Estimating Species Richness Laboratory

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.

1 Table 1. Species No. individuals detected 1 20 2 15 3 9 4 5 5 3 6 3 7 2 8 1 9 1 10 1 11 1 12 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. 100 Enter the total number of species observed: 50 Enter the number of species observed with only 1 individual: 20 Enter the number of species observed with exactly 2 individuals: 10 Enter the number of species observed with 3 individuals: 5 Enter the number of species observed with 4 individuals: 9 Enter the number of species observed with 5 individuals:

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).

3 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 No of spp. observed 2x 25 20 No of spp. observed 3x 10 20 No of spp. observed 4x 2 15 No of spp. observed 5x 3 15

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

4 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. Loma Linda San Gabriel Observed no. of species 22 28 No. of species observed 1x 16 16 No. of species observed 2x 4 7 No. of species observed 3x 2 1 No. of species observed 4x 0 0 No. of species observed 5x 0 2 Estimated no. of species 46.3 44.0 Standard error of the estimate 8.5 5.7 Range of estimate 37-55 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.

5 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. Plantation Islands Controls Observed no. of species 4 9 2 No. of species observed 1x 1 6 2 No. of species observed 2x 0 1 0 No. of species observed 3x 1 1 0 No. of species observed 4x 2 1 0 No. of species observed 5x 0 0 0 Estimated no. of species 5 16.8 10.1 Standard error of the estimate 1.4 4.4 6.4 Range of estimate 3.6-6.4 12.4-21.2 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.