Comparing the performance of three common species distribution modelling frameworks for freshwater environments through application to eel species in New Zealand
Bibliographic record
Abstract
Globally, many freshwater species are depleting and require population-level assessments. Many species distribution modelling frameworks are available for such assessments, but comparisons are needed to understand their predictive performance under different settings. K-fold cross-validation techniques were employed to compare the performance of three commonly used frameworks: machine learning, spatiotemporal modelling, and Gaussian process (GP) modelling. Through application to New Zealand populations of longfin eel ( Anguilla dieffenbachii) and shortfin eel ( Anguilla australis), area under the receiver operating characteristic curve (AUC) and true skill statistic (TSS) model performance metrics were estimated. All modelling frameworks produced approximately consistent distribution maps but differed in predictive performance. AUC and TSS results indicated that model predictions from the spatiotemporal modelling framework were the most accurate, followed by GP modelling. However, all modelling frameworks performed similarly when training and test data were spatially independent. In addition to having the best predictive performance, the spatiotemporal modelling framework showed the greatest promise for advancement in population-level assessment and is therefore recommended. The results are useful for freshwater ecologists and resource managers to make informed decisions on the appropriateness of a modelling framework for their research objective.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".