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Record W2012638886 · doi:10.1111/oik.01061

Tradeoffs and scaling of functional traits in <i>Sphagnum</i> as drivers of carbon cycling in peatlands

2014· article· en· W2012638886 on OpenAlexaff
C. Laing, Gustaf Granath, Lisa R. Belyea, Kathryn Allton, H. Rydin

Bibliographic record

VenueOikos · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsMcMaster University
FundersNatural Environment Research CouncilSight Research UK
KeywordsSphagnumCanopyPeatEnvironmental scienceBiomass (ecology)ShootCarbon cycleBogEcologyBiologyBiomass partitioningCyclingCarbon fibersBotanyAgronomyEcosystemMathematicsGeography

Abstract

fetched live from OpenAlex

Growth and decomposition of Sphagnum controls turnover of a large global store of soil organic carbon. We investigated variation in morphological and physiological traits of Sphagnum shoots, and related this variation to canopy variables relevant to peatland carbon cycling. We sampled Sphagnum along a bog plateau‐swamp forest gradient and measured a suite of shoot traits and canopy variables. Major axes of variation were identified using principal component analysis and correlated with canopy variables such as growth, biomass and decomposition. We also examined scaling of shoot traits with one another and with canopy variables. Two distinct tradeoffs in shoot traits emerged. From dry to wet habitats, individual metabolic rates and capitulum size increased while numerical density decreased, leading to faster growth and elongation on an individual basis. From treed to open habitats, photosynthetic efficiency decreased and photosynthetic biomass increased, driving faster growth on an area basis and slower litter mass loss. The tradeoffs identified have important implications for peatlands undergoing climate‐related changes in water and light availability. Sphagnum trait comparisons, combined with scaling analyses, offer a promising approach to understanding and predicting the effects of environmental change on peatland carbon cycling.

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 score0.019
Threshold uncertainty score0.262

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 head0.007
GPT teacher head0.200
Teacher spread0.193 · 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

Citations69
Published2014
Admission routes1
Has abstractyes

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