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Predominantly, two modeling frameworks are implemented to simulate granulation processes: population balance modeling (PBM) and discrete element methods (DEM).
Selection pressure, co-evolutionary processes, population genetics, bio-geographical variables, and gene dynamics (gene flow, drift, mating systems, etc).
Geographical modeling of genetic information may therefore greatly contribute to our understanding of landscape processes, population dynamics, adaptation and evolutionary potential in river systems.
Signatures identified here are robust to the presence of recombination, extinction-colonization processes, population bottlenecks, and expansion as well as skewed offspring distributions.
Although it is less clear how phenotypic variation will respond to the same processes, population genetics theory suggests that a decrease in phenotypic variation should also be evident [ 50], especially in the case of morphological traits [ 51].
However, characteristics of the supply system such as the type of water source (e.g., river and lake), water treatment processes, population served, system size and hydraulic conditions vary from one system to another.
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Four main diagnostic scatterplots were used in the model selection processes: population-predicted vs. observed concentrations, population-weighted residuals vs. population-predicted concentrations, individual-predicted vs. observed concentrations, and population-weighted residuals vs. time.
The GA processes populations of chromosomes, successively replacing one population by another.
The evolutionary signatures underlying eQTLs in different biological processes, populations[ 44] and systems vary widely.
Stochastic partial differential systems are usually used to describe physical and engineering phenomena such as heat process, population dynamics, chemical reactors, fluid dynamics, etc. and have been widely investigated (for instance, [1 10] and references therein).
In order to simulate the sampling process, population frequency tables (PFT) with R rows and C columns were randomly generated (figure 3).
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