Species richness of both native and invasive aquatic plants influenced by environmental conditions and human activity
Bibliographic record
Abstract
Invasive plants alter community structure, threatening ecosystem function and biodiversity, but little information is available on whether invasive species richness responds to environmental conditions in the same way that richness of native plants does. We surveyed submerged and floating-leaved plants in 99 Connecticut (northeast USA) lakes and ponds, collecting quantitative data on abundance and frequency. We used multiple linear and logistic regression to determine which environmental conditions were correlated with species richness of invasive and native plants. Independent variables included lake area, maximum depth, pH, alkalinity, conductivity, phosphorus concentration, productivity, and dominance (the proportional abundance of the most abundant and frequently found species), plus two estimates of human activity. Species richness of both native and invasive richness was correlated with alkalinity and human activity. Native richness also increased with water clarity, lake area, and productivity; invasive species richness also rose with pH. We found no evidence that richness of one group affected richness of the other. We also investigated patterns of dominance and found that native plants were as likely to become dominant as invasive species. Dominance occurred overwhelmingly in shallow lakes with high productivity.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".