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Record W1964792697 · doi:10.1111/oik.00878

A temporal dimension to the stress gradient hypothesis for intraspecific interactions

2014· article· en· W1964792697 on OpenAlexafffundabout
Shekhar R. Biswas, Helene H. Wagner

Bibliographic record

VenueOikos · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicEcosystem dynamics and resilience
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIntraspecific competitionInterspecific competitionEcologyDominance (genetics)BiologyForbEnvironmental gradientDensity dependenceOld fieldEnvironmental stressCompetition (biology)Field experimentHabitatDemographyGrasslandPopulation

Abstract

fetched live from OpenAlex

Space and time are the two fundamental drivers of ecological dynamics. Studies exploring the Stress gradient hypothesis (SGH) – which predicts that the patterns of interspecific interactions shift from negative to positive with increasing environmental severity – conceptualize environmental severity predominantly from a spatial perspective. Here, from a temporal perspective and for intraspecific interactions, we asked: do the predictions of the SGH at the intraspecific level apply to seasonal change in environmental severity? We conducted a field experiment, which was complemented by a two‐year field survey of natural populations of the non‐native biennial forb Alliaria petiolata at the Koffler Scientific Reserve, Ontario, Canada. In both experiment and field survey studies we found statistically significant negative density‐dependent survival in the productive summer period and positive density‐dependent survival over the severe winter period. Effects were stronger in the field experiment than in the survey of natural populations. We suggest that the SGH at the intraspecific level may be applicable to seasonal variation in environmental severity, though our ability to detect its effect in natural communities may depend on other factors such as species dominance and environmental heterogeneity.

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 score0.479
Threshold uncertainty score0.553

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 studies0.0000.000
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 head0.010
GPT teacher head0.216
Teacher spread0.206 · 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

Citations25
Published2014
Admission routes3
Has abstractyes

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