3.4.7 Investigating diversity

How to compare genetic diversity:

Genetic biodiversity can be assessed by calculating the percentage of gene variants (alleles) in a genome within isolated populations. 

-A high genetic biodiversity means there is a large variety of alleles in the population's gene pool.

-Measuring genetic diversity also allows quantification of diversity within and between species.

-Genetic diversity can be compared by:

• Comparing the frequency of measurable or observable characteristics.

• Comparing the base sequence of DNA.

• Comparing the base sequence of mRNA.

• Comparing the amino acid sequence of the proteins encoded by DNA and mRNA.

Limitations of examining observable phenotypic traits

-Traditionally, genetic variation was assessed by examining observable phenotypic traits.

-However, this method has its limitations:

• Traits are often polygenic - This means they are influenced by several genes, which makes it challenging to separate the influence of individual genes.

• Environmental factors can alter traits - This leads to variations that are not genetically based.

• Traits may change gradually across a range - Traits not falling into distinct categories makes them difficult to differentiate.

Comparative biochemistry

Comparative biochemistry involves studying the molecular aspects of organisms to uncover genetic diversity.

For instance, DNA sequencing allows direct observation of DNA base sequences, providing a more accurate measure of genetic diversity.

Useful molecules to compare genetic diversity:

• DNA - Populations that are more genetically diverse will have more differences in their DNA sequences.

• Messenger RNA - Base sequences of mRNA are complementary to DNA so can assess genetic diversity.

• Amino acids - Populations that are more genetically diverse will have more differences in their amino acid sequences because they are determined by mRNA and DNA.

Measuring variation

-Random sampling:

• It is often impractical or impossible to measure every single individual when investigating population traits. Random sampling is important to avoid bias and ensure the samples are representative of the whole population.

-Random sampling involves:

• Randomly generating coordinates across an area to prevent sampling bias.

• Collecting samples from these random coordinates to represent the population.

• Repeating this several times as a large sample size minimises the effects of chance.

• Analysing the data collected to identify any relationships.

Most samples with values either side of the mean will be normally distributed. A normal distribution curve is bell-shaped and symmetrical around a central mean value.

Standard deviation:

-The standard deviation measures spread of values around the mean within a sample.

• Small standard deviation - The values are fairly consistent and clustered around the mean.

• Large standard deviation - The values are fairly inconsistent and widely spread around the mean.

-Error bars:

Error bars display the standard deviation visually:

• They are plotted one standard deviation above and below the mean.

• The error bar length shows the degree of value spread in the sample.

• Longer error bars indicate more variation around the mean within that sample.