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DOE OSTI2021

Adaptive Phenotypic Plasticity Stabilizes Evolution in Fluctuating Environments

Fluctuating environmental conditions are ubiquitous in natural systems, and populations have evolved various strategies to cope with such fluctuations. The particular mechanisms that evolve profoundly influence subsequent evolutionary dynamics. One such mechanism is phenotypic plasticity, which is the ability of a single genotype to produce alternate phenotypes in an environmentally dependent context. Here, we use digital organisms (self-replicating computer programs) to investigate how adaptive phenotypic plasticity alters evolutionary dynamics and influences evolutionary outcomes in cyclically changing environments. Specifically, we examined the evolutionary histories of both plastic populations and non-plastic populations to ask: (1) Does adaptive plasticity promote or constrain evolutionary change? (2) Are plastic populations better able to evolve and then maintain novel traits? And (3), how does adaptive plasticity affect the potential for maladaptive alleles to accumulate in evolving genomes? We find that populations with adaptive phenotypic plasticity undergo less evolutionary change than non-plastic populations, which must rely on genetic variation from de novo mutations to continuously readapt to environmental fluctuations. Indeed, the non-plastic populations undergo more frequent selective sweeps and accumulate many more genetic changes. We find that the repeated selective sweeps in non-plastic populations drive the loss of beneficial traits and accumulation of maladaptive alleles, whereas phenotypic plasticity can stabilize populations against environmental fluctuations. This stabilization allows plastic populations to more easily retain novel adaptive traits than their non-plastic counterparts. In general, the evolution of adaptive phenotypic plasticity shifted evolutionary dynamics to be more similar to that of populations evolving in a static environment than to non-plastic populations evolving in an identical fluctuating environment. All natural environments subject populations to some form of change; our findings suggest that the stabilizing effect of phenotypic plasticity plays an important role in subsequent adaptive evolution.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Genomic selection in algae with biphasic lifecycles: A Saccharina latissima (sugar kelp) case study

Introduction Sugar kelp ( Saccharina latissima ) has a biphasic life cycle, allowing selection on both thediploid sporophytes (SPs) and haploid gametophytes (GPs). Methods We trained a genomic selection (GS) model from farm-tested SP phenotypic data and used a mixed-ploidy additive relationship matrix to predict GP breeding values. Topranked GPs were used to make crosses for further farm evaluation. The relationship matrix included 866 individuals: a) founder SPs sampled from the wild; b) progeny GPs from founders; c) Farm-tested SPs crossed from b); and d) progeny GPs from farm-tested SPs. The complete pedigree-based relationship matrix was estimated for all individuals. A subset of founder SPs ( n = 58) and GPs ( n = 276) were genotyped with Diversity Array Technology and whole genome sequencing, respectively. We evaluated GS prediction accuracy via cross validation for SPs tested on farm in 2019 and 2020 using a basic GBLUP model. We also estimated the general combining ability (GCA) and specific combining ability (SCA) variances of parental GPs. A total of 11 yield-related and morphology traits were evaluated. Results The cross validation accuracies for dry weight per meter ( r ranged from 0.16 to 0.35) and wet weight per meter ( r ranged 0.19 to 0.35) were comparable to GS accuracy for yield traits in terrestrial crops. For morphology traits, cross validation accuracy exceeded 0.18 in all scenarios except for blade thickness in the second year. Accuracy in a third validation year (2021) was 0.31 for dry weight per meter over a confirmation set of 87 individuals. Discussion Our findings indicate that progress can be made in sugar kelp breeding by using genomic selection.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

A stable 15-member bacterial SynCom promotes Brachypodium growth under drought stress

Introduction: Rhizosphere microbiomes are known to drive soil nutrient cycling and influence plant fitness during adverse environmental conditions. Field-derived robust Synthetic Communities (SynComs) of microbes mimicking the diversity of rhizosphere microbiomes can greatly advance a deeper understanding of such processes. However, assembling stable, genetically tractable, reproducible, and scalable SynComs remains challenging. Methods: Here, we present a systematic approach using a combination of network analysis and cultivation-guided methods to construct a 15-member SynCom from the rhizobiome of Brachypodium distachyon. This SynCom incorporates diverse strains from five bacterial phyla. Genomic analysis of the individual strains was performed to reveal encoded plant growth-promoting traits, including genes for the synthesis of osmoprotectants (trehalose and betaine) and Na+/K+ transporters, and some predicted traits were validated by laboratory phenotypic assays. Results: The SynCom demonstrates strong stability both in vitro and in planta. Most strains encoded multiple plant growth-promoting functions, and several of these were confirmed experimentally. The presence of osmoprotectant and ion transporter genes likely contributed to the observed resilience of Brachypodium to drought stress, where plants amended with the SynCom recovered better than those without. We further observed preferential colonization of SynCom strains around root tips under stress, likely due to active interactions between plant root metabolites and bacteria. Discussion: Our results demonstrate that trait-informed construction of synthetic communities can yield stable, functionally diverse consortia that enhance plant resilience under drought. Preferential colonization near root tips points to active, localized plant-microbe signaling as a component of stress-responsive recruitment. This stable SynCom provides a scalable platform for probing mechanisms of plant-microbe interaction and for developing microbiome-based strategies to improve soil and crop performance in variable environments.

Yadav, Archana↗

DOE OSTI2020

Editorial: Proceedings of ICPSBBB 2018 - 2nd International Conference on Plant Synthetic Biology, Bioengineering and Biotechnology

Plant synthetic biology combines engineering principles with plant biology toward the design, construction, and testing of plants with new traits (Liu and Stewart, 2015). Synthetic biology applies engineering principles, using defined genetic parts with the aim of achieving predictable outcomes. The field aims at providing fuels, pharmaceuticals, bioactive metabolites and increased crop yield. Synthetic biology is increasingly forming a framework in genetic transformation of plants allowing us to improve traits and crop performance. The conference on Plant Synthetic Biology, Bioengineering, and Biotechnology covers broad aspects of plant synthetic biology, ranging from tools to the design of predictable functions and precision genome editing, to re-designed photosynthesis, plant bioengineering techniques, and rational design of novel plant traits. This Frontiers in Plant Science virtual issue on the “Research Topic Proceedings of ICPSBBB 2018 - 2nd International Conference on Plant Synthetic Biology, Bioengineering and Biotechnology” consists of 8 publications, including four reviews and four original research articles, which fall into the following general topics: quantitative and genetic parts, evolution, expression of transgenes and epigenetic changes, engineering of stress tolerance and bioproduction.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Intraspecific Variation for Leaf Physiological and Root Morphological Adaptation to Drought Stress in Alfalfa ( Medicago sativa L.)

Drought stress reduces crop biomass yield and the profitability of rainfed agricultural systems. Evaluation of populations or accessions adapted to diverse geographical and agro-climatic environments sheds light on beneficial plant responses to enhance and optimize yield in resource-limited environments. This study used the morphological and physiological characteristics of leaves and roots from two different alfalfa subspecies during progressive drought stress imposed on controlled and field conditions. Two different soils (Experiments 1 and 2) imposed water stress at different stress intensities and crop stages in the controlled environment. Algorithm-based image analysis of leaves and root systems revealed key morphological and physiological traits associated with biomass yield under stress. The Medicago sativa subspecies (ssp.) sativa population, PI478573, had smaller leaves and maintained higher chlorophyll content (CC), leaf water potential, and osmotic potential under water stress. In contrast, M. sativa ssp. varia, PI502521, had larger leaves, a robust root system, and more biomass yield. In the field study, an unmanned aerial vehicle survey revealed PI502521 to have a higher normalized difference vegetation index (vegetation cover and plant health characteristics) throughout the cropping season, whereas PI478573 values were low during the hot summer and yielded low biomass in both irrigated and rainfed treatments. RhizoVision Explorer image analysis of excavated roots revealed a smaller diameter and a narrow root angle as target traits to increase alfalfa biomass yield irrespective of water availability. Root architectural traits such as network area, solidity, volume, surface area, and maximum radius exhibited significant variation at the genotype level only under limited water availability. Different drought-adaptive strategies identified across subspecies populations will benefit the plant under varying levels of water limitation and facilitate the development of alfalfa cultivars suitable across a broad range of growing conditions. The alleles from both subspecies will enable the development of drought-tolerant alfalfa with enhanced productivity under limited water availability.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Generating Novel Aroma Phenotypes Using Commercial Wine Samples to Characterize an F1 Population

Due to their disease tolerance and cold hardy nature, interspecific hybrid grapes are widely grown in the Midwestern and Northeastern United States, with additional interest worldwide in the face of increased abiotic and biotic stresses from climate change. However, the aroma profile of these hybrids is unique and generally less popular in comparison with Vitis vinifera grapes. One of the challenges in any phenotyping project is first defining the traits of interest. As wine quality was our ultimate metric of interest, the aroma profile of commercial wines produced from the parents of a breeding population ( Vitis aestivalis derived ‘Norton’ x V. vinifera . ‘Cabernet Sauvignon’) was first assessed for traits of interest. We investigated 11 commercial wines each of Norton, a popular hybrid in Missouri and Cabernet Sauvignon (Cab) for their volatile profiles using the more inclusive metabolomics-based workflow. We then analyzed 21 Norton and 21 Cab grapes from different sites and vintages for the free and bound volatile compounds using HS-SPME-GCMS to validate the differences in wine. The GCMS data was processed using XCMS software to find features that were different between the two cultivars. The two cultivars were found to have differences in their volatile profiles, with 304 features different for wine volatiles, 418 features different for free volatiles, and 302 features different for bound volatiles at 0.05 significance level and with at least a 1.5-fold change between the two cultivars. Those features were used to identify several odor-active compounds in both grapes and wines, including β-damascenone, β-ionone, eugenol, 1,1,6-trimethyl-1,2-dihydronaphthalene (TDN), and methyl salicylate. Some of the identified compounds were higher in Norton than Cab; however, several features were higher in Cab. Using the identified aroma compounds as markers, we phenotyped an F1 population of Norton and Cab. The F1 population was found to be segregating for many aroma compounds with some genotypes demonstrating an even higher concentration of aroma volatiles than either of the parents. Ultimately, using commercially available samples paired with untargeted analysis proved to be an efficient way to determine phenotypes of interest for further analysis and may offer an easy way to choose potential parents with desired traits for breeding.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

PhytoOracle: Scalable, modular phenomics data processing pipelines

As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to ( i ) improve data processing efficiency; ( ii ) provide an extensible, reproducible computing framework; and ( iii ) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area).

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Engineering custom morpho- and chemotypes of Populus for sustainable production of biofuels, bioproducts, and biomaterials

Humans have been modifying plant traits for thousands of years, first through selection (i.e., domestication) then modern breeding, and in the last 30 years, through biotechnology. These modifications have resulted in increased yield, more efficient agronomic practices, and enhanced quality traits. Precision knowledge of gene regulation and function through high-resolution single-cell omics technologies, coupled with the ability to engineer plant genomes at the DNA sequence, chromatin accessibility, and gene expression levels, can enable engineering of complex and complementary traits at the biosystem level. Populus spp., the primary genetic model system for woody perennials, are among the fastest growing trees in temperate zones and are important for both carbon sequestration and global carbon cycling. Ample genomic and transcriptomic resources for poplar are available including emerging single-cell omics datasets. To expand use of poplar outside of valorization of woody biomass, chassis with novel morphotypes in which stem branching and tree height are modified can be fabricated thereby leading to trees with altered leaf to wood ratios. These morphotypes can then be engineered into customized chemotypes that produce high value biofuels, bioproducts, and biomaterials not only in specific organs but also in a cell-type-specific manner. For example, the recent discovery of triterpene production in poplar leaf trichomes can be exploited using cell-type specific regulatory sequences to synthesize high value terpenes such as the jet fuel precursor bisabolene specifically in the trichomes. By spatially and temporally controlling expression, not only can pools of abundant precursors be exploited but engineered molecules can be sequestered in discrete cell structures in the leaf. The structural diversity of the hemicellulose xylan is a barrier to fully utilizing lignocellulose in biomaterial production and by leveraging cell-type-specific omics data, cell wall composition can be modified in a tailored and targeted specific manner to generate poplar wood with novel chemical features that are amenable for processing or advanced manufacturing. Precision engineering poplar as a multi-purpose sustainable feedstock highlights how genome engineering can be used to re-imagine a crop species.

09 BIOMASS FUELS↗

DOE OSTI2022

Editorial: Artificial Intelligence and Machine Learning Applications in Plant Genomics and Genetics

In plants, as in other species, genotypic variation in combination with environmental variation leads to differences in the biochemical makeup of cells, measurable as molecular phenotypes. These in turn influence physiological and developmental traits such as organ formation and plant growth, and eventually traits relevant in agriculture, such as yield and stress tolerance. Thus, relating genotypes to phenotypes yields fundamental insights into the regulation of important processes in plant development and physiology, but also provides the ability to predict yield and quality traits in specific environments, which is essential in basic plant science and molecular breeding of resilient plants in changing environments.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Evolutionary Origins of Metabolic Reprogramming in Cancer

Metabolic changes that facilitate tumor growth are one of the hallmarks of cancer. These changes are not specific to tumors but also take place during the physiological growth of tissues. Indeed, the cellular and tissue mechanisms present in the tumor have their physiological counterpart in the repair of tissue lesions and wound healing. These molecular mechanisms have been acquired during metazoan evolution, first to eliminate the infection of the tissue injury, then to enter an effective regenerative phase. Cancer itself could be considered a phenomenon of antagonistic pleiotropy of the genes involved in effective tissue repair. Cancer and tissue repair are complex traits that share many intermediate phenotypes at the molecular, cellular, and tissue levels, and all of these are integrated within a Systems Biology structure. Complex traits are influenced by a multitude of common genes, each with a weak effect. This polygenic component of complex traits is mainly unknown and so makes up part of the missing heritability. Here, we try to integrate these different perspectives from the point of view of the metabolic changes observed in cancer.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Data from: Biomass yield, yield components and growing season phenotypic measurements of Miscanthus

For sustainable biomass production of Miscanthus × giganteus (hereafter miscanthus), understanding the impact of stand age and nitrogen (N) fertilization on biomass yield is crucial. This study investigated the effects of varying N fertilization rates (0, 56, 112, and 168 kg N ha-1) on yield components (tiller height, density, and weight) and their correlations with end-of-season biomass yield in miscanthus. We also explored end-of-season biomass yield prediction using in-season traits (canopy height, leaf area index (LAI), and leaf chlorophyll content (LCC)). The study was conducted at two sites in Illinois: a previously unfertilized 10-year-old miscanthus research stand at Urbana and a 16-year-old commercial stand at Pesotum with a history of annual 56N application. Results from 2018-2021 in Urbana and 2020-2021 in Pesotum showed increased biomass yields with N fertilization, varying by rate, year, and location. Biomass yield in Pesotum peaked at 56N, while in Urbana, it increased significantly at 112 kg N ha-1. Biomass yield was strongly correlated with tiller height and weight measured at Urbana across N rates. Morphological traits measured every 2-3 weeks during the 2020 and 2021 growing seasons showed that canopy height was the strongest single predictor of miscanthus biomass yield, followed by LCC. Mid-August to September measurements of these traits were the best predictors of biomass yield. Multiple regressions involving the canopy height and LCC further improved yield predictions. We conclude that while N enhances biomass yields of aging miscanthus, the optimum rate depends on the site, environmental conditions, and management.

Aging↗

DOE OSTI2023

A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0)

Tropical forest dynamics play a crucial role in the global carbon, water, and energy cycles. However, realistically simulating the dynamics of competition and coexistence between different plant functional types (PFTs) in tropical forests remains a significant challenge. This study aims to improve the modeling of PFT coexistence in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a vegetation demography model implemented in the Energy Exascale Earth System Model (E3SM) land model (ELM), ELM-FATES. Specifically, we explore (1) whether plant trait relationships established from field measurements can constrain ELM-FATES simulations and (2) whether machine learning (ML)-based surrogate models can emulate the complex ELM-FATES model and optimize parameter selections to improve PFT coexistence modeling. We conducted three ensembles of ELM-FATES experiments at a tropical forest site near Manaus, Brazil. By comparing the ensemble experiments without (Exp-CTR) and with (Exp-OBS) consideration of observed trait relationships, we found that accounting for these relationships slightly improves the simulations of water, energy, and carbon variables when compared to observations but degrades the simulation of PFT coexistence. Using ML-based surrogate models trained on Exp-CTR, we optimized the trait parameters in ELM-FATES and conducted another ensemble of experiments (Exp-ML) with these optimized parameters. The proportion of PFT coexistence experiments significantly increased from 21 % in Exp-CTR to 73 % in Exp-ML. After filtering the experiments that allow for PFT coexistence to agree with observations (within 15 % tolerance), 33 % of the Exp-ML experiments were retained, which is a significant improvement compared to the 1.4 % in Exp-CTR. Exp-ML also accurately reproduces the annual means and seasonal variations in water, energy, and carbon fluxes and the field inventory of aboveground biomass. This study represents a reproducible method that utilizes machine learning to identify parameter values that improve model fidelity against observations and PFT coexistence in vegetation demography models for diverse ecosystems. Our study also suggests the need for new mechanisms to enhance the robust simulation of coexisting plants in ELM-FATES and has significant implications for modeling the response and feedbacks of ecosystem dynamics to climate change.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Full spectrum (350-2500 nm) canopy spectral reflectance, Seward Peninsula, Alaska, 2018

Full-range (350-2500 nm) canopy reflectance spectra of various Arctic tundra ecosystem endmembers, including canopy-scale species endmember spectra, plot-scale spectra, and transect spectra as well as non-vegetated surface (NVS) spectra. The datasets were collected at the three core NGEE-Arctic watersheds, Kougarok, Teller, and Council, as well as the supplemental Kougarok Mile 80 site within the larger Seward Peninsula, Alaska region. The data were collected in July 2018 using a full-range Spectra Vista Corporation (SVC) HR-1024i spectroradiometer. Canopy-scale reflectance was collected with an 8-degree field-of-view (FOV) foreoptic lens. All spectral measurements were collected as calibrated surface radiance and converted to surface reflectance using a 99.99% reflective Spectralon white reference standard. For those canopy spectra collected with associated functional trait data, the FOV of the instrument was positioned to include the same leaves harvested for functional trait measurements, including leaf mass per area (LMA) and foliar carbon and nitrogen content (see associated dataset). Species included in the spectra and functional trait dataset include Alnus viridis, Betula nana, Betula glandulosa, Arctous alpina, and Salix pulchra. This data package includes 35 files in a variety of formats including processed canopy spectra (*.csv), processed dGPS locations (*.csv and *.kmz), digital photographs of spectral targets (*.jpg) and raw data from spectroradiometer and dGPS instruments (compressed as tar.gz). Metadata files include data descriptions (_dd.csv) for tabular data and a key to species symbols used in data files. All included files are listed and described in NGA208_flmd.csv.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Full spectrum (350-2500 nm) leaf and canopy spectral reflectance, Seward Peninsula, Alaska, 2019

Full-range (350 - 2500 nm) leaf and canopy reflectance spectra of various Arctic tundra ecosystem endmembers, including species-level leaf reflectance, canopy-scale species endmember spectra, plot-scale spectra, and transect spectra. These data were collected in support of ongoing NASA ABoVE AVIRIS data synthesis work. The datasets were collected at the NGEE-Arctic watershed sites, Kougarok MM64 and MM80, and Teller MM27 on the Seward Peninsula, Alaska. The data were collected in July 2019 using a full-range Spectra Vista Corporation (SVC) HR-1024i spectroradiometer. Leaf-level spectra were collected with the original SVC leaf clip/plant probe connected to the spectrometer through a 1.15 meter long fiber optic cable, while canopy-scale reflectance was collected with an 8-degree field-of-view (FOV) foreoptic lens. All spectral measurements were collected as calibrated surface radiance and converted to surface reflectance using a 99.99% reflective Spectralon white reference standard. For those canopy spectra collected with associated functional trait data, the FOV of the instrument was positioned to include the same leaves harvested for functional trait measurements, including leaf mass per area (LMA) and foliar carbon and nitrogen content (see leaf trait data package for these data). Canopy spectra were obtained for 23 different species, and leaf spectra for 8 species. This data package includes processed canopy and leaf spectra (*.csv), processed dGPS locations (*.csv and *.kmz), digital photographs of spectral targets (*.jpg) and raw data from spectroradiometer and dGPS instruments (compressed as tar.gz). Metadata files include data descriptions (_dd.csv) for tabular data and a key to species symbols used in data files. All included files are listed and described in NGA212_flmd.csv.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1983

What changes and what endures - The capabilities and limitations of training and selection

The contributions of psychology to aviation in the areas of selection, training, and evaluation, and the implementation of new technologies are discussed. The concept of personality traits versus modification of human behavior through principles of learning are analyzed. Particular consideration is given to achievement motivation (defined in terms of mastery, work, and competitiveness) and the differences between traits and attitudes. It is argued that personality traits are important dimensions of the self and are useful measures of individual differences. The selection of individuals with desired personality characteristics and the training of personnel to improve crew coordination, flight-deck management, and interpersonal efficacy are examined.

Helmreich, Robert L.↗

NASA NTRS2000

Predictors of Behavior and Performance in Extreme Environments: The Antarctic Space Analogue Program

To determine which, if any, characteristics should be incorporated into a select-in approach to screening personnel for long-duration spaceflight, we examined the influence of crewmember social/ demographic characteristics, personality traits, interpersonal needs, and characteristics of station physical environments on performance measures in 657 American men who spent an austral winter in Antarctica between 1963 and 1974. During screening, subjects completed a Personal History Questionnaire which obtained information on social and demographic characteristics, the Deep Freeze Opinion Survey which assessed 5 different personality traits, and the Fundamental Interpersonal Relations Orientation-Behavior (FIRO-B) Scale which measured 6 dimensions of interpersonal needs. Station environment included measures of crew size and severity of physical environment. Performance was assessed on the basis of combined peer-supervisor evaluations of overall performance, peer nominations of fellow crewmembers who made ideal winter-over candidates, and self-reported depressive symptoms. Social/demographic characteristics, personality traits, interpersonal needs, and characteristics of station environments collectively accounted for 9-17% of the variance in performance measures. The following characteristics were significant independent predictors of more than one performance measure: military service, low levels of neuroticism, extraversion and conscientiousness, and a low desire for affection from others. These results represent an important first step in the development of select-in criteria for personnel on long-duration missions in space and other extreme environments. These criteria must take into consideration the characteristics of the environment and the limitations they place on meeting needs for interpersonal relations and task performance, as well as the characteristics of the individuals and groups who live and work in these environments.

Palinkas, Lawrence A.↗

NASA NTRS2003

Quantitative analysis of bristle number in Drosophila mutants identifies genes involved in neural development

BACKGROUND: The identification of the function of all genes that contribute to specific biological processes and complex traits is one of the major challenges in the postgenomic era. One approach is to employ forward genetic screens in genetically tractable model organisms. In Drosophila melanogaster, P element-mediated insertional mutagenesis is a versatile tool for the dissection of molecular pathways, and there is an ongoing effort to tag every gene with a P element insertion. However, the vast majority of P element insertion lines are viable and fertile as homozygotes and do not exhibit obvious phenotypic defects, perhaps because of the tendency for P elements to insert 5' of transcription units. Quantitative genetic analysis of subtle effects of P element mutations that have been induced in an isogenic background may be a highly efficient method for functional genome annotation. RESULTS: Here, we have tested the efficacy of this strategy by assessing the extent to which screening for quantitative effects of P elements on sensory bristle number can identify genes affecting neural development. We find that such quantitative screens uncover an unusually large number of genes that are known to function in neural development, as well as genes with yet uncharacterized effects on neural development, and novel loci. CONCLUSIONS: Our findings establish the use of quantitative trait analysis for functional genome annotation through forward genetics. Similar analyses of quantitative effects of P element insertions will facilitate our understanding of the genes affecting many other complex traits in Drosophila.

Non-NASA Center↗

NASA NTRS2005

Team Performance and Error Management in Chinese and American Simulated Flight Crews: The Role of Cultural and Individual Differences

This report describes results of a study conducted for NASA-Langley Research Center. This study is part of a program of research conducted for NASA-LARC that has focused on identifying the influence of national culture on the performance of flight crews. We first reviewed the literature devoted to models of teamwork and team performance, crew resource management, error management, and cross-cultural psychology. Davis (1999) reported the results of this review and presented a model that depicted how national culture could influence teamwork and performance in flight crews. The second study in this research program examined accident investigations of foreign airlines in the United States conducted by the National Transportation Safety Board (NTSB). The ability of cross-cultural values to explain national differences in flight outcomes was examined. Cultural values were found to covary in a predicted way with national differences, but the absence of necessary data in the NTSB reports and limitations in the research method that was used prevented a clear understanding of the causal impact of cultural values. Moreover, individual differences such as personality traits were not examined in this study. Davis and Kuang (2001) report results of this second study. The research summarized in the current report extends this previous research by directly assessing cultural and individual differences among students from the United States and China who were trained to fly in a flight simulator using desktop computer workstations. The research design used in this study allowed delineation of the impact of national origin, cultural values, personality traits, cognitive style, shared mental model, and task workload on teamwork, error management and flight outcomes. We briefly review the literature that documents the importance of teamwork and error management and its impact on flight crew performance. We next examine teamwork and crew resource management training designed to improve teamwork. This is followed by discussion of the potential influence of national culture on teamwork and crew resource management. We then examine the influence of other individual and team differences, such as personality traits, cognitive style, shared mental model, and task workload. We provide a heuristic model that depicts the influence of national culture and individual differences on teamwork, error management and flight outcomes. The results demonstrate the usefulness of the model for future research.

Davis, Donald D.↗