Partial life cycle analysis: a model for pre-breeding census data

Authors

  • Madan K. Oli,

  • Bertram Zinner


M. K. Oli, Dept of Wildlife Ecology and Conservation, Univ. of Florida, 303 Newins-Ziegler Hall, Gainesville, FL 32611-0430, USA (olim@wec.ufl.edu). – B. Zinner, Dept of Discrete and Statistical Sciences, Math Annex, Auburn Univ., Auburn, AL 36849, USA.

Abstract

Matrix population models have become popular tools in research areas as diverse as population dynamics, life history theory, wildlife management, and conservation biology. Two classes of matrix models are commonly used for demographic analysis of age-structured populations: age-structured (Leslie) matrix models, which require age-specific demographic data, and partial life cycle models, which can be parameterized with partial demographic data. Partial life cycle models are easier to parameterize because data needed to estimate parameters for these models are collected much more easily than those needed to estimate age-specific demographic parameters. Partial life cycle models also allow evaluation of the sensitivity of population growth rate to changes in ages at first and last reproduction, which cannot be done with age-structured models. Timing of censuses relative to the birth-pulse is an important consideration in discrete-time population models but most existing partial life cycle models do not address this issue, nor do they allow fractional values of variables such as ages at first and last reproduction. Here, we fully develop a partial life cycle model appropriate for situations in which demographic data are collected immediately before the birth-pulse (pre-breeding census). Our pre-breeding census partial life cycle model can be fully parameterized with five variables (age at maturity, age at last reproduction, juvenile survival rate, adult survival rate, and fertility), and it has some important applications even when age-specific demographic data are available (e.g., perturbation analysis involving ages at first and last reproduction). We have extended the model to allow non-integer values of ages at first and last reproduction, derived formulae for sensitivity analyses, and presented methods for estimating parameters for our pre-breeding census partial life cycle model. We applied the age-structured Leslie matrix model and our pre-breeding census partial life cycle model to demographic data for several species of mammals. Our results suggest that dynamical properties of the age-structured model are generally retained in our partial life cycle model, and that our pre-breeding census partial life cycle model is an excellent proxy for the age-structured Leslie matrix model.

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