Links between fish abundance and ocean biogeochemistry as recorded in marine sediments
Bibliographic record
Abstract
Fish populations are linked to ocean biogeochemistry by their reliance on primary production for food, and dissolved oxygen to breathe. It is also possible that marine fish modify biogeochemical dynamics, as do freshwater fish, through top-down trophic cascades, but there has been relatively little consideration of this possibility. This lack of consideration may reflect a lack of importance; alternatively, it may simply reflect the lack of appropriate observations with which to constrain such relationships. Here, we draw attention to the potential use of marine sediments as long-term simultaneous monitors of both fish abundance and marine biogeochemical dynamics. We compile published sediment proxy records of fish abundance from the west coasts of the Americas, and compare them with biogeochemical proxy measurements made at the same sites. Despite the challenges of using sediment records and the potential convolution of ecological and climatic signals, we find a small number of statistically significant relationships between fish debris and biogeochemical variables, at least some of which are likely to reflect causal relationships. Considering TOC, the most commonly-measured biogeochemical variable, some positive correlations with fish abundance are found, consistent with bottom-up control of fish abundance by primary production, or a planktivore-herbivore-phytoplankton trophic cascade. Negative correlations are also found, which could reflect sedimentary processes, the influence of upwelling-driven oxygen and nutrient dynamics on primary production and fish populations, and/or impacts of fish stocks on carbon fluxes by altering the recycling of carbon within the water column. Although the number of available measurements is too small to draw strong conclusions, the results point to plausible cases of bottom-up forcing, trophic cascades, and influence of dissolved oxygen concentrations on fish habitat.
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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 | 0.002 |
| 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 | 0.000 | 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".