Comparing Historical and Contemporary Observations of Avian Fauna on the Yáláƛi (Goose Island) Archipelago, British Columbia, Canada
Bibliographic record
Abstract
In an era of global change, historical natural history data can improve our understanding of ecological phenomena, particularly when evaluated with contemporary Indigenous and place-based knowledge. The Yáláƛi (Goose Island) Archipelago is a group of islands in Heiltsuk (Haíɫzaqv) territory on the Central Coast of British Columbia, Canada. Not only has this region been important to the Heiltsuk for millennia but also it is both a federally and internationally recognized important bird area. In this study, we compare data collected by Charles J. Guiguet, a biologist who documented bird communities at Yáláƛi in the summer of 1948, to three different contemporary surveys and to citizen-science data. We find that the relative abundances of forest bird species (i.e., birds that use the terrestrial island ecosystems) in 1948 differed to those observed in systematic surveys in 2011. While Orange-crowned Warblers, Dark-eyed Juncos, and Red Crossbills comprised 55% of detections by Guiguet in 1948, the three most abundant species in 2011 were Bald Eagles, Varied Thrushes, and Pacific Wrens, and these accounted for only 25% of detections. Although we could not make a quantitative comparison, we provide summaries of each species observed in surveys or reported on eBird. We also incorporate Heiltsuk place-based knowledge to enrich our discussion of the variability in bird communities over time, from how changes in mammal communities and human use may have shaped vegetation dynamics to how large-scale natural phenomena impacted topography. To understand which birds are present and how their communities are changing over time, we recommend continued monitoring of the bird communities at Yáláƛi.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".