Use of Permanent Plots to Monitor Trends in Burrow-nesting Seabird Populations in British Columbia
Bibliographic record
Abstract
We describe the use of permanent plots for monitoring population trends of burrow-nesting seabirds in British Columbia and test the assumption that trends in plot counts mirror trends in overall population size.A total of 97 plots for Ancient Murrelets Synthliboramphus antiquus, Cassin's Auklets Ptychoramphus aleuticus, Rhinoceros Auklets Cerorhinca monocerata, and Tufted Puffins Fratercula cirrhata were established in the 1980s.Plots were subjectively distributed in higher-density nesting areas of major colonies.Since then, numbers of Ancient Murrelet, Rhinoceros Auklet, and Tufted Puffin burrows increased or remained stable at monitored colonies, except on Pine Island, where burrows decreased for Rhinoceros Auklets.Declines were apparent for Cassin's Auklets, especially on Triangle Island, where numbers of burrows in plots declined 2.5% per year, resulting in a 40% decline in 20 years-a potential loss in that region of more than 20% of the estimated world breeding population.A serious threat to a majority of the world's Ancient Murrelet population from introduced predators was undetected by the permanent plot scheme because colonies with predators were not sampled.This highlights the need for a broad sampling of colonies and the importance of additional surveillance and study of breeding populations.Close agreement was found in the trend information provided by permanent monitoring plots and full-colony transect surveys.Both methods revealed significant differences when burrow numbers changed 3-4% annually.Results suggest that six to eight subjectively placed permanent plots reveal accurate trends in burrow numbers within a colony.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".