Demographic studies focusing on age‐specific mortality rates are becoming increasingly common throughout the fields of life‐history evolution, ecology and biogerontology. Well‐defined statistical techniques for quantifying patterns of mortality within a cohort and identifying differences in age‐specific mortality among cohorts are needed. Here I discuss using maximum likelihood (ML) statistical methods to estimate the parameters of mathematical models, which are used to describe the change in mortality with age. ML provides a convenient and powerful framework for choosing an adequate mortality model, estimating model parameters and testing hypotheses about differences in parameters among experimental or ecological treatments. Simulations suggest that experiments designed to estimate age‐specific mortality should involve at least 100‐500 individuals per cohort per treatment. Significant bias in the estimation of model parameters is introduced when the mortality model is misspecified and samples are too small to detect the true mortality pattern. Furthermore, the lack of simple and efficient procedures for comparing different mortality models has forced the use of the Gompertz model, which specifies an exponentially increasing mortality with age, and which may not apply to the majority of experimental systems.

Evolutionary biologists, ecologists and experimental gerontologists have increasingly used estimates of age‐specific mortality as a critical component in studies of a range of important biological processes. However, the analysis of age‐specific mortality rates is plagued by specific statistical challenges caused by sampling error. Here we discuss the nature of this ‘demographic sampling error’, and the way in which it can bias our estimates of (1) rates of ageing, (2) age at onset of senescence, (3) costs of reproduction and (4) demographic tests of evolutionary models of ageing. We conducted simulations which suggest that using standard statistical techniques, we would need sample sizes on the order of tens of thousands in most experiments to effectively remove any bias due to sampling error. We argue that biologists should use much larger sample sizes than have previously been used. However, we also present simple maximum likelihood models that effectively remove biases due to demographic sampling error even at relatively small sample sizes.

No abstract