Modeling vital rates improves estimation of population projection matrices Journal Article uri icon



  • AbstractPopulation projection matrices are commonly used by ecologists and managers to analyze the dynamics of stage‐structured populations. Building projection matrices from data requires estimating transition rates among stages, a task that often entails estimating many parameters with few data. Consequently, large sampling variability in the estimated transition rates increases the uncertainty in the estimated matrix and quantities derived from it, such as the population multiplication rate and sensitivities of matrix elements. Here, we propose a strategy to avoid overparameterized matrix models. This strategy involves fitting models to the vital rates that determine matrix elements, evaluating both these models and ones that estimate matrix elements individually with model selection via information criteria, and averaging competing models with multimodel averaging. We illustrate this idea with data from a population of Silene acaulis (Caryophyllaceae), and conduct a simulation to investigate the statistical properties of the matrices estimated in this way. The simulation shows that compared with estimating matrix elements individually, building population projection matrices by fitting and averaging models of vital‐rate estimates can reduce the statistical error in the population projection matrix and quantities derived from it.

publication date

  • January 1, 2006

Date in CU Experts

  • December 18, 2014 9:06 AM

Full Author List

  • Gross K; Morris WF; Wolosin MS; Doak DF

author count

  • 4

Other Profiles

International Standard Serial Number (ISSN)

  • 1438-3896

Electronic International Standard Serial Number (EISSN)

  • 1438-390X

Additional Document Info

start page

  • 79

end page

  • 89


  • 48


  • 1