Dispersal barriers and opportunities drive multiple levels of phylogeographic concordance in the Southern Alps of New Zealand
Bibliographic record
Abstract
Phylogeographic concordance, or the sharing of phylogeographic patterns among codistributed species, suggests similar responses to topography or climatic history. While the orientation and timing of breaks between lineages are routinely compared, spatial dynamics within regions occupied by individual lineages provide a second opportunity for comparing responses to past events. In environments with complex topography and glacial history, such as New Zealand's South Island, geographically nested comparisons can identify the processes leading to phylogeographic concordance between and within regional genomic clusters. Here, we used single nucleotide polymorphisms (obtained via ddRADseq) for two codistributed forest beetle species, Agyrtodes labralis (Leiodidae) and Brachynopus scutellaris (Staphylinidae), to evaluate the role of climate change and topography in shaping phylogeographic concordance at two, nested spatial scales: do species diverge over the same geographic barriers, with similar divergence times? And within regions delimited by these breaks, do species share similar spatial dynamics of directional expansion or isolation-by-distance? We found greater congruence of phylogeographic breaks between regions divided by the strongest dispersal barriers (i.e., the Southern Alps). However, these shared breaks were not indicative of shared spatial dynamics within the regions they delimit, and the most similar spatial dynamics between species occurred within regions with the strongest gradients in historical climatic stability. Our results indicate that lack of concordance as traditionally detected by lineage turnover does not rule out the possibility of shared histories, and variation in the presence and type of concordance may provide insights into the different processes shaping phylogeographic patterns across geologically dynamic regions.
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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 | 0.000 |
| 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".