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Knowledge of species' functional traits is essential for understanding biodiversity patterns, predicting the impacts of global environmental changes, and assessing the efficiency of conservation measures. Bats are major components of mammalian diversity and occupy a variety of ecological niches and geographic distributions. However, an extensive compilation of their functional traits and ecological attributes is still missing. Here we present EuroBaTrait 1.0, the most comprehensive and up-to-date trait dataset covering 47 European bat species. The dataset includes data on 118 traits including genetic composition, physiology, morphology, acoustic signature, climatic associations, foraging habitat, roost type, diet, spatial behaviour, life history, pathogens, phenology, and distribution. We compiled the bat trait data obtained from three main sources: (i) a systematic literature and dataset search, (ii) unpublished data from European bat experts, and (iii) observations from large-scale monitoring programs. EuroBaTrait is designed to provide an important data source for comparative and trait-based analyses at the species or community level. The dataset also exposes knowledge gaps in species, geographic and trait coverage, highlighting priorities for future data collection.
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http://dx.doi.org/10.1038/s41597-023-02157-4 | DOI Listing |
Genome Biol
September 2025
Department of Clinical Pharmacy, Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA, 90089, USA.
Background: Recent advances in high-throughput sequencing technologies have enabled the collection and sharing of a massive amount of omics data, along with its associated metadata-descriptive information that contextualizes the data, including phenotypic traits and experimental design. Enhancing metadata availability is critical to ensure data reusability and reproducibility and to facilitate novel biomedical discoveries through effective data reuse. Yet, incomplete metadata accompanying public omics data may hinder reproducibility and reusability and limit secondary analyses.
View Article and Find Full Text PDFBrief Bioinform
August 2025
School of Computer Science, Xi'an Polytechnic University, 710048, Xi'an, China.
Cancer, with its inherent heterogeneity, is commonly categorized into distinct subtypes based on unique traits, cellular origins, and molecular markers specific to each type. However, current studies primarily rely on complete multi-omics datasets for predicting cancer subtypes, often overlooking predictive performance in cases where some omics data may be missing and neglecting implicit relationships across multiple layers of omics data integration. This paper introduces Multi-Layer Matrix Factorization (MLMF), a novel approach for cancer subtyping that employs multi-omics data clustering.
View Article and Find Full Text PDFAppl Microbiol Biotechnol
September 2025
School of Plant Sciences, The University of Arizona, 1140 E South Campus Drive, Forbes 303, Tucson, AZ, 85721, USA.
Fungal endophytes and epiphytes associated with plant leaves can play important ecological roles through the production of specialized metabolites encoded by biosynthetic gene clusters (BGCs). However, their functional capacity, especially in crops like lettuce (Lactuca sativa L.), remains poorly understood.
View Article and Find Full Text PDFJDS Commun
September 2025
Institute of Agricultural Sciences in the Tropics, University of Hohenheim, Stuttgart, 70599, Germany.
Accurate estimation of individual feed intake is essential for calculating feed efficiency, planning diets, monitoring cow herds, and managing grazing cows. This study aimed to evaluate the performance and applicability of estimation equations developed to predict pasture herbage DMI (PHDMI) in dairy cows using behavioral traits recorded and scored by the RumiWatch system. The study had 4 primary objectives: (1) to compare the behavioral characteristic outputs of 2 versions of the RumiWatch converter (0.
View Article and Find Full Text PDFJDS Commun
September 2025
Geno Breeding and AI Association, 2317 Hamar, Norway.
It is of interest to examine whether methane (CH) emission is genetically the same trait in young bulls and lactating dairy cows. The aim was therefore to estimate the genetic correlation between CH emissions for Norwegian Red young bulls and lactating cows. Measures of CH from GreenFeed (GF) were available from Geno's test station for young bulls and from GF units installed across 14 commercial dairy herds.
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