Interaction of landscape and life history attributes on genetic diversity, neutral divergence and gene flow in a pristine community of salmonids
Bibliographic record
Abstract
Landscape genetics holds promise for the forecasting of spatial patterns of genetic diversity based on key environmental features. Yet, the degree to which inferences based on single species can be extended to whole communities is not fully understood. We used a pristine and spatially structured community of three landlocked salmonids (Salvelinus fontinalis, Salmo salar, and Salvelinus alpinus) from Gros Morne National Park (Newfoundland, Canada) to test several predictions on the interacting effects of landscape and life history variation on genetic diversity, neutral divergence, and gene flow (m, migration rate). Landscape factors consistently influenced multispecies genetic patterns: (i) waterfalls created strong dichotomies in genetic diversity and divergence between populations above and below them in all three salmonids; (ii) contemporary m decreased with waterway distance in all three species, while neutral genetic divergence (theta) increased with waterway distance, albeit in only two taxa; (iii) river flow generally produced downstream-biased m between populations when waterfalls separated these, but not otherwise. In contrast, we expected differential life history to result in a hierarchy of neutral divergence (S. salar > S. fontinalis > S. alpinus) based on disparities in dispersal abilities and population size from previous mark-recapture studies. Such hierarchy additionally matched varying degrees of spatial genetic structure among species revealed through individual-based analyses. We conclude that, whereas key landscape attributes hold power to predict multispecies genetic patterns in equivalent communities, they are likely to interact with species-specific life history attributes such as dispersal, demography, and ecology, which will in turn affect holistic conservation strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".