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The global biodiversity crisis due to anthropogenic pressures jeopardizes marine ecosystem functioning and services. Community responses to these environmental changes can be assessed through functional diversity, a biodiversity component related to organism-environment interactions, and estimated through biological traits related to organism functions (locomotion, feeding mode, and reproduction). Fish play a key role in marine systems functioning and supply proteins for billions of humans worldwide, yet most of the knowledge is limited to several commercial species and little is known about the intraspecific variability of their functional traits. The data provided here consist of 867 records of individuals from 85 species of ray-finned (Actinopterygii) and cartilaginous (Chondrichthyes) fish sampled in the Bay of Biscay (Atlantic, France) between autumn 2017 and 2019. We provided for each individual the taxonomic classification, 16 ecomorphological measures (5 directly made on fresh individuals and 11 realized using individual pictures) that were converted into nine ecomorphological traits classically documented in the literature (biomass, protrusion, oral gape shape, surface and position, eye size and position, body transversal shape and surface, pectoral fin position and caudal peduncle throttling) and eight life history traits obtained from FishBase (maximum length, average depth, depth range, trophic level, reproduction mode, fertilization mode, parental care, vertical position in the water column). These traits document several functions such as dispersion, feeding mode, habitat use, position in the food web, and reproduction. To improve the development of new traits, we provided a picture of each individual with an ROI file containing the different morpho-anatomical measures made using "ImageJ" software and an R function to extract them. In addition, we provided the metadata from each sampling site (years, dates, stations, sampling hours, strata, gears, latitudes, longitudes, and depths) and environmental variables measured in situ (conductivity, salinity, water temperature, water density, and air temperature). This data set accounting for the intraspecific variability among 85 fish species is of interest to better understand the effects of environmental forcing in a global change context as in the Bay of Biscay, a highly fished transition zone harboring mixed assemblages of boreal, temperate, and subtropical fish species that are susceptible to display variability in functional trait to adapt to changing conditions. The data set is freely available without copyright restrictions; users should cite this paper in research products (publications, presentations, reports, etc.) derived from the data set.
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http://dx.doi.org/10.1002/ecy.3924 | DOI Listing |
J Org Chem
September 2025
State Key Laboratory of Fine Chemicals, School of Chemical Engineering, Ocean and Life Sciences, Dalian University of Technology, Panjin 124221, P. R. China.
The Buchwald-Hartwig (B-H) reaction graph, a novel graph for deep learning models, is designed to simulate the interactions among multiple chemical components in the B-H reaction by representing each reactant as an individual node within a custom-designed reaction graph, thereby capturing both single-molecule and intermolecular relationship features. Trained on a high-throughput B-H reaction data set, B-H Reaction Graph Neural Network (BH-RGNN) achieves near-state-of-the-art performance with an score of 0.971 while maintaining low computational costs.
View Article and Find Full Text PDFJ Chem Inf Model
September 2025
Department of Chemistry, Delaware State University, Dover, Delaware 19901, United States.
The calculation of the highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gap for chemical molecules is computationally intensive using quantum mechanics (QM) methods, while experimental determination is often costly and time-consuming. Machine Learning (ML) offers a cost-effective and rapid alternative, enabling efficient predictions of HOMO-LUMO gap values across large data sets without the need for extensive QM computations or experiments. ML models facilitate the screening of diverse molecules, providing valuable insights into complex chemical spaces and integrating seamlessly into high-throughput workflows to prioritize candidates for experimental validation.
View Article and Find Full Text PDFMycologia
September 2025
Herbarium, University of Michigan, 3600 Varsity Drive, Ann Arbor, Michigan 48108, USA.
Marthamycetales species are widely distributed, non-lichenized, apothecial ascomycetes that are associated with various woody plants and grasses. Most species are presumed to be saprobes, although a few are pathogens. Apothecia are small and erumpent, with farinose discs that are encircled by ragged, projecting flaps of degraded plant tissue.
View Article and Find Full Text PDFChaos
September 2025
School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA.
Although many real-world time series are complex, developing methods that can learn from their behavior effectively enough to enable reliable forecasting remains challenging. Recently, several machine-learning approaches have shown promise in addressing this problem. In particular, the echo state network (ESN) architecture, a type of recurrent neural network where neurons are randomly connected and only the read-out layer is trained, has been proposed as suitable for many-step-ahead forecasting tasks.
View Article and Find Full Text PDFAust J Rural Health
October 2025
AgHealth Australia, School of Rural Health, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
Objective: To describe the pattern and estimated direct economic burdens associated with unintentional deaths and injuries on Australian farms over the past 11 years (2013-2023).
Design: Descriptive retrospective epidemiological study of National Coronial Information System (NCIS) data for persons fatally injured on a farm and workers' compensation injuries data from the National Data Set.
Setting: Australia.