Relationships between air pollution, population density, and lichen biodiversity in the Niagara Escarpment World Biosphere Reserve
Bibliographic record
Abstract
Abstract The fragmented ecosystems along the Niagara Escarpment World Biosphere Reserve provide important habitats for biota including lichens. Nonetheless, the Reserve is disturbed by dense human populations and associated air pollution. Here we investigated patterns of lichen diversity within urban and rural sites at three different locations (Niagara, Hamilton, and Owen Sound) along the Niagara Escarpment in Ontario, Canada. Our results indicate that both lichen species richness and community composition are negatively correlated with increasing human population density and air pollution. However, our quantitative analysis of community composition using canonical correspondence analysis (CCA) indicates that human population density and air pollution is more independent than might be assumed. The CCA analysis suggests that the strongest environmental gradient (CCA1) associated with lichen community composition includes regional pollution load and climatic variables; the second gradient (CCA2) is associated with local pollution load and human population density factors. These results increase the knowledge of lichen biodiversity for the Niagara Escarpment and urban and rural fragmented ecosystems as well as along gradients of human population density and air pollution; they suggest a differential influence of regional and local pollution loads and population density factors. This study provides baseline knowledge for further research and conservation initiatives along the Niagara Escarpment World Biosphere Reserve.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".