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The MAJIS VIS-NIR Channel: detectors’ characterization and radiative transfer modeling of Jupiter’s atmosphere
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Répertoire photographique du mobilier des sanctuaires de Belgique : Province de Hainaut : Canton de Gosselies
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Comparative genetic analysis of invasive mosquito species in Belgium supports diverse introduction pathways
No full textInvasive mosquitoes present challenges to both public health and ecosystems, demanding a comprehension of their dispersal patterns and genetic makeup. This study integrates genetic analyses of two invasive mosquito species in Belgium, Aedes albopictus and Aedes japonicus, to clarify their introduction pathways and dispersal dynamics. Data were gathered through two mosquito monitoring programs, MEMO and MEMO+, with specimens collected at various points of entry (PoE), including international import companies, highway parking lots, and residential areas. A total of 254 Ae. albopictus and 292 Ae. japonicus specimens were analysed. The northward expansion of Ae. albopictus into Belgium was investigated via active monitoring at PoEs and citizen science initiatives. Thirteen distinct COI haplotypes were identified, with one prevalent haplotype across sampling locations. The disparity in haplotype composition at import companies (indicative of long-distance introductions) versus parking lots/residential areas (suggestive of medium-distance introductions) in Belgium corroborates field observations, where tiger mosquitoes are presumed to hitchhike from established populations in neighbouring countries via passive ground transport. The analysis of seven microsatellite loci within Ae. japonicus populations disclosed genetic disparities between specimens collected at one PoE before and after eradication attempts, hinting at potential new introductions after elimination campaigns. Furthermore, clustering analysis unveiled a genetic correlation between Belgian specimens collected at the border with Germany and populations in western Germany, underscoring the influence of human-mediated transport on invasion pathways. These comparative genetic analyses underscore the significance of vigilant monitoring and targeted control strategies. The genetic investigation of Ae. albopictus and Ae. japonicus enhances our comprehension of their introduction pathways, which is important for effective management and mitigation of invasive species' impacts on public health and ecosystems
SmartWoodID: Smart classification of Congolese timbers: deep learning techniques for enforcing forest conservation
No full textA substantial part of the timber traded each year is still illegal. Illegal logging is the most profitable biodiversity crime. It involves a high risk of irreversible damage to forests since it often implicates overexploitation of highly sought after, sometimes protected, species. This is especially pertinent for tropical species, as it is estimated that 30-90% of the tropical timber volume is harvested illegally (Deklerck et al., 2020; Hirschberger, 2008; Hoare, 2015; Vlam et al., 2018). Timber regulations are already active (CITES, FLEGT, EUTR , Amendment to the U.S. Lacey Act), but implementation and enforcement are a challenge. Wood identification is crucial in the enforcement process when it comes to verify whether the shipment corresponds with the products mentioned on the accompanying documents. For this reason, there is a growing demand for timber identification tools that can be applied by law enforcement officers. SmartwoodID aims at improving both identification success and speed by non-experts. The project aims at automating part of the wood identification process by applying artificial intelligence techniques for the analysis of wood anatomical images of timber species of the Democratic Republic of the Congo. The project focusses on 970 Congolese timbers to create a database with high-resolution scans of the endgrain surface along with expert wood anatomical descriptions. The study material comes from the Tervuren Xylarium. This because said database offers the most complete collection of reference material for the development of wood classification and identification approaches for Congolese species, comprising more than 2000 woody species from the DRC (timber trees, small trees, shrubs, dwarf shrubs and lianas). The resulting database is used to make an illustrated key for wood identification. The project also takes advantage of the power of modern deep learning approaches. The scans and anatomical descriptions will therefore serve as annotated training data to develop a machine learning assisted illustrated key for wood identification
