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Predator-mediated apparent competition persists in a rapidly changing Subarctic ecosystem

2023· preprint· en· W4322495264 on OpenAlexaff
Milly Hong, James D. Roth, Laura McKinnon

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of ManitobaYork University
Fundersnot available
KeywordsPredationSubarctic climateEcologyBiologyArcticPredatorNest (protein structural motif)Abundance (ecology)PopulationCompetition (biology)Arctic foxSeabirdDemography

Abstract

fetched live from OpenAlex

The Alternative Prey Hypothesis (APH) states that predators switch to relatively more abundant prey when their main prey is scarce. In the High Arctic, lemming population cycles indirectly affect predation risk on alternative prey such as shorebird nests as they share a main predator, the arctic fox. In this study, we examined the indirect effects of arvicoline rodent cycles on alternative prey in the Subarctic where arctic and red fox coexist as predators of primary (lemmings, voles) and alternative prey (shorebird nests). Using 10 years of field data, our results indicate that interannual variation in daily nest survival of Dunlin was best explained by an interactive effect of arvicoline rodent abundance and arctic fox (not red fox) abundance. During high rodent years, shorebird nest survival appeared to be buffered from variation in arctic fox abundance but when rodents were absent, nest survival declined. We found no relationship between shorebird nest survival and red fox abundance despite red foxes being found in much higher abundance in the study area. Our results indicate that despite the presence of other predators and multiple primary prey species, predator-mediated interactions common to High Arctic sites, still hold true for the Subarctic in regards to the arctic fox, arvicoline rodents and shorebirds.

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)0.001
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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
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 head0.021
GPT teacher head0.228
Teacher spread0.206 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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