2529693
Project Grant
Overview
Grant Description
Star: Modeling the phylogenetic architecture of biodiversity
Understanding how, when, and why organisms differ in their biological characteristics is key to interpreting the bewildering biodiversity of our planet.
By providing insights into how lifeforms adapt, survive, and respond, this knowledge is crucial not only for ecology and evolutionary biology, but also for research in agriculture, biomedicine, and beyond.
At the heart of this effort are statistical methods that enable data-driven investigations into the evolutionary and ecological mechanisms driving variation across individuals, populations, and species.
Yet, progress is often hindered by analytical challenges involved in effectively linking variability observed in nature with the true phylogenetic history of organisms and their traits.
These challenges are only expected to compound as the scale and scope of modern biological datasets continue to expand, demanding new, scalable solutions that consider the complexities of evolution and the shared ancestry of related species.
Through an integrated analytics initiative, this project will synergize evolutionary biology and statistical learning to tackle unanswered questions in biodiversity research.
Research-driven inquiry, infrastructure, and outreach will enrich statistical and biological learning, education, and mentoring in an EPSCoR state, and well beyond, through intensive education, training, and research missions driving intensive STEM workforce training.
As a component of this work, this project will launch a new program to introduce STEM trainees to phylogenetics, biodiversity, and data science simultaneously.
Early-career researchers will benefit from mentoring, training, networking, and exposure to STEM careers, as well as transdisciplinary skillsets in computational biology, data science, software development, and machine learning.
This project will develop new statistical solutions for testing, predicting, and linking biological variation with the phylogenetic past when studying diverse traits, taxa, and questions.
Specifically, this research seeks to transform the ways in which we evaluate evidence for hypotheses concerning the phylogenetic ancestry of species and their inherited characteristics.
Researchers face a sea of phylogenetic trees of varying confidence, reliability, reproducibility, and applicability in the genomics era.
There is therefore an urgent need to both understand the consequences of this uncertainty, and to create robust algorithms for modeling evolution in the context of complex, and often conflicting, phylogenetic information.
This research will address both challenges through integrated studies that combine theoretical modeling, simulation, and empirical applications spanning diverse evolutionary and experimental conditions in comparative phylogenetics.
By doing so, it aims to establish a framework for phylogenetic architectural analysis that is resilient to uncertainty and adaptable to the growing complexity of comparative data.
This research therefore promises to advance evolutionary data science and build new frameworks for explicitly testing phylogenetic hypotheses about the historical processes underlying species-level variation in nature.
Project deliverables will span a wealth of new analytical approaches that will be implemented in open-source, open-access software for the community to apply to most any similar trait, research question, and study.
The research results and new approaches will benefit the research community and general public through advancements for comparative biological studies with fundamental, biomedical, and agricultural applications.
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.
Subawards are not planned for this award.
Understanding how, when, and why organisms differ in their biological characteristics is key to interpreting the bewildering biodiversity of our planet.
By providing insights into how lifeforms adapt, survive, and respond, this knowledge is crucial not only for ecology and evolutionary biology, but also for research in agriculture, biomedicine, and beyond.
At the heart of this effort are statistical methods that enable data-driven investigations into the evolutionary and ecological mechanisms driving variation across individuals, populations, and species.
Yet, progress is often hindered by analytical challenges involved in effectively linking variability observed in nature with the true phylogenetic history of organisms and their traits.
These challenges are only expected to compound as the scale and scope of modern biological datasets continue to expand, demanding new, scalable solutions that consider the complexities of evolution and the shared ancestry of related species.
Through an integrated analytics initiative, this project will synergize evolutionary biology and statistical learning to tackle unanswered questions in biodiversity research.
Research-driven inquiry, infrastructure, and outreach will enrich statistical and biological learning, education, and mentoring in an EPSCoR state, and well beyond, through intensive education, training, and research missions driving intensive STEM workforce training.
As a component of this work, this project will launch a new program to introduce STEM trainees to phylogenetics, biodiversity, and data science simultaneously.
Early-career researchers will benefit from mentoring, training, networking, and exposure to STEM careers, as well as transdisciplinary skillsets in computational biology, data science, software development, and machine learning.
This project will develop new statistical solutions for testing, predicting, and linking biological variation with the phylogenetic past when studying diverse traits, taxa, and questions.
Specifically, this research seeks to transform the ways in which we evaluate evidence for hypotheses concerning the phylogenetic ancestry of species and their inherited characteristics.
Researchers face a sea of phylogenetic trees of varying confidence, reliability, reproducibility, and applicability in the genomics era.
There is therefore an urgent need to both understand the consequences of this uncertainty, and to create robust algorithms for modeling evolution in the context of complex, and often conflicting, phylogenetic information.
This research will address both challenges through integrated studies that combine theoretical modeling, simulation, and empirical applications spanning diverse evolutionary and experimental conditions in comparative phylogenetics.
By doing so, it aims to establish a framework for phylogenetic architectural analysis that is resilient to uncertainty and adaptable to the growing complexity of comparative data.
This research therefore promises to advance evolutionary data science and build new frameworks for explicitly testing phylogenetic hypotheses about the historical processes underlying species-level variation in nature.
Project deliverables will span a wealth of new analytical approaches that will be implemented in open-source, open-access software for the community to apply to most any similar trait, research question, and study.
The research results and new approaches will benefit the research community and general public through advancements for comparative biological studies with fundamental, biomedical, and agricultural applications.
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.
Subawards are not planned for this award.
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "DIVISION OF ENVIRONMENTAL BIOLOGY (CORE PROGRAMS)", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF24543
Grant Program (CFDA)
Awarding Agency
Place of Performance
Fayetteville,
Arkansas
72704-6898
United States
Geographic Scope
Single Zip Code
Related Opportunity
Division Of Agriculture Of The University Of Arkansas was awarded
Project Grant 2529693
worth $399,831
from Emerging Frontiers and Multidisciplinary Activities in August 2025 with work to be completed primarily in Fayetteville Arkansas United States.
The grant
has a duration of 3 years and
was awarded through assistance program 47.074 Biological Sciences.
The Project Grant was awarded through grant opportunity Division of Environmental Biology.
Status
(Ongoing)
Last Modified 8/21/25
Period of Performance
8/1/25
Start Date
7/31/28
End Date
Funding Split
$399.8K
Federal Obligation
$0.0
Non-Federal Obligation
$399.8K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2529693
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Public/State Controlled Institution Of Higher Education
Awarding Office
490801 DIVISION OF ENVIRONMENTAL BIOLOGY
Funding Office
490804 EMERGING FRONTIERS
Awardee UEI
WJNTJ7LBL823
Awardee CAGE
3MXV8
Performance District
AR-03
Senators
John Boozman
Tom Cotton
Tom Cotton
Modified: 8/21/25