A Macrogenetic Analysis of Isolation Mechanisms Reveals Habitat Fragmentation as the Primary Driver of Genetic Divergence in Mammals.
Bibliographic record
Abstract
jabbrv-ltwa-all.ldf jabbrv-ltwa-en.ldf Understanding the processes that drive spatial genetic differentiation is essential for understanding how populations adapt to environmental change. By evaluating the relative influence of these drivers, we can gain insights into evolutionary dynamics and the potential for species to respond to shifting landscapes. Three well-accepted drivers of spatial patterns in genetic variation are isolation-by-distance (IBD), where individuals are more genetically similar the closer they are geographically; isolation-by-environment (IBE), where gene flow is reduced due to selection against migrants in unsuitable ecological conditions; and isolation-by-resistance (IBR), where landscape features limit dispersal. We employed a macrogenetic approach, conducting a multi-species, multi-driver, meta-analysis of published genomic SNP data to identify general patterns driving spatial genetic differentiation of mammals globally. Three species distribution models were built per species to test different aspects of IBR, using combinations of landscape and bioclimatic variables. Using two model selection techniques, we find that landscape resistance models better explain genetic differentiation than bioclimatic resistance models. Among the three drivers, IBR was most frequently selected as the best model of genetic differentiation in mammals across both model selection tests, with IBD a close second and IBE the worst performing model. However, the importance of IBE increased with increasing spatial scale, with populations spread over larger distances more likely to be diverging due to IBE than IBR or IBD. Our findings suggest that anthropogenic habitat fragmentation significantly shapes genetic variation in mammals worldwide, underscoring the importance of mitigating the impacts of habitat fragmentation to prevent isolation and extinction of mammalian species.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".