Dimensions of Biodiversity

What is the best way to calculate biodiversity in fish communities? Is it to count of the number of species? Is it better to calculate phylogenetic diversity, which is the distance among fishes in a sample across the fish tree of life? What about functional diversity, which is a measure of the ecological role of a species? How does abundance, where some species are super abundant and others are super rare, or biomass, where some species are bigger and take up more space within the community, influence biodiversity?

It turns out there is no one way to calculate biodiversity. Lots of different calculations can be made, and they all take into account different aspects of an organism’s biology, ecology, and evolutionary history.

A big unknown in ecology is understanding how these different biodiversity metrics play out over space and time. Do all biodiversity methods paint the same picture? Which metrics agree with one another, and how do they differ?

In Lefcheck et al. 2014, our biodiversity class at VIMS tackled some of these questions. We analyzed a 10-year data set of Chesapeake Bay Multispecies Monitoring and Assessment Program data and compared several different metrics of biodiversity. We calculated things like Richness, Evenness, Gini-Simpson Diversity, Functional Diversity, Phylogenetic Diversity, and Taxonomic Diversity. Some of these metrics are common and widely used in ecology. Others are used less frequently. We even weighted the species data by abundance and biomass to see what that did to our results. For the big analysis, we compared how all of these different types of biodiversity assessment methods stacked up with each other, what they told us about biodiversity across Chesapeake Bay, and how they held up over multiple seasons.

There was a lot of coding in R. I mostly left this to my classmates. I was happy to help calculate Functional Diversity, which included a trip to the Smithsonian National Museum of Natural History to measure fish proportions, and get gene sequence data for the calculation of Phylogenetic Diversity.

It turns out that, most of the methods we used to calculate diversity gave the same basic patterns. Evenness wasn’t such a good measure, but all of the others compared favorably. Honestly, I was a bit surprised by the results. I assumed that the metrics would agree some or most of the time but fall apart when compared across seasons. The fish community in Chesapeake Bay follows some regular seasonal patterns, and almost every method we used picked up the patterns. There were a few fuzzy areas where the results did not always match up perfectly, but, by and large, several methods gave the same story.

Taxonomic Diversity, Phylogenetic Diversity, and Functional Diversity, which all take into account very different types of data to calculate biodiversity, were nearly redundant. Richness and Gini-Simpson Diversity also performed well. The only metric that was different from the rest was Evenness. The areas where the different metrics failed to match are interesting areas to look at in the future. What is it about those particular spots within Chesapeake Bay that caused the different biodiversity metrics to disagree?

Before this project, I hadn’t thought much about the subtle differences given by different methods used to measure biodiversity. This class offered an opportunity to explore some of those areas. Now, I try to incorporate multiple aspects of biodiversity measurements in my ongoing ecological studies. It’s funny how a single class project can influence the direction of your research moving forward. This is a topic that I would likely never have explored on my own, but, because I had seven other students and two professors who bought into this project, I developed a skill set that I am using today.

What is your favorite biodiversity metric? Let me know in the comments below, by email at [email protected], or on Twitter!