Applying landscape genetics to evaluate threats affecting endangered Atlantic salmon populations
Bibliographic record
Abstract
Landscape genetics affords a potential analysis framework to evaluate the effect of contemporary land use on endangered species at a population level. However, historical patterns of population connectivity need to be accounted for prior to testing for the contemporary effect of threats. The goals of this study were to (1) optimally describe historical patterns in population connectivity for a diadromous fish species before (2) evaluating whether residual genetic variation was correlated with ecological changes arising from several types of land use. Using endangered Atlantic salmon populations as a case study, we evaluated whether historical patterns in population connectivity were more likely to result from dispersal limitation (isolation by distance) relative to habitat choice and reproductive success (isolation by environment). Second, we used Reciprocal Causal Modeling to identify the types of land use contributing to three threat indices, and subsequently Multiple Regression on Distance Matrices to evaluate the relative severity of each. These analyses suggest that straying Atlantic salmon avoid watersheds with reduced water quality (resulting from acidification and abandoned mines) and higher road density, yet are not responding to watershed fragmentation (from road-river crossings and dams) at a population level. This study is among the first to explicitly compare alternate behavioural hypotheses leading to dispersal patterns for diadromous fishes and to quantitatively assess freshwater threats for Atlantic salmon at a population level using landscape genetics.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".