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Record W2955948037 · doi:10.1111/oik.06506

Non‐resource effects of foundation species on meta‐ecosystem stability and function

2019· article· en· W2955948037 on OpenAlexafffund
Julien Massé Jodoin, Frédéric Guichard

Bibliographic record

VenueOikos · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEcosystemMetapopulationEcologyFoundation speciesEcosystem engineerEnvironmental scienceAbundance (ecology)PopulationBiologyBiological dispersal

Abstract

fetched live from OpenAlex

Ecosystems such as forests and mussel beds, that are driven by foundation species can be characterized by the slow accumulation of matter that affect their structural stability. This non‐resource effect of matter on ecosystems can lead to disturbances and to pulsed release and transport of matter over regional scales. However, non‐resource effects of endogenous pulses of matter on meta‐ecosystem stability and function remain largely unknown. Using a two‐patch meta‐ecosystem model of mussel bed dynamics, we show that non‐resource effects of matter on the structural stability of mussel beds promote pulsed releases of matter and fluctuations in population abundance. These pulsed fluctuations explain the maintenance of meta‐ecosystem heterogeneity in the distribution of abundance and matter through out‐of‐phase synchrony and asynchrony over a broad range of connectivity. These regimes of spatial (a)synchrony explain a tradeoff between the regional retention of matter (ecosystem function) and metapopulation persistence. These results reveal how foundation species can cause local and catastrophic changes that can promote regional asynchrony and stability, even under strong connectivity.

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 score0.030
Threshold uncertainty score0.999

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)0.0020.000

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 head0.016
GPT teacher head0.218
Teacher spread0.202 · 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

Citations9
Published2019
Admission routes2
Has abstractyes

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