Ecology Lab, PCB 3043L
The structure of ecological communities is measured with a number of different metrics, including food web parameters that also address functional relationships among the species of a community. Species diversity and species richness have been important measures of community structure for many years. Whereas species diversity is a single, non-dimensional number generated for a given community, species richness is a measure of number of species per unit area. Because of the problems associated with mathematical measures of species richness (see p. 190 of your lab manual), most ecologists use one of the measures of species diversity presented below as an estimate of species richness.
We face two main problems when quantifying differences in the abundances of species in communities. First, the total number of species found varies with the sample size, because you are more likely to find a rare species as you sample more. This means that diversity cannot be compared between communities that were sampled at different intensities. Second, the number of individuals representing a species may not be a good indication of the functional importance of that species to the community. To some degree, the functional roles that species play in a community varies in proportion to their overall abundance. There are cases where this is not the case, however. An excellent example of this is a keystone predator, which may number few individuals but plays a critical role in the way food webs are structured in a community. Thus, it is best to have some measure of the functional role of a species in a community as well as measuring simply the numbers of individuals.
There is an easy way to identify when we have sampled enough individuals in a community to determine species diversity with some level of confidence. It is called the rarefaction curve (Figure 32.1 of your lab manual). Rarefaction curves plot the total number of individuals counted with repeated samplings versus the total number of species found in those samplings. The result is a curve that increases steeply at first, then gradually levels off. The point at which it levels off is the point where additional sampling is yielding no additional information about the number of species. This is the optimal sample size. As you might guess, the total number of species in a community strongly determines how many individuals must be counted to reach this optimum, and the number of rare species plays a critical role as well.
The two most common indices of species diversity are Simpson’s Index and the Shannon-Weaver Index (both are presented below). In both cases, these indices are calculated from the proportions of total individuals sampled (ntotal) that are represented by a given species (i), such that:
Simpson’s Index (D) is calculated as:
Ds = [N(N-1)] / [jn(n-1)]
The Shannon-Weaver Index (H) is calculated as:
Today,
you will use the plant sampling methods that you used in your first lab to
quantify plant species diversity in a quasi-natural community on campus—the
field just west of the ECS building that is being recolonized by various
wildflowers, forbs, etc. You will
be using 0.25 m2 quadrats (50 cm X 50 cm) to sample this area today.
From the data taken by your entire lab, you will generate a rarefaction
curve and determine the optimal sample size (as both number of individuals and
number of haphazardly tossed quadrats) for this community.
You will then calculate both D and H (above) using only your group’s
data and using your entire lab’s data, and compare the two.
1.
Generate several testable hypotheses as a class that you can test with
today’s exercise.
2.
Set up field data sheets for today’s work, remembering the importance
of noting total numbers of all species in each quadrat and thus the importance
of keeping track of all plant species you find.
3.
Divide into groups and work as teams in the field.
Work should be divided up so that all team members get to experience each
aspect of the exercise. In other
words, don’t make one person record data for the entire lab exercise!
4.
Be sure that you have all field sampling equipment that you will need.
Read below and make a list before you leave the lab.
5.
All field teams should participate in sampling as many quadrats as
possible. After sampling, return to
the lab and your TA will pool data from all teams to generate larger datasets
for each habitat.
1.
Since you will be comparing/contrasting data from your group with data
from your entire lab, it is absolutely critical that everyone in the lab be
identifying the same plants as the same species.
To that end, before any sampling can begin you need to come up with some
kind of “plant identification key”
for your study area. The best way
to do this is to select certain “voucher specimens” of every species you can find and identify
them—either by genus and species, if possible, or by some other identifier
(such as A, B, C, etc or “Amy, Bob, Chuck, etc.). Then be sure that every group has access to your collection
of voucher specimens so that individuals are counted as the correct species.
This is very important. Take
as much time as necessary to generate this collection of voucher specimens, and
be sure everyone in your lab agrees on the identification labels you will use
for each species.
2.
Your group should haphazardly toss your 0.25 m2 quadrat and
sample it as many times as possible. With each toss, first identify all species present in the
quadrat using your voucher specimens. Then
count and note the number of individuals of each species present.
3.
If your group identifies a plant that is NOT in your lab voucher
collection, it may be a rarer species.
It is VERY important that you add this plant to the voucher collection,
and be sure that everyone in the lab knows that you have done this.
4.
Save your lab voucher collection when you return to the lab to collate
all group datasets into a single lab dataset.