Topographic Complexity Facilitates Persistence Compared to Signals of Contraction and Expansion in the Adjacent Subdued Landscape
Bibliographic record
Abstract
Topographically heterogeneous areas are likely to act as refugia for species because they facilitate survival during regional climatic stress due to availability of a range of microenvironments. The Stirling Ranges are a topographically complex area in the generally subdued and ancient landscape of south-western Australia. We investigated the influence of these landscape features on the evolutionary history of the rare woody shrub, Banksia brownii through a combined approach using phylogeographic analysis of sequence data from three chloroplast sequences, the trnV–ndhC, trnQ–rps16, and rpl32–ndhF intergenic spacer regions, and species distribution modeling. The Stirling Ranges showed high genetic diversity and differentiation among populations consistent with localized persistence and maintenance of large populations in an area that species distribution modeling identified as providing habitat stability at the Last Glacial Maximum as well as under warmer conditions. In contrast, populations in the adjacent subdued lowlands showed signals of low diversity, suggesting contraction, and subsequent expansion from localized refugia in the west. Cool summers are an important climatic variable for the species and species distribution modeling showed suitable habitat identified at the LGM suggesting expansion at this time following likely contraction during earlier warmer climatic oscillations. The isolated, coastal population at Vancouver Peninsula showed low diversity but no differentiation and it may have been established in more recent historical times, possibly through Aboriginal movement of seed. Our analysis of B. brownii highlights the complex evolutionary history of the species and the influence of topographic complexity and habitat heterogeneity in this global biodiversity hotspot.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".