Ancient islands acted as refugia and pumps for conifer diversity
Bibliographic record
Abstract
Island species are thought to be extinction-prone because of small population sizes, restricted geographical distribution and limited dispersal ability. However, the topographical and environmental heterogeneity, geographical isolation and stability of islands over long timescales could create refugia for taxa whose source area is threatened by environmental changes. We address this possibility by inferring the evolution of the New Caledonia (NC) and New Zealand (NZ) conifer diversity, which represents over 10% of the world's diversity for this group. We estimate speciation and extinction rates in relation to the presence/absence on these islands, and dispersal rates between the islands and surrounding areas. We also test the Eocene submersion of NC and the Oligocene drowning of NZ by comparing the fit of biogeographical scenarios using ancestral area estimations. We find that extinction rates were significantly lower for island species, and dispersal "out of islands" was higher. A model including a diversification shift when NC emerged better explains the diversification dynamics. Biogeographical analyses corroborate that conifers experienced high continental extinctions, but survived on islands. NC and NZ have thus contributed to the world's conifer diversity as "island refugia", by maintaining early-diverging lineages from continents during environmental changes on continents. These ancient islands also acted as "species pumps", providing species into adjacent areas. Our study highlights the important but neglected role of islands in promoting the evolution and conservation of biodiversity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".