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Record W4391215349 · doi:10.1126/science.adg1464

Disruption of an ant-plant mutualism shapes interactions between lions and their primary prey

2024· article· en· W4391215349 on OpenAlexaff
Douglas Kamaru, Todd M. Palmer, Corinna Riginos, Adam T. Ford, Jayne Belnap, Robert Chira, John Githaiga, Benard Gituku, Brandon R. Hays, Cyrus M. Kavwele, Alfred K. Kibungei, Clayton T. Lamb, Nelly J. Maiyo, Patrick Milligan, Samuel Mutisya, Caroline C. Ng’weno, Michael Ogutu, Alejandro G. Pietrek, Brendon Wildt, Jacob R. Goheen

Bibliographic record

VenueScience · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsMutualism (biology)PredationPantheraBiologyEcologyInvasive speciesWildebeestPheidoleANTNational park

Abstract

fetched live from OpenAlex

Mutualisms often define ecosystems, but they are susceptible to human activities. Combining experiments, animal tracking, and mortality investigations, we show that the invasive big-headed ant ( Pheidole megacephala ) makes lions ( Panthera leo ) less effective at killing their primary prey, plains zebra ( Equus quagga ). Big-headed ants disrupted the mutualism between native ants ( Crematogaster spp.) and the dominant whistling-thorn tree ( Vachellia drepanolobium ), rendering trees vulnerable to elephant ( Loxodonta africana ) browsing and resulting in landscapes with higher visibility. Although zebra kills were significantly less likely to occur in higher-visibility, invaded areas, lion numbers did not decline since the onset of the invasion, likely because of prey-switching to African buffalo ( Syncerus caffer ). We show that by controlling biophysical structure across landscapes, a tiny invader reconfigured predator-prey dynamics among iconic species.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.057
GPT teacher head0.260
Teacher spread0.204 · 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

Citations52
Published2024
Admission routes1
Has abstractyes

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