The relationship of variance and mean informs researchers about the spread of data. If a researcher calculates the mean abundance per unit area of a species, and then calculates the variance, the relationship between mean and variance will reflect the distribution pattern. Which distribution pattern pictured below will have variance greater than the mean?

The distribution pattern that will have variance greater than mean is one where the population of species is clustered and thus far from the mean abundance of species per unit area.

This distribution pattern can be found, using the POISSON distribution.

Step-by-step explanation:

Variance is a measure of dispersion while Mean is a measure of central tendency.

The mean is the average of all values (in this case, the abundance or concentration of species per unit area). It is the sum total of all values, divided by the number of values there are.

The variance of a given set of data, on the other hand, is a measure of the spread or distance or dispersal of the data from the mean. It measures the spread between each datum/value and the mean value.

The relationship between mean and variance surely reflects the pattern that the distribution will take. The kind of distribution pattern that will have a greater variance than mean is a Poisson distribution. Sample size is usually large here. Since the variance is greater than the mean, the population is a clustered or clumped distribution.