ECOLOGY OF NORTH AMERICAN RED SQUIRRELS ACROSS CONTRASTING HABITATS: RELATING NATAL DISPERSAL TO HABITAT
Bibliographic record
Abstract
Because natal dispersal affects both individual fitness and population persistence, it is important to understand how dispersers are affected by habitat heterogeneity. To explore the effect of habitat on dispersal, we compared the ecology and natal dispersal of red squirrels (Tamiasciurus hudsonicus) originating from mature forest and adjacent commercially thinned forest. Because individuals living along the edge between the 2 forest types were more likely to have experience in both habitats, we classified squirrels according to habitat type (mature or thinned) and position (edge or deep within forest). Using livetrapping and radiotelemetry, we compared 4 habitats in terms of juvenile settlement patterns, surrogate measures of fitness, and population demography. Mature forest appeared to represent the highest quality habitat: mean density, mean overwinter survival, probability of surviving the field season, and success at raising ≥1 juveniles to emergence were higher in mature forest. However, the majority of juveniles from all habitats settled close to their natal territory, and with the exception of juveniles living along the edge of mature forest, juveniles settled within their habitat of origin. Juveniles living along mature edge biased their settlement for deep within mature forest. It appears that dispersal outcomes were affected by a combination of experience and opportunity. There are few, if any, other studies that have simultaneously compared demography, dispersal movements, and settlement patterns across contrasting habitats. While rare, studies such as this that link individual behavior and population theory are vital to effective population and landscape management.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".