A pan-European epidemiological study reveals honey bee colony survival depends on beekeeper education and disease control

Reports of honey bee population decline has spurred many national efforts to understand the extent of the problem and to identify causative or associated factors. However, our collective understanding of the factors has been hampered by a lack of joined up trans-national effort. Moreover, the impacts of beekeeper knowledge and beekeeping management practices have often been overlooked, despite honey bees being a managed pollinator. Here, we established a standardised active monitoring network for 5 798 apiaries over two consecutive years to quantify honey bee colony mortality across 17 European countries. Our data demonstrate that overwinter losses ranged between 2% and 32%, and that high summer losses were likely to follow high winter losses. Multivariate Poisson regression models revealed that hobbyist beekeepers with small apiaries and little experience in beekeeping had double the winter mortality rate when compared to professional beekeepers. Furthermore, honey bees kept by professional beekeepers never showed signs of disease, unlike apiaries from hobbyist beekeepers that had symptoms of bacterial infection and heavy Varroa infestation. Our data highlight beekeeper background and apicultural practices as major drivers of honey bee colony losses. The benefits of conducting trans-national monitoring schemes and improving beekeeper training are discussed

Recrutement et auto-organisation : Vers un modèle multi-agent complet d’une colonie d’abeilles

International audienceLes Systèmes Multi-Agents (SMA) ont montré depuis plusieurs années leur adéquation à modéliser et simuler les systèmes complexes. Nous suivons cette approche pour modéliser une colonie d'abeilles située dans une ruche Dadant, où plusieurs dizaine de milliers d'individus interagissent, dans le but d'évaluer l'impact d'actions locales au niveau des abeilles (e.g. pratiques apicoles) sur la colonie. Nous nous concentrons ici sur l'activité de butinage, en nous intéressant plus particulièrement aux interactions des butineuses avec l'environnement extérieur de la ruche, très différent en terme de granularité et d'échelle. Nous présentons un module paramétrable et compatible agent, dont le but est de modéliser et de simuler le butinage en fonction de la météo et des sources de nourriture environnantes. Les premiers résultats montrent qu'un phénomène d'auto-organisation des butineuses, résultant de leur comportement et des mécanismes de recrutement, les amène à sélectionner les meilleures sources disponibles, et offrent une première vérification de notre modèle

Modèle multi-agent d’auto-organisation pour le butinage au sein d’une colonie d’abeilles

International audienceThe agent-based approach has been successfully used in the past years to model and simulate complex systems. We use this approach on a honeybee colony in a Dadant hive, where several tens of thousands of bees interact, in order to evaluate the impact of local actions at the bee-level (such as beekeeping practices) on the global system. In this article, we focus on the foraging activity, its self-organisation mechanisms and the behaviour of foraging bees, and how these bees interact with the environment of the hive, greatly different in granularity and scale. We present a customizable, agent-compliant module that aims at modelling and simulating the foraging, according to the weather and the surrounding nectar sources. The results of two experimentations provide a first validation of our model, showing that the agents’ behaviours lead to a self-organizing process of the best available sources’ selection.Les Systèmes Multi-Agents (SMA) ont montré depuis plusieurs années leur adéquation à modéliser et simuler les systèmes complexes. Nous suivons cette approche pour modéliser une colonie d’abeilles située dans une ruche Dadant, où plusieurs dizaines de milliers d’individus interagissent, dans le but d’évaluer l’impact d’actions locales au niveau des abeilles (e.g. pratiques apicoles) sur la colonie. Nous nous concentrons ici sur l’activité de butinage, en nous intéressant plus particulièrement au phénomène d’auto-organisation qui conduit les butineuses à sélectionner les meilleures sources de nourriture disponibles. Les interactions des butineuses avec l’environnement extérieur de la ruche, qui diffère de l’intérieur en termes de granularité des actions et d’échelle, sont simulées grâce à un module paramétrable et compatible agent, en fonction de la météo et des sources de nourriture environnantes. Les résultats de deux expérimentations du modèle, l’une sur une année complète, et l’autre sur une journée, montrent que le phénomène d’auto-organisation des butineuses résulte du comportement des butineuses et des mécanismes de recrutement implantés, et offrent une première validation de notre modèle

O-glycosylation as a novel control mechanism of peptidoglycan hydrolase activity

Acm2, the major autolysin of Lactobacillus plantarum, is a tripartite protein. Its catalytic domain is surrounded by an O-glycosylated N-terminal region rich in Ala, Ser, and Thr (AST domain), which is of low complexity and unknown function, and a C-terminal region composed of five SH3b peptidoglycan (PG) binding domains. Here, we investigate the contribution of these two accessory domains and of O-glycosylation to Acm2 functionality. We demonstrate that Acm2 is an N-acetylglucosaminidase and identify the pattern of O-glycosylation (21 mono-N-acetylglucosamines) of its AST domain. The O-glycosylation process is species-specific as Acm2 purified from Lactococcus lactis is not glycosylated. We therefore explored the functional role of O-glycosylation by purifying different truncated versions of Acm2 that were either glycosylated or non-glycosylated. We show that SH3b domains are able to bind PG and are responsible for Acm2 targeting to the septum of dividing cells, whereas the AST domain and its O-glycosylation are not involved in this process. Notably, our data reveal that the lack of O-glycosylation of the AST domain significantly increases Acm2 enzymatic activity, whereas removal of SH3b PG binding domains dramatically reduces this activity. Based on this antagonistic role, we propose a model in which access of the Acm2 catalytic domain to its substrate may be hindered by the AST domain where O-glycosylation changes its conformation and/or mediates interdomain interactions. To the best of our knowledge, this is the first time that O-glycosylation is shown to control the activity of a bacterial enzyme

Risk indicators affecting honeybee colony survival in Europe : one year of surveillance

The first pan-European harmonized active epidemiological surveillance program on honeybee colony mortality (EPILOBEE) was set up across 17 European Member States to estimate honeybee colony mortality over winter and during the beekeeping season. In nine Member States, overwinter losses were higher and statistically different from the empirical level of 10 % under which the level of overwinter mortality was considered as acceptable with usual beekeeping conditions. In four other countries, these losses were lower. Using multivariable Poisson regression models, it was showed that the size of the operation and apiary and the clinically detected varroosis, American foulbrood (AFB), and nosemosis before winter significantly affected 2012-2013 overwinter losses. Clinically detected diseases, the size of the operation and apiary, and the non-participation to a common veterinary treatment significantly affected 2013 summer losses. EPILOBEE was a prerequisite to implement future projects studying risk factors affecting colony health such as multiple and co-exposure to pesticides