Harnessing aquatic plant growth forms to apply European nutrient‐enrichment bioindicators to Canadian waters
Bibliographic record
Abstract
Premise: Aquatic macrophyte species abundance and nutrient affinity are used in metrics to assess the trophic condition of lakes and rivers. The development of these indices is often regional, with inter-regional comparisons being complicated by the lack of taxonomic overlap. Here, we use a traits-based approach to expand the geographic scope of existing metrics. Methods: We generalized European trophic affinity values using the response of plant growth form to the light-nutrient gradient, then applied these values to sites in Canada. We evaluated the method's performance against the measured total phosphorus concentration (TP). Results: Free-floating and emergent growth forms were associated with enriched waters (>0.2 mg/L TP), whereas rosette forms were associated with oligotrophic conditions (<0.05 mg/L TP). The responses were longitudinally consistent, and the site scores among indices were highly collinear. Growth form-based scores were more strongly correlated with TP than were species-based scores (0.42-0.56 versus 0.008-0.25). Discussion: We leveraged the ecological relationship between increased surface water nutrient enrichment and the dominance of particular aquatic plant growth forms to generalize aquatic plant trophic indices. We demonstrated an approach for adapting species-based indices to plant traits to facilitate a broader geographic application and simpler data collection, which could be used to develop an easily applied trait-based method of assessing water nutrient status.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".