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Record W4391751659 · doi:10.1111/fwb.14230

Use of trait concepts and terminology in freshwater ecology: Historic, current, and future perspectives

2024· article· en· W4391751659 on OpenAlexfundno aff
Cayetano Gutiérrez‐Cánovas, Rachel Stubbington, Daniel von Schiller, Rossano Bolpagni, Miriam Colls, Thibault Datry, Rafael Marcé, Daniel Bruno

Bibliographic record

VenueFreshwater Biology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
FundersH2020 EnvironmentHorizon 2020 Framework ProgrammeGlobal Lake Ecological Observatory NetworkEusko JaurlaritzaConsejo Superior de Investigaciones CientíficasMinisterio de Economía y CompetitividadGeneralitat de CatalunyaEuropean Cooperation in Science and Technology“la Caixa” Foundation
KeywordsTraitEcologyBiodiversityTerminologyFreshwater ecosystemBiologyEcosystemEnvironmental resource managementEnvironmental scienceComputer science

Abstract

fetched live from OpenAlex

Abstract Trait‐based approaches have received increasing interest among freshwater scientists given their capacity to predict community structure and biodiversity effects on ecosystem functioning. However, the inconsistent development and use of trait concepts and terms across freshwater scientific disciplines may have limited realisation of the potential of traits. Here, we reviewed trait definitions and terms use to provide recommendations for their consistent application in freshwater science. To do so, we first reviewed literature to identify established trait definitions, historical and current use of trait terms and challenges restricting the application of trait‐based approaches in freshwater science. Next, we surveyed 414 freshwater researchers from 54 countries to assess variability in the current use of trait terminology in relation to respondent characteristics (i.e., professional experience, geographical region, research discipline, and focal freshwater ecosystem, biotic group, and ecosystem function). Our literature review identified two well‐established trait definitions, which emphasise individual phenotypic characteristics that influence either eco‐evolutionary aspects (i.e., organism performance and fitness) or ecosystem dynamics and processes (i.e., responses to the environment and/or effects on ecosystem functioning). Publications used a range of trait‐related terms and their frequency of use varied among scientific fields. The term functional trait dominated fields such as biodiversity conservation, environmental sciences and ecology, plant sciences and microbiology. In contrast, the terms biological trait , functional trait , and species trait were used with similar frequencies in fields such as entomology, fisheries, marine and freshwater biology, and zoology. We also found that well‐established trait definitions are difficult to apply to freshwater unicellular organisms, colonial multicellular organisms, genomic information, and cultural traits. Our survey revealed highly inconsistent use of trait terms among freshwater researchers. Terms including biological trait , functional trait , structural measure , and ecosystem function were commonly used to describe the same traits or functions. Variability in the use of terms was generally explained by research discipline, geographical region, and focal biotic group and ecosystem functions. We propose making the trait concept flexible enough to be applicable to all freshwater biota and their characteristics, while keeping and integrating links to eco‐evolutionary and ecosystem aspects. Specifically, our new definition expands the established functional trait definition by considering also supra‐individual scales of trait measurement (colonial‐ or community‐mean traits), genotypic traits (e.g., functional gene markers of enzymes) and cultural traits (e.g., feeding behaviours, communication skills). To reduce terminological ambiguity, we also recommend that researchers define trait terms, prioritising the use of functional trait as an overarching term over alternative terms (e.g., biological trait ), and restricting specific terms (e.g., morphological trait ) to situations in which such precision is desirable. The findings of our integrative study could help to improve terminological consistency across freshwater disciplines and to better recognise the potential of traits to elucidate the mechanisms behind ecological patterns.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.776
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0160.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.033
GPT teacher head0.283
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2024
Admission routes1
Has abstractyes

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