On the role of natural water level fluctuation in structuring littoral benthic macroinvertebrate community composition in lakes
Bibliographic record
Abstract
We used traditional hydrologic endpoints and extracted novel water‐level fluctuation (WLF) characteristics through principal components analysis (PCA), to determine the relationship between WLF and rocky littoral benthic macroinvertebrate (BMI) richness in 16 boreal lakes. Yearly WLF amplitude (maximum minus minimum water levels) ranged from 35.9 cm to 157.5 cm. Using PCA we derived a new variable D80‐D210 (31 March minus 01 August) as a surrogate for change in mean water level and potential habitat squeeze (loss). Analyses of BMI richness with several physicochemical variables, including water quality (8), habitat variability (4), lake and basin morphology (6), land classification (28), water temperature (8), hydrology (9), and PCA axes (10) resulted in only three significant relationships. We found a classic species‐area relationship, as BMI richness increases with increasing lake area (r2 = 0.38linear, r2 = 0.69unimodal). Similarly, as littoral slope increases macroinvertebrate richness decreases (r2 = 0.32linear). Most importantly, lower water levels, quantified using D80‐D210, have higher macroinvertebrate richness (r2 = 0.38linear). Together these results suggest that a habitat squeeze in littoral areas is the direct result of lower mean water levels and that relatively small changes in natural WLF can be associated with changes in BMI communities.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".