Nine cells have 0 individuals, 22 have 1 individual, 6 have 2, 2 have 3, 1 has 4 and none of the quadrats/cells have 5 or more individuals (Table 1). In Excel, the formula would look like this: =(µ^x)/((EXP(µ))*(FACT(x))) For example, we sample 40 sub-quadrats/cells. …where e = the base of the natural log = 2.7182818, µ = mean, and x = the frequency category. This equation is called the ‘Poisson expression’ by Cox (2001), and it looks like this: We will use an equation to generate a Poisson distribution with the characteristics that we would expect from a randomly dispersed plant species that has a mean number of plants per sub-quadrat equal to our sample population. The chi-square test compares a given distribution to the Poisson distribution. To analyze the data from the quadrat method we will use a chi-square test of hypothesis. If the individuals in the population are clumped in dispersion, there will be a few quadrats with many individuals, and many quadrats with no individuals (see Figure 4.1 C & F). If the individuals in the population are uniformly dispersed, there will be the same number of individuals in each sub-quadrat (see Figure 4.1 B & E). If the individuals within the population are randomly dispersed, there will be a random number of individuals in each quadrat, centered about the mean (see Figure 4.1 A & D). Quadrat Method The quadrat method involves counting the frequency of occurrences of the species of interest in each of the 100 individual 10 X 10 cm sub-quadrats that compose the 1 m2 quadrat. In today’s lab exercise, we will utilize two different techniques to characterize the dispersion pattern of our focal species: (1) a quadrat-based method and (2) a point-to-plant method. Deviations from the predicted, random pattern suggest that the population under study exhibits either a uniform or clumped dispersion pattern. Analyses of population dispersion patterns usually follow a standard method in which observed patterns are compared to predicted, random dispersion patterns modeled on the Poisson distribution. With mobile organisms, this requires intensive sampling therefore, we will measure the dispersion patterns of less mobile species. Measuring population dispersion Population dispersion is commonly quantified by population ecologists. Note that Figure D is derived from a randomly dispersed population, and that it indicates a Poisson distribution.Ĥ-2 II. Figures D, E, and F indicate the number of individuals within each sub-quadrat. The entire square indicates the entire quadrat, and each small square indicates one sub-quadrat. Figures A, B, and C represent the spacing of individuals within a population relative to each other. B.įigure 4.1: Common dispersion patterns are represented above. In addition to species traits, the distribution of resources or microhabitats links population dispersion patterns to the surrounding abiotic environment.

Species traits such as territoriality, other social behaviors, dispersal ability, and allelochemistry will shape individual dispersal (i.e., movements within a population), emigration, and immigration, all of which affect population dispersion patterns. Generally, dispersion can follow one of three basic patterns: random, uniform (evenly spaced or hyper-dispersed), or clumped (aggregated or contiguous see Figure 4.1). Different species and different populations of the same species can exhibit drastically different dispersion patterns. Introduction to population dispersion patterns The dispersion of individuals in a population describes their spacing relative to each other. 4-1 Population ecology Lab 4: Population dispersion patterns I.

Dispersio patters of of orgaisms