Computation of Self-recruitment in Fish Larvae using Forward- and Backward-in-Time Particle Tracking in a Lagrangian Model (SWIM-v2.0) of the Simulated Circulation of Lake Erie (AEM3D-v1.1.2)
Bibliographic record
Abstract
Abstract. Accurately estimating self-recruitment (SR), the fraction of recruits in a location that originated locally, is critical for understanding population connectivity. Biophysical models have been typically applied to compute SR by releasing a certain number of larval particles from each assumed source location and tracking them forward in time. However, various strategies have been employed for releasing these larval particles: including randomly, consistently, or a number proportional to the location’s area or larval production, which causes ambiguous results. We demonstrate, using theoretical arguments and numerical simulations from Lake Whitefish (Coregonus clupeaformis) larvae in Lake Erie, that SR depends on larval production at each source location. This dependency suggests that SR may not be computed unambiguously in these models unless realistic larval production is released from all potential source locations. In contrast, parentage analysis studies typically computed SR by assessing the fraction of sampled juveniles that originate locally at a settlement location, instead of identifying larval production at all sources. Therefore, tracking larval particles backward from the settlement location is proposed as a straightforward approach for computing SR. Our findings demonstrate that SR is independent of the number of larval recruits at the settlement location, supporting the employment of backtracking models with randomly released larval particles. In this way, considerable effort and resources, that would otherwise be spent on identifying all potential sources and their larval output, in forward tracking can be saved. We believe this result will have important implications for studies on larval dispersal and recruitment in aquatic systems.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".