skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: The Pelagic Species Trait Database, an open data resource to support trait-based ocean research
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.  more » « less
Award ID(s):
2011031
PAR ID:
10501881
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ; ; ;
Publisher / Repository:
Springer Nature
Date Published:
Journal Name:
Scientific Data
Volume:
11
Issue:
1
ISSN:
2052-4463
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ; ; ; ; ; et al (, Scientific Data)
    Abstract In our changing world, understanding plant community responses to global change drivers is critical for predicting future ecosystem composition and function. Plant functional traits promise to be a key predictive tool for many ecosystems, including grasslands; however, their use requires both complete plant community and functional trait data. Yet, representation of these data in global databases is sparse, particularly beyond a handful of most used traits and common species. Here we present the CoRRE Trait Data, spanning 17 traits (9 categorical, 8 continuous) anticipated to predict species’ responses to global change for 4,079 vascular plant species across 173 plant families present in 390 grassland experiments from around the world. The dataset contains complete categorical trait records for all 4,079 plant species obtained from a comprehensive literature search, as well as nearly complete coverage (99.97%) of imputed continuous trait values for a subset of 2,927 plant species. These data will shed light on mechanisms underlying population, community, and ecosystem responses to global change in grasslands worldwide. 
    more » « less
  2. ; ; ; ; ; ; ; ; (, Scientific Data)
    Abstract Trait-based approaches are revolutionizing our understanding of high-diversity ecosystems by providing insights into the principles underlying key ecological processes, such as community assembly, species distribution, resilience, and the relationship between biodiversity and ecosystem functioning. In 2016, the Coral Trait Database advanced coral reef science by centralizing trait information for stony corals (i.e., Subphylum Anthozoa, Class Hexacorallia, Order Scleractinia). However, the absence of trait data for soft corals, gorgonians, and sea pens (i.e., Class Octocorallia) limits our understanding of ecosystems where these organisms are significant members and play pivotal roles. To address this gap, we introduce the Octocoral Trait Database, a global, open-source database of curated trait data for octocorals. This database houses species- and individual-level data, complemented by contextual information that provides a relevant framework for analyses. The inaugural dataset, OctocoralTraits v2.2, contains over 97,500 global trait observations across 98 traits and over 3,500 species. The database aims to evolve into a steadily growing, community-led resource that advances future marine science, with a particular emphasis on coral reef research. 
    more » « less
  3. ; ; ; ; ; ; ; ; ; ; et al (, Global Ecology and Biogeography)
    Abstract MotivationTraits are increasingly being used to quantify global biodiversity patterns, with trait databases growing in size and number, across diverse taxa. Despite growing interest in a trait‐based approach to the biodiversity of the deep sea, where the impacts of human activities (including seabed mining) accelerate, there is no single repository for species traits for deep‐sea chemosynthesis‐based ecosystems, including hydrothermal vents. Using an international, collaborative approach, we have compiled the first global‐scale trait database for deep‐sea hydrothermal‐vent fauna – sFDvent (sDiv‐funded trait database for theFunctionalDiversity ofvents). We formed a funded working group to select traits appropriate to: (a) capture the performance of vent species and their influence on ecosystem processes, and (b) compare trait‐based diversity in different ecosystems. Forty contributors, representing expertise across most known hydrothermal‐vent systems and taxa, scored species traits using online collaborative tools and shared workspaces. Here, we characterise the sFDvent database, describe our approach, and evaluate its scope. Finally, we compare the sFDvent database to similar databases from shallow‐marine and terrestrial ecosystems to highlight how the sFDvent database can inform cross‐ecosystem comparisons. We also make the sFDvent database publicly available online by assigning a persistent, unique DOI. Main types of variable containedSix hundred and forty‐six vent species names, associated location information (33 regions), and scores for 13 traits (in categories: community structure, generalist/specialist, geographic distribution, habitat use, life history, mobility, species associations, symbiont, and trophic structure). Contributor IDs, certainty scores, and references are also provided. Spatial location and grainGlobal coverage (grain size: ocean basin), spanning eight ocean basins, including vents on 12 mid‐ocean ridges and 6 back‐arc spreading centres. Time period and grainsFDvent includes information on deep‐sea vent species, and associated taxonomic updates, since they were first discovered in 1977. Time is not recorded. The database will be updated every 5 years. Major taxa and level of measurementDeep‐sea hydrothermal‐vent fauna with species‐level identification present or in progress. Software format.csv and MS Excel (.xlsx). 
    more » « less
  4. ; ; (, Ecography)
    Plant traits are important for understanding community assembly and ecosystem processes, yet our understanding of intraspecific trait variation (ITV) is limited. This gap in our knowledge is partially because collecting trait data across a species' entire range is impractical, let alone across the ranges of multiple species within a plant family. Using machine learning techniques to predict spatial ITV is an attractive and cost‐effective alternative to sampling across a species range, although this has not been applied beyond regional scales. We compiled a trait database of over 1000 grass species (family: Poaceae), encompassing six key functional traits: specific leaf area (SLA), leaf dry matter content (LDMC), plant height, leaf area, leaf nitrogen (Nmass) and leaf phosphorus content (Pmass). Using a random forest machine learning approach, we predicted local trait values within species' ranges considering climate, soil type, phylogeny, lifespan, and photosynthetic pathway as influential factors. An iterative random forest modeling technique incorporated correlations between traits, resulting in improved model performance (observed versus predicted R range of 0.72–0.91). Our models also highlight the importance of climate in predicting trait variation. For a subset of species (n = 860), we projected trait predictions across their known distribution, informed by expert maps from Royal Botanic Gardens, Kew, to create global maps of ITV for grasses. Such maps have the potential to inform conservation efforts and predictions of grazing and fire dynamics in grasslands worldwide. Overall, our research demonstrates the value and ecological applications of predicting plant traits. 
    more » « less
  5. ; ; (, Global Ecology and Biogeography)
    Abstract MotivationBiodiversity in many areas is rapidly declining because of global change. As such, there is an urgent need for new tools and strategies to help identify, monitor and conserve biodiversity hotspots. This is especially true for frugivores, species consuming fruit, because of their important role in seed dispersal and maintenance of forest structure and health. One way to identify these areas is by quantifying functional diversity, which measures the unique roles of species within a community and is valuable for conservation because of its relationship with ecosystem functioning. Unfortunately, the functional trait information required for these studies can be sparse for certain taxa and specific traits and difficult to harmonize across disparate data sources, especially in biodiversity hotspots. To help fill this need, we compiled Frugivoria, a trait database containing ecological, life‐history, morphological and geographical traits for mammals and birds exhibiting frugivory. Frugivoria encompasses species in contiguous moist montane forests and adjacent moist lowland forests of Central and South America—the latter specifically focusing on the Andean states. Compared with existing trait databases, Frugivoria harmonizes existing trait databases, adds new traits, extends traits originally only available for mammals to birds also and fills gaps in trait categories from other databases. Furthermore, we create a cross‐taxa subset of shared traits to aid in analysis of mammals and birds. In total, Frugivoria adds 8662 new trait values for mammals and 14,999 for birds and includes a total of 45,216 trait entries with only 11.37% being imputed. Frugivoria also contains an open workflow that harmonizes trait and taxonomic data from disparate sources and enables users to analyse traits in space. As such, this open‐access database, which aligns with FAIR data principles, fills a major knowledge gap, enabling more comprehensive trait‐based studies of species in this ecologically important region. Main Types of Variable ContainedEcological, life‐history, morphological and geographical traits. Spatial Location and GrainNeotropical countries (Mexico, Guatemala, Costa Rica, Panama, El Salvador, Belize, Nicaragua, Ecuador, Colombia, Peru, Bolivia, Argentina, Venezuela and Chile) with contiguous montane regions. Time Period and GrainIUCN spatial data: obtained February 2023, spanning range maps collated from 1998 to 2022. IUCN species data: obtained June 2019–September 2022. Newly included traits: span 1924 to 2023. Major Taxa and Level of MeasurementClasses Mammalia and Aves; 40,074 species‐level traits; 5142 imputed traits for 1733 species (mammals: 582; birds: 1147) and 16 sub‐species (mammals). Software Format.csv; R. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email