Natural variation of profundal macroinvertebrate communities in boreal lakes is related to lake morphometry: implications for bioassessment
Bibliographic record
Abstract
Modern biological assessment of aquatic systems is often based on the reference condition approach, which requires characterization of biota in undisturbed conditions. For this approach, it is essential to recognize the origins and degree of natural variation in communities. We used data from 55 minimally disturbed Finnish lake basins to investigate the natural variation of profundal macroinvertebrate community composition in relation to environmental factors. Partial canonical correspondence analysis showed that most (68% combined, 39% uniquely) of the total explained compositional variation (29%) was correlated with environmental variables insensitive to human activities, especially lake morphometry. In contrast, the unique contributions of geographical location and variables influenced by human activities (dissolved oxygen and total phosphorus) were substantially smaller (5.5% and 22%, respectively). All of the explained variation (38%–64%) of three widely used community metrics was also correlated with human-insensitive environmental factors. The results suggest that variation in undisturbed profundal invertebrate communities of reference sites is strongly dependent on variables insensitive to human activities and especially on those related to lake morphometry, suggesting that reference communities for bioassessment of impacted lakes should be rather predictable from these variables.
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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 | 0.000 | 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) | 0.000 | 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".