1. To understand the concept of species diversity, including the contribution of both species richness and “evenness” to it.
2. To understand some of the ways in which species diversity is measured and/or quantitatively modeled.
3. To understand the relationship between species diversity and area sampled.
4. To understand how the equilibrium theory of island biogeography can apply to the design of nature preserves.
5. To gain some familiarity with identification of leaf litter fauna.
If Stafford County had some money to buy land for new parks, would you suggest that they spend it all on one big parcel or on several smaller ones having the same total area?
One of the primary purposes of national parks and other kinds of nature reserves is to protect and conserve biological diversity – different kinds of living things. And while we all agree that conserving biological diversity is a good thing, we also realize that doing so comes only at some cost – development, timber harvest, mining, etc. all promote economic well-being, at least over the short term. So we have to be able to give more practical advice to policy makers than “protect everything!” This two-week field/lab problem is meant to give you some basic background on species diversity and island biogeography, as well as to get you to think about the above problem. In doing so, you should see some important contributions that ecological theory can make to very practical problems. Such contributions form the basis of conservation biology.
To understand the factors affecting diversity, we must first decide how we’ll measure and analyze diversity. This lab will ask you to apply a few common approaches:
Species Richness (S): This is simply the number of different species present in a given area. This is a good first approximation to diversity, but it doesn’t allow us to distinguish between a community that has 10 species represented by 10 individuals each, and one that has one species represented by 91 individuals and nine other species represented by one individual each. To do that, ecologists have developed approaches that incorporate the “evenness” of abundances, because we feel that the former community is more “diverse.”
Shannon Index: Shannon’s index (H’) is a calculation that takes into account both the number of species found (S) and the “evenness” or “equitability” of distribution of total individuals among the species. Values of H’ tend to range between 1.5 (lower diversity) and 3.5 (higher diversity). The Shannon index is given by the equation: where , the proportion of total individuals in the sample made up by individuals of species i.
Dominance-diversity curves: A graphical way of representing species diversity is to plot the log of each species’ abundance vs. its rank (from most abundant to least abundant). If we had 10 species, represented by 100, 80, 53, 41, 35, 33, 29, 14, 9, and 6 individuals, respectively, our dominance-diversity curve would look like Figure 1. Though it doesn’t yield a simple number for comparison, this way of representing diversity conveys more information. Note that the greater the slope of the line, the less “even” the sample, and the smaller the slope, the more “even” the sample.
Applying island biogeography to forest litter communities
The Equilibrium Theory of Island Biogeography (MacArthur and Wilson 1967) predicts the species diversity of islands as a function of their size and distance from the mainland. Reserves are often isolated patches of natural habitat surrounded by human-altered landscapes (i.e., they are effectively islands). Animals living under cover objects are similarly isolated from similar habitats, so we can use them to consider how island biogeography might relate to reserves.
The essence of MacArthur and Wilson’s idea is simple. They assume that the number of species on an island is determined by a balance between the rate of immigration of new species from the “source pool” (generally the nearby mainland) and the rate of local extinction. Both processes are assumed to be continuous, with species continually going (locally) extinct and being replaced through immigration, although not necessarily by the same species. The balance of extinction and immigration results in an equilibrium species richness () for the island (Figure 2).
Consider immigration first. At first, the immigration rate will be high, since each new individual reaching the island will represent a new species. As more and more individuals arrive, however, fewer and fewer represent new species. The immigration rate reaches zero when all of the “source pool” species (i.e., those present on the adjacent mainland) are present on the island. Immigration rate should also vary as a function of the island’s isolation, i.e., its distance from the mainland. So the graph has two immigration curves – one for a near island and one for a far one.
Now consider extinction rate. It is bound to be zero when there are no species present, but will increase in proportion to the number of species already present, due to there being more opportunities for extinction and the exclusion of species that are poor competitors. Extinction should also be a function of island size, both because larger islands support larger populations, which makes them less vulnerable to “bad luck” extinction events, and because larger islands are likely to contain a greater diversity of habitats than small ones. We can now predict that:
1) Species richness will become roughly constant through time, even though there will be continual turnover in species composition. Thus, the equilibrium reached is dynamic rather than static.
2) Large islands should support more species than small islands.
3) Near islands (to the mainland source pool) should support more species than far islands.
There are lots of data from real communities that support the above predictions. The potential application of this for the design of nature reserves is not so simple, however, as highlighted by the “single large or several small” (SLOSS) debate on this issue. On the one hand, it seems very clear that we should expect a decline in species diversity when a patch of land is separated from the “mainland” by human-altered habitat, since distance from the mainland is by definition increased from zero. In addition, a larger preserve should support more species than a smaller one. On the other hand, if we consider the question of whether to make a single large reserve or several smaller ones with the same total area, things get complicated. Small reserves might be more insulated against the spread of an epidemic disease. Small reserves might also allow for survival of poor competitor species in patches that just happen (by chance) to be without superior competitors. Similarly, small reserves might allow for the survival of prey species in patches that happen to lack major predators (Huffaker 1958). Given these tradeoffs, it’s clear that island biogeography theory shouldn’t be the sole factor in designing reserves. Other factors, like the biological needs of all the species involved and the physical features (e.g., topography, habitat diversity, spatial position) of the land in question, must also be considered. Procedure for week 1
We’re going to survey the animal community living under fallen logs and compare it to the community found in nearby open areas that lack a distinct cover item. The procedure is simple: you will flip the log and count and/or collect all the animals you find living within its footprint. You may use hands, forceps, aspirators, or nets to capture animals. Physical variables to collect for each log will include the length and width of the item (in meters), its proximity to the nearest other cover item (also in meters), and any other characteristics that seem relevant. You will then repeat that procedure in a nearby uncovered area that is a similar distance from neighboring logs. Our goal is for each group to survey at least three log-open site pairs. Aim to include both logs that are “isolated” (>3 m from another cover item) and “well-connected” (<1 m from another item) in your samples. To be confident of our identifications, we will collect examples of each invertebrate we find, and take photos of any vertebrates we find. Be sure to keep samples from each site separate and label them appropriately. We will complete identification of the samples in week 2.
Procedure for week 2
Your goal this week is to identify as many distinct “morphospecies” as you can within your samples. While it would be good to identify each animal to species, it is more important for our purposes is to accurately assess how many distinct taxa are present in the samples. As a result, you may identify species as “red centipede” or “striped brown spider” as you go. The key is for the names used by each group to be consistent. To encourage consistency, we will maintain a collection of examples of each species we identify. Before you record an animal as a new morphospecies, check whether that animal has already been given a name in the collection. Enter your group’s data into the Excel template your instructor provided and email it to them. As you're identifying and recording the morphospecies present in your sample, give some thought to the trophic habits (i.e., detritivore (consumer of rotting vegetation, bacteria, fungi, etc.), herbivore (living plants), or carnivore (other animals)) of each species encountered. Doing so will help you to interpret your results (e.g., you might find that spiders, which are carnivores, correlate with high diversity under a log). Write-up: Use the full class data for your Figures.
1. Use island biogeography theory to form good scientific hypotheses for each of the following questions. Remember that good hypotheses are testable explanations for an observation.
How will the species diversity under large items differ from smaller ones?
How will the species diversity under isolated items differ from more-connected ones?
How will the species diversity and trophic habits of animals in “under log” samples differ from “open” samples?
2. For two samples, __CH1c____ and_____TS2c_, show how you’d calculate S and H’. Compare your results to the values your instructor calculated for those samples. The following example should help: Suppose our samples contained 400 individuals in 10 species, and that taxa 1 through 10 were represented by 100, 80, 53, 41, 35, 33, 29, 14, 9 and 6 individuals, respectively. Table 1: Sample calculation of Shannon index (H’)
3. Figure 1: Plot the relationship of the Shannon index (y-axis) versus the area of cover item for each sample. For this and the other figures, you can plot the “under log” and “open” samples as different plots, or as two data series on the same plot.
4. Figure 2: Plot the relationship of the Shannon index (y-axis) versus the “isolation” (distance from nearest cover item) for each sample. 5. Figure 3: Make dominance-diversity curves for “covered” and “open” samples. Place the log (Abundance) for each species on the y-axis, and the species’ rank in terms of abundance on the x-axis. To make this plot, you’ll first have to sort the data with the most common species  first and the rarest species last. 6. Do Figures 1 and 2 support the predictions from island biogeography theory (see #1A and #1B)? Explain.
7. Do the data in Figure 3 support your hypothesis in #1C? Describe the ecological reasons (not methodological or error ) why you think the dominance- diversity curves are different (if they are) for “under log” and “open” samples. Be sure to compare both the richness and evenness of the samples. Consider things like: degree of isolation from similar habitat, rate of desiccation of habitat, abundance or lack of food, abundance or lack of predators, abundance or lack of plants, etc. Be specific and refer to the names of actual taxa, rather than generalizing over all species. Spiders are very different ecologically from roaches!
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Huffaker, C.B. 1958. Experimental studies on predation: dispersion factors and predator-prey oscillations. Hilgardia 27: 343-383.
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Explanations for your hypotheses should be based in IBT, especially in A and B
Note that the rankings will be different for the Covered and Open samples. In other words, you'll have to sort those data separately.
Not measurement error!