from the hive in the low brood group stopped functioning in the second half of March so data stopped abruptly for this colony. In these two experimental datasets, the distribution of colony mortality between treatment groups suggested no or weak effects of the treatments on colony survival and we kept all groups for the present study. In this dataset, the distribution of colony mortality between treatment groups suggested no or weak effects of the treatments on colony survival. By the end of the experiment, 3 colonies died, 2 were considered to have low brood, and 8 were considered healthy.

In our third dataset, 60 healthy hives of similar strength with open-mated queens obtained from a commercial source were selected and managed by a commercial beekeeper in California, USA over a year, in a migratory pollination operation located across 3 environments in southern California, USA, from August 2016 to March 2017 (Meikle et al., 2020). In that study differences in hive temperature were observed among the different environments; as the analysis presented here is intended to be robust across different environments, data from all hives were used. By the end of the experiment, 6 colonies died, 14 were considered to have low brood, and 40 were considered healthy.

### 2.2. Measurements

Temperature was recorded every 15 min using sensors with a 0.0625 °C precision (iButton Thermochron model DS1922). A single sensor was used in each hive, hanging from a wire in the middle of the hive between frames 4 and 5. Every two months, sensors were removed for inspection, data download and replacement, occasionally creating gaps in the temperature records. Because accessing sensors required opening the hive and thus some level of colony disturbance, records for the first 2 h after sensor installation were removed.

Regular inspections were conducted during which brood frames were brushed free of bees, photographed and capped brood cells counted using CombCount (Colin et al., 2018). Honeybee development typically takes about 21 days, with 9 days as an uncapped larva and 12 days as capped larva or pupa (Winston, 1991). Brood amount data from the last evaluation in each dataset were used to classify colonies into “low brood” colonies and “healthy” colonies based on gaps in the
distribution of colonies' brood amounts, which were found to resemble a left-skewed normal distribution (Supplementary Material Fig. S1).

Colonies in the Sydney 2018 experiment were assessed for health and activity monthly, by conducting a full health assessment and photographing all brood, and every two weeks, by monitoring flight activity on sunny days. Colonies in the Sydney 2020 experiment were assessed weekly for flight activity, and every two weeks for health. Colonies in the California 2016–17 dataset were assessed for brood in August, October, January, February and March (Meikle et al., 2020). In general, colonies were considered dead and removed as soon as they were found to contain less than 300 workers to prevent the spread of pests and disease.

## 2.3. Analyses

All analyses were conducted in R v4.2.3, data handling was done using 'tidyverse' v2.0.0 and 'lubridate' v1.9.2, breakpoints for the receiver operating characteristic (ROC) curves were obtained using 'segmented' v1.6.4.

Datasets were analysed separately. Temperature averages, minima, maxima, standard deviations and lag-1 autocorrelation coefficients were calculated for each colony and day using base R function ('mean', 'min', 'max', 'sd' and 'acf' with 'lag = 1') (Figs. 1 to 3), and those data used to calculate Z-scores. A Z-score corresponds to the difference between the observed value x and the sample mean μ, divided by the standard deviation of the sample σ:

$$Z = \frac{|x - \mu|}{\sigma}$$

<figure><img src="image_3.png" /><figcaption>Fig. 1. Daily hive temperature parameters for the California 2016–17 dataset. Bee colonies were either considered alive, dead, or had abnormally low brood levels at the end of the experiment (SM1). The within-day lag-1 autocorrelation coefficients, daily standard deviations, daily averages, daily minima and daily maxima suggest differences between alive, dead and low brood bee colonies that increase during winter. Black lines show the daily data, blue lines generalised additive models (default parameters from geom.smooth in R v4.0.0). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Daily hive temperature parameters for the Sydney 2018 dataset. Bee colonies were either considered alive, dead, or had abnormally low brood levels at the end of the experiment (SM1). Daily autocorrelations, daily standard deviations, daily averages, daily minima and daily maxima suggest differences between alive, dead and low brood bee colonies that increase during winter. Black lines show the daily data, blue lines generalised additive models (default parameters from *geom.smooth* in R v4.0.0; note in this dataset one colony survives significantly longer than the others, weighing more at the end of the study on the GAM). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

A Z-score was calculated for each colony and each day as the absolute difference between the variable average of one focal hive and that of all the other hives, divided by the variable standard deviation for all the other hives on that day. All colonies were included in the calculation of the Z-score, regardless of their outcome in terms of survival or brood population, as that would correspond to the perspective of a beekeeper.

A Z-score threshold was established to classify observations in
groups. To quantify the effect of a threshold on the ratio of true positive
to that of false negative rate we used a ROC curve (Boettiger and
Hastings, 2012). The ROC curves we obtained (Fig. 4) show the per-
formance of a classifier at every possible classification value. For each
focal variable, the true positive rate (number of true positives divided by
the sum of true positives and false negatives) and the false positive rate
(number of false positives divided by the sum of true negatives and false
positives) were calculated across threshold values as thresholds varied

from 0 to the maximum Z-score, with a step of 0.1.

The curves show a clear breakpoint for low values of true positive
rates and false positive rates after which the slope of the curve growth is
reduced. Typically, this breakpoint is used as a strict threshold because it
corresponds to the point after which the true positive rate grows slower
than the false positive rate (Metze, 1978). In our case, the false negative
rate is inflated because of a practical limitation: we cannot determine
exactly when colonies that died during experiments stopped being
"healthy". It is likely that colonies in the dead or low brood groups
stayed healthy for a few weeks before their health started to decline and
low brood amounts or death were detected. This conservative approach
inflates false negative rates and reduces the AUC values below their real
predictive power. The ROC breakpoint threshold value was calculated
for the daily average measurements and then used to classify the Z-
scores of the average daily temperature (Figs. 5 and 6). Colonies with Z-
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Daily hive temperature parameters for the Sydney 2020 dataset. Bee colonies were either considered alive, dead, or had abnormally low brood levels at the end of the experiment (SM1). Daily autocorrelations, daily standard deviations, daily averages, daily minima and daily maxima suggest differences between alive, dead and low brood bee colonies that increase during winter. Black lines show the daily data, blue lines generalised additive models (default parameters from *geom.smooth* in R v4.0.0). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

scores above the thresholds were considered at risk of death. This led to flickering warnings during hot and cold weeks in all colonies, so we removed warnings that occurred for less than three consecutive days, an arbitrary value chosen here to facilitate data interpretation. In practice, increased or decreased warning filtering would be used to increase sensitivity or decrease the number of false positives. We then calculated the number of days from the first warning to the classification of the colony as dying or dead.

# 3. Results

## 3.1. Area Under the Curve (AUC)

AUCs were calculated for all indicators to select the best early warning signal of colony failure. Note that these numbers are expected to be lower here than in a typical ROC approach as data from bee colonies declining before they are marked as dead are included in our

analysis. This lowers the AUC for all variables measured. Overall, AUCs were similar for the different parameters (Fig. 4), with the daily average temperature the highest in two datasets and the daily autocorrelation the lowest in two and second lowest in one dataset.

For the Sydney 2018 dataset, the AUC was the highest for the daily maximum (0.61), followed by daily standard deviation (0.57), the daily average and daily minimum (0.55), and daily autocorrelation (0.50) (Fig. 4).

For the Sydney 2020 dataset, the AUC was the highest for the daily average and daily minimum and daily average (0.74), followed by the daily maximum (0.69), daily standard deviation (0.68) and daily autocorrelation (0.59) (Fig. 4).

For the California 2016–17 dataset, the AUC was the highest for the daily average (0.65), followed by the daily minimum (0.64), the daily maximum (0.61), the daily autocorrelation (0.59) and the daily standard deviation (0.58) (Fig. 4).
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Receiver operating characteristic (ROC) curves showing the relationship between the true positive rate and the false positive rate for the five variables measured, based on the classification of z-scores. Left, California: AUC daily mean = 0.65, AUC daily minimum = 0.64, AUC daily maximum = 0.61, AUC autocorrelation = 0.59, AUC standard deviation = 0.58; Center, Sydney 2018: AUC daily mean = 0.55, AUC daily minimum = 0.55, AUC daily maximum = 0.61, AUC autocorrelation = 0.50, AUC standard deviation = 0.57; Right, Sydney 2020: AUC daily mean = 0.74, AUC daily minimum = 0.74, AUC daily maximum = 0.69, AUC autocorrelation = 0.59, AUC standard deviation = 0.68. Although the area under the curve is higher for the autocorrelation, the true positive rate is mainly high when the false positive rate is also high. Curves from the Sydney 2020 datasets are less smooth because the experiment was shorter, so less data were collected.</figcaption></figure>

## 3.2. True and false positive detection rates

We determined the Z-score threshold corresponding to the main breakpoint of the ROC curve in each dataset and considered that a colony should exceed the Z-score threshold for three consecutive days to be considered at risk of dying or having low brood. Given the similarity between the AUCs of most parameters, we selected only the daily average temperature for these further analyses. Using these values, we were able to detect 5/6 colonies in the California 2016–17 dataset that died later (true positives), 10/14 low brood colonies (true positives) with 9/40 false positives (note that in this dataset where a commercial beekeeper cared for the hives, failing colonies might have been rescued) (Figs. 5 and 6). Dying colonies were on average first detected at risk of dying 98 days before death was confirmed (29, 35, 61, 155 and 210 days). When expressed as the percentage of days with warnings for each group, we found that warnings occurred in 35.4 % of the days for the dead colonies group, 20.3 % of the days for the low brood group, and that only 1.0 % of the days had false positive warnings in the healthy colonies group (Figs. 5 and 6). This percentage includes days where colonies that were left with low brood or died by the end of our study were still healthy, in particular at the beginning of the experiment.

In the Sydney 2018 dataset, we detected 8/9 colonies that died, half of the low brood colonies (2/4), but false positives occurred in the healthy colonies group (5/8). Note that the probability that most colonies would have at least one false-detection increases with the duration of the experiment, and it is not surprising that this year-long dataset, the longest of all, has the largest number of false-positives. These false positives were however short-lived. False positives in the healthy colonies group represented only 2.0 % of all days, while warnings occurred for 25.1 % of days in the dead colony group and 6.3 % of days in the low brood group (Figs. 5 and 6). The eight colonies that failed were detected on average 56 days before they died (9, 9, 30, 33, 59, 59, 119 and 133 days before they were observed dead or were considered too small to recover and were removed to prevent the spread of disease or pests).

In the Sydney 2020 dataset we were able to detect 3/3 that later died. We detected one low brood colony (1/2), and few false positives occurred in the healthy colonies group (3/10). Among the three dead colonies detected as failing, hives were detected on average 20 days before death was confirmed (8, 21 and 31 days). Estimating the overall percentage of daily warnings for each group, we found that dead colonies were detected as dying 58.6 % of the days, low brood colonies 20 % of the days, and false positives represented 3.8 % of the total number of days in the healthy group (Figs. 5 and 6).

## 4. Discussion

In this study we applied two widely used early warning signals, the changes in the autocorrelation and standard deviation (Dai et al., 2012; Dakos et al., 2024), along with records of daily beehive temperature to predict the death of honeybee colonies. Standard deviation and autocorrelation are expected to increase when a system is close to a tipping point due to either an increase in system sensitivity to stochastic fluctuations, or a decrease in system capacity to recover from rapid environmental changes (Dakos et al., 2024). While daily average temperature is useful as a proxy for the overall capacity of colony's ability to maintain a stable temperature over time, minimum and maximum hive temperatures reflected a colony's ability to control temperature in spite of fluctuations in ambient temperature.

Simple thresholds cannot be used to predict the death of colonies as these parameters are expected to vary in all bee colonies due to changes as colonies approach winter and transition to a natural overwintering state (Döke et al., 2015; Minaud et al., 2024) and because of extreme weather changes. This is visible on Figs. 1 to 3, where all colonies, regardless of their survival outcome, experience a drop in average temperature from Autumn to Spring. Instead, we compared the daily temperature parameters (autocorrelation, standard deviation, minimum, maximum and average) of each colony with those of all other colonies from the same experiment, regardless of their survival outcome,
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Z-scores of individual colonies over time, for survival categories and location. In this example, colonies are considered healthy (green) below a Z-score threshold determined from the ROC curves (main breakpoint), or at risk of dying or containing low brood (red) when above the threshold for 3 consecutive days. A consequence of the method is that as fewer unhealthy colonies remain, their Z-scores become larger (ex. Sydney 2020, only one unhealthy hive remaining in the dataset from mid-March). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

to obtain an individual Z-score for each individual hive on each specific day. The Z-score represents the difference between a single colony and the mean value of all other colonies in the same environmental conditions, for any given variable.

To determine the utility of temperature at detecting colony failure, we used datasets where the survival outcome and brood population were known for each colony. In our analyses, we first ignored this knowledge to simulate a real-time scenario similar to what beekeepers experience. We then checked our results against the known outcomes. We purposefully ignored known failure mechanisms and additional information about beekeeping practices and environmental stress as we were interested in developing an early warning indicator that functioned irrespective of specific stressors. Typically, the development of early warning systems has relied on understanding the existence and mechanisms behind tipping points to model the system (Dakos et al., 2024). Obtaining and integrating such knowledge in early warning systems

likely improves their accuracy and is an inviting scientific prospect but is not practical and is often unfeasible. Instead, we showed that useful early warning signals can be obtained for practical applications even without the knowledge needed to build mechanistic models, qualify tipping points and demonstrate critical transitions.

We determined a threshold based on the change in the ratio of true to false positive simply to illustrate our findings. We applied our threshold to the Z-score of the daily average temperature, one of the parameters with the highest AUC along with the daily minimum and daily maximum temperature. Of these three, the daily average temperature is easiest to implement in practical systems, as more frequent sampling may be necessary to capture the minimum or maximum daily temperature than to estimate a daily average temperature. Our results contained very few days of false-positives and detected almost all colonies at risk of failure. We do not recommend the use of these particular thresholds as universal thresholds for all apicultural situations, as they influence the number of
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Threshold warnings obtained from the Z-score applied to the average daily temperature of hives. Red lines show hives considered at risk of death; green lines show hives considered healthy. Note that winter occurs at different times in California (November–February) and Sydney (June–September). The detection of dying hives using a Z-score of the daily average temperature remains reliable even as bee colonies shift into a stable winter state during which bees minimise energy expenditure by reducing thermoregulation. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

false-positives (leading to unnecessary efforts from beekeepers) or false-
negatives (decreasing the number of colonies rescued). This trade-off
would be better resolved by stakeholders with knowledge of the costs
and benefits specific to their own operations. These have been selected
as “strict thresholds” (Metze, 1978) to provide an example of a possible
application. Because thresholds influence the number of false-positives
(leading to unnecessary efforts from beekeepers) or false-negatives
(decreasing the number of colonies rescued), this trade-off would be
better solved regionally by stakeholders with knowledge of the costs and
benefits specific to their own operations. A strict threshold could be a
starting point, but we recommend that beekeepers lower thresholds
when the cost of false positives is higher than the cost of false negatives,
which is expected to occur when labour and travelling to an apiary is
more costly than losing a few hives. We recommend that beekeepers
increase thresholds when the cost of false negatives is higher than the

cost of false positives, which would happen when the cost of losing bee
colonies is higher than the cost of running unnecessary hive checks.
Beekeepers or sensor providers may refine their thresholds as they
collect data on false positives and false negatives and as more hives are
equipped with sensors. Our method requires several colonies to be
equipped with sensors within a geographical area to provide accurate
results, which is expected as beekeepers gradually seem to adopt these
technologies (Sattler et al., 2025). The predictions of our smallest
dataset comprising 15 colonies (Sydney 2020) were still accurate
enough to detect all dying colonies early. We expect this method to be
especially accurate for commercial beekeepers, who typically own
several dozen hives often placed in a same location (Sattler et al., 2025).
Amateur beekeepers typically keep fewer hives (Sattler et al., 2025) but
are often clustered around cities, so we expect this method to be relevant
to a vast majority of hobbyists in places where enough of them adopt
temperature sensors.

Here we focused on temperature regulation as a possible early warning signal for a colony. Currently, temperature is the most affordable and most reliable variable that can be measured in a hive. A variety of other sensors are available for beekeeping (Meikle and Holst, 2015; Sattler et al., 2025). Scales can measure weight changes reflecting variation in food stores or worker activity (Arias-Calluari et al., 2023; Meikle et al., 2016), although hive scales are typically expensive and fragile and measurements can be imprecise in windy or rainy conditions (McMinn-Sauder et al., 2024; Sattler et al., 2025). Hygrometers can be placed within hives and bees actively control moisture to cool the hive and prevent the desiccation of larvae, or limit the growth of pests and pathogens, but bees tend to block these sensors with propolis. Microphones and bee counters can be placed within hives but often get in the way of beekeeping practices and the dataset produced are large and sensors consume significant power, which is not compatible with low-maintenance Internet of Devices systems and practical applications (Sattler et al., 2025). If future technological improvements were to help reduce these limitations, combining data could help reduce false positives and false negatives or detect failing colonies earlier. Similarly, we focused on simple early warning indicators to facilitate the uptake of our methodology by the beekeeping and pollination industries, but more recent and sophisticated albeit somewhat complex approaches based on relaxation time (Ma et al., 2022; Ma et al., 2024), dynamical eigenvalue (Grziwotz et al., 2023) and deep learning (Bury et al., 2021) could improve predictions.

Most of our detections would be practically early enough to give beekeepers time to rescue bee colonies, usually occurring for the first time a month before collapse (Brasil et al., 2013; Çakmak et al., 2023). When colonies are considered beyond rescuing, beekeepers can merge weak bee colonies to save workers, or cull colonies early to prevent the spread of pests and diseases to nearby hives and damages to the wax frames. Global data on bee colony rescue are rare and additional experiments quantifying the number of bee colonies saved across climates and seasons would provide valuable insights to the field. Similarly, datasets that include long-term repeated measurements of colony survival, health, brood quantity, and internal hive temperature—especially in apiaries where a proportion of the colonies ultimately dies—remain scarce. While our study comprises data from hives in Arizona and Sydney, the collection of additional datasets in different climates will be necessary to validate the reliability and generalisability of our results globally.

Early warning signals can guide the development of markers of biological systems at risk of failing or transitioning to sub-optimal states. Our application of this method to detect failing bee colonies is the first example of the use of early warning signals in a biological or agricultural system where it can realistically be used to minimise losses. We hope this will encourage the development of similar methods in related fields. We suggest that the development of early warning tools for critical transitions in biological and eco-systems should focus on measuring and analysing processes that are highly dynamic, self-regulated by the system and very variable. In our changing world, there are an abundance of biological systems similar to bee colonies (Dakos et al., 2024), where stable equilibria occur, and failures have costly consequences to society. The insights from this early warning study demonstrate that early warning signals can be applied to rapid processes in a meaningful way to solve urgent environmental and agricultural issues, such as the unsustainable losses of bee colonies worldwide.

### CRediT authorship contribution statement

**Theotime Colin:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.
**Vasilis Dakos:** Writing – review & editing, Visualization, Validation, Methodology, Investigation,

Conceptualization. **Andrew B. Barron:** Writing – review & editing, Validation, Resources, Project administration, Funding acquisition, Conceptualization. **William G. Meikle:** Writing – review & editing, Validation, Resources, Project administration, Methodology, Investigation, Data curation, Conceptualization. **Eduardo G. Altmann:** Writing – review & editing, Visualization, Validation, Project administration, Methodology, Investigation, Conceptualization. **Tanya Latty:** Writing – review & editing, Validation, Project administration, Funding acquisition, Conceptualization.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgments

This work was supported by the Australian Research Council (Grant DP190101994 to TL, VD, EGA), the Lord Mayor's Charitable Foundation (TC, ABB) and as part of the project "Best practices for evaluation of bee health technology" (PH22003) funded through Hort Innovation Frontiers with co-investment from Macquarie University and contributions from the Australian Government (TC, ABB).

### Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ecoinf.2025.103445.

### Data availability

The data and code that support the findings of this study are openly available in Dryad at https://doi.org/10.5061/dryad.2547d7x46.

### References

Aizen, M.A., Harder, L.D., 2009. The global stock of domesticated honey bees is growing slower than agricultural demand for pollination. Curr. Biol. 19, 915–918. https://doi.org/10.1016/j.cub.2009.03.071.

Araújo, M.B., Rahbek, C., 2006. How does climate change affect biodiversity? Science 313, 1396–1397. https://doi.org/10.1126/science.1131758.

Arias-Calluari, K., Colin, T., Latty, T., Myerscough, M., Altmann, E.G., 2023. Modelling daily weight variation in honey bee hives. PLoS Comput. Biol. 19, e1010880. https://doi.org/10.1371/journal.pcbi.1010880.

Arias-Calluari, K., Colin, T., Latty, T., Myerscough, M., Altmann, E.G., 2025. Assessing Honey Bee Colony Health Using Temperature Time Series. https://doi.org/10.4850/arXiv.2506.00602.

Boettiger, C., Hastings, A., 2012. Quantifying limits to detection of early warning for critical transitions. J. R. Soc. Interface 9, 2527–2539. https://doi.org/10.1098/rsif.2012.0125.

Brasil, D. de F., Guimarães, M. de O., Barbosa Filho, J.A.D., Freitas, B.M., 2013. Internal ambience of bee colonies submitted to strengthening management by adding broods. Engenharia Agrícola 33, 902–909. https://doi.org/10.1590/S0100-69162013000500002.

Bryden, J., Gill, R.J., Mitton, R.A.A., Raine, N.E., Jansen, V.A.A., 2013. Chronic sublethal stress causes bee colony failure. Ecol. Lett. 16, 1463–1469. https://doi.org/10.1111/ele.12188.

Bury, T.M., Sujith, R., Pavithran, I., Scheffer, M., Lenton, T.M., Anand, M., Bauch, C.T., 2021. Deep learning for early warning signals of tipping points. Proc. Natl. Acad. Sci. 118, e2106140118. https://doi.org/10.1073/pnas.2106140118.

Cailleret, M., Dakos, V., Jansen, S., Robert, E.M.R., Aakala, T., Amoroso, M.M., Antos, J. A., Bigler, C., Bugmann, H., Caccianaga, M., Camarero, J.J., Cherubini, P., Coyea, M. R., Cufar, K., Das, A.J., Davi, H., Gea-Izquierdo, G., Gillner, S., Haavik, L.J., Hartmann, H., Hereş, A.M., Hultine, K.R., Janda, P., Kane, J.M., Kharuk, V.I., Kitzberger, T., Klein, T., Levanic, T., Linares, J.C., Lombardi, F., Mäkinen, H., Mészáros, I., Metsaranta, J.M., Oberhuber, W., Papadopoulos, A., Petritan, A.M., Rohner, B., Sangüesa-Barreda, G., Smith, J.M., Stan, A.B., Stojanovic, D.B., Suarez, M.L., Svoboda, M., Trotsiuk, V., Villalba, R., Westwood, A.R., Wyckoff, P.H., Martínez-Vilalta, J., 2019. Early-warning signals of individual tree mortality based on annual radial growth. Front. Plant Sci. 9, 1–14. https://doi.org/10.3389/fpls.2018.01964.

Çakmak, I., Kul, B., Ben Abdelkader, F., Seven Çakmak, S., 2023. Effects of temperature adjustment with a heating device in weak honey bee colonies in cold seasons. Int. J. Biometeorol. 67, 1765–1774. https://doi.org/10.1007/s00484-023-02537-w.
Camarero, J.J., Gazol, A., Sangüesa-Barreda, G., Oliva, J., Vicente-Serrano, S.M., 2015. To die or not to die: early warnings of tree dieback in response to a severe drought. J. Ecol. 103, 44–57. https://doi.org/10.1111/1365-2745.12295.

Clements, C.F., Blanchard, J.L., Nash, K.L., Hindell, M.A., Ozgul, A., 2017. Body size shifts and early warning signals precede the historic collapse of whale stocks. Nat. Ecol. Evol. 1, 1–6. https://doi.org/10.1038/s41559-017-0188.

Colin, T., Bruce, J., Meikle, W.G., Barron, A.B., 2018. The development of honey bee colonies assessed using a new semi-automated brood counting method: Combcount. PLoS One 13, 1–14. https://doi.org/10.1371/journal.pone.0205816.

Colin, T., Meikle, W.G., Paten, A.M., Barron, A.B., 2019. Long-term dynamics of honey bee colonies following exposure to chemical stress. Sci. Total Environ. 677, 660–670. https://doi.org/10.1016/j.scitotenv.2019.04.402.

Colin, T., Forster, C.C., Westacott, J., Wu, X., Meikle, W.G., Barron, A.B., 2021. Effects of late miticide treatments on foraging and colony productivity of European honey bees (*Apis mellifera*). *Apidologie* 52, 474–492. https://doi.org/10.1007/s13592-020-00837-3.

Dai, L., Vorselen, D., Korolev, K.S., Gore, J., 2012. Generic indicators for loss of resilience before a tipping point leading to population collapse. Science 336, 1175–1177. https://doi.org/10.1126/science.1219805.

Dakos, V., Boulton, C.A., Buxton, J.E., Abrams, J.F., Arellano-Nava, B., Armstrong McKay, D.I., Bathiany, S., Blaschke, L., Boers, N., Dylewsky, D., López-Martínez, C., Parry, I., Ritchie, P., Van Der Bolt, B., Van Der Laan, L., Weinans, E., Kéfi, S., 2024. Tipping point detection and early warnings in climate, ecological, and human systems. Earth Syst. Dynam. 15, 1117–1135. https://doi.org/10.5194/esd-15-1117-2024.

Döke, M.A., Frazier, M., Grozinger, C.M., 2015. Overwintering honey bees: biology and management. Curr. Opin. Insect Sci. 10, 185–193. https://doi.org/10.1016/j.cois.2015.05.014.

Döke, M.A., McGrady, C.M., Otieno, M., Grozinger, C.M., Frazier, M., 2019. Colony size, rather than geographic origin of stocks, predicts overwintering success in honey bees (*Hymenoptera: Apidae*) in the northeastern United States. J. Econ. Entomol. 112, 525–533. https://doi.org/10.1093/jee/toy377.

Eswaran, H., Lal, R., Reich, P.F., 2019. Land degradation: an overview. In: Response to Land Degradation, pp. 20–35. https://doi.org/10.1201/9780429187957.

Goulson, D., Nicholls, E., Botias, C., Rotheray, E.L., 2015. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 347, 1255957. https://doi.org/10.1126/science.1255957.

Grziwotz, F., Chang, C.-W., Dakos, V., van Nes, E.H., Schwarzländer, M., Kamps, O., Heßler, M., Tokuda, I.T., Telschow, A., Hsieh, C., 2023. Anticipating the occurrence and type of critical transitions. Sci. Adv. 9, eabq4558. https://doi.org/10.1126/sciadv.abq4558.

Harbo, J.R., 1986. Effect of population size on brood production, worker survival and honey gain in colonies of honeybees. J. Apic. Res. 25, 22–29. https://doi.org/10.1080/00218839.1986.11100687.

Hoffmann, W.A., Geiger, E.L., Gotsch, S.G., Rossatto, D.R., Silva, L.C.R., Lau, O.L., Haridasan, M., Franco, A.C., 2012. Ecological thresholds at the savanna-forest boundary: how plant traits, resources and fire govern the distribution of tropical biomes. Ecol. Lett. 15, 759–768. https://doi.org/10.1111/j.1461-0248.2012.01789.x.

Jones, J.C., Oldroyd, B.P., 2006. Nest thermoregulation in social insects. Adv. Insect Physiol. https://doi.org/10.1016/S0065-2806(06)33003-2.

Jones, J.C., Helliwell, P., Beekman, M., Maleszka, R., Oldroyd, B.P., 2005. The effects of rearing temperature on developmental stability and learning and memory in the honey bee, *Apis mellifera*. J. Comp. Physiol. A, Neuroethol. Sensory Neural Behav. Physiol. 191, 1121–1129. https://doi.org/10.1007/s00359-005-0035-z.

Klein, A.M., Vaissière, B.E., Cane, J.H., Steffan-Dewenter, I., Cunningham, S.A., Kremen, C., Tscharntke, T., 2007. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B Biol. Sci. 274, 303–313. https://doi.org/10.1098/rspb.2006.3721.

Ma, Z., Zeng, C., Zheng, B., 2022. Relaxation time as an indicator of critical transition to a eutrophic lake state: the role of stochastic resonance. Europhys. Lett. 137, 42001. https://doi.org/10.1209/0295-5075/ac5b99.

Ma, Z., Zeng, C., Liu, W.-M., 2024. Relaxation time as early warning signal of avalanches in self-organizing systems. Phys. Rev. Res. 6, 013013. https://doi.org/10.1103/PhysRevResearch.6.013013.

McMinn-Sauder, H.B.G., Colin, T., Gaines Day, H.R., Quinlan, G., Smart, A., Meikle, W. G., Johnson, R.M., Sponsler, D.B., 2024. Next-generation colony weight monitoring: a review and prospectus. Apidologie 55, 13. https://doi.org/10.1007/s13592-023-01050-8.

Meikle, W.G., Holst, N., 2015. Application of continuous monitoring of honeybee colonies. Apidologie 46, 10–22. https://doi.org/10.1007/s13592-014-0298-x.

Meikle, W.G., Weiss, M., Stilwell, A.R., 2016. Monitoring colony phenology using within-day variability in continuous weight and temperature of honey bee hives. Apidologie 47, 1–14. https://doi.org/10.1007/s13592-015-0370-1.

Meikle, W.G., Weiss, M., Beren, E., 2020. Landscape factors influencing honey bee colony behavior in Southern California commercial apiaries. Sci. Rep. 10, 1–16. https://doi.org/10.1038/s41598-020-61716-6.

Meikle, W.G., Colin, T., Adamczyk, J.J., Weiss, M., Barron, A.B., 2022. Traces of a neonicotinoid pesticide stimulate different honey bee colony activities, but do not increase colony size or longevity. Ecotoxicol. Environ. Saf. 231, 113202. https://doi.org/10.1016/j.ecoenv.2022.113202.

Metze, C.E., 1978. Basic principles of ROC analysis. Semin. Nucl. Med. 8, 283–298. https://doi.org/10.1016/S0001-2998(78)80014-2.

Minaud, E., Rebaudo, F., Mainardi, G., Vardakas, P., Hatjina, F., Steffan-Dewenter, I., Requier, F., 2024. Temperature in overwintering honey bee colonies reveals brood status and predicts colony mortality. Ecol. Indic. 169, 112961. https://doi.org/10.1016/j.ecolind.2024.112961.

Neumann, P., Elzen, P.J., 2004. The biology of the small hive beetle (*Aethina tumida*, Coleoptera: Nitidulidae): gaps in our knowledge of an invasive species. Apidologie 35, 229–247. https://doi.org/10.1051/apido:2004010.

Perry, C.J., Sovik, E., Myerscough, M.R., Barron, A.B., 2015. Rapid behavioral maturation accelerates failure of stressed honey bee colonies. Proc. Natl. Acad. Sci. 112, 3427–3432. https://doi.org/10.1073/pnas.1610243113.

Rosenkranz, P., Aumeier, P., Ziegelmann, B., 2010. Biology and control of Varroa destructor. J. Invertebr. Pathol. 103, S96-S119. https://doi.org/10.1016/j.jip.2009.07.016.

Sattler, C., Barron, A.B., Colin, T., 2025. Beekeeping in the digital age: prospects and pitfalls of hive sensors in commercial beekeeping. J. Apic. Res. 0, 1–11. https://doi.org/10.1080/00218839.2025.2552530.

Scheffer, M., Bascompte, J., Brock, W.A., Brovkin, V., Carpenter, S.R., Dakos, V., Held, H., Van Nes, E.H., Rietkerk, M., Sugihara, G., 2009. Early-warning signals for critical transitions. Nature 461, 53–59. https://doi.org/10.1038/nature08227.

vanEngelsdorp, D., Evans, J.D., Saegerman, C., Mullin, C., Haubruge, E., Nguyen, B.K., Frazier, M., Frazier, J., Cox-Foster, D., Chen, Y., Underwood, R., Tarpy, D.R., Pettis, J.S., 2009. Colony collapse disorder: a descriptive study. PLoS One 4, e6481. https://doi.org/10.1371/journal.pone.0006481.

Wang, R., Dearing, J.A., Langdon, P.G., Zhang, E., Yang, X., Dakos, V., Scheffer, M., 2012. Flickering gives early warning signals of a critical transition to a eutrophic lake state. Nature 492, 419–422. https://doi.org/10.1038/nnnnnnnn11655.

Winston, M.L., 1991. *The biology of the honey bee*. Harvard University Press.