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Record W4229005434 · doi:10.1126/sciadv.abn3368

Drought soil legacy alters drivers of plant diversity-productivity relationships in oldfield systems

2022· article· en· W4229005434 on OpenAlexaff
Nianxun Xi, Dongxia Chen, Michael Bahn, Hangyu Wu, Chengjin Chu, Marc W. Cadotte, Juliette Bloor

Bibliographic record

VenueScience Advances · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersAgence Nationale de la Recherche
KeywordsSpecies richnessBiodiversityEcosystemEcologyMesocosmProductivityPlant communityComplementarity (molecular biology)Species diversityThreatened speciesBiologyEnvironmental scienceGeographyHabitat

Abstract

fetched live from OpenAlex

Ecosystem functions are threatened by both recurrent droughts and declines in biodiversity at a global scale, but the drought dependency of diversity-productivity relationships remains poorly understood. Here, we use a two-phase mesocosm experiment with simulated drought and model oldfield communities (360 experimental mesocosms/plant communities) to examine drought-induced changes in soil microbial communities along a plant species richness gradient and to assess interactions between past drought (soil legacies) and subsequent drought on plant diversity-productivity relationships. We show that (i) drought decreases bacterial and fungal richness and modifies relationships between plant species richness and microbial groups; (ii) drought soil legacy increases net biodiversity effects, but responses of net biodiversity effects to plant species richness are unaffected; and (iii) linkages between plant species richness and complementarity/selection effects vary depending on past and subsequent drought. These results provide mechanistic insight into biodiversity-productivity relationships in a changing environment, with implications for the stability of ecosystem function under climate change.

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)0.001
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 score0.214
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.017
GPT teacher head0.231
Teacher spread0.215 · 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

Citations55
Published2022
Admission routes1
Has abstractyes

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