Bibliographic record
Abstract
Understanding the scale of dispersal is an important consideration in the conservation and management of many species. However, in species in which the high-dispersal stage is characterized by tiny gametes or offspring, it may be difficult to estimate dispersal directly. This is the case for many marine species, whose pelagic larvae are dispersed by ocean currents by several days or weeks before beginning a benthic, more sedentary, adult stage. As consequence of the high-dispersal larval stage, many marine species have low genetic structure on large spatial scales (Waples 1998; Hellberg 2007). Despite the high capacity for dispersal, some tagging studies have found that a surprising number of larvae recruit into the population they were released from (self-recruitment). However, estimates of self-recruitment are not informative about mean dispersal between subpopulations. To what extent are limited dispersal estimates from tagging studies compatible with high potential for dispersal and low genetic structure? In this issue, a study on five species of coral reef fish used isolation by distance (IBD) between individuals to estimate mean dispersal distances (Puebla et al. 2012). They found that mean dispersal was unexpectedly small (<50 km), given relatively low IBD slopes and long pelagic durations. This study demonstrates how low genetic structure is compatible with limited dispersal in marine species. A comprehensive understanding of dispersal in marine species will involve integrating methods that estimate dispersal over different spatial and temporal scales. Genomic data may increase power to resolve these issues but must be applied carefully to this question.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".