Low effective population size in the genetically bottlenecked Australian sea lion is insufficient to maintain genetic variation
Bibliographic record
Abstract
Abstract Genetic bottlenecks can reduce effective population sizes ( N e ), increase the rate at which genetic variation is lost via drift, increase the frequency of deleterious mutations and thereby accentuate inbreeding risk and lower evolutionary potential. Here, we tested for the presence of a genetic bottleneck in the endangered Australian sea lion ( Neophoca cinerea ), estimated N e and predicted future losses of genetic variation under a range of scenarios. We used 2238 genome‐wide neutral single‐nucleotide polymorphisms (SNPs) from 72 individuals sampled from colonies off the southern (SA) and western (WA) coastline of Australia. Coalescent analyses using approximate Bayesian computation (ABC) methods indicated that both the SA and WA populations have experienced a historical genetic bottleneck. Using LD‐based methods, we estimated contemporary N e to be 160 (CI = 146–178) and 424 (CI = 397–458) for the WA and SA populations respectively. Modelled future population declines suggested that disease epidemics prompted the highest increases in inbreeding relative to fishery‐related mortalities and other modelled threats. Small effective sizes and relatively low genetic variation leave this species vulnerable, and these risks may be compounded if current population declines are not reversed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".