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Record W2081633838 · doi:10.1086/658066

A Stoichiometric Model of Early Plant Primary Succession

2011· article· en· W2081633838 on OpenAlexafffund
Justin N. Marleau, Yu Jin, John G. Bishop, William F. Fagan, Mark A. Lewis

Bibliographic record

VenueThe American Naturalist · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of Alberta
FundersPacific Institute for the Mathematical Sciences
KeywordsEcological successionPrimary successionEcosystemPlant communityBiogeochemical cycleEcologyEnvironmental scienceSecondary successionBiomass (ecology)Biology

Abstract

fetched live from OpenAlex

The relative importance of plant facilitation and competition during primary succession depends on the development of ecosystem nutrient pools, yet the interaction of these processes remains poorly understood. To explore how these mechanisms interact to drive successional dynamics, we devised a stoichiometric ecosystem-level model that considers the role of nitrogen and phosphorus limitation in plant primary succession. We applied this model to the primary plant community on Mount St. Helens, Washington State, to check the validity of the proposed mechanisms. Our results show that the plant community is colimited by nitrogen and phosphorus, and they confirm previous suggestions that the presence of a nitrogen-fixing legume, Lupinus lepidus, can enhance community biomass. In addition, the observed nutrient supply rates may promote alternative successional trajectories that depend on the initial plant abundances, which may explain the observed heterogeneity in community development. The model further indicates the importance of mineralization rates and other ecosystem parameters to successional rates. We conclude that a model framework based on ecological stoichiometry allows integration of key biotic processes that interact nonlinearly with biogeochemical aspects of succession. Extension of this approach will improve the understanding of the process of primary succession and its application to ecosystem rehabilitation.

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 score0.024
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.020
GPT teacher head0.233
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 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

Citations33
Published2011
Admission routes2
Has abstractyes

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