Effects of sea cucumber fishing on tropical seagrass productivity
Bibliographic record
Abstract
Abstract Fishing can drive major ecological change in coastal ecosystems and is typically examined through top‐down trophic impacts. However, the massive removal of biomass can also disrupt key ecological bottom‐up processes, though how these effects shape ecosystems is poorly understood. Here, we examined the ecological roles of two species of commercially exploited sea cucumbers thought to promote primary productivity in nutrient‐poor environments through nutrient provisioning and sediment processing. Using a large‐scale field experiment, we tested whether simulated sea cucumber fishing affected seagrass productivity in a natural system comprising reef and seagrass patches that varied in abundance of vertebrate nutrient providers (i.e., fishes). Our findings were scale‐ and metric‐specific: while we could not detect a change in patch‐level seagrass productivity in response to simulated sea cucumber fishing, individual leaf extension rates were ~15% lower at sites where all sea cucumbers were removed, relative to the highest density, unfished sites. Interestingly, there was no concomitant effect of nutrients from the more abundant reef‐associated fishes, which contribute far more nutrients overall than sea cucumbers. This suggests that sea cucumbers are likely mediating seagrass growth through mechanisms other than direct nutrient provisioning, perhaps through processes associated with sediment processing. Our study demonstrates the potential consequences of under‐regulated and unmonitored sea cucumber fishing on foundation species like seagrasses, while highlighting the importance of taking a community‐based approach to these types of field experiments.
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 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 | 0.000 | 0.000 |
| 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 | 0.001 |
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".