Aquatic macrophytes and periphyton communities as bioindicators of lake trophic status in Riding Mountain National Park, Manitoba
Bibliographic record
Abstract
Aquatic conservation practitioners at Riding Mountain National Park of Canada (RMNP) are concerned with maintaining and restoring the ecological integrity of lakes within the park; thus, there is a need to identify lakes potentially at risk of eutrophication. The ability to identify at-risk lakes would allow lake managers to alleviate stressors, such as excess nutrients, before irreversible changes occur in the ecosystems. Aquatic macrophytes and periphyton have potential as bioindicators of lake trophic status. Both have a widespread distribution, their growth is tightly coupled with water clarity and they obtain their nutrients directly from the water column or lake bed, making them sensitive to changes in water quality. Previous studies have identified species diversity, and macrophyte and periphyton species as reliable predictors of lake ecological status. Aquatic macrophyte and periphyton surveys were conducted in forty-five and thirty lakes, respectively, in and immediately surrounding Riding Mountain National Park, Manitoba. Non-metric multidimensional scaling, redundancy analysis and generalized linear models identified submerged aquatic macrophyte species diversity, Ruppia maritima, Potamogeton pectinatus and Chara spp. as having potential use as bioindicators of ecological change in Riding Mountain National Park lakes. Periphyton taxon diversity and individual taxa were not recommended for inclusion in future monitoring in Riding Mountain National Park without further study. It is my recommendation to incorporate aquatic macrophyte species and diversity monitoring along with established aquatic monitoring programs in Riding Mountain National Park.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".