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Record W4213385384 · doi:10.1017/s0266467422000050

Limited influence of experimentally induced predation risk on granivory in a tropical forest

2022· article· en· W4213385384 on OpenAlexaff
Alys Granados, Henry Bernard, Jedediah F. Brodie

Bibliographic record

VenueJournal of Tropical Ecology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPredationPredatorBiologySeed predationForagingEcologyZoologyPopulation

Abstract

fetched live from OpenAlex

Abstract Seed predation by rodents can strongly influence plant recruitment and establishment. The extent to which predation risk indirectly alters plant survival in tropical forests via impacts on granivory is unclear, making it difficult to assess the cascading impacts of widespread predator loss on tree recruitment and species composition. Experimental field studies that manipulate predation risk can help address these knowledge gaps and reveal whether antipredator responses among small mammals influence plant survival. We used camera traps and seed predation experiments to test the effects of perceived predation risk (via predator urine gel) on foraging behaviour of and seed removal by murid rodents in an unlogged and unhunted rainforest in Malaysian Borneo. We also explored the influence of seed traits (e.g., seed size) on removal by granivores and assessed whether granivore preferences for particular species were affected by predator urine. Murid visits to seed plots were positively related to overall seed removal, but were not affected by predator scent. Granivory was the lowest for the largest-seeded (>6 g) plant in our study, but was not influenced by predation risk. Predator urine significantly affected removal of one seed taxon ( Dimoocarpus , ∼0.8 g), suggesting that removal by granivores may be affected by predation risk for some seed species but not others. This could have implications for plant species composition but may not affect the overall level of granivory.

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 score0.009
Threshold uncertainty score0.921

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.257
Teacher spread0.243 · 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
Published2022
Admission routes1
Has abstractyes

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