The effects of bark quality on corticolous lichen community composition in urban parks of southern Ontario
Bibliographic record
Abstract
Tree bark characteristics influence lichen colonization. To better understand how urban parks can be managed to maximize lichen biodiversity, we examined trees in seven parks throughout the City of Guelph in southern Ontario. We measured bark characteristics and lichen communities on four common tree species that have a wide range of pH: Acer platanoides L., Acer × freemanii E. Murray, Pinus resinosa Aiton, and Pinus strobes L. We recorded the lichen species on 99 trees, calculated the pH and fissuring of the bark, and determined the diameter at breast height (DBH) as a proxy for age. Gamma diversity on all trees included 18 lichen taxa. We used graphite bark rubbings analyzed in ImageJ 1.47v to calculate the degree of bark fissuring. We collected bark samples from each tree trunk and determined the acidity with a pH meter. Using multivariate analyses we show that lichen community composition is positively correlated with DBH and tree species, but the degree of fissuring did not have a significant effect. We could not statistically analyze pH independent of tree species, but our results suggest pH is not a significant variable. We show lichen biodiversity in urban parks can be increased by planting a variety of tree species at different ages.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".