A multivariate analysis of fine‐scale species density in the plant communities of a saltwater lagoon – the importance of disturbance intensity
Bibliographic record
Abstract
Interactions between resources and abiotic conditions control local diversity and productivity, often in a complex fashion. In this study, I estimated the relative causal effects of several environmental variables known to influence diversity and productivity in plant communities. Two sites differing in disturbance intensity (i.e. wrack deposition) were studied along a saltwater lagoon, at Îles de la Madeleine, Québec, Canada. A larger proportion of the variance in species density (82%) and plant cover (81%) was explained by the environmental factors at the most disturbed site, while only 35% of the variance in species density and 47% of the variance in plant cover were explained at the other, less disturbed site. At the most disturbed site, environmental factors associated with distance from the shoreline (e.g. salinity, anoxia, granulometry) indirectly controlled species density through their effects on plant cover, while at the less disturbed site, environmental factors influenced both plant cover and species density. At low disturbance intensity, the species pool may be more significant than productivity per se in restricting local diversity; however, at higher intensity of disturbance, productivity (directly influenced by resources and abiotic conditions) may be more important in controlling diversity.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".