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Dr Daniel Maynard
Dr Daniel Maynard profile picture
  • Honorary Associate Professor
  • Genetics, Evolution & Environment
  • Div of Biosciences
  • Faculty of Life Sciences
I'm currently an honorary professor in the Department of Genetics, Evolution and Environment at UCL, and a lead scientist and group leader at ETH Zürich. In summer of 2023, I'll be joining UCL full-time as Associate Professor of Quantitative Ecology, as a member of CBER and the new People and Nature Lab at UCL East. 

Before focusing on Ecology, my undergraduate training was originally in mathematics and algebraic theory, and I went on to receive a master's degree in biostatistics from Harvard University in 2006. After working in industry for several years, I returned to school, earning a master's in Natural Resources from the University of New Hampshire, and my PhD in Ecology from Yale University in 2017. My dissertation, under the supervision of Dr. Mark Bradford, focused on adapting network approaches to better understand the structure and function of wood-decomposing fungi. Since then, I've worked as a postdoc in Dr. Stefano Allesina's lab at the University of Chicago, developing new statistical tools for understanding coexistence mechanisms, before moving to ETH Zürich to join Dr. Thomas Crowther's group as a lead scientist and group leader in the Institute of Integrative Biology. For the last several years my research has focused on applying coexistence theory to global datasets, with a specific interest in forest dynamics and functional biogeography. 

Research Groups
Research Themes
Research Summary
My research lies at the intersection of community ecology and macroecology, using a broad array of experimental and observational approaches to explore how biodiversity is maintained in hyper-diverse ecosystems. My group focuses on three broad themes, each of which explores how emergent patterns relate to underlying ecological processes: 

1. The development of pattern-based models to predict how communities will respond to environmental changes. Instead of trying to make predictions about community composition by building a mechanistic, dynamical model from the ground up, my work focuses on building probabilistic models that best recovers the emergent patterns of the system (e.g., which species can coexist with each other). For this work, I use both global forest inventory datasets as well as experimental data generated by a network of colleagues. By avoiding the need to mechanistically model the dynamics of the community, this approach bypasses many of the challenges that plague traditional efforts to predict species' and community responses to disturbances, giving us the potential to make real-time projections of how coexistence and community structure will respond to environmental change. 

2. Disentangling how biotic and abiotic processes structure broad-scale biodiversity patternsThe resilience of ecosystems is tightly coupled to the underlying community assembly processes, which govern the functional diversity of the community. In places where habitat filtering is the overriding filter on community composition, slight changes in environmental conditions can lead to drastic changes in species occurrences. In locations where biotic processes dominate, changes in environmental conditions should alter the strength of interactions, thereby shuffling the relative abundance of species. In this work, I identify unifying trends that allow us to infer underlying assembly processes from patterns in functional and phylogenetic diversity, with an emphasis on how these patterns shift across scales, habitats, and traits. 

3. The maintenance of biodiversity through the lens of functional ecology. Instead of using the traditional taxonomic approach, much of my work adopts a functional approach to understanding the drivers of biodiversity. Doing so has provided key insight into trait trade-offs governing biogeographical patterns, the demarcation of species' niches and the drivers of coexistence and diversity in spatially structured, sessile microbial communities. By focusing on traits rather than taxonomic identities, my work seeks to provide a scalable and mechanistically-informed approach to understanding ecological patterns and processes in hyper-diverse natural ecosystems. 

By addressing these questions at various scales and systems -- most notably using global forest inventory data and experimental microbial systems -- we look to find overarching principles that extend across study systems. In doing so, we aim to develop new tools and approaches that can help address the biodiversity and climate crises in real time.

Teaching Summary
I've taught a wide range of courses, including more mathematical-focused topics such as theoretical ecology and biostatistics, to subject-specific courses such as forest management and economics, wildlife ecology, and soil ecology. I'm currently teaching a course at ETH Zürich on the ecological foundations underpinning restoration and conservation, and am helping to plan the new course "AI for the Environment" through the People and Nature Lab at UCL East. 
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