FUN Azores: a FUNctional trait database for the meio-, macro-, and megafauna from the Azores Marine Park (Mid-Atlantic Ridge)

نویسندگان

چکیده

Trait-based approaches that complement taxonomy-based studies have increased in popularity among the scientific community over last decades. The collection of biological and ecological characteristics species (i.e., traits) provides insight into ecosystem vulnerability to environmental anthropogenic changes, as well functioning. Here, we present FUN Azores trait database, describe our approach, evaluate its scope, compare it other marine databases, explore spatial distribution traits with “functional maps.” While most available databases date contain essential information understand functional diversity a taxonomic or group, ecosystem-based approach comprehensive assessment diverse fauna meio-, macro-, megafauna) from benthic pelagic environments Marine Park; including ridges, seamounts, hydrothermal vents, overlying water column. We used collaborative involving 30 researchers different expertise develop which contains compiled data on 14 representing morphological, behavioral, life history for 1,210 across 10 phyla. maps” show distinct two common size classes, suggesting communities functionalities. following had best scoring coverage >95% scored): maximum body size, form, skeleton material, feeding structure, motility, position, substratum affinity, distribution, depth range; while related behavior (e.g., sociability aggregation tendencies) developmental mechanism) lower coverage, highlighting need further research fill these knowledge gaps. found larger number compared environment differing composition between areas within Park resulting varying biodiversity, types, sampling effort, methodologies used. database will foster facilitate trait-based area, framework expansion cross-ecosystem cross-taxa elsewhere, improve understanding conservation requirements.

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ژورنال

عنوان ژورنال: Frontiers in Ecology and Evolution

سال: 2023

ISSN: ['2296-701X']

DOI: https://doi.org/10.3389/fevo.2023.1050268