About

Elizabeth Ainsworth is a professor in the School of Integrative Biology at the University of Illinois. Her research focuses on plant physiological and molecular responses to global change, including photosynthesis and carbohydrate metabolism. She investigates how rising carbon dioxide and tropospheric ozone concentrations affect crop productivity, aiming to understand and integrate genetic, molecular, biochemical, and physiological plant responses to global climate change. Her work involves using meta-analyses to quantify plant responses to climate factors, developing high-throughput tools for investigating molecular and physiological responses, and identifying genes and loci responsible for variation in species' responses. The ultimate goal of her research is to provide fundamental knowledge to maximize crop yields and plant productivity in a future with elevated carbon dioxide, ozone, higher temperatures, and increased drought stress.

Research topics

• Computer Science
• Environmental resource management
• Environmental science
• Ecology
• Geography
• Natural resource economics
• Biology
• Mathematics

Selected publications

• The Global Spectra-Trait Initiative: A database of paired leaf spectroscopy and functional traits associated with leaf photosynthetic capacity

  Earth system science data · 2026-01-09

  articleOpen access

  Abstract. Accurate assessment of leaf functional traits is crucial for a diverse range of applications from crop phenotyping to parameterizing global climate models. Leaf reflectance spectroscopy offers a promising avenue to advance ecological and agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development of robust hyperspectral models for predicting leaf photosynthetic capacity and associated traits from reflectance data has been hindered by limited data availability across species and environments. Here we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. The GSTI repository currently encompasses over 7500 observations from 397 species and 41 sites gathered from 36 published and unpublished studies, thereby offering a key resource for developing and validating hyperspectral models of leaf photosynthetic capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti, last access: 4 January 2026) and published to ESS-DIVE https://doi.org/10.15485/2530733, Lamour et al., 2025). It includes gas exchange data, derived photosynthetic parameters, and key leaf traits often associated with traditional gas exchange measurements such as leaf mass per area and leaf elemental composition. By providing a standardized repository for data sharing and analysis, we present a critical step towards creating hyperspectral models for predicting photosynthetic traits and associated leaf traits for terrestrial plants.

  PublisherOA PDFDOI

• Data for Impact of Vertical and Seasonal Variation in Leaf Traits on Simulating Soybean Canopy Photosynthesis via 1D and 3D Modeling

  Illinois Data Bank · 2026-01-01

  datasetOpen access

  Accurate modeling of photosynthesis is crucial for predicting crop productivity and quantifying the carbon cycle in agroecosystems. Leaf traits are essential inputs for modeling canopy photosynthesis. Yet, many existing models still use fixed plant functional type (PTF)-based values to parameterize leaf traits under a big-leaf or two-big-leaf assumption, neglecting their vertical profiles and seasonal changes. This simplification may introduce significant uncertainties in estimating gross primary productivity (GPP). In this study, we simulated soybean GPP and tested the effects of vertical and seasonal variation in three key leaf photosynthetic traits: the maximum carboxylation rate at 25 °C (Vcmax25), leaf chlorophyll content (LCC), and leaf mass per area (LMA) in the 1D-SCOPE and 3D-Helios models. Weekly field measurements were conducted during the growing season of 2024 to support the simulation. We designed ten leaf trait parameterization schemes by incorporating different combinations of vertical profiles and seasonal changes, while assuming homogeneous canopy architecture in both models. Our results revealed that Vcmax25 vertical and seasonal variation had the strongest influence on simulated GPP in both 1D and 3D models, while LCC and LMA effects were minimal. Particularly, the scheme with an empirically parameterized Vcmax25 profile achieved comparable performance to the scheme with the measured Vcmax25 profile. Both 1D-SCOPE and 3D-Helios accurately modeled GPP (SCOPE: R2 = 0.87, Bias = 0.55 µmol m⁻² s⁻¹; Helios: R2 = 0.9, Bias = 0.22 µmol m⁻² s⁻¹) under the most complex scheme, and their responses to vertical and seasonal variation in leaf traits were consistent, demonstrating the robustness of our findings. Based on our findings, we propose a scalable framework for parameterizing leaf traits to improve GPP simulations. This study contributes to improving the representation of leaf trait dynamics in canopy-level photosynthesis models, potentially enhancing our ability to predict crop productivity and understand agroecosystem carbon dynamics.

  PublisherDOI

• Registration of four soybean lines in a common genetic background with contrasting leaf shape

  Journal of Plant Registrations · 2026-01-01 · 1 citations

  articleOpen accessSenior authorCorresponding

  Abstract Four soybean [ Glycine max (L.) Merr.] lines, two with lanceolate (narrow) leaf shape (LD11‐NL‐1 [Reg. no. GP‐549, PI 708601], LD11‐NL‐2 [Reg. no GP‐550, PI 708602]) and two with ovate (broad) leaf shape (LD11‐BL‐1 [Reg. no GP‐547, PI 708599], LD11‐BL‐2 [Reg. no GP‐548, PI 708600]) were developed at the University of Illinois Urbana‐Champaign and released to investigate the effects of leaf shape on soybean physiology and productivity. These lines were developed through three generations of backcrossing (BC 3 ) the ln allele for narrow leaf shape from the narrow‐leaved donor parents PI 612713A and PI 547745 into the elite broad‐leaved cultivar ‘LD11‐2170’, followed by selfing to generate BC 3 F 2 ‐derived F 4 lines. Marker‐assisted selection using JAG1 gene‐specific Kompetitive Allele Specific polymerase chain reaction markers was conducted during backcrossing and in the BC 3 F 2 generation. The four selected near‐isogenic lines share ∼95% genetic similarity with the recurrent parent LD11‐2170, and differ principally at the GmJAG1 locus, enabling precise study of the effects of leaf shape on soybean development, growth, and productivity. Leaf Area Index (LAI) in the narrow‐leaved lines was 14%–16% lower on average across growth environments compared with broad‐leaf lines. There were modest differences in the time to canopy closure, but no yield advantage was observed for narrow‐leaf lines across environments and row spacings. Notably, narrow‐leaved lines produced significantly more 4‐seeded pods (34.2% vs. 1.8%). These germplasm resources will facilitate investigation into the physiological mechanisms underlying canopy architecture optimization and enable breeding for improved radiation‐use efficiency in soybean, helping address the excessive LAI often seen in modern varieties.

  PublisherDOI

• Investigation of the impacts of elevated ozone on maize and soybean using the Terrestrial Ecosystem Model in R (TEMIR) version 2.0: differential responses of biomass and photosynthetic rates to cumulative stomatal ozone uptake in different crops

  2026-03-14

  articleOpen access

  Surface ozone air pollution impairs carbon assimilation in terrestrial ecosystems. For crop species, ozone pollution reduces biomass and crop yield and therefore poses challenges on food security in regions with large populations such as India and China. The ozone impacts on crop yield can be examined with a mechanistic crop model, which explicitly simulates plant physiological responses (e.g., gas exchange rate, leaf area index) to changes in environmental conditions. In mechanistic crop models, ozone-induced yield loss is primarily determined by the sensitivity parameter (asen) of photosynthetic rate loss to cumulative stomatal ozone uptake. Derivation of asen follows different approaches: one based on statistical relationships between relative yield or biomass loss and cumulative ozone uptake, as described in Sitch et al. (2007); another based on relationships between gas exchange rate losses (photosynthesis and stomatal conductance) and cumulative ozone uptake, as described in Lombardozzi et al. (2015).In this study, gas-exchange measurement data from multiple elevated ozone exposure experiments for maize and soybean are used to calibrate asen following Lombardozzi et al. (2015). Validation simulations are conducted using the Terrestrial Ecosystem Model in R (TEMIR) version 2.0, a mechanistic crop model akin to those in land surface models such as JULES and CLM4.5, implemented with two plant-ozone damage schemes following Sitch et al. (2007) and Lombardozzi et al. (2015).With the newly calibrated asen, modeled ozone-induced relative yield loss shows good agreement with observed values for soybean, with a mean error of less than 5 percentage points across different ozone levels. Simulations using the calibrated asen following Lombardozzi et al. (2015) exhibit superior performance compared to those using the default asen from Lombardozzi et al. (2015) or the calibrated asen following Sitch et al. (2007), both of which have mean errors exceeding 25 percentage points in the modeled ozone-induced relative yield loss. The low mean error from the simulations using the calibrated asen following Lombardozzi et al. (2015) suggests the sensitivity of relative photosynthetic rate loss to ozone is similar to that for relative yield loss in soybean. In contrast, for maize, with the calibrated asen following Lombardozzi et al. (2015), the model overestimates relative ozone-induced yield loss by about 30 percentage points at the highest ozone concentration (~100 ppbv). Sensitivity simulations with varying values of asen indicate that the parameter calibrated to photosynthetic rate loss must be reduced to about one-third of its original value to align modeled and observed relative yield and biomass losses for maize. Modelers should account for these differential responses of photosynthetic rates versus yield and biomass losses among crops species, when assessing future ozone impacts on crop productivity.

  PublisherDOI

• Stephen P. Long: Visionary, teacher, and doer

  Proceedings of the National Academy of Sciences · 2026-02-04

  articleOpen access1st author

  Stephen Long pioneered a multidisciplinary approach to advance our knowledge of photosynthesis, by integrating research at the molecular, cellular, organismal, and ecological levels along with a pragmatic understanding of the implications for agriculture. This involved development of new mathematical models, patented equipment for analyzing photosynthetic efficiency, and the world’s largest open-air laboratory for understanding crop responses to atmospheric change. Long fundamentally changed our understanding of how crops respond to rising CO 2 and ozone. He inspired and led efforts to engineer photosynthesis to increase yield and respond maximally to rising CO 2 . He discovered that C 4 plants, that have highly efficient photosynthesis due to a CO 2 concentrating mechanism, could thrive in cold climates, which was a true paradigm shift as previously C 4 photosynthesis had been considered limited to tropical and subtropical climates. He provided a theoretical explanation, and his research led to the discovery that Miscanthus x giganteus could achieve the high yields in cool northern climate achieved by other C 4 plants in the tropics. This seminal finding led Long to experiment on the closely related and much more widely used maize plant, and he showed how it could be adapted to cooler conditions and achieve a significant yield jump in the Corn Belt. At a time when society critically needed new ways to achieve increases in productivity in ecologically sustainable ways, Long’s research and leadership changed the way we think about choices of plants and cropping systems.

  PublisherOA PDFDOI

• Natural leaf shape variation reveals diverse transcriptional targets of <i>GmJAG1</i> during soybean leaf development

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-11

  articleOpen accessSenior author

  Abstract The JAGGED transcription factor family regulates lateral organ development across angiosperms. In soybean ( Glycine max Merr.), a D9H mutation in the EAR repression motif of GmJAG1 causes a narrow leaflet phenotype and explains over 70% of phenotypic variance in leaf shape. Because this mutation does not affect the zinc finger DNA-binding domain, both alleles bind identical targets but differ in repressor recruitment. Previous studies mapped GmJAG1 binding sites, but the functional targets controlling leaf morphology are uncharacterized. Here, we used comparative transcriptomics across four soybean genotypes with contrasting leaf shape, spanning a developmental time series from shoot apex to mature leaf, and identified 1,567 candidate target genes. GmJAG1 expression was confined to the shoot apex, yet 99.1% of candidate targets maintained differential expression throughout development. We found that neither Kip-Related Protein ( KRP ) cell cycle inhibitors nor Cyclin-Dependent Kinases (CDKs) showed differential expression despite binding evidence in Arabidopsis. However, D-type cyclins were upregulated in narrow-leaf genotypes suggesting cyclin-mediated rather than KRP -mediated cell cycle regulation in soybean. Pathway analysis revealed enrichment of auxin (1.8-fold, P = 0.02) and salicylic acid (4-fold, P = 0.016) genes among JAG1 D9H targets. Filtering by differential expression, binding data, phenotype correlation, and co-expression network membership identified 79 high-confidence targets, including orthologs of NPH3 (phototropin-mediated leaf flattening), MIK2 (cell wall integrity sensing), RD22 (ABA-responsive stress signaling), and SCL23 (GRAS transcription factor in bundle sheath development). These candidates provide targets for functional validation and breeding in legumes.

  PublisherDOI

• Environmental factors have a greater influence on photosynthetic capacity in C4 plants than C4 biochemical subtypes or growth forms

  2025-04-14

  preprintOpen access

  • Our understanding of how photosynthesis varies among C4 species and across different growth and measurement conditions remains limited. • We collated 1,696 CO2 response curves of net CO2 assimilation rate (A/Ci curves) from C4 species grown and measured at various environmental conditions and used these data to estimate the apparent maximum carboxylation activity of phosphoenolpyruvate carboxylase (VpmaxA) and CO2-saturated net photosynthetic rate (Amax), two key parameters describing C4 photosynthetic capacity. We examined how VpmaxA and Amax vary with species-specific traits, growth and measurement conditions. • We show that VpmaxA and Amax do not differ between C4 biochemical subtypes or growth forms, and highlight that growth temperature and measurement conditions are major factors determining photosynthetic capacity. We found no evidence that common C4 model species (e.g., maize, sorghum and Setaria viridis) differ in photosynthetic capacity from other C4 species when grown in controlled environments. However, C4 model species showed up to twice the photosynthetic capacity of other C4 species when grown in the field. • Our multivariate model accounts for 47-51% of the variation reported in VpmaxA and Amax, and we argue that environmental conditions have a greater influence on C4 photosynthetic capacity than inherent biochemical subtypes or growth forms.

  PublisherOA PDFDOI

• <scp>C₄</scp> photosynthesis, trait spectra, and the fast‐efficient phenotype

  New Phytologist · 2025-03-27 · 17 citations

  reviewOpen access

  Summary It has been 60 years since the discovery of C 4 photosynthesis, an event that rewrote our understanding of plant adaptation, ecosystem responses to global change, and global food security. Despite six decades of research, one aspect of C 4 photosynthesis that remains poorly understood is how the pathway fits into the broader context of adaptive trait spectra, which form our modern view of functional trait ecology. The C 4 CO 2 ‐concentrating mechanism supports a general C 4 plant phenotype capable of fast growth and high resource‐use efficiencies. The fast‐efficient C 4 phenotype has the potential to operate at high productivity rates, while allowing for less biomass allocation to root production and nutrient acquisition, thereby providing opportunities for the evolution of novel trait covariances and the exploitation of new ecological niches. We propose the placement of the C 4 fast‐efficient phenotype near the acquisitive pole of the world‐wide leaf economic spectrum, but with a pathway‐specific span of trait space, wherein selection shapes both acquisitive and conservative adaptive strategies. A trait‐based perspective of C 4 photosynthesis will open new paths to crop improvement, global biogeochemical modeling, the management of invasive species, and the restoration of disturbed ecosystems, particularly in grasslands.

  PublisherOA PDFDOI

• The Global Spectra-Trait Initiative: A database of paired leaf spectroscopy and functional traits associated with leaf photosynthetic capacity

  2025-05-21 · 2 citations

  preprintOpen access

  Abstract. Accurate assessment of leaf functional traits is crucial for a diverse range of applications from crop phenotyping to parameterizing global climate models. Leaf reflectance spectroscopy offers a promising avenue to advance ecological and of robust hyperspectral models for predicting leaf photosynthetic capacity and associated traits from reflectance data has been hindered by limited data availability across species and environments. Here we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. The GSTI repository currently encompasses over 7500 observations from 397 species and 41 sites gathered from 36 published and unpublished studies, thereby offering a key resource for developing and validating hyperspectral models of leaf photosynthetic agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti) and published to ESS-dive https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2530733, Lamour et al., 2025). It includes gas exchange data, derived photosynthetic parameters, and key leaf traits often associated with traditional gas exchange measurements such as leaf mass per area and leaf elemental composition. By providing a standardized repository for data sharing and analysis, we present a critical step towards creating hyperspectral models for predicting photosynthetic traits and associated leaf traits for terrestrial plants.

  PublisherOA PDFDOI

• Impact of vertical and seasonal variation in leaf traits on simulating soybean canopy photosynthesis via 1D and 3D modeling

  Agricultural and Forest Meteorology · 2025-11-22 · 1 citations

  articleOpen access

  • Vcmax 25 variation is more critical than LCC and LMA in GPP simulation. • Both measured and empirically parameterized Vcmax 25 profiles mitigated GPP overestimation. • The 1D and 3D models show similar response to the vertical and seasonal variation in leaf traits. • A scalable leaf traits parameterization is proposed for GPP modeling. • Effectiveness of the proposed parameterization is confirmed at the study site. Accurate modeling of photosynthesis is crucial for predicting crop productivity and quantifying the carbon cycle in agroecosystems. Leaf traits are essential inputs for modeling canopy photosynthesis. Yet, many existing models still use fixed plant functional type (PTF)-based values to parameterize leaf traits under a big-leaf or two-big-leaf assumption, neglecting their vertical profiles and seasonal changes. This simplification may introduce significant uncertainties in estimating gross primary productivity (GPP). In this study, we simulated soybean GPP and tested the effects of vertical and seasonal variation in three key leaf photosynthetic traits: the maximum carboxylation rate at 25 °C (Vcmax 25 ), leaf chlorophyll content (LCC), and leaf mass per area (LMA) in the 1D-SCOPE and 3D-Helios models. Weekly field measurements were conducted during the growing season of 2024 to support the simulation. We designed ten leaf trait parameterization schemes by incorporating different combinations of vertical profiles and seasonal changes, while assuming homogeneous canopy architecture in both models. Our results revealed that Vcmax 25 vertical and seasonal variation had the strongest influence on simulated GPP in both 1D and 3D models, while LCC and LMA effects were minimal. Particularly, the scheme with an empirically parameterized Vcmax 25 profile achieved comparable performance to the scheme with the measured Vcmax 25 profile. Both 1D-SCOPE and 3D-Helios accurately modeled GPP (SCOPE: R 2 = 0.87, Bias = 0.55 µmol m⁻² s⁻¹; Helios: R 2 = 0.9, Bias = 0.22 µmol m⁻² s⁻¹) under the most complex scheme, and their responses to vertical and seasonal variation in leaf traits were consistent, demonstrating the robustness of our findings. Based on our findings, we propose a scalable framework for parameterizing leaf traits to improve GPP simulations. This study contributes to improving the representation of leaf trait dynamics in canopy-level photosynthesis models, potentially enhancing our ability to predict crop productivity and understand agroecosystem carbon dynamics.

  PublisherDOI

Recent grants

• MCA-PGR: Genetic and Genomic Approaches to Understand and Improve Maize Responses to Ozone

  NSF · $5.7M · 2013–2019

Frequent coauthors

• Andrew D. B. Leakey

  University of Illinois Urbana-Champaign

  124 shared

• Carl J. Bernacchi

  University of Illinois Urbana-Champaign

  120 shared

• Donald R. Ort

  University of Illinois Urbana-Champaign

  100 shared

• Alistair Rogers

  Lawrence Berkeley National Laboratory

  93 shared

• Stephen P. Long

  90 shared

• Kaiyu Guan

  87 shared

• Christopher M. Montes

  University of Illinois Urbana-Champaign

  60 shared

• Craig R. Yendrek

  University of Illinois Urbana-Champaign

  53 shared

Education

• PhD, Crop Sciences

  University of Illinois at Urbana-Champaign

  2003

Awards & honors

• 2019 NAS Prize in Food and Agriculture Sciences
• 2018 Presidential Award, Crop Science Society of America
• 2016, 2017 Thomson Reuters/Clarivate Analytics Highly Cited…
• 2016 Service Recognition Award, College of ACES, UIUC
• 2015 USDA ARS Outreach, Diversity and Equal Opportunity Awar…