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Record W6929861399 · doi:10.5061/dryad.r5j0s3n

Data from: Applying modern coexistence theory to priority effects

2019· dataset· en· W6929861399 on OpenAlexaff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2019
Typedataset
Languageen
FieldSocial Sciences
TopicEvolutionary Game Theory and Cooperation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCoexistence theoryPairwise comparisonCompetition (biology)Competitive exclusionEmpirical researchPopulationAbiotic component

Abstract

fetched live from OpenAlex

Modern coexistence theory is increasingly used to explain how differences between competing species lead to coexistence versus competitive exclusion. Although research testing this theory has focused on deterministic cases of competitive exclusion, in which the same species always wins, mounting evidence suggests that competitive exclusion is often historically contingent, such that whichever species happens to arrive first excludes the other. Coexistence theory predicts that historically contingent exclusion, known as priority effects, will occur when large destabilizing differences (positive frequency-dependent growth rates of competitors), combined with small fitness differences (differences in competitors’ intrinsic growth rates and sensitivity to competition), create conditions under which neither species can invade an established population of its competitor. Here we extend the empirical application of modern coexistence theory to determine the conditions that promote priority effects. We conducted pairwise invasion tests with four strains of nectar-colonizing yeasts to determine how the destabilizing and fitness differences that drive priority effects are altered by two abiotic factors characterizing the nectar environment: sugar concentration and pH. We found that higher sugar concentrations increased the likelihood of priority effects by reducing fitness differences between competing species. In contrast, higher pH did not change the likelihood of priority effects, but instead made competition more neutral by bringing both fitness differences and destabilizing differences closer to zero. This study demonstrates how the empirical partitioning of priority effects into fitness and destabilizing components can elucidate the pathways through which environmental conditions shape competitive interactions.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0060.005
Research integrity0.0000.001
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 head0.047
GPT teacher head0.329
Teacher spread0.282 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations1
Published2019
Admission routes1
Has abstractyes

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