Beyond a diagnostic tool: Validating standardized Mahalanobis distance as a species distribution model for invasive alien species in North America
Bibliographic record
Abstract
Abstract Species distribution models (SDMs) are useful tools for predicting where new invasive species can establish within a country, supporting both preparatory and response activities. National Plant Protection Organizations use SDMs to inform risk assessment and surveillance activities for emerging plant pests. However, SDMs face multiple and difficult statistical challenges, including multi-collinearity of input variables, correlation structures in climate variables that vary through time and space, limited species observation data, and they often lack formal tests of model performance. We have implemented a previously-reported extrapolation-detection tool as an SDM, rather than a diagnostic tool of SDMs. This method characterizes the observed multivariate climate envelope by using Mahalanobis distance to take advantage of the correlation between climate variables, and identifies areas where the climatic conditions are outside the range of the observed climate envelope. Model outputs include climate suitability maps, and most-important covariate analyses to identify the environmental drivers of the results while assisting in variable reduction. We performed a formal test to assess the ability of the SDM to identify areas invaded by invasive plant pests in North America. Using a list of 23 species from the Canadian Food Inspection Agency’s regulated plant pest list, we demonstrate that this method achieves a high level of accuracy (> 85%) for determining climate suitability for North American plant pest invasions, especially when combined with most-important covariate-guided variable reduction. This suggests that the model is suitable for identifying areas of North America that are susceptible to future invasions. We show that many of the errors occur at the edge of climate suitable areas, where we would expect greater uncertainty in model predictions due to potential over-constraining and geospatial averaging. We present additional analyses to support recommendations on the use and limitations of this SDM in a regulatory context.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".