Use of trait concepts and terminology in freshwater ecology: Historic, current, and future perspectives
Bibliographic record
Abstract
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".