Nikunj is one of our 2021 Stengl-Wyer Fellows. He is a theoretical biogeographer working in the lab of Dr. Tim Keitt at the Department of Integrative Biology. He is broadly interested in understanding how dispersal generates and maintains biodiversity. As a Stengl-Wyer Fellow, he is building mathematical and statistical theory to understand how human dispersal pathways facilitate the spread of zebra mussels in the inland USA by commercial shipping network. Nikunj took some time to tell us about how he got started and where he's going.
Tell us where you came from before UT, and what you studied then?
I got into ecology and evolution during my second year of undergrad at the Indian Institute of Science (IISc) through a series of serendipitous events. Back then, I was majoring in Physics and had planned to pursue experimental physics. One day, a biologist friend told me about a course on mathematical ecology and evolution. At first, I was surprised—I never thought math and biology could go together. Frankly, I disliked biology because we had to mug up so many details, but I loved math. I took the course out of sheer curiosity, not realizing that this decision would shape my scientific career.
I instantly fell in love with the course. This was partly because the instructor, Vishwesha (Vishu) Guttal, was also a physicist. He knew how to engage students with quantitative backgrounds with little biology training. I started to enjoy biology as math allowed me to make sense of the ecological and evolutionary patterns from a mechanistic perspective. I changed my research focus from physics to ecology. I worked with Vishu for my senior thesis on regime shifts in complex ecological systems, resulting in my first publication. Even after finishing undergrad, Vishu's course was incredibly useful in my later research.
In 2015, I moved to the United States to pursue my graduate degree in ecology at Yale. I worked with Carla Staver on understanding how dispersal maintains the distribution of tropical savanna and forest biomes using reaction-diffusion models that Vishu taught in his class. A big takeaway from that research was that biome patterns were not only determined by climatic factors, such as precipitation, but also by continental-scale source-sink dynamics and dispersal barriers. This work was awarded the best paper and best talk awards by the ESA's Theoretical Ecology and Vegetation sections.
What got you interested in studying biological invasions, and focusing on zebra mussels?
In 2018, I attended a month-long workshop on complexity science at the Santa Fe Institute in New Mexico, where I got introduced to network-based models of disease spread. There I stumbled on a paper by Brockmann and Helbing (2014) on modeling the spread of epidemics using global airline networks. The central idea of the paper is quite intuitive. Instead of visualizing the disease spread in geographical space, one could analyze the same problem in network space where the effective distance between locations is a function of the intensity of human travel rather than geographical distance. This change in perspective from geographical to network space made the problem very simple in the sense that effective distances characterized both the spread pathway and the time of arrival of the disease.
I realized the potential for this mathematical framework to understand human-mediated invasions. After all, disease spread is an invasion by pathogens, and most contemporary invasions are human-mediated. Zebra mussel was the obvious species to study. They are the poster child for human-mediated biological invasions. Zebra mussels were introduced in Great Lakes in the late ’80s and have subsequently spread to the inland river system by commercial shipping network, costing billions of dollars in infrastructure damage every year and incurring huge biodiversity loss.
Using ideas from Brockmann and Helbing’s (2014) paper, I developed a network-based model of zebra mussel spread. As a Stengl-Wyer Fellow, I am working on fitting this network model to the invasion history of zebra mussels using a hierarchical Bayesian approach to answer questions like (a) when and where the species was introduced? (b) what was the invasion pathway? (c) where will it go next? Answering these questions can be valuable in informing real-time mitigation strategies.
Even though my research focuses on zebra mussels, the lessons learned from the research are general. For instance, the mathematical and statistical models I am developing can easily be co-opted to forecast future biological invasions.
Where do you see your research agenda heading here at UT?
Global change is an unavoidable reality of the Anthropocene. Ecosystems are changing at an unprecedented rate due to human activities, posing a threat to biodiversity, ecosystem services, and human well-being. A natural question to ask is—what should be the conceptual basis of the theory of global change biology? I argue that global change theory should be embedded within the framework of population demography. After all, any change in population results from four demographic processes—birth, death, immigration, and emigration.
In my thesis work, I adopt a demographic approach to address problems in global change biology at population margins. These problems range from understanding determinants of species range limits to the evolution of dispersal at invasion fronts to the spread of human-mediated invasions. I focus on population margins because, in contrast to the population core, margins are subjected to a unique set of ecological and evolutionary pressures. Elucidating population dynamics under these novel conditions might allow us to gain new biological insights. I take a two-pronged approach in which I combine mathematical theory with data using state-of-the-art Bayesian statistical tools.
I believe adopting a demographic agenda to study global change patterns at the margins offers several advantages. First, a general birth-death-dispersal model provides a unified perspective on the issue of global change. Second, a biogeographer can make assumptions to reduce the birth-death-dispersal model into a mathematically tractable problem that may provide testable predictions. Corroborating these predictions with experiments and observations allows the biogeographer to draw mechanistic inferences. Most importantly, when a simplified model fails to match the reality, one can return to the general framework and ask, “Am I missing a critical assumption or making an incorrect one?’. This feedback loop between the general framework and a simplified model tested with data can yield a better scientific understanding of global change biology.
