Biodiversity is a grand and wonderful thing, but it is also a notoriously hard thing to measure. What makes a habitat or landscape “diverse”? This was the topic of last week’s Cons. Sci. lecture, which touched upon patterns of biodiversity and the different metrics we use to quantify biological diversity in the world around us.
To make the lecture more interactive, we thought we would calculate diversity metrics live in class (*code and files below*), as we talked about them, on a dataset collected by the students. However, sending the students out “in the field” would have been impractical in the time frame we had – and frankly, Scotland in October is not the best time to wander around with a butterfly net. Instead, phone in hand, we sent them in the wondrous virtual world of Pokémon Go to see what they could find.
Students (and devoted tutors) had a week to make a daily screenshot of the Pokémon’s found around them, and were then asked to compile the species observed along with the time and location of “sampling”. Our research question was: “Does the diversity of Pokémon species vary across campuses of the University of Edinburgh?”
From a quick glance at our data above, we see that many more species were sighted at King’s Buildings, the southern campus, compared to the Main campus. However, this seemed mostly due to the fact that observers were mostly based at KB, which biased our sampling effort. We also noticed that some species were seen more often than others. Those Pidgeys, Weedles and Rattatas are everywhere!
We first calculated the alpha diversity in each building where observations were made, and although we did not follow up with a statistical test it seems that the Main Library was less Pokémon-rich than other places.
We then calculated the Shannon-Wiener index, which considers evenness (i.e. whether we have many species with similar abundances or just a few dominant species making most of the individuals recorded). The Shannon-Wiener index was 0.88 for both campuses, which means that the assemblages in both cases are quite diversified and even.
Finally, we wanted to have an idea of how these assemblages varied spatially: did buildings that are close together hold more similar communities than those further apart? We calculated pairwise Jaccard’s distances, which indicate dissimilarity, and confirmed our hypothesis that buildings from different campuses were indeed more dissimilar.
Although simple, this exercise allowed us to put into practice some diversity metrics, test hypotheses, learn bits of R code and reflect on sampling bias and the challenges of monitoring biodiversity. What do these same sampling biases mean for biodiversity data collection in the real world? – Our in class discussions taught us that this is a big issue facing biodiversity science, if we want a global monitoring programme to capture how biodiversity is changing on planet earth.
We have since then found out that another course had designed a – rather more ambitious – protocol to explore community ecology in New York City, also using Pokémon Go. We can’t wait to see their results!
*If you want to have a closer look at the code, you can download the R script and data files here