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Record W4409570120 · doi:10.1002/oik.11092

The effect of biodiversity on productivity changes over time in duckweed communities

2025· article· en· W4409570120 on OpenAlexafffund
Alex‐Anne Couture, Mégane Déziel, Jacques Brisson, Mark Davidson Jewell, Alain Paquette

Bibliographic record

VenueOikos · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsConcordia UniversityUniversité de MontréalUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaUniversité du Québec à Montréal
KeywordsComplementarity (molecular biology)PolycultureBiodiversityBiologyProductivityMonocultureDominance (genetics)EcologyAquacultureFisheryFish <Actinopterygii>Economics

Abstract

fetched live from OpenAlex

Several studies have shown that increased biodiversity can lead to increased productivity in plant communities. This relationship can be explained by a selection effect caused by the dominance of one or a few species that drive productivity, and by a complementarity effect resulting from the use of resources by the different species. However, these effects are likely to change over time, which cannot be observed in short‐term experiments (relative to lifespan) that make up most of the literature on this topic. To study the change over time of the diversity–productivity relationship (DPR) and its underlying mechanisms, we developed an experimental system using small, fast‐generating plants: duckweeds (Lemnaceae). In a growth chamber, we grew four species in monocultures and polycultures with all possible combinations for 60 days (about six generations of growth). At 20‐day intervals, we measured the biomass of each species, as well as their functional traits. Our results show a clear increase in the net effect of biodiversity on productivity over time, mainly caused by an increase in the complementarity effect and the presence of one species in particular. We also show that, in the presence of other species, the plants showed phenotypic plasticity; however, these changes did not contribute to an enhancement of the complementarity effect. Our results reinforce the importance of long‐term DPR studies and underline the effectiveness of duckweeds as an experimental model.

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 score0.016
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)0.0000.000

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 head0.004
GPT teacher head0.192
Teacher spread0.188 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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