A framework to identify priority wetland habitats and movement corridors for urban amphibian conservation
Bibliographic record
Abstract
Abstract Cities worldwide are expanding in area and human population, posing multiple challenges to amphibian populations, including habitat loss from removal of wetlands and terrestrial upland habitat, habitat fragmentation due to roads and the built environment, and habitat degradation from pollutants, extensive human use and introduced species. We developed an eight‐step urban amphibian conservation framework based on established monitoring, analytical methods and community engagement to enable amphibian conservation in a large urban centre. The framework outlines a process used to conserve biodiversity in a complex landuse and decision‐making environment supported by a series of successive complementary modelling techniques to measure amphibian presence, priority habitat and functional connectivity. We applied the framework in Calgary, Alberta, Canada to illustrate its potential. Here, urbanization has reduced wetlands by 90% and ecological knowledge on amphibians was poor. We improved knowledge on amphibian diversity and distribution, identified core wetlands and movement pathways for amphibian species and identified barriers in the wetland network where construction or restoration measures could re‐establish amphibians or increase their densities. This knowledge was shared with ecologists and city planners for implementation through appropriate policies and plans. Our framework provides a series of stepwise products to improve an urban municipality's ability to restore or conserve priority habitat and movement pathways necessary for amphibian survival under pressure from multiple land uses. The framework provides a platform to identify city plans, policy and or programmes and to derive necessary information to support amphibian conservation.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".