MétaCan
Menu
Back to cohort
Record W4293696738 · doi:10.1111/ecog.06166

Circum‐Arctic distribution of chemical anti‐herbivore compounds suggests biome‐wide trade‐off in defence strategies in Arctic shrubs

2022· article· en· W4293696738 on OpenAlexafffund
Elin Lindén, Mariska te Beest, Ilka N. Abreu, Thomas Möritz, Maja K. Sundqvist, Isabel C. Barrio, Julia Boike, John P. Bryant, Kari Anne Bråthen, Agata Buchwał, C. Guillermo Bueno, Alain Cuerrier, Dagmar Egelkraut, Bruce C. Forbes, Martin Hallinger, Monique Heijmans, Luise Hermanutz, David S. Hik, Annika Hofgaard, Milena Holmgren, Diane C. Huebner, Toke T. Høye, Ingibjörg S. Jónsdóttir, Elina Kaarlejärvi, Emilie Kissler, Timo Kumpula, Juul Limpens, Isla H. Myers‐Smith, Signe Normand, Eric Post, Adrian V. Rocha, Niels Martin Schmidt, Anna Skarin, Eeva M. Soininen, Aleksandr Sokolov, James D. M. Speed, Lorna E. Street, Nikita Tananaev, Jean‐Pierre Tremblay, Christine Urbanowicz, David A. Watts, Heike Zimmermann, Johan Olofsson

Bibliographic record

VenueEcography · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversité LavalMemorial University of NewfoundlandSimon Fraser UniversityUniversité de MontréalEspace pour la vie
FundersNatural Environment Research CouncilNatural Sciences and Engineering Research Council of CanadaKnut och Alice Wallenbergs StiftelseSuomen KulttuurirahastoSvenska Forskningsrådet FormasSight Research UKNorges ForskningsrådArcticNetParks CanadaVetenskapsrådetNational Science Foundation
KeywordsBiomeHerbivoreArcticEcologyDistribution (mathematics)The arcticArctic ecologyChemical ecologyArctic vegetationGeographyTundraBiologyEnvironmental scienceEcosystemOceanographyGeology

Abstract

fetched live from OpenAlex

Spatial variation in plant chemical defence towards herbivores can help us understand variation in herbivore top–down control of shrubs in the Arctic and possibly also shrub responses to global warming. Less defended, non‐resinous shrubs could be more influenced by herbivores than more defended, resinous shrubs. However, sparse field measurements limit our current understanding of how much of the circum‐Arctic variation in defence compounds is explained by taxa or defence functional groups (resinous/non‐resinous). We measured circum‐Arctic chemical defence and leaf digestibility in resinous ( Betula glandulosa , B. nana ssp. exilis ) and non‐resinous ( B. nana ssp. nana , B. pumila ) shrub birches to see how they vary among and within taxa and functional groups. Using liquid chromatography–mass spectrometry (LC–MS) metabolomic analyses and in vitro leaf digestibility via incubation in cattle rumen fluid, we analysed defence composition and leaf digestibility in 128 samples from 44 tundra locations. We found biogeographical patterns in anti‐herbivore defence where mean leaf triterpene concentrations and twig resin gland density were greater in resinous taxa and mean concentrations of condensing tannins were greater in non‐resinous taxa. This indicates a biome‐wide trade‐off between triterpene‐ or tannin‐dominated defences. However, we also found variations in chemical defence composition and resin gland density both within and among functional groups (resinous/non‐resinous) and taxa, suggesting these categorisations only partly predict chemical herbivore defence. Complex tannins were the only defence compounds negatively related to in vitro digestibility, identifying this previously neglected tannin group as having a potential key role in birch anti‐herbivore defence. We conclude that circum‐Arctic variation in birch anti‐herbivore defence can be partly derived from biogeographical distributions of birch taxa, although our detailed mapping of plant defence provides more information on this variation and can be used for better predictions of herbivore effects on Arctic vegetation.

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 score0.028
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.005
GPT teacher head0.205
Teacher spread0.199 · 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

Citations4
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueEcographySame topicFire effects on ecosystemsFrench-language works237,207