What is a population

A population is generally defined as a potentially interbreeding group of individuals that belong to the same species and live within a restricted geographical area. In theory this definition may seem fairly straightforward (at least for sexually reproducing species), but in practice there are a number of reasons why

Molecular Ecology Joanna Freeland © 2005 John Wiley & Sons, Ltd.

Figure 3.1 A pair of copulating common green darner dragonflies (Anax Junius). Juvenile development in this species is phenotypically plastic, depending on the temperature and photoperiod during the egg and larval stages. Photograph provided by Kelvin Conrad and reproduced with permission

populations are seldom delimited by obvious boundaries. One confounding factor may be that species live in different groups at different times of the year. This is true of many bird species that breed in northerly temperate regions and then migrate further south for the winter, because any one of these overwintering 'populations' may comprise birds from several distinct breeding populations.

The situation is even more complex in the migratory common green darner dragonfly, Anax junius (Figure 3.1). Throughout part of its range, A. junius has two alternative developmental pathways in which larvae take either 3 or 11 months to develop into adults (Trottier, 1966). Individuals that develop at different rates will not be reproductively active at the same time and therefore cannot interbreed. If developmental times are fixed there would be two distinct A. junius populations within a single lake or pond, but preliminary genetic data suggest that development in this species is an example of phenotypic plasticity (Freeland et al., 2003). This means that, although some individuals are unable to interbreed within a particular mating season, their offspring may be able to interbreed in the following year; therefore, individuals that follow different developmental pathways can still be part of the same population.

Prolonged diapause (delayed development) also may cause researchers to underestimate the size or boundaries of a population, because seeds or other propagules that are in diapause will often be excluded from a census count. Many plants fall into this category, such as the flowering plant Linanthus parryae that thrives in the Mojave desert when conditions are favourable. When the environment becomes unfavourable, seeds can lay dormant for up to 6 years in a seed bank, waiting for conditions to improve before they germinate (Epling, Lewis and Ball, 1960). Similarly, the sediment-bound propagules of many species of freshwater zooplankton can survive for decades (Hairston, Van Brunt and Kearns, 1995).

Another complication that arises when we are defining populations is that their geographical boundaries are seldom fixed. Boundaries may be particularly unpredictable if reproduction within a population depends on an intermediate species. The population limits of a flowering plant, for example, may depend on the movements of pollinators, which can vary from one year to the next. Populations of the post-fire wood decay fungus Daldinia loculata, which grows in the wood of deciduous trees that have been killed by fire, are also influenced by vectors. Pyrophilous insect species moving between trees can disperse fungal conidia (clonal propagules that act as male gametes) across varying distances. Genetic data from a forest site in Sweden suggested that insects sometimes transfer conidia between trees, thereby increasing the range of potentially interbreeding individuals beyond a single tree (Guidot et al., 2003).

It should be apparent from the preceding examples that population boundaries are seldom precise, although in a reasonably high proportion of cases they should correspond more or less to the distribution of potential mates. Biologists often identify discrete populations at the start of their research programme, if only as a framework for their sampling design, which often will specify the minimum number of individuals required from each presumptive population. Nevertheless, populations should not be treated as clear-cut units, and the boundaries are sometimes revised after additional ecological or genetic data have been acquired. Bearing in mind that molecular ecology is primarily concerned with wild populations, which by their very nature are variable (Box 3.1) and often unpredictable, we shall start to look at ways in which molecular genetics can help us to understand the dynamics of single populations.

Box 3.1 Summarizing data

Ecological studies, molecular and otherwise, are often based on measurements of a trait or characteristic that have been taken from multiple individuals. These data may quantify phenotypic traits, such as wing lengths in birds, or genotypic traits, such as allele frequencies in different populations. Consider the following data set on wing lengths:

Sample 1 Sample 2

23 21

24 24

20 26 23 19 27

There are a number of ways in which we can summarize these wing measurements, including the arithmetic mean, or average, which is calculated as:

where Xi is the value of the variable in the ith specimen, so

X =(23 + 21 + 23 + 24 + 24)/5 = 23 for population 1, and

X =(20 + 26 + 23 + 19 + 27)/5 = 23 for population 2

In this case both populations have the same average wing length, but this is telling us nothing about the variation within each population. The range of measurements (the minimum value subtracted from the maximum value, which equals 3 and 8 in samples 1 and 2, respectively), can give us some idea about the variability of the sample, although a single unusually large or unusually small measurement can strongly influence the range without improving our understanding of the variability. An alternative measure is variance, which reflects the distribution of the data around the mean. Variance is calculated as:

= [(23 - 23)2 + (21 - 23)2 + (23 - 23)2 + (24 - 23)2

= [(20 - 23)2 + (26 - 23)2 + (23 - 23)2 + (19 - 23)2

This shows that, although the mean is the same in both samples, the variation in sample 2 is an order of magnitude higher than that in sample 1. Variance is described in square units and therefore can be quite difficult to visualize so it is sometimes replaced by its square root, which is known as the standard deviation (S), calculated as:

= \/L5 = 1.225 for population 1, and = V12.5 = 3.536 for population 2

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