Unpacking the ‘black box’: Improving ecological interpretation of regression‐based models
Bibliographic record
Abstract
Abstract Aim Many tree species distribution models use black‐box machine learning techniques that often neglect interpretative aspects and instead focus mainly on maximizing predictive accuracy. In this study, we outline an interpretative modelling framework to gain better ecological insights while mapping abundance patterns of six North American species. Location Continental United States and Canada. Methods We develop an innovative procedure using regression trees by stabilizing variance, and mapping dominant rules which we term ‘optimized regression tree bagging for interpretation and mapping’ (ORTBIM). We apply this technique to understand ecological features influencing the abundance patterns of three eastern (Pinus strobus, Acer saccharum and Quercus montana), and three western (Picea engelmannii, Pinus ponderosa and Pseudotsuga menziesii) tree species in North America. For these species, we assess and map the dominant climate–terrain interactions that partly determine abundance patterns in the eastern and western regions. In the process, we examine the role of varying responses and scales and explore finer‐scale species climate–terrain niches and non‐linear relationships. Results Our study emphasizes the prominent role of elevation and heat–moisture variables in the west and the greater importance of seasonal precipitation and seasonal temperature in the east. The abundance patterns under future climate (SSP5‐8.5) show climate–terrain habitats shifting northward and westward into Canada and Alaska for the eastern species, and predominantly north‐westward for the western species. Conclusion Our interpretative modelling framework can be used to gain a more comprehensive understanding of the abundance patterns across the full species range, formulate better predictive models and facilitate improved management practices under climate change.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".