A population-viability-based risk assessment of Marbled Murrelet nesting habitat policy in British Columbia
Bibliographic record
Abstract
The Marbled Murrelet (Brachyramphus marmoratus Gmelin) is a small threatened seabird of the Pacific coast of North America. Through simulation modelling we varied the long-term minimum amounts and quality (nesting density) of old-forest nesting habitat to examine effects on murrelet population viability, our measure of population resilience. Applying diffusion approximations we estimated population longevity and persistence probability under uncertainties of at-sea demography and onshore edge effects affecting nesting success, time scale, spatial scale, and subpopulation structure. We cast our analysis in a Bayesian belief and decision network framework. We also applied the framework to spatially explicit land-use and murrelet inventory data for the northern mainland region of the British Columbia coast. We found a diminishing expected value of persistence probability (EVP), for a single independent population, below a nesting capacity of ≈5000 nesting pairs (≈15 000 birds), accelerating below 2000 pairs. A strategy of multiple semi-independent subpopulations provided a higher joint EVP across a wide range of total nesting capacity. There was little improvement in EVP, for any number of subpopulations, above 10 000 – 12 000 pairs (≈36 000 birds, 45%–60% of coastwide population estimate in 2001). Depending on estimates of nesting density, 12 000 pairs would require between 0.6 and 1.2 million ha of potential old-forest nesting habitat.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".