Stephen A. Smith - US grants

The tree of life links all biodiversity through a shared evolutionary history. This project will produce the first online, comprehensive first-draft tree of all 1.8 million named species, accessible to both the public and scientific communities. Assembly of the tree will incorporate previously published results, with strong collaborations between computational and empirical biologists to develop, test and improve methods of data synthesis. This initial tree of life will not be static; instead, we will develop tools for scientists to update and revise the tree as new data come in. Early release of the tree and tools will motivate data sharing and facilitate ongoing synthesis of knowledge.

Biological research of all kinds, including studies of ecological health, environmental change, and human disease, increasingly depends on knowing how species are related to each other. Yet there is no single resource that unites knowledge of the tree of life. Instead, only small parts of the tree are individually available, generally as printed figures in journal articles. This project will provide the global community of scientists who study the tree of life with a means to share and combine their results, and will enable large-scale studies of Earth's biodiversity. It will also create a resource where students, educators and citizens can go to explore and learn about life's evolutionary history.

Biologists have long known that traits and gene sequences evolve at different rates in different lineages of organisms, but it has been difficult to understand the causes for these shifts in evolutionary rate. For example, the Caryophyllales contain ~6% of all flowering plant species and exhibit extreme life history diversity, including tropical trees, temperate herbs, long-lived succulent cacti, and a diverse array of carnivorous plants. The rate of DNA sequence evolution among these lineages differs greatly. This project will leverage recent advances in genome sequencing technologies and computational methods to evaluate the extent to which changes in life history and ecophysiology in plants are correlated with changes in the evolutionary rate over the entire genome. In collaboration with researchers worldwide, key traits will be characterized and >10,000 genes will be sequenced for 300 representative species of Caryophyllales. Analyses of evolutionary rate shifts in both traits and the genome will be used to assess how life history and ecophysiology have influenced genomic evolution, and vice versa.

This project provides the first rigorous assessment of the relationship between shifts in ecology and life history and genome-wide changes in evolutionary rate. It will yield unprecedented insight into the evolution of several genetic pathways of fundamental importance in flowering and crop plants, including those associated with flower development, photosynthesis, and pigmentation. Furthermore, this research will include the development of new and refined bioinformatic tools of broad use to biologists. The project will support the mentorship and training of a postdoctoral researcher, graduate students, and numerous undergraduates on all aspects of the project, and will fund professional development workshops for high school teachers and plant biologists.

Extracting biological knowledge from complex datasets such as those now being compiled requires integration of powerful computational tools. Recent developments in computational biology as well as rich new data sources provide novel opportunities for integrating massive amounts of biological data. This perfect storm of new data and advanced data acquisition, management, and integration afford the unique opportunity to drive the discovery of new, complex patterns in biology. The project will leverage NSF's considerable investment in biodiversity tools provided by Open Tree of Life (the framework for the project), Lifemapper (which handles geospatial data), iDigBio (data from~1 billion museum specimens that carry locality data and their ecological information), and Arbor (computer tools that permit new analyses from the sources noted). It will create much needed computational connections among these tools. It will then build upon these new linkages and tools, enabling novel research in biodiversity. These linkages will provide researchers the opportunity to rapidly synthesize datasets and use them to address diverse evolutionary questions. The tools and infrastructure the project will build will connect species relationships with species distribution models, climate projections, genes and traits. The project will transform future studies of biodiversity; it will provide a global integration of powerful tools that will permit new data-driven discovery in "next generation" biodiversity science. It will provide interdisciplinary post-doc and graduate student training in bioinformatics, use of digitized specimen data, and complex analyses (e.g. ecological analyses), preparing the biodiversity scientists of the future. The project will recruit underrepresented students and women and develop an undergrad course that will help train students with the integrative skills (field biology to computational biology) needed in the workforce. We will further develop this module for wider classroom use. We will introduce an annual week-long course at University of Florida (UF) for students and post-docs on the use of the resources developed. With education specialists at UF, the project will produce video materials and a coordinated display for general audiences on the importance of digitized specimen data, and their utility for studies of climate change.

The project will develop a computational framework linking diverse data (trees of species relationships, morphology, ecology, fossils, geography, and climate) across research tools used by the biological community, including Open Tree of Life, which will serve as the framework to which all other biological data - traits, genes, genomes, and especially specimens - will be linked, as well as Lifemapper, iDigBio, and Arbor. Use of the large, hyper-diverse plant group Saxifragales will provide precisely what is needed to drive the development of these tools--a comprehensive dataset that covers morphology, ecology, geography, fossils, and climate provides a test case for refining the tools the project will develop and their integration. The project will: 1. Facilitate new synergistic research of broad utility at the interface of phylogenetics, ecology, evolutionary biology, biogeography and biodiversity science, enabling scientists to address novel questions relating phenotypic and ecological biodiversity, spatial and temporal variation, community assembly, and diversification across landscapes and through time. 2. Increase visibility and accessibility of iDigBio, Open Tree of Life, Arbor, and Lifemapper resources by linking them together and making them available through multiple access points (e.g., pre-existing tools associated with Arbor and Lifemapper) in a variety of appropriate formats. 3. Develop a complete, multifaceted species-level dataset for a large clade (Saxifragales), which will not only fill in this branch on the ToL, but will produce a resource of great utility for the scientific community to explore. 4. Demonstrate the utility of iDigBio, Open Tree, Lifemapper, and Arbor resources with a comprehensive analysis using near complete sampling of Saxifragales, for which we will add the following data layers: DNA sequences, morphology, fossils, ontologies, geospatial and environmental data, digitized voucher specimens, and link to the Encyclopedia of Life (EOL). The project will: 1) provide interdisciplinary post-doc and graduate student training in bioinformatics, large-scale phylogeny reconstruction, use of digitized specimen data, and complex post-tree analyses (e.g. niche modeling, niche diversification), preparing the integrative biodiversity scientists of the future; 2) recruit underrepresented students and women; 3) developed an undergrad course that uses field collection, herbarium specimens, digitized data (iDigBio), and niche modeling (with climate change; 4) introduce an annual week-long course (UF) for students and post-docs on the use of the resources produced; 5) produce video materials and a coordinated display for general audiences on the importance of digitized specimen data, their utility for modeling niche evolution through time and implications for climate change. The project will provide a platform that will enable other researchers to take the same integrated approach in other groups. It will also establish web links to EOL and 1) build species pages; 2) place morphological and other trait data on TraitBank, making these widely available; 3) work with EOL and iNaturalist to engage citizen scientists.

The tropical Andes region of South America is among earth's most species-rich biodiversity hotspots. For example, 15% of all plant species (>45,000 species) occur in this region that covers only about 1% of the planet's land surface. To explain this unexpectedly high species diversity, this research team will investigate how environmental changes associated with the formation of the Andes Mountains influenced the distribution, diversification and evolution of tropical tree species in the region. Understanding the relationships between mountain uplift, environmental change, and species distributions is not only important to identify the causes of the remarkable Andean diversity, but also provides insight into how species respond to large-scale changes in environmental conditions. This information, in turn, can guide regional conservation and management aimed at conserving diverse and resilient biological communities. This project is unique because it is conducted over vast areas and deep evolutionary time scales. Because of the broad scope, project results are likely to significantly advance understanding of the long-term consequences of the emergence of novel environments for the formation and organization of biologically diverse communities.

This project will test the the importance of elevational gradients for shaping community composition by integrating datasets on plant species distributions, functional traits and evolutionary relationships. Following mountain uplift, new environments at different elevations are hypothesized to promote (1) rapid adaptive diversification of clades, (2) immigration and ecological sorting of pre-adapted clades, or (3) a combination of both processes. This study will simultaneously explore these mechanisms and disentangle their relative contributions to the assembly of a hyper-diverse regional flora. This project leverages data from the Madidi Project that has already documented the elevational distribution of tree species in the Bolivian Andes. Distributional and elevation data will be integrated with a comprehensive database of plant-specimens for across the New World and elevational surveys of 10 plant functional traits. A large phylogeny of all seed plants will be developed and used to study turnover in species and functions among plant communities at different elevations and among biogeographic regions across the Neotropics. The project is somewhat risky because database uncertainties can be propagated in process modeling, but the geographic density of sampling could preclude such problems. However, there is high potential for revealing new insights into community assembly across shifting environmental gradients.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The physical attributes and behaviors of species are shaped by evolution. These traits determine how individuals interact with their environment (ecology) which then influences the course of evolution. Thus, ecology and evolution are inextricably intertwined. Bridging the fields of evolution and ecology is challenging because the processes involved can operate on similar, or very different, time scales. African savanna acacia trees have evolved to survive and reproduce under harsh conditions not tolerated by many other tree species. Acacias can tolerate fires, droughts, herbivory by giraffes and elephants, and competition from other plants. But how acacias have adapted to these conditions in the past will influence their response to the current changing environment on the African continent. The goal of this study is to develop a comprehensive understanding of the evolutionary history and ecological distribution of African acacias and to explore how species traits and distributions changed over time in response to change in the savanna climate. Ultimately, this knowledge will inform predictions about how acacia habitats will be affected by ongoing climate change. This project not only has broader societal benefit but also it will train undergraduate and graduate students and postdoctoral fellows in evolutionary biology, ecology, plant physiology, and molecular genomics. In addition, the project will expand the content and reach of a successful undergraduate teaching module of ecology and evolution featuring Serengeti National Park and will begin a new bioinformatics training course in partnership with universities in Africa.

This project will bridge phylogenetic approaches to diversification with direct ecological field measurement of trait responses and gene expression. The activities include: (1) constructing new, detailed models of the phylogenetic history and ecological distribution of species and traits in the African Acacia Clade, (2) using phylogenomic analyses to study selection, introgression, and gene family expansion in relation to the Savanna Syndrome, (3) measuring acacia trait responses to Savanna Syndrome components (drought, fire, herbivory, grass competition) in a common garden experiment in Arusha, Tanzania, and (4) analyzing the molecular aspects of the phenotypic response through analysis of acacia transcriptomic profiles collected both on site in Tanzania and in controlled greenhouse experiments. Collectively, these linked lines of evidence will provide crucial information about the past evolution of the savanna community, the rapid rise of savannas across Africa that occurred in the Miocene, and its likely response to present ecological change.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The Tree of Life is marked by short periods of rapid innovation where groups emerge with dramatically altered forms and diversify quickly. This has happened multiple times throughout geologic history including with the rise of birds, mammals, the transition of plants from water to land, and the origination of flowering plants. The rapid emergence and diversification of flowering plants in particular represents one of the most remarkable episodes in the history of life on earth. However, while fundamental to understanding the ecology and evolution of modern ecosystems, this episode remains unexplained and leads to one of the grand challenges in the biological sciences – determining what processes may be responsible for such rapid changes in form and function across the Tree of Life. A major impediment to addressing this question is the availability of data and methods for analyzing those data. The goal of this study is to use and develop new machine learning approaches to gathering data for both fossil and living plant species and to use these data to help develop new techniques. These techniques will help identify what contributed to the rapid change in plants that resulted in their dominance in the environment today. This project will train undergraduates, graduate students, and postdoctoral fellows in machine learning methods, evolutionary biology, and techniques for working with both fossil and living specimens. The project will also include resource development and training for middle schoolers, high schoolers, undergraduates, and the broader research community.<br/><br/>This project aims to evaluate the evolutionary processes underlying the emergence of innovation, using flowering plants as a case study. Specifically, the project will examine Cretaceous radiations of flowering plants characterized by rapid evolution, a rich fossil record, and the origin of innovations and lineages of great ecological significance. The central goals of the proposed research are to (a) generate a large morphological dataset for flowering plants using novel machine learning methods, (b) develop new statistical methods for modeling evolution, and (c) use these advances in data collection and methods to identify the processes that led to the episodic and rapid emergence of novelty across the Tree of Life. Collectively, these new developments in machine learning techniques, morphological data collection, and analytical techniques for addressing evolutionary processes will be potentially transformative to several fields including the biological sciences, computational biology, and machine learning. The large scope and scale of this project, together with its highly integrative nature, creates the potential to address one of the most important standing questions in evolutionary biology.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.