Use of trait concepts and terminology in freshwater ecology: Historic, current, and future perspectives
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».