MétaCan
Menu
Retour à la cohorte
Enregistrement W2172952647 · doi:10.5287/ora-z65vy8q41

Environmental effects on great tit life-histories

2006· dissertation· en· W2172952647 sur OpenAlexfundno aff
Teddy A. Wilkin

Notice bibliographique

RevueOxford University Research Archive (ORA) (University of Oxford) · 2006
Typedissertation
Langueen
DomaineEnvironmental Science
ThématiqueAvian ecology and behavior
Établissements canadiensnon disponible
Organismes subventionnairesNatural Sciences and Engineering Research Council of CanadaBiotechnology and Biological Sciences Research CouncilDirectorate for Biological Sciences
Mots-clésWoodlandEcologyPopulation densityHabitatGeographyBiologyPopulationLife history theoryLife historyDemography

Résumé

récupéré en direct d'OpenAlex

Explaining variation between individuals is a central concept in ecology. Phenotypic variation is the product of genes, environments and their interactions. In contrast to genotypes which are fixed within individuals, environments vary considerably in time and space and have measurable effects on phenotypic quality between and within individuals. The aim of the current work was to identify environmental sources of life-history variation in a wild population of the great tit. The size of Thiessen polygons formed around c. 8000 nestboxes occupied over a 41 year period was used to estimate breeding density at the level of the individual. Linear mixed modelling showed that birds breeding in large territories laid more eggs and produced heavier fledglings that were more likely to survive to breed, than those in smaller territories. Systematic capping of territory sizes revealed that birds breeding in territories more than 2ha in size were unconstrained by density. This method of measuring individual density identified important relationships between density and life-histories and allowed for the accurate separation of other environmental effects usually confounded by density. For example, the life-histories and breeding density of woodland passerines often both vary with distance from the woodland edge. Using the Thiessen polygons to control for density we were able to independently examine edge effects on life-histories. Results confirmed higher density at edges and independently showed that birds near the woodland edge tended to lay smaller clutches of larger eggs later in the season, than birds away from the edge, probably due differences in habitat quality. A further use of Thiessen polygons was to determine the scale at which to measure oak availability in the vicinity of each occupied nestbox. Birds breeding in oak rich polygons laid larger clutches, earlier in the season and had heavier nestlings than birds in oak poor polygons, independently of density and edge effects. What's more, including oaks in life-history models, reduced or eliminated the effect of the Thiessen polygons, suggesting that density dependent life-histories are to some extent explained by reduced oak availability at high density. Clutch size, fledgling mass and recruitment were also found to correlate with local soil calcium. Analyses performed at several spatial scales found the greatest effect of calcium at scales of c.500m. This figure may indicate the average distance females were travelling to obtain calcium rich food during periods of high demands. That breeding environments strongly affect life-histories has been demonstrated by the above work. However, no correlations were found between natal environment and the subsequent life-histories of recruited individuals, probably due to high mortality in great tits, which favours current condition over any character that conveys benefits later in life. This result shows that long-term effects of rearing environments cannot be assumed as it depends on the life-history conditions under which they are found. The results of this study suggest a pervasive role of fine-scale environment variation in determining the life-histories of individual great tits. Moreover, the study demonstrates the efficacy of GIS to model such variation and applying it to explaining life-history variation in long-term databases.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,006

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,012
Tête enseignante GPT0,228
Écart entre enseignants0,216 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2006
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueOxford University Research Archive (ORA) (University of Oxford)Même sujetAvian ecology and behaviorTravaux en français237 207