Modelling the dynamic biogeography of the wildcat: implications for taxonomy and conservation
Bibliographic record
Abstract
Abstract There is still no clear consensus on how to relate geographical variation in the morphology and genetics of the globally widespread wildcat Felis silvestris to its taxonomy and systematics. Reconstructing the dynamic biogeography of the wildcat provides insight into how current geographical patterns of morphological and molecular variation may have developed. A geographical information system was used to infer climate‐change influences using a deduced distribution model (DDM) to reconstruct the wildcat's geographical distribution at four points in time from the Last Glacial Maximum [LGM; 18 000 years before present ( bp )] until today. The DDM for 9000 bp , when mean global temperatures were 2 °C more than today, provides insight into how current global warming will affect the wildcat's distribution 50–100 years into future. Modelled distributions were assessed against known geographical barriers or unsuitable habitats, which may have separated populations and led to known morphological and genetic divergence. The DDM today corresponds well with known contemporary wildcat distribution records, except where wildcats would be expected to be excluded (e.g. high human population densities, potential competitors, inaccessible islands). The DDM today also corresponds closely with the results of recent studies on skull morphometrics and phylogeography, which support hypothesized colonizations of Africa and Asia from Europe during the late Pleistocene. Although DDM palaeo‐distributions are more uncertain, they correspond to expected dramatic declines in northern Eurasia during the LGM, and significant distributional decline in central Asia, the Sahara and southern Africa, owing to increased aridity during climate cooling. From the DDM 9000 model moderate global warming is hypothesized to impact minimally on wildcats, except in the Middle East and south‐west Asia.
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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 | 0.000 |
| 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) | 0.000 | 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".