The amount of methane released to the atmosphere from the Nord Stream subsea pipeline leaks remains uncertain, as reflected in a wide range of estimates. A lack of information regarding the temporal variation in atmospheric emissions has made it challenging to reconcile pipeline volumetric (bottom-up) estimates with measurement-based (top-down) estimates. Here we simulate pipeline rupture emission rates and integrate these with methane dissolution and sea-surface outgassing estimates to model the evolution of atmospheric emissions from the leaks.
View Article and Find Full Text PDFThe ease with which genome-wide data can nowadays be collected allows complicated phylogenetic questions to be re-evaluated. Phylogenetic relationships among newts have often proven difficult to resolve due to the prevalence of incomplete lineage sorting and introgressive hybridization. For the newt genus Lissotriton, phylogenetic relationships are not settled and there is controversy surrounding the species status of several taxa.
View Article and Find Full Text PDFIn this study, explainable machine learning techniques are applied to predict the toxicity of mussels in the Gulf of Trieste (Adriatic Sea) caused by harmful algal blooms. By analysing a newly created 28-year dataset containing records of toxic phytoplankton in mussel farming areas and diarrhetic shellfish toxins in mussels (Mytilus galloprovincialis), we train and evaluate the performance of machine learning (ML) models to accurately predict diarrhetic shellfish poisoning (DSP) events. Based on the F1 score, the random forest model provided the best prediction of toxicity results at which the harvesting of mussels is stopped according to EU regulations.
View Article and Find Full Text PDFResolving the order of speciation events that occurred in rapid succession is inherently hard and typically requires a phylogenomic approach. A case in point concerns the previously unresolved phylogeny of the three species of banded newt (genus Ommatotriton). We obtain c.
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