Award Abstract # 1926345
Collaborative Research: MRA: A lineage-based framework to advance grassland macroecology and Earth System Modeling

NSF Org: DEB
Division Of Environmental Biology
Recipient: KANSAS STATE UNIVERSITY
Initial Amendment Date: August 12, 2019
Latest Amendment Date: March 20, 2020
Award Number: 1926345
Award Instrument: Standard Grant
Program Manager: Matthew Kane
mkane@nsf.gov
 (703)292-7186
DEB
 Division Of Environmental Biology
BIO
 Directorate for Biological Sciences
Start Date: January 1, 2020
End Date: December 31, 2024 (Estimated)
Total Intended Award Amount: $298,621.00
Total Awarded Amount to Date: $306,381.00
Funds Obligated to Date: FY 2019 = $298,621.00
FY 2020 = $7,760.00
History of Investigator:
  • Jesse Nippert (Principal Investigator)
    nippert@ksu.edu
Recipient Sponsored Research Office: Kansas State University
1601 VATTIER STREET
MANHATTAN
KS  US  66506-2504
(785)532-6804
Sponsor Congressional District: 01
Primary Place of Performance: Kansas State University
116 Ackert Hall
Manhattan
KS  US  66506-1100
Primary Place of Performance
Congressional District:
01
Unique Entity Identifier (UEI): CFMMM5JM7HJ9
Parent UEI:
NSF Program(s): Integrtv Ecological Physiology,
MacroSysBIO & NEON-Enabled Sci
Primary Program Source: 01001920DB NSF RESEARCH & RELATED ACTIVIT
01002021DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7657, 7959, 9150, 9251
Program Element Code(s): 765700, 795900
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.074

ABSTRACT

Humanity depends on grasses like corn, wheat, and switchgrass for food, fuel, and fiber. Grasslands and savannas play an important role in carbon and water cycling. They are heavily impacted by humans. They are widespread ground cover for many other ecosystems. Despite their importance, grasses are often omitted from plant databases and studies so have less attention than trees and other woody plants. This project will advance predictability of grassy ecosystem responses to global change by measuring many grass species traits. The new data will be incorporated into new modeling approaches. The project will enhance understanding of grass ecology, with many applications in agriculture and natural resource management. It will also provide important training opportunities for young scientists and will share results with K-12 teachers for use into their classrooms.

In this work, a novel, integrative framework that reorganizes grass vegetation types around phylogeny-driven functional diversity will be developed. Lineage-based trait coordination and distribution will be investigated along environmental gradients in North America at select NEON and LTER sites by collecting an unprecedented suite of trait and leaf spectral data and integrating the information with existing databases. The method is fundamentally different from previous approaches, as it uses phylogenetic relatedness to create lineage-based functional types (LFTs), which anchors trait data in an evolutionary context. Developing and implementing LFTs will increase the accuracy of site-, regional-, and Earth-System-Model-scale predictions, and provide a synthesis of grass functional ecology that is critical for forecasting how grassy biomes will respond to increasing CO2, climate change, and disturbance. This project will provide training and career development opportunities for interdisciplinary research to 3 graduate students and 1 postdoctoral researcher. The education and training centerpiece will be an immersive graduate student workshop that will train dozens of graduate students in state-of-the-art ecophysiological and spectroscopic techniques that underpin a broad swath of plant ecology and precision agriculture.

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Donnelly, Ryan C and Nippert, Jesse B and Wedel, Emily R and Ferguson, Carolyn J "Grass leaf structural and stomatal trait responses to climate gradients assessed over the 20th century and across the Great Plains, USA" AoB PLANTS , v.16 , 2024 https://doi.org/10.1093/aobpla/plae055 Citation Details
Griffith, Daniel M. and Osborne, Colin P. and Edwards, Erika J. and Bachle, Seton and Beerling, David J. and Bond, William J. and Gallaher, Timothy J. and Helliker, Brent R. and Lehmann, Caroline E. R. and Leatherman, Lila and Nippert, Jesse B. and Pau, S "Lineagebased functional types: characterising functional diversity to enhance the representation of ecological behaviour in Land Surface Models" New Phytologist , v.228 , 2020 https://doi.org/10.1111/nph.16773 Citation Details

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.

Our interdisciplinary team has made significant progress in understanding and representing grass-dominated ecosystems and their role in the earth system. Extensive field-based data on grass physiology, anatomy, biogeochemistry, and spectra were collected across multiple key NEON sites (WOOD, CPER, KONZ, JORN, as well as the Cedar Creek LTER), providing robust insights into grass traits, biogeography, and functional ecology. Additionally, a NEON AOP flight we commissioned at Cedar Creek as part of our project expanded our remote sensing capabilities and ability to extend our findings to other sites. These various field-based and remotely sensed datasets are foundational to our modeling and analysis efforts, allowing us to explore the connections among evolutionary history, spectral signals, and ecosystem processes.

Our research has generated a strong publication record. Multiple papers have been published in leading journals, exploring the role of evolutionary lineages in trait variation, grass distributions, and novel applications of remote sensing. Furthermore, numerous conference presentations at major scientific gatherings have showcased our groundbreaking work to a global audience. With substantial results achieved, we have several datasets to be analyzed with forthcoming publications. These publications will further refine our understanding of grass diversity and provide powerful tools for enhancing Earth System Models. Through continued research, data dissemination, and training, this project will leave a lasting legacy in the fields of ecology and global change science.

A crucial component of this project was knowledge transfer and training. As part of this we co-organized and partially supported two different intensive graduate student and postdoctoral scholar ecophysiological training courses: PhysFest 3 and PhysFest 4. PhysFest 3 was held at the Colorado State Mountain Campus in 2021, and PhysFest 4 was held at the University of New Mexico’s Sevilleta field station in 2023. Both courses were enthusiastically received by the 30-40 participants (primarily graduate students and postdocs but including a handful of early career faculty from teaching colleges and universities). The courses provided an intensive training experience for participants, equipping them with cutting-edge techniques in ecophysiology such as gas exchange, hydraulics, chlorophyll fluorescence, environmental monitoring, and thermal and hyperspectral imaging. Evenings were focused on topics like career development, science communication, and activities designed to broaden participation. Our commitment to mentorship has additionally supported undergraduate research experiences, culminating in presentations at high-profile conferences.

We are deeply grateful for the generous support of the NSF Macrosystems Biology program, which has made this work possible, and we are excited about the project's forthcoming contributions to our understanding of grass-dominated ecosystems and their representation in Earth System Models.

Peer-reviewed manuscripts that include Nippert's Lab:

Donnelly, R.C., J.B. Nippert, E.R. Wedel, C. Ferguson (2024) Grass leaf structural and stomatal trait responses to climate gradients assessed over the 20th century and across the Great Plains, USA AOB Plants 16:plae055 https://doi.org/10.1093/aobpla/plae055

Slapikas, R., S. Pau., R.C. Donnelly, C-Ling Ho, J.B. Nippert, B.R. Helliker, W.J. Riley, C.J. Still, D.M. Griffith (2024) Grass evolutionary lineages can be identified using hyperspectral leaf reflectance. Journal of Geophysical Research – Biogeosciences  129: e2023JG007852 https://doi.org/10.1029/2023JG007852

Donnelly, R, E.R. Wedel, J.H.Taylor, J.B. Nippert, B.R. Helliker, W. Riley, C.J. Still, D. Griffith (2023) Evolutionary lineage explains trait variation among 75 coexisting grass species New Phytologist 239:875-887 doi: 10.1111/nph.18983

Bachle, S., J.B. Nippert (2022) Climate variability supercedes grazing to determine the anatomy and physiology of a dominant grassland species. Oecologia 198:345–355 https://doi.org/10.1007/s00442-022-05106-x

Pau, S, JB Nippert, R Slapikas, DM Griffith, S Bachle, BR Helliker, RC O’Connor, WJ Riley, CJ Still, M Zaricor. (2022) Poor relationships between NEON AOP data and field-based vegetation traits at a mesic grassland site Ecology e 03590 https://doi.org/10.1002/ecy.3590

Bachle, S, JB Nippert. (2021) Microanatomical variation tracks climate variation for a dominant C4 grass species across the Great Plains, USA. Annals of Botany 127: 451–459 https://doi.org/10.1093/aob/mcaa146

Griffith, DM, C Osborne, EJ Edwards, S Bachle, DJ Beerling, WJ Bond, TJ Gallaher, BR Helliker, CER Lehmann, L Leatherman, JB Nippert, S Pau, F Qui, WJ Riley, MD Smith, CAE Stromberg, L Taylor, M Ungerer, CJ Still. (2020) Lineage Functional Types (LFTs): Characterizing functional diversity to enhance the representation of ecological behavior in Land Surface Models. New Phytologist 228: 15-23 https://doi.org/10.1111/nph.16773

Peer-reviewed book chapters that include Nippert's Lab:

Nippert, J.B., Helliker, B.R. (in press) Physiological and anatomical traits of grass species reflect evolutionary diversification and facilitate persistence across environmental gradients in grassy ecosystems. IN: “Routledge Handbook of Grasslands”, D. Gibson, H. Hager, J. Newman, Eds. Routledge Publishing

Peer-reviwed manuscripts in review that include Nippert's Lab: 

Pau, S., Slapikas, R., Ho, C-L, Bayliss, S., Donnelly, R., Abdullah, R., Helliker, B., Nippert, J., Riley, W., Still, C., Wedel, E., Griffith, D. Hyperspectral leaf reflectance of grasses varies with evolutionary lineage more than with site. Revised submission, Ecosphere.  

 

 

 

 

 

 

 

 

 

 

 

 


Last Modified: 01/29/2025
Modified by: Jesse B Nippert

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