Ecology. Michael Begon

when currently available data are sparse. Lynch et al. (2010) therefore reviewed what was known about the survivorship of captive mammals – 37 species, including primates, artiodactyls (cattle, sheep, deer, etc.), carnivores, bats, seals and the giant panda – and some of their results are summarised in Figure 4.12. They were more interested in the shapes of the survivorship curves (and for example whether they were type 1, 2 or 3) than in absolute values, and all data sets were therefore scaled to the maximum longevity of the species concerned. They then fitted all datasets to a general survivorship function with two shape parameters, α and β, which allowed the different curves to be classified and either grouped together or distinguished (Figure 4.12). Broadly speaking, with increasing values of α/β, mortality shifted towards being more evenly distributed throughout life, rather than being concentrated at the start; and with decreasing values of αβ, mortality shifted towards including senescence – a period of increased mortality at the end of life – rather than decreasing steadily with age.

Figure 4.12 Distribution of the shapes of survivorship curves for 37 species of animals kept in zoos. (For a full list of species names, see the original text.) A generalised survivorship function with two parameters, α and β was fitted to all datasets, allowing each to be located in logα–logβ space. The shapes themselves are illustrated in the insets, referring to the four starred locations, as survivorship on linear and semilogarithmic scales (solid and dashed lines, respectively; see Figure 4.11) and as distributions of mortality (histograms) between birth and maximum longevity (L). Among those species, the locations of artiodactyls (), carnivores () and primates () are indicated, plus five further species ().

      Source: After Lynch et al. (2010).

      Despite wide variations in size, longevity and taxonomic affiliation, most of the curves were, in essence, type 2, with some element of type 1 (senescence) or type 3 (early mortality). The variation that did exist was significantly associated with the species’ taxonomic order: the artiodactyls showed the least evidence of senescence, the carnivores the most, with the primates somewhere in between (Figure 4.12). This taxonomic variation was in turn associated with variations in age to weaning (relative to lifespan) and litter size, suggesting ‘syndromes’ of associated life history traits. We return to the whole topic of the patterns in life histories and their possible causes in the next chapter. For now, though, the results do provide us with grounds for believing that, based on this analysis, even for species where we have little or no prior knowledge, managers in zoos can make educated predictions with some confidence about likely patterns of mortality, and act accordingly.

      4.6.3 Static life tables

      Many of the species that ecologists study, and for which life tables would therefore be valuable, have repeated breeding seasons like the marmots, or continuous breeding as in the case of humans, but constructing life tables here is complicated, largely because these populations have individuals of many different ages living together. Building a cohort life table is sometimes possible, as we have seen, but this is relatively uncommon. Apart from the mixing of cohorts in the population, it can be difficult simply because of the longevity of many species.

      useful – if used with caution

Another approach is to construct a static life table (Figure 4.9). The data look like a cohort life table – a series of different numbers of individuals in different age classes – but these come simply from the age structure of the population captured at one point in time. Hence, great care is required: they can only be treated and interpreted in the same way as a cohort life table if patterns of birth and survival in the population have remained much the same since the birth of the oldest individuals – and this will happen only rarely. Nonetheless, there is often no alternative and useful insights can still be gained. This is illustrated for a population of small dinosaurs, Psittacosaurus lujiatunensis, recovered as fossils from the Lower Cretaceous Yixian Formation in China, where the alternative of following a cohort is obviously not available (Figure 4.13). They appear to have perished simultaneously in a volcanic mudflow, which might therefore have captured a representative snapshot of the population at the time, around 125 million years ago.

Figure 4.13 Static life tables can be informative, especially when alternatives are not available. (a) The age structure (and hence the static life table) of a population of dinosaurs, Psittacosaurus lujiatunensis, recovered as fossils from the Lower Cretaceous Yixian Formation in China. Age was estimated from the length of the femur, which had been shown in a subsample of specimens to correlate very strongly with the number of ‘growth lines’ (one per year) in the bone. (b) A survivorship curve (log(l_x) plotted against age) derived from the life table.

      Source: After Erickson et al. (2009).

      It appears that mortality rates were high amongst the dinosaurs until around the age of three, after which there was another period of around five years during which mortality rates were low even though the animals continued to grow rapidly, which they did until the age of around nine or 10 years (Figure 4.13b). Mortality rates then seem to have increased again, just as the animals were attaining their maximum size, and broadly coinciding with the appearance in the fossils of characteristics associated with sexual maturity (e.g. enlarged, flaring ‘jugal’ horns). As we shall see in the next chapter, many organisms suffer a cost of reproduction in terms of reductions in growth and/or survival.

      Notwithstanding this successful use of a static life table, the interpretation of static life tables generally, and the age structures from which they stem, is fraught with difficulty: usually, age structures offer no easy short cuts to understanding the dynamics of populations.

      4.6.4 The importance of modularity

Finally here, we turn to the difficulties of constructing any