Ecological correlates of the distribution of territorial Svalbard rock ptarmigan (<i>Lagopus muta hyperborea</i>)
Bibliographic record
Abstract
Predictive habitat models have become important research and management tools for monitoring the spatial distribution and abundance of wildlife species. In this paper we develop and evaluate statistical habitat models for presence of territorial Svalbard rock ptarmigan ( Lagopus muta hyperborea Sundevall, 1845) cocks in spring and apply the best model to assess ptarmigan habitat selection in a larger extrapolated region. Terrain variables were extracted at detailed (10 m digital elevation model (DEM)) and coarse (50 m DEM) scales to compare model performance. Sets of candidate environmental variables related to terrain and vegetation cover were developed and explanatory variables were calculated at increasing distances from the count site to well above the typical size of ptarmigan territory. We used ecological niche factor analysis to describe the difference between used and available sites. Survey sites used by cocks were characterized by a restricted range of altitude, a high degree of terrain heterogeneity, and dense vegetation cover compared with overall site availability in the survey region. We then used model selection criteria (AICc) to find the most parsimonious logistic regression models estimating habitat resource selection functions for cocks. Detailed terrain variables were better predictors than coarse terrain variables. The normalized difference vegetation index (NDVI) was a good predictor of presence of territorial cocks, but not as good as the most preferred habitat type. Owing to limited availability of high-quality vegetation maps, the best model containing NDVI and 10 m DEM variables was used for extrapolation of male ptarmigan habitat. Our results show that it is possible to obtain a model with a high ability to rank habitats using a low number of map-derived variables. Such rankings can then be used to improve field sampling designs and are therefore a useful tool for management and conservation of ptarmigan and wildlife in Arctic and alpine areas.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".