Population demography maintains biogeographic boundaries
Bibliographic record
Abstract
Global biodiversity is organised into biogeographic regions that comprise distinct biotas. The contemporary factors maintaining differences in species composition between regions are poorly understood. Given evidence that populations with sufficient genetic variation can adapt to fill new habitats, it is surprising that more homogenisation of species assemblages across regions has not occurred. Theory suggests that expansion across biogeographic regions could be limited by reduced adaptive capacity due to demographic variation along environmental gradients, but this possibility has not been empirically explored. Using three independently curated data sets describing continental patterns of mammalian demography and population genetics, we show that populations near biogeographic boundaries have lower effective population sizes and genetic diversity, and are more genetically differentiated. These patterns are consistent with reduced adaptive capacity in areas where one biogeographic region transitions into the next. That these patterns are replicated across mammals suggests they are stable and generalisable in their contribution to long-term limits on biodiversity homogenisation. Understanding the contemporary processes that maintain compositional differences among regional biotas is crucial for our understanding of the current and future organisation of global 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".