Autoregressive model is a regression model in which the value of the dependent variable is predicted from values of the same variable in previous time steps or in neighboring locations in space. Most statistical models of population dynamics are autoregressive. In spatio-temporal models, population density in a specific location in the next year can be predicted from the current density in the same location as well as in neighboring locations. Autoreggressive models may include external factors, i.e., climatic factors. The mechanisms that cause correlations between population density and factors are not represented in these models .
Prediction is based solely on the previous behavior of the system.
If in the past the system exhibited specific behavior in particular situations, then it is most probable that it behave in a similar way in the future. Statistical modeling is based on the assumption that system behavior remains the same. Thus, these models have very limited value for predicting new behaviors of the system . For example, if we develop a new method for controlling pest populations or a new strategy for conservation of some species, then stochastic models based on the previous dynamics of these populations may be misleading. In some cases stochastic models may help to understand some mechanisms of population dynamics.