Biology subjects

Basso, B.

Publications and source records attributed to Basso, B..

11 recordsLinked to original sources

bioRxiv2026

Beekeepers' preferences for honeybee breeding goals: a French case study

Honeybee breeding plans are relatively recent in most countries. In France, diverse small-scale breeding groups are emerging. Beekeepers are highly diverse in their motivations, farm productions and services, practices and management techniques. Yet, little is known about what beekeepers would consider as relevant breeding goals in the design of breeding plans. We therefore conducted an online survey answered by about 250 French beekeepers, mostly professionals, to assess their perceived importance of including 20 pre-defined traits in breeding goals and to identify how beekeeping profiles might influence these priorities. Respondents rated each trait as essential, useful, or useless, and indicated if they wished useful or essential traits to be genetically improved or merely maintained at their current level. Results indicated a strong preference for multi-trait selection, with a median of 13 traits considered useful or essential. Honey yield, disease resistance, swarming tendency, gentleness, and summer feed autonomy, emerged as the main traits of interest with about 90% of beekeepers finding them at least useful. About 40% or more only wished to maintain these traits at their current level rather than to directionally improve them. A major exception to this was disease resistance, that 75% wanted to improve. Bees genetic background influenced the most the importance attributed to breeding goal traits, while other beekeeping profile characteristics only had a marginal effect on breeding goal trait priorities. Some poorly studied traits, such as summer and winter feed autonomy, winter diapause, and longevity, were considered at least useful in a breeding goal by over 70% of beekeepers. Future research is needed to explore possible selection criteria for these traits and estimate the potential for their genetic improvement. ImplicationsOur survey shows that French beekeepers wish to improve or maintain through selective breeding usual colony production and behavioral traits, but also colony resilience, especially disease resistance and feed autonomy. However, trait priorities differ depending on the genetic background of the bees used. This knowledge is essential for designing breeding programs that truly match beekeeper needs and for identifying which traits deserve research attention. In France, beekeepers are increasingly starting breeding efforts to adapt their bees to current conditions, facing growing pressures from climate change, diseases, invasive species, and pesticides. Well-designed breeding programs can support sustainable beekeeping and essential pollination services.

genetics↗

bioRxiv2025

A Multi-Model Ensemble Reveals Soil Carbon Gains from Regenerative Practices in the U.S. Midwest Cropland

Process-based cropping systems models (CSMs) are key components of measurement, monitoring, reporting, and verification (MMRV) frameworks of carbon markets, but their application suffers from model-specific differences that keep any one model from working well across all combinations of soils, climates, crops, and agronomic practices at varying scales. Multi-model ensemble (MME), successfully used to quantify soil, management and climate impact on crop productivity, provide an opportunity to better estimate changes in soil organic carbon (SOC) outcomes for agronomic practices that have the potential to mitigate SOC loss at scale. We used an MME across 46 million hectares of US Midwest cropland at a resolution of 4- km2 to assess the aggregate ability of different regenerative practices to sequester SOC at this scale compared to their dynamic baselines. MME was validated with long-term experimental data and compared to its constituent CSMs, showing greater accuracy and lower uncertainty. The results show that adopting no-till combined with cover crops increased SOC stocks by 0.36 {+/-} 0.12 Mg ha-1 yr-1 aggregated across the entire U.S. Midwest cropland. At the regional scale, this corresponds to a net SOC gain of 16.4 Tg C yr-1 compared to business-as-usual baselines. These benefits are approximately halved when each management change is practiced individually, and the modest gains are only fully realized when continued over the long-term in soils with low initial carbon stock. Results demonstrate the power of MMEs run at high resolution for providing robust estimates of environmental outcomes following agricultural practice change, and for pinpointing locations for most effective intervention. This approach can alleviate many producer carbon market participation barriers and help address market issues while ultimately supporting large-scale regenerative agriculture initiatives.

ecology↗

bioRxiv2024

How partial phenotyping to reduce generation intervals can help to increase annual genetic gain in selected honeybee populations

Honeybee breeding is organized around annual cycles, following seasonal change. Generation intervals are thus commonly multiples of whole years. Most queens are generally raised during spring or early summer in temperate climates. A generation interval of 1 year limits phenotyping to early recordable traits, before the spring following queens births. Some traits, however, can only be recorded later, as is typically the case for total honey yield. Their recording on selection candidates thus increases the generation interval to at least 2 years, a common interval on the dam path. Using stochastic simulation, we investigated the impact of halving the dam generation interval and therefore recording only early traits on candidate dams. The generation interval on the sire path remained at 2 years with complete phenotyping. Breeding goals with varying weights on early and late traits were considered, as well as different genetic correlations between traits, from negative to positive. The acceleration of the breeding scheme generally resulted in an increased genetic gain for the two-trait breeding goals, from 0% up to +47% after 20 years of selection. Although inbreeding rates per generation were slightly lower in the accelerated breeding scheme, associate inbreeding levels were about 20-30% higher after 20 years of selection due to the 33% increased generation turnover. To avoid too high inbreeding, shortening the generation interval should thus be accompanied by strategies to limit inbreeding while still retaining most of the genetic gain, such as increasing the breeding nucleus size by relaxing selection intensity.

genetics↗

bioRxiv2024

Inferring long-term and short-term determinants of genetic diversity in honey bees: Beekeeping impact and conservation strategies

Bees are vital pollinators in natural and agricultural landscapes around the globe, playing a key role in maintaining flowering plant biodiversity and ensuring food security. Among the honey bee species, the Western honey bee (Apis mellifera) is particularly significant, not only for its extensive crop pollination services but also for producing economically valuable products such as honey. Here, we analyzed whole-genome sequence data from four Apis species to explore how honey bee evolution has shaped current diversity patterns. Using Approximate Bayesian Computation, we first reconstructed the demographic history of A. mellifera in Europe, finding support for postglacial secondary contacts, therefore predating human-mediated transfers linked to modern beekeeping. However, our analysis of recent demographic changes then reveals significant bottlenecks due to beekeeping practices, which have notably affected genetic diversity. Black honey bee populations from conservatories, particularly those on islands, exhibit considerable genetic loss, raising concerns about the long-term effectiveness of current conservation strategies. Additionally, we observed a high degree of conservation in the genomic landscapes of nucleotide diversity across the four species, despite a divergence gradient spanning over 15 million years, consistent with a long-term conservation of the recombination landscapes. Taken together, our results provide the most comprehensive assessment of diversity patterns in honey bees to date and offer insights into the optimal management of resources to ensure the long-term persistence of honey bees and their invaluable pollination services.

evolutionary biology↗

bioRxiv2024

Sequence-based genome-wide association studies reveal the polygenic architecture of Varroa destructor resistance in Western honey bees Apis mellifera

Honey bees, Apis mellifera, have experienced the full impacts of globalisation, including the recent invasion by the parasitic mite Varroa destructor which has become one of the main causes of colony losses worldwide. Despite its lethal effects, some colonies have developed defence strategies conferring colony resistance and, assuming non-null heritability, selective breeding of naturally resistant bees could be a sustainable way to fight infestations. Here we report on the largest genome-wide association study performed on honey bees to understand the genetic basis of multiple phenotypes linked to varroa resistance. This study was performed on whole genome sequencing of more than 1,500 colonies belonging to different ancestries and combined in a meta-analysis. Results show that varroa resistance is polygenic. A total of 60 genetic markers were identified as having a significant impact in at least one of the tested populations pinpointing several regions of the honey bee genome. Our results also support strategies for genomic selection in honey bee breeding.

genetics↗

bioRxiv2023

Uncertainty in the mating strategy causes bias and inaccuracy in estimates of genetic parameters in honeybees

BackgroundWith the increased number of honeybee breeding plans worldwide, records from queens with diversified mating strategies need to be considered. Breeding queens might be inseminated with drones produced by a single drone-producing queen (DPQ), or by a group of sister-DPQs. Often, only the dam of DPQ(s) is reported in the pedigree. Furthermore, datasets might include colony phenotypes from DPQs that were open mated in different locations. Using simulation, we investigated the impact of the mating strategy on estimates of genetic parameters and breeding values, when the DPQs were treated in different ways in the statistical evaluation model. We quantify the bias and standard error of estimates when breeding queens are mated to a single or a group of DPQs, assuming that this information is either known or not. We also investigated two alternative strategies to accommodate phenotypes of open-mated DPQs in the genetic evaluation, adding either a dummy pseudo sire in the pedigree, or a non-genetic effect to the statistical evaluation model to account for the origin of the DPQs mates. ResultsWhen breeding queens were inseminated with semen from drones of a single DPQ and this was known, estimates of genetic parameters and genetic trends were more precise. If they were inseminated using drones from a single or a group of DPQs, and this information was not known, erroneous assumptions led to considerable bias in the estimates. For colony phenotypes of open-mated DPQs, adding a dummy pseudo sire in the pedigree for each mating location led to considerable overestimation of genetic variances, while correcting for the mating area by adding a non-genetic effect in the evaluation model gave unbiased estimates. ConclusionsKnowing only the dam of the DPQ(s) in the mating may lead to erroneous assumptions on how DPQs were used and cause severe biases in estimates of genetic parameters and genetic trends. Therefore, keeping track in the pedigree of which DPQ(s), and not only which dam of DPQ(s) are used, is recommended. Records from DPQ colonies with queens open mated to a heterogeneous drone population can be integrated by adding non-genetic effects to the statistical evaluation model.

genetics↗

bioRxiv2021

From group to individual - Genotyping by pool sequencing eusocial colonies

BackgroundEusocial insects play a central role in many ecosystems, and particularly the important pollinator honeybee (Apis mellifera). One approach to facilitate their study in molecular genetics, is to consider whole colonies as single individuals by combining DNA of multiple individuals in a single pool sequencing experiment. Such a technique comes with the drawback of producing data requiring dedicated analytical methods to be fully exploited. Despite this limitation, pool sequencing data has been shown to be informative and cost-effective when working on random mating populations. Here, we present new statistical methods for exploiting pool sequencing data of eusocial colonies in order to reconstruct the genotype of the colony founder, the queen. This leverages the possibility to monitor genetic diversity, perform genomic-based studies or implement selective breeding. ResultsUsing simulations and honeybee real data, we show that the methods allow for a fast and accurate estimation of the genetic ancestry, with correlations of 0.9 with that obtained from individual genotyping, and for an accurate reconstruction of the queen genotype, with 2% genotyping error. We further validate the inference using experimental data on colonies with both pool sequencing and individual genotyping of drones. ConclusionIn this study we present statistical models to accurately estimate the genetic ancestry and reconstruct the genotype of the queen from pool sequencing data from workers of an eusocial colony. Such information allows to exploit pool sequencing for traditional population genetics, association studies and selective breeding. While validated in Apis mellifera, these methods are applicable to other eusocial hymenoptera species.

genomics↗

bioRxiv2021

Complex population structure and haplotype patterns in Western Europe honey bee from sequencing a large panel of haploid drones

Honey bee subspecies originate from specific geographic areas in Africa, Europe and the Middle East. The interest of beekeepers in specific phenotypes has led them to import subspecies to regions outside of their original range. The resulting admixture complicates population genetics analyses and population stratification can be a major problem for association studies. As a typical example, the case of the French population is studied here. We sequenced 870 haploid drones for SNP detection and identified nine genetic backgrounds in 629 samples. Five correspond to subspecies, two to isolated populations and two to human-mediated population management. We also highlight several large haplotype blocks, some of which coincide with the position of centromeres. The largest is 3.6 Mb long on chromosome 11, representing 1.6 % of the genome and has two major haplotypes, corresponding to the two dominant genetic backgrounds identified.

genetics↗

bioRxiv2020

The chaos in calibrating crop models

Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of system models and has an important impact on simulated values. Here we propose and illustrate a novel method of developing guidelines for calibration of system models. Our example is calibration of the phenology component of crop models. The approach is based on a multi-model study, where all teams are provided with the same data and asked to return simulations for the same conditions. All teams are asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices. HighlightsO_LIWe propose a new approach to deriving calibration recommendations for system models C_LIO_LIApproach is based on analyzing calibration in multi-model simulation exercises C_LIO_LIResulting recommendations are holistic and anchored in actual practice C_LIO_LIWe apply the approach to calibration of crop models used to simulate phenology C_LIO_LIRecommendations concern: objective function, parameters to estimate, software used C_LI

plant biology↗

bioRxiv2020

Multi-model evaluation of phenology prediction for wheat in Australia

Predicting wheat phenology is important for cultivar selection, for effective crop management and provides a baseline for evaluating the effects of global change. Evaluating how well crop phenology can be predicted is therefore of major interest. Twenty-eight wheat modeling groups participated in this evaluation. Our target population was wheat fields in the major wheat growing regions of Australia under current climatic conditions and with current local management practices. The environments used for calibration and for evaluation were both sampled from this same target population. The calibration and evaluation environments had neither sites nor years in common, so this is a rigorous evaluation of the ability of modeling groups to predict phenology for new sites and weather conditions. Mean absolute error (MAE) for the evaluation environments, averaged over predictions of three phenological stages and over modeling groups, was 9 days, with a range from 6 to 20 days. Predictions using the multi-modeling group mean and median had prediction errors nearly as small as the best modeling group. About two thirds of the modeling groups performed better than a simple but relevant benchmark, which predicts phenology by assuming a constant temperature sum for each development stage. The added complexity of crop models beyond just the effect of temperature was thus justified in most cases. There was substantial variability between modeling groups using the same model structure, which implies that model improvement could be achieved not only by improving model structure, but also by improving parameter values, and in particular by improving calibration techniques.

plant biology↗

bioRxiv2019

How well do crop models predict phenology, with emphasis on the effect of calibration?

Predicting phenology is essential for adapting varieties to different environmental conditions and for crop management. Therefore, it is important to evaluate how well different crop modeling groups can predict phenology. Multiple evaluation studies have been previously published, but it is still difficult to generalize the findings from such studies since they often test some specific aspect of extrapolation to new conditions, or do not test on data that is truly independent of the data used for calibration. In this study, we analyzed the prediction of wheat phenology in Northern France under observed weather and current management, which is a problem of practical importance for wheat management. The results of 27 modeling groups are evaluated, where modeling group encompasses model structure, i.e. the model equations, the calibration method and the values of those parameters not affected by calibration. The data for calibration and evaluation are sampled from the same target population, thus extrapolation is limited. The calibration and evaluation data have neither year nor site in common, to guarantee rigorous evaluation of prediction for new weather and sites. The best modeling groups, and also the mean and median of the simulations, have a mean absolute error (MAE) of about 3 days, which is comparable to the measurement error. Almost all models do better than using average number of days or average sum of degree days to predict phenology. On the other hand, there are important differences between modeling groups, due to model structural differences and to differences between groups using the same model structure, which emphasizes that model structure alone does not completely determine prediction accuracy. In addition to providing information for our specific environments and varieties, these results are a useful contribution to a knowledge base of how well modeling groups can predict phenology, when provided with calibration data from the target population.

plant biology↗