An experimental analysis of juvenile survival and dispersal in snowshoe hares
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».