Influence of forest decline on the abundance and diversity of Raphidioptera and Mecoptera species dwelling in oak canopies

International audienceTrees in a state of decline exhibit a reduced foliage density and accumulate dead branches in their crowns. Consequently, forest decline can markedly affect both the habitats and sources of food for canopy-dwelling insects. The decline-induced increase in canopy openness may also modify the understory, shrub and ground layers, and have cascading effects on associated species. Flight interception traps and green Lindgren traps were used to survey the canopy-dwelling insects in stands of healthy and declining oak trees, in particular two insect orders: Raphidioptera, saproxylic insects associated with canopies, and Mecoptera, necrophagous or opportunistic species associated with the herbaceous or shrub strata. Overall, green Lindgren traps caught more of these insects than flight interception traps. The traps caught five species of Raphidioptera. Three of them, Subilla confinis, Phaeostigma major and, to a lesser extent, Phaeostigma notata, were more abundant in stands or plots with declining trees. However, the other two species of Raphidioptera, Atlantoraphidia maculicollis and Xanthostigma xanthostigma exhibited a reverse trend. Two species of Mecoptera, Panorpa germanica and Panorpa communis, were particularly abundant, but unaffected by the level of decline. Our results show that declining forests can either host more or fewer species of Raphidioptera with saproxylic larvae, whereas Mecoptera with ground-living larvae were unaffected. Seasonal phenology and sex ratio of the species are also discussed

Toward an Operational Monitoring of Oak Dieback With Multispectral Satellite Time Series: A Case Study in Centre-Val De Loire Region of France

This article studies the monitoring of oak dieback in forests of the Centre-Val de Loire region (France), where drought-induced dieback has become a major concern due to climate change. The main objective of the study is to evaluate the applicability of multispectral satellite time series for operational monitoring of forest dieback. Using in situ data collected from 2017 to 2022 on approximately 2700 oak plots, a multiyear mapping of the analyzed region was performed using the random forest algorithm and Sentinel-2 images. Our results show that it is possible to detect oak dieback accurately (average overall accuracy = 80% and average balanced accuracy = 79%). A spatial cross-validation analysis also evaluates the performance of the model on regions that were never encountered during training, across all years, resulting in a slight decrease in accuracy ($\sim$5%). The study also highlights the importance of measuring the stability and performance of the classification model over time, in addition to standard cross-validation metrics. A feature analysis shows that the shortwave infrared part of the spectrum is the most important for mapping forest dieback, while the red-edge portion of the spectrum can increase the stability of the model over time. Overall, both in situ data and model predictions showed evidence of forest decline in many areas of the study region. Our results suggest that large areas of forest can decline over short periods of time, highlighting the interest of satellite data to provide timely and accurate information on forest status