Reviews and syntheses: Insights into deep-sea food webs and global environmental gradients revealed by stable isotope ( <i>δ</i> <sup>15</sup> N, <i>δ</i> <sup>13</sup> C) and fatty acid trophic biomarkers
Bibliographic record
Abstract
Abstract. Biochemical markers developed initially for food-web studies of terrestrial and shallow-water environments have only recently been applied to deep-sea ecosystems (i.e., in the early 2000s). For the first time since their implementation, this review took a close look at the existing literature in the field of deep-sea trophic ecology to synthesize current knowledge. Furthermore, it provided an opportunity for a preliminary analysis of global geographic (i.e., latitudinal, along a depth gradient) trends in the isotopic (δ15N, δ13C) and fatty acid composition of deep-sea macro- and megafauna from heterotrophic systems. Results revealed significant relationships along the latitudinal and bathymetric gradients. Deep-sea animals sampled at temperate and polar latitudes displayed lower isotopic ratios and greater proportions of essential ω3 long-chain polyunsaturated fatty acids (LC-PUFAs) than did tropical counterparts. Furthermore, δ15N and δ13C ratios as well as proportions of arachidonic acid increased with increasing depth. Since similar latitudinal trends in the isotopic and fatty acid composition were found in surface water phytoplankton and particulate organic matter, these results highlight the link across latitudes between surface primary production and deep-water communities. Because global climate change may affect quantity and quality (e.g., levels of essential ω3 PUFAs) of surface primary productivity, and by extension those of its downward flux, the dietary intake of deep-sea organisms may likely be altered. In addition, because essential ω3 PUFAs play a major role in the response to temperature variations, climate change may interfere with the ability of deep-sea species to cope with potential temperature shifts. Importantly, methodological disparities were highlighted that prevented in-depth analyses, indicating that further studies should be conducted using standardized methods in order to generate more reliable global predictions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".