DOES DENSITY REFLECT HABITAT QUALITY FOR NORTH AMERICAN RED SQUIRRELS DURING A SPRUCE-CONE FAILURE?
Bibliographic record
Abstract
Predicting animal populations over time often is done using models of density–habitat relationships that assume animal density is a reflection of habitat quality. We explored whether this assumption was true for the North American red squirrel (Tamiasciurus hudsonicus), a species present at different densities in 3 conifer habitats: white spruce (Picea glauca; high resource quality, unstable availability), mixed conifer, and lodgepole pine (Pinus contorta; lower resource quality, stable availability). We documented density, body condition, survival, reproduction, and turnover in these 3 habitats during a failure of spruce-cone crop, when the potential for the relative importance of habitats to change was greatest. During this time squirrel densities in white spruce decreased by 66% to match with those found in pine and mixed-conifer forests. Red squirrels in spruce forests experienced lower survival and fewer females successfully weaned young, and juvenile production was lower. Adult and juvenile immigration was more important than local juvenile production in replacing squirrel mortality in spruce and mixed-conifer forests. Our results indicate that density does not always reflect habitat quality for red squirrels, and we question the historical high-quality rating of white spruce habitat for this species. Further, our findings suggest that movement of ostensibly highly territorial adults in late winter and early spring is an important mechanism in determining annual squirrel densities regardless of habitat type.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".