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Record W6948291645 · doi:10.5061/dryad.pg4f4qrpf

Variable strength of predator-mediated effects on species occurrence in an arctic terrestrial vertebrate community

2021· dataset· en· W6948291645 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typedataset
Languageen
FieldComputer Science
TopicData Management and Algorithms
Canadian institutionsUniversité de SherbrookeUniversité LavalUniversité du Québec à Rimouski
Fundersnot available
KeywordsPredationTrophic levelHabitatFood webVertebrateHerbivorePredatorCompetition (biology)Arctic

Abstract

fetched live from OpenAlex

Indirect effects resulting from species sharing the same enemy can shape spatio-temporal variations in species occurrence. The strength of such effects remains poorly known in natural communities composed of species from different trophic levels interacting in heterogeneous landscapes. Benefiting from a well-known arctic vertebrate community and marked spatio-temporal variations in the density of key prey species, we examined the effects of direct predator-prey and indirect predator-mediated effects on species occurrence in the landscape. We found both positive effects of one prey (lemmings), as well as negative indirect effects of another prey (colonial nesting snow geese) on the occurrence of species (ground-nesting birds) belonging to different guilds and trophic levels but sharing a common predator (arctic fox). However, species using prey refuges available in the landscape were not or less affected by predator-mediated effects. Similarly, the smallest (a passerine) and the largest and most dangerous species (an owl) for the shared predator were not affected by these effects. Our study provides one of the rare empirical evidence of predator-mediated effects ascending the food web (i.e., negative indirect effect of an herbivore on avian predators) and underlines how habitat structure and species traits can modulate the strength of indirect effects in natural communities.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0050.004
Research integrity0.0000.001
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 head0.040
GPT teacher head0.252
Teacher spread0.212 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2021
Admission routes1
Has abstractyes

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