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These studies suggest that evolutionary processes in populations can be studied and modeled using statistical models, Markov chain Monte Carlo algorithms, evolutionary dynamics on graphs, and integrating molecular, systems, and population scales.
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Studying and modeling evolutionary processes in populations is a fundamental aspect of evolutionary biology. This involves understanding how genetic variations arise, spread, and are maintained within populations over time. Researchers employ various statistical, computational, and theoretical models to infer and predict evolutionary dynamics.
Ornstein-Uhlenbeck Process for Phenotypic Evolution:
Phylogenetic Comparative Methods:
Likelihood-Based Approaches for Population Histories:
Evolutionary Dynamics on Graphs:
Bridging Molecular and Population Scales:
Stochastic and Deterministic Models:
Predictive Models of Evolution:
The study and modeling of evolutionary processes in populations involve a diverse array of methods, from statistical and computational models to theoretical frameworks. Key approaches include the Ornstein-Uhlenbeck process for phenotypic evolution, phylogenetic comparative methods, likelihood-based population history inference, evolutionary dynamics on graphs, and bridging molecular to population scales. Both stochastic and deterministic models play crucial roles, and recent efforts are increasingly directed towards predictive models of evolution. These methodologies collectively enhance our understanding of how evolutionary processes shape the diversity and distribution of species.
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