H. Charles J. Godfray
Understanding what determines the average abundance of species, why their numbers fluctuate, and how they interact with each other is a major part of modern ecology often united under the term population ecology. Of course, the boundaries of population ecology are ill-defined and porous: on the one hand the field grades into physiological ecology—how individuals interact with the environment—and on the other hand into community ecology—the study of large assemblages of species. Population ecology is part of the larger subject of population biology that encompasses both the evolutionary and the ecological processes affecting populations.
The human race has always been concerned with the abundance and fluctuations of the plants and animals that share its environment, not least because they provide its food. But the modern study of populations begins with Thomas Malthus (1798), who, in his Essay on the Principle of Population, realized that if birth and death rates remain constant with the former greater than the latter, then population size will grow geometrically until some extrinsic factor comes into play. The conclusions that Malthus, an upper-class English vicar, drew from his insights were of the importance of doing something about the “irresponsibly fecund lower orders” (as well as the need to attend to other “problems” such as “liberal women” and the French!). Fortunately, Malthus is not remembered as a politician, but his writing hugely influenced the first generation of biologists to think about animal populations, and in particular Charles Darwin, who realized that geometric population growth implied massive mortality and hence a huge advantage to any heritable trait that helped individuals in the struggle for survival. Today we use the Malthusian parameter, the population’s rate of geometric growth assuming demographic parameters remain the same, as an index of the state of the population. A closely related parameter, the growth rate of a rare mutation, is intimately connected to notions of evolutionary fitness. Calculating population growth rates (population projection) is quite straightforward for some species, for example, those with discrete generations. It can be much more complicated when there are overlapping generations and where the population is composed of individuals of different classes (differing in age, size, or other variable), issues discussed in chapter II.1.
But demographic rates do not remain the same forever, and in particular, as population densities increase, birthrates go down or death rates go up. It is these density-dependent effects that are critical in determining the typical range of abundance of different organisms, as discussed in chapter II.2. Densitydependent effects may increase smoothly as population size gets larger but may also be much more capricious, cutting in only above a threshold, the latter itself possibly varying from year to year. The chief factor determining observed population densities at any particular time is often a density-independent process such as the weather, and the densities of some populations may fluctuate in a random way for many generations before they become large enough for densitydependent processes to come into play. However, no population can be regulated, that is, persist indefinitely within certain bounds, without density dependence occurring.
Where density-dependent processes act instantaneously and increase gently with population size, the outcome of population regulation will be a stable equilibrium (though in nature random perturbations will mean that an absolutely constant population density is unlikely to be observed). But if there is a time lag between population increase and the impact of density dependence, or if density dependence is very strong, then overcompensation may occur, and the population will show cycles. As was first realized by ecological theoreticians, particularly by Robert May, in the 1970s, stronger density dependence and larger time lags may lead to population fluctuations that are chaotic—purely deterministic yet impossible to predict in detail. Hastings (chapter II.3) explores these issues and discusses recent findings about how deterministic