Climate niche conservatism and complex topography illuminate the cryptic diversification of Asian shrew‐like moles
Bibliographic record
Abstract
Abstract Aim The drivers of extraordinary species diversity and endemism in biodiversity hotspots remain elusive. To identify such drivers, it is necessary to understand the origin of allopatric cryptic diversity that formed as an important part of the biodiversity in low‐latitude montane areas. Here, we test hypotheses regarding the patterns and processes that underlie the diversity of Asian shrew‐like moles ( Uropsilus , Uropsilinae, Talpidae), which exhibit strikingly high cryptic diversity. Specifically, we test the hypotheses that niche conservatism and complex topography explain the largely cryptic diversification of these small montane mammals. Location The mountains of Southwest China ( MSC ), which are a biodiversity hotspot, and adjacent areas. Materials and methods A total of 186 specimens that include all seven species of Uropsilus were collected from key geographical areas of the MSC . One mitochondrial and six nuclear genes were sequenced for phylogenetic and phylogeographical analyses. We reconstructed the phylogeny and delimited species boundaries within Uropsilus using multiple methods. We also tested the hypothesis of phylogenetic niche conservatism and examined the effect of topography on genetic divergence. Furthermore, we implemented a hierarchical examination of spatial‐temporal dynamics in our study system. Results Phylogenetic and species delimitation analyses discovered vastly more cryptic diversity than had been identified in morphology‐based taxonomy. Significant niche similarity between sibling phylogroups was detected and the genetic structure of Uropsilus accorded well with the topography of the MSC . Relatively stable biogeographical diffusion and demography, as well as in situ persistence during the last glacial cycle, were detected. Main conclusions Our analysis indicates that much genetic diversification has occurred without evident niche divergence; hence topographical diversity has provided strongly geographical isolation and ecological gradients which reinforce niche conservatism for sedentary organisms. Cryptic species, as the consequence of a lack of variability in the traits, is attributed to stabilizing selection by the optimal ecological and/or climatic envelopes over evolutionary time‐scales. Our findings indicate that global biodiversity in certain areas could be underestimated. Analyses of other biological systems can determine the universality of niche conservatism in the MSC.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".