Pacific Great Blue Heron Population Monitoring on Vancouver Island and the Surrounding Gulf Islands
Bibliographic record
Abstract
The Pacific or Great Blue Heron fannini subspecies winters and breeds on Vancouver Island in locations within 5 km of the marine shoreline. Its visibility and close connection to the Salish Sea make it an important flagship species. Due to population concerns and threats associated with urban development, the Province of BC has coordinated monitoring of Great Blue Heron colonies on Vancouver Island and the adjacent Gulf Islands from 1997 through 2015. Starting in 2005, I trained volunteer stewards and technicians to use a standardized methodology to locate and assess heron colonies, count active nests, determine nest success and follow a sample of visible nests to determine productivity of young herons that lived until fledgling age. The data is housed at the BC Conservation Data Centre and provided to local governments and interested parties to support conservation bylaws and urban planning. Each breeding season we assessed between 18 and 37 colonies and analyzed the population variables to provide indications of Pacific Great Blue Heron population viability. Overall colony success rate ranged from a low of 43% in 2008 to 90% in 2010 with an average of 66%. The number of active nests in the study area does not show a significant trend with a mean of 536 + 9 active nests annually. Nest success in sampled nests ranged from a low of 30% in 2005 to 100% in 2015 with an overall average of 65% + 21%. Vennesland and Butler (2008 COSEWIC report) estimated that at least 63% of active nests would need to fledge young to maintain Pacific Great Blue Heron populations which occurred in 9 of 14 years analysed. As well, the average annual productivity on Vancouver Island was 1.4 chicks per sampled nest. In only 2006, 2010 and 2012 the productivity exceeded the 1.9 chicks per nest population maintenance threshold estimated by Henny and Bethers in 1971. Bald Eagle predation on chicks and adults appears to be responsible for lower productivity and nest success and colony failures. Another factor in the annual variation of nesting success appears to be cool wet springs which affect egg production and timing of nesting. Great Blue Heron colonies on Vancouver Island appear to be locating closer to urban centres possibly to deter Bald Eagle predation, but this poses vulnerability to disturbance from noise, tree cutting and various building pressures.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".