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Record W2132197712 · doi:10.5194/bg-11-2991-2014

Can current moisture responses predict soil CO <sub>2</sub> efflux under altered precipitation regimes? A synthesis of manipulation experiments

2014· article· en· W2132197712 on OpenAlexaff
Sara Vicca, Michael Bahn, Marc Estiarte, E. Emiel van Loon, Rodrigo Vargas, Giorgio Alberti, Per Ambus, M. Altaf Arain, Claus Beier, Lisa Patrick Bentley, Werner Borken, Nina Buchmann, Scott L. Collins, Giovanbattista de Dato, Jeffrey S. Dukes, Cristina Escolar, Philip A. Fay, Gabriele Guidolotti, Paul J. Hanson, Ansgar Kahmen, György Kröel‐Dulay, Thomas Ladreiter‐Knauss, Klaus Steenberg Larsen, Eszter Lellei‐Kovács, Edwin Lebrija‐Trejos, Fernando T. Maestre, Sven Marhan, Miles R. Marshall, Patrick Meir, Y. Miao, Jan Muhr, Pascal A. Niklaus, R. Ogaya, Josep Peñuelas, Christian Poll, Lindsey E. Rustad, K. E. Savage, Andreas Schindlbacher, Inger Kappel Schmidt, Andrew R. Smith, Eleneide Doff Sotta, Vidya Suseela, Albert Tietema, Natasja van Gestel, Oliver van Straaten, Shiqiang Wan, Ulrich Weber, Ivan A. Janssens

Bibliographic record

VenueBiogeosciences · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsMcMaster University
FundersEuropean Social FundNatural Environment Research CouncilU.S. Geological SurveyVlaamse regeringEcological Society of AmericaBundesministerium für Bildung und ForschungBritish Ecological SocietySight Research UKOffice of ScienceAustrian Science FundU.S. Department of AgricultureU.S. Department of EnergyNational Science Foundation
KeywordsPrecipitationEffluxChemistryMoistureEnvironmental scienceBiochemistryMeteorologyPhysics

Abstract

fetched live from OpenAlex

Abstract. As a key component of the carbon cycle, soil CO2 efflux (SCE) is being increasingly studied to improve our mechanistic understanding of this important carbon flux. Predicting ecosystem responses to climate change often depends on extrapolation of current relationships between ecosystem processes and their climatic drivers to conditions not yet experienced by the ecosystem. This raises the question of to what extent these relationships remain unaltered beyond the current climatic window for which observations are available to constrain the relationships. Here, we evaluate whether current responses of SCE to fluctuations in soil temperature and soil water content can be used to predict SCE under altered rainfall patterns. Of the 58 experiments for which we gathered SCE data, 20 were discarded because either too few data were available or inconsistencies precluded their incorporation in the analyses. The 38 remaining experiments were used to test the hypothesis that a model parameterized with data from the control plots (using soil temperature and water content as predictor variables) could adequately predict SCE measured in the manipulated treatment. Only for 7 of these 38 experiments was this hypothesis rejected. Importantly, these were the experiments with the most reliable data sets, i.e., those providing high-frequency measurements of SCE. Regression tree analysis demonstrated that our hypothesis could be rejected only for experiments with measurement intervals of less than 11 days, and was not rejected for any of the 24 experiments with larger measurement intervals. This highlights the importance of high-frequency measurements when studying effects of altered precipitation on SCE, probably because infrequent measurement schemes have insufficient capacity to detect shifts in the climate dependencies of SCE. Hence, the most justified answer to the question of whether current moisture responses of SCE can be extrapolated to predict SCE under altered precipitation regimes is "no" – as based on the most reliable data sets available. We strongly recommend that future experiments focus more strongly on establishing response functions across a broader range of precipitation regimes and soil moisture conditions. Such experiments should make accurate measurements of water availability, should conduct high-frequency SCE measurements, and should consider both instantaneous responses and the potential legacy effects of climate extremes. This is important, because with the novel approach presented here, we demonstrated that, at least for some ecosystems, current moisture responses could not be extrapolated to predict SCE under altered rainfall conditions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.245
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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

Citations109
Published2014
Admission routes1
Has abstractyes

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