Urban sharks: residency patterns of marine top predators in relation to a coastal metropolis
Bibliographic record
Abstract
Understanding and ultimately predicting how marine organisms will respond to urbanization is central for effective wildlife conservation and management in the Anthropocene. Sharks are upper trophic level predators in virtually all marine environments, but if and how their behaviors are influenced by coastal urbanization remains understudied. Here, we examined space use and residency patterns of 14 great hammerheads Sphyrna mokarran , 13 bull sharks Carcharhinus leucas , and 25 nurse sharks Ginglymostoma cirratum in proximity to the coastal metropolis of Miami, Florida, using passive acoustic telemetry. Based on the terrestrial urban carnivore literature, we predicted sharks would exhibit avoidance behaviors of areas close to Miami, with residency patterns in these urban areas increasing during periods of lower human activity, such as during nocturnal hours and weekdays, and that dietary specialists (great hammerhead) would exhibit comparatively lower affinity towards highly urbanized areas relative to dietary generalists (bull and nurse shark). However, we did not find empirical support for these predictions. Space use patterns of tracked sharks were consistent with that of ‘urban adapters’ (species that exhibit partial use of urban areas). Modeling also revealed that an unmeasured spatial variable was driving considerable shark residency in areas exposed to high urbanization. We propose several hypotheses that could explain our findings, including food provisioning from shore-based activities that could be attracting sharks to urban areas. Ultimately, the lack of avoidance of urban areas by sharks documented here, as compared to terrestrial carnivores, should motivate future research in the growing field of urban ecology.
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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 | 0.000 | 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 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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".