Hydrological regime and plant functional traits jointly mediate the influence of Salix spp. on soil organic carbon stocks in a High Arctic tundra

Evidence points out that increasing plant productivity associated with greater erect shrub abundance alters soil organic carbon (SOC) stocks in the Arctic. However, the underlying plant economic traits remain poorly examined, which limits our understanding of plant–environment interactions driving tundra carbon cycling. We explored how erect shrub abundance leads to SOC variation in a High Arctic tundra (Bylot Island, Nunavut, Canada), where the only erect shrub, Salix richardsonii, has settled along currently active and abandoned channel zones of alluvial fans. The effects of vegetation and local environmental changes on SOC were evaluated through a paired sampling of soil materials and plant aboveground functional traits associated with plant carbon supply and nutrient demand processes. The occurrence of S. richardsonii, characterized by a tenfold increase in aboveground biomass, induced a 28% increase in SOC compared to adjacent plots dominated by prostrate shrubs and graminoids. Yet, this vegetation effect was solely observed along active channels, where higher SOC was associated with greater leaf and stem biomass. A path analysis showed that shrub leaf area index and total leaf nutrient content best represented plant carbon supply and nutrient demand dynamics, respectively, and jointly regulated SOC variation. This study underscores that vegetation structural changes associated with increasing erect shrub abundance in the Arctic can promote soil organic carbon storage, but that this pattern may be mediated by strong plant–environment interactions. Accounting for changes in functional traits driving plant carbon supply and nitrogen demand proves important for a better mechanistic understanding of how shrubification impacts tundra carbon cycling

Dynamics of aero-spora of Eutypa lata (Pers. : Fr.) Tul., the causal pathogen of eutypa dieback of grapevine

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Dynamique de la sporee aerienne de l'Eutypa lata dans les vignoble de la facade atlantique

International audienc

Dynamique de la sporee aerienne de l'Eutypa lata dans les vignoble de la facade atlantique

International audienc

Identifying contaminants in astronomical images using convolutional neural networks

In this work, we propose to use convolutional neural networks to detect contaminants in astronomical images. Each contaminant is treated in a one vs all fashion. Once trained, our network is able to detect various contaminants such as cosmic rays, hot and bad pixel defaults, persistence effects, satellite trails or fringe patterns in images of various field properties. The convolutional neural network is performing semantic segmentation: it can output a probability map, assigning to each pixel its probability to belong to the contaminant or the background class. Training and testing data have been gathered from real or simulated data

Study of receptivity of pruning wounds to Eutypa lata, agent of eutypa dieback in grapevine : defence mechanisms of plant

International audienc

Study of receptivity of pruning wounds to Eutypa lata, agent of eutypa dieback in grapevine : defence mechanisms of plant

International audienc

Development of a preventative measure against eutypa dieback of grapevine

International audienc

Development of a preventative measure against eutypa dieback of grapevine

International audienc