Modelling the partially unknown distribution of wall lizards (Podarcis) in North Africa: ecological affinities, potential areas of occurrence, and methodological constraints
Bibliographic record
Abstract
Species distribution modelling (SDM) is a powerful tool to investigate various biological questions with a spatial component, but is also sensitive to presence-data characteristics, particularly data precision and clustering. Here, we investigate the effect of these two factors on SDM using Maxent as the modelling technique and wall lizards (genus Podarcis Wagler, 1830) from North Africa as a model system. Podarcis are not ubiquitous in Africa as they are in Europe, but their ecological and distributional characteristics in this area are poorly known. Our results show that the most important environmental factors related to the distribution of this genus in North Africa are humidity, habitat type, and temperature. The areas of potential distribution predicted by models based on data sets with different precision and clustering characteristics show high relatedness to coastal areas and mountain ranges and extend to areas were presence records for these lizards are lacking. Our comparison of models based on different data sets indicates that finer scale models, even if based on fewer presence locations, outperform coarser scale ones. Data clustering does not have a negative effect on model performance, but is rather overcome by sample-size effects. Similar approaches may be of general application to other stenoic species for which available locations are scarce in comparison with the extension of the study area.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".