Ocean acidification does not overlook sex: Review of understudied effects and implications of low pH on marine invertebrate sexual reproduction
Bibliographic record
Abstract
Sexual reproduction is a fundamental process essential for species persistence, evolution, and diversity. However, unprecedented oceanographic shifts due to climate change can impact physiological processes, with important implications for sexual reproduction. Identifying bottlenecks and vulnerable stages in reproductive cycles will enable better prediction of the organism, population, community, and global-level consequences of ocean change. This article reviews how ocean acidification impacts sexual reproductive processes in marine invertebrates and highlights current research gaps. We focus on five economically and ecologically important taxonomic groups: cnidarians, crustaceans, echinoderms, molluscs and ascidians. We discuss the spatial and temporal variability of experimental designs, identify trends of performance in acidified conditions in the context of early reproductive traits (gametogenesis, fertilization, and reproductive resource allocation), and provide a quantitative meta-analysis of the published literature to assess the effects of low pH on fertilization rates across taxa. A total of 129 published studies investigated the effects of ocean acidification on 122 species in selected taxa. The impact of ocean acidification is dependent on taxa, the specific reproductive process examined, and study location. Our meta-analysis reveals that fertilization rate decreases as pH decreases, but effects are taxa-specific. Echinoderm fertilization appears more sensitive than molluscs to pH changes, and while data are limited, fertilization in cnidarians may be the most sensitive. Studies with echinoderms and bivalve molluscs are prevalent, while crustaceans and cephalopods are among the least studied species even though they constitute some of the largest fisheries worldwide. This lack of information has important implications for commercial aquaculture, wild fisheries, and conservation and restoration of wild populations. We recommend that studies expose organisms to different ocean acidification levels during the entire gametogenic cycle, and not only during the final stages before gametes or larvae are released. We argue for increased focus on fundamental reproductive processes and associated molecular mechanisms that may be vulnerable to shifts in ocean chemistry. Our recommendations for future research will allow for a better understanding of how reproduction in invertebrates will be affected in the context of a rapidly changing environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".