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Record W6929911028 · doi:10.5061/dryad.41752p5

Data from: The ‘filtering’ metaphor revisited: competition and environment jointly structure invasibility and coexistence

2018· dataset· en· W6929911028 on OpenAlexaff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2018
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsCompetition (biology)Competitor analysisCompetitive exclusionPersistence (discontinuity)Coexistence theoryStorage effect

Abstract

fetched live from OpenAlex

‘Filtering’, or the reduction in species diversity that occurs because not all species can persist in all locations, is thought to unfold hierarchically, controlled by the environment at large scales and competition at small scales. However, the ecological effects of competition and the environment are not independent, and observational approaches preclude investigation into their interplay. We use a demographic approach with 30 plant species to experimentally test (i) the effect of competition on species persistence in two soil moisture environments, and (ii) the effect of environmental conditions on mechanisms underlying competitive coexistence. We find that competitors cause differential species persistence across environments even when effects are lacking in the absence of competition, and that the traits that determine persistence depend on the competitive environment. If our study had been observational and trait-based, we would have erroneously concluded that the environment filters species with low biomass, shallow roots, and small seeds. Changing environmental conditions generated idiosyncratic effects on coexistence outcomes, increasing competitive exclusion of some species while promoting coexistence of others. Our results highlight the importance of considering environmental filtering in light of, rather than in isolation from, competition, and challenge community assembly models and approaches to projecting future species distributions.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science
Consensus categoriesOpen science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.053
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0060.017
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 head0.053
GPT teacher head0.275
Teacher spread0.222 · 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; both teacher heads agree on what is shown here.

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
Published2018
Admission routes1
Has abstractyes

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