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Record W4416945637 · doi:10.1038/s41467-025-65959-7

Dynamic omnivory shapes the functional role of large carnivores under global change

2025· article· en· W4416945637 on OpenAlexaff
Jörg Albrecht, Hervé Bocherens, Keith A. Hobson, Dorothée G. Drucker, Agnieszka Sergiel, Jon E. Swenson, Andreas Zedrosser, Adrian Marciszak, Elisabeth Iregren, Leena Drenzel, René Kyselý, Grzegorz Lipecki, Daniel Makowiecki, Jan Wagner, Tomasz Zwijacz‐Kozica, Susanne A. Fritz, Eloy Revilla, Nuria Selva

Bibliographic record

VenueNature Communications · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicEvolution and Paleontology Studies
Canadian institutionsEnvironment and Climate Change Canada
FundersLeibniz-GemeinschaftDeutsche ForschungsgemeinschaftDeutscher Akademischer AustauschdienstNarodowe Centrum Badań i RozwojuNational Research Centre
KeywordsTrophic levelEcosystemTrophic cascadeFood webHerbivoreOmnivoreEnvironmental changeGlobal change

Abstract

fetched live from OpenAlex

Omnivory is increasingly recognized as a dynamic stabilizing force under environmental change. Despite its ubiquity across ecosystems, trophic levels and spatiotemporal scales, our empirical understanding of how omnivores respond to changing conditions in terrestrial ecosystems is limited. Here we combine macroecological and paleoecological approaches across seven bear species-the largest terrestrial carnivores-and discover they dynamically adapt their trophic position in food webs to resource availability. Throughout their ranges, bears shift to carnivory in unproductive ecosystems with short growing seasons and to herbivory in productive ecosystems with long growing seasons. In line with this, isotopic evidence from the Late Pleistocene and Holocene reveals a sharp decrease in the trophic position of the European brown bear in response to increasing net primary productivity and growing season length. These findings reveal a mechanism of trophic rewiring that alters the functional role of large carnivores in ecosystems and may simultaneously stabilize food web dynamics under global change.

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: none
Teacher disagreement score0.926
Threshold uncertainty score0.646

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.022
GPT teacher head0.293
Teacher spread0.270 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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