Of detectability and camouflage: evaluating Pollard Walk rules using a common, cryptic butterfly
Bibliographic record
Abstract
Abstract Estimating distribution and abundance of species depends on the probability at which individuals are detected. Butterflies are of conservation interest worldwide, but data collected with Pollard walks—the standard for national monitoring schemes—are often analyzed assuming that changes in detectability are negligible within recommended sampling criteria. The implications of this practice remain poorly understood. Here, we evaluated the effects of sampling conditions on butterfly counts from Pollard walks using the Arctic fritillary, a common but cryptic butterfly in boreal forests of Alberta, Canada. We used an open population binomial N‐mixture model to disentangle the effects of habitat suitability and phenology on abundance of Arctic fritillaries, and its detectability by sampling different conditions of temperature, wind, cloud cover, and hour of the day. Detectability varied by one order of magnitude within the criteria recommended for Pollard walks (P varying between 0.04 and 0.45), and simulations show how sampling in suboptimal conditions increases substantially the risk of false‐absence records (e.g., false‐absences are twice as likely than true‐presences when sampling 10 Arctic fritillaries at P = 0.04). Our results suggest that the risk of false‐absences is highest for species that are poorly detectable, low in abundance, and with short flight periods. Analysis with open population binomial N‐mixture models could improve estimates of abundance and distribution for rare species of conservation interest, while providing a powerful method for assessing butterfly phenology, abundance, and behavior using counts from Pollard walks, but require more intensive sampling than conventional monitoring schemes.
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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".