title: Stochastic development in biologically structured population models.
abstract: Variation in organismal development is ubiquitous in nature but omitted from most age- and stage-structured population models. I give a general approach for formulating and analyzing its role in density-independent population models using the framework of integral projection models. The approach allows flexible assumptions, including correlated development times among multiple life stages. I give a new Monte Carlo numerical integration approach to calculate long-term growth rate, its sensitivities, stable age-stage distributions and reproductive value. This method requires only simulations of individual life schedules, rather than iteration of full population dynamics, and has practical and theoretical appeal because it ties easily implemented simulations to numerical solution of demographic equations. I show that stochastic development is demographically important using two examples. For a desert cactus, many stochastic development models, with independent or correlated stage durations, can generate the same stable stage distribution (SSD) as the real data, but stable age-within-stage distributions and sensitivities of growth rate to demographic rates differ greatly among stochastic development scenarios. For Mediterranean fruit flies, empirical variation in maturation time has a large impact on population growth. The systematic model formulation and analysis approach given here should make consideration of variable development models widely accessible and readily extendible.
authors.affiliation: Department of Environmental Science, Policy, and Management, University of California-Berkeley, 137 Hilgard Hall #3114, Berkeley, California 94720-3114, USA. pdevalpine@berkeley.edu
authors.forename: Perry
authors.initials: P
authors.lastname: De Valpine
mesh_terms: D000375:Aging; D000818:Animals; D033622:Ceratitis capitata / Q000502:physiology; D008954:Models, Biological; D009010:Monte Carlo Method; D011157:Population Dynamics; D013269:Stochastic Processes; D013997:Time Factors