Permeability of a heterogeneous urban landscape to the movements of forest songbirds
Bibliographic record
Abstract
1. Urbanization represents a major threat to biodiversity world-wide because it causes permanent degradation and fragmentation of biologically rich natural communities. This is particularly acute in coastal plains and river valleys, where cities are typically located. Although movement is essential to the persistence of populations in fragmented landscapes, little is known about how development and transportation corridors affect the movements of wildlife in the urban context. 2. We conducted a series of translocation experiments within the urban landscape of Calgary, Alberta, Canada, to assess the permeability of selected landscape elements for two species of forest songbirds with contrasting adaptabilities to urban development and migratory behaviours: the black-capped chickadee Poecile atricapillus, an urban-adaptable year-round resident, and the yellow warbler Dendroica petechia, an urban-sensitive Neotropical migrant. 3. Birds were caught in riparian habitats and translocated either within the riparian corridor of origin or across the urban matrix. Riparian treatments included continuous forest, one or several transportation bridges and a major river. In the urban matrix, birds were translocated across a single major road, well- or poorly-treed developed areas, or multiple gaps. 4. Using Cox regression we found that the presence of gaps in forest cover explained more variation in return time than the amount of forest cover for both species. Multiple gaps, in particular, resulted in significantly longer return times compared with continuous forest. Chickadees exhibited longer return times when translocated across linear gaps associated with bridges or roads. In contrast, yellow warbler movements appeared to be more constrained by urban development. 5. Synthesis and applications. Our results suggest that improving the permeability of urban landscapes for songbirds can be achieved by preserving connectivity along riparian corridors and other major swaths of natural vegetation while minimizing gaps in vegetation throughout the urban matrix. Our study also demonstrated a cumulative effect of multiple barriers, species-specific response thresholds to canopy cover and gap width, and an important effect of distance-to-territory on movement behaviour. Finally, we demonstrated the utility of ‘most forested route’ as a new, animal-based approach for quantifying the permeability of heterogeneous landscapes.
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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) | 0.002 | 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 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".