Method selection for species distribution modelling: are temporally or spatially independent evaluations necessary?
Bibliographic record
Abstract
To assess the realism of habitat projections in the context of climate change, we conduct independent evaluations of twelve species distribution models, including three novel ecosystem‐based modelling techniques. Habitat hindcasts for 24 western North American tree species were validated against 931 palaeoecological records from 6000, 11000, 14000, 16000 and 21000 yr before present. In addition, we evaluate regional extrapolations based on geographic splits of >55000 sample plots. Receiver operating characteristic analyses indicated excellent predictive accuracy for cross‐validations (median AUC of 0.90) and fair accuracy for independent regional and palaeoecological validations (0.78 and 0.75). Surprisingly, we found little evidence for over‐parameterisation in any method. Also, given high correlations found between model accuracies in non‐independent and independent evaluations, we conclude that non‐independent evaluations are effective model selection tools. Ecosystem‐based modelling approaches performed below average with respect to model sensitivity but excelled in specificity statistics and robustness against extrapolations far beyond training data, suggesting that they are well suited to reconstruct historical biogeographies and glacial refugia.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.038 | 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 teacher head, 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".