Modeling the population dynamics of lemon sharks

Area of Science:

• Marine biology
• Population dynamics
• Conservation science

Background:

• Long-lived marine megavertebrates face high vulnerability to human-caused mortality.
• Assessing population dynamics requires detailed models considering age structure, density-dependence, and stochasticity.
• Lemon sharks (Negaprion brevirostris) are challenging to study due to their pelagic adult phase, but juveniles aggregate in coastal nurseries.

Purpose of the Study:

• To model lemon shark population dynamics using a stage-structured, Markov-chain stochastic approach.
• To identify key drivers of population fluctuations, including age structure, density-dependence, and demographic stochasticity.
• To estimate natural mortality rates for older lemon shark age classes and assess the impact of time-series length on ecological pattern detection.

Main Methods:

• Utilized a 17-year longitudinal dataset from a lemon shark nursery in Bimini, Bahamas.
• Developed a stage-structured, Markov-chain stochastic model to simulate population dynamics.
• Analyzed the contribution of delayed breeding, density-dependence, and demographic stochasticity to population size variance.

Main Results:

• The interplay of delayed breeding, density-dependence, and demographic stochasticity explained 33-49% of the variance in lemon shark population size.
• Demographic stochasticity accounted for all random effects, suggesting unmodeled environmental factors drive interannual fluctuations.
• The model successfully estimated natural mortality rates for older lemon sharks and highlighted limitations of even long time series in capturing rare events.

Conclusions:

• Environmental factors likely play a significant role in interannual population fluctuations of lemon sharks.
• The developed modeling approach is valuable for inferring population dynamics of other large vertebrates where age structure and stochasticity are critical.
• Long-term monitoring is essential, yet even extended datasets may miss crucial rare ecological events.