Lampreys adjust, mammals non-plussed, birds robust: how ecological and environmental features influence genetic diversity
Bibliographic record
Abstract
Evolutionary forces are intrinsically tied to environments and ecosystems species occupy. Gene flow is shaped by conduits and impediments in landscape features and conditions, and spatially separate populations often divergently adapt to heterogenous environments. Understanding the nexus of environments and genetic diversity is vital when managing species, especially in the context of rapid global climate change and increasing anthropogenic disturbance. I used two avenues of study to explore this relationship on large spatial scales. I analyzed whole-genome sequencing data for 209 invasive sea lampreys in the Laurentian Great Lakes, and publicly archived, raw microsatellite data from 1,008 bird and mammal population data points across Canada and the United States. For the former, I hypothesized that as an invasive species recently introduced to a novel environmental gradient, the sea lamprey populations would be locally adapted to conditions in the Great Lakes. For the latter, I hypothesized that the human footprint index (HFI), when used as a resistance surface, would best explain genetic distances in bird and mammal populations in North America. For both studies, I used statistical approaches to look for patterns of genetic diversity among and across populations of animals and identify environmental covariates of these patterns. I found evidence of local adaptation in sea lamprey populations, whereby the adaptive divergence of populations significantly correlated with levels of human population density. Bird and mammal populations were also shaped by human influence, with a positive and negative effect, respectively, of the HFI on genetic distance. Though direction and degree of effects on genetic diversity varied across taxonomic groups, these results indicate the overarching influence environmental variables—particularly, human disturbances—have on spatial distribution and genetic diversity across taxonomic groups. Be it an invasive species, or species of conservation concern, understanding the link between environmental gradients and genetic diversity of animal populations is of both biological and managerial interest.
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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 | 0.000 | 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) | 0.001 | 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".