“Butterflies are often a first point of introduction for many into nature,” writes Vaughn Shirey, first author of an astounding paper published in a very prestigious Nature journal, Scientific Data, that presents the largest and most comprehensive global compilation of butterfly trait data to date.
Shirey, who is also an alum of Drexel University’s Biodiversity, Earth and Environmental Science (BEES) Department and a previous co-op at the Academy, spoke with us about their life-changing student experiences that led to this research success, as well as the importance of studying butterflies to better understand climate change’s ecological impact on insects.
Tell us more about yourself.
I am a current biology PhD candidate in the Ries Lab of Butterfly Informatics at Georgetown University. Previously, I worked as a Fulbright Study/Research Fellow with the Laboratory for Integrative Biodiversity Research at the Finnish Museum of Natural History of University of Helsinki and received my bachelor’s degree in Environmental Science from the BEES Department at Drexel University. I also worked in the Gelhaus Lab in the Entomology Department at the Academy of Natural Sciences of Drexel University.
I started off my career at Drexel as a computer science major but transferred to BEES in my second year. BEES gave me a wide breadth of opportunities to get engaged with entomology-related research. I’ve always wanted to combine computing with biodiversity science and my first co-op with the Academy’s Curator of Entomology Jon Gelhaus allowed me to do just that.
What is especially unique about the program is that professors and staff are super excited to leverage individual skillsets and interest to really make co-op and work experiences meaningful. I definitely wouldn’t be where I am today without the support and encouragement from Jon Gelhaus and Steve Dilliplane who have been great mentors throughout my career, and Rick McCourt who advised my senior thesis on bacterial communities around algae, which really tested my bioinformatics skills.
Since then, I worked closely with the Entomology Department and a biodiversity informatics group on issues related to mobilizing biodiversity data. My visiting research position with Pedro Cardoso marked the start of using biodiversity data to look at how it could be used in species conservation and my jump to Georgetown allowed me to continue this line of research through butterflies and climate change research.
What work went into this major publication in Nature?
Our publication in Nature, “LepTraits 1.0 A globally comprehensive dataset of butterfly traits,” is the result of a massive collaborative project, ButterflyNet, which aims to assemble trait, distribution and phylogenetic data for every butterfly species on the planet. Butterflies are ideal study organisms. Not only are they widely admired, collected and written about, but, like most insects, they are especially sensitive to environmental change. This makes butterfly communities useful tools to study the effects of climate change on large spatial scales.
I personally got involved during my first year at Georgetown under my advisor Leslie Ries. Our lab was responsible for much of the trait data collection which involved extracting data from textbooks, field guides and other literature and then organizing and assembling them into a larger dataset. The paper presents data that took about six to seven years to fully collect, process and organize.
Our work represents the largest dataset of butterfly trait information available on the planet. These trait data will be useful in both ecological and evolutionary research and can be used to examine thousands of potential research questions. For example, we can look at which species might be more greatly impacted by climate change and how attributes of those species (e.g. the number of host-plant families they can feed from or in which life stage they overwinter) could affect responses.
One of the most rewarding aspects of this work was the amount of dialogue and consensus-building that went on in order to decide how to present trait data in a way that would be appealing and usable to a global research audience. I hope what follows is more research (and lots of it)!
