Artificial intelligence in plant breeding.

Harnessing cutting-edge technologies to enhance crop productivity is a pivotal goal in modern plant breeding. Artificial intelligence (AI) is renowned for its prowess in big data analysis and pattern recognition, and is revolutionizing numerous scientific domains including plant breeding. We explore the wider potential of AI tools in various facets of breeding, including data collection, unlocking genetic diversity within genebanks, and bridging the genotype-phenotype gap to facilitate crop breeding. This will enable the development of crop cultivars tailored to the projected future environments. Moreover, AI tools also hold promise for refining crop traits by improving the precision of gene-editing systems and predicting the potential effects of gene variants on plant phenotypes. Leveraging AI-enabled precision breeding can augment the efficiency of breeding programs and holds promise for optimizing cropping systems at the grassroots level. This entails identifying optimal inter-cropping and crop-rotation models to enhance agricultural sustainability and productivity in the field.

Phenological mismatches mitigate the ecological impact of a biological invader on amphibian communities.

Horizon scans have emerged as a valuable tool to anticipate the incoming invasive alien species (IAS) by judging species on their potential impacts. However, little research has been conducted on quantifying actual impacts and assessing causes of species-specific vulnerabilities to particular IAS due to persistent methodological challenges. The underlying interspecific mechanisms driving species-specific vulnerabilities therefore remain poorly understood, even though they can substantially improve the accuracy of risk assessments. Given that interspecific interactions underlying ecological impacts of IAS are often shaped by phenological synchrony, we tested the hypothesis that temporal mismatches in breeding phenology between native species and IAS can mitigate their ecological impacts. Focusing on the invasive American bullfrog (Lithobates catesbeianus), we combined an environmental DNA (eDNA) quantitative barcoding and metabarcoding survey in Belgium with a global meta-analysis, and integrated citizen-science data on breeding phenology. We examined whether the presence of native amphibian species was negatively related to the presence or abundance of invasive bullfrogs and whether this relationship was affected by their phenological mismatches. The field study revealed a significant negative effect of increasing bullfrog eDNA concentrations on native amphibian species richness and community structure. These observations were shaped by species-specific vulnerabilities to invasive bullfrogs, with late spring- and summer-breeding species being strongly affected, while winter-breeding species remained unaffected. This trend was confirmed by the global meta-analysis. A significant negative relationship was observed between phenological mismatch and the impact of bullfrogs. Specifically, native amphibian species with breeding phenology differing by 6 weeks or less from invasive bullfrogs were more likely to be absent in the presence of bullfrogs than species whose phenology differed by more than 6 weeks with that of bullfrogs. Taken together, we present a novel method based on the combination of aqueous eDNA quantitative barcoding and metabarcoding to quantify the ecological impacts of biological invaders at the community level. We show that phenological mismatches between native and invasive species can be a strong predictor of invasion impact regardless of ecological or methodological context. Therefore, we advocate for the integration of temporal alignment between native and IAS's phenologies into invasion impact frameworks.

PidTools: Algorithm and web tools for crop pedigree identification analysis.

Crop pedigrees incorporate information on the kinship and genetic evolutionary history of breeding materials. Complete and accurate pedigree information is vital for effective genetic improvement of crops and maximal exploitation of heterosis in crop production. It is difficult for breeders to accurately extrapolate the selection of germplasm resources with missing genealogical information based on breeding experience. In this study, an algorithm called PidTools was developed, consisting of five sets of algorithms from three core modules, for accurate pedigree identification analysis. The algorithms and associated tools are suitable for all crops, for the reconstruction and visualization of a complete pedigree for breeding materials. The algorithm and tools were validated with the model crop maize. A genotype database was constructed using Maize6H-60K array data from 5791 maize inbred lines. The pedigree of the maize inbred line Jing72464 was identified without prior provision of any parental information. The pedigree information for Zheng58 was fully identified at the genome-wide scale. With regard to group identification, the parents of a doubled-haploid group were identified based on the genotyping data. The pedigree of 21 Dan340 derived lines were visualized using PidTools. The algorithms are incorporated into a user-friendly online analytical platform, PidTools-WS, with an associated customizable toolkit program, PidTools-CLI. These analytical tools and the present results provide useful information for future maize breeding. The PidTools online analysis platform is available at https://PidTools.plantdna.site/.

Development and characterisation of novel durum wheat-H. chilense 4Hch chromosome lines as a source for resistance to Septoria tritici blotch.

The use of wild species as a source of genetic variability is a valued tool in the framework of crop breeding. Hordeum chilense Roem. et Schult is a wild barley species that can be a useful genetic donor for sustainable wheat breeding which carries genes conferring resistance to some diseases or increasing grain quality, among others. Septoria tritici blotch (STB), caused by the Zymoseptoria tritici fungus, is one of the most important wheat diseases worldwide, affecting both bread and durum wheat and having a high economic impact. Resistance to STB has been previously described in H. chilense chromosome 4Hch. In this study, we have developed introgression lines for H. chilense chromosome 4Hch in durum wheat using interspecific crosses, advanced backcrosses, and consecutive selfing strategies. Alien H. chilense chromosome segments have been reduced in size by genetic crosses between H. chilense disomic substitution lines in durum wheat and durum wheat lines carrying the Ph1 deletion. Hordeum chilense genetic introgressions were identified in the wheat background through several plant generations by fluorescence in situ hybridisation (FISH) and simple sequence repeat (SSR) markers. An STB infection analysis has also been developed to assess STB resistance to a specific H. chilense chromosome region. The development of these H. chilense introgression lines with moderate to high resistance to STB represents an important advance in the framework of durum breeding and can be a valuable tool for plant breeders.

Sparse testing designs for optimizing predictive ability in sugarcane populations.

Sugarcane is a crucial crop for sugar and bioenergy production. Saccharose content and total weight are the two main key commercial traits that compose sugarcane's yield. These traits are under complex genetic control and their response patterns are influenced by the genotype-by-environment (G×E) interaction. An efficient breeding of sugarcane demands an accurate assessment of the genotype stability through multi-environment trials (METs), where genotypes are tested/evaluated across different environments. However, phenotyping all genotype-in-environment combinations is often impractical due to cost and limited availability of propagation-materials. This study introduces the sparse testing designs as a viable alternative, leveraging genomic information to predict unobserved combinations through genomic prediction models. This approach was applied to a dataset comprising 186 genotypes across six environments (6×186=1,116 phenotypes). Our study employed three predictive models, including environment, genotype, and genomic markers as main effects, as well as the G×E to predict saccharose accumulation (SA) and tons of cane per hectare (TCH). Calibration sets sizes varying between 72 (6.5%) to 186 (16.7%) of the total number of phenotypes were composed to predict the remaining 930 (83.3%). Additionally, we explored the optimal number of common genotypes across environments for G×E pattern prediction. Results demonstrate that maximum accuracy for SA ( ρ = 0.611 ) and for TCH ( ρ=0.341 ) was achieved using in training sets few (3) to no common (0) genotype across environments maximizing the number of different genotypes that were tested only once. Significantly, we show that reducing phenotypic records for model calibration has minimal impact on predictive ability, with sets of 12 non-overlapped genotypes per environment (72=12×6) being the most convenient cost-benefit combination.

Beyond plant genetics: microbiome-enhancing heterosis in plants.

Plant heterosis has been recognized as being primarily dependent on the genetics of contrasting parents. However, in recent work, Liu et al. describe 'endophytic microbiome-induced heterosis', showing distinct and diverse seed microbiomes in hybrids, which boosted seed germination compared with their parents. Here, we discuss the possible impact of this finding for sustainable agriculture.

Single- and multiple-trait quantitative trait locus analyses for seed oil and protein contents of soybean populations with advanced breeding line background.

Soybean seed oil and protein contents are negatively correlated, posing challenges to enhance both traits simultaneously. Previous studies have identified numerous oil and protein QTLs via single-trait QTL analysis. Multiple-trait QTL methods were shown to be superior but have not been applied to seed oil and protein contents. Our study aimed to evaluate the effectiveness of single- and multiple-trait multiple interval mapping (ST-MIM and MT-MIM, respectively) for these traits using three recombinant inbred line populations from advanced breeding line crosses tested in four environments. Using original and simulated data, we found that MT-MIM did not outperform ST-MIM for our traits with high heritability (H2 > 0.84). Empirically, MT-MIM confirmed only five out of the seven QTLs detected by ST-MIM, indicating single-trait analysis was sufficient for these traits. All QTLs exerted opposite effects on oil and protein contents with varying protein-to-oil additive effect ratios (-0.4 to -4.8). We calculated the economic impact of the allelic variations via estimated processed values (EPV) using the National Oilseed Processors Association (NOPA) and High Yield + Quality (HY + Q) methods. Oil-increasing alleles had positive effects on both EPVNOPA and EPVHY+Q when the protein-to-oil ratio was low (-0.4 to -0.7). However, when the ratio was high (-4.1 to -4.8), oil-increasing alleles increased EPVNOPA and decreased EPVHY+Q, which penalizes low protein meal. In conclusion, single-trait QTL analysis is adequately effective for high heritability traits like seed oil and protein contents. Additionally, the populations' elite pedigrees and varying protein-to-oil ratios provide potential lines for further yield assessment and direct integration into breeding programs. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-024-01489-2.

Stem traits promote wheat climate-resilience.

Introduction: Wheat grain filling processes under post-anthesis stress scenarios depend mainly on stem traits and remobilization of stem water-soluble carbohydrates (WSC). Methods: A diverse panel of advanced semi-dwarf spring wheat lines, representing a natural variation in stem traits (WSC content, stem diameter, peduncle length, and stem wall width), was used to identify specific traits that reliably reflect the relationship between WSC and grain yield. The panel was phenotyped under various environmental conditions: well-watered, water-limited, and heat stress in Mexico, and terminal-drought in Israel. Results: Environmental stresses reduced grain yield (from 626 g m-2 under well-watered to 213 g m-2 under heat), lower internode diameter, and peduncle length. However, stem-WSC generally peaked 3-4 weeks after heading under all environmental conditions except heat (where it peaked earlier) and expressed the highest values under water-limited and terminal-drought environments. Increased investment in internode diameter and peduncle length was associated with a higher accumulation of stem WSC, which showed a positive association with yield and kernel weight. Across all environments, there were no apparent trade-offs between increased crop investment in internode diameter, peduncle length, and grain yield. Discussion: Our results showed that selecting for genotypes with higher resource investment in stem structural biomass, WSC accumulation, and remobilization could be a valuable strategy to ameliorate grain size reduction under stress without compromising grain yield potential. Furthermore, easy-to-measure proxies for WSC (stem diameter at specific internodes and length of the last internode, i.e., the peduncle) could significantly increase throughput, potentially at the breeding scale.

Current status of global conservation and characterisation of wild and cultivated Brassicaceae genetic resources.

BACKGROUND: The economic importance of the globally distributed Brassicaceae family resides in the large diversity of crops within the family and the substantial variety of agronomic and functional traits they possess. We reviewed the current classifications of crop wild relatives (CWRs) in the Brassicaceae family with the aim of identifying new potential cross-compatible species from a total of 1,242 species using phylogenetic approaches. RESULTS: In general, cross-compatibility data between wild species and crops, as well as phenotype and genotype characterisation data, were available for major crops but very limited for minor crops, restricting the identification of new potential CWRs. Around 70% of wild Brassicaceae did not have genetic sequence data available in public repositories, and only 40% had chromosome counts published. Using phylogenetic distances, we propose 103 new potential CWRs for this family, which we recommend as priorities for cross-compatibility tests with crops and for phenotypic characterisation, including 71 newly identified CWRs for 10 minor crops. From the total species used in this study, more than half had no records of being in ex situ conservation, and 80% were not assessed for their conservation status or were data deficient (IUCN Red List Assessments). CONCLUSIONS: Great efforts are needed on ex situ conservation to have accessible material for characterising and evaluating the species for future breeding programmes. We identified the Mediterranean region as one key conservation area for wild Brassicaceae species, with great numbers of endemic and threatened species. Conservation assessments are urgently needed to evaluate most of these wild Brassicaceae.

The impact of management on the fecal microbiome of endangered greater sage-grouse (Centrocercus urophasianus) in a zoo-based conservation program.

Greater sage-grouse (Centrocercus urophasianus) are a critically endangered species in Canada with fewer than 140 individuals remaining on native habitats in southern Alberta and Saskatchewan. In 2014, the Wilder Institute/Calgary Zoo initiated North America's only zoo-based conservation breeding program for this species to bolster declining wild populations through conservation reintroductions. Within the managed population of sage-grouse, morbidity and mortality have primarily been associated with intestinal bacterial infections. As a preliminary study to assess the gastrointestinal health of this species in managed care, the fecal bacterial microbiome of adult and juvenile captive sage-grouse was characterized with 16S rRNA sequencing. The composition of the microbiome at the phylum level in greater sage-grouse is consistent with previous studies of the avian microbiome, with Bacillota as the most abundant phyla, and Actinomycetota, Bacteroidota and Pseudomonadota also being highly abundant. Antibiotic use and sex did not have a significant impact on the diversity or composition of the microbiome, but the management of juvenile sage-grouse did influence the development of the microbiome. Juveniles that were raised outdoors under maternal care developed a microbiome much more similar to adults when compared to chicks that were incubated and hand-raised. The local environment and parental care appear to be important factors influencing the diversity and composition of the gastrointestinal microbiome in this species.

Theileria orientalis Ikeda infection does not negatively impact growth performance or breeding soundness exam results in young beef bulls at bull test stations.

Introduction: Theileria orientalis Ikeda genotype is an emerging cattle disease in the US. Since 2017, when T. orientalis Ikeda was discovered in beef cattle in two counties in Virginia, cattle infections have risen to include ~67% of Virginia counties and 14 states. Consistent with New Zealand studies, many infected herds in Virginia were >90% positive upon initial testing without overt evidence of infection. Central bull tests present a unique opportunity to study the effects of T. orientalis Ikeda infections, as bulls from multiple source herds are consolidated. The objective of this study was to determine if infection with T. orientalis Ikeda affected the average daily gain (ADG), adjusted yearling weight (AYW) and breeding soundness of bulls at two test stations in Virginia over a period of years. Materials and methods: The bulls were fed and housed similarly to compare their growth performance and breeding soundness. For T. orientalis Ikeda testing, DNA was extracted from whole blood for quantitative polymerase chain reaction. Results: The number of bulls infected with T. orientalis Ikeda at initial delivery to the stations increased significantly over the years studied. Multivariable linear regression models, using Angus bulls from Virginia test stations, indicated no significant effect on ADG or AYW in bulls that became test positive during the test or were positive for the duration, compared to Angus bulls that were negative for the duration. At LOC A, the odds of passing a breeding soundness exam (BSE) were not significantly different for bulls that turned positive during the test or were positive for the duration, compared to bulls that were negative for the duration of the test. At LOC B, bulls that became positive during the test were 2.4 times more likely (95% CI: 1.165-4.995, p = 0.016) to pass their BSE compared to bulls that remained negative throughout the test. Discussion: We do not suppose that an obscured infection of T. orientalis Ikeda is protective for bulls to pass a BSE. However, this study demonstrates an obscured infection of T. orientalis Ikeda does not negatively affect weight gain or achievement of a satisfactory BSE rating at the central bull test stations in Virginia.

Integrative genomics reveals the polygenic basis of seedlessness in grapevine.

Seedlessness is a crucial quality trait in table grape (Vitis vinifera L.) breeding. However, the development of seeds involved intricate regulations, and the polygenic basis of seed abortion remains unclear. Here, we combine comparative genomics, population genetics, quantitative genetics, and integrative genomics to unravel the evolution and polygenic basis of seedlessness in grapes. We generated the haplotype-resolved genomes for two seedless grape cultivars, "Thompson Seedless" (TS, syn. "Sultania") and "Black Monukka" (BM). Comparative genomics identified a ∼4.25 Mb hemizygous inversion on Chr10 specific in seedless cultivars, with seedless-associated genes VvTT16 and VvSUS2 located at breakpoints. Population genomic analyses of 548 grapevine accessions revealed two distinct clusters of seedless cultivars, and the identity-by-descent (IBD) results indicated that the origin of the seedlessness trait could be traced back to "Sultania." Introgression, rather than convergent selection, shaped the evolutionary history of seedlessness in grape improvement. Genome-wide association study (GWAS) analysis identified 110 quantitative trait loci (QTLs) associated with 634 candidate genes, including previously unidentified candidate genes, such as three 11S GLOBULIN SEED STORAGE PROTEIN and two CYTOCHROME P450 genes, and well-known genes like VviAGL11. Integrative genomic analyses resulted in 339 core candidate genes categorized into 13 functional categories related to seed development. Machine learning-based genomic selection achieved a remarkable prediction accuracy of 97% for seedlessness in grapevines. Our findings highlight the polygenic nature of seedlessness and provide candidate genes for molecular genetics and an effective prediction for seedlessness in grape genomic breeding.

Integrating evolutionary genomics of forest trees to inform future tree breeding amidst rapid climate change.

Global climate change is leading to rapid and drastic shifts in environmental conditions, posing threats to biodiversity and nearly all life forms worldwide. Forest trees serve as foundational components of terrestrial ecosystems and play a crucial and leading role in combating and mitigating the adverse effects of extreme climate events, despite their own vulnerability to these threats. Therefore, understanding and monitoring how natural forests respond to rapid climate change is a key priority for biodiversity conservation. The recent progress of evolutionary genomics, primarily driven by cutting-edge multi-omics technologies, offer powerful new tools to address several key issues. These include the precise delineation of species and evolutionary units, inference of past evolutionary histories and demographic fluctuations, identification of environmental adaptive variants, and measurement of genetic load levels. As the urgency to deal with more extreme environmental stresses grows, understanding the genomics of evolutionary history, local adaptation, future responses to climate change, and the conservation and restoration of natural forest trees will be critical for research at the nexus of global change, population genomics and conservation biology. In this review, we explore the application of evolutionary genomics to assess the effects of global climate change using multi-omics approaches and discuss the outlook for breeding climate-adapted trees.

Does Scaling Up Pig Farming Promote Carbon Neutrality Among Pig Farmers?

Pig farming is a major source of carbon emissions in China's livestock industry, and the promotion and application of carbon neutral technologies dedicated to carbon reduction and sequestration is a key measure to promote the realization of carbon neutrality in pig farming industry. In the absence of a system for carbon emission reduction in agriculture, it is important to explore whether large-scale pig farming can spontaneously promote the application of carbon neutral technologies by pig farmers. Combined with the technical background of carbon neutrality in pig farming industry and using the field survey data of 468 pig farmers in Shandong Province, this paper empirically analyzed the effect of large-scale pig farming on the carbon neutral behavior of pig farmers. The findings are threefold. Firstly, because of the effect of economies of scale in the application of carbon neutral technologies, large-scale pig farming can promote the carbon neutral behavior of pig farmers. However, the effect of economies of scale in the application of carbon neutral technologies has a boundary, and the annual output of pigs should be maintained at 3000 to 4999. Secondly, among the effects of pig breeding scale on the application and its degree of composting project, state of planting and breeding cycle has no regulating effect. Thirdly, among the effects of pig breeding scale on the application and its degree of biogas engineering, both state of planting and breeding cycle and subsidy for construction of biogas digester have negative regulating effects. In this paper, the pig farming industry is taken as a typical example to reveal the endogenous incentive mechanism of promoting agricultural carbon emission reduction, which provides theoretical reference and experience enlightenment for the formulation of effective policies to promote agricultural carbon neutrality, which are different from industrial industries.

Enhancing genomic prediction with Stacking Ensemble Learning in Arabica Coffee.

Coffee Breeding programs have traditionally relied on observing plant characteristics over years, a slow and costly process. Genomic selection (GS) offers a DNA-based alternative for faster selection of superior cultivars. Stacking Ensemble Learning (SEL) combines multiple models for potentially even more accurate selection. This study explores SEL potential in coffee breeding, aiming to improve prediction accuracy for important traits [yield (YL), total number of the fruits (NF), leaf miner infestation (LM), and cercosporiosis incidence (Cer)] in Coffea Arabica. We analyzed data from 195 individuals genotyped for 21,211 single-nucleotide polymorphism (SNP) markers. To comprehensively assess model performance, we employed a cross-validation (CV) scheme. Genomic Best Linear Unbiased Prediction (GBLUP), multivariate adaptive regression splines (MARS), Quantile Random Forest (QRF), and Random Forest (RF) served as base learners. For the meta-learner within the SEL framework, various options were explored, including Ridge Regression, RF, GBLUP, and Single Average. The SEL method was able to predict the predictive ability (PA) of important traits in Coffea Arabica. SEL presented higher PA compared with those obtained for all base learner methods. The gains in PA in relation to GBLUP were 87.44% (the ratio between the PA obtained from best Stacking model and the GBLUP), 37.83%, 199.82%, and 14.59% for YL, NF, LM and Cer, respectively. Overall, SEL presents a promising approach for GS. By combining predictions from multiple models, SEL can potentially enhance the PA of GS for complex traits.

Biochemical foundation of the aroma and antioxidant activity of Indian traditional rice landrace Maharaji and the effect of radiation-induced mutagenesis on its metabolome.

Maharaji rice, an aromatic variety with medium slender grains, is traditionally cultivated in the central regions of India. This study aimed to identify the biochemical compounds responsible for Maharaji rice's distinctive fragrance and enhance its agro-morphological traits through mutation breeding. Using Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) analysis, forty major metabolites were identified which may be responsible for its characteristic aroma. The bioactive compounds included terpenes, flavonoids, and amino acids. Maharaji brown rice extract exhibited potent radical scavenging activity. Radiation-induced mutation breeding improved the agro-morphological traits and also triggered biochemical diversification in different mutants. Maharaji Mutant-2 exhibited improved aroma due to higher abundance of aromatic compounds, improved yield and morphological characters as compared to the parent. This study, for the first time identifies the compounds associated with the characteristic aroma of Maharaji rice. Global metabolomics may, therefore, expedite the selection of mutants with suitable aroma and desirable biological properties.

[Genetic diversity of SP1 population of space mutagenized Pueraria montana].

To explore the mutagenic effect of the space environment on Pueraria montana and select the elite germplasm with good growth conditions and high isoflavone content, this study observed the agronomic traits, determined the flower isoflavone content, and labeled amplified fragment length polymorphism(AFLP) fluorescent molecular markers of 79 P. montana plants exposed to space mutagenesis(SP1 group) and 10 control plants of P. montana(CK group). Excel 2019, SPSS 25.0, NTSYSpc-2.11F, and Popgen 32 were employed to analyze the genetic diversity and perform the cluster analysis. The results showed that the SP1 group presented changed leaf hairy attitude and flower structure and higher CV and H' of quantitative traits than the CK group. The cluster analysis screened out five plants in the SP1 group. Ten P. montana plants in the SP1 group had higher content of 6″-O-xylosyl-tectoridin and tectoridin in the flowers than the control group, with the total content of both exceeding 11%. After clustering, 9 plants in the SP1 group were separated. Nine pairs of polymorphic primers were screened out frrom 64 pairs of primers. A total of 1 620 polymorphic loci were detected, with the average percentage of polymorphic loci(PPL) of 83.33%. The average Nei's gene diversity index(H) and Shannon's information index(I) were 0.192 2 and 0.305 2, respectively. After clustering, 4 plants in the SP1 group were screened out. According to the above results, plants No. 30, No. 66, and No. 89 in the SP1 group were subjected to greater mutagenic effect by the space environment and presented better growth and higher flower isoflavone content. Moreover, plant No. 30 showed the flower structure variation and flower weight two times of that in the CK group. These plants can be used as key materials for the subsequent experiments.

Generation of Anti-Mastitis Gene-Edited Dairy Goats with Enhancing Lysozyme Expression by Inflammatory Regulatory Sequence using ISDra2-TnpB System.

Gene-editing technology has become a transformative tool for the precise manipulation of biological genomes and holds great significance in the field of animal disease-resistant breeding. Mastitis, a prevalent disease in animal husbandry, imposes a substantial economic burden on the global dairy industry. In this study, a regulatory sequence gene editing breeding strategy for the successful creation of a gene-edited dairy (GED) goats with enhanced mastitis resistance using the ISDra2-TnpB system and dairy goats as the model animal is proposed. This included the targeted integration of an innate inflammatory regulatory sequence (IRS) into the promoter region of the lysozyme (LYZ) gene. Upon Escherichia Coli (E. coli) mammary gland infection, GED goats exhibited increased LYZ expression, showing robust anti-mastitis capabilities, mitigating PANoptosis activation, and alleviating blood-milk-barrier (BMB) damage. Notably, LYZ is highly expressed only in E. coli infection. This study marks the advent of anti-mastitis gene-edited animals with exogenous-free gene expression and demonstrates the feasibility of the gene-editing strategy proposed in this study. In addition, it provides a novel gene-editing blueprint for developing disease-resistant strains, focusing on disease specificity and biosafety while providing a research basis for the widespread application of the ISDra2-TnpB system.

How does climate change impact social bees and bee sociality?

Climatic factors are known to shape the expression of social behaviours. Likewise, variation in social behaviour can dictate climate responses. Understanding interactions between climate and sociality is crucial for forecasting vulnerability and resilience to climate change across animal taxa. These interactions are particularly relevant for taxa like bees that exhibit a broad diversity of social states. An emerging body of literature aims to quantify bee responses to environmental change with respect to variation in key functional traits, including sociality. Additionally, decades of research on environmental drivers of social evolution may prove fruitful for predicting shifts in the costs and benefits of social strategies under climate change. In this review, we explore these findings to ask two interconnected questions: (a) how does sociality mediate vulnerability to climate change, and (b) how might climate change impact social organisation in bees? We highlight traits that intersect with bee sociality that may confer resilience to climate change (e.g. extended activity periods, diet breadth, behavioural thermoregulation) and we generate predictions about the impacts of climate change on the expression and distribution of social phenotypes in bees. The social evolutionary consequences of climate change will be complex and heterogeneous, depending on such factors as local climate and plasticity of social traits. Many contexts will see an increase in the frequency of eusocial nesting as warming temperatures accelerate development and expand the temporal window for rearing a worker brood. More broadly, climate-mediated shifts in the abiotic and biotic selective environments will alter the costs and benefits of social living in different contexts, with cascading impacts at the population, community and ecosystem levels.