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Overview
Overview
Quantifying diversity is needed as the number and type of organisms and species differ across communities. This lab explores how data on diversity is collected and analyzed (graphically and numerically).
Background Reading
Background Reading
Objectives
Objectives
Students should be able to
Define a survey and transect
Propose an appropriate sampling design for measuring diversity
Create and interpret species accumulation curves
Calculate diversity metrics including richness, evenness, Simpson's Reciprocal Index, and Jaccard's Index
Use diversity indices to justify conservation decisions
Introduction
Introduction
Diversity is a central focus on ecology and evolutionary studies that considers the variation in species that are present in a a community. Studies of diversity can range from analysis of genetic diversity within a single species at a site to a common focus on taxonomic diversity, or the identity and relative abundance of species at a location. Diversity studies require data to be gathered from a site and then analyzed. We will consider this work by sampling Pokemon diversity in New York city.
Before data can be analyzed, efforts should be made to ensure a community has been adequately and fairly sampled. This requires a clear definition of what a study is trying to sample; most diversity studies focus on a single type or group of species due to practical constraints. To verify the adequacy of your sampling, we can create species accumulation curves ( also called Collector’s curve). A species accumulation curve indicates the number of novel species captured per sampling event or site by plotting the total number of species observed against the total number of samples or sites. For example, imagine if in your first survey of a community you observed 3 species A and 2 species B. In this case the number of species you encountered would be 2. Therefore, you will add your first data point at y = 2.
In the next location, you captured 0 species A, 1 species B, 1 species C. This time the only unique species in this group that you didn’t find in the previous site was species C, so you have only observed 3 total species. Therefore you add the next data point at y = 3.
In the third location, you found no new species. Therefore you data point for this site would be the same y-value as the site 2, y = 3 .
In the fourth location, new species D and E were found in addition to more specimens of species A and C. In this case you will need to increase the number of species by 2 in y-axis.
You will continue to grow the curve by adding data from your other stations. Hopefully you note that the end of the curve is reaching an asymptote (Fig.5-5); this is how you know that you are getting close to capturing all species present in your study area.
Once a site has been adequately sampled diversity metrics can be calculated. Common taxonomic measures of diversity include Species Richness and Species Evenness. Species richness, which is commonly abbreviated as S, is simply a number of species within an area, while species evenness refers to how evenly species are distributed in an area. The following images show two different communities of Pokémon. Pokémon (an abbreviation for Pocket Monsters!) are a highly diverse (digital) life form that are present in a wide variety of ecological niches on the earth. Both communities have have 12 individuals, however, the community on the right has a higher species richness than the other community. If you take a closer look, it contains 9 species while the other has only 1 species.
In the next example, the communities below have the same number of individuals and species, but the evenness differs between the two. The community on the left has a total of 5 species; Abra (N = 2), Beedrill (N = 3), Bellsprout (N = 4), Bulbasaur (N = 2), Butterfree (N = 1). The other community also contains 5 species; Ekans (N = 6), Sandshrew (N = 2), Nidoran ♂&♀ (N = 2), Nidorina (N = 1), Nidoqueen (N = 1). As you can see, we can tell that the community on the left ({2, 3, 4, 2, 1} )is more evenly distributed than the community on the right ( {6, 2, 2, 1, 1}), which has a more dominant species. Therefore, we can say that one community has a greater evenness than the other.
Species evenness can also be numerically quantified in several ways. One way is to actually consider the opposite of evenness, or dominance. Simpson's Index considers dominance by summing the square of the relative proportion for each species in a community. The relative proportion for each species is simply the fraction of individuals in a community that are a given species. For example, the last observed community above has 6 Ekans out of 12 observed species, so the proportion ofr Ekans is .5. If we find the relative proportion of each species, we can calculate Simpson's Index as
Simpson's Index (D) = Σpi2
Σ means " sum of", so Σpi2 means sum of Pi2. Pi is the fractional abundance of the ith species in an area.
This sounds complicated, but consider what it implies. Relative proportion goes from 1 (when only one species is in a community) to zero as more species are evenly represented in a community; it's minimum is 1/S, which occurs when S species are represented equally in a community. For another example, consider the shape community below.
Community 1
True species diversity metrics combine information on richness and evenness. This is essential for comparing different sites. There are many conventional methods to calculating species diversity. Here, we will use a standard index called Simpson Reciprocal Index, 1/D where it is a literally a reciprocal of Simpson Index D. Remember,
Simpson Index (D) = Σpi2
Σ means " sum of", so Σpi2 means sum of Pi2. Pi is the fractional abundance of the ith species in an area.
Simpson Reciprocal Index = 1/Simpson Index = 1/Σpi2
Since D goes from 1 (when a community has only one species) to 1/S (when a community has a equal number of S species), 1/D goes from 1 (when community has only one species) to S (when a community has an equal number of S species). Simpson Reciprocal Index takes into account both species richness and species evenness . The higher the reciprocal index value, the higher the diversity. Here are some examples of how the two aspects interplay to determine diversity.
Community 1
In this community we found 6 individuals of circle species and 4 individuals of triangle species. The proportion of each species is determined by dividing the number of individuals of each species by the total individual number from that site. For example, for circle species; 6 /10 = 0.6. The sum of Pi2 values is 0.52, and therefore the Simpson Reciprocal Index is 1.92 (calculated as 1/0.52).
Simpson Reciprocal Index = 1 / ((0.6)2 + (0.4)2) = 1/ΣPi2= 1/0.52 = 1.92
Community 2
In this community, again circle and triangle species were sampled, but the the abundance of the two species are now evenly distributed; proportion of circle species is 0.5, and the proportion of Triangle species is also 0.5. Now if you pay attention to the diversity calculated for site 2, the index is higher than the first location. This example shows that diversity increases with increasing evenness.
Simpson Reciprocal Index = 1 / ((0.5)2 + (0.5)2) = 1/Σ Pi2 = 1/0.5 = 2
Community 3
In the third community, we found two individuals of Circle, Triangle, Pentagon, Heart, and Square species. The similarity between the second and third sites were that both of them exhibited even distributions; site 2 {5:5}, site 3 {2:2:2:2:2}. However, the third site had a higher number of species than the second site (the third site exhibit 5 species in the group where the second site only contains two species). As a result, the third site has a Simpson Reciprocal Index of 5, which is higher than the second site’s index of 2. This example concludes that diversity increases with increasing species richness .
Simpson Reciprocal Index = 1 / ((0.2)2 + (0.2)2 + (0.2)2 + (0.2)2 + (0.2)2 ) 1/ΣPi2= 1/0.2 = 5
In summary, Simpson's reciprocal Diversity Index takes into account both species richness and evenness where the higher species richness and evenness result in a greater index value, hence greater diversity.
Community similarity:
Note that measures of specie richness and eveness (and thus diversity) do not consider species identity. For example, 2 sites with totally different species could both have a richness of 4 and a diversity of 2.98. Here we will measure how different communities are from one another using the Jaccard coefficient of community similarity. We will contrast community distinctiveness between all possible pairs of sites.
The Jaccard coefficient of community similarity:
CCJ = c/S
c is the number of species common to both locations, and S is the total number of species present in the two locations.
For example, two sites A and B both contains two species, but one of which is common between two sites. Therefore;
c = 1 (number of common species: Square species)
S = 3 (total the total number of species present in the two communities)
CCJ = c/S = 1/3 = 0.33
This index ranges from 0 (when no species are found in common between communities) to 1 (when all species are found in both communities).
You will need to calculate this index to compare each pair of sites separately. For example, comparing Site A with Site B, Site A with Site C, Site A with Site D, etc… for however number of sites we sampled as all of our sampling sites together.
Methods
Methods
In this lab exercise, we will assist Pokémon researcher, Professor Willow to collect Pokémon in New York City. This lab will take place over several days. Data on Pokemon diversity will first be collected by surveying sites using Pokemon go! If you haven't played Pokemon go, it’s a free game you can download on your mobile phone. Instructions on sampling techniques, locations, data entry, and due dates will be provided by your instructor. Next you will analyze the data collected by students in order to determine species richness, species diversity, and community similarity of the sampled locations using Excel spreadsheets, Google Sheets or equivalent (please bring your laptop to this lab and download the spreadsheets provided below if possible).
Materials:
Materials:
Mobile device (iphone, Andriod)
Spreadsheet software (Microsoft Excel, Google Drive, etc) and laptop (available for Baruch students)
Pokémon Go (free game)
Data collection
Data collection
Students will collect Pokémon data (your instructor may have you work in pairs) from designated locations designated (examples may include local parks or your own neighborhood). Possibilities may include
Madison Square Park
Bryant Park
Union Square Park
Washington Square Park
To complete the sampling, students will need to download Pokémon Go on their phone. You can either use your Facebook, Google or Pokémon Trainer club account to log-in (Trainer club log-in may not be very straightforward, so using Google or Facebook accounts is recommended).
Sampling needs to be consistent across all samples (why?), so all sampling efforts should last for 30 minutes. Over 30 min document and attempt to catch every Pokémon you encounter. To minimize bias amongst samples, do not use incense since those will artificially inflate your encounter rates. Also, avoid using PokeStops that have lures.
For each capture effort, collect information as noted by your instructors. Examples may include
Location
Where did you sample for Pokemon?
Species Observed
what species did you see?
# Balls (Thrown to capture or before pokemon escaped)
How many balls did it take to capture the Pokemon (or how many did use before it escaped)?
Captured?
was it captured (yes or no)?
It may be useful to write data down during your sampling event. An example data sheet is available here, but your instructor may modify as needed. Scientists use notebooks and sheets like this all the time!
Once you complete your collection, you will submit a copy of collected data to your instructor to verify your surveying activity. This may be via email or by uploading information using a shared link provided by your instructor. This sheet will allow you to enter the number and identify of each Pokemon species you collected or observed (since some may have escaped). This activity shows the value of shared names (What if you called a Charizard a Pikachu?) and explains why scientific names are so important to real efforts to catalog diversity!
On the shared sheet provided by your instructor (example below), you will enter the general following information:
Your basic information under the Collection Info tab (include both student names). This is metadata, or data about the collection event.
Information on the number and species of Pokemon you collected. The sheet may use dropdown tabs to help standardize names among collections.
If your instructor assigned specific locations, there may be tabs for each site. Make sure to use the correct tab if provided.
There may also be information on observed vs collected pokemon and other factors.
Data Analysis
Data Analysis
Your instructor will provide a data sheet with combined information from all collections. Now you are ready for analysis!
Before comparing diversity among sites, you want to make sure you thoroughly sampled an area. Having a thorough inventory is important, because more comprehensive inventories will lead to more accurate diversity measures. But how do we know that we have surveyed Pokémon diversity in your sites adequately so that we have a little to no Pokémon species left to capture? To verify the adequacy of your sampling, we can create species accumulation curves ( also called Collector’s curve) for each sampled site. Do this using the class data. For each site the class sampled, you should have a graph with “Number of samples” on the x-axis and “Number of Species” on the y-axis.
One way to do this is demonstrated on the sheet shared below. On this sheet, the first column of cleaned data is replicated below the original data. The following columns add this data to the original data to form columns that show the cumulative number of specimens collected for each species as sample size increases. Using these columns, the number of species can be found using the countif command, where cells with values greater than 0 are counted (e.g., =countif(B49:B91, ">0") ). This data can be combined with data on sample number to create the required chart.
If your curve does not reach an asymptote, what does that mean?
Contrasting Pokémon Diversity:
To understand Pokémon diversity at each site, we will look at the three community characteristics noted above:
• Species richness within each location
• Species diversity within each location (using Simpson's Reciprocal Index)
• The similarity of Pokemon communities between locations (Using Jaccard's Index)
A sheet to help you with these calculations can be found here! Note you should only modify the cells that are highlighted green; the rest are calculations or output (highlighted in yellow). However, you may need to extend the formulas region (pull down the appropriate cells; ask your instructor for help).
The Taxonomic Diversity Calculations tab to find information on richness and Simpson's Reciprocal Index after you input a species list . After calculating the final number of species collected at each park for your species accumulation curves, you should be able to copy and paste species names and number collected into your copy of this sheet.
The Jaccard's Index tab will calculate a measure of similarity among two sites that you enter data for; make sure the species list/order is the same for both sites!
Make sure you can answer the following questions.
Make sure you can answer the following questions.
In your accumulation curves were the curves still increasing or close to being flat? How do you interpret our sampling effort for each location?
Which site had the highest diversity?
Which site had the most distinct community among all others (only answerable if your semester sampled more than 2 locations. Why?)?
What would you do differently next time to improve the accuracy of your Pokémon research?
If you were asked which of the sites you sampled was most "essential" for Pokemon conservation, how would you answer and why?
Extension: Functional Diversity
Extension: Functional Diversity
To consider functional diversity of the Pokemon, find relevant trait data for each of the Pokemon you collected from a Pokedex. For example, you could characterize the type of Pokemon or a continuous trait for each (such as speed or HP). After collecting this information, you can calculate the functional richness for categorical traits (how many different "types" of Pokemon did you find? For Pokemon that are considered to be more than one type count them as both) and the community-weighted mean for a continuous trait. You can see an example of the calculations under the Functional Diversity Calculations tab. Considering this analysis (along with taxonomic diversity), make sure you can answer:
Which site had the highest functional diversity? Did it depend on the trait you focused on?
How did your analysis of functional diversity compare with taxonomic measures?
Did your analysis of functional diversity change your list of "essential" Pokemon conservation site? Why or why not?
Note that if you have data for a species for which you collected 0 specimens (maybe because you copied a sheet..), the functional richness measure will still count! One way to fix this is to remove trait data for any species for which you collected 0 specimens at a given site.
References:
References:
Davis-Berg, E. C., Drew, J., Sardelis, S. (2018). Pokemon Go and Ecology. QUBES Educational Resources. doi:10.25334/Q4K994
https://labroides.org/exploring-community-ecology-using-pokemon-go/
https://pdfs.semanticscholar.org/db95/0293cc96b866a53371d7794baac940aab961.pdf
Gibbs et al. What is Biodiversity. Lessons in Conservation, Volume 8, Issue 1.
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