Community Diversity

An ecosystem is a community of living and non-living components that interact in an area. This can be as large as the earth itself or a forest full of plants and animals, or as small as a stomach with its array of microbial inhabitants. Ecosystem health can be measured in different ways, but one good indicator is biodiversity, a measure of the variety of biological species in an ecosystem.

We can evaluate biodiversity using a measure called the Shannon-Wiener Index, proposed by Claude Shannon and Norbert Wiener in the 1940s. The Shannon-Wiener Index is a unitless measurement, calculated as H. The index uses two variables: species richness, or the number of species present, indicated by K, and species evenness, or proportion of each species, indicated by PI. Species richness is the number of unique species in an ecosystem. This example has seven. Species evenness measures how species in an ecosystem are distributed. This means that an ecosystem in which the different species have similar numbers is more even than one where one or two species account for most of the organisms, like this one, relatively overrun with hares.

Now that we know what to measure, let's learn how to measure biodiversity. Since ecosystems can be huge, ecologists had to figure out ways to randomly sample large areas quickly. The simplest of these is the quadrat, which is a frame of fixed size, typically placed at random within a sampling site. Scientists survey the area inside the quadrat, logging the species richness and also their evenness. Using these little windows placed at multiple spots over an ecosystem, they can extrapolate their findings to give diversity estimates for the entire study area. But although this technique allows for a manageable assessment of a large area, it's not perfect. Random placement of quadrants may miss individuals, resulting in under-representation of species and incorrectly low biodiversity estimates.

This limitation means that some communities, especially patchy or non-uniform ones, are better sampled by a more structured approach. Here, our meadow borders the forest and, in reality, consists of different habitat patches. To cover this community fairly, we can divide the landscape into core habitats and importantly, edge habitats. Often, edge areas have more biodiversity because species from both habitat types might live there. But this might get missed in traditional random sampling. Instead, to measure the biodiversity across a community like this, a good strategy may be to first sample the core areas randomly using quadrats. The data collected can then be used to calculate the H value for each core habitat. Next, a transect or sampling line can be laid across each core habitat, extending over into the edge area. Species richness and evenness can then be recorded using quadrats placed at set points along these lines. Now using the Shannon-Wiener formula, the diversity index can be calculated for each distance along the transect, and these can be compared. An area with a greater H value is generally more diverse than one with a lower H. This combined random quadrat and transect technique gives a versatile and more fair assessment of how the different habitats within the same community compare in terms of diversity.

In addition to species measures, scientists may also take measures of the chemical components of an ecosystem to assess its health. For example, they might check for pollution, or examine soil quality by measuring the nutrients present. This information can also be useful to help explain changes in patterns of biodiversity in different regions of a community. So, in our example, if the meadow has lower quality soil than the forest and the edge, it might support less species.

In this lab, you'll measure biodiversity and soil quality in two different core regions, and then examine how biodiversity is affected along an edge habitat.