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Healthy honey bee colonies follow predictable patterns of weight change through the season, gaining weight when resources are abundant and losing weight during periods of scarcity. Divergence from this pattern can indicate trouble in the colony, necessitating beekeeper intervention. While colony weight monitoring has long been used to evaluate colony progress and diagnose potential problems, research has been limited by the labor associated with manual weight measurements. The introduction of next generation colony weight monitoring permits the collection of hive weight data continuously and remotely, enhancing the range of questions that can be answered with these data. However, there is currently no central guide for researchers aiming to use hive scales in their research. Here, we review the literature and describe current methods used to process and analyze within-day, or diel, and seasonal colony weight changes. Diel weight dynamics are based around the circadian rhythm of the colony, resulting from the departure and arrival of foragers and the intake, consumption, and dehydration of food stores. Seasonal weight dynamics can be used to assess colony survival and productivity, often in relation to large-scale patterns of climate, landscape, and floral resource phenology. In addition to describing methods, we highlight future applications of hive weight monitoring, including monitoring weight across ecological gradients and physiological time, coupling of weight monitoring with other colony monitoring techniques, and the practical use of weight monitoring in commercial beekeeping operations. This paper serves as a tool for those wishing to conduct research using colony weight monitoring, and guides the future of remote weight monitoring in honey bee research.

The lack of seasonally sustained floral resources (i.e. pollen and nectar) is considered a primary global threat to pollinator health. However, the ability to predict the abundance of flowering resources for pollinators based upon climate, weather, and land cover is difficult due to insufficient monitoring over adequate spatial and temporal scales. Here we use spatiotemporally distributed honey bee hive scales that continuously measure hive weights as a standardized method to assess nectar intake. We analyze late summer colony weight gain as the response variable in a random forest regression model to determine the importance of climate, weather, and land cover on honey bee colony productivity. Our random forest model predicted resource acquisition by honey bee colonies with 71% accuracy, highlighting the detrimental effects of warm, wet regions in the Northcentral United States on nectar intake, as well as the detrimental effect of years with high growing degree day accumulation. Our model also predicted that grassy–herbaceous natural land had a positive effect on the summer nectar flow and that large areas of natural grassy–herbaceous land around apiaries can moderate the detrimental effects of warm, wet climates. These patterns characterize multi-scale ecological processes that constrain the quantity and quality of pollinator nutritional resources. That is, broad climate conditions constrain regional floral communities, while land use and weather act to further modify the quantity and quality of pollinator nutritional resources. Observing such broad-scale trends demonstrates the potential for utilizing hive scales to monitor the effects of climate change on landscape-level floral resources for pollinators. The interaction of climate and land use also present an opportunity to manage for climate-resilient landscapes that support pollinators through abundant floral resources under climate change.

Roughly a third of described insect species visit flowers, making the flower–insect interface one of the chief pillars of global biodiversity. Studying flower–insect relationships at the scale of communities and landscapes has been hindered, however, by the methodological challenges of quantifying landscape‐scale floral resources. This challenge is especially acute in urban landscapes, where traditional floral surveying techniques are ill‐suited to the unique constraints of built environments. To surmount these challenges, we devised a “honey bee foraging assay” approach to floral resource surveying, wherein continuous colony weight tracking and DNA metabarcoding of pollen samples are used to capture both the overall availability and taxonomic composition of floral resources. We deploy this methodology in the complex urban ecosystem of Philadelphia, Pennsylvania, USA. Our results reveal distinct seasonality of floral resource availability, with pulses of high availability in May, June, and September, and a period of prolonged scarcity in August. Pollen genus richness mirrored this pattern, with peak richness in May and June. The taxonomic composition of pollen samples varied seasonally, reflecting underlying floral phenology, with especially strong turnover between May and June samples and between August and September samples delineating well‐defined spring, summer, and fall floral resource communities. Trait analysis also revealed seasonal structure, with spring samples characterized by trees and shrubs, summer samples including a stronger presence of herbaceous “weeds”, and fall samples dominated by woody vines. Native flora predominated in spring, giving way to a preponderance of exotic flora in summer and fall. At a basic level, this yields insight into the assembly of novel urban floral resource communities, showcasing, for example, the emergence of a woody vine‐dominated fall flora. At an applied level, our data can inform urban land management, such as the design of ecologically functional ornamental plantings, while also providing practical guidance to beekeepers seeking to adapt their management activities to floral resource seasonality. Methodologically, our study demonstrates the potential of the honey bee foraging assay as a powerful technique for landscape‐scale floral resource surveying, provided the inherent biases of honey bee foraging are accounted for in the interpretation of the results.

Sensor technologies have sufficiently advanced to provide low-cost devices that can quantify carbon dioxide levels in honeybee hives with high temporal resolution and in a small enough package for hive deployment. Recent publications have shown that summer carbon dioxide levels vary throughout the day and night over ranges that typically exceed 5000 ppm. Such dramatic changes in a measurable parameter associated with bee physiology are likely to convey information about the colony health. In this work, we present data from four UK-based hives collected through the winter of 2022/2023, with a focus on seeing if carbon dioxide can indicate when colonies are at risk of failure. These hives have been fitted with two Sensirion SCD41 photoacoustic non-dispersive infrared (NDIR) carbon dioxide sensors, one in the queen excluder, at the top of the brood box, and one in the crown board, at the top of the hive. Hive scales have been used to monitor the hive mass, and internal and external temperature sensors have been included. Embedded accelerometers in the central frame of the brood box have been used to measure vibrations. Data showed that the high daily variation in carbon dioxide continued throughout the coldest days of winter, and the vibrational data suggested that daily fanning may be responsible for restoring lower carbon dioxide levels. The process of fanning will draw in colder air to the hive at a time when the bees should be using their energy to maintain the colony temperature. Monitoring carbon dioxide may provide feedback, prompting human intervention when the colony is close to collapse, and a better understanding may contribute to discussions on future hive design.

The use of modern technology is becoming part of both industry and agriculture. These technologies can also be used in beekeeping, where they can help to monitor the operation of the hive remotely. Beekeepers can remotely monitor the weight of their hives, their temperature, humidity, and other parameters. The aim of this paper is to map the beekeepers in the use of products with monitoring system for remote bee detection in beekeeping in Czechia. To map the issue, qualitative research using semi-structured interviews was conducted with beekeepers, manufacturers/providers of smart devices in beekeeping, and other entities involved in beekeeping. The findings showed that the interest of manufacturers and sellers to offer these smart devices is significant, but the interest of beekeepers is rather less, due to e.g., the purchase price, weaker IT knowledge, traditional beekeeping practices, higher age of beekeepers and the joy of being personally with bees. The novelty of the paper is not to look at the provision of ICT in beekeeping from a technical perspective, but from the perspective of users (beekeepers) and manufacturers of these technologies. Through interviews with beekeepers as well as others in the apiculture sphere, a comprehensive view of the issue is developed. Moreover, this is the first piece of research on this area in Czechia.

Winter loss rates of honey bee colonies may fluctuate highly between years in temperate climates. The present study combined survey data of autumn and winter loss rates in Germany (2012–2021) with estimates of honey flow—assessed with automated hive scales as the start of honey flow in spring and its magnitude in summer—with the aim of understanding annual fluctuations in loss rates. Autumn colony loss rates were positively and significantly correlated with winter loss rates, whereas winter loss rates were inversely related to loss rates in autumn of the following year. An early start of net honey flow in spring predicted high loss rates in both autumn and winter, whereas high cumulative honey flow led to lower loss rates. The start of net honey flow was related to temperature sums in March. Combined, the results implied that the winter loss rate in one year was influenced by the loss rate of the preceding winter and shaped by honey flow dynamics during the following year. Hence, the rate of colony loss in winter can be viewed as a cumulative death process affected by the preceding one and a half years.

Colony monitoring devices used to track and assess the health status of honey bees are becoming more widely available and used by both beekeepers and researchers. These devices monitor parameters relevant to colony health at frequent intervals, often approximating real time. The fine-scale record of hive condition can be further related to static or dynamic features of the landscape, such as weather, climate, colony density, land use, pesticide use, vegetation class, and forage quality. In this study, we fit commercial honey bee colonies in two apiaries with pollen traps and digital scales to monitor floral resource use, pollen quality, and honey production. One apiary was situated in low-intensity agriculture; the other in high-intensity agriculture. Pollen traps were open for 72 h every two weeks while scales recorded weight every 15 min throughout the growing season. From collected pollen, we determined forage quantity per day, species identity using DNA sequencing, pesticide residues, amino acid content, and total protein content. From scales, we determined the accumulated hive weight change over the growing season, relating to honey production and final colony weight going into winter. Hive scales may also be used to identify the occurrence of environmental pollen and nectar dearth, and track phenological changes in plant communities. We provide comparisons of device-derived data between two apiaries over the growing season and discuss the potential for employing apiary monitoring devices to infer colony health in the context of divergent agricultural land use conditions.

The use of modern technology is becoming part of both industry and agriculture. These technologies can also be used in beekeeping, where they can help to monitor the operation of the hive remotely. Beekeepers can remotely monitor the weight of their hives, their temperature, humidity, and other parameters. The aim of this paper is to map the beekeepers in the use of products with monitoring system for remote bee detection in beekeeping in Czechia. To map the issue, qualitative research using semi-structured interviews was conducted with beekeepers, manufacturers/providers of smart devices in beekeeping, and other entities involved in beekeeping. The findings showed that the interest of manufacturers and sellers to offer these smart devices is significant, but the interest of beekeepers is rather less, due to e.g., the purchase price, weaker IT knowledge, traditional beekeeping practices, higher age of beekeepers and the joy of being personally with bees. The novelty of the paper is not to look at the provision of ICT in beekeeping from a technical perspective, but from the perspective of users (beekeepers) and manufacturers of these technologies. Through interviews with beekeepers as well as others in the apiculture sphere, a comprehensive view of the issue is developed. Moreover, this is the first piece of research on this area in Czechia.

This review focuses on critical milestones in the development path for the use of bees, mainly honey bees and bumble bees, as sentinels and biosensors. These keystone species comprise the most abundant pollinators of agro-ecosystems. Pollinating 70%–80% of flowering terrestrial plants, bees and other insects propel the reproduction and survival of plants and themselves, as well as improve the quantity and quality of seeds, nuts, and fruits that feed birds, wildlife, and us. Flowers provide insects with energy, nutrients, and shelter, while pollinators are essential to global ecosystem productivity and stability. A rich and diverse milieu of chemical signals establishes and maintains this intimate partnership. Observations of bee odor search behavior extend back to Aristotle. In the past two decades great strides have been made in methods and instrumentation for the study and exploitation of bee search behavior and for examining intra-organismal chemical communication signals. In particular, bees can be trained to search for and localize sources for a variety of chemicals, which when coupled with emerging tracking and mapping technologies create novel potential for research, as well as bee and crop management.