HAL-IRD
Not a member yet
83947 research outputs found
Sort by
Scaling law links plant growth variation to grain yield in wheat stands
No full textGrowth rate, a fundamental biological trait influencing plant resource use, scales predictably with plant mass, following remarkably consistent allometric power laws shaped by biophysical constraints and natural selection. However, how these laws apply to crop plants shaped by artificial selection, and how they manifest in agronomic traits, remains undefined. Under controlled greenhouse conditions, we quantified the relationship between plant mass and growth rate in 195 European winter wheat cultivars. We uncovered genetic variation in allometry linked to plant size, where increased leaf allocation and faster development elevated allometric exponents. Phenotypic and genetic analyses revealed adaptive strategies, ranging from large, slow-growing genotypes that support reproductive initiation to small, fast-growing genotypes that enhance reproductive effort.A shared genetic basis-associated with Photoperiod response-1 (Ppd-1)-linked growth allometry in the greenhouse to genotype-by-environment interactions for grain yield in field trials. This variation in growth allometry, shaped by breeding in diverse environments, reflects strategies that enhance adaptation to weather conditions. Our findings demonstrate that growth allometry is biologically robust and agronomically important because it scales to wheat yield under diverse, realistic field conditions.</div
Hidden decomposers: Revisiting saprotrophy among soil protists and its potential impact on carbon cycling
No full textInternational audienceSoil protists are increasingly recognized as key players in organic matter turnover, yet their role as direct decomposers (i.e., saprotrophs) remains underexplored compared to that of bacteria and fungi. Here, we synthesize ecological, physiological, and genomic evidence to highlight the potential of protists to actively decompose organic matter and influence soil carbon cycling. We distinguish two saprotrophic strategies within protists—lysotrophic (extracellular) and phagotrophic (intracellular)—with the latter being unique to protists among microbial decomposers. By directly ingesting particulate or dissolved organic matter, phagotrophic saprotrophic protists may bypass constraints associated with extracellular decomposition, potentially providing an advantage in breaking down recalcitrant substrates. In contrast, lysotrophic saprotrophy in protists involves the secretion of enzymes, similar to bacterial and fungal decomposers. We propose that integrating protist saprotrophy into conceptual and quantitative models of soil organic matter decomposition could address critical knowledge gaps. This integration involves employing functional genomics and functional ecology methodologies to determine, in vitro, the capacity of protists to function as saprotrophs, elucidate the genetic pathways underpinning saprotrophic activities, and assess, in situ, their direct contributions to organic matter decomposition processes. Ultimately, a clearer view of the organic matter decomposition capacities of soil protists will refine our understanding of microbially driven carbon fluxes
Simulation et analyse des dynamiques conjointes de transformation et de diffusion dans des domaines irréguliers : application à la modélisation computationnelle de la décomposition microbienne dans les milieux poreux
No full textMany computational modeling challenges, particularly within the scope of natural phenomena, can be represented by coupled transformation and diffusion processes occurring in complex (3D) geometries. The spatialization of dynamics through numerical simulations provides a significantly improved understanding of process behaviors as they relate to the geometry in which they occur. This thesis presents a general framework for simulating coupled transformation and transport processes within any domain contained within a voxelized 3D representation. The proposed methodology describes the 3D domain of the dynamics through a hierarchy of attributed relational graphs. These graphs are constructed using advanced geometrical modeling methods. Transformation processes are assumed to occur locally at each graph node, while transport processes are modeled as mass exchange between adjacent graph nodes using Fick's law, which governs the relationship between diffusion flux and concentration gradients. To simulate these dynamics efficiently, we propose numerically stable schemes that reduce the computational complexity by focusing on graph updating instead of solving the corresponding Partial Differential Equations (PDEs) directly. Local adaptive diffusional conductance coefficients are incorporated into the graph to accurately represent the heterogeneity of the medium and the variability of transport properties between connected spatial units. In order to improve transport simulation, we use data-driven approach to calculate these coefficients accurately. The graph updating significantly lowers simulation costs, particularly in real-world applications where sensor data describing the spatial domain often result in extremely large meshes, making direct PDE solving computationally impossible. Moreover, this methodological framework based on valuated graph updating allow to take into account temporal changes of the domain by updating the graph structure. This general framework for computational modeling is applicable to a wide range of natural phenomena in irregular geometries. Its validation is demonstrated in the context of simulating diffusion and microbial decomposition in porous media derived from 3D computed tomography images. Specifically, we show that the framework reproduces a validated model of microbial decomposition of organic matter in soil, consistent with empirical data. The results highlight the framework's ability to handle complex transformation and diffusion processes inherent in real-world systems while maintaining computational efficiency. Additionally, we conduct a mathematical analysis of microbial decomposition dynamics, formulating the model as a nonlinear parabolic PDE and proving the existence of a global attractor, which ensures the long-term stability of the system.De nombreux défis de modélisation informatique, notamment dans le domaine des phénomènes naturels, peuvent être décrits par des processus couplés de transformation et de diffusion au sein de géométries complexes (3D). La spatialisation des dynamiques à travers des simulations numériques améliore considérablement la compréhension des processus en fonction de leur géométrie. Cette thèse propose un cadre général pour simuler ces processus dans tout domaine représenté sous forme voxélisée 3D. La méthodologie décrit le domaine via une hiérarchie de graphes relationnels attribués, construits à l'aide de méthodes avancées de modélisation géométrique. Les transformations sont supposées se produire localement à chaque nœud du graphe, tandis que les transports sont modélisés comme un échange de masse entre nœuds adjacents selon la loi de Fick, qui lie le flux de diffusion aux gradients de concentration. Pour simuler efficacement ces dynamiques, nous proposons des schémas numériques stables réduisant la complexité en mettant à jour le graphe plutôt qu'en résolvant directement les EDP associées. Des coefficients locaux adaptatifs de conductance sont incorporés au graphe pour représenter l'hétérogénéité du milieu et la variabilité des propriétés de transport. Pour améliorer la simulation, une approche data-driven est utilisée pour calculer ces coefficients avec précision. La mise à jour du graphe réduit considérablement les coûts de simulation, particulièrement dans les applications où les données des capteurs produisent des maillages très larges, rendant la résolution directe des EDP impraticable. Ce cadre méthodologique intègre aussi les changements temporels du domaine via la mise à jour de la structure du graphe. Ce cadre général de modélisation informatique s'applique à un large spectre de phénomènes naturels dans des géométries irrégulières. Sa validation est démontrée dans le contexte de la simulation de la diffusion et de la décomposition microbienne dans les milieux poreux dérivés d'images tomographiques 3D. Plus précisément, nous montrons qu'il reproduit un modèle de décomposition microbienne de la matière organique dans le sol validé par des données empiriques. Les résultats des simulations illustrent la capacité de ce cadre général à gérer la complexité et la variabilité des systèmes réels tout en maintenant l'efficacité des calculs. De plus, nous réalisons une analyse mathématique des dynamiques de décomposition microbienne, en formulant le modèle sous forme d'une EDP parabolique non linéaire et en prouvant l'existence d'un attracteur global, garantissant ainsi la stabilité à long terme du système
Observing social and environmental change in a large regulated river: the Rhône Valley Human-Environment Observatory
No full textInternational audienceFollowing the major floods of the early 2000s, the Rhône river has been the focus of an integrated management plan, the Plan Rhône . This plan advocates a new way of managing the river from a sustainable development perspective. The Rhône Valley Human-Environment Observatory (OHM VR) was set up in 2010 with sustainable development as a foundation applied to the scale of the river. The scientific objectives of the OHM VR have been to monitor the effects of this new management approach in a perspective of a highly anthropised socio-ecosystem, by studying the socio-environmental responses and adaptations in the Rhône corridor following a change in the mode of river management. Priority scientific themes were developed with the aim of covering most of the questions raised by this new mode of management: to achieve a better understanding of the geo-historical trajectory of the river to situate temporally the crisis events and their consequences; to provide insight into the new modes of management from a socio-political point of view and their reception by the local populations; to study the socio-economic processes in a context of ecological restoration actions; to analyse the environmental risks, in particular pollution; and to develop new tools to support the research works and the diffusion of scientific results. Interdisciplinarity and relations with local stakeholders provide the framework for these investigations. This article provides an overview of the activities of the OHM VR and their evolution since its creation, with a particular focus on two interdisciplinary case studies that have shaped collective scientific thinking in recent years
A wet chemical extraction protocol for measuring biogenic silica in sediments of marginal seas and open ocean
No full textInternational audienceThis study describes a wet chemical extraction protocol for measuring the biogenic silica (bSi) in sediments from diverse marine environments. The protocol lists the reagents, materials, equipment, and sample preparation procedures, and provides a detailed explanation of the methods for examining the alkaline-leachable silicon (Si), and calculating bSi content. Although, the protocol was primarily developed for measuring bSi in sediments from the Chinese marginal seas, it was also validated using sediments from the Chesapeake Bay, the Atlantic Ocean, and the Southern Ocean. The protocol can be used to quantify bSi in recently deposited and aged sediments from the Holocene period. The protocol contributes to the ongoing efforts to minimize the methodological bias that exist in bSi quantification and the bSi burial flux evaluation, thereby assisting in our understanding of Si cycling in the modern ocean.• This protocol provides a step-by-step wet chemical extraction procedures and the measurement of dissolved Si in an alkaline solution using spectrophotometer.• This protocol is easy to set up and reproduce, and determines bSi content with high precision.• The protocol can be used to determine bSi in sediments of marginal seas and the open ocean
ClimBurst: A Dynamic Visualization Tool to Display Climatological Anomalies over Time and Space
No full textInternational audienceDetecting abnormal climate events across temporal and spatial scales is crucial to the understanding of local and regional climate trends. This demonstration introduces ClimBurst, a dynamic tool to detect climate bursts, which are unusually high or low values of one or more climate variables over some time interval. ClimBurst detects bursts without prior assumptions about their temporal duration. The demonstration will allow users to interact directly with our system to see both a summary showing the presence/absence of bursts over a user-specified year and spatial range. The demonstration will also allow users to perform time-travel queries to see how bursts propagate over space and time
Variability and Trends in Cloud Properties Over 17 Years From CALIPSO Space Lidar Observations
No full textInternational audienceIn this paper we search for fingerprints of cloud changes over 17 years (2006–2023) within the record of cloud properties collected by the CALIPSO space lidar. Indeed, climate model projections suggest that clouds are rising up in altitude, shrinking in cover and changing rapidly in the Arctic under the influence of human induced climate warming. We describe how changes in CALIPSO space lidar operations over the mission have impacted the stability of the cloud detection. Removing contaminated data reduces the record to 11 full years (2008–2018). For these 11 years, we analyze de‐seasonalized anomalies of cloud cover, altitude, emissivity and vertical profile, to identify trends at global scale and in specific latitude bands. Results show a decrease in opaque cloud cover and a rise in opaque cloud altitude, consistent in sign with climate model projected changes, but statistically insignificant in the observations. Observed changes do not extend beyond the natural variability in any statistically significant way. This suggests that if cloud changes exist, the CALIPSO record might be too short in time or not precise enough to detect them with confidence. This study underscores the need for extending the cloud record from space lidars and building a harmonized record from observations made by successive space lidars
Metabolic resistance of the tiger mosquito to pyrethroid insecticides in La Réunion Island likely results from local adaptation
No full textAbstract The resistance of mosquitoes to insecticides is a valuable model system for studying the genetic bases of xenobiotic adaptation in insects. The spread of the Asian tiger mosquito Aedes albopictus combined to the massive use of pyrethroid insecticides to limit arbovirus transmission resulted in the rise of resistance in various continents. Here, we investigated the genetic mechanisms underlying the recent adaptation of this mosquito to deltamethrin in La Réunion island. Bioassays confirmed the presence of resistance alleles in field populations. The resistance phenotype was further enhanced in the laboratory following a few generations of controlled selection. Combining whole genome Pool-seq and RNA-seq revealed no evidence of target-site resistance mutations but the over-expression and variant selection of detoxification enzymes associated with pyrethroid metabolism including cytochrome P450s, transferases and ABC-transporters. Among over-expressed detoxification genes, only one was linked to a gene duplication while polymorphism data suggest most of them being trans-regulated. Genome-wide selection signatures revealed a 9 Mb inverted superlocus responding to insecticide selection whose phenotypical importance remains uncertain. Altogether, this study indicates that the multigenic metabolic resistance phenotype observed in this insular territory mainly results from local adaptation. From an applied perspective, this study provides a set of markers to track pyrethroid resistance in the tiger mosquito in the South-West Indian Ocean. As this region is subjected to recurrent arbovirus outbreaks, the additive resistance phenotype that may arise from the introduction of Kdr mutations from other territories also calls for improving resistance surveillance at the regional scale. Author summary While novel vector control strategies are being developed, chemical insecticides remain widely used to control mosquitoes transmitting human diseases such as the Asian tiger mosquito. However, the recurrent use of insecticides resulted in the emergence of resistance which can ultimately affect vector control efficacy. Here, we investigate the genetic bases underlying the resistance of the Asian tiger mosquito to the pyrethroid insecticide deltamethrin in La Réunion island. By combining two complementary genomic approaches, we showed that resistance is mainly caused by an increased insecticide detoxification while classical ‘Knock down resistance’ mutations affecting the target of the insecticide were not detected. We also showed that resistance is underlain by multiple genetic changes spread across the genome, supporting the local selection of resistance rather than the introduction of resistance alleles. Furthermore, we identified a large inverted supergene responding to insecticide selection. This study provides valuable insights into the genetic bases of insecticide resistance, enabling the implementation of molecular makers to improve the tracking of insecticide resistance in this major mosquito vector across the Indian Ocean
Rheological control of crystal fabrics on Antarctic ice shelves
No full textInternational audienceIce crystal fabrics can exert significant rheological control on ice sheets and ice shelves, potentially softening or hardening anisotropic ice by several orders of magnitude compared to isotropic ice. We introduce an anisotropic extension of the Shallow Shelf Approximation (SSA), allowing for fabric-induced viscous anisotropy to affect the flow of ice shelves in coupled, transient simulations. We show that the viscous anisotropy of synthetic ice shelves can be parameterized using an isotropic flow enhancement factor, suggesting that existing SSA flow models could, with little effort, approximate the effect of fabric on flow. Next, we propose a new way to directly solve for SSA fabric fields using satellite-derived velocities, assuming velocities are approximately steady and that fabric evolution is dominated by lattice rotation with or without discontinuous dynamic recrystallization. We apply our method to the Ross and Pine Island ice shelves, Antarctica, suggesting that these regions might experience significant fabric-induced hardening and softening depending on the relative strength of lattice rotation and recrystallization. Our results emphasize the icedynamical relevance of needing to better constrain the strength of fabric processes. This calls for more widespread fabric and temperature measurements from the field, since measurements are currently too sparse for model validation
Paving the way toward foundation models for irregular and unaligned Satellite Image Time Series
No full textAlthough recently several foundation models for satellite remote sensing imagery have been proposed, they fail to address major challenges of real/operational applications. Indeed, embeddings that don’t take into account the spectral, spatial and temporal dimensions of the data as well as the irregular or unaligned temporal sampling are of little use for most real world uses. As a consequence, we propose an ALIgned Sits Encoder (ALISE), a novel approach that leverages the spatial, spectral, and temporal dimensions of irregular and unaligned SITS while producing aligned latent representations. Unlike SSL models currently available for SITS, ALISE incorporates a flexible query mechanism to project the SITS into a common and learned temporal projection space. Additionally, thanks to a multi-view framework, we explore integration of instance discrimination along a masked autoencoding task to SITS. The quality of the produced representation is assessed through three downstream tasks: crop segmentation (PASTIS), land cover segmentation (MultiSenGE), and a novel crop change detection dataset. Furthermore, the change detection task is performed without supervision. The results suggest that the use of aligned representations is more effective than previous SSL methods for linear probing segmentation tasks