Construction economics of plant roots exhibit predictable relationships with root growth, death, and nutrient uptake strategies. Plant taxa with inexpensively constructed roots tend to more precisely explore nutrient hotspots than do those with costly constructed roots but at the price of more frequent tissue turnover. This trade-off underlies an acquisitive to conservative continuum in resource investment, described as the “root economics spectrum (RES).” Yet the adaptive role and genetic basis of RES remain largely unclear. Different ecotypes of switchgrass (Panicum virgatum) display root features exemplifying the RES, with costly constructed roots in southern lowland and inexpensively constructed roots in northern upland ecotypes. We used an outbred genetic mapping population derived from lowland and upland switchgrass ecotypes to examine the genetic architecture of the RES. We found that absorptive roots (distal first and second orders) were often “deciduous” in winter. The percentage of overwintering absorptive roots was decreased by northern upland alleles compared with southern lowland alleles, suggesting a locally-adapted conservative strategy in warmer and acquisitive strategy in colder regions. Relative turnover of absorptive roots was genetically negatively correlated with their biomass investment per unit root length, suggesting that the key trade-off in framing RES is genetically facilitated. We also detected strong genetic correlations among root morphology, root productivity, and shoot size. Overall, our results reveal the genetic architecture of multiple traits that likely impacts the evolution of RES and plant aboveground–belowground organization. In practice, we provide genetic evidence that increasing switchgrass yield for bioenergy does not directly conflict with enhancing its root-derived carbon sequestration.

Ionomics measures elemental concentrations in biological organisms and provides a snapshot of physiology under different conditions. In this study, we evaluate genetic variation of the ionome in outbred, perennial switchgrass in three environments across the species’ native range, and explore patterns of genotype-by-environment interactions. We grew 725 clonally replicated genotypes of a large full sib family from a four-way linkage mapping population, created from deeply diverged upland and lowland switchgrass ecotypes, at three common gardens. Concentrations of 18 mineral elements were determined in whole post-anthesis tillers using ion coupled plasma mass spectrometry (ICP-MS). These measurements were used to identify quantitative trait loci (QTL) with and without QTL-by-environment interactions (QTLxE) using a multi-environment QTL mapping approach. We found that element concentrations varied significantly both within and between switchgrass ecotypes, and GxE was present at both the trait and QTL level. Concentrations of 14 of the 18 elements were under some genetic control, and 77 QTL were detected for these elements. Seventy-four percent of QTL colocalized multiple elements, half of QTL exhibited significant QTLxE, and roughly equal numbers of QTL had significant differences in magnitude and sign of their effects across environments. The switchgrass ionome is under moderate genetic control and by loci with highly variable effects across environments.

In this chapter we compile data on intraspecific variation in plant reproductive, growth, and physiological responses to changes in atmospheric CO₂, temperature, and water availability. In total, we extracted data from 71 studies comprising a total of 79 species representing all major growth forms, functional groups, and biomes. Cumulatively, these studies examined responses to environmental change in 1154 genotypes. We used these data to examine: (1) the extent to which natural populations and genotypes within species vary in their response to increasing CO₂, warmer temperatures, and reduced water availability, and (2) whether intraspecific variation in these responses differs among growth forms, functional groups, biomes, and the phenotypic trait. In general, genotypes or populations of many species showed a wide range of responses to elevated CO₂, warming, and reduced water availability. However, probability values (p-values) for genotype-by-environment interaction terms (usually from analysis of variance) varied from <0.0001 to >0.90 depending upon the study design (and species), the environmental factor, and the scale of the trait. More studies reported significant intraspecific variation in plant responses to increasing temperature and decreasing water availability than intraspecific variation in plant responses to increasing CO₂. Thus, warmer and drier conditions may be more likely to result in evolutionary changes within species than increasing CO₂ alone. We also find that intraspecific variation in plant responses to environmental change is generally higher for reproductive and growth traits than for leaf-scale physiological traits. Even so, moderate intraspecific variation in physiological responses could result in substantial variation in growth and reproductive responses among genotypes. We conclude by discussing our understanding of genetic features that influence genotype-by-environment interactions. We go on to identify future research directions for advancing our understanding of the causes and consequences of intraspecific variation in plant responses to global change.

Genotype-environment interaction (GxE) has a great impact on wheat physiology, morphology and grain yield (GY). We evaluated an association mapping panel of spring wheat advanced lines for chlorophyll content, canopy temperature (CT), and yield-related traits under three different watering regimes in two consecutive growing seasons. Genome-wide association mapping identified 457 SNPs, with significant effects that varied with the watering regimes and growing seasons, of which 199 and 69 SNPs showed pleiotropic and conditionally neutral effects, respectively, on the measured traits. We mapped 61 SNPs with effects higher than 10% on all traits, showing antagonistic pleiotropic effects on CT, corresponding to 46 genes; some of these genes represent good candidates to control wheat response to water availability. Surprisingly, no significant SNPs were mapped in the semi-dwarfing genes, Rht-B1b or Rht-D1b. However, haplotype analysis of the SNPs located at the positions of both genes revealed significant interactions of GY with the watering regimes for Rht-B1b and with the growing season for Rht-D1b. We selected genotypes that outperformed two local check cultivars; some of them overlapped across the three watering regimes and could be used to create a multi-parent population to further unravel the genetic factors underlying yield component traits across drought stress. Our results demonstrate the importance of incorporating GxE in mapping models to better understand wheat response to different watering regimes and to select stable markers for selection.

We develop a method to artificially select for rhizosphere microbiomes that confer salt tolerance to the model grass Brachypodium distachyon grown under sodium salt stress or aluminum salt stress. In a controlled greenhouse environment, we differentially propagated rhizosphere microbiomes between plants of a nonevolving, highly inbred plant population; therefore, only microbiomes evolved in our experiment, but the plants did not evolve in parallel. To maximize microbiome perpetuation when transplanting microbiomes between plants and, thus, maximize response to microbiome selection, we improved earlier methods by (i) controlling microbiome assembly when inoculating seeds at the beginning of each selection cycle; (ii) fractionating microbiomes before transfer between plants to harvest, perpetuate, and select on only bacterial and viral microbiome components; (iii) ramping of salt stress gradually from minor to extreme salt stress with each selection cycle to minimize the chance of overstressing plants; (iv) using two nonselection control treatments (e.g., nonselection microbial enrichment and null inoculation) that permit comparison to the improving fitness benefits that selected microbiomes impart on plants. Unlike previous methods, our selection protocol generated microbiomes that enhance plant fitness after only 1 to 3 rounds of microbiome selection. After nine rounds of microbiome selection, the effect of microbiomes selected to confer tolerance to aluminum salt stress was nonspecific (these artificially selected microbiomes equally ameliorate sodium and aluminum salt stresses), but the effect of microbiomes selected to confer tolerance to sodium salt stress was specific (these artificially selected microbiomes do not confer tolerance to aluminum salt stress). Plants with artificially selected microbiomes had 55 to 205% greater seed production than plants with unselected control microbiomes.

Long-term climate change and periodic environmental extremes threaten food and fuel security¹ and global crop productivity^2,3,4. Although molecular and adaptive breeding strategies can buffer the effects of climatic stress and improve crop resilience⁵, these approaches require sufficient knowledge of the genes that underlie productivity and adaptation⁶—knowledge that has been limited to a small number of well-studied model systems. Here we present the assembly and annotation of the large and complex genome of the polyploid bioenergy crop switchgrass (Panicum virgatum). Analysis of biomass and survival among 732 resequenced genotypes, which were grown across 10 common gardens that span 1,800 km of latitude, jointly revealed extensive genomic evidence of climate adaptation. Climate–gene–biomass associations were abundant but varied considerably among deeply diverged gene pools. Furthermore, we found that gene flow accelerated climate adaptation during the postglacial colonization of northern habitats through introgression of alleles from a pre-adapted northern gene pool. The polyploid nature of switchgrass also enhanced adaptive potential through the fractionation of gene function, as there was an increased level of heritable genetic diversity on the nondominant subgenome. In addition to investigating patterns of climate adaptation, the genome resources and gene–trait associations developed here provide breeders with the necessary tools to increase switchgrass yield for the sustainable production of bioenergy.

Telomeres are highly repetitive DNA sequences found at the ends of chromosomes that protect the chromosomes from deterioration duringcell division. Here, using whole-genome re-sequencing and terminal restriction fragment assays, we found substantial natural intraspecific variation in telomere length in Arabidopsis thaliana, rice (Oryza sativa), and maize (Zea mays). Genome-wide association study (GWAS) mapping in A. thaliana identified 13 regions with GWAS-significant associations underlying telomere length variation, including a region that harbors the telomerase reverse transcriptase (TERT) gene. Population genomic analysis provided evidence for a selective sweep at the TERT region associated with longer telomeres. We found that telomere length is negatively correlated with flowering time variation not only in A. thaliana, but also in maize and rice, indicating a link between life-history traits and chromosome integrity. Our results point to several possible reasons for this correlation, including the possibility that longer telomeres may be more adaptive in plants that have faster developmental rates (and therefore flower earlier). Our work suggests that chromosomal structure itself might be an adaptive trait associated with plant life-history strategies.

In recent years, the root microbiome (i.e., microorganisms growing inside, on, or in close proximity to plant roots) has been shown to play an important role in plant health and productivity. Despite its importance, the root microbiome is challenging to study because of its complexity, heterogeneity, and subterranean location. Fortunately, root microbiome research has seen a tremendous influx of novel technologies (e.g., imaging tools, robotics, and molecular analyses), experimental platforms (e.g., micro- and mesocosms), and data integration, modeling, and prediction tools in the past decade that have greatly increased our ability to dissect the complex network of interactions between above- and belowground environmental parameters, plants, bacteria, and fungi that dictate soil and broader ecosystem health. Herein, we discuss methods that are currently used in root microbiome research and that can be expanded to phytobiome research in general ranging from laboratory studies to mesocosm-scale studies and, finally, to field studies; evaluate their relevance to ecosystem studies; and discuss future root microbiome research directions.

Geographic patterns of within-species genomic diversity are shaped by evolutionary processes, life history and historical and contemporary factors. New genomic approaches can be used to infer the influence of such factors on the current distribution of infraspecific lineages. In this study, we evaluated the genomic and morphological diversity as well as the genetic structure of the C₄ grass Panicum hallii across its complex natural distribution in North America. We sampled extensively across the natural range of P. hallii in Mexico and the USA to generate double-digestion restriction-associated DNA (ddRAD) sequence data for 423 individuals from 118 localities. We used these individuals to study the divergence between the two varieties of P. hallii, P. hallii var. filipes and P. hallii var. hallii as well as the genetic diversity and structure within these groups. We also examined the possibility of admixture in the geographically sympatric zone shared by both varieties, and assessed distribution shifts related with past climatic fluctuations. There is strong genetic and morphological divergence between the varieties and consistent genetic structure defining seven genetic clusters that follow major ecoregions across the range. South Texas constitutes a hotspot of genetic diversity with the co-occurrence of all genetic clusters and admixture between the two varieties. It is likely a recolonization and convergence point of populations that previously diverged in isolation during fragmentation events following glaciation periods.

• Nighttime stomatal conductance (g_sn) varies among plant functional types and species, but factors shaping the evolution of g_sn remain unclear. Examinations of intraspecific variation in g_sn as a function of climate and co‐varying leaf traits may provide new insight into the evolution of g_sn and its adaptive significance.
• We grew 11 genotypes of Panicum virgatum (switchgrass) representing differing home‐climates in a common garden experiment and measured nighttime and daytime leaf gas exchange, as well as stomatal density (SD) and size during early‐, mid‐, and late‐summer. We used piecewise structural equation modelling to determine direct and indirect relationships between home‐climate, gas exchange, and stomatal traits.
• We found no direct relationship between home‐climate and g_sn. However, genotypes from hotter climates possessed higher SD, which resulted in higher g_sn. Across genotypes, higher g_sn was associated with higher daytime stomatal conductance and net photosynthesis.
• Our results indicate that higher g_sn may arise in genotypes from hotter climates via increased SD. High SD may provide benefits to genotypes from hotter climates through enhanced daytime transpirational cooling or by permitting maximal gas exchange when conditions are suitable. These results highlight the role of climate and trait coordination in shaping genetic differentiation in g_sn.

The C4 perennial grass Panicum virgatum (switchgrass) is a native species of the North American tallgrass prairie. This adaptable plant can be grown on marginal lands and is useful for soil and water conservation, biomass production, and as a forage. Two major switchgrass ecotypes, lowland and upland, differ in a range of desirable traits, and the responsible underlying loci can be localized efficiently in a pseudotestcross design. An outbred four-way cross (4WCR) mapping population of 750 F 2 lines was used to examine the genetic basis of differences in leaf surface wax load between two lowland (AP13 and WBC) and two upland (DAC and VS16) tetraploid cultivars. The objective of our experiments was to identify wax compositional variation among the population founders and to map underlying loci responsible for surface wax variation across environments. GCMS analyses of surface wax extracted from …

Minirhizotron technology is widely used to study root growth and development. Yet, standard approaches for tracing roots in minirhiztron imagery is extremely tedious and time consuming. Machine learning approaches can help to automate this task. However, lack of enough annotated training data is a major limitation for the application of machine learning methods. Transfer learning is a useful technique to help with training when available datasets are limited. In this paper, we investigated the effect of pre-trained features from the massive-scale, irrelevant ImageNet dataset and a relatively moderate-scale, but relevant peanut root dataset on switchgrass root imagery segmentation applications. We compiled two minirhizotron image datasets to accomplish this study: one with 17,550 peanut root images and another with 28 switchgrass root images. Both datasets were paired with manually labeled ground truth masks. Deep neural networks based on the U-net architecture were used with different pre-trained features as initialization for automated, precise pixel-wise root segmentation in minirhizotron imagery. We observed that features pre-trained on a closely related but relatively moderate size dataset like our peanut dataset were more effective than features pre-trained on the large but unrelated ImageNet dataset. We achieved high quality segmentation on peanut root dataset with 99.04% accuracy at the pixel-level and overcame errors in human-labeled ground truth masks. By applying transfer learning technique on limited switchgrass dataset with features pre-trained on peanut dataset, we obtained 99% segmentation accuracy in switchgrass imagery using only 21 images for training (fine tuning). Furthermore, the peanut pre-trained features can help the model converge faster and have much more stable performance. We presented a demo of plant root segmentation for all models under https://github.com/GatorSense/PlantRootSeg.

In this paper, multiple instance learning (MIL) algorithms to automatically perform root detection and segmentation in minirhizotron imagery using only image-level labels are proposed. Root and soil characteristics vary from location to location, and thus, supervised machine learning approaches that are trained with local data provide the best ability to identify and segment roots in minirhizotron imagery. However, labeling roots for training data (or otherwise) is an extremely tedious and time-consuming task. This paper aims to address this problem by labeling data at the image level (rather than the individual root or root pixel level) and train algorithms to perform individual root pixel level segmentation using MIL strategies. Three MIL methods (multiple instance adaptive cosine coherence estimator, multiple instance support vector machine, multiple instance learning with randomized trees) were applied to root detection and compared to non-MIL approaches. The results show that MIL methods improve root segmentation in challenging minirhizotron imagery and reduce the labeling burden. In our results, multiple instance support vector machine outperformed other methods. The multiple instance adaptive cosine coherence estimator algorithm was a close second with an added advantage that it learned an interpretable root signature which identified the traits used to distinguish roots from soil and did not require parameter selection.

Summary

 

• Local adaptation is an important process in plant evolution, which can be impacted by differential pathogen pressures along environmental gradients. However, the degree to which pathogen resistance loci vary in effect across space and time is incompletely described.
• To understand how the genetic architecture of resistance varies across time and geographic space, we quantified rust (Puccinia spp.) severity in switchgrass (Panicum virgatum ) plantings at eight locations across the central USA for 3 yr and conducted quantitative trait locus (QTL) mapping for rust progression.
• We mapped several variable QTLs, but two large‐effect QTLs which we have named Prr1 and Prr2 were consistently associated with rust severity in multiple sites and years, particularly in northern sites. By contrast, there were numerous small‐effect QTLs at southern sites, indicating a genotype‐by‐environment interaction in rust resistance loci. Interestingly, Prr1 and Prr2 had a strong epistatic interaction, which also varied in the strength and direction of effect across space.
• Our results suggest that abiotic factors covarying with latitude interact with the genetic loci underlying plant resistance to control rust infection severity. Furthermore, our results indicate that segregating genetic variation in epistatically interacting loci may play a key role in determining response to infection across geographic space.

• Plant responses to major environmental drivers like precipitation can influence important aspects of carbon (C) cycling like soil CO₂ efflux (  ). These responses may be predicted by two independent classes of drivers: plant size—larger plants respire more and produce a larger quantity of labile C, and plant economics—plants possessing more acquisitive plant economics strategies (i.e. high metabolic rate and tissue nutrient content) produce higher‐quality tissue that respires rapidly and decomposes quickly.
• At two sites in central Texas, USA with similar climates and differing soil characteristics, we examined the response of eight Panicum virgatum genotypes to three annual precipitation levels defined by the driest, average and wettest years from each site's precipitation history. We evaluated the individual and joint influence of plant genotypes and precipitation on  and traits related to plant economics and plant size. We then used confirmatory path analysis to evaluate whether effects of precipitation on  were in part related to effects of precipitation on plant economics traits or size (‘mediated’ effects).
• These genotypes exhibited variation in plant economics traits and above‐ground net primary productivity (ANPP), an above‐ground measure of plant size. Increasing precipitation increased  and ANPP more than plant economics traits. At both sites, ANPP was the best predictor of  . Moreover, the sites differed in the ways that plant size and plant economics traits combined with precipitation to influence  . At the Austin site, the positive effect of precipitation on  was mediated primarily by ANPP, offset by a smaller effect of leaf nitrogen content; no direct precipitation effect was detected. At the Temple site, increasing precipitation had positive direct and ANPP‐mediated effects on  . This suggests that greater water limitation at Austin may strengthen the links between plant size and  .
• Synthesis. Estimates of C cycling can be improved by accounting for mediation of precipitation effects on  by plant economics traits and plant size in resource‐limited environments.

Multi-environment trials (METs) are widely used to assess the performance of promising crop germplasm. Though seldom designed to elucidate genetic mechanisms, MET datasets are often much larger than could be duplicated for genetic research and, given proper interpretation, may offer valuable insights into the genetics of adaptation across time and space. The Cooperative Dry Bean Nursery (CDBN) is a MET for common bean (Phaseolus vulgaris) grown for over 70 years in the United States and Canada, consisting of 20 to 50 entries each year at 10 to 20 locations. The CBDN provides a rich source of phenotypic data across entries, years, and locations that is amenable to genetic analysis. To study stable genetic effects segregating in this MET, we conducted genome-wide association (GWAS) using best linear unbiased predictions (BLUPs) derived across years and locations for 21 CDBN phenotypes and genotypic data (1.2M SNPs) for 327 CDBN genotypes. The value of this approach was confirmed by the discovery of three candidate genes and genomic regions previously identified in balanced GWAS. Multivariate adaptive shrinkage (mash) analysis, which increased our power to detect significant correlated effects, found significant effects for all phenotypes. Mash found two large genomic regions with effects on multiple phenotypes, supporting a hypothesis of pleiotropic or linked effects that were likely selected on in pursuit of a crop ideotype. Overall, our results demonstrate that statistical genomics approaches can be used on MET phenotypic data to discover significant genetic effects and to define genomic regions associated with crop improvement.

Intraspecific trait variation is caused by genetic and plastic responses to environment. This intraspecific diversity is captured in immense natural history collections, giving us a window into trait variation across continents and through centuries of environmental shifts. Here we tested if hypotheses based on life history and the leaf economics spectrum explain intraspecific trait changes across global spatiotemporal environmental gradients. We measured phenotypes on a 216‐year time series of Arabidopsis thaliana accessions from across its native range and applied spatially varying coefficient models to quantify region‐specific trends in trait coordination and trait responses to climate gradients. All traits exhibited significant change across space or through time. For example, δ¹⁵N decreased over time across much of the range and leaf C:N increased, consistent with predictions based on anthropogenic changes in land use and atmosphere. Plants were collected later in the growing season in more recent years in many regions, possibly because populations shifted toward more spring germination and summer flowering as opposed to fall germination and spring flowering. When climate variables were considered, collection dates were earlier in warmer years, while summer rainfall had opposing associations with collection date depending on regions. There was only a modest correlation among traits, indicating a lack of a single life history/physiology axis. Nevertheless, leaf C:N was low for summer‐ versus spring‐collected plants, consistent with a life history–physiology axis from slow‐growing winter annuals to fast‐growing spring/summer annuals. Regional heterogeneity in phenotype trends indicates complex responses to spatiotemporal environmental gradients potentially due to geographic genetic variation and climate interactions with other aspects of environment. Our study demonstrates how natural history collections can be used to broadly characterize trait responses to environment, revealing heterogeneity in response to anthropogenic change.

Host–parasite coevolution can maintain high levels of genetic diversity in traits involved in species interactions. In many systems, host traits exploited by parasites are constrained by use in other functions, leading to complex selective pressures across space and time. Here, we study genome-wide variation in the staple crop Sorghum bicolor (L.) Moench and its association with the parasitic weed Striga hermonthica (Delile) Benth., a major constraint to food security in Africa. We hypothesize that geographic selection mosaics across gradients of parasite occurrence maintain genetic diversity in sorghum landrace resistance. Suggesting a role in local adaptation to parasite pressure, multiple independent loss-of-function alleles at sorghum LOW GERMINATION STIMULANT 1 (LGS1) are broadly distributed among African landraces and geographically associated with S. hermonthica occurrence. However, low frequency of these alleles within S. hermonthica-prone regions and their absence elsewhere implicate potential trade-offs restricting their fixation. LGS1 is thought to cause resistance by changing stereochemistry of strigolactones, hormones that control plant architecture and below-ground signaling to mycorrhizae and are required to stimulate parasite germination. Consistent with trade-offs, we find signatures of balancing selection surrounding LGS1 and other candidates from analysis of genome-wide associations with parasite distribution. Experiments with CRISPR–Cas9-edited sorghum further indicate that the benefit of LGS1-mediated resistance strongly depends on parasite genotype and abiotic environment and comes at the cost of reduced photosystem gene expression. Our study demonstrates long-term maintenance of diversity in host resistance genes across smallholder agroecosystems, providing a valuable comparison to both industrial farming systems and natural communities.

Intraspecific trait variation is caused by genetic and plastic responses to environment. This intraspecific diversity is captured in immense natural history collections, giving us a window into trait variation across continents and through centuries of environmental shifts. Here we tested if hypotheses based on life history and the leaf economics spectrum explain intraspecific trait changes across global spatiotemporal environmental gradients. We measured phenotypes on a 216‐year time series of Arabidopsis thaliana accessions from across its native range and applied spatially varying coefficient models to quantify region‐specific trends in trait coordination and trait responses to climate gradients. All traits exhibited significant change across space or through time. For example, δ¹⁵N decreased over time across much of the range and leaf C:N increased, consistent with predictions based on anthropogenic changes in land use and atmosphere. Plants were collected later in the growing season in more recent years in many regions, possibly because populations shifted toward more spring germination and summer flowering as opposed to fall germination and spring flowering. When climate variables were considered, collection dates were earlier in warmer years, while summer rainfall had opposing associations with collection date depending on regions. There was only a modest correlation among traits, indicating a lack of a single life history/physiology axis. Nevertheless, leaf C:N was low for summer‐ versus spring‐collected plants, consistent with a life history–physiology axis from slow‐growing winter annuals to fast‐growing spring/summer annuals. Regional heterogeneity in phenotype trends indicates complex responses to spatiotemporal environmental gradients potentially due to geographic genetic variation and climate interactions with other aspects of environment. Our study demonstrates how natural history collections can be used to broadly characterize trait responses to environment, revealing heterogeneity in response to anthropogenic change.

Telomeres cap the physical ends of eukaryotic chromosomes to ensure complete DNA replication and genome stability. Heritable natural variation in telomere length exists in yeast, mice, plants and humans at birth; however, major effect loci underlying such polymorphism remain elusive. Here, we employ quantitative trait locus (QTL) mapping and transgenic manipulations to identify genes controlling telomere length set point in a multi-parent Arabidopsis thaliana mapping population. We detect several QTL explaining 63.7% of the total telomere length variation in the Arabidopsis MAGIC population. Loss-of-function mutants of the NOP2A candidate gene located inside the largest effect QTL and of two other ribosomal genes RPL5A and RPL5B establish a shorter telomere length set point than wild type. These findings indicate that evolutionarily conserved components of ribosome biogenesis and cell proliferation pathways promote telomere elongation.