We can also calculate R0 by dividing the total number of offspring produced during one generation (∑Fx, meaning the sum of the values in the Fx column) by the original number of individuals. That is:
(4.3)
For Gilia (Table 4.1), R0 is calculated very simply (no summation required) since only the flowering class produces seed. Its value is 38.27 for the inland subspecies and 11.47 for the coastal subspecies: a clear indication that the inland subspecies thrived, comparatively, at this inland site. (Though the annual rate of reproduction would not have been this high, since, no doubt, a proportion of these would have died before the start of the 1994 cohort. In other words, another class of individuals, ‘winter seeds’, was ignored in this study.)
For the marmots, R0 = 0.67: the population was declining, each generation, to around two‐thirds its former size. However, whereas for Gilia the length of a generation is obvious, since there is one generation each year, for the marmots the generation length must itself be calculated. We address the question of how to do this in Section 4.7, but for now we can note that its value, 4.5 years, matches what we can observe ourselves in the life table: that a ‘typical’ period from an individual’s birth to giving birth itself (i.e. a generation) is around four and a half years. Thus, Table 4.2 indicates that each generation, every four and a half years, this particular marmot population was declining to around two‐thirds its former size.
4.6.2 Survivorship curves
It is also possible to study the detailed pattern of decline in a cohort. Figure 4.10a, for example, shows the numbers of marmots surviving relative to the original population – the lx values – plotted against the age of the cohort. However, this can be misleading. If the original population is 1000 individuals, and it decreases by half to 500 in one time interval, then this decrease looks more dramatic on a graph like Figure 4.9a than a decrease from 50 to 25 individuals later in the season. Yet the risk of death to individuals is the same on both occasions. If, however, lx values are replaced by log(lx ) values, that is, the logarithms of the values, as in Figure 4.10b (or, effectively the same thing, if lx values are plotted on a log scale), then it is a characteristic of logs that the reduction of a population to half its original size will always look the same. Survivorship curves are, therefore, conventionally plots of log(lx ) values against cohort age. Figure 4.10b shows that for the marmots, there was a steady, more or less constant rate of decline until around the eighth year of life, then three further years at a slightly higher rate (until breeding ceased), followed by a brief period with effectively no mortality, after which the few remaining survivors died.
Figure 4.10 Representations of the survival of a cohort of the yellow‐bellied marmot (Table 4.2). (a) When lx is plotted against cohort age, it is clear that most individuals are lost relatively early in the life of the cohort, but there is no clear impression of the risk of mortality at different ages. (b) By contrast, a survivorship curve plotting log(lx ) against age shows a virtually constant mortality risk until around age eight, followed by a brief period of slightly higher risk, and then another brief period of low risk after which the remaining survivors died.
a classification of survivorship curves
Life tables provide a great deal of data on specific organisms. But ecologists search for generalities – patterns of life and death that we can see repeated in the lives of many species – conventionally dividing survivorship curves into three types in a scheme that goes back to 1928, generalising what we know about the way in which the risks of death are distributed through the lives of different organisms (Figure 4.11).
Figure 4.11 Classification of survivorship curves plotting log(lx) against age, above, with corresponding plots of the changing risk of mortality with age, below. The three types are discussed in the text.
Source: After Pearl (1928) and Deevey (1947).
In a type 1 survivorship curve, mortality is concentrated toward the end of the maximum life span. It is perhaps most typical of humans in developed countries and their carefully tended zoo animals and pets. A type 2 survivorship curve is a straight line signifying a constant mortality rate from birth to maximum age. It describes, for instance, the survival of buried seeds in a seed bank. In a type 3 survivorship curve there is extensive early mortality, but a high rate of subsequent survival. This is typical of species that produce many offspring. Few survive initially, but once individuals reach a critical size, their risk of death remains low and more or less constant. This appears to be the most common survivorship curve among animals and plants in nature.
These types of survivorship curve are useful generalisations, but in practice, patterns of survival are usually more complex. We saw with the marmots, for example, that survivorship was broadly type 2 throughout much of their lives, but not at the end (Figure 4.10b). Similarly, with the dinosaurs we will meet in the next section, survivorship followed the typical type 3 pattern until they reached sexual maturity, but again failed to conform to such a simple classification thereafter (see Figure 4.13). More generally, we see examples approximating to each of the three types in the survey in Figure 4.2, but also more examples where the shape changes as individuals pass through the different phases of their lives.
APPLICATION 4.2 The survivorship curves of captive mammals
Opinions naturally differ regarding both the ethics and the practical benefits of keeping wild animals in captivity in zoos, but the current reality is that zoos play an integral role in the conservation of many species, especially those, like many mammals, that are large and inherently attractive to the general public. Hence, in managing these animals, we need to understand their patterns of survivorship, and to know in particular if there are general rules organising these patterns that would not only describe the species for which we have good data, but also allow us