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

2001· article· en· W2057684442 on OpenAlexaffabout
Daniel Fortin

Bibliographic record

VenueFunctional Ecology · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsForagingPredationBiologyFunctional responseEcologyOptimal foraging theoryMetabolic rateHerbivoreBehavioral ecologyPredator

Abstract

fetched live from 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.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.022
GPT teacher head0.225
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations17
Published2001
Admission routes2
Has abstractyes

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