U.S. winter wheat yield loss attributed to compound hot-dry-windy events.

Climate extremes cause significant winter wheat yield loss and can cause much greater impacts than single extremes in isolation when multiple extremes occur simultaneously. Here we show that compound hot-dry-windy events (HDW) significantly increased in the U.S. Great Plains from 1982 to 2020. These HDW events were the most impactful drivers for wheat yield loss, accounting for a 4% yield reduction per 10 h of HDW during heading to maturity. Current HDW trends are associated with yield reduction rates of up to 0.09 t ha-1 per decade and HDW variations are atmospheric-bridged with the Pacific Decadal Oscillation. We quantify the "yield shock", which is spatially distributed, with the losses in severely HDW-affected areas, presumably the same areas affected by the Dust Bowl of the 1930s. Our findings indicate that compound HDW, which traditional risk assessments overlooked, have significant implications for the U.S. winter wheat production and beyond.

Precise multispecies agricultural gas flux determined using broadband open-path dual-comb spectroscopy.

Advances in spectroscopy have the potential to improve our understanding of agricultural processes and associated trace gas emissions. We implement field-deployed, open-path dual-comb spectroscopy (DCS) for precise multispecies emissions estimation from livestock. With broad atmospheric dual-comb spectra, we interrogate upwind and downwind paths from pens containing approximately 300 head of cattle, providing time-resolved concentration enhancements and fluxes of CH4, NH3, CO2, and H2O. The methane fluxes determined from DCS data and fluxes obtained with a colocated closed-path cavity ring-down spectroscopy gas analyzer agree to within 6%. The NH3 concentration retrievals have sensitivity of 10 parts per billion and yield corresponding NH3 fluxes with a statistical precision of 8% and low systematic uncertainty. Open-path DCS offers accurate multispecies agricultural gas flux quantification without external calibration and is easily extended to larger agricultural systems where point-sampling-based approaches are insufficient, presenting opportunities for field-scale biogeochemical studies and ecological monitoring.

Large-effect flowering time mutations reveal conditionally adaptive paths through fitness landscapes in Arabidopsis thaliana.

Contrary to previous assumptions that most mutations are deleterious, there is increasing evidence for persistence of large-effect mutations in natural populations. A possible explanation for these observations is that mutant phenotypes and fitness may depend upon the specific environmental conditions to which a mutant is exposed. Here, we tested this hypothesis by growing large-effect flowering time mutants of Arabidopsis thaliana in multiple field sites and seasons to quantify their fitness effects in realistic natural conditions. By constructing environment-specific fitness landscapes based on flowering time and branching architecture, we observed that a subset of mutations increased fitness, but only in specific environments. These mutations increased fitness via different paths: through shifting flowering time, branching, or both. Branching was under stronger selection, but flowering time was more genetically variable, pointing to the importance of indirect selection on mutations through their pleiotropic effects on multiple phenotypes. Finally, mutations in hub genes with greater connectedness in their regulatory networks had greater effects on both phenotypes and fitness. Together, these findings indicate that large-effect mutations may persist in populations because they influence traits that are adaptive only under specific environmental conditions. Understanding their evolutionary dynamics therefore requires measuring their effects in multiple natural environments.

Estimating parametric phenotypes that determine anthesis date in Zea mays: Challenges in combining ecophysiological models with genetics.

Ecophysiological crop models encode intra-species behaviors using parameters that are presumed to summarize genotypic properties of individual lines or cultivars. These genotype-specific parameters (GSP's) can be interpreted as quantitative traits that can be mapped or otherwise analyzed, as are more conventional traits. The goal of this study was to investigate the estimation of parameters controlling maize anthesis date with the CERES-Maize model, based on 5,266 maize lines from 11 plantings at locations across the eastern United States. High performance computing was used to develop a database of 356 million simulated anthesis dates in response to four CERES-Maize model parameters. Although the resulting estimates showed high predictive value (R2 = 0.94), three issues presented serious challenges for use of GSP's as traits. First (expressivity), the model was unable to express the observed data for 168 to 3,339 lines (depending on the combination of site-years), many of which ended up sharing the same parameter value irrespective of genetics. Second, for 2,254 lines, the model reproduced the data, but multiple parameter sets were equally effective (equifinality). Third, parameter values were highly dependent (p<10-6919) on the sets of environments used to estimate them (instability), calling in to question the assumption that they represent fundamental genetic traits. The issues of expressivity, equifinality and instability must be addressed before the genetic mapping of GSP's becomes a robust means to help solve the genotype-to-phenotype problem in crops.

Circadian rhythms vary over the growing season and correlate with fitness components.

Circadian clocks have evolved independently in all three domains of life, suggesting that internal mechanisms of time-keeping are adaptive in contemporary populations. However, the performance consequences of either discrete or quantitative clock variation have rarely been tested in field settings. Clock sensitivity of diverse segregating lines to the environment remains uncharacterized as do the statistical genetic parameters that determine evolutionary potential. In field studies with Arabidopsis thaliana, we found that major perturbations to circadian cycle length (referred to as clock period) via mutation reduce both survival and fecundity. Subtler adjustments via genomic introgression of naturally occurring alleles indicated that clock periods slightly >24 hr were adaptive, consistent with prior models describing how well the timing of biological processes is adjusted within a diurnal cycle (referred to as phase). In segregating recombinant inbred lines (RILs), circadian phase varied up to 2 hr across months of the growing season, and both period and phase expressed significant genetic variances. Performance metrics including developmental rate, size and fruit set were described by principal components (PC) analyses and circadian parameters correlated with the first PC, such that period lengths slightly >24 hr were associated with improved performance in multiple RIL sets. These experiments translate functional analyses of clock behaviour performed in controlled settings to natural ones, demonstrating that quantitative variation in circadian phase is highly responsive to seasonally variable abiotic factors. The results expand upon prior studies in controlled settings, showing that discrete and quantitative variation in clock phenotypes correlates with performance in nature.

Fluctuating, warm temperatures decrease the effect of a key floral repressor on flowering time in Arabidopsis thaliana.

The genetic basis of growth and development is often studied in constant laboratory environments; however, the environmental conditions that organisms experience in nature are often much more dynamic. We examined how daily temperature fluctuations, average temperature, day length and vernalization influence the flowering time of 59 genotypes of Arabidopsis thaliana with allelic perturbations known to affect flowering time. For a subset of genotypes, we also assessed treatment effects on morphology and growth. We identified 17 genotypes, many of which have high levels of the floral repressor FLOWERING LOCUS C (FLC), that bolted dramatically earlier in fluctuating - as opposed to constant - warm temperatures (mean = 22°C). This acceleration was not caused by transient VERNALIZATION INSENSITIVE 3-mediated vernalization, differential growth rates or exposure to high temperatures, and was not apparent when the average temperature was cool (mean = 12°C). Further, in constant temperatures, contrary to physiological expectations, these genotypes flowered more rapidly in cool than in warm environments. Fluctuating temperatures often reversed these responses, restoring faster bolting in warm conditions. Independently of bolting time, warm fluctuating temperature profiles also caused morphological changes associated with shade avoidance or 'high-temperature' phenotypes. Our results suggest that previous studies have overestimated the effect of the floral repressor FLC on flowering time by using constant temperature laboratory conditions.

Ecological genomics and process modeling of local adaptation to climate.

Locally adapted genotypes have higher fitness in their native site in comparison to foreign genotypes. Recent studies have demonstrated both local adaptation to and genomic associations with a range of climate variables. For climate adaptation, the most common genomic pattern is conditional neutrality, as proven by weak across-environment correlations, frequent SNP×environment interactions, and the topology of some developmental and physiological pathways potentially involved in local adaptation. Genomic association approaches readily translate to non-model systems, and genetically explicit climate envelope models will predict future species' distributions under changing climates. Here, we review recent evidence for local adaptation to climate, focusing primarily on the model system, Arabidopsis thaliana, and on studies incorporating genomic tools into field studies or climate analyses.

Tapping unsustainable groundwater stores for agricultural production in the High Plains Aquifer of Kansas, projections to 2110.

Groundwater provides a reliable tap to sustain agricultural production, yet persistent aquifer depletion threatens future sustainability. The High Plains Aquifer supplies 30% of the nation's irrigated groundwater, and the Kansas portion supports the congressional district with the highest market value for agriculture in the nation. We project groundwater declines to assess when the study area might run out of water, and comprehensively forecast the impacts of reduced pumping on corn and cattle production. So far, 30% of the groundwater has been pumped and another 39% will be depleted over the next 50 y given existing trends. Recharge supplies 15% of current pumping and would take an average of 500-1,300 y to completely refill a depleted aquifer. Significant declines in the region's pumping rates will occur over the next 15-20 y given current trends, yet irrigated agricultural production might increase through 2040 because of projected increases in water use efficiencies in corn production. Water use reductions of 20% today would cut agricultural production to the levels of 15-20 y ago, the time of peak agricultural production would extend to the 2070s, and production beyond 2070 would significantly exceed that projected without reduced pumping. Scenarios evaluate incremental reductions of current pumping by 20-80%, the latter rate approaching natural recharge. Findings substantiate that saving more water today would result in increased net production due to projected future increases in crop water use efficiencies. Society has an opportunity now to make changes with tremendous implications for future sustainability and livability.

Paths to selection on life history loci in different natural environments across the native range of Arabidopsis thaliana.

Selection on quantitative trait loci (QTL) may vary among natural environments due to differences in the genetic architecture of traits, environment-specific allelic effects or changes in the direction and magnitude of selection on specific traits. To dissect the environmental differences in selection on life history QTL across climatic regions, we grew a panel of interconnected recombinant inbred lines (RILs) of Arabidopsis thaliana in four field sites across its native European range. For each environment, we mapped QTL for growth, reproductive timing and development. Several QTL were pleiotropic across environments, three colocalizing with known functional polymorphisms in flowering time genes (CRY2, FRI and MAF2-5), but major QTL differed across field sites, showing conditional neutrality. We used structural equation models to trace selection paths from QTL to lifetime fitness in each environment. Only three QTL directly affected fruit number, measuring fitness. Most QTL had an indirect effect on fitness through their effect on bolting time or leaf length. Influence of life history traits on fitness differed dramatically across sites, resulting in different patterns of selection on reproductive timing and underlying QTL. In two oceanic field sites with high prereproductive mortality, QTL alleles contributing to early reproduction resulted in greater fruit production, conferring selective advantage, whereas alleles contributing to later reproduction resulted in larger size and higher fitness in a continental site. This demonstrates how environmental variation leads to change in both QTL effect sizes and direction of selection on traits, justifying the persistence of allelic polymorphism at life history QTL across the species range.

An augmented Arabidopsis phenology model reveals seasonal temperature control of flowering time.

• In this study, we used a combination of theoretical (models) and experimental (field data) approaches to investigate the interaction between light and temperature signalling in the control of Arabidopsis flowering. • We utilised our recently published phenology model that describes the flowering time of Arabidopsis grown under a range of field conditions. We first examined the ability of the model to predict the flowering time of field plantings at different sites and seasons in light of the specific meteorological conditions that pertained. • Our analysis suggested that the synchrony of temperature and light cycles is important in promoting floral initiation. New features were incorporated into the model that improved its predictive accuracy across seasons. Using both laboratory and field data, our study has revealed an important seasonal effect of night temperatures on flowering time. Further model adjustments to describe phytochrome (phy) mutants supported our findings and implicated phyB in the temporal gating of temperature-induced flowering. • Our study suggests that different molecular pathways interact and predominate in natural environments that change seasonally. Temperature effects are mediated largely during the photoperiod during spring/summer (long days) but, as days shorten in the autumn, night temperatures become increasingly important.

The iPlant Collaborative: Cyberinfrastructure for Plant Biology.

The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address Grand Challenges in new ways, to stimulate and facilitate cross-disciplinary research, to promote biology and computer science research interactions, and to train the next generation of scientists on the use of cyberinfrastructure in research and education. Meeting humanity's projected demands for agricultural and forest products and the expectation that natural ecosystems be managed sustainably will require synergies from the application of information technologies. The iPlant cyberinfrastructure design is based on an unprecedented period of research community input, and leverages developments in high-performance computing, data storage, and cyberinfrastructure for the physical sciences. iPlant is an open-source project with application programming interfaces that allow the community to extend the infrastructure to meet its needs. iPlant is sponsoring community-driven workshops addressing specific scientific questions via analysis tool integration and hypothesis testing. These workshops teach researchers how to add bioinformatics tools and/or datasets into the iPlant cyberinfrastructure enabling plant scientists to perform complex analyses on large datasets without the need to master the command-line or high-performance computational services.

Simultaneous structure discovery and parameter estimation in gene networks using a multi-objective GP-PSO hybrid approach.

This paper presents a hybrid algorithm based on Genetic Programming (GP) and Particle Swarm Optimisation (PSO) for the automated recovery of gene network structure. It uses gene expression time series data as well as phenotypic data pertaining to plant flowering time as its input data. The algorithm then attempts to discover simple structures to approximate the plant gene regulatory networks that produce model gene expressions and flowering times that closely resemble the input data. To show the efficacy of the proposed approach, simulation results applied to flowering time control in Arabidopsis thaliana are demonstrated and discussed.

Effects of genetic perturbation on seasonal life history plasticity.

Like many species, the model plant Arabidopsis thaliana exhibits multiple different life histories in natural environments. We grew mutants impaired in different signaling pathways in field experiments across the species' native European range in order to dissect the mechanisms underlying this variation. Unexpectedly, mutational loss at loci implicated in the cold requirement for flowering had little effect on life history except in late-summer cohorts. A genetically informed photothermal model of progression toward flowering explained most of the observed variation and predicted an abrupt transition from autumn flowering to spring flowering in late-summer germinants. Environmental signals control the timing of this transition, creating a critical window of acute sensitivity to genetic and climatic change that may be common for seasonally regulated life history traits.

Resolving the genetic basis of invasiveness and predicting invasions.

Considerable effort has been invested in determining traits underlying invasiveness. Yet, identifying a set of traits that commonly confers invasiveness in a range of species has proven elusive, and almost nothing is known about genetic loci affecting invasive success. Incorporating genetic model organisms into ecologically relevant studies is one promising avenue to begin dissecting the genetic underpinnings of invasiveness. Molecular biologists are rapidly characterizing genes mediating developmental responses to diverse environmental cues, i.e., genes for plasticity, as well as to environmental factors likely to impose strong selection on invading species, e.g., resistance to herbivores and competitors, coordination of life-history events with seasonal changes, and physiological tolerance of heat, drought, or cold. Here, we give an overview of molecular genetic tools increasingly used to characterize the genetic basis of adaptation and that may be used to begin identifying genetic mechanisms of invasiveness. Given the divergent traits that affect invasiveness, "invasiveness genes" common to many clades are unlikely, but the combination of developmental genetic advances with further evolutionary studies and modeling may provide a framework for identifying genes that account for invasiveness in related species.