Effects of White Spruce Mast Crop on Female Red Squirrel (Tamiasciurus hudsonicus) Nest-site Selection
Bibliographic record
Abstract
Nest site and habitat selection play an integral part in litter success within mammalian populations. These decisions are made due to a variety of factors, one of the largest being predation. In many mammalian populations, some non-parental males commit infanticide, the conspecific killing of young, which can pose a large threat to a females’ reproductive success. There are a few theories as to why individuals commit infanticide, but it is still greatly understudied. In some species, like Tamiasciurus hudsonicus (North American red squirrel), risks of infanticide increase during years of greater resource competition. The North American red squirrels’ main food source is the seeds of white spruce (Picea glauca), a species that undergoes an episodic masting of cones. During mast years, trees produce a surplus of cones. In this study, I aimed to understand how female red squirrel behavior changed during a mast year through nest site selection to reduce the risk of infanticide. I collected field data during 2024 and used previous data from a population of red squirrels in the Yukon Territory, Canada. As predicted, I found that the distance from territory to nest site increased significantly during one mast year compared to a corresponding non-mast year and that litter success during mast years was also significantly less than during non-mast years. I found that there was a slightly positive trend in distance traveled on survival during the mast year and a negative trend during the non-mast year, but these results were not statistically significant. I also found no differences between years for the nest-site habitat type and spruce density. Since my study found that although females do travel further distances to select nest sites, there was no correlation that this combats the risk of infanticide, it needs to be studied in more depth. Infanticide is a critically understudied phenomenon within evolutionary research and nest site selection may play a fundamental role within this.
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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 | 0.001 |
| 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) | 0.003 | 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".