The aim of the Life On Trees (LOT) program is to generate baseline knowledge about the number of eukaryotic species a single large aged tropical tree can host and to understand how these communities of organisms are assembled. The program is conducted in the Amazon and Andes biodiversity hotspots. Our first project, LOT-Amazon 2022, was performed on a spectacular Dussia tree (Fabaceae), which was 50 m high and 45 m wide. The sampling was carried out by professional climbers, guided by experts of the different eukaryotic groups studied (plants, fungi, animals, protists). To better understand the contribution of different tree components (bark, leaves, fruits, flowers, living and dead wood) to overall tree biodiversity, we assigned observations into communities based on height zone or microhabitat and will examine similarities and nestedness in the composition of these communities. The first results show that a single tree can host a tremendous diversity (e.g., 42 orchids, 28 ferns, and more than 200 bryophytes, 180 lichen species identified, which are world records considering the 400m elevation). This confirms that large old tropical trees are important pools of biodiversity probably in relation with the variety of local microhabitats and tree age. Funding: Fonds de Dotation Biotope pour la Nature Web and/or Twitter account: www.lifeontrees.org
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RBINS Staff Publications 2023
Mytilopsis leucophaeata is a biofouling bivalve causing major problems in the cooling water system of BASF, Antwerp NV, Belgium, a large water-using industrial facility. This study aimed to develop a statistical model to predict the response of M. leucophaeata larvae to environmental conditions in estuarine ecosystems. Multiple logistic regression, taking into account temporal autocorrelation, was applied on a large dataset allowing the prediction of the probability of occurrence of M. leucophaeata larvae at BASF NV as a response to the environmental variables. The final model made it possible to predict larval presence in the water column solely by monitoring water temperature. The results from subsampling indicated that the model was stable. The model was tested with 2005 data, demonstrating a 98\% precise prediction of the occurrence of M. leucophaeata larvae in the water column, with a sensitivity of 100\% and a specificity of 97\%, even though autumn 2005 was exceptionally warm, which led to an extended presence of the larvae.
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