Lipid, Fatty Acid and Energy Density Profiles of White Sharks: Insights into the Feeding Ecology and Ecophysiology of a Complex Top Predator
Bibliographic record
Abstract
Lipids are major sources of metabolic energy in sharks and are closely linked to environmental conditions and biological cycles, such as those related to diet, reproduction and migration. In this study, we report for the first time, the total lipid content, lipid class composition and fatty acid profiles of muscle and liver tissue of white sharks, Carcharodon carcharias, of various lengths (1.5-3.9 m), sampled at two geographically separate areas off southern and eastern Australia. Muscle tissue was low in total lipid content (<0.9% wet mass, wm) and was dominated by phospholipids (>90% of total lipid) and polyunsaturated fatty acids (34±12% of total fatty acids). In contrast, liver was high in total lipid which varied between 51-81% wm and was dominated by triacylglycerols (>93%) and monounsaturated fatty acids (36±12%). With knowledge of total lipid and dry tissue mass, we estimated the energy density of muscle (18.4±0.1 kJ g-1 dm) and liver (34.1±3.2 kJ g-1 dm), demonstrating that white sharks have very high energetic requirements. High among-individual variation in these biochemical parameters and related trophic markers were observed, but were not related to any one biological or environmental factor. Signature fatty acid profiles suggest that white sharks over the size range examined are generalist predators with fish, elasmobranchs and mammalian blubber all contributing to the diet. The ecological applications and physiological influences of lipids in white sharks are discussed along with recommendations for future research, including the use of non-lethal sampling to examine the nutritional condition, energetics and dietary relationships among and between individuals. Such knowledge is fundamental to better understand the implications of environmental perturbations on this iconic and threatened species.
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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 | 0.000 |
| 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) | 0.000 | 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 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".