Demography when history matters: construction and analysis of second-order matrix population models.

History matters when individual prior conditions contain important information about the fate of individuals. We present a general framework for demographic models which incorporates the effects of history on population dynamics. The framework incorporates prior condition into the -state variable and includes an algorithm for constructing the population projection matrix from information on current state dynamics as a function of prior condition. Three biologically motivated classes of prior condition are included: prior stages, linear functions of current and prior stages, and equivalence classes of prior stages. Taking advantage of the matrix formulation of the model, we show how to calculate sensitivity and elasticity of any demographic outcome. Prior condition effects are a source of inter-individual variation in vital rates, i.e., individual heterogeneity. As an example, we construct and analyze a second-order model of , a long-lived herb. We present population growth rate, the stable population distribution, the reproductive value vector, and the elasticity of to changes in the second-order transition rates. We quantify the contribution of prior conditions to the total heterogeneity in the stable population of using the entropy of the stable distribution.