Firefly tourism: Advancing a global phenomenon toward a brighter future
Bibliographic record
Abstract
Abstract Fireflies are charismatic beetles with attractive bioluminescent courtship displays that have recently been swept onto the global stage of nature tourism. Here, we provide the first comprehensive review of the geographic scope, magnitude, focal species, and other attributes of the major firefly tourism sites worldwide. Through targeted interviews and surveys, we estimate that in recent years over one million tourists travelled annually to sites located in at least 12 countries for firefly‐watching. Rapid proliferation of firefly tourism provides a timely opportunity to examine how such activities may impact local firefly populations, and to highlight the biological factors that make certain species especially vulnerable to tourism‐associated threats. We offer science‐based best practices for firefly ecotourism that can be tailored to fit local circumstances, including recommendations to: (1) Conserve the habitats required for all life stages to thrive, (2) involve local communities as key stakeholders, and (3) provide training programs for guides and interpretive materials for visitors. Finally, we provide suggestions for transforming tourist behavior to minimize impact on firefly populations. By developing management plans that incorporate these recommendations, firefly tourist sites can enhance the visitor experience, protect natural resources, benefit local communities, and help promote the conservation of invertebrate biodiversity.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".