Life history traits modulate the influence of environmental stressors on biodiversity: The case of fireflies, climate and artificial light at night
Bibliographic record
Abstract
Abstract Aim Artificial light at night (ALAN) is an unprecedented stressor recently introduced in the abiotic milieu of natural landscapes. As such, understanding how ALAN and other natural stressors act in concert to shape the spatial distribution of biodiversity is a core goal in conservation ecology. Here, we aim at understanding how ALAN and climate interact with life history traits and courtship signalling systems to dictate the composition of firefly communities in a global biodiversity hotspot. Location An extensive elevational gradient in the Atlantic rain forest (Brazil) currently known as the hottest hotspot of fireflies on Earth. Methods We used multivariate species distribution models to understand how species traits and courtship signalling systems interact with climate and ALAN to determine species abundance within firefly communities. We also investigated how species‐specific responses to climate and ALAN scale up to determine compositional changes in firefly communities along the elevational gradient. Results We found that climate shapes communities by filtering species according to their body size and trophic position. ALAN dictates the dominant courtship signalling system within communities by affecting the abundance of species that use bioluminescence or a combination of bioluminescence and pheromones in courtship. We also found that associations between beta‐diversity and ALAN were non‐stationary, being higher in regions under low levels of light pollution. This suggests that even incipient increases in ALAN within protected areas can yield fast changes in the composition of firefly communities. Main Conclusions Firefly responses to climate and ALAN are modulated by traits associated with different facets of their life histories. Given the alarming changes in both stressors predicted for the foreseeable future, our findings indicate that firefly communities are vulnerable to compositional changes even within protected areas.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".