Estimating Species Richness Laboratory
Zoogeography
The number of species inhabiting an area, species richness, is a basic biological
variable that scientists would often like to quantify. For example, a scientist may want to
compare the species richness of two areas. Or, he/she may want to determine whether the
species richness of an area has changed from one point in time to another. However, it
can be difficult to determine the number of species in an area, particularly when
individuals are mobile, like many animals, or when some species are more conspicuous
than others. In addition, environments vary and it may be easier to detect any given
species in one environment, for example a grassland, compared to in a forest. This
laboratory will focus on ways to estimate species richness given a particular set of data.
Sampling the species richness of an area. Sampling is measuring a small part
of an entity assuming that this small part is representative of the larger entity. When
sampling for species richness we typically detect a subset of the species in a community
and assume this subset is representative of the total number of species in the community.
To make sure it is representative, one must plan the sampling so that each species in a
community, or each location in an area, has an equal chance of being sampled. For
example, if one wants to determine the species richness of frogs in a woodlot and one
samples for frogs in the terrestrial part of the woodlot but not in the pond in the woodlot,
the species richness number that results is not representative of the whole woodlot but
only of the terrestrial portion of the woodlot. Similarly, if one wants to determine the
species richness of arthropods in a field, one must use a technique that has an equal
chance of catching terrestrial and flying arthropods or, one must use one technique that is
good for catching terrestrial arthropods and another that is good for catching flying
arthropods. Once the sampling results in a dataset, then one must estimate the species
richness from the dataset.
Estimating species richness. Why does one have to estimate the species richness
from the sampled data? Sampling generally does not detect every species in an area,
particularly when species are mobile, like most animals, and detections of individuals
from sampling generally do not have a one-to-one correspondence with the number of
individuals of a species actually present. This is because species vary in their
detectability. For example, for every cardinal sighted in Baker Woodlot on a winter
morning, there may actually be two cardinals present. For every downy woodpecker
sighted in Baker Woodlot, there may actually be four woodpeckers present. The
cardinals thus have a higher detectability than the woodpeckers. On some mornings
downy woodpeckers may not be detected at all, even though they are present. Therefore,
the number of species sampled is not equivalent to the number of species in the
community, although in many cases investigators present the sampled number as the
actual number. It is more accurate to say that the number of species sampled is an
estimate of the species richness of the community. However, it may not be a very good
estimate. Consider the following example data (Table 1) for a butterfly community. The
community was sampled by observations of flying butterflies.
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Table 1.
Species
1
2
3
4
5
6
7
8
9
10
11
12
No. individuals detected
20
15
9
5
3
3
2
1
1
1
1
1
If we take the number of species detected as the species richness estimate, then we would
say there are 12 species in this community. However, look at the distribution of the
number of individuals detected for the various species. A couple of species were
relatively common. However, many of the species detected were only observed once.
These data suggest that many of the species in the community are rare. Therefore it is
likely that our sampling missed a number of rare species. How can we make our
estimates of species richness better, given a set of data? We can use the distribution of
the number of individuals detected per species—this information can be used in a
software program, SPECRICH, to improve species richness estimates.
The program is part of the Patuxent Wildlife Research Center’s software archive. The
Patuxent Wildlife Research Center has many different software programs that can be
used for ecological analyses. There are also other ways to estimate species richness. We
are just looking at one today.
Go to the following webpage:
http://www.mbr-pwrc.usgs.gov/software/specrich.html
You should see the following:
Table 2.
Enter the total number of species observed:
100
Enter the number of species observed with only 1 individual:
50
Enter the number of species observed with exactly 2 individuals:
Enter the number of species observed with 3 individuals:
Enter the number of species observed with 4 individuals:
Enter the number of species observed with 5 individuals:
20
10
5
9
2
In the space below, draw a bar graph showing the distribution of observations of
species in this community (Table 2).
What can you say about the proportions of rare and common species from these
data, considering a rare species to be one that was observed once and common
species those that were observed 4 or 5 times?
A higher proportion of species are rare than common.
Push the Compute Species Richness Button
The interpolated N at the bottom of the table is the estimated number of species from the
data and the standard error of the interpolated N is a measure of the potential range of the
estimate. In other words, with the N of 167 and the standard error of 14, this suggests the
true number of species is 167 ± 14 or between 153 and 181. The columns in the table
provide details related to the statistical procedure used to arrive at the estimate. For those
of you interested in statistics, you can see me for questions about these columns and/or
see the following paper: Burnham, K. P. and W. S. Overton. 1979. Robust estimation of
population size when capture probabilities vary among animals. Ecology 60:927-936.
K is the order of the jackknife estimator, T(K) is a test statistic and P(K) is the probability
of obtaining a value for T(K) higher than the one in the chart.
Put the data from Table 1 into the appropriate boxes in the program and compute
the interpolated N and standard error for this dataset. **The program does not
include boxes for species observed more than five times—in developing the formula to
calculate the estimate the authors discovered that species observed more than five times
did not influence the estimates very much. However, because you include the total no. of
species, the program “knows” how many species were observed more than five times.**
What is the interpolated N, standard error, and range of the estimate for the dataset
in Table 1? (The range can be calculated from the N and the standard error).
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N = 17.0
SE = 3.2
Range = 14-20
Consider two communities of arthropods that inhabit leaf litter of the forest floor.
One hundred species were observed in each community. See Table 3 for the distribution
of the number of detections of the different species in both communities.
Table 3.
Community A
Community B
No of spp. observed 1x
60
30
25
20
10
20
2
15
3
15
No of spp. observed 2x
No of spp. observed 3x
No of spp. observed 4x
No of spp. observed 5x
Which community do you think will have the higher estimated species richness?
Explain your reasoning.
Community A is likely to have a higher species richness because it has a greater number
of rare species (those observed only one time). This finding suggests that more species in
community A likely were not detected at all, compared to community B and so the true
species richness for A should be greater.
Fill in Table 4 by putting the values from Table 3 into the program.
Table 4.
Estimated species
richness (interpolated Standard error of
Range of
N)
estimate
estimate
Community A
174.3
14.1
160-188
Community B
130.0
7.7
122-138
I will send you Table 5 at the beginning of class. Save this file on the computer. For
the following you will be working with the sheet called “San Gabriel”. The data are from
two sites where Dr. Lindell and some colleagues have conducted a forest restoration
experiment in Costa Rica. Seedlings were planted in both sites in 2004 and the seedlings
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today are trees up to 8 m tall. The San Gabriel site is primarily surrounded by pasture
while the Loma Linda site is primarily surrounded by forest.
Based on the information in the last sentence above, do you think the estimated
species richness will be higher for the San Gabriel or Loma Linda site? Explain
your reasoning.
I accepted a variety of answers here. Tropical forested sites typically have higher
species richness than pasture sites so one would probably expect a higher species
richness at Loma Linda, due to spillover from the forest.
Calculate the estimated species richness for the two sites using the SPECRICH
program. You will have to figure out how to get the information from the dataset that
you need to enter into the program. Please write down the steps you use to get this
information (Hint: you should sort the data in the Excel file to make it easier to get the
information—if you don’t know how to sort, please ask). Fill in Table 6.
Table 6.
Observed no. of species
No. of species observed 1x
No. of species observed 2x
No. of species observed 3x
No. of species observed 4x
No. of species observed 5x
Estimated no. of species
Standard error of the estimate
Range of estimate
Loma Linda
22
16
4
2
0
0
46.3
8.5
37-55
San Gabriel
28
16
7
1
0
2
44.0
5.7
38-50
Does one site have higher estimated species richness than the other? Are the results
what you would have expected, given the information in the table? Explain.
Loma Linda has a slightly higher species richness. One would have expected this,
looking at the information in the table. Although fewer species overall were detected in
Loma Linda, a greater proportion of species were observed only once (16/22), compared
to San Gabriel (16/28). Also, San Gabriel had two very common species (observed 5
times). This suggests that Loma Linda has more rare species that likely weren’t detected
at all.
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Use the data in Table 7 to calculate the species richness of Neotropical migrant birds
in three types of restoration sites in the same experiment as described above.
Table 7.
Observed no. of species
No. of species observed 1x
No. of species observed 2x
No. of species observed 3x
No. of species observed 4x
No. of species observed 5x
Estimated no. of species
Standard error of the estimate
Range of estimate
Plantation
4
1
0
1
2
0
5
1.4
3.6-6.4
Islands
9
6
1
1
1
0
16.8
4.4
12.4-21.2
Controls
2
2
0
0
0
0
10.1
6.4
3.7-16.5
Describe three advantages of using species richness estimates as calculated by
SPECRICH, as compared to the raw no. of species observed.
1. Uses information from the distribution of detections to make inferences about the
relative number of rare and potentially undetected species in communities, leading to
more robust estimates of species richness than the raw data.
2. Provides a range for estimates of species richness, rather than one number.
3. Imprecise numbers of very common species don’t affect estimates because species
detected more than 5 times aren’t included in estimation procedure.
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