An experimental analysis of juvenile survival and dispersal in snowshoe hares
Bibliographic record
Abstract
If spacing behaviour of snowshoe hares limits juvenile survival and recruitment during summer, removal of this behaviour should produce an increase in these parameters. During the summers of 1980 and 1981 I removed all adults from an 8 ha trapping grid and all first litter juveniles from another. Experiments were conducted in the southwestern Yukon during a period when hare populations were at peak densities. The experimental removals did not increase survival, but recruitment relative to control areas was higher to the adult removal grid in 1980 and to both the adult removal and juvenile removal grids in 1981. To determine whether juveniles trapped for the first time were residents or immigrants, I implanted adult females with, calcium-45. This was passed to nursing young and could be detected by scintillation counting of a sample of bone tissue taken from new recruits. Any juvenile without radioactive calcium was classed as an immigrant. The increase in recruitment on the removal areas was due to increased immigration. The number of resident recruits was equal on all study areas. Results support the hypothesis that spacing behaviour limits juvenile immigration but not survival. However, immigration to control areas was also high with immigrants making up 70% of the total number of juveniles present on the areas in October. If food limits snowshoe hare numbers, addition of food should lead to increased numbers through higher survival and immigration. If food supply influences spacing behaviour of hares, home range size should decrease with food addition. I supplied peak (1980) and declining (1981) hare populations on 8 ha grids (one in 1980 and 2 in 1981) with laboratory rabbit chow for 1 - 4 months during March through June. Population size was determined by live-trapping and movements of animals were monitored by radio telemetry. Food addition decreased weight loss and improved survival of hares in both years. Onset of breeding was advanced in males but not females. In 1980, the number of males on the food addition area was 1.4 times higher than those on the control area while the number of females did not differ. In 1981, numbers of males and females were up to 3.6 and 3.2 times higher respectively on the food addition area as compared to those on ;the control area. The differences were due mainly to increased immigration. Residents responded to food addition by decreasing home range size in 1980 but not in 1981. Movement of immigrants, as monitored by telemetry, to the food addition area indicated that some established home ranges there while others returned to their old home ranges. Results support the hypothesis that hare densities are limited by winter food supply during the early decline phase of the cycle and possibly during the peak phase as well. A decrease in home range size was not necessary for immigration to occur. To examine the relationship of dispersal to changes in snowshoe hare numbers, I monitored dispersal of hares during a population increase, peak, and early decline (1978-1982). Two methods were used: 1) a conventional removal grid in which all animals caught each trapping session were removed and 2) telemetry monitoring of radio-collared individuals. The number of animals caught on the removal area was correlated with density on the control area but per capita dispersal rate was not. Both the number of dispersers and the per capita dispersal rate were highest during the period of peak densities on the control area. Dispersal, as measured by the removal grid, was not density dependent. Only 23 of 265 radio-collared animals dispersed during the study. Dispersal accounted for an average of 11% of the losses of radio-collared animals during the population decline. Results from both telemetry and the removal grid indicated that the decline in hare numbers was not due to dispersal. The amount of dispersal as determined by the removal grid was much higher than that determined by telemetry. The difference was more pronounced during the population peak and early decline. This was due to the removal grid over-estimating the average amount of dispersal that was occurring because it attracted animals to it. These results point to the need to be more critical of the underlying assumptions of the removal grid method as a way of monitoring dispersal.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".