Urban wildlife and arborists: environmental governance and the protection of wildlife during tree care operations
Bibliographic record
Abstract
Abstract When working with urban trees, arborists can negatively impact urban wildlife. There have been recent efforts to strengthen wildlife protection and conservation during arboricultural practices, both legislatively and voluntarily through arboriculture organizations. To examine arborists’ perceptions of these environmental policies and understand their experiences with urban wildlife, we conducted an international online survey of 805 arborists. Many respondents (n = 481, 59.8%) reported being involved in tree work that resulted in wildlife injury or death, despite most respondents reportedly modifying work plans or objectives after encountering wildlife (n = 598, 74.3%). Decisions to modify or cease work were most heavily influenced by the legal protection of species, wildlife having young, and the overall management objectives. Support for new wildlife best management practices (BMPs) was high (n = 718, 90.3%), as was awareness of wildlife and arboriculture-related legislation (n = 611, 77.2%). The findings demonstrate support amongst arborists for the implementation of wildlife policies to protect wildlife in urban forestry; however, implementation of such policies would require a non-prescriptive approach that is relevant to a diversity of wildlife concerns globally, causing concern amongst arborists about the applicability of such a document. Concerns also included the economic impacts of voluntary wildlife protection policies in arboriculture, where competitors may not adhere to industry standards or best practices. Given the support of arborists for increased wildlife protection policies, we recommend the development of international wildlife-focused BMPs for arboriculture, especially as an intermediary until legislation can be implemented or more rigorously enforced.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".