Ecology. Michael Begon

Figure 4.16 Populations with constant rates of survival and fecundity eventually reach a constant rate of growth and a stable age structure. A population growing according to the life cycle graph shown in Figure 4.15a, with parameter values as shown in the insert here. The starting conditions were 100 individuals in class 1 (n₁ = 100), 50 in class 2, 25 in class 4 and 10 in class 4. On a logarithmic (vertical) scale, exponential growth appears as a straight line. Thus, after about 10 time steps, the parallel lines show that all classes were growing at the same rate (R = 1.25) and that a stable class structure had been achieved.

      Hence, a population projection matrix allows us to summarise a potentially complex array of survival, growth and reproductive processes, and characterise that population succinctly by determining the per capita rate of increase, R, implied by the matrix. But crucially, this ‘asymptotic’ R can be determined directly, without the need for a simulation, by application of the methods of matrix algebra (these are beyond our scope here, but see Caswell (2001)). Moreover, such algebraic analysis can also indicate whether a simple, stable class structure will indeed be achieved, and what that structure will be. It can also determine the importance of each of the different components of the matrix in generating the overall outcome, R – a topic to which we turn shortly. By convention, R in population projection matrices and related approaches (see below) is often referred to as λ. Here, for continuity with previous sections, we will continue to refer to the net reproductive rate as R.

      integral projection models

Before we do so, however, we should acknowledge that the differences between individuals in a population, and hence the differences in their contribution to R, are not always best described in terms of age classes or stages. Often, demographic forces – birth rates, death rates and do on – vary with a continuous character, of which organism size is the most obvious example. In such cases, not a matrix model but an integral projection model (IPM) is appropriate. And where there is a mix of continuous and discrete characters, so‐called generalised IPMs can be used (see Rees & Ellner (2009), Merow et al. (2014) and Rees et al. (2014) for accessible guides). An example of an IPM in action is shown in Figure 4.17, based on data from the females in a population of Soay sheep (Ovis aries) that have been intensively and extensively studied on the island of Hirta in the St Kilda archipelago in Scotland (Coulson, 2012). Here, rather than variations in survival, birth and so on being represented by successive elements in a projection matrix, field data are used to derive relationships between body weight and growth over the following year, survival to the next year, the production of offspring over the next year, and the size of those offspring (Figure 4.17a–d, respectively). As with the matrices, it is not necessary here to go into technical (mathematical) details, but it is easy to understand that fitting statistical models to the data in Figure 4.17 allows us, for example, to predict the probability of survival over the next year of a female, given her weight, in the same way as a value in a projection matrix allows us to predict the survival of that individual, given its age class. Doing this for each of the relationships allows us in turn to estimate R₀, the mean lifetime reproductive success (Equation 4.4) and especially R, the fundamental net reproductive rate (Equations 4.11–4.13). This, then, allows us to assess the current viability of the population, overall. (In this case, R was around 1.3 and hence the population was projected to increase from its current size.) It allows us, too, to predict the stable stage (here, size) distribution, which in this case has two peaks – one for lambs, and one for older individuals that have survived to adulthood but slowed or stopped in their rate of growth.