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Enregistrement W2057684442 · doi:10.1046/j.1365-2435.2001.00490-2.x

An adjustment of the extended contingency model of Farnsworth & Illius (1998)

2001· article· en· W2057684442 sur OpenAlexaffabout
Daniel Fortin

Notice bibliographique

RevueFunctional Ecology · 2001
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueWildlife Ecology and Conservation
Établissements canadiensUniversity of Guelph
Organismes subventionnairesnon disponible
Mots-clésForagingPredationBiologyFunctional responseEcologyOptimal foraging theoryMetabolic rateHerbivoreBehavioral ecologyPredator

Résumé

récupéré en direct d'OpenAlex

Farnsworth & Illius (1998) modified the classical contingency model (Stephens & Krebs 1986) so that it could take into account the overlap between searching and handling time observed in large herbivores (Spalinger & Hobbs 1992; Laca, Ungar & Demment 1994). Their model predicts an optimal diet based on estimates of prey encounter rate (λ, prey/min), digestible energy (e, in kJ/prey), handling time (h, in min/prey), and the proportion of h exclusive from searching activity (η). Given that 1 > η > 0, which will be assumed throughout this paper, animals can search for the next prey while chewing the last bite during the portion (1 − η)h of handling time, whereas they cannot search during the portion ηh of handling time devoted to cropping a food item. Farnsworth & Illius (1998) also distinguished between foraging that is limited by encounter rate and by handling time. Interestingly, they showed that if more than one prey is required to make a diet handling-limited, the last prey should often be consumed at a rate lower than the encounter rate. This contrasts with the 0–1 rule characteristic of the classical foraging models (Stephens & Krebs 1986). The model of Farnsworth & Illius (1998) illuminates our understanding of foraging decisions in large herbivores, but algebraic errors in some of its equations lead to inaccurate predictions during handling-limited foraging. My objective is to provide revised equations that indicate (1) when a diet is handling-limited, (2) the rate at which the last type of a multi-prey type diet should be accepted to make the diet just handling-limited, and (3) the energy intake rate provided by a handling-limited diet based on the partial consumption of a given prey type. Farnsworth & Illius (1998) stated on p. 76 that a prey type (i) is subject to handling-limited foraging when ‘hI ≥ 1/λi’, which could also be written as (1 − ηi)hi + ηihi ≥ 1/λi. Because herbivores can search for new bites while they are masticating a previous bite, the proper conflict is between expected time to the next bite to be encountered 1/λi and the expected time to chew the last bite (1 − ηi)hi. When (1 − ηi)hi ≥ 1/λi, the forager is limited by handling, because bite rate = 1/hi. In contrast, when (1 − ηi)hi < 1/λi, then the bite rate = λi/(1 + λiηihi). Extending this argument to multispecies foraging, and based on equation 9 of Farnsworth & Illius (1996) and equation 2 of Farnsworth & Illius (1998), it can be derived that handling-limited foraging should rather occur when: where m is the total number of prey types included in the diet. The optimal diet is obtained by ranking prey by increasing profitability (e/h), and then expanding the diet until foraging becomes handling-limited. If consumption of the most profitable prey type is limited by handling time, the animal should specialize on this prey type. Hence, the intake rate would simply correspond to profitability of the highest ranked prey (e1/h1). When more than one prey type are required to reach handling limitation, the last type should be partially accepted at a rate that relates to the encounter rate for the more highly ranked items already in the diet. The acceptance rate of prey type m can be expressed as: . By definition, partial acceptance of prey type m entails that these plants are accepted at a rate lower than their encounter rate, and thus that . Because nm represents the minimal amount of prey m that would make the diet handling-limited, the following equality is implied: With the rearrangement of equation 2 we can find nm from: which corresponds to the time that would be spent searching after chewing (i.e. during ‘pure’ search) if only m − 1 prey types were accepted in the diet divided by the time that can be spent searching while handling prey m. Because ηi > 0, equation 3 would provide higher nm than equation 17 of Farnsworth & Illius (1998). However, their equation reflected encounter-limited foraging rather than handling-limited foraging, as it should have. Given the acceptance of all the m − 1 prey encountered and the partial acceptance rate of prey m, the energy intake rate of the handling-limited diet becomes: The diet would be optimum only if the handling-limited foraging provides a greater intake rate than the rate for encounter-limited foraging without inclusion of the last prey type: If inequality (5) does not hold, only m − 1 prey types should be included in the diet. I believe that the study of Farnsworth & Illius (1998) constitutes an important contribution to foraging theory, and my revision of their equations now allows the prediction of optimal diet also during the handling-limited foraging of large herbivores. The funding for this study was provided by Parks Canada, University of Guelph, and scholarships from FCAR and OGS. I thank John Fryxell for his advice and constructive comments on this paper. I am grateful to Keith Farnsworth and Andrew Illius for encouraging me to pursue this work.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
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 enseignants0,037
Score d'incertitude au seuil0,998

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
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)0,0030,000

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 Opus0,022
Tête enseignante GPT0,225
Écart entre enseignants0,203 · 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 tête enseignante, pas un consensus.

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

Citations17
Publié2001
Routes d'admission2
Résumé présentoui

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