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.

Aquaculture has become a crucial component of global food production, yet catfish (10.8% of global finfish production) breeding programs often lack sufficient genetic data to fully utilize their production potential. In the last 15 years, there have been improvements in this field as two high-density (HD) single nucleotide polymorphism (SNP) arrays (250K and 690K) and low-density panels have been developed for North American channel catfish (Ictalurus punctatus) and blue catfish (Ictalurus furcatus). This lack of genomic tools hinders genetic improvement efforts in other commercially relevant catfish species besides them. Therefore, this review investigated the reason behind the lack of SNP chip usage in genetic-based selections in most catfish breeding programs and the cross-species applicability of the already existing high-density SNP arrays for genotyping members of the Clariidae, African catfish (Clarias gariepinu), and Siluridae, European catfish (Silurus glanis), families. This paper systematically reviews the literature of more than 16 SNP arrays, with 66 non-target species, and assesses the possibility of adapting catfish SNP arrays to the catfish families of interest. With lowered filtering (e.g., MAF > 0) thresholds, the Affymetrix Axiom 250K and Axiom Catfish 690K Genotyping Array could potentially be used on important market species like African and European catfishes. In the long term, chip development would be the solution for these species, but, until then, cross-application is a viable alternative. Despite low polymorphic SNPs (~1%) and call rates (~0%), this SNP array could aid researchers and breeders, improving catfish aquaculture and management.

One of the most important issues in improving the competitiveness of the fish production sector is to improve the growth rate of fish. The genetic background to this trait is at present poorly understood. In this study, we compared the relative gene expression levels of the Akt1s1, FGF, GH, IGF1, MSTN, TLR2, TLR4 and TLR5 genes in blood in groups of common carps (Cyprinus carpio), which belonged to different growth types and phenotypes. Fish were divided into groups based on growth rate (normal group: n = 6; slow group: n = 6) and phenotype (scaled group: n = 6; mirror group: n = 6). In the first 18 weeks, we measured significant differences (p < 0.05) between groups in terms of body weight and body length. Over the next 18 weeks, the fish in the slow group showed more intense development. In the same period, the slow group was characterized by lower expression levels for most genes, whereas GH and IGF1 mRNA levels were higher compared to the normal group. We found that phenotype was not a determining factor in differences of relative expression levels of the genes studied.

The conservation of genetic diversity in historically structured horse breeds requires fi-ne-scale population genetic evaluation beyond conventional pedigree-based approaches. The present study assessed genetic diversity, population structure, and mare family differentiation in a Lipizzan horse population using 16 ISAG/FAO-recommended microsatellite markers. A total of 172 mares representing 29 mare families were genotyped. All loci were polymorphic, with a mean number of alleles per locus of 6.69 and a mean effective number of alleles of 3.56. The average polymorphism information content (PIC = 0.66) indicated high marker informativeness. Mean observed heterozygosity was slightly lower than expected heterozygosity, although the overall negative inbreeding coefficient suggested general heterozygote excess and low population-level inbreeding, whilst the genetic differentiation among mare families was moderate. The principal components and the STRUCTURE analysis indicated admixture among mare families. Seven private alleles were detected across six loci, highlighting the presence of rare lineage-specific variants. These findings emphasize the importance of mare family-based analyses for identifying hidden genetic structures that may not be captured by population-level averages. The integration of microsatellite-based diversity evaluation provides a robust framework for de-signing sustainable breeding and conservation strategies aimed at preserving both overall variability and rare lineage-specific genetic components.

The Carpathian water buffalo represents a locally adapted but under-characterized genetic group found in Central and Eastern Europe. Genome-wide information on its genetic diversity, population structure and potential adaptive variation remains limited, particularly for Hungarian and Romanian populations. In this study, we genotyped 263 water buffalo individuals from Hungary and Romania using the GeneSeek Genomic Profiler Bovine 100K SNP array to evaluate genetic diversity, the population structure, runs of homozygosity (ROH) and candidate genomic regions showing signatures of selection. After quality control, 214 Hungarian and 33 Romanian individuals and 6605 SNPs were retained for downstream analyses. Both populations showed moderate genetic diversity, with the Romanian population displaying higher minor allele frequency, observed heterozygosity and nucleotide diversity than the Hungarian population. In contrast, the Hungarian buffalo showed a higher burden of runs of homozygosity, including a larger proportion of long ROH segments, suggesting stronger recent autozygosity or a more restricted breeding structure. Principal component analysis and neighbor-joining phylogeny separated the two populations, whereas ADMIXTURE indicated shared ancestry and a within-population substructure rather than complete population-specific differentiation. The integration of standardized F_ST, absolute allele-frequency differences and ROH islands identified six candidate regions under a positive signature of selection in each population. These regions harbored genes previously associated with immune response, reproduction, growth, milk production and thermotolerance in bovids. Functional enrichment was limited, with significant Gene Ontology terms detected only in the Hungarian candidate regions. Our results provide a regional genomic baseline for the future conservation and breeding management of Carpathian water buffalo. Given the use of a cross-species SNP array and unequal sample sizes, the candidate selection signals should be interpreted as hypothesis-generating and warrant validation using higher-density buffalo-specific genomic data.