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Record W4403413201 · doi:10.1098/rspb.2024.0683

The moon’s influence on the activity of tropical forest mammals

2024· article· en· W4403413201 on OpenAlexaff
Richard Bischof, Andrea F. Vallejo‐Vargas, Asunción Semper‐Pascual, Simon D. Schowanek, Lydia Beaudrot, Daniel Turek, Patrick A. Jansen, Francesco Rovero, Steig E. Johnson, Marcela Guimarães Moreira Lima, Fernanda Santos, Eustrate Uzabaho, Santiago Espinosa, Jorge Ahumada, Robert Bitariho, Julia Salvador, Badru Mugerwa, Moses N. Sainge, Douglas Sheil

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2024
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsUniversity of Calgary
FundersNorges Forskningsråd
KeywordsMoonlightFull moonNocturnalEcologyAstrobiologyRainforestAttractionMammalGeographyBiology

Abstract

fetched live from OpenAlex

Changes in lunar illumination alter the balance of risks and opportunities for animals, influencing activity patterns and species interactions. We examined if and how terrestrial mammals respond to the lunar cycle in some of the darkest places: the floors of tropical forests. We analysed long-term camera trapping data on 86 mammal species from 17 protected forests on three continents. Conservative categorization of activity during the night revealed pronounced avoidance of moonlight (lunar phobia) in 12 species, compared with pronounced attraction to moonlight (lunar philia) in only three species. However, half of all species in our study responded to lunar phases, either changing how nocturnal they were, altering their overall level of activity, or both. Avoidance of full moon was more common, exhibited by 30% of all species compared with 20% of species that exhibited attraction. Nocturnal species, especially rodents, were over-represented among species that avoided full moon. Artiodactyla were more prominent among species attracted to full moon. Our findings indicate that lunar phases influence animal behaviour even beneath the forest canopy. Such impacts may be exacerbated in degraded and fragmented forests. Our study offers a baseline representing relatively intact and well-protected contexts together with an intuitive approach for detecting activity shifts in response to environmental change.

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 score0.444
Threshold uncertainty score0.886

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 studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.312
Teacher spread0.262 · 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

Citations14
Published2024
Admission routes1
Has abstractyes

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