Seasonality in functional connectivity: A case study with the American marten in Forillon National Park
Bibliographic record
Abstract
Abstract Protected areas are essential tools for reducing loss of global biodiversity. To fulfill their ecological functions, protected areas must be connected to their surroundings, a requirement that is difficult to meet in landscapes intensively disturbed by anthropogenic activities. Therefore, protecting movement corridors at the edges of protected areas is crucial, especially for species with broad habitat needs, such as the American marten (Martes americana). However, habitat selection and space use patterns are dynamic processes, so we could expect that functional connectivity would vary temporally in response to changing environmental conditions and levels of human activities. In this study, we aimed at predicting the location of movement corridors for the American marten in Forillon National Park and its periphery during two contrasted periods (snow‐free: May–November; snow‐covered: December–April). We used “seasonal” resource selection functions to identify core areas (interpreted as circuit “nodes”) and CircuitScape to delineate movement corridors between them based on the electrical circuit theory. Habitat selection patterns differed between periods, with martens avoiding open areas, high elevations and road proximity during the snow‐free period, while avoiding areas closer to secondary roads but selecting areas closer to primary roads and housing structures during the snow‐covered period. Consequently, the location of movement corridors differed partially between periods. Functional connectivity was favored by the presence of forest patches for both periods, while being constrained by open environments, especially during the snow‐free period. Our study highlights the importance of modeling functional connectivity at fine temporal scales in order to provide movement corridors that fulfill the requirements of a species at each period of its annual cycle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".