ABSTRACT

There is an increasing trend toward the use of population models that include variables such as age, size, stage, and space to describe how individuals differ. These models have for the most part been written in terms of distributions of numbers over these individual state variables, but there has also been increasing use of models that describe each individual in a population explicitly, as being potentially different from all others. I outline reasons for the general use of individual state structure in models used to (1) increase our understanding of population dynamics, and (2) predict population behavior for management. The logical basis underlying both of these requires that models be realistic , that is that their component structure be comparable to the population itself, through observation. This realism can be achieved by adequate description of the state of individuals in a population and the state of the population. Over the past 15 years of study of recruitment to marine populations, the use of age, size, and spatially structured models has led to advances in our understanding of stability and the effects of random environment not possible with traditional lumped models. The fact that population models with individual state structure connect the population level of integration to the individual level can be used to draw implications for large scale (both spatial and temporal) population behavior from small scale studies at the individual level. For some populations, modelers turn to models which keep track of all individuals explicitly because of convenience or efficiency, but in other cases this approach is required because of the nature of interactions within the population. Although it is not clear just when this approach is absolutely necessary, it seems to be most appropriate for populations that are locally small, with little mixing of individuals in the individual state space. Continued development of a general understanding of population dynamics will require cautious use of this new approach as we move slowly away from the mathematical foundations of distribution-based approaches in increments small enough to allow complete understanding of new aspects of population behavior.