High-Throughput Genome-Wide Genotyping To Optimize the Use of Natural Genetic Resources in the Grassland Species Perennial Ryegrass (Lolium perenne L.)

The natural genetic diversity of agricultural species is an essential genetic resource for breeding programs aiming to improve their ecosystem and production services. A large natural ecotype diversity is usually available for most grassland species. This could be used to recombine natural climatic adaptations and agronomic value to create improved populations of grassland species adapted to future regional climates. However describing natural genetic resources can be long and costly. Molecular markers may provide useful information to help this task. This opportunity was investigated for Lolium perenne L., using a set of 385 accessions from the natural diversity of this species collected right across Europe and provided by genebanks of several countries. For each of these populations, genotyping provided the allele frequencies of 189,781 SNP markers. GWAS were implemented for over 30 agronomic and/or putatively adaptive traits recorded in three climatically contrasted locations (France, Belgium, Germany). Significant associations were detected for hundreds of markers despite a strong confounding effect of the genetic background; most of them pertained to phenology traits. It is likely that genetic variability in these traits has had an important contribution to environmental adaptation and ecotype differentiation. Genomic prediction models calibrated using natural diversity were found to be highly effective to describe natural populations for almost all traits as well as commercial synthetic populations for some important traits such as disease resistance, spring growth or phenological traits. These results will certainly be valuable information to help the use of natural genetic resources of other species

Canonical correlations reveal adaptive loci and phenotypic responses to climate in perennial ryegrass

Germplasm from perennial ryegrass (Lolium perenne L.) natural populations is useful for breeding because of its adaptation to a wide range of climates. Climate‐adaptive genes can be detected from associations between genotype, phenotype and climate but an integrated framework for the analysis of these three sources of information is lacking. We used two approaches to identify adaptive loci in perennial ryegrass and their effect on phenotypic traits. First, we combined Genome‐Environment Association (GEA) and GWAS analyses. Then, we implemented a new test based on a Canonical Correlation Analysis (CANCOR) to detect adaptive loci. Furthermore, we improved the previous perennial ryegrass gene set by de novo gene prediction and functional annotation of 39,967 genes. GEA‐GWAS revealed eight outlier loci associated with both environmental variables and phenotypic traits. CANCOR retrieved 633 outlier loci associated with two climatic gradients, characterized by cold‐dry winter versus mild‐wet winter and long rainy season versus long summer, and pointed out traits putatively conferring adaptation at the extremes of these gradients. Our CANCOR test also revealed the presence of both polygenic and oligogenic climatic adaptations. Our gene annotation revealed that 374 of the CANCOR outlier loci were positioned within or close to a gene. Co‐association networks of outlier loci revealed a potential utility of CANCOR for investigating the interaction of genes involved in polygenic adaptations. The CANCOR test provides an integrated framework to analyse adaptive genomic diversity and phenotypic responses to environmental selection pressures that could be used to facilitate the adaptation of plant species to climate change

Pleistocene climate changes, and not agricultural spread, accounts for range expansion and admixture in the dominant grassland species <i>Lolium perenne</i> L.

International audienceAim: Grasslands have been pivotal in the development of herbivore breeding since the Neolithic and still represent the most widespread agricultural land use across Europe. However, it remains unclear whether the current large‐scale genetic variation of plant species found in natural grasslands of Europe is the result of human activities or natural processes. Location: Europe. Taxon: Lolium perenne L. (perennial ryegrass). Methods: We reconstructed the phylogeographic history of L. perenne, a dominant grassland species, using 481 natural populations, including 11 populations of closely related taxa. We combined Genotyping‐by‐Sequencing (GBS) and pool‐Sequencing (pool‐Seq) to obtain high‐quality allele frequency calls of ~500 k SNP loci. We performed genetic structure analyses and demographic reconstructions based on the site frequency spectrum (SFS). We additionally used the same genotyping protocol to assess the genomic diversity of a set of 32 cultivars representative of the L. perenne cultivars widely used for forage purposes. Results: Expansion across Europe took place during the Würm glaciation (12–110 kya), a cooling period that decreased the dominance of trees in favour of grasses. Splits and admixtures in L. perenne fit historical climate changes in the Mediterranean basin. The development of agriculture in Europe (7–3.5 kya), that caused an increase in the abundance of grasslands, did not have an effect on the demographic patterns of L. perenne. We found that most modern cultivars are closely related to natural diversity from north-western Europe. Thus, modern cultivars do not represent the wide genetic variation found in natural populations. Main conclusions: Demographic events in L. perenne can be explained by the changing climatic conditions during the Pleistocene. Natural populations maintain a wide genomic variability at continental scale that has been minimally exploited by recent breeding activities. This variability constitutes valuable standing genetic variation for future adaptation of grasslands to climate change, safeguarding the agricultural services they provide

Monthly estimation of the surface water extent in France at a 10-m resolution using Sentinel-2 data

International audienceThe first national product of Surface Water Dynamics in France (SWDF) is generated on a monthly temporal scale and 10-m spatial scale using an automatic rule-based superpixel (RBSP) approach. The current surface water dynamic products from high resolution (HR) multispectral satellite imagery are typically analyzed to determine the annual trend and related seasonal variability. Annual and seasonal time series analyses may fail to detect the intra-annual variations of water bodies. Sentinel-2 allows us to investigate water resources based on both spatial and temporal high-resolution analyses. We propose a new automatic RBSP approach on the Google Earth Engine platform. The RBSP method employs combined spectral indices and superpixel techniques to delineate the surface water extent; this approach avoids the need for training data and benefits large-scale, dynamic and automatic monitoring. We used the proposed RBSP method to process Sentinel-2 monthly composite images covering a two-year period and generate the monthly surface water extent at the national scale, i.e., over France. Annual occurrence maps were further obtained based on the pixel frequency in monthly water maps. The monthly dynamics provided in SWDF products are evaluated by HR satellite-derived water masks at the national scale (JRC GSW monthly water history) and at local scales (over two lakes, i.e., Lake Der-Chantecoq and Lake Orient, and 200 random sampling points). The monthly trends between SWDF and GSW were similar, with a coefficient of 0.94. The confusion matrix-based metrics based on the sample points were 0.885 (producer's accuracy), 0.963 (user's accuracy), 0.932 (overall accuracy) and 0.865 (Matthews correlation coefficient). The annual surface water extents (i.e., permanent and maximum) are validated by two HR satellite image-based water maps and an official database at the national scale and small water bodies (ponds) at the local scale at Loir-et-Cher. The results show that the SWDF results are closely correlated to the previous annual water extents, with a coefficient >0.950. The SWDF results are further validated for large rivers and lakes, with extraction rates of 0.929 and 0.802, respectively. Also, SWDF exhibits superiority to GSW in small water body extraction (taking 2498 ponds in Loir-et-Cher as example), with an extraction rate improved by approximately 20%. Thus, the SWDF method can be used to study interannual, seasonal and monthly variations in surface water systems. The monthly dynamic maps of SWDF improved the degree of land surface coverage by 25% of France on average compared with GSW, which is the only product that provides monthly dynamics. Further harmonization of Sentinel-2 and Landsat 8 and the introduction of enhanced cloud detection algorithm can fill some gaps of no-data regions

Large and small water bodies monitoring exploiting the ExtraEO processing chain : case of the Lake Fitri (Chad) and of the pounds in the Grand East Region (France)

International audienceA flat-bottomed lake, geometry not well-known with a core part and flood spreading areas. High intra-and inter-annual variability related to West African monsoon,+ over decades trends in increasing of water surfaces

Towards enhanced regionalization of Hydrologic-hydraulic river networks models with assimilation of multi-source data and SWOT hydraulic visibility

In context of increasing observation of the earth by satellites and airborne sensors, this contribution investigates information extraction from those data combined with in situ data for hydrologicalhydraulic modeling of river networks. This work is based on recent multi-satellite datasets over two relatively large catchment river networks with contrasted and complex hydrological variabilities and flow features (anabranching reaches, confluences, tidal effects) : the Maroni River basin in French Guyana (SWOT Cal/val site) and the Adour River basin in metropolitan France. The dataset contains Multimission altimetry, optical and radar image of rivers in addition to in situ data and recent SWOT data. The forward modeling approach consists in dynamic shallow water flow models of river networks, in 1D following or multi-D inflowed by catchment scale hydrological models. The inverse modeling approach uses variational data assimilation and enables to optimize spatially distributed bathymetry-friction patterns to reduce the misfit to in-situ and satellite observables as well as to optimize simultaneously inflow hydrographs

Large and small water bodies monitoring exploiting the ExtraEO processing chain : case of the Lake Fitri (Chad) and of the pounds in the Grand East Region (France)

International audienceA flat-bottomed lake, geometry not well-known with a core part and flood spreading areas. High intra-and inter-annual variability related to West African monsoon,+ over decades trends in increasing of water surfaces

Towards enhanced regionalization of Hydrologic-hydraulic river networks models with assimilation of multi-source data and SWOT hydraulic visibility

In context of increasing observation of the earth by satellites and airborne sensors, this contribution investigates information extraction from those data combined with in situ data for hydrologicalhydraulic modeling of river networks. This work is based on recent multi-satellite datasets over two relatively large catchment river networks with contrasted and complex hydrological variabilities and flow features (anabranching reaches, confluences, tidal effects) : the Maroni River basin in French Guyana (SWOT Cal/val site) and the Adour River basin in metropolitan France. The dataset contains Multimission altimetry, optical and radar image of rivers in addition to in situ data and recent SWOT data. The forward modeling approach consists in dynamic shallow water flow models of river networks, in 1D following or multi-D inflowed by catchment scale hydrological models. The inverse modeling approach uses variational data assimilation and enables to optimize spatially distributed bathymetry-friction patterns to reduce the misfit to in-situ and satellite observables as well as to optimize simultaneously inflow hydrographs

Towards enhanced regionalization of Hydrologic-hydraulic river networks models with assimilation of multi-source data and SWOT hydraulic visibility

International audienceIn context of increasing observation of the earth by satellites and airborne sensors, this contribution investigates information extraction from those data combined with in situ data for hydrologicalhydraulic modeling of river networks. This work is based on recent multi-satellite datasets over two relatively large catchment river networks with contrasted and complex hydrological variabilities and flow features (anabranching reaches, confluences, tidal effects) : the Maroni River basin in French Guyana (SWOT Cal/val site) and the Adour River basin in metropolitan France. The dataset contains Multimission altimetry, optical and radar image of rivers in addition to in situ data and recent SWOT data. The forward modeling approach consists in dynamic shallow water flow models of river networks, in 1D following or multi-D inflowed by catchment scale hydrological models. The inverse modeling approach uses variational data assimilation and enables to optimize spatially distributed bathymetry-friction patterns to reduce the misfit to in-situ and satellite observables as well as to optimize simultaneously inflow hydrographs