• demography;
  • elasticity;
  • matrix population model;
  • population growth rate;
  • population viability analysis;
  • sampling


  • 1
    Matrix population models have become important tools in many fields of ecology and conservation biology, and are the most commonly used method in population viability analysis (PVA). There is a large literature concerned with different aspects of matrix model analysis, but relatively little attention has been paid to how data are collected.
  • 2
    In most demographic population viability studies, data are sampled in permanent plots, often resulting in poor representation of some stages. It has been suggested that by using previous knowledge of species’ demography it is possible to sample demographic data more efficiently. Here we propose an alternative method that is much simpler and does not rely on any assumptions, namely sampling an equal number of individuals per stage.
  • 3
    By using demographic data from 32 species we showed that sampling an equal number of individuals per stage provides more precise estimates of both population growth rate and elasticity than the traditional plot-based method. In some cases it is also better than the estimates gained using the method based on previous knowledge of the species’ demography. The conclusions of the latter method are very sensitive to the quality of the previous knowledge of the species’ demography. In contrast, collecting demographic data from an equal number of individuals per stage is independent of any assumptions.
  • 4
    Synthesis and applications. A central aim for management of threatened species is to develop robust and accurate methods for assessing population viability that are also efficient in terms of costs and labour. A key issue is how to collect the data on which viability assessments are based. We show that it is possible to increase considerably the accuracy and robustness of demographic PVA without increasing sampling effort, by using a simple method based on sampling an equal number of individuals per life-cycle stage. Improved PVA model performance will be important to guide conservation efforts and to evaluate different management options.