Can inverse calibration help improving process-explicit species distribution models?
Bibliographic record
Abstract
Process-explicit models (PEMs) are expected to provide reliable projections of species range shifts because they explicitly model the biological mechanisms that drive species responses to climate. However, their application is often limited by the need for diverse and detailed datasets, which are only available for a limited number of species. Inverse calibration has been identified as an avenue to help calibrate PEMs for many species, but it is still unclear whether it can provide biologically meaningful parameter estimates. Here, we investigated the potential of inverse calibration techniques to improve the accuracy of PEMs. We examined the discrepancies in parameter estimates obtained by classical and inverse calibration approaches. We evaluated two inverse calibration strategies: (i) calibrating all parameters simultaneously and (ii) focusing only on critical parameters. We assessed the realism of the obtained parameter estimates and the simulated processes by comparing them with measurements and observations across Europe. We show that when the entire model is calibrated at once, the model structure alone may not sufficiently constrain parameter estimation, leading to unrealistic parameter values. However, selective application of the inverse calibration approach – focusing on critical parameters – can improve model performance while still simulating realistic biological mechanisms.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".