Arborists and Urban Foresters Support for Urban Wildlife and Habitat Sustainability: Results of an Urban Ecology-Focused Survey of Arborists
Bibliographic record
Abstract
Urbanization is causing fragmentation of natural areas and impacting urban wildlife populations. Sustainability of wildlife and their habitat in arboriculture has focused on three key areas: retaining wildlife snags and beneficial-tree features (e.g., hollows/cavities), education of arborists and the public, and the adoption of systems-level thinking into arboriculture (i.e., the consideration of wildlife in risk matrices and pruning objectives). We surveyed 805 arborists using an international online survey to examine how arborists perceive these key areas of wildlife conservation and sustainability in urban forest management. Systems-level thinking was the highest rated method for arborists to support urban wildlife, followed by the retaining of wildlife snags. Education and the involvement of conservation groups received lower ratings, and the retainment of branches with hollows or cavities received the lowest ratings. In selecting important factors for wildlife snag retainment, arborists were most concerned with tree risk and targets, followed by setting (urban versus rural) and use of the tree by wildlife. Other factors that are the concern of urban ecologists were less important to respondents. Our findings support continued urban ecology education for arborists which focuses on whole/complex systems thinking to develop sustainable urban forest management practices which benefit urban wildlife.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".