Assessing population genetic structure of three New Zealand stream insects using mitochondrial and nuclear DNA markers
Bibliographic record
Abstract
Assessing genetic differentiation among natural populations can aid understanding of dispersal patterns and connectivity among habitats. Several molecular markers have become increasingly popular in determining population genetic structure for this purpose. Here, we compared the resolution of mitochondrial cytochrome c oxidase subunit I (COI) and nuclear single nucleotide polymorphism (SNP) markers for detecting population structure among stream insects at small spatial scales. Individuals of three endemic taxa— Coloburiscus humeralis (Ephemeroptera), Zelandobius confusus (Plecoptera), and Hydropsyche fimbriata (Trichoptera)—were collected from forested streams that flow across open pasture in the North Island of New Zealand. Both COI and SNP data indicated limited population structure across the study area, and small differences observed among these species were likely related to their putative dispersal abilities. For example, fine-scale genetic differentiation between and among neighbouring stream populations for H. fimbriata suggests that gene flow, and hence dispersal, may be more limited for this species relative to the others. Based on the generally similar results provided by both types of markers, we suggest that either COI or SNP markers can provide suitable initial estimates of fine-scale population genetic differentiation in stream insects.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".