Re‐envisioning urban landscapes: lichens, liverworts, and mosses coexist spontaneously with us
Bibliographic record
Abstract
Current conceptions of “urban biodiversity” address only particular taxa, ignoring the full richness of species within cities. Despite their exclusion from these conceptions, tree‐dwelling lichens, mosses, and liverworts (collectively, “epiphytes”) are recognized as bioindicators of urbanization, but their inherent contributions to biodiversity are largely unrecognized. Here, we report on a survey of epiphytes in the city of Vancouver, Canada. Using Bayesian multilevel models, we asked the following questions: how diverse are epiphytes in this large temperate city, and what urban and host‐tree factors determine their distribution? We found 39 macrolichen, 32 moss, and seven liverwort species on Vancouver street trees, establishing them as rich microenvironments influenced by a network of interacting factors previously unaccounted for. Our results challenge the idea that pollution and urban heat islands primarily regulate urban epiphyte diversity; instead, we identify host‐tree genus as having strong effects on all epiphytes. Expanding urban biodiversity to include epiphyte diversity recharacterizes urban landscapes as rewilded spaces of interdependent coexistence.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".