Exam 2 Take Home Sheet FINAL - James, John, Gavin, Alejandra, Maya

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Anthropology

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Apr 3, 2024

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1 Species Richness and Diversity Exercise You may work in groups of up to 5 people/group. Reading Richness and Diversity Recall that biodiversity is one of the most important features of ecological research and often serves as a measure of the overall health of an ecosystem. Declining biodiversity may indicate that the ecosystem is undergoing some type of environmental stress. Species richness and diversity are two of the most widely used metrics of biodiversity. Species richness refers to the actual number of species present on a landscape, whereas diversity takes into account both the number of species and their relative abundances ( sometimes referred to as species evenness ). For example, species richness would estimate that there are 34 species of vertebrate species in a woodlot, whereas diversity would take into account that 12% of all vertebrate individuals are song sparrows. Unfortunately, in many instances species richness and diversity cannot be observed directly as, typically, some cryptic species are overlooked or the overall complexity of the system prohibits a complete census of all species present being conducted. Over the next few classes, we will be looking at several approaches to sample ecosystems and to estimate species richness, abundance, and site occupancy. Each method we will look at has advantages and drawbacks. Most estimates of diversity or richness are unit-less. This means that the estimates are essentially relativistic, or that they have little meaning by themselves. Rather, they become useful when we compare estimates of diversity and richness from one ecosystem to another similar ecosystem or the same ecosystem sampled at different times. In today’s exercise, we are going to work with a couple of the most basic estimates of species richness and diversity. 1) What is the most basic method you can think of to estimate ecosystem diversity? Count the number of species present in the ecosystem. 2) What might be some problems with using this estimate of ecosystem diversity? You won’t be able to reliably detect every single species. The number can be inflated by very few individuals of many different species. Additionally, if you are only focusing on the species level, you might miss out on measures of diversity at different taxonomic levels (i.e – many species, but all vertebrates). A slightly more refined method of estimating ecosystem diversity over raw richness is Margalef’s Diversity Index (Margalef 1958). Diversity = s − 1 log N Names: James Shugart, John Morrow, Gavin Roberts, Alejandra Gage, Maya Madern (equation. 1)

2 Where s is the number of species observed and N is the number of individuals observed. Margalef was a limnologist (aquatic ecologist) which meant he typically worked with very complex ecosystems where both N and s were huge. His diversity index works well when s and N are about the same for all ecosystems being assessed or they are both large values. However, when these assumptions are violated, as is true in many terrestrial ecosystems, the Margalef Diversity Index tends to be less useful due to a high bias. The bias exists because the Margalef Index does not account for species evenness (i.e. relative abundance of each species). The Shannon Diversity Index (Shannon 1948) adjusts for species evenness by using information theory. The Shannon Diversity Index measures the degree of uncertainty in the system. If diversity is low, then the certainty of picking a particular species at random is high. If diversity is high, then the certainty of predicating the identity of a randomly chosen individual from the population is low. Thus, high diversity = high uncertainty. The Shannon Index is calculated as: H ' =− ∑ i = 1 S pi ( ln ( pi ) ) Where S is the total number of species and pi is the relative abundance of each species ( see equation 3 ). Notice the negative sign in front of the summation symbol; this means the index will almost always be positive. pi = n i N Where n i is the number of individuals of species i and N is the total number of individuals for all species. Rarity Another attribute of ecosystems is the occurrence of rare species within them--Rarity. Rarity is the scarcity of a particular species across an ecosystem. In many cases species of high conservation value are species that occur rarely on a landscape. Consequently, if a particularly rare species of conservation concern, or a species of high conservation value occurs at a particular location, that location might have high conservation value, even if it doesn’t have high biodiversity or species richness. Rarity Weighted Richness (RWR) is an accounting method that gives sites a conservation value score based on having either 1) a large number of species presence or 2) the occurrence of a few rare or highly valuable species or 3) a combination of both factors. This can be an important metric for use in conservation planning, where a goal might be to identify a set of sites or the bounds of a reserve that represents all conservation targets in as efficient a manner as possible. Williams et al.(1996) proposed that the rarity value of a species can be characterized by the inverse of the number of sites or planning units in which it occurs. Thus if a species is found in only 1 site, the species would have the maximum rarity score of 1/1 = 1, and a species that occurs in 20 sites would have a rarity score of 1/20 = 0.05. Williams et al. also proposed that the rarity scores of all species in the site can be summed to yield a single RWR value for the site: (equation. 2) (equation. 3) (equation. 4)

3 ∑ 1 n ( 1 ci ) where ci is the number of sites occupied by species i, and the values are summed for the n species that occur in that site. Thus, sites that have the only record of a particular species will receive higher scores as will sites that contain all detected species. Recently, Albuquerque and Gregory (2017) demonstrated that the use of RWR is a more efficient way of designing priority bird conservation reserve networks for Natura 2000 and IUCN sites throughout Europe. To calculate RWR you will need to keep track of the occurrence of each species at each site and the total number of sites at which a species occurs. Similarity Similarity of sites can be thought of as the antithesis of diversity and Rarity. Whereas both of the previous discussed sets of metrics look at differences within and among sample locations and biological communities, similarity evaluates how alike sites are. Similarity indices are frequently used to study the coexistence of species or the similarity of sampling sites. The main aim of this type of analysis is to discover distributional patterns common to different species and groups, or areas with similar biota (Birks 1987). There have been >60 different similarity indices proposed for use with ecological data. However, the Jaccard's index (Jaccard, 1908) is one of the simplest and most widely used. Jaccard’s index is used frequently in conservation because it may be applied to the power function of the relationship between species and areas to determine a measure for the optimum size for natural protection reserves (Higgs and Usher, 1980). Jaccard's index is independent of the number of operational taxonomic units OTUs studied. One of the simplest and most commonly used. Jaccard's index may be expressed in several ways. A common approach is the following: J= C A + B − C where A is the number of attributes present at site a, B is the number of attributes present at site b, and C is the number of attributes present in both sites a and b. In this case Jaccard’s index can be thought of as the proportion of species found at both sites compared to total species found at only one site or the other, but not both. Jaccard's index can also be expressed as: J= C A + B + C where A is the number of attributes present in a but absent in b, B is the number of attributes present in b but absent a, and C is the same as in Equation 5. In this case Jaccard’s index can be thought of as the proportion of species found at both sites compared to total species found at all sites. A third way of expressing Jaccard's index is as follows: J= C N in which C is the same as in Equations 5& 6 and N is the total number of species in the system. In this case Jaccard’s index is the proportion of shared species. (equation. 5) (equation. 6) (equation. 7)

4 The Jaccard’s index represented in equation 7 is the easiest and most accurate to use with a large number of sample sites. Whereas the other two indices are best used when comparing a few sites to each other. Regardless all three methods are equally valid and one really only needs define how the metric was calculated to justify its use. In this exercise, you may use any method you wish, but once you choose a method you must maintain fidelity to it throughout the analysis. I suggest using equation 7 as it will be the easiest to calculate and has the most straightforward interpretation. Similarly to RWR, to calculate a Jaccard index, you will need to collect data on the identity of each species present at each site. Thus, in Tables 1&2 the most important data that you will collect are the data in column 1 (species ID) and column 2 (number of occurrences of that species/site). In-class Exercise In today’s class, we are going to calculate each of the above metrics of richness, diversity and similarity using some data that I have provided on bird species richness at three sites in Texas. Objective #1: Students will be able to use the Shannon diversity estimator, Jaccard indices and a Rarity weighted richness index to estimate and compare species diversity of a complex ecological system. Objective #2: Students will be able to identify and correct for common sources of error in field data collection. Table 1 . Sample Bellevue Study Site diversity calculation data.

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