Modelling daily weight variation in honey bee hives.

A quantitative understanding of the dynamics of bee colonies is important to support global efforts to improve bee health and enhance pollination services. Traditional approaches focus either on theoretical models or data-centred statistical analyses. Here we argue that the combination of these two approaches is essential to obtain interpretable information on the state of bee colonies and show how this can be achieved in the case of time series of intra-day weight variation. We model how the foraging and food processing activities of bees affect global hive weight through a set of ordinary differential equations and show how to estimate the parameters of this model from measurements on a single day. Our analysis of 10 hives at different times shows that the estimation of crucial indicators of the health of honey bee colonies are statistically reliable and fall in ranges compatible with previously reported results. The crucial indicators, which include the amount of food collected (foraging success) and the number of active foragers, may be used to develop early warning indicators of colony failure.

The final frontier: ecological and evolutionary dynamics of a global parasite invasion.

Studying rapid biological changes accompanying the introduction of alien organisms into native ecosystems can provide insights into fundamental ecological and evolutionary theory. While powerful, this quasi-experimental approach is difficult to implement because the timing of invasions and their consequences are hard to predict, meaning that baseline pre-invasion data are often missing. Exceptionally, the eventual arrival of Varroa destructor (hereafter Varroa) in Australia has been predicted for decades. Varroa is a major driver of honeybee declines worldwide, particularly as vectors of diverse RNA viruses. The detection of Varroa in 2022 at over a hundred sites poses a risk of further spread across the continent. At the same time, careful study of Varroa's spread, if it does become established, can provide a wealth of information that can fill knowledge gaps about its effects worldwide. This includes how Varroa affects honeybee populations and pollination. Even more generally, Varroa invasion can serve as a model for evolution, virology and ecological interactions between the parasite, the host and other organisms.

Traces of a neonicotinoid pesticide stimulate different honey bee colony activities, but do not increase colony size or longevity.

How neonicotinoid contamination affects honey bees remains controversial. Studies have yielded contradictory results, and few have examined effects on colony development. Here we report the results of a comprehensive five-year study of the effects of the neonicotinoid imidacloprid on honey bee colonies. Colonies fed 5 ng/g (ppb) imidacloprid in sugar syrup showed increased brood production, lower temperature variability, higher CO2 production and had more foragers compared to control colonies fed unadulterated syrup, but treatment did not affect adult bee numbers or average hive temperatures, and did not increase food stores, daily food acquisition or colony survivorship. These results suggest that imidacloprid contamination increased colony metabolism without improving colony productivity, and helps explain why some studies have reported no, or even positive, effects of neonicotinoids. Effect sizes were generally small but that could be attributed at least in part to variability in uncontrolled factors such as weather. We provide an explanation for the diverse effects of pesticide contamination on honey bees, and an improved understanding of how colonies are impacted.

Traces of a Neonicotinoid Induce Precocious Foraging and Reduce Foraging Performance in Honey Bees.

There is increasing worldwide concern about the impacts of pesticide residues on honey bees and bee colony survival, but how sublethal effects of pesticides on bees might cause colony failure remains highly controversial, with field data giving very mixed results. To explore how trace levels of the neonicotinoid pesticide imidacloprid impacted colony foraging performance, we equipped bees with RFID tags that allowed us to track their lifetime flight behavior. One group of bees was exposed to a trace concentration (5 µg/kg, ppb) of imidacloprid in sugar syrup while in the larval stage. The imidacloprid residues caused bees to start foraging when younger as adults and perform fewer orientation flights, and reduced their lifetime foraging flights by 28%. The magnitude of the effects of a trace imidacloprid concentration delivered only during larval stage highlights the severity of pesticide residues for bee foraging performance. Our data suggest that neonicotinoids could impact colony function by imbalancing the normal age based division of labor in a colony and reducing foraging efficiency. Understanding this mechanism will help the development of interventions to safeguard bee colony health.

Non-additive gene interactions underpin molecular and phenotypic responses in honey bee larvae exposed to imidacloprid and thymol.

Understanding the cumulative risk of chemical mixtures at environmentally realistic concentrations is a key challenge in honey bee ecotoxicology. Ecotoxicogenomics, including transcriptomics, measures responses in individual organisms at the molecular level which can provide insights into the mechanisms underlying phenotypic responses induced by one or more stressors and link impacts on individuals to populations. Here, fifth instar honey bee larvae were sampled from a previously reported field experiment exploring the phenotypic impacts of environmentally realistic chronic exposures of the pesticide imidacloprid (5 µg.kg-1 for six weeks) and the acaricide thymol (250 g.kg-1 applied via Apiguard gel in-hive for four weeks), both separately and in combination. RNA-seq was used to discover individual and interactive chemical effects on larval gene expression and to uncover molecular mechanisms linked to reported adult and colony phenotypes. The separate and combined treatments had distinct gene expression profiles which represented differentially affected signaling and metabolic pathways. The molecular signature of the mixture was characterised by additive interactions in canonical stress responses associated with oxidative stress and detoxification, and non-additive interactions in secondary responses including developmental, neurological, and immune pathways. Novel emergent impacts on eye development genes correlated with long-term defects in visual learning performance as adults. This is consistent with these chemicals working through independent modes of action that combine to impact common downstream pathways, and highlights the importance of establishing mechanistic links between molecular and phenotypic responses when predicting effects of chemical mixtures on ecologically relevant population outcomes.

Long-term dynamics of honey bee colonies following exposure to chemical stress.

Pesticide residues have been linked to reduced bee health and increased honey bee colony failure. Most research to date has investigated the role of pesticides on individual honey bees, and it is still unclear how trace levels of pesticides change colony viability and productivity over seasonal time scales. To address this question we exposed standard bee colonies to chemical stressors known to have negative effects on individual bees, and measured the productivity of bee colonies across a whole year in two environments: near Tucson Arizona and Sydney Australia. We exposed hives to a trace amount of the neonicotinoid imidacloprid and to the acaricide thymol, and measured capped brood, bee and honey production, as well as the temperature and foraging force of the colonies. The effect of imidacloprid on colony dynamics differed between the two environments. In Tucson we recorded a positive effect of imidacloprid treatment on bee and brood numbers. Thymol was associated with short-term negative effects on bee numbers at both locations, and may have affected colony survival at one location. The overall benefits of thymol for the colonies were unclear. We conclude that long-term and colony-level measures of the effects of agrochemicals are needed to properly understand risks to bees.

The development of honey bee colonies assessed using a new semi-automated brood counting method: CombCount.

Precise, objective data on brood and honey levels in honey bee colonies can be obtained through the analysis of hive frame photographs. However, accurate analysis of all the frame photographs from medium- to large-scale experiments is time-consuming. This limits the number of hives than can be practically included in honeybee studies. Faster estimation methods exist but they significantly decrease precision and their use requires a larger sample size to maintain statistical power. To resolve this issue, we created 'CombCount' a python program that automatically detects uncapped cells to speed up measurements of capped brood and capped honey on photos of frames. CombCount does not require programming skills, it was designed to facilitate colony-level research in honeybees and to provide a fast, free, and accurate alternative to older methods based on visual estimations. Six observers measured the same photos of thirty different frames both with CombCount and by manually outlining the entire capped areas with ImageJ. The results obtained were highly similar between both the observers and the two methods, but measurements with CombCount were 3.2 times faster than with ImageJ (4 and 13 min per side of the frame, respectively) and all observers were faster when using CombCount rather than ImageJ. CombCount was used to measure the proportions of capped brood and capped honey on each frame of 16 hives over a year as they developed from packages to full-size colonies over about 60 days. Our data describe the formation of brood and honey stores during the establishment of a new colony.

Using within-day hive weight changes to measure environmental effects on honey bee colonies.

Patterns in within-day hive weight data from two independent datasets in Arizona and California were modeled using piecewise regression, and analyzed with respect to honey bee colony behavior and landscape effects. The regression analysis yielded information on the start and finish of a colony's daily activity cycle, hive weight change at night, hive weight loss due to departing foragers and weight gain due to returning foragers. Assumptions about the meaning of the timing and size of the morning weight changes were tested in a third study by delaying the forager departure times from one to three hours using screen entrance gates. A regression of planned vs. observed departure delays showed that the initial hive weight loss around dawn was largely due to foragers. In a similar experiment in Australia, hive weight loss due to departing foragers in the morning was correlated with net bee traffic (difference between the number of departing bees and the number of arriving bees) and from those data the payload of the arriving bees was estimated to be 0.02 g. The piecewise regression approach was then used to analyze a fifth study involving hives with and without access to natural forage. The analysis showed that, during a commercial pollination event, hives with previous access to forage had a significantly higher rate of weight gain as the foragers returned in the afternoon, and, in the weeks after the pollination event, a significantly higher rate of weight loss in the morning, as foragers departed. This combination of continuous weight data and piecewise regression proved effective in detecting treatment differences in foraging activity that other methods failed to detect.