Modeling Marbled Murrelet nesting habitat: a quantitative approach using airborne laser scanning data in British Columbia, Canada
Bibliographic record
Abstract
The Marbled Murrelet (Brachyramphus marmoratus) is a tree-nesting seabird found along the Pacific coast of North America whose forest nesting habitat has dramatically declined over the last century. Mapping the remaining nesting habitat is a core step in the conservation of this species at risk. Fine-scale mapping efforts are expected to be enhanced by airborne laser scanning (ALS) data (an application of lidar technology), which provides quantitative measures of forest structure. We present an ALS-informed model for Marbled Murrelet nesting habitat in British Columbia (BC), Canada, using ecologically relevant predictors. Two rare species modeling approaches, i.e., ensembles of small models (ESMs) and maximum entropy modeling (MaxEnt), were evaluated to link 58 nest locations with ALS and non-ALS predictors at a 100-m resolution in Desolation Sound (DS). An independent model transfer was conducted in Clayoquot Sound (CS) with 21 nests. The top model that balanced parsimony with strong performance in both areas was built with MaxEnt using only two predictors, forest vertical complexity and internal forest gaps. This model achieved good performance in DS (AUC = 0.77, Boyce index = 0.99) and reasonable performance in CS (AUC = 0.63, Boyce index = 0.67). On average, the most suitable nesting habitat was found in small clusters (< 2 ha) within older forest and less suitable habitat within younger forest. Our results broadly align with existing maps of nesting habitat produced from low-level aerial surveys. However, we predicted additional suitable nesting areas not detected by aerial habitat mapping and found large variations in habitat quality within mapped areas of suitable forest. This study offers a quantitative method to predict the nesting habitat of an elusive species with a small sample of occurrence records. With successful field verification, our model could be a valuable tool for Marbled Murrelet management in BC.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".