Larval dispersal in a changing ocean with an emphasis on upwelling regions
Bibliographic record
Abstract
Abstract Dispersal of benthic species in the sea is mediated primarily through small, vulnerable larvae that must survive minutes to months as members of the plankton community while being transported by strong, dynamic currents. As climate change alters ocean conditions, the dispersal of these larvae will be affected, with pervasive ecological and evolutionary consequences. We review the impacts of oceanic changes on larval transport, physiology, and behavior. We then discuss the implications for population connectivity and recruitment and evaluate life history strategies that will affect susceptibility to the effects of climate change on their dispersal patterns, with implications for understanding selective regimes in a future ocean. We find that physical oceanographic changes will impact dispersal by transporting larvae in different directions or inhibiting their movements while changing environmental factors, such as temperature, pH, salinity, oxygen, ultraviolet radiation, and turbidity, will affect the survival of larvae and alter their behavior. Reduced dispersal distance may make local adaptation more likely in well‐connected populations with high genetic variation while reduced dispersal success will lower recruitment with implications for fishery stocks. Increased dispersal may spur adaptation by increasing genetic diversity among previously disconnected populations as well as increasing the likelihood of range expansions. We hypothesize that species with planktotrophic (feeding), calcifying, or weakly swimming larvae with specialized adult habitats will be most affected by climate change. We also propose that the adaptive value of retentive larval behaviors may decrease where transport trajectories follow changing climate envelopes and increase where transport trajectories drive larvae toward increasingly unsuitable conditions. Our holistic framework, combined with knowledge of regional ocean conditions and larval traits, can be used to produce powerful predictions of expected impacts on larval dispersal as well as the consequences for connectivity, range expansion, or recruitment. Based on our findings, we recommend that future studies take a holistic view of dispersal incorporating biological and oceanographic impacts of climate change rather than solely focusing on oceanography or physiology. Genetic and paleontological techniques can be used to examine evolutionary impacts of altered dispersal in a future ocean, while museum collections and expedition records can inform modern‐day range shifts.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".