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Record W2747048957 · doi:10.1093/jmammal/gyx090

Increased foraging success or competitor avoidance? Diel activity of sympatric large carnivores

2017· article· en· W2747048957 on OpenAlexaff
Badru Mugerwa, Byron du Preez, Lucy Tallents, Andrew J. Loveridge, David W. Macdonald

Bibliographic record

VenueJournal of Mammalogy · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsWestern University
FundersBeit Trust
KeywordsDiel vertical migrationPantheraForagingLeopardPredationSympatric speciationNocturnalEcologyBiologyInterspecific competition

Abstract

fetched live from OpenAlex

The temporal activity of sympatric carnivores reflects trade-offs between avoidance of competitors and predators, and optimizing foraging success. Closely related species may experience greater interspecific competition for resources due to similar morphologies and ecological requirements. Although the mechanisms by which lions (Panthera leo) and leopards (Panthera pardus) partition diet and habitat have been investigated, the degree to which they avoid each other temporally with possible compromises for foraging success remains less clear. In a wildlife conservancy in Zimbabwe, we used camera trap data to investigate the factors influencing the diel activity of lions and leopards. We modeled diel activity using circular statistics and calculated coefficients of overlap using kernel density functions and non-negative trigonometric sums models. Both leopards and lions were predominately nocturnal, with highly overlapping diel activity. The diel activity of leopards also coincided with that of some prey species, especially common duikers (Sylvicapra grimmia). Therefore, we suggest that leopards may prioritize hunting success and prey acquisition over diel avoidance of dominant competitors like lions.

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)0.000
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 score0.002
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.014
GPT teacher head0.259
Teacher spread0.245 · 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

Citations22
Published2017
Admission routes1
Has abstractyes

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