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Record W4290018568 · doi:10.1111/1365-2664.14267

Emerging stability of forest productivity by mixing two species buffers temperature destabilizing effect

2022· article· en· W4290018568 on OpenAlexaff
Miren del Rı́o, Hans Pretzsch, Ricardo Ruíz‐Peinado, Hervé Jactel, Lluís Coll, Magnus Löf, Jorge Aldea, Christian Ammer, Admir Avdagić, Ignacio Barbeito, Kamil Bielak, Felipe Bravo, Gediminas Brazaitis, Jakub Černý, Catherine Collet, Sonia Condés, Lars Drößler, Marek Fabrika, Michael Heym, Stig‐Olof Holm, Gro Hylén, Āris Jansons, Viktor Kurylyak, Fabio Lombardi, Bratislav Matović, Marek Metslaid, Renzo Motta, Thomas Nord‐Larsen, Arne Nothdurft, J. den Ouden, Maciej Pach, Marta Pardos, Charlotte Poeydebat, Quentin Ponette, Thomas Pérot, Ditlev Otto Juel Reventlow, Roman Sitko, Vít Šrámek, Mathias Steckel, Miroslav Svoboda, Kris Verheyen, Sonja Vospernik, Barbara Wolff, Tzvetan Zlatanov, Andrés Bravo‐Oviedo

Bibliographic record

VenueJournal of Applied Ecology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsUniversity of British Columbia
FundersResearch Committee, Aristotle University of ThessalonikiAgentúra na Podporu Výskumu a VývojaHorizon 2020 Framework ProgrammeMinisterstwo Edukacji i NaukiEesti Teadusagentuur
KeywordsProductivityMixing (physics)Environmental scienceEcologyStability (learning theory)AgroforestryAtmospheric sciencesBiologyEconomicsPhysicsComputer science

Abstract

fetched live from OpenAlex

Abstract The increasing disturbances in monocultures around the world are testimony to their instability under global change. Many studies have claimed that temporal stability of productivity increases with species richness, although the ecological fundamentals have mainly been investigated through diversity experiments. To adequately manage forest ecosystems, it is necessary to have a comprehensive understanding of the effect of mixing species on the temporal stability of productivity and the way in which it is influenced by climate conditions across large geographical areas. Here, we used a unique dataset of 261 stands combining pure and two‐species mixtures of four relevant tree species over a wide range of climate conditions in Europe to examine the effect of species mixing on the level and temporal stability of productivity. Structural equation modelling was employed to further explore the direct and indirect influence of climate, overyielding, species asynchrony and additive effect (i.e. temporal stability expected from the species growth in monospecific stands) on temporal stability in mixed forests. We showed that by adding only one tree species to monocultures, the level (overyielding: +6%) and stability (temporal stability: +12%) of stand growth increased significantly. We identified the key effect of temperature on destabilizing stand growth, which may be mitigated by mixing species. We further confirmed asynchrony as the main driver of temporal stability in mixed stands, through both the additive effect and species interactions, which modify between‐species asynchrony in mixtures in comparison to monocultures. Synthesis and applications . This study highlights the emergent properties associated with mixing two species, which result in resource efficient and temporally stable production systems. We reveal the negative impact of mean temperature on temporal stability of forest productivity and how the stabilizing effect of mixing two species can counterbalance this impact. The overyielding and temporal stability of growth addressed in this paper are essential for ecosystem services closely linked with the level and rhythm of forest growth. Our results underline that mixing two species can be a realistic and effective nature‐based climate solution, which could contribute towards meeting EU climate target policies.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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)0.0020.000

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 head0.005
GPT teacher head0.206
Teacher spread0.201 · 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 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

Citations58
Published2022
Admission routes1
Has abstractyes

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