SEE-U 2000: Biosphere 2 - Module 5

## Module 5: Community Ecology: An Introduction to the Players and How to Measure Communities

Predictable patterns often emerge when groups of objects or individuals are put together. When we mix flour, yeast, water, sugar, and salt and then bake in the proper manner, we get bread. If the proper combination of metal tubes, gears, cables, chain, and rubber are put together in the correct manner, we can produce a bicycle. However, if the components of our mixtures are not at the proper ratios and assembled in the proper fashion, we will not get the desired outcome. There is a dynamic tension between the components, their relative presence, and the assembly process.

We can often learn quite a bit about the eventual outcome (the bread) by studying the nature and behavior of the components (the flour, yeast, water, sugar, and salt). Similarly, communities of species can be better understood when we explore the actions and interactions between and within species. Understanding the functioning of communities is essential to conserve endangered ecosystems and the species therein.

### Definition of Community

How would you define a community? Is it merely any aggregation of species that are found in a given area? Can we create communities by putting together a random assortment of species or do they have to have been associated with each other and evolving together? Most ecologists define a community as an association of interacting populations of many species that are defined by the nature of their interactions or the place in which they live.

By this definition, migrant species (such as long-distance dragonfly or bird migrants) would not be community members along their migration route, even though they would be collected during most sampling methods. Also, artificial aggregates of species would not be considered natural communities. Community members must have been evolving together for many generations; otherwise the aggregate will not be stable.

A good example of some of the consequences of assembling unnatural communities is Biosphere 2. When the assortment of plants and animals were chosen for inclusion into the domes, the designers wanted to include a variety of plants and animals from many ecosystems and continents. Interestingly, most of the animal species and some of the plant species died off soon thereafter, possibly because they did not form a natural community and were not adapted to coexist in that ecological setting.

What could comprise a community is scalable. A community could range in geographic scale from the community of bacteria on the face of a deer, to the community of parasites living on the entire body of the deer, to the plants and animals with which the deer interacts. Generally, the above definition of a community can be applied to any given geographic scale, which will be determined by the dispersal ability of the organisms under study.

### Community Structure

Irrespective of the scale at which you find them, a way to think about how communities are assembled and structured is to use two analogies. The first analogy is that communities are like a body, in that each species in the community has a role to play. In other words, the species come together because they "need" each other to exist in the same way that the organs in our body cannot function without each other’s assistance.

Alternatively, others suggest that communities are not at all akin to an organism. Instead advocates of this second analogy posit that species in a community are often found together merely because communities are like a group of sports fans. In other words, species that are found in any given community are found together only because they have the same ecophysiological requirements. In this analogy, species come together because they need the same temperature regime, to have the same amount of moisture, and so on. Similarly, sports fans do not usually go to the baseball stadium to meet each other but rather to see the baseball game, which can be thought of as their shared ecophysiological requirement.

In truth, most communities come together because of a mixture of both of these forces, with the relative importance of each differing from community to community, and at each spatial scale. Irrespective of which of these analogies is best supported by the data, we can make an empirical distinction between open and closed communities based on the frequency with which certain species co-occur in nature.

Open communities do not have clear-cut boundaries between adjacent communities. In other words, there is no sharp transition zone present between these types of communities. Open communities tend to blend into each other, with many species from each community frequently co-occurring. In contrast, closed communities have strong transition zones and have very tightly overlapping species distributions. The species of a perfectly closed community will not be found without a specific set of other species.

The transition zones, or ecotones, between communities are very interesting ecological regions. Some scientists have found that intraspecific genetic diversity is greatest in these areas, which may then serve as the generator of the raw material upon which natural selection works to create novel species. Ecotones often come about because some of the most important species (often called keystone species) were missing. Keystone species strongly interact with many other species and are tightly woven into the fabric of the food web. Keystone species often structure and assemble the community by their activities such that the removal of the keystone species leads to a total breakdown of the food web and thus the community. The term keystone was taken from architecture, and is an appropriate word. In architecture, a keystone is the block at the top of the archway that locks all the other blocks in place

### Measuring Communities

If we wanted to compare or summarize a community, what features would we measure? What are the relative merits of each feature? There are four main community aspects that we can measure: overall richness (the total number of species that are found or collected in an area), overall abundance (the total number of individuals), the number of trophic levels (the number of links in the food chain), or the number of feeding guilds (the method or location of foraging). Although each of these can be used to individually summarize a community, overall species richness and abundance are the simplest, most apparent, and most commonly used.

Many diversity indexes have been produced by combining the overall richness and abundance values. The first diversity indices are called alpha-level indices because they summarize the community on a local scale only. Beta-level diversity indices compare the communities along changes in a gradient (e.g., altitude) or between different sites. The last diversity indices are called gamma-level indices and compare changes across a very large geographic scale. During our class, we will emphasize the alpha and beta-level indices, as they are the simplest and most commonly used among ecologists.

Within each of these diversity index levels, we can analyze the data either by assuming that all species are of equal merit and importance or by imposing some degree of subjectivity and thereby rank some species as being important. When all species are treated equally, the indices are a type of cardinal index. In contrast, if we place greater weight on one or a few species in a community, the indices are a type of ordinal index. Most commonly, ecologists use ordinal indices.

Irrespective of the level at which they operate and whether they place greater emphasis on a few species, each index emphasizes different combinations of the above features. Let us begin by looking at the two most commonly used alpha-level ordinal diversity indices, the most commonly cited summaries of diversity.

The most commonly used alpha diversity index is the Shannon-Wiener (S-W) index, which assumes that all species in a community have been sampled and that the sampling was done randomly. If all of the species are evenly represented, the Shannon-Wiener Index value (H’) will be higher, with 4.5 being towards the upper limit. If one species dominates, the value will be close to zero.

The Simpson index estimates the probability of picking two organisms at random that are of different species. This index tends to be weighted toward the abundance of the most common species and as such is a type of dominance measure. As the value for the Simpson index (D) increases in the original calculation, the probability of any two species being of the same species increases. Therefore, in communities where one species numerically dominates all others, such as an unhealthy lake where one species of algae has taken over, the original formulation of D increases and communities with higher diversity have lower D values. However, researchers have reformulated and inverted the D value so that it is more intuitive. The reformulated D values can range from 1.0 in a community containing one species, to infinity for a community in which every individual belongs to a different species.

Because of the strong bias in the Simpson index in favor of the most abundant species, ecologists prefer to use the Shannon-Wiener index if its base assumptions can be met. Other important alpha-level ordinal diversity indices that we will not discuss here are the Rarefaction Curve (R1), the Relative Abundance Curve, the Evenness Measure (E), and the Brillouin index (HB). Similarly, important beta-level ordinal diversity indices used to compare communities include Whittaker’s Measure (b W), the Jaccard Index (CJ), the Sorenson Index (CS), and the Morisita-Horn (CmH) Index.

Choosing the most appropriate diversity index is often one of the most difficult parts of community ecology and should be done only after analyzing the assumptions, sampling methodology, and desired use of the index by the researcher. There are almost as many ways to measure communities as there are researchers studying those communities. In short, there are a diversity of diversity indices!