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Diversity effects on invasion vary with life history stage in marine macroalgae

2007· article· en· W2057563181 on OpenAlexaffabout
Laura F. White, Jonathan B. Shurin

Bibliographic record

VenueOikos · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEcologyFucalesBiologyInvasive speciesIntroduced speciesInvertebrateAlgae

Abstract

fetched live from OpenAlex

Most experimental studies of diversity effects on invasibility have reported negative relationships while observational studies have often found positive correlations between the numbers of exotic and native taxa. Nearly all of these studies have been done with terrestrial plants or aquatic invertebrates. We investigated effects of native macroalgal diversity on invasion success of the introduced macroalga Sargassum muticum (Yendo) Fensholt (Phaeophyceae: Fucales) on the west coast of Vancouver Island. We conducted both observational field surveys of the correlation between native diversity and exotic cover, and experimental manipulations of native diversity in constructed 25×25 cm communities. Field surveys found higher cover of S. muticum in plots with low native diversity, suggesting a negative relationship between diversity and invasibility at the neighbourhood scale. The experiment found initial cover of S. muticum germlings was highest in plots with greater diversity. Over the duration of the experiment cover of settled germlings increased fastest in the low diversity plots, so that there was a weak negative effect of diversity on final cover of the invader after 77 days. The slope of the relationship reversed over time, with field patterns and experimental results converging at the end of the experiment. Our results suggest native diversity has contrasting effects on different stages of invasion. Diversity facilitates invader recruitment of S. muticum but decreases growth and or survivorship.

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 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 score0.139
Threshold uncertainty score0.998

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)0.0030.000

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 head0.013
GPT teacher head0.175
Teacher spread0.162 · 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

Citations31
Published2007
Admission routes2
Has abstractyes

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