For example, we may hypothesize that the population at some time in the future depends not only on the population dynamics, but also on genetic variability, environmental conditions, and interspecific interactions. For example, the steelhead population model might be modified to incorporate genetic variability and interspecific competition. And further, we may wish to couple the model directly to a global climate change model in order to investigate potential effects on steelhead populations if global warming occurs. It would be nice if the reverse were true, i.e., that coupling simple models together accurately predicts general properties, but this too is rarely the case. The specific population parameters may then be used in more complex models, or the estimates may be compared, spatially or temporally, to those from other populations.
Population models can be deterministic or stochastic.
Despite its simple nature, this model is useful for predicting the future population size of laboratory animals, insect populations, and humans. This is an unrealistic result, because the exact size of the population at time t is uncertain. We know that birth and death rates vary from season to season and year to year depending on a variety of factors such as food, weather, the ability to find mates, etc. For example, we might assume that the parameter r follows a normal distribution with a certain mean and variance, and let the population at the next time step vary accordingly. A further modification to the above model might include the addition of stochasticity to K, the carrying capacity, because the carrying capacity also may vary seasonally.