Habitat suitability modeling from empirical data : application to mule deer in the interior of British Columbia
Bibliographic record
Abstract
Habitat suitability modeling has both strengths and weaknesses as a land management tool. Its utility is highly dependent on the ecological interactions and spatial and temporal scales that are pertinent to land management concerns and the species of interest. To maximize the usefulness of mathematical habitat suitability models, it is important that they are constructed using all the reliable a priori information available, and selected using a method that consistently selects models of an appropriate level of complexity. Application is exemplified here to produce winter and summer habitat suitability models for mule deer (Odocoileus hemionus) in young, intensively managed lodgepole pine (Pinus contorta) stands in the interior of British Columbia. The building of all models likely to have good explanatory power was informed by a comprehensive literature review of mule deer habitat requirements. After models were built, multivariate correlations between predictor variables and the dependent variable of standardized pellet-group densities were analyzed to ensure that no strong and sensible relationships suggested by the data were left out of the model set. Akaike's Information Criterion (AIC) was used for model selection, as it is currently the best readily available model selection criterion when 'truth' is of near-infinite complexity. To improve robustness of inference and prediction error estimates, final models are produced as AIC weighted averages of the models most strongly supported by the data. Although models should ideally be validated using independent data, error was estimated here based on the same data used for model fitting.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".