| NSF Org: |
DBI Division of Biological Infrastructure |
| Recipient: |
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| Initial Amendment Date: | July 31, 2019 |
| Latest Amendment Date: | July 31, 2019 |
| Award Number: | 1927286 |
| Award Instrument: | Standard Grant |
| Program Manager: |
David Liberles
dliberle@nsf.gov (703)292-0000 DBI Division of Biological Infrastructure BIO Directorate for Biological Sciences |
| Start Date: | September 1, 2019 |
| End Date: | August 31, 2024 (Estimated) |
| Total Intended Award Amount: | $340,212.00 |
| Total Awarded Amount to Date: | $340,212.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
1523 UNION RD RM 207 GAINESVILLE FL US 32611-1941 (352)392-3516 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
Rm 358 Dickinson Hall Gainesville FL US 32611-0001 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
Infrastructure Innovation for, Cross-BIO Activities, Systematics & Biodiversity Sci |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.074 |
ABSTRACT
An unprecedented opportunity to advance understanding of the biological rules that govern the diversity and dynamics of life now exists thanks to the large quantity and variety of data that are becoming increasingly available. This goal of understanding biodiversity dynamics is enabled at a critical moment when human systems are disrupting those very dynamics. However, the scientific and computational tools needed to derive understanding from data are still missing. Such tools need to be accessible to a broad community of users, thereby catalyzing involvement and innovation. This project will (1) build a computational model for multiple aspects of biodiversity-species abundance, genetic, functional, and phylogenetic; (2) use and refine this model by testing major hypotheses about the generation and maintenance of biodiversity in three exemplar systems; (3) make the model accessible to the scientific community by building an open-source platform to prepare diverse data sources and run the model; and (4) create pedagogically effective courses and workshops to enable students, researchers, and stakeholders from many backgrounds to understand biodiversity theory and the data science tools needed to test those theories with data.
The Rules of Life Engine (RoLE) model will be a mechanistic, simulation-based hypothesis-testing and data synthesis framework enabling scientists with multi-dimensional biodiversity data to generate and test hypotheses about the processes driving biodiversity patterns. The RoLE model will apply new techniques in machine learning to fit models to high dimensional, cross-scale data. The model will simulate eco-evolutionary community assembly building from individual-based ecological and genetic neutral models with added non-neutral, trait-based competition and environmental filtering. New species and traits will arise through long time scale evolution in the meta-community and rapid evolution in the local community. Population genetics and species abundances in the local community will be modeled through birth, death, immigration, and mutation. The project research team will refine and illustrate the use of the RoLE model by testing four hypothesized rules of life across three bio-geographic systems for which multi-scale biodiversity data are now available. The hypotheses address the relative roles of immigration versus speciation in community assembly, how species interactions influence diversity, how different assembly histories determine the strength of species interactions, and whether/how systems come to equilibrium. The project leaders have established a network of 14 collaborators, including the National Ecological Observatory Network, who will use the RoLE model in their diverse systems and propagate wider adoption. In order to further reduce barriers to use, the RoLE model framework will be made available as open source software, including an R language Shiny App interface with standardized metadata outputs to promote reproducibility and sharing. The insights gained from the RoLE model are of direct relevance to conservation, e.g., whether or not communities are assembled primarily by speciation or immigration strongly determines their response to anthropogenic pressures and optimal conservation management. To encourage participation in quantitative biodiversity research, the project leaders will develop a massively open online course through the Santa Fe Institute?s Complexity Explorer program using the RoLE model as an interactive teaching tool. In conjunction with Data Carpentry and Software Carpentry, the research team will also provide an in-person data science training workshop. Results from the RoLE project can be found at https://role-model.github.io.
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.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Intellectual Merit: The RoLE model project was a collaborative effort to develop and implement process-based models for understanding how biodiversity is assembled over space and time. Biodiversity has multiple dimensions, facets and scales, including genetic, species and community-level views. Because of this, models need to be able to account for these different dimensions or facets. Our modeling framework includes a key simulation-based, mechanistic model for multiple dimensions of biodiversity, and in particular incorporates population genetic, phylogenetic and functional components. This framework provides a basis for testing hypotheses about generation, maintenance and how biodiversity and ecosystems may shift under future disturbance. Implementation of the modeling framework was a particular focus of this work and the work at UF, and we focused on developing the main functions and wrapper, including fitting algorithms, using the R statistical ecosystem, while also assuring best possible performance by running some analyses in more performant languages. As well, we simplified the full installation of the RoLE models and use of them on higher end computing platforms using containers. Finally, we developed a user interface to RoLE models by developing an R Shiny Application
The end result is a released R package, R Shiny App, simplied installation acrosss different computing platforms, documentation and website that can used to run simulation or test hypotheses. I
Broader Impacts: The RoLE model team also created a pedagogically effective set of course materials and ran a workshop that enabled researchers to understand biodiversity theory and the data science tools needed to test those theories with empirical data. Our work extends development of process models that incorporate more realism about ecological and evolutionary processes and can be used by the community to test key hypotheses about biodiversity generation and maintenance.
Finally, this work not only provided new tools, documentation and training in use of model construction, it also enabled training for graduate students and coordination between students and developers with expertise in modeling, computational biology, and biodiversity.
Last Modified: 01/09/2025
Modified by: Robert P Guralnick
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