Reducing the rate of false absences of cryptic species in inventory and sampling work
Bibliographic record
Abstract
When doing inventory for cryptic and rare species, it can be difficult to determine with great confidence that a sampled area has no occurrences of the target species. Boreal Felt lichen (Erioderma pedicellatum (Hue.) P.M.Jørg.) and Vole Ears lichen (Erioderma mollissimum (G.Sampaio) Du Rietz) are two rare species of cyanolichens that have several populations in North America, including Nova Scotia, Newfoundland and Alaska. These lichens occur in small numbers and are difficult to spot with the untrained eye; therefore, they are likely to be overlooked in standard sampling protocols. In this paper, we develop and test a sampling method that enables us to determine with confidence that a sample site has zero occurrences of the species of interest within a defined area (i.e., an absence of detections indicates an absence of the target lichen species and is not a false absence). On 50 sites, we randomly assigned “decoy lichen” treatments (small pieces of felt that resemble boreal felt lichen) and three seekers with different survey experience and time limits carried out their respective searches for these decoys. This sampling method is very applicable to sessile, rare organisms, such as lichens and mosses. Using circular sample plots of 5m in radius, we determined that 20 minutes is the required search effort to detect at least one rare and cryptic lichen individuals within the plot. We also found that decoy density on a plot had a strong influence on decoy detectability, regardless of seeker experience. Detection reliability was greater for the two seekers with prior cryptic survey experience compared to the seeker with none. High confidence in the “true absence” rate is useful for comparative studies of optimal and non-optimal habitat, and the methods here are useful to estimate detection rates for other cryptic organisms.
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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 | 0.095 | 0.210 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".