Trophic ecology shapes spatial ecology of two sympatric predators, the great hammerhead shark (Sphyrna mokarran) and bull shark (Carcharhinus leucas)
Bibliographic record
Abstract
Information on how the trophic ecology of predators shapes their movement patterns and space-use is fundamental to understanding ecological processes across organisational levels. Despite this, studies combining spatial and trophic ecology to determine how prey preference and/or resource availability shape space use are lacking in marine predators as these can occur at low density and are often difficult to track over extended periods. Furthermore, many exhibit behavioural variability within species and among closely related, sympatric species adding further complexity. We applied a context-focused, multi-method approach to the understudied great hammerhead shark ( Sphyrna mokarran ) to test if movement and home ranges relate to prey preference and availability. Movement data from satellite and acoustic telemetry in Queensland, Australia, were combined with stable-isotope analysis, drone surveys, and videos of hunting behaviour. Limited dispersal, and small home ranges in S. mokarran were linked to trophic specialisation on stingray prey. Drone surveys and videos showed predation events on stingrays and demonstrated high, year-round availability of this prey in shallow, inshore habitats, which may allow the majority of S. mokarran to remain resident. This affinity for inshore habitats suggests that critical life-history requirements are performed over local or regional scales, although some larger movements were evident. These results were interpreted in comparison to the well-studied bull shark ( Carcharhinus leucas ), which showed reliance on pelagic food webs. Carcharhinus leucas had high individual variability in movement, with both large-scale migrations and residency. This could indicate that only some individuals are locally sustained on dynamic, pelagic food webs, while others undergo large-scale excursions over distant habitats. The specialised foraging of S. mokarran indicates they play an apex predator role in shallow, inshore habitats, potentially shaping space-use, and foraging behaviour of batoids. As inshore habitats are disproportionately affected by anthropogenic stressors, S. mokarran’s trophic specialisation and limited demographic connectivity may make the species particularly vulnerable to anthropogenic threats.
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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) | 0.001 | 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".