Nuno Capela

The yellow-legged hornet ( Vespa velutina nigrithorax ) is an invasive species in Europe that has created serious environmental issues by feeding on insect pollinators, and it is a major cause of concern for beekeepers due to its predatory activity against Apis mellifera colonies. Predation can play a role in transmitting pathogens between bees and hornets, thereby contributing to the spread of diseases in the environment. This study assessed the prevalence and abundance of honey bee- and bumblebee-associated pathogens in different developmental stages of V. v. nigrithorax individuals collected from nests in the Aveiro and Coimbra regions of Portugal. A total of 534 samples (larvae, pupae, adults, and queens) within 38 nests were analysed using qPCR for the main bee pathogens. The most prevalent and abundant pathogen was DWV (70.41%; 4.66 × 10 ¹⁰ ), followed by Nosema ceranae (42.70%; 6.17 × 10 ⁶ ), BQCV (44.76%; 1.64 × 10 ⁵ ) and CBPV (33.90%; 2.17 × 10 ⁹ ). DWV and other pathogens were detected across all developmental stages, indicating widespread pathogen detection and suggesting multiple exposure routes within the nest with possible vertical transmission. Pathogen loads were significantly higher in the Aveiro region, possibly due to environmental factors and nest maturity. These findings confirm that V. v. nigrithorax harbours multiple replicative bee-associated pathogens, raising concerns about interspecific transmission.

Deformed wing virus (DWV) is a significant pathogen affecting Apis mellifera populations globally (Wilfert et al., 2016). This virus, also transmitted by the ectoparasitic mite (Varroa destructor),...

The yellow-legged hornet, Vespa velutina subs. nigrithorax Buysson, 1905, is an invasive species in Europe, posing substantial ecological and economic threats. Its biology and behavior, marked by rapid reproduction and aggressive predation, endanger native insects. This has negative impacts on agriculture, biodiversity, and human safety. Effective control is therefore essential to prevent and minimize its spread and ecological impact. This study assessed the acute oral toxicity of four commercial insecticide formulations containing acetamiprid, spinosad, cypermethrin, and a mixture of natural pyrethrins as active ingredients on V. v. nigrithorax workers. Our findings indicate that acetamiprid and spinosad are the most promising compounds, with acetamiprid inducing rapid mortality at low doses, and spinosad causing both lethal and sublethal effects, potentially disrupting hornet behavior and colony viability. Given that oral exposure generally resulted in higher toxicity than contact exposure, pest control strategies should incorporate multiple exposure pathways. Further investigations are necessary to confirm efficacy under field conditions and to evaluate potential ecological risks to non-target species. Overall, this study provides critical data to improve V. v. nigrithorax control and supports the development of more effective and environmentally sustainable control strategies against this invasive species.

A novel ascomycetous yeast species of the genus Zygosaccharomyces is proposed, based on isolates obtained in Japan, Portugal and the USA, and on a combination of conventional DNA barcode sequence analyses and whole-genome phylogenies. The new species is described as Zygosaccharomyces progenitor sp. nov. (PYCC 7198 ^T ) and was found in association with bees ( Apis mellifera and Bombus spp.) and their honey, besides being found in plants and soil. The novel species belongs to a Zygosaccharomyces subclade that also harbours Zygosaccharomyces rouxii . Although the two species can be differentiated at the sequence level, they cannot be distinguished at the phenotypic level. As is typical of the genus, Z. progenitor sp. nov. produces spherical ascospores after cell conjugation.

The aim of the study was to collect data on plant phenology, density of flowers and production of floral resources in European countries, using published and grey literature written in local languages. The search was conducted in 11 European languages (Danish, Dutch, French, German, Greek, Italian, Norwegian Bokmål, Portuguese, Romanian, Spanish and Swedish) and included published and unpublished data from local journals, books, databases or master and doctoral theses. The collection contains 2382 records for 1132 plant species from 113 families. Most of the data collected is on flowering phenology, with a relatively large amount of data on nectar/sugar production and less on pollen production and floral density (1474, 1141, 325 and 152 records, respectively). Our study is unique in collecting data on floral resource traits in local European languages. The data collected are a valuable addition to existing floral resource trait databases and can help to quantify the floral resources available to pollinators and other organisms that depend on them for food in different habitats and ecosystems. At the same time, our collection, in combination with other databases on floral resource traits, allows the identification of plant genera and families for which information is scarce, as well as the best‐studied plants and countries where research on floral resource traits has a long tradition. Synthesis and applications. Our research shows the value of using data published in local languages, often as grey literature, especially when building ecological databases of various kinds. Much of the basic data we collected on floral resource traits is available in literature published long ago, making it even more difficult for the research community to access. We discussed several technical issues that can be encountered when collecting such floristic data, especially if the data are to be further used in modelling.

The yellow-legged hornet, Vespa velutina subs. nigrithorax Buysson, 1905, originally from Southeast Asia, has become an invasive species in Europe since its introduction in France around 2004. Its rapid proliferation and voracious predatory behavior pose a significant threat to native insects, particularly honeybees and other pollinators, impacting agricultural production, biodiversity, and human safety. Eradication in Europe seems now impossible, and the control efforts are hindered by the lack of standardized application protocols, including for insecticide use, leading to potential indiscriminate pesticide application and, consequently, environmental damages. Our study evaluated the acute contact toxicity on V. v. nigrithorax workers of four commercially available formulations containing acetamiprid, cypermethrin, a mix of natural pyrethrins, and Spinosad as active ingredients. These tests were performed in laboratory conditions, offering novel data for the chemical control of this invasive species. Our results suggest acetamiprid and spinosad as promising candidates for the yellow-legged hornet control. Further research is needed to validate their efficacy under field conditions and assess ecological impacts of these pesticides on non-target organisms. Integrated pest management strategies should prioritize insecticides with low non-target toxicity and minimal environmental persistence to mitigate resistance development and ensure effective pest control. Comprehensive assessments considering multiple factors beyond mortality are essential for informing sustainable pest control strategies.

Implementation of marker-assisted selection (MAS) in modern beekeeping would improve sustainability, especially in breeding programs aiming for resilience against the parasitic mite Varroa destructor. Selecting honey bee colonies for natural resistance traits, such as brood-intrinsic suppression of varroa mite reproduction, reduces the use of chemical acaricides while respecting local adaptation. In 2019, eight genomic variants associated with varroa non-reproduction in drone brood were discovered in a single colony from the Amsterdam Water Dune population in the Netherlands. Recently, a new study tested the applicability of these eight genetic variants for the same phenotype on a population-wide scale in Flanders, Belgium. As the properties of some variants varied between the two studies, one hypothesized that the difference in genetic ancestry of the sampled colonies may underly these contribution shifts. In order to frame this, we determined the allele frequencies of the eight genetic variants in more than 360 Apis mellifera colonies across the European continent and found that variant type allele frequencies of these variants are primarily related to the A. mellifera subspecies or phylogenetic honey bee lineage. Our results confirm that population-specific genetic markers should always be evaluated in a new population prior to using them in MAS programs.

Introduction: Both intrinsic colony mechanisms and external environmental variables affect the honey bee colony development rates and response and a key aspect of this is the use of resources within the landscape by honey bees. Although several models have been developed to explore the foraging behaviour of bees, none fully considered the spatial and temporal dynamics of landscape resources and the role of the colony in the process. Methodology: Here, we introduce a new honey bee foraging model being developed as a part of the ApisRAM honey bee colony model. Based on agent-based modelling, we used a dynamic ALMaSS landscape model enhanced with floral resource modelling to assess the impacts of weather conditions and resource availability on the possible foraging behaviour of honey bees. Several possible mechanisms (defined, based on honey bee traits) for scouting and foraging were investigated, separately for nectar and pollen collection, including prioritising foraging polygons for nectar foraging according to their distance to the colony, the quality or the energetic efficiency and, for pollen foraging, according to their distance to the colony and pollen quantity. Results: If model foraging bees prioritised the polygons, based on their distance from the colony, the number of unsuccessful flights increased compared to other tested strategies and the total amount of sugar collected showed a high variability. Contrary to expectations, the energetic efficiency strategy did not provide the colony with the highest amount of sugar. Overall, the tested strategies provide different outcomes on the collection of resources, the number of performed flights and their success rate, evidencing that the model's outcome at the colony level arises from the individual types of behaviour. Conclusions and Relevance: Variability in the mass of collected nectar and pollen was found mostly when scout bees applied the distance strategy. This higher variability in sugar collection means that model bees were not able to find the most profitable foraging sites at the landscape level, but only at the local level. Other strategies showed less dependence on the surrounding landscape (i.e. quality or random), but it comes at a cost (i.e. lower production for both nectar and pollen collection). These outputs help us evaluate which strategies could be used for future model development and confirm the models' ability to create dynamic responses. These responses at the colony level were only possible thanks to the implementation of a dynamic landscape model and dynamic spatiotemporal resource model, as well as implementing a social communication mechanism for bees to share information about the resources. Plant nectar production and quality information is essential to predict honey bee foraging distribution. In future model development, the implementation of pollen quality should also be explored to evaluate if it influences the overall pollen collection.

Honey bee colonies have great societal and economic importance. The main challenge that beekeepers face is keeping bee colonies healthy under ever-changing environmental conditions. In the past two decades, beekeepers that manage colonies of Western honey bees (Apis mellifera) have become increasingly concerned by the presence of parasites and pathogens affecting the bees, the reduction in pollen and nectar availability, and the colonies’ exposure to pesticides, among others. Hence, beekeepers need to know the health condition of their colonies and how to keep them alive and thriving, which creates a need for a new holistic data collection method to harmonize the flow of information from various sources that can be linked at the colony level for different health determinants, such as bee colony, environmental, socioeconomic, and genetic statuses. For this purpose, we have developed and implemented the B-GOOD (Giving Beekeeping Guidance by computational-assisted Decision Making) project as a case study to categorize the colony’s health condition and find a Health Status Index (HSI). Using a 3-tier setup guided by work plans and standardized protocols, we have collected data from inside the colonies (amount of brood, disease load, honey harvest, etc.) and from their environment (floral resource availability). Most of the project’s data was automatically collected by the BEEP Base Sensor System. This continuous stream of data served as the basis to determine and validate an algorithm to calculate the HSI using machine learning. In this article, we share our insights on this holistic methodology and also highlight the importance of using a standardized data language to increase the compatibility between different current and future studies. We argue that the combined management of big data will be an essential building block in the development of targeted guidance for beekeepers and for the future of sustainable beekeeping.

Honey bees play an important role in agricultural landscapes by providing pollination services. Throughout the season, colonies increase their population and collect resources from the available flowering plants. Besides internal mechanisms, such as the amount of brood or the availability of bees to perform foraging flights, colonies are also influenced by the climate and the surrounding landscape. Therefore, exposure to different environmental contexts leads to distinct development rates. In this study, we show how colonies develop under three different landscape contexts and explore which external variables (mostly climate and resources availability) influence the colonies’ development. We installed three apiaries in three different landscapes in the Iberian Peninsula, with temporal and spatial variation in climatic conditions and resource availability. The availability of resources and their use, as well as the development of colonies throughout the season, were thoroughly investigated. These data were used to take the first step into creating an ecologically relevant landscape by calculating the number of available resources in the landscape at different points in time, based on plants’ beekeeping interest as well as nectar and production. Furthermore, climatic variables were transformed into the amount of available foraging minutes that bees had to collect resources, and a theoretical threshold of optimal vs. sub-optimal conditions was also explored. Interestingly, the main drivers of colony development (measured by daily weight increase) were not the same in the tested apiaries, evidencing how colonies are indeed intrinsically connected with the surrounding environmental scenario. Therefore, results from field testing are extremely context-dependent and should be interpreted with caution when being extrapolated to other environmental scenarios.