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Record W2095481932 · doi:10.1086/499382

The Prerequisites for and Likelihood of Generalist‐Specialist Coexistence

2006· article· en· W2095481932 on OpenAlexaff
Peter A. Abrams

Bibliographic record

VenueThe American Naturalist · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsToronto ZooUniversity of Toronto
Fundersnot available
KeywordsGeneralist and specialist speciesForagingHabitatResource (disambiguation)EcologyBiologyOptimal foraging theoryVariation (astronomy)Range (aeronautics)Environmental resource managementComputer scienceEconomics

Abstract

fetched live from OpenAlex

Mathematical models of three-consumer-two-resource systems are used to explore the possibility of coexistence when one consumer is a generalist utilizing both resources, and the other two are specialists utilizing only one. Such coexistence requires strongly saturating functional or numerical responses in at least one consumer and the presence of sustained asynchronous variation in resource abundances. Given these conditions, the effects of three dichotomous factors on the range of parameters allowing coexistence are examined: flexible versus inflexible resource choice by the generalist, endogenous or exogenous cause of resource cycles, and location of the two resources in a single habitat versus two habitats. Coexistence of all three species is found to be possible for all combinations of these factors except for inflexible choice in a two-habitat environment. Generalists experience frequency-dependent fitness because, when they are abundant, they synchronize resource cycles and/or reduce their amplitude. When the generalist can adaptively adjust its relative foraging on the two resources, coexistence conditions are broadened considerably, and coexistence commonly occurs readily with exogenous variation in resource growth and with resources located in distinct habitats. Adaptive behavior increases the generalist's ability to both synchronize and dampen resource cycles.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.668
Threshold uncertainty score0.409

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
GPT teacher head0.250
Teacher spread0.245 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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".

Quick stats

Citations117
Published2006
Admission routes1
Has abstractyes

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