Buzzing with Intelligence: Current Issues in Apiculture and the Role of Artificial Intelligence (AI) to Tackle It.

Honeybees (Apis mellifera L.) are important for agriculture and ecosystems; however, they are threatened by the changing climate. In order to adapt and respond to emerging difficulties, beekeepers require the ability to continuously monitor their beehives. To carry out this, the utilization of advanced machine learning techniques proves to be an exceptional tool. This review provides a comprehensive analysis of the available research on the different applications of artificial intelligence (AI) in beekeeping that are relevant to climate change. Presented studies have shown that AI can be used in various scientific aspects of beekeeping and can work with several data types (e.g., sound, sensor readings, images) to investigate, model, predict, and help make decisions in apiaries. Research articles related to various aspects of apiculture, e.g., managing hives, maintaining their health, detecting pests and diseases, and climate and habitat management, were analyzed. It was found that several environmental, behavioral, and physical attributes needed to be monitored in real-time to be able to understand and fully predict the state of the hives. Finally, it could be concluded that even if there is not yet a full-scale monitoring method for apiculture, the already available approaches (even with their identified shortcomings) can help maintain sustainability in the changing apiculture.

Honey bee (Apis mellifera) wing images: a tool for identification and conservation.

BACKGROUND: The honey bee (Apis mellifera) is an ecologically and economically important species that provides pollination services to natural and agricultural systems. The biodiversity of the honey bee in parts of its native range is endangered by migratory beekeeping and commercial breeding. In consequence, some honey bee populations that are well adapted to the local environment are threatened with extinction. A crucial step for the protection of honey bee biodiversity is reliable differentiation between native and nonnative bees. One of the methods that can be used for this is the geometric morphometrics of wings. This method is fast, is low cost, and does not require expensive equipment. Therefore, it can be easily used by both scientists and beekeepers. However, wing geometric morphometrics is challenging due to the lack of reference data that can be reliably used for comparisons between different geographic regions. FINDINGS: Here, we provide an unprecedented collection of 26,481 honey bee wing images representing 1,725 samples from 13 European countries. The wing images are accompanied by the coordinates of 19 landmarks and the geographic coordinates of the sampling locations. We present an R script that describes the workflow for analyzing the data and identifying an unknown sample. We compared the data with available reference samples for lineage and found general agreement with them. CONCLUSIONS: The extensive collection of wing images available on the Zenodo website can be used to identify the geographic origin of unknown samples and therefore assist in the monitoring and conservation of honey bee biodiversity in Europe.

Using the Software DeepWings© to Classify Honey Bees across Europe through Wing Geometric Morphometrics.

DeepWings© is a software that uses machine learning to automatically classify honey bee subspecies by wing geometric morphometrics. Here, we tested the five subspecies classifier (A. m. carnica, Apis mellifera caucasia, A. m. iberiensis, Apis mellifera ligustica, and A. m. mellifera) of DeepWings© on 14,816 wing images with variable quality and acquired by different beekeepers and researchers. These images represented 2601 colonies from the native ranges of the M-lineage A. m. iberiensis and A. m. mellifera, and the C-lineage A. m. carnica. In the A. m. iberiensis range, 92.6% of the colonies matched this subspecies, with a high median probability (0.919). In the Azores, where the Iberian subspecies was historically introduced, a lower proportion (85.7%) and probability (0.842) were observed. In the A. m mellifera range, only 41.1 % of the colonies matched this subspecies, which is compatible with a history of C-derived introgression. Yet, these colonies were classified with the highest probability (0.994) of the three subspecies. In the A. m. carnica range, 88.3% of the colonies matched this subspecies, with a probability of 0.984. The association between wing and molecular markers, assessed for 1214 colonies from the M-lineage range, was highly significant but not strong (r = 0.31, p < 0.0001). The agreement between the markers was influenced by C-derived introgression, with the best results obtained for colonies with high genetic integrity. This study indicates the good performance of DeepWings© on a realistic wing image dataset.

Geographical Variation of Honey Bee (Apis mellifera L. 1758) Populations in South-Eastern Morocco: A Geometric Morphometric Analysis.

In Morocco, there are two well-recognised honey bee (Apis mellifera L.) subspecies: A. m. intermissa in the north and A. m. sahariensis in the south-east. The latter subspecies is found in the arid and semiarid climates of the Sahara Desert. In this study, we used honey bees from four areas of south-eastern Morocco which are, to some degree, isolated by arid zones. We analysed the shape and size of the forewings, using the method of geometric morphometrics. The bees from the four areas of south-eastern Morocco differed significantly in terms of wing shape. Moreover, bees from traditional hives were smaller than those from modern hives. The bees from south-eastern Morocco were clearly different from the reference samples obtained from the Morphometric Bee Data Bank in Oberursel, Germany, representing most of the global variation in honey bees. Surprisingly, the bees were also different from A. m. sahariensis, which should occur in the study area, according to earlier studies. This difference could have been caused by introgression with non-native subspecies imported by beekeepers. The distinct honey bees from south-eastern Morocco deserve to be protected. We provide a method for identifying them, which can help protect them.

Further Evidence of Population Admixture in the Serbian Honey Bee Population.

Socioeconomic interests and beekeeper preferences have often taken precedence over the conservation of locally native honey bee subspecies, leading to the predominance of admixture populations in human-dominated areas. To assess the genetic diversity of contemporary managed Serbian honey bee colonies, we used 14 microsatellite loci and analyzed 237 worker bees from 46 apiaries in eight localities of northern and southern Serbia. Furthermore, we compared data for nine microsatellite loci with 338 individuals from Italy, Hungary, Poland, and Spain. The standard parameters of genetic diversity in Serbian honey bee populations were in line with other analyses, although somewhat smaller. STRUCTURE analysis showed the existence of two equally distributed genetic clusters and Analysis of molecular variances could not confirm the presence of a geographically discrete population but showed local differences. Discriminant analysis of principal components showed overlapping of worker bees from different parts of Serbia. Clear genetic differentiation can be observed when comparing all populations between geographical regions and their corresponding subspecies. The absence of the A. m. macedonica subspecies from its historical distribution range in southern Serbia as well as the lack of distinctive geographical groups suggest that selective breeding, queen import, and migratory beekeeping practices strongly influence the genetic structure and diversity of honey bees, leading to the genetic uniformization and creation of the admixture population.

Body Remains Left by Bird Predators as a Reliable Source for Population Genetic Studies in the Great Capricorn Beetle Cerambyx cerdo, a Veteran Oak Specialist.

Obtaining biological material for DNA extraction is often challenging in organisms of conservation interest. Non-invasive sampling (i.e., sampling without injuring or disturbing an animal) is preferred as it carries no risk to the population's survival. Here, we tested the possibility of using the body remains left by bird predators for microsatellite genotyping in Cerambyx cerdo, a veteran oak specialist. We compared results obtained from such potentially degraded samples with samples of fresh beetle tarsi (i.e., invasive and destructive but non-lethal samples). Using 10 SSR loci, we genotyped 28 fresh, and 28 remains samples. The analysis indicated that PCR amplification efficiency was not influenced by sample type but allele length and individual heterozygosity. Allele frequencies were perfectly correlated for both sample types (R2 = 0.94). Additionally, null allele frequencies and genotyping failure rates were not significantly different from zero. Although the point estimates of individual inbreeding rates (fi) were higher in remains than fresh samples (medians 0.08 vs. 0.02, respectively), both groups were not significantly different from each other and zero. Our study demonstrated that non-invasive remains samples could provide satisfactory data for population-genetic studies. However, we highlight the problem of biased inbreeding estimates, which may result from samples affected by allelic dropout.

Mitochondrial DNA Suggests the Introduction of Honeybees of African Ancestry to East-Central Europe.

In Europe, protecting the genetic diversity of Apis mellifera is usually perceived in the context of limiting the spread of the evolutionary C-lineage within the original range of the M-lineage. However, due to climate change and large-scale ongoing movement of breeding individuals, the expansion of bees from the African A-lineage could represent another threat. This issue has not yet been investigated in detail, although A-mitotypes occur in South-West and South Europe due to natural gene flow. Here, we determine the diversity of mtDNA in honey bees from East-Central Europe. We sequenced the COI-COII region in 427 bees sampled along two 900 km transects (17.5° N and 23° E). We found that 1.64% of bees (95% CI: 0.66-3.35 %) had A-mitotypes. It is unlikely that their presence in the area resulted from natural migration but instead human driven introductions of hybrids of African ancestry. This expansion deserves more attention, as it may contribute to the dissemination of undesirable traits, parasites and diseases.

Identification of determinants of pollen donor fecundity using the hierarchical neighborhood model.

Individual differences in male reproductive success drive genetic drift and natural selection, altering genetic variation and phenotypic trait distributions in future generations. Therefore, identifying the determinants of reproductive success is important for understanding the ecology and evolution of plants. Here, based on the spatially explicit mating model (the neighborhood model), we develop a hierarchical probability model that links co-dominant genotypes of offspring and candidate parents with phenotypic determinants of male reproductive success. The model accounts for pollen dispersal, genotyping errors as well as individual variation in selfing, pollen immigration, and differentiation of immigrant pollen pools. Unlike the classic neighborhood model approach, our approach is specially designed to account for excessive variation (overdispersion) in male fecundity. We implemented a Bayesian estimation method (the Windows computer program available at: https://www.ukw.edu.pl/pracownicy/plik/igor_chybicki/1806/) that, among others, allows for selecting phenotypic variables important for male fecundity and assessing the fraction of variance in fecundity (R2 ) explained by selected variables. Simulations showed that our method outperforms both the classic neighborhood model and the two-step approach, where fecundities and the effects of phenotypic variables are estimated separately. The analysis of two data examples showed that in wind-pollinated trees, male fecundity depends on both the amount of produced pollen and the ability to pollen spread. However, despite that the tree size was positively correlated with male fecundity, it explained only a fraction of the total variance in fecundity, indicating the presence of additional factors. Finally, case studies highlighted the importance of accounting for pollen dispersal in the estimation of fecundity determinants.

Climatic oscillations in Quaternary have shaped the co-evolutionary patterns between the Norway spruce and its host-associated herbivore.

During the Last Glacial Maximum in the Northern Hemisphere, expanding ice sheets forced a large number of plants, including trees, to retreat from their primary distribution areas. Many host-associated herbivores migrated along with their host plants. Long-lasting geographic isolation between glacial refugia could have been led to the allopatric speciation in separated populations. Here, we have studied whether the migration history of the Norway spruce Picea abies in Quaternary has affected its host-associated herbivorous beetle-Monochamus sartor. By using microsatellite markers accompanied by the geometric morphometrics analysis of wing venation, we have revealed the clear geographic structure of M. sartor in Eurasia, encompassing two main clusters: southern (Alpine-Carpathian) and eastern (including northeastern Europe and Asia), which reflects the northern and southern ecotypes of its host. The two beetles' lineages probably diverged during the Pleniglacial (57,000-15,000 BC) when their host tree species was undergoing significant range fragmentation and experienced secondary contact during post-glacial recolonization of spruce in the Holocene. A secondary contact of divergent lineages of M. sartor has resulted in the formation of the hybrid zone in northeastern Europe. Our findings suggest that the climatic oscillations during the Pleistocene have driven an insect-plant co-evolutionary process, and have contributed to the formation of the unique biodiversity of Europe.

Seed and pollen gene dispersal in Taxus baccata, a dioecious conifer in the face of strong population fragmentation.

Background and Aims: Dispersal is crucial due to its direct impact on dynamics of a species' distribution as well as having a role in shaping adaptive potential through gene flow. In plants forming scarce and small populations, knowledge about the dispersal process is required to assess the potential for colonizing new habitats and connectivity of present and future populations. This study aimed to assess dispersal potential in Taxus baccata, a dioecious gymnosperm tree with a wide but highly fragmented distribution. Methods: Seed and pollen dispersal kernels were estimated directly in the framework of the spatially explicit mating model, where genealogies of naturally established seedlings were reconstructed with the help of microsatellite markers. In this way, six differently shaped dispersal functions were compared. Key Results: Seed dispersal followed a leptokurtic distribution, with the Exponential-Power, the Power-law and Weibull being almost equally best-fitting models. The pollen dispersal kernel appeared to be more fat-tailed than the seed dispersal kernel, and the Lognormal and the Exponential-Power function showed the best fit. The rate of seed immigration from the background sources was not significantly different from the rate of pollen immigration (13.1 % vs. 19.7 %) and immigration rates were in agreement with or below maximum predictions based on the estimated dispersal kernels. Based on the multimodel approach, 95 % of seeds travel <109 m, while 95 % of pollen travels <704 m from the source. Conclusions: The results showed that, at a local spatial scale, yew seeds travel shorter distances than pollen, facilitating a rapid development of a kinship structure. At the landscape level, however, although yew exhibits some potential to colonize new habitats through seed dispersal, genetic connectivity between different yew remnants is strongly limited. Taking into account strong population fragmentation, the study suggests that gene dispersal may be a limiting factor of the adaptability of the species.

Population structure of honey bees in the Carpathian Basin (Hungary) confirms introgression from surrounding subspecies.

Carniolan honey bees (Apis mellifera carnica) are considered as an indigenous subspecies in Hungary adapted to most of the ecological and climatic conditions in this area. However, during the last decades Hungarian beekeepers have recognized morphological signs of the Italian honey bee (Apis mellifera ligustica). As the natural distribution of the honey bee subspecies can be affected by the importation of honey bee queens or by natural gene flow, we aimed at determining the genetic structure and characteristics of the local honey bee population using molecular markers. All together, 48 Hungarian and 84 foreign (Italian, Polish, Spanish, Liberian) pupae and/or workers were used for mitochondrial DNA analysis. Additionally, 53 sequences corresponding to 10 subspecies and the Buckfast hybrid were downloaded from GenBank. For the nuclear analysis, 236 Hungarian and 106 foreign honey bees were genotyped using nine microsatellites. Heterozygosity values, population-specific alleles, FST values, principal coordinate analysis, assignment tests, structure analysis, and dendrograms were calculated. Haplotype and nucleotide diversity values showed moderate values. We found that one haplotype (H9) was dominant in Hungary. The presence of the black honey bee (Apis mellifera mellifera) was negligible, but a few individuals resembling other subspecies were identified. We proved that the Hungarian honey bee population is nearly homogeneous but also demonstrated introgression from the foreign subspecies. Both mitochondrial DNA and microsatellite analyses corroborated the observations of the beekeepers. Molecular analyses suggested that Carniolan honey bee in Hungary is slightly affected by Italian and black honey bee introgression. Genetic differences were detected between Polish and Hungarian Carniolan honey bee populations, suggesting the existence of at least two different gene pools within A. m. carnica.

Weak isolation by distance in Diaperis boleti, a fungivorous saproxylic beetle.

Living in unstable habitats is expected to decrease the intensity of isolation by distance in populations through the need for frequent movements of individuals. Insects associated with fruiting bodies of fungi therefore are supposed to have weak spatial genetic structure of populations compared with those living in more stable habitats. With the use of an amplified fragment length polymorphism technique, this study investigated the isolation by distance, inbreeding, and genetic diversity in Diaperis boleti (L.) (Coleoptera: Tenebrionidae), a fungivorous saproxylic beetle that inhabits sporocarps of Laetiporus sulphureus (Bulliard) Murrill (Polyporales) on trees growing in highly-fragmented agricultural landscapes. Isolation by distance was tested with spatial autocorrelation analysis of kinship (individual-based approach) and correlating matrices of genetic and geographic distances with the Mantel test (population-based approach). These results were compared with the results obtained for saproxylic beetles living in the same landscape but differing in ecological preferences. It was shown that the species dependent on sporocarps of wooddecomposing fungi had higher variability, lower individual inbreeding, and less intensive isolation by distance pattern than saproxylic beetles living in tree hollows. It was also demonstrated that spatial autocorrelation analysis of kinship is a more sensitive approach for detecting finescale spatial genetic structure than the Mantel test.