Broadscale effects of landscape on genetic structure of polar bears
Bibliographic record
Abstract
The interplay between landscape features and genetic processes (e.g., gene flow, genetic drift) ultimately shapes population structure and species distributions. Understanding the evolutionary processes linking species and their environments can inform species’ responses to stochastic or human-induced change. Individual dispersal and connectivity among populations can be studied using the rapidly advancing field of landscape genetics through integrative study of spatial genetic patterns and their relation to landscape variables. Studying these dynamics through time is not often done, yet offers greater potential to evaluate the effects of landscapes on gene flow, and enables study of genetic change. The polar bear (Ursus martimus) is a circumpolar, apex arctic predator, considered to be in peril in the context of rapidly changing arctic environments. It is of great cultural and spiritual importance to Inuit peoples, and is hunted across the Arctic. Using over two decades (1997–2020) of polar bear harvest sample data collected by Inuit throughout Nunavut and the Inuvialuit Settlement Region, I conduct a comparison of polar bear spatial genetic structure and landscape resistance through time. I use resistance models to relate landscape variables to genetic structure for each of two periods of sampling across a consistent distribution (1997–2008 and 2009–2020). I observe local changes in spatial genetic structure across the Arctic Archipelago between periods. Resistance models emphasize the importance of both sea ice extents and landcover across this distribution for each period. Spatial autoregressive lag models indicate genetic change is predicted by sea ice resistance change between periods. I thus detect change in genetic structure resulting from environmental change, and identify sea ice as the leading underlying factor. Temporal landscape-genetic studies offer valuable insight on wide-ranging, continuously distributed species for conservation and management decisions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".