Assessing long‐term effects of artificial light at night on insects: what is missing and how to get there
Bibliographic record
Abstract
Abstract Widespread and significant declines of insect population abundances and biomass are currently one of the most pressing issues in entomology, ecology and conservation biology. It has been suggested that artificial light at night is one major driver behind this trend. Recent advances in the gathering and analysis of long‐term data sets of insect population and biomass trends, however, have mostly focused on the effects of climate change and agricultural intensification. We posit here that adequate assessment of artificial night at light that would be required to evaluate its role as a driver of insect declines is far from trivial. Currently its implementation into entomological monitoring programmes and long‐running ecological experiments is hampered by several challenges that arise due to (i) its relatively late appearance as a biodiversity threat on the research agenda and (ii) the interdisciplinary nature of the research field where biologists, physicists and engineers still need to develop a set of standardised assessment methods that are both biologically meaningful and easy to implement. As more studies that address these challenges are urgently needed, this article aims to provide a short overview of the few existing studies that have attempted to investigate longer‐term effects of artificial light at night on insect populations. To improve the quality and relevance of studies addressing artificial light at night and its effect on insects, we present a set of best practise recommendations where this field needs to be heading in the coming years and how to achieve it.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".