Identifying the source of species invasions: sampling intensity vs. genetic diversity
Bibliographic record
Abstract
Population geneticists and community ecologists have long recognized the importance of sampling design for uncovering patterns of diversity within and among populations and in communities. Invasion ecologists increasingly have utilized phylogeographical patterns of mitochondrial or chloroplast DNA sequence variation to link introduced populations with putative source populations. However, many studies have ignored lessons from population genetics and community ecology and are vulnerable to sampling errors owing to insufficient field collections. A review of published invasion studies that utilized mitochondrial or chloroplast DNA markers reveals that insufficient sampling could strongly influence results and interpretations. Sixty per cent of studies sampled an average of less than six individuals per source population, vs. only 45% for introduced populations. Typically, far fewer introduced than source populations were surveyed, although they were sampled more intensively. Simulations based on published data forming a comprehensive mtDNA haplotype data set highlight and quantify the impact of the number of individuals surveyed per source population and number of putative source populations surveyed for accurate assignment of introduced individuals. Errors associated with sampling a low number of individuals are most acute when rare source haplotypes are dominant or fixed in the introduced population. Accuracy of assignment of introduced individuals is also directly related to the number of source populations surveyed and to the degree of genetic differentiation among them (F(ST)). Incorrect interpretations resulting from sampling errors can be avoided if sampling design is considered before field collections are made.
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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 | 0.043 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".