Predicting the occurrence of an endangered salamander in a highly urbanized landscape
Bibliographic record
Abstract
Effective protection of threatened species living in highly urbanized landscapes requires detailed information on their population distribution. For species that are difficult to detect, species distribution models (SDMs) can be valuable tools for predicting their occurrence. We created an SDM to predict breeding pond locations of the endangered Jefferson salamander Ambystoma jeffersonianum in southern Ontario, the most highly developed and populated region in Canada. Using a maximum entropy modelling algorithm (Maxent), we combined known breeding pond occurrences with climate, land type, soil, and topography data to capture the ecological niche of the Jefferson salamander. Our SDM showed excellent performance (AUC = 0.919), with land type being the most important predictor variable. We produced a continuous habitat suitability map that predicted most hotspots of suitable habitat to occur along the Niagara Escarpment, with small patches in hedge rows and forest fragments in surrounding agricultural areas. Our refined presence-absence map predicted a high suitability area of 305 km 2 with high specificity and moderate overall accuracy. Over half of this area was within the Ontario Greenbelt, demonstrating the importance of protecting this land from future development. Our work demonstrates how SDMs can be used to inform decisions on endangered species and direct conservation efforts towards critical habitats.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.034 | 0.001 |
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; both teacher heads agree on what is shown here.
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".