Larval traits interaction with the Amazon River Plume determines its role as a dispersal barrier
Bibliographic record
Abstract
The Amazon River Plume (ARP) functions as a dynamic and porous biogeographic barrier whose permeability to larval dispersal depends on the interplay between species’ biological traits and oceanographic processes. Using biophysical modeling combined with a dual analytical framework a Multivariate Regression Tree (MRT) and a Generalized Additive Model (GAM) this study quantifies the factors regulating this permeability for eggs and larvae in the Western Tropical Atlantic. Our results reveal a clear hierarchy of controls. Planktonic Larval Duration (PLD) emerged as the primary determinant, explaining most of the variation in dispersal distances (58%). Diel Vertical Migration (DVM) was the second biologic key factor (11%), modulating whether larvae were locally retained (with DVM) or exported to distant regions (without DVM). Seasonality and the geographic context of spawning habitats further shaped larval interactions with the plume, reinforcing the spatial and temporal complexity of this system. Overall, the ARP acts not as an absolute barrier but as a continuum of permeability a selective filter that restricts dispersal of coastal species with short PLDs and low physiological tolerance, yet facilitates exchange for communities with greater dispersal capacity and behavioral plasticity.
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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 | 0.000 | 0.000 |
| 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".