Does a short Pollard walk transect capture butterfly and bee diversity? A test to inform pollinator monitoring and community science initiatives
Bibliographic record
Abstract
Abstract Widespread declines in insects will threaten ecosystem functioning and services. Nevertheless, a lack of data hinders assessments of population and biodiversity trends for many insect groups and thus effective conservation actions. Implementing cost‐effective, unbiased, and accurate monitoring programmes targeting different groups across a larger geographical range has therefore become a key conservation priority. We evaluated a sampling protocol designed for community science initiatives targeting butterflies and bees. Specifically, we tested how well a short (200‐m long) version of traditional Pollard walk transects, designed to be accessible for large numbers of community scientists, captures changes in alpha and beta diversity of these two pollinator groups. We used resampling methods to simulate and assess scenarios varying in sampling intensity and frequency. We found that alpha and beta diversity of butterflies and bees were estimated at similar accuracies across different scenarios, which suggests that even short transects can provide useful information on diversity patterns for both taxa. However, common sampling frequencies resulted in low accuracies (e.g. one sample every 10 days finds on average ~50% of the species present at a site). We discuss our results in the context of developing large scale, structured monitoring systems for multiple insect taxa, and how information on biodiversity patterns can inform the expansion of monitoring schemes. We explain why, moving forward, even rapid sampling designs similar to the approach tested here will be useful given a higher potential to involve community scientists, data integration techniques, and the opportunities to sample under‐represented habitat types
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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".