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Record W2953361443 · doi:10.1098/rsbl.2018.0460

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

2018· article· en· W2953361443 on OpenAlexafffund
Rachel M. Germain, Margaret M. Mayfield, Benjamin Gilbert

Bibliographic record

VenueBiology Letters · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of TorontoUniversity of British Columbia
FundersConnaught FundNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaKillam Trusts
KeywordsBiologyCompetition (biology)EcologyPersistence (discontinuity)TraitCompetitor analysisBiomass (ecology)Coexistence theoryStorage effectEconomics

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 which 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 the 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.010
Scholarly communication0.0030.005
Open science0.0010.002
Research integrity0.0010.001
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 head0.011
GPT teacher head0.223
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations124
Published2018
Admission routes2
Has abstractyes

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