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Abstract Trait-based frameworks are increasingly used for predicting how ecological communities respond to ongoing global change. As species range shifts result in novel encounters between predators and prey, identifying prey ‘guilds’, based on a suite of shared traits, can distill complex species interactions, and aid in predicting food web dynamics. To support advances in trait-based research in open-ocean systems, we present the Pelagic Species Trait Database, an extensive resource documenting functional traits of 529 pelagic fish and invertebrate species in a single, open-source repository. We synthesized literature sources and online resources, conducted morphometric analysis of species images, as well as laboratory analyses of trawl-captured specimens to collate traits describing 1) habitat use and behavior, 2) morphology, 3) nutritional quality, and 4) population status information. Species in the dataset primarily inhabit the California Current system and broader NE Pacific Ocean, but also includes pelagic species known to be consumed by top ocean predators from other ocean basins. The aim of this dataset is to enhance the use of trait-based approaches in marine ecosystems and for predator populations worldwide.
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LepTraits 1.0 A globally comprehensive dataset of butterfly traits
Abstract Here, we present the largest, global dataset of Lepidopteran traits, focusing initially on butterflies (ca. 12,500 species records). These traits are derived from field guides, taxonomic treatments, and other literature resources. We present traits on wing size, phenology,voltinism, diapause/overwintering stage, hostplant associations, and habitat affinities (canopy, edge, moisture, and disturbance). This dataset will facilitate comparative research on butterfly ecology and evolution and our goal is to inspire future research collaboration and the continued development of this dataset.
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- PAR ID:
- 10368574
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Data
- Volume:
- 9
- Issue:
- 1
- ISSN:
- 2052-4463
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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