Background: Long-lived marine megavertebrates (e.g. sharks, turtles, mammals, and seabirds) are inherently vulnerable to anthropogenic mortality. Although some mathematical models have been applied successfully to manage these animals, more detailed treatments are often needed to assess potential drivers of population dynamics. In particular, factors such as age-structure, density-dependent feedbacks on reproduction, and demographic stochasticity are important for understanding population trends, but are often difficult to assess. Lemon sharks (Negaprion brevirostris) have a pelagic adult phase that makes them logistically difficult to study. However, juveniles use coastal nursery areas where their densities can be high.
Results: We use a stage-structured, Markov-chain stochastic model to describe lemon shark population dynamics from a 17-year longitudinal dataset at a coastal nursery area at Bimini, Bahamas. We found that the interaction between delayed breeding, density-dependence, and demographic stochasticity accounts for 33 to 49% of the variance in population size.
Conclusions: Demographic stochasticity contributed all random effects in this model, suggesting that the existence of unmodeled environmental factors may be driving the majority of interannual population fluctuations. In addition, we are able to use our model to estimate the natural mortality rate of older age classes of lemon sharks that are difficult to study. Further, we use our model to examine what effect the length of a time series plays on deciphering ecological patterns. We find that-even with a relatively long time series-our sampling still misses important rare events. Our approach can be used more broadly to infer population dynamics of other large vertebrates in which age structure and demographic stochasticity are important.
Reviewers: This article was reviewed by Yang Kuang, Christine Jacob, and Ollivier Hyrien.
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http://dx.doi.org/10.1186/1745-6150-9-23 | DOI Listing |
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Department of Mechanical Engineering, Indian Institute of Technology Delhi (IITD), New Delhi, DL, India.
This research determines the potential impact of reducing food waste on future energy consumption and pollutant emissions. The study uses system dynamics modelling to simulate the complex link between population, food demand, food waste output and their interactions with energy consumption in the food system and carbon dioxide (CO) emissions. Scenarios are developed by considering two elements: a reduction in food waste and an increase in energy output.
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March 2025
Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic.
Longitudinal neuroimaging studies offer valuable insight into brain development, ageing, and disease progression over time. However, prevailing analytical approaches rooted in our understanding of population variation are primarily tailored for cross-sectional studies. To fully leverage the potential of longitudinal neuroimaging, we need methodologies that account for the complex interplay between population variation and individual dynamics.
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School of Nursing, Nanjing Medical University, 101 Longmian Avenue, Jiangning District, Nanjing, 211166, China.
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Cells
February 2025
Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9PT, UK.
Heart failure (HF) is a prominent fatal cardiovascular disorder afflicting 3.4% of the adult population despite the advancement of treatment options. Therefore, a better understanding of the pathogenesis of HF is essential for exploring novel therapeutic strategies.
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Orygen, Parkville, VC, Australia.
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