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Record W2135174480

Declining interspecific competition during character displacement: Summoning the ghost of competition past

2001· article· en· W2135174480 on OpenAlexaff
John Pritchard, Dolph Schluter

Bibliographic record

VenueEvolutionary ecology research · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCharacter displacementInterspecific competitionSympatric speciationBiologySticklebackCompetition (biology)GasterosteusEcologyNicheSympatryEvolutionary biologyFisheryFish <Actinopterygii>
DOInot available

Abstract

fetched live from OpenAlex

Prevailing theories of biotic diversification incorporate resource competition as a leading cause of divergence between new species. In support of this, many cases of divergent character displacement between close relatives (congeners) are known. Yet, experimental tests of underlying mechanisms are uncommon. In a pond experiment with threespine sticklebacks (Gasterosteus spp.), we tested the prediction that competition between species should decline as character divergence proceeds, yielding descendants whose present-day interaction is a ‘ghost’ of its former strength. Competition’s impact on the marine threespine stickleback (G. aculeatus) was contrasted between two treatments simulating early and late stages of a hypothesized character displacement series that began at the end of the last ice age when marine sticklebacks colonized lakes containing an earlier descendant. Growth rate and niche specialization of marine sticklebacks were higher in the ‘post-displacement’ treatment than in the ‘pre-displacement’ treatment, suggesting a decline in competition strength through time. The result supports the idea that interspecific competition favoured divergence between sympatric sticklebacks, with reduced competition the outcome. The influence of other interactions on divergence between sympatric species may be tested with analogous experimental designs.

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)0.001
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 score0.014
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.032
GPT teacher head0.315
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 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".

Quick stats

Citations85
Published2001
Admission routes1
Has abstractyes

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