Spatial genetic structure in the rock hyrax (<i>Procavia capensis</i>) across the Namaqualand and western Fynbos areas of South Africa — a mitochondrial and microsatellite perspective
Bibliographic record
Abstract
The interplay between biotic and abiotic environments is increasingly recognized as a major determinant of spatial genetic patterns. Among spatial genetic studies, saxicolous or rock-dwelling species remain underrepresented in spite of their strict dependence on landscape structure. Here we investigated patterns and processes operating at different spatial (fine and regional scales) and time scales (using mitochondrial and microsatellite markers) in the rock hyrax (Procavia capensis (Pallas, 1766)). Our focus was on the western seaboard of South Africa and included two recognized biodiversity hotspots (Cape Floristic Region and Succulent Karoo). At fine spatial scale, significant genetic structure was present between four rocky outcrops in an isolated population, likely driven by the social system of this species. At a broader spatial scale, ecological dependence on rocky habitat and population-level processes, in conjunction with landscape structure, appeared to be the main drivers of genetic diversity and structure. Large areas devoid of suitable rocky habitat (e.g., the Knersvlakte, Sandveld, and Cape Flats, South Africa) represent barriers to gene flow in the species, although genetic clusters closely follow climatic, geological, and phytogeographic regions, possibly indicating ecological specialization or adaptation as contributing factors enforcing isolation. Taken together, our study highlights the need to consider both intrinsic and extrinsic factors when investigating spatial genetic structures within species.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".