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A multivariate analysis of fine‐scale species density in the plant communities of a saltwater lagoon – the importance of disturbance intensity

2005· article· en· W2077055061 on OpenAlexfundaboutno aff
Gilles Houle

Bibliographic record

VenueOikos · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAbiotic componentIntermediate Disturbance HypothesisProductivityEcologyDisturbance (geology)Environmental scienceSpecies diversityPlant communityPlant coverBiologySpecies richness

Abstract

fetched live from OpenAlex

Interactions between resources and abiotic conditions control local diversity and productivity, often in a complex fashion. In this study, I estimated the relative causal effects of several environmental variables known to influence diversity and productivity in plant communities. Two sites differing in disturbance intensity (i.e. wrack deposition) were studied along a saltwater lagoon, at Îles de la Madeleine, Québec, Canada. A larger proportion of the variance in species density (82%) and plant cover (81%) was explained by the environmental factors at the most disturbed site, while only 35% of the variance in species density and 47% of the variance in plant cover were explained at the other, less disturbed site. At the most disturbed site, environmental factors associated with distance from the shoreline (e.g. salinity, anoxia, granulometry) indirectly controlled species density through their effects on plant cover, while at the less disturbed site, environmental factors influenced both plant cover and species density. At low disturbance intensity, the species pool may be more significant than productivity per se in restricting local diversity; however, at higher intensity of disturbance, productivity (directly influenced by resources and abiotic conditions) may be more important in controlling diversity.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
Threshold uncertainty score1.000

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 head0.014
GPT teacher head0.207
Teacher spread0.194 · 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 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

Citations7
Published2005
Admission routes2
Has abstractyes

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