Mixed Metrics and the Need to Adjust Remote-sensing Data in the Evaluation of Key Biodiversity Areas for Colonial-nesting Seabirds: An example with Glaucous-winged Gulls <i>Larus glaucescens</i>
Bibliographic record
Abstract
Conservation initiatives such as the Key Biodiversity Areas (KBA) Programme use standardized criteria based on estimates of species abundance to identify critical habitats. They therefore depend on accurate estimates of population sizes for target species. It is essential that the metrics used to measure abundance at a candidate site are consistent with those used to estimate total abundance at national or global scales, because only then can it be determined whether abundance at a site meets threshold criteria. Imagery gathered by remotely piloted aircraft systems (RPAS, or drones) has rapidly become a tool for determining abundance of surface-nesting seabirds and, therefore, can assist with the designation of KBAs. However, abundance data derived from drone imagery are often in different units, such as numbers of birds or numbers of incubating adults visible on photographs, than data derived from in-person counts, which generally measure the number of nests or breeding pairs. Therefore, drone data may not be directly comparable to data that have been historically collected to estimate overall breeding-population sizes. This study considered a candidate colony of Glaucous-winged Gulls Larus glaucescens located in the Salish Sea in southwestern Canada, which has been surveyed both by drone and by traditional ground surveys. We developed a conversion factor that at least partially translates counts of incubating Glaucous-winged Gulls detected on drone imagery to an estimate of breeding pairs. Compensating for only nests without incubating adults, results suggest that numbers of incubating adults detected by drone likely represent between 63% and 84% of the total number of breeding pairs. Applying this conversion increased the population estimate for the colony and changed former conclusions about whether the site met recommended criteria for designation as a national or global KBA for Glaucous-winged Gulls.
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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 | 0.021 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".