MétaCan
Menu
Back to cohort
Record W4408843997 · doi:10.1111/geb.70013

Synchrony and Tail‐Dependent Synchrony Have Different Effects on Stability of Terrestrial and Freshwater Communities

2025· article· en· W4408843997 on OpenAlexaff
Shyamolina Ghosh, Blake Matthews, Sarah R. Supp, Roel van Klink, Francesco Pomati, James A. Rusak, Imran Khaliq, Niklaus E. Zimmermann, Thomas Wohlgemuth, Ole Seehausen, Christian Rixen, Martin M. Goßner, Anita Narwani, Jonathan M. Chase, Catherine H. Graham

Bibliographic record

VenueGlobal Ecology and Biogeography · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsQueen's University
FundersHorizon 2020 Framework ProgrammeBoard of the Swiss Federal Institutes of TechnologyH2020 European Research CouncilUniversität ZürichEuropean Commission
KeywordsEcologyStability (learning theory)BiologyGeographyEnvironmental science

Abstract

fetched live from OpenAlex

ABSTRACT Aim Global change can impact the stability of biological communities by affecting species richness and synchrony. While most studies focus on terrestrial ecosystems, our research includes both terrestrial and aquatic realms. Previous works measure overall community synchrony as co‐variation among co‐occurring species, ignoring the tail dependence—when species fluctuate together at extreme abundance levels. We used community time‐series data to test two hypotheses across realms: a positive relationship between diversity (richness) and stability, and a negative relationship between synchrony and stability. Additionally, we explored how tail‐dependent synchrony contributes to variations in community stability. Location Global. Time Period 1923–2020. Major Taxa Studied 7 taxa across freshwater (fish, plants, invertebrates) and terrestrial (birds, plants, invertebrates, mammals) realms. Methods We synthesised 20+ years of species abundance/biomass data from 2668 communities across seven taxonomic groups. Using a variance‐ratio approach and copula models, we measured overall and tail‐dependent synchrony. Hierarchical linear mixed‐effects models in a Bayesian framework were used to assess the effects of richness and both synchrony types on stability. Results We found a positive diversity–stability relationship in terrestrial but not in freshwater communities, with terrestrial stability being nearly three times higher. A negative synchrony –stability relationship was found in both realms. The best model explaining stability included realm differences, richness and both types of synchronies. For freshwater, only overall synchrony significantly impacted stability, while richness and both synchrony types were key predictors for terrestrial stability. Notably, the model overestimates terrestrial stability when tail‐dependent synchrony is excluded. Main Conclusions Richness strongly enhanced terrestrial stability, offering the most extensive support for this relationship to date. In addition, tail‐dependent synchrony provides key insights into stability differences across ecosystems. As extreme environmental events increase, incorporating tail‐dependent synchrony in future stability studies is crucial.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.216
Teacher spread0.211 · 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 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 routes1
Has abstractyes

Explore more

Same venueGlobal Ecology and BiogeographySame topicIsotope Analysis in EcologyFrench-language works237,207