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Record W4415145981 · doi:10.1007/s00114-025-02031-w

Herbivory rate is elevated but orb-weaver spider growth unaffected by artificial light at night in subtropical forest

2025· article· en· W4415145981 on OpenAlexaff
John A. Allcock, Caroline Dingle, Louise A. Ashton, Timothy C. Bonebrake

Bibliographic record

VenueDie Naturwissenschaften · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicImpact of Light on Environment and Health
Canadian institutionsCapilano University
FundersUniversity of Hong Kong
KeywordsSubtropicsHerbivoreAbundance (ecology)Tropical and subtropical moist broadleaf forestsBiodiversityEcosystemTropical forest

Abstract

fetched live from OpenAlex

Artificial light at night (ALAN) is a growing threat to biodiversity and ecosystems globally. However, a limited number of studies have focused on the effect of ALAN in the tropical or subtropical regions, and the impact of ALAN on species interactions and ecological processes is particularly understudied. We introduced ALAN into light-naïve forest plots to examine whether ALAN affects herbivory rate and alters the growth and abundance of Nephila pilipes, a common orb-weaver spider. We found illuminated plots had a higher herbivory rate than control plots in the early wet season. The growth rate and abundance of the spider species, however, were not affected by ALAN. Our results indicate that ecological processes in tropical ecosystems, such as herbivory, are potentially sensitive to ALAN. Additionally, this study highlights the importance of a more mechanistic understanding of the sensitivities of tropical species interactions to ALAN and the possible complications caused by environmental variation.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.006
GPT teacher head0.236
Teacher spread0.230 · 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
Published2025
Admission routes1
Has abstractyes

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