Discrete time modeling is a way of determining population sizes at certain time intervals. The equations involve growth rates, a pre-determined population size (such as X_0), and the input of a time-step, which results in the output of the population size at that time interval. Growth rates greater than 1 signify growth, rates less Essentially, your x is a time-step, and your y is the population at that given time.
Perhaps one of the most interesting examples, or rather ideas, from discrete time modeling is the ability to predict population growth or decline through time. The ability to predict how much time is needed/left before populations hit a certain point is incredibly valuable to my future profession, in that it can help develop management plans for over-abundant species, or how to go about implementing conservation measures for species in decline.
I am, however, slightly confused by two topics under this umbrella of discrete time modeling. Primarily, the implementation of multiple different formulas that describe the same thing -yet are used in different contexts- are confusing to me. Remembering and knowing when to use them will be a challenge. Additionally, measuring multiple variables at once went over my head a bit, but I'm sure with some practice I'll actually understand it.
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