NSF Biological Integration Institute INSITE
Research Mission:
Create novel tools to predict the impact of climate change on symbiotic biodiversity.

Research Vision
Our vision is to identify key indicators of climate change through a microbial lens, develop methods to predict the potential for biodiversity loss, and provide conservation tools to address these climate change impacts, thereby offering insights to mitigate such devastation. To better predict the trajectory of biodiversity under climate change, we need to assess how hosts and their microbes will respond to Earth’s rapidly shifting climate. This information is essential for developing mathematical and statistical models that accurately predict how climate change will impact species and whether these species can adapt. However, to incorporate symbiotic systems into conservation frameworks, we must initially develop foundational knowledge across the dominant types of symbioses. First, we must determine the short-term vulnerability and acclimatization ability of symbioses to projected climate futures. Second, we need to enhance our understanding of underlying mechanisms across biological scales that govern organismal responses to climate change. Finally, it is crucial to ascertain how resilient symbiotic systems are in their capacity to adapt to new climate realities. Each component is necessary to expand our fundamental knowledge of symbioses and to inform both immediate and long-term conservation strategies.
Research Goal
To accomplish these goals, we have selected a set of three emergent model systems that allow us to integrate empirical and theoretical evidence in order to understand how climate change will affect symbiotic systems from molecular to phenotypic levels, across ecological and evolutionary timeframes, and from laboratory to the natural environment. Since our systems represent the fundamental types of symbiosis, we will use this data to develop predictive mathematical models that extend our knowledge to other, less-studied symbiotic systems.