A stressor-independent test for biodiversity – ecosystem function relationships during a 23-year whole-lake experimentThis paper is part of the series “Forty Years of Aquatic Research at the Experimental Lakes Area”.
Bibliographic record
Abstract
Anthropogenic stressors are the current drivers of loss of global biodiversity and deterioration of ecosystem function (e.g., primary production). However, it is debatable whether human stressors or associated changes in biodiversity better predict the impairment of ecosystem function. Variation in plankton communities during a whole-lake experiment (Lake 302S, Experimental Lakes Area, Canada) was examined to test whether the stressor treatment effect or subsequent stressor-independent variation in species richness best explained interannual variation in aggregate functional properties, such as productivity or net total biomass. Although significant “biodiversity – ecosystem function” relationships were detected, these correlations were confounded by the negative effect of experimental acidification on species richness. The stressor effect was removed by plotting functional properties against the residuals from the species richness – pH regressions, which generated either negative or nonsignificant relationships. The lack of significant stressor-independent positive relationships between functional properties and species richness highlights the potential greater importance of other mediating factors, such as interactions among multiple stressors, species identity, and altered trophic interactions, at the whole-ecosystem scale.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".