A cohort projection method to follow deb-structured populations with periodic, synchronized and iteroparous reproduction

The need to follow structured populations, as opposed to unstructured ones, is well-recognized. The most detailed category of population models are the individual-based population models (IBMs), also called agent-based population models (ABMs). Their analysis is generally by simulation in time followed by statistical analysis of the numerical results. A less detailed method is the physiologically structured population model approach (PSPMs) leading originally to continuous-time partial differential equations (pdes) for the p(opulation)-states such as number(-density) with respect to time and i(ndividual)-state such as age and/or size and later to a delay equation formulation. Their mathematical analysis and computational methods are generally complex. Discrete-time matrix population models (MPMs) are much simpler to analyse in all respects, but the applicability is limited due to stringent modelling assumptions made. We discuss here a class of models we call the Cohort Projection Models (CPMs), which were formerly introduced as a special case of PSPMs with pulsed reproduction. CPMs follow cohorts of identical individuals in a Lagrangian way of which the changes of their i-states such as, size, energy reserves and maturity, are described by age dependent ordinary differential equations (ode)s from DEB theory. Simultaneously the p-states, such as number of individuals are described by time dependent odes obeying conservation laws. The population is subdivided in generations on the assumption that seasonal cycles synchronize reproduction events among cohorts and all eggs that are produced by different generations are the same. Feedback from the environment can be included via specification of food dynamics that accommodates competition. Temperature follows a specified periodic trajectory in time. This allows for the definition of a projection map of i-states and p-states, from one reproduction event to the next. The projection interval is typically one year for seasonal variability. The properties of the map can be studied using nonlinear dynamical system theory, such as existence and stability of fixed points and, thereby, the long-term dynamics of the food-population system. We demonstrate this using deb parameter values from the Add-my-Pet (AmP) collection for over 2000 animal species, which were estimated from empirical data. CPMs are meant to match the relative simplicity of the analysis of MPMs with the realism of the deb models for the dynamics of the population individuals.