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Global Change Asymmetrically Rewires Ecosystems

2025· preprint· en· W4407007515 on OpenAlexaff
Charlotte Ward, Tyler D. Tunney, Ian Donohue, Carling Bieg, Kayla R. S. Hale, Bailey C. McMeans, John C. Moore, Kevin S. McCann

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicEcosystem dynamics and resilience
Canadian institutionsUniversity of TorontoFisheries and Oceans CanadaUniversity of Guelph
Fundersnot available
KeywordsEcosystemBusinessEnvironmental scienceEconomicsEcologyBiology

Abstract

fetched live from OpenAlex

Global change is altering ecosystems in ways that threaten the critical functions on which biodiversity depends. Despite this, we know very little about how drivers of global change broadly affect food webs. While an industry of studies documents shifts in whole carbon pathways within food webs in response to anthropogenic pressures, a comprehensive synthesis is lacking. To address this, we provide empirical examples across diverse ecosystems and conduct a systematic literature review to reveal the prevalence of asymmetric rewiring -- a phenomenon whereby drivers of global change consistently but disproportionally alter the flow of some carbon pathways relative to others. Further, using food web models, we show how asymmetric rewiring erodes resilience and disrupts key functions, such as primary and secondary production. Global change is complex and multidimensional, making it challenging to understand how human activities affect ecosystem processes. Our work critically synthesizes empirical evidence to uncover a remarkably general response in food webs to global environmental change that needs to be better understood to protect nature and the services that human societies rely on in a rapidly changing world.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.928
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.016
GPT teacher head0.257
Teacher spread0.241 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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