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Record W2411144234 · doi:10.1111/gcb.13384

Plant invasion is associated with higher plant–soil nutrient concentrations in nutrient‐poor environments

2016· review· en· W2411144234 on OpenAlexaff
Jordi Sardans, Mireia Bartrons, Olga Margalef, Albert Gargallo‐Garriga, Ivan A. Janssens, Phillipe Ciais, Michael Obersteiner, Bjarni D. Sigurðsson, Han Y. H. Chen, Josep Peñuelas

Bibliographic record

VenueGlobal Change Biology · 2016
Typereview
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsLakehead University
FundersEuropean Research Council
KeywordsNutrientEcosystemPlant litterInvasive speciesBiologyNutrient cycleAgronomyEcologyBotany

Abstract

fetched live from OpenAlex

Plant invasion is an emerging driver of global change worldwide. We aimed to disentangle its impacts on plant-soil nutrient concentrations. We conducted a meta-analysis of 215 peer-reviewed articles and 1233 observations. Invasive plant species had globally higher N and P concentrations in photosynthetic tissues but not in foliar litter, in comparison with their native competitors. Invasive plants were also associated with higher soil C and N stocks and N, P, and K availabilities. The differences in N and P concentrations in photosynthetic tissues and in soil total C and N, soil N, P, and K availabilities between invasive and native species decreased when the environment was richer in nutrient resources. The results thus suggested higher nutrient resorption efficiencies in invasive than in native species in nutrient-poor environments. There were differences in soil total N concentrations but not in total P concentrations, indicating that the differences associated to invasive plants were related with biological processes, not with geochemical processes. The results suggest that invasiveness is not only a driver of changes in ecosystem species composition but that it is also associated with significant changes in plant-soil elemental composition and stoichiometry.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.051
GPT teacher head0.280
Teacher spread0.229 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations226
Published2016
Admission routes1
Has abstractyes

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