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Fine roots and N uptake efficiency shape genotypic variability in early vigor of rapeseed under low nitrogen conditions
No full textEarly vigor is a determining factor for successful winter oilseed rape cultivation in low-input systems, as efficient establishment enhances weed competitiveness and tolerance to early pest pressure. Yet the physiological and genetic bases of early vigor remain poorly understood, particularly regarding root traits. Here, we combined high-throughput phenotyping and genetic analysis to dissect early vigor determinants under low nitrogen conditions.A population of 100 recombinant inbred lines and four commercial cultivars were grown under low nitrogen supply up to BBCH 16 in RhizoTubes, allowing non-destructive imaging of root and shoot development. Forty traits were measured or derived, including traits related to carbon and nitrogen metabolism and detailed root system architecture descriptors. Random forest models and QTL mapping were used to identify ecophysiological and genetic determinants of early vigor.Early vigor -assessed by total dry weight, shoot dry weight, leaf area, and projected leaf area -showed strong positive correlations with fine-root dry weight and nitrogen uptake efficiency (NUpE), but weak relationships with fine-scale root system architecture traits. Random forest analyses confirmed fine-root dry weight and NUpE as the main contributors to genotypic variation in early vigor. QTL mapping identified 17 QTLs across 10 chromosomes, including a locus on A01 co-localizing for total, root, and fine-root dry weight, highlighting shared genetic control of shoot and fine-root growth.Altogether, under low nitrogen supply, early vigor is primarily governed by traits related to nitrogen capture and fine-root growth, highlighting fine-root development and nitrogen uptake as promising breeding targets for sustainable winter oilseed rape
Soil Moisture Reduction Accelerates CO 2 Emissions in Near‐Saturated Cold and Wet Ecosystems
No full textInternational audienceAbstract Global cold and wet ecosystems, such as permafrost, northern peatlands, Arctic tundra, boreal wetlands, and alpine swamp meadows, store large amounts of soil organic carbon (SOC) and are typically water‐rich. While it is well recognized that these ecosystems are highly vulnerable to climate warming as it accelerates SOC decomposition, how soil water levels regulate SOC decomposition and CO 2 emissions specifically by constraining oxygen (O 2 ) availability during the growing season remains poorly understood at large spatial scales. Here, we integrate field observations, global data sets, and process‐based models to quantify how soil water dynamics influence CO 2 emissions by regulating O 2 diffusion across these ecosystems. Results from 107 field sites consistently reveal a protective effect of high soil water levels against SOC decomposition and CO 2 release during the peak growing season (representing one‐third of the year), suggesting that, beyond warming, the loss of this protection due to soil moisture reduction under near‐saturated conditions is a critical driver of increased CO 2 emissions. However, global data‐driven data sets and process‐based model simulations show divergent correlations between soil CO 2 emissions and soil water levels. Many models, which rely on simplified soil moisture scalars or empirical functions to represent soil water level effects, inadequately reproduce the effects of soil water levels on SOC decomposition and CO 2 emissions in cold and wet ecosystems. Our study underscores the urgent need to incorporate more mechanistic representations of soil water‐mediated SOC decomposition into models and to improve spatially explicit hydraulic parameters, thereby enhancing projections of soil carbon‐climate feedback
Two new species of Caloptilia (Lepidoptera, Gracillariidae) from New Caledonia inducing galls on Glochidion billardierei (Phyllanthaceae) and redescription of C. xanthopharella (Meyrick, 1880)
No full textInternational audienceNew Caledonia is a biodiversity hotspot with high levels of micro-endemism, yet its gracillariid fauna remains poorly documented. Here, two new species of Caloptilia Hübner, 1825 (Gracillariidae) are described from Glochidion J.R.Forst. & G.Forst. (Phyllanthaceae) host plants in Parc des Grandes Fougères, New Caledonia: Caloptilia augeas Guiguet, Lopez-Vaamonde, van Nieukerken & Ohshima, sp. nov ., and Caloptilia ceryneia Guiguet, Lopez-Vaamonde, van Nieukerken & Ohshima, sp. nov . Both species induce leaf galls on Glochidion billardierei Baill., co-occurring on the same host species, sometimes even on the same leaf. They exhibit distinct wing patterns, but very similar male and female genitalia, and DNA barcoding supports their status as separate species. These findings provide evidence for potential within-host sympatric speciation, as documented in other gall-inducing insects. The larval biology of C. augeas and C. ceryneia reveals a unique frass disposal behaviour, whereby waste is excreted through a hole and the aperture is subsequently sealed—an adaptation not previously reported in gall-inducing Lepidoptera. Our findings double the known number of gall-inducing species in Gracillariidae, highlighting that this life history strategy may be more common than currently appreciated. We also provide new information on distribution and host plants of Caloptilia xanthopharella (Meyrick, 1880), a leaf roller found on the same host plant, G. billardierei . These findings mark the first records of the subfamily Gracillariinae in New Caledonia. This study underscores the underexplored diversity of New Caledonian gracillariids and emphasises the conservation value of Parc des Grandes Fougères. Further surveys in the Indo-Pacific region may reveal additional yet undescribed Caloptilia species associated with Phyllanthaceae and help clarify the evolutionary mechanisms underpinning their diversification
Efficient species segmentation throughout the growing season of oilseed rape-service plant intercropping using transfer learning
No full textInternational audienceSemantic segmentation methods have become increasingly popular in the field of agronomy for their ability to accurately and efficiently analyse images of crops. These methods use machine learning algorithms to assign semantic labels to each pixel in an image and require extensive amount of labelled data. Currently, most of the available models focus on crop and weed identification and target a single growth stage. In this paper, we propose a robust semantic segmentation method for estimating the dynamics of multi-species cover in intercropping systems, using only 50 images for training the model. We applied transfer learning on the well-established convolutional neural network DeepLab to decrease the image annotation effort. Three models are trained using field images from a three-year field trial with canopy densities ranging from early development at 1.2% to well-developed canopy covers of 98.7%. Overall, we propose a two-class segmentation model to differentiate vegetation from soil, obtaining 96.8% mean accuracy. Two methods for three-class segmentation models to identify soil, oilseed rape and the other plants are proposed, reaching best mean accuracy of 96.2%. The proposed method is able to differentiate oilseed rape from service plant mixtures at all growing stages, allowing for accurate assessment of the dynamic competition for light between these species. Hence, semantic segmentation methods in agronomy have the potential to support study and management of crops, enabling more accurate and efficient data collection and analysis.Les méthodes de segmentation sémantique sont devenues de plus en plus populaires dans le domaine de l’agronomie, grâce à leur capacité à analyser précisément et efficacement les images des cultures. Ces méthodes utilisent des algorithmes de machine learning pour classer chaque pixel de l’image, et nécessitent un grand nombre de données. Actuellement, la plupart des modèles disponibles proposent l’identification d’une culture et des adventices et ne ciblent qu’un seul stade de développement. Dans cet article, nous proposons une méthode de segmentation sémantique robuste permettant d’estimer la dynamique de couverture d’associations complexes de cultures, en n’utilisant que 50 images pour entraîner le modèle. Nous avons utilisé une méthode de transfer learning sur le modèle de réseaux de neurones DeepLab déjà entraîné pour réduire l’effort d’annotation des images. Trois modèles ont été entraînés à partir d’images obtenues dans un essai au champ reconduit trois ans, pendant la période de développement de la culture allant d’une couverture du sol de 1,2 % jusqu’à 98,7 %. Nous proposons un modèle de segmentation en deux classes, qui différentie le sol de la végétation, et obtient une précision moyenne de 96,8 %. Deux méthodes pour des modèles de segmentation à 3 classes, identifiant le sol, le colza et les autres espèces végétales sont proposées, et ont une précision moyenne de 96,2 %. Les méthodes proposées sont capables de différentier le colza des mélanges de plantes de services à tous les stades du développement végétatif, et permettent ainsi une évaluation précise de la dynamique de compétition pour la lumière entre les espèces. Ainsi, les méthodes de segmentation sémantique peuvent contribuer à l’étude des associations de cultures et être utilisées dans des outils d’aide à la décision, car elles simplifient et rendent plus précise la collecte et l’analyse des données
Ecological forecasts highlight opposing effects of long‐term climate change on population demography
No full textInternational audienceAbstract The multifaceted impacts of global climate change on biota challenge our understanding and capability of anticipating the long‐term viability of wild populations, which is an emergent property of ecological systems. Using Bayesian integrated population modeling, sensitivity analyses, and ecological forecasting, we investigate how climate variability shapes the long‐term population dynamics of a species highly sensitive to climate change: the emperor penguin ( Aptenodytes forsteri ). Leveraging a multi‐decadal database from Pointe Géologie, East Antarctica, we assess penguin sensitivity to multiple environmental drivers and produce anticipatory projections of the emerging population trajectories under the noise of forecasted climatic changes. We found that receding fast ice during chick‐rearing, leading to reduced commuting distances to open water, improves breeding success. Conversely, ocean warming and stronger winds negatively impact adult survival, possibly due to changes in Antarctic marine productivity. These contrasting effects of ocean warming and sea ice contractions on adult survival and breeding success, the most important contributors to the realized population growth rate, indicate opposing effects of climate change on penguins. Using forecasts, we explored how these opposing forces will jointly determine long‐term emperor penguin population dynamics. We found that the increased breeding success linked to reductions in fast ice may buffer and delay population declines by over a decade. However, ocean warming and its likely repercussions to the food web and adult survival will ultimately drive population declines. While forecasting is well established in climate science, ecological forecasting faces distinct challenges, including shorter and less defined predictability horizons, greater stochasticity, and limited long‐term data. Yet, forecasts can be used to understand and anticipate population responses, which is particularly valuable, given the urgent need to define proactive conservation plans. Here, forecasts reveal contrasting demographic impacts of sea ice loss and ocean warming on emperor penguins. Our approach, adaptable to other species and systems, highlights the value of anticipatory projections for disentangling and quantifying drivers of long‐term population change
Dynamic Efficiency With Private Information
No full textNational audienceThis paper studies the efficient allocation of capital and consumption in a production economy with many agents, private information, and aggregate risk. It extends the influential work of Andrew Atkeson and Robert E. Lucas Jr. (1992), who analyzed a related problem in an exchange economy. In a dynamic production setting, the planner faces a fundamental trade-off between providing some insurance against privately observed idiosyncratic risk and sustaining productive investment and economic growth. Using mean-field control techniques, we derive the infinite dimensional Hamilton–Jacobi–Bellman equation that characterizes constrained-efficient allocations. Under constant relative risk aversion preferences, the solution admits a simple characterization. We show that constrained-efficient allocations can be decentralized through a competitive market in which goods trade against a single safe asset supplied by fiscal or monetary authorities. Dynamic efficiency requires setting the growth rate of the safe asset to balance the demand of agents for insurance with the investment needed to maintain optimal growth
Should we stop the COPs ?
No full textNational audienceMore than a decade after COP21, carbon emission trajectories remain far above the 1.5° C threshold, due to lack of international consensus. Departing from cost-benefit approaches, we assess the maximum reduction in carbon emissions that could be accepted by all countries. We characterize the target-consistent mechanism that minimizes global emissions subject to the participation constraint of each country. The mechanism can be implemented either via a uniform carbon tax or as a cap-and-trade system. Calibrated to data from 69 countries, including GDP, carbon intensities, and observed tax rates, our model suggests — for our baseline scenario — that the maximum uniform carbon price politically acceptable for all countries is $250 per ton. It could reduce global emissions by 35%, but would require unprecedented international transfers: up to 3% of world GDP, with a large redistribution from high-income, low-emission countries to carbon-intensive emerging economies. Our analysis highlights the structural ambition gap imposed by voluntary cooperation and identifies two levers to overcome it: convergence in green technologies and stronger political support for mitigation. Without progress in these dimensions, international climate policy remains constrained to deliver only modest results
Quelle place des acteurs de l'intervention sociale dans les gouvernances alimentaires territoriales ?
No full textInternational audienc
Optimization of α‐Synuclein and Tau Detection by Immunoblot in Enteroendocrine Cell Lines
No full textInternational audienceABSTRACT Background Enteroendocrine cells (EECs) are dispersed along the intestinal mucosa and transduce luminal stimuli into hormonal signals. EECs exhibit neuron‐like features and express both α‐synuclein and tau, two proteins pathologically and genetically linked to Parkinson disease (PD). These observations support the hypothesis that EECs may be involved in disease development in the “body‐first” PD subtype. Cellular models represent invaluable tools for studying the role of α‐synuclein and tau in PD pathogenesis. However, the sensitivity and specificity of commercial antibodies for detecting α‐synuclein and tau in EEC cell lines remain unclear. Methods We tested by immunoblot a panel of commercial total‐α‐synuclein and total‐tau antibodies on protein lysates from three EEC cell lines: GLUTag, NCI‐H716, and STC‐1. Pharmacological and biochemical manipulations were applied to assess the specificity of antibodies against phosphorylated α‐synuclein and tau. Key Results Five antibodies detected total α‐synuclein in NCI‐H716 lysates, whereas the antibodies D1M9X and Tau12 detected total tau in GLUTag and NCI‐H716 lysates, respectively. α‐synuclein and tau protein levels were comparable between naïve and differentiated NCI‐H716 cells. Four phospho‐specific antibodies revealed phospho‐α‐synuclein S129 in NCI‐H716. Of the nine phospho‐tau antibodies tested, six recognized tau phosphorylated at specific epitopes (T181, S199, T231, S356, S396, and S404) in GLUTag cells. Conclusions and Inferences Our results indicate that NCI‐H716 cells represent an ideal EEC model for studying α‐synuclein expression and phosphorylation, whereas GLUTag cells are preferable for investigating tau protein biology. This work provides a comprehensive antibody toolbox to dissect the physiological and pathological role of α‐synuclein and tau in EECs
Possibilities, challenges, and limitations of dinosaur eggshells LA-ICP-MS U-Pb dating based on samples from Provence (Upper Cretaceous, France)
No full textObtaining accurate chronostratigraphic constraints on continental deposits is challenging, necessitating innovative dating approaches. Here, we investigate the feasibility of Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) U-Pb dating of dinosaur eggshell fragments from the Late Cretaceous of Provence, France. Preliminary optical, Scanning Electron Microscopy (SEM), and cathodoluminescence (CL) analyses were critical for identifying zones of optimal preservation and mitigating potential diagenetic contamination. Results from two samples attributed to Megaloolithus mamillare and Cairanoolithus dughii yielded ages of 68.1 ± 4.7 Ma and 68.7 ± 10.2 Ma, respectively, broadly consistent with regional stratigraphic markers. Element mapping reveals significant spatial variation in U and Pb concentrations within individual eggshells, with zones of high contamination contrasting with well-preserved cores. The data highlight the crucial role of diagenesis and organic matter in influencing U-Pb system behavior, although it appears to be limited to early diagenesis. While LA-ICP-MS U-Pb dating of dinosaur eggshells presents substantial challenges, this study demonstrates its potential with careful sample selection and nuanced interpretation, paving the way for further refinement and broader application