No abstract

We present BeechCOSTe52; a database of European beech (Fagus sylvatica) phenotypic measurements for several traits related to fitness measured in genetic trials planted across Europe. The dataset was compiled and harmonized during the COST-Action E52 (2006–2010), and subsequently cross-validated to ensure consistency of measurement data among trials and provenances. Phenotypic traits (height, diameter at breast height, basal diameter, mortality, phenology of spring bud burst and autumn–leaf discoloration) were recorded in 38 trial sites where 217 provenances covering the entire distribution of European beech were established in two consecutive series (1993/95 and 1996/98). The recorded data refer to 862,095 measurements of the same trees aged from 2 to 15 years old over multiple years. This dataset captures the considerable genetic and phenotypic intra-specific variation present in European beech and should be of interest to researchers from several disciplines including quantitative genetics, ecology, biogeography, macroecology, adaptive management of forests and bioeconomy.

This chapter provides a practical worldwide overview of the environmental applications of poplars and willows. The chapter aims to synthesize the latest knowledge on these applications with respect to sustainable livelihoods, land use and restoration. The applications covered include land protection, watershed stabilization, waste management and other ecosystem services.

Anomaly Detection (AD) in images is a fundamental computer vision problem and refers to identifying images that deviate significantly from normality. State-of-the-art AD algorithms commonly learn a model of normality from scratch using task specific datasets in either semi-supervised or selfsupervised manner. We follow an alternative approach, and model the distribution of normal data in deep feature representations learned from ImageNet via a multivariate Gaussian (MVG). This lightweight approach achieves a new state of the art in AD on the public MVTec AD dataset. In addition to the empirical benefits, we give a clear motivation for the seemingly simplistic approach via the ties between deep generative and discriminative modeling revealed recently. We further elucidate why ImageNet representations are discriminative in the transfer learning AD setting using Principal Component Analysis. Here, we find that the principal components containing little variance in normal data are the ones crucial for discriminating between normal and anomalous instances, giving an explanation for the unreasonable effectiveness of our approach. We also investigate setting the working point of our approach by selecting acceptable False Positive Rate thresholds based on the MVG assumption as well as the resistance of our approach to unlabeled anomalies in the dataset. Finally, we investigate whether our approach is prone to exploiting spurious correlations using explainable AI techniques. Code is publicly available at https://github.com/ORippler/gaussian-ad-mvtec.

Due to its beneficial effects on river ecosystems, black alder (Alnus glutinosa) is one of the tree species selected for planting on riverbanks in the cross-border area encompassing Wallonia in Belgium, Lorraine in France, and Luxembourg. The preservation of this species, however, is threatened by an invasive pathogen that particularly targets and kills young alder individuals. The objectives of this study were to characterize the genetic diversity and the genetic structure of A. glutinosa at this local level with the aim of assisting the conservation and replanting strategies and to determine if a germplasm collection comprising individuals from the same cross-border area captures the diversity present in the region. Nuclear simple sequence repeat (SSR) and chloroplastic DNA (cpDNA) markers were used to analyze four local wild populations and the germplasm collection which is representative of two river catchments and six legal provenance regions. Three populations distant from the studied area were also included. A panel of 14 nuclear SSR loci revealed high allelic diversity and very low differentiation among wild populations (mean F ST = 0.014). The germplasm collection displayed a range of alleles that were representative of the different populations, and no significant differentiation between the germplasm collection and the local wild populations was observed, making this collection, as far as allelic diversity is concerned, suitable for providing trees for riverbank replanting programs. Using SSR markers, various statistical approaches consistently indicated the lack of a significant geographical structure at the level of the river catchments or provenance regions. In contrast, two cpDNA haplotypes were detected and displayed a cross-border geographically structured distribution that could be taken into account in defining new cross-border provenance regions.