Seasonal trait variation and functional niche overlap of macrophyte growth forms
Bibliographic record
Abstract
Macrophytes provide many ecosystem processes and functions which support freshwater ecosystem services, and the ecological role of a macrophyte is related to its growth form (emergent, free-floating, floating rooted, submerged). Differences between growth forms and the relationships between ecosystem functioning and environmental conditions can be described by functional traits. Seasonal variation in functional trait expression can lead to alterations in ecosystem functioning. As such, when inferring trait-environment relationships, a species’ functional niche should capture this temporal variation. However, it is unknown how functional traits in macrophytes vary seasonally, and the importance of between-growth form variation. Using hypervolume analysis and linear mixed effect modelling, we demonstrate that seasonal trait variation within-growth forms is stronger than between-growth form variation over time. We found that emergent macrophytes have significantly (p < 0.01) higher specific leaf area in June compared to September. Whereas leaf nutrients (total nitrogen and phosphorus), are significantly higher early in the growing season for emergent, floating rooted, and submerged plants (p < 0.05). We show that the large functional niche of submerged macrophytes is shaped by seasonal variation, and that the functional niches of all macrophyte growth forms overlap, suggesting redundancies in the maintenance of ecosystem functions. Together, this study demonstrates the influence of seasonal variation on macrophyte functional traits. Thus, seasonality is relevant to our understanding of aquatic ecosystem functioning and must be considered when determining the ecological role of macrophytes across a season. This study provides rationale for further examinations of between-growth form redundancies in the ecological role of macrophytes. • Seasonal trait variation within-growth forms exceeds between-growth form variation. • Specific leaf area and leaf nutrients are highest early in the growing season. • The functional niche of macrophytes varies by growth form. • Submerged plants have the largest functional niche, driven by seasonal variation. • Variation in leaf functional traits between growth forms is limited.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".