A field‐validated species distribution model to support management of the critically endangered Poweshiek skipperling ( <scp> <i>Oarisma poweshiek</i> </scp> ) butterfly in Canada
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The Poweshiek skipperling ( Oarisma poweshiek ) is a critically endangered grassland butterfly with six populations remaining in the United States and Canada. The single Canadian population, with the largest remaining contiguous habitat, includes less than ~50 observed individuals and extirpation is potentially imminent. Captive breeding is underway and there is a need to locate suitable sites for reintroduction and habitat management. Species distribution models (SDMs) predict habitat quality and guide management decisions. Most SDMs rely on statistical validation as a surrogate metric for accuracy, with presence‐only SDMs usually reporting area under the curve (AUC). Although experts have long cautioned against relying on statistical validation alone, accuracy is rarely field‐validated. We developed a presence‐only SDM using the maximum entropy (Maxent) method to predict probability of occurrence for the Poweshiek skipperling and determine environmental covariates associated with high probability of occurrence. We collected two independent datasets to (a) calibrate our model to predict categories of habitat quality (using factor analysis) and (b) compare expected and observed habitat quality to calculate model accuracy. Statistical validation showed that we predicted presence‐absence of training data with high accuracy (AUC = 0.98). Covariates responsible for most of the variation in probability of occurrence included soil drainage, habitat patch size, and land use type. Only 0.4% of the study area was expected to represent good‐excellent habitat with the remaining 99.6% medium‐poor. Our model predicted novel habitat quality with 81% accuracy (better than chance). Poor‐medium habitat was predicted more accurately (92%) than good‐excellent habitat (54%). Our model showed better accuracy than most other field‐validated SDMs reviewed. We reiterate calls for greater field‐validation of SDMs: if we had relied on statistical validation alone, perceived accuracy of our model would be inflated. Finally, managers can use our results to reliably exclude predicted poor‐medium habitats as candidates for Poweshiek skipperling habitat management or reintroduction.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it