Conservation Genetics 5: 25–37, 2004. © 2004 Kluwer Academic Publishers. Printed in the Netherlands. 25 Dispersal in a stream dwelling salmonid: Inferences from tagging and
Bibliographic record
Abstract
We used both direct (mark-recapture) and indirect (microsatellite analysis) methodologies to investigate dispersal between two putative populations of brook charr (Salvelinus fontinalis) in Freshwater River, Cape Race, Newfound-land, Canada. Over a 5-year study period, mark-recapture data revealed some movement by fish, but the proportion of recaptured fish migrating from one population area to another was low (0–4.1%). Additionally, during sampling periods in the spawning seasons, no fish was found in the alternate population area to that of its first capture. Despite this pattern of limited movement, microsatellite analysis based on sixteen polymorphic loci provided no evidence of genetic differentiation. Indirect estimates of dispersal parameters varied greatly between different methods of analysis. While use of a coalescent-based model yielded estimated migration rates congruent with the results of the mark-recapture study, other methods resulted in much higher estimates of migration between the populations. In particular, the lack of genetic differentiation coupled with likely violations of the assumed island model prevented generation of meaningful estimates of dispersal using Fst. The disparities between migration rates estimated from the mark-recapture work and from the different indirect methods highlight the difficulties of using indirect methods to estimate dispersal on an ecological timescale. However, mark-recapture methods can fail to detect historical or episodic movement that is important in an evolutionary context, and we therefore argue that a combination of direct and indirect methods can provide a more complete picture of dispersal than either approach alone.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.007 |
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".