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Record W2884540437 · doi:10.1111/ele.13124

Ecological correlates of the spatial co‐occurrence of sympatric mammalian carnivores worldwide

2018· article· en· W2884540437 on OpenAlexafffund
Courtney L. Davis, Lindsey N. Rich, Zach J. Farris, Marcella J. Kelly, Mario S. Di Bitetti, Yamil Di Blanco, Sebastián Albanesi, Mohammad S. Farhadinia, Navid Gholikhani, Sandra Hamel, Bart J. Harmsen, Claudia Wultsch, Mamadou Kané, Quinton Martins, Asia Murphy, Robin Steenweg, Sunarto Sunarto, Atieh Taktehrani, Kanchan Thapa, Jody M. Tucker, Jesse Whittington, Febri Anggriawan Widodo, Nigel G. Yoccoz, David A. Miller

Bibliographic record

VenueEcology Letters · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsParks CanadaAlberta Environment and Protected Areas
FundersU.S. Forest ServiceMinistry of EnvironmentWildlife Conservation SocietyUnited States Agency for International DevelopmentParks CanadaNational Science Foundation
KeywordsCarnivoreSympatric speciationEcologyCo-occurrenceBiologyCamera trapEcosystemGeographyHabitatPredation

Abstract

fetched live from OpenAlex

The composition of local mammalian carnivore communities has far-reaching effects on terrestrial ecosystems worldwide. To better understand how carnivore communities are structured, we analysed camera trap data for 108 087 trap days across 12 countries spanning five continents. We estimate local probabilities of co-occurrence among 768 species pairs from the order Carnivora and evaluate how shared ecological traits correlate with probabilities of co-occurrence. Within individual study areas, species pairs co-occurred more frequently than expected at random. Co-occurrence probabilities were greatest for species pairs that shared ecological traits including similar body size, temporal activity pattern and diet. However, co-occurrence decreased as compared to other species pairs when the pair included a large-bodied carnivore. Our results suggest that a combination of shared traits and top-down regulation by large carnivores shape local carnivore communities globally.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.998

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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.

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

Citations110
Published2018
Admission routes2
Has abstractyes

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