Relationships between habitat area, habitat quality, and populations of nesting Marbled Murrelets
Bibliographic record
Abstract
We review relationships between the area and quality of apparently suitable nesting habitat (as defined by canopy structure) and the population size of Marbled Murrelets (Brachyramphus marmoratus) which such habitat might support. This information is important to manage the old seral forest nesting habitat of this threatened seabird. Studies at different spatial scales indicate that linear relationships provide good, biologically feasible fits between murrelet counts and areas of apparently suitable habitat when the effects of habitat quality are unknown. A large-scale analysis across Washington, Oregon, and California showed a strong linear relationship between murrelet numbers and area of habitat within large conservation regions. Seven separate watershed-level radar studies (six in British Columbia and one in Washington) support a linear relationship and also indicate that when logging reduces habitat, the murrelets do not aggregate in the remaining habitat at higher densities. Tree-climbing studies show similar trends at stand levels: compared to more pristine habitat, nest densities were not higher in remnant old-growth patches in depleted, highly fragmented areas. Do murrelets nest at higher densities in higher-quality habitat? The sparse information on this topic suggests a correspondence between nest locations and habitat quality as assessed by algorithms, air photo interpretation, and low-level aerial surveys. Most nests (92% and 86% in pooled data from aerial surveys or air photo interpretation, respectively) were found in habitat rated as Moderate, High, or Very High, and few (8% and 14%, respectively) in those rated Low, Very Low, or Nil. The relationship between perceived quality and the likelihood of nesting is, however, non-linear and it is premature to assume that murrelet nest densities will be significantly higher within the upper ranks of suitable habitat assessed from forest features.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".