Assessing the connectivity value of roadway structures for terrestrial mammals across the Northern Appalachian forest of Vermont
Bibliographic record
Abstract
Landscape connectivity is often negatively impacted by road networks that fragment habitat and result in genetic and demographic consequences for wildlife. Existing roadway structures like bridges, culverts, and underpasses can facilitate connectivity and reduce the barrier effect of roads by providing less risky areas for animals to cross. Estimating areas of high wildlife movement near roads is beneficial for prioritizing transportation investments for wildlife. We used an omnidirectional circuit theory approach to model the movements of eight terrestrial mammal species across the state of Vermont, a forested region central to the globally important Northern Appalachian ecoregion. We combined expert-derived landscape resistance surfaces with wildlife occurrence data to develop species-specific connectivity models at statewide (23,873 km2, 30 m resolution) and roadway structure (100 m radius around 5,912 structures, 0.5 m resolution) scales. The flow of animal movement across the landscape, depicted as electrical current density, was highest for forest-obligate species along the forested, mid-elevation foothills of the Green Mountains in central Vermont and lowest in the agricultural Champlain Valley; however, for more urban- and agriculture-adapted species, flow was highest in developed areas and lower elevation valleys. Average current density was highest for black bear (Ursus americanus), and lowest for striped skunk (Mephitis mephitis) at the statewide scale and highest for raccoon (Procyon lotor) and lowest for moose (Alces alces) at the finer structure scale. Results at both scales revealed different patterns of expected animal movement that reflect the relative extent of connectivity. We then scored connectivity for each structure across all species by combining both scales using four different methods to capture a range of management interests. Rankings varied greatly depending on the method used, highlighting the need to clearly articulate objectives when scoring structures or other features in a landscape. Resistance, occupancy, and current maps also indicated the broad importance of intact forest for connectivity and may be particularly important for identifying priority regions for protection under Vermont's Community Resilience and Biodiversity Protection Act that mandates protecting 50% of the state by 2050.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".