Data from: Assessing conservation risks to populations of an anadromous Arctic salmonid, the northern Dolly Varden (Salvelinus malma malma), via estimates of effective and census population sizes and approximate Bayesian computation
Bibliographic record
Abstract
Abstract Census population size (Nc) is crucial to the development of resource management strategies, however, monitoring the effective population size (Ne) of managed populations has proliferated because of this parameter’s relationship to the short-term impacts of genetic stochasticity and long-term population viability. Thus, having a sound understanding of both Nc and Ne, including population connectivity, provides valuable insights into both the demographic and genetic risks to extinction. Here, we assessed microsatellite DNA variation in four (of five known) anadromous northern Dolly Varden (NDV, Salvelinus malma malma) populations from Canada’s western Arctic region, to estimate Ne using both temporal-based and single-sample estimators and to test for associations between Ne and Nc. We also employed approximate Bayesian computation (ABC) to evaluate several evolutionary scenarios that have potentially shaped contemporary population structure in this species, focusing particularly on population size and connectivity. We found evidence for moderate to large contemporary and historical Ne, suggesting that short- and long-term extinction risks are low for these populations. Estimates of contemporary and long-term Ne were variable within and among populations and overall estimates could not be reliably linked with Nc or available spawning habitat. The overall estimate of Ne/Nc, was 0.152 and ranged from 0.024 to 0.442 when including errors around the estimate of Ne and Nc. Finally, ABC analyses suggest that NDV had a common origin followed by divergence in isolation while maintaining large effective sizes, but also that these populations were bottlenecked in the past, likely the result of post-glacial colonization processes. These results corroborate indications of limited gene flow at present, indicating independent demographic and evolutionary trajectories that imply NDV is best managed on a per-river-population basis. Overall, the results of this study further our general understanding of Ne, Ne/Nc and demographic independence in NDV, and provide a comprehensive and quantitative assessment of the potential genetic and demographic risk status of Arctic anadromous salmonids, including baselines for future monitoring.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".