Habitat suitability modeling for mink passage activity: A cautionary tale
Bibliographic record
Abstract
ABSTRACT Many studies have evaluated wildlife passage effectiveness, but few have explored how accurately passage activity can be modeled. I created a habitat suitability index (HSI) model for American mink (Neovison vison) using a geographic information system with 17 wildlife passages located in Quebec, Canada as validation for the model. I addressed how well HSI modeling using constrained habitat data could determine mink passage activity and tested model sensitivity to different parameterizations. Uncertainty analysis revealed that the HSI model was sensitive to extreme changes in factor weights and scale. I used a generalized linear model to test how well the constrained HSI model explained the variability in passage activity (counts). For the HSI model (and all alternatives) the HSI scores were negatively associated with passage use. The predictive power of all models greatly improved after including aspects related to passage construction, with the pseudo‐R2 increasing by 64–73%. These findings suggest that the constrained HSI models are a poor predicator of passage activity for mink, but wildlife passage characteristics are highly predictive. Transportation agencies would benefit from the ability to make informed planning decisions; however, greater care is required to determine passage suitability. The proper implementation of these tools requires knowledge of habitat preferences and how movement is influenced by the wildlife passages themselves. © 2017 The Wildlife Society.
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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 | 0.013 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".