Low levels of climate niche conservatism may explain clade diversity patterns in the South African genus <i>Pelargonium</i> (Geraniaceae)
Bibliographic record
Abstract
PREMISE OF THE STUDY: Sharp climatic gradients in South Africa and in particular in the Cape Floristic Region (CFR) provide a diversity of niches over short distances that may have promoted ecological diversification in local clades. Here we measured the extent to which closely related species occupy divergent climates and test whether niche lability is correlated with higher species diversity in the genus. METHOD: We integrated phylogenetic information and environmental niche models (ENM) to assess the levels of climate niche conservatism. ENMs for 113 species of Pelargonium were calculated using maximum entropy. We used two tests, one assessing climate niche equivalency and the other testing niche similarity between sister species and within sections. We also examined whether niche similarity was correlated with phylogenetic relatedness across the genus. KEY RESULTS: Niche similarity was mostly independent of phylogenetic relationships. Compared to random expectations, 23% of closely related species pairs had climate niches that were more similar, and only 6.5% were more disparate; the remaining 70% of comparisons had similarities that fell within random expectations. Similar trends were observed when analyses were restricted to only sister species pairs. Although the overall proportion of niche divergence was low, this was significantly related to sectional diversity. We also found a negative relationship between diversity and the proportion of random niches. CONCLUSIONS: Lack of widespread niche conservatism in a highly heterogeneous landscape and few instances of significant climate niche lability suggest that an adaptive divergence process was implicated in the Pelargonium radiation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".