A generalist-specialist trade-off between switchgrass cytotypes impacts climate adaptation and geographic range.

Polyploidy results from whole-genome duplication and is a unique form of heritable variation with pronounced evolutionary implications. Different ploidy levels, or cytotypes, can exist within a single species, and such systems provide an opportunity to assess how ploidy variation alters phenotypic novelty, adaptability, and fitness, which can, in turn, drive the development of unique ecological niches that promote the coexistence of multiple cytotypes. Switchgrass, Panicum virgatum, is a widespread, perennial C4 grass in North America with multiple naturally occurring cytotypes, primarily tetraploids (4×) and octoploids (8×). Using a combination of genomic, quantitative genetic, landscape, and niche modeling approaches, we detect divergent levels of genetic admixture, evidence of niche differentiation, and differential environmental sensitivity between switchgrass cytotypes. Taken together, these findings support a generalist (8×)­specialist (4×) trade-off. Our results indicate that the 8× represent a unique combination of genetic variation that has allowed the expansion of switchgrass' ecological niche and thus putatively represents a valuable breeding resource.

The genetic basis for panicle trait variation in switchgrass (Panicum virgatum).

KEY MESSAGE: We investigate the genetic basis of panicle architecture in switchgrass in two mapping populations across a latitudinal gradient, and find many stable, repeatable genetic effects and limited genetic interactions with the environment. Grass species exhibit large diversity in panicle architecture influenced by genes, the environment, and their interaction. The genetic study of panicle architecture in perennial grasses is limited. In this study, we evaluate the genetic basis of panicle architecture including panicle length, primary branching number, and secondary branching number in an outcrossed switchgrass QTL population grown across ten field sites in the central USA through multi-environment mixed QTL analysis. We also evaluate genetic effects in a diversity panel of switchgrass grown at three of the ten field sites using genome-wide association (GWAS) and multivariate adaptive shrinkage. Furthermore, we search for candidate genes underlying panicle traits in both of these independent mapping populations. Overall, 18 QTL were detected in the QTL mapping population for the three panicle traits, and 146 unlinked genomic regions in the diversity panel affected one or more panicle trait. Twelve of the QTL exhibited consistent effects (i.e., no QTL by environment interactions or no QTL × E), and most (four of six) of the effects with QTL × E exhibited site-specific effects. Most (59.3%) significant partially linked diversity panel SNPs had significant effects in all panicle traits and all field sites and showed pervasive pleiotropy and limited environment interactions. Panicle QTL co-localized with significant SNPs found using GWAS, providing additional power to distinguish between true and false associations in the diversity panel.

QTL × environment interactions underlie adaptive divergence in switchgrass across a large latitudinal gradient.

Local adaptation is the process by which natural selection drives adaptive phenotypic divergence across environmental gradients. Theory suggests that local adaptation results from genetic trade-offs at individual genetic loci, where adaptation to one set of environmental conditions results in a cost to fitness in alternative environments. However, the degree to which there are costs associated with local adaptation is poorly understood because most of these experiments rely on two-site reciprocal transplant experiments. Here, we quantify the benefits and costs of locally adaptive loci across 17° of latitude in a four-grandparent outbred mapping population in outcrossing switchgrass (Panicum virgatum L.), an emerging biofuel crop and dominant tallgrass species. We conducted quantitative trait locus (QTL) mapping across 10 sites, ranging from Texas to South Dakota. This analysis revealed that beneficial biomass (fitness) QTL generally incur minimal costs when transplanted to other field sites distributed over a large climatic gradient over the 2 y of our study. Therefore, locally advantageous alleles could potentially be combined across multiple loci through breeding to create high-yielding regionally adapted cultivars.

Geographic variation in the genetic basis of resistance to leaf rust between locally adapted ecotypes of the biofuel crop switchgrass (Panicum virgatum).

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.

Modifying the National Research Council weight gain model to estimate daily gain for stockers grazing bermudagrass in the southern United States.

The energy requirements, feed intake, and performance of grazing animals vary daily due to changes in weather conditions, forage nutritive values, and plant and animal maturity throughout the grazing season. Hence, realistic simulations of daily animal performance can be made only by the models that can address these changes. Given the dearth of simple, user-friendly models of this kind, especially for pastures, we developed a daily gain model for large-frame stockers grazing bermudagrass sCynodon dactylon (L.) Pers.], a widely used warm-season perennial grass in the southern United States. For model development, we first assembled some of the classic works in forage-beef modeling in the last 50 yr into the National Research Council (NRC) weight gain model. Then, we tested it using the average daily gain (ADG) data obtained from several locations in the southern United States. The evaluation results showed that the performance of the NRC model was poor as it consistently underpredicted ADG throughout the grazing season. To improve the predictive accuracy of the NRC model to make it perform under bermudagrass grazing conditions, we made an adjustment to the model by adding the daily departures of the modeled values from the data trendline. Subsequently, we tested the revised model against an independent set of ADG data obtained from eight research locations in the region involving about 4,800 animals, using 30 yr (1991-2020) of daily weather data. The values of the various measures of fit used, namely the Willmott index of 0.92, the modeling efficiency of 0.75, the R2 of 0.76, the root mean square error of 0.13 kg d-1, and the prediction error relative to the mean observed data of 24%, demonstrated that the revised model mimicked the pattern of observed ADG data satisfactorily. Unlike the original model, the revised model predicted more closely the ADG value throughout the grazing season. The revised model may be useful to accurately reflect the impacts of daily weather conditions, forage nutritive values, seasonality, and plant and animal maturity on animal performance.

Modification of the summative equation to estimate daily total digestible nutrients for bermudagrass pasture.

In forage-animal nutrition modeling, diet energy is estimated mainly from the forage total digestible nutrients (TDN). As digestibility trials are expensive, TDN is usually estimated using summative equations. Early summative equations assumed a fixed coefficient to compute digestible fiber using the lignin-to-neutral detergent fiber (NDF) ratio. Subsequently, a structural coefficient (φ) was added to the summative equations to reflect an association between lignin and cell wall components. Additional modifications to the summative equations assumed a constant φ value, and they have been used as a standard method by many commercial laboratories and scientists. For feeds with nutritive values that do not change much over time, a constant φ value may suffice. However, for forages with nutritive values that keep changing during the grazing season owing to changes in weather and plant maturity, a constant φ value may add a systematic bias to prediction because it is associated with the variable lignin-to-NDF ratio. In this study, we developed a model to estimate φ as a function of the day of the year by using the daily TDN values of bermudagrass [Cynodon dactylon (L.) Pers.], a popular warm-season perennial grass in the southern United States. The variable φ model was evaluated by using it in the TDN equation and comparing the estimated values with the observed ones obtained from several locations. Values of the various measures of fit used-the Willmott index (WI), the modeling efficiency (ME), R2, root mean square error (RMSE), and percent error (PE)-showed that using the variable φ vis-à-vis the constant φ improved the TDN equation significantly. The WI, ME, R2, RMSE, and PE values of 0.94, 0.80, 0.80, 2.5, and 4.7, respectively, indicated that the TDN equation with the variable φ model was able to mimic the observed values of TDN satisfactorily. Unlike the constant φ, the variable φ predicted more closely the forage nutritive value throughout the grazing season. The variable φ model may be useful to forage-beef modeling in accurately reflecting the impacts of plant maturity and weather on daily forage nutritive value and animal performance.

Relationship between the rumen microbiome and residual feed intake-efficiency of Brahman bulls stocked on bermudagrass pastures.

Residual feed intake (RFI) testing has increased selection pressure on biological efficiency in cattle. The objective of this study was to assess the association of the rumen microbiome in inefficient, positive RFI (p-RFI) and efficient, negative RFI (n-RFI) Brahman bulls grazing 'Coastal' bermudagrass [Cynodondactylon (L.) Pers.]under two levels of forage allowance (high and low stocking intensity). Sixteen Brahman bulls were previously fed in confinement for 70 d to determine the RFI phenotype. Bulls were then allotted 60 d stocking on bermudagrass pastures to estimate RFI using the n-alkane technique. At the conclusion of the grazing period, rumen liquid samples were collected from each bull by stomach tube to evaluate the rumen microbiome. Extraction of DNA, amplification of the V4-V6 region of the 16S rRNA gene, and 454 pyrosequencing were performed on each sample. After denoising the sequences, chimera checking, and quality trimming, 4,573 ± 1,287 sequences were generated per sample. Sequences were then assigned taxonomy from the Greengenes database using the RDP classifier. Overall, 67.5 and 22.9% of sequences were classified as Bacteroidetes and Firmicutes, respectively. Within the phylum Bacteroidetes, Prevotella was the most predominant genus and was observed in greater relative abundance in p-RFI bulls compared with n-RFI bulls (P = 0.01). In contrast, an unidentified Bacteroidales family was greater in relative abundance for n-RFI bulls than p-RFI (26.7 vs. 19.1%; P = 0.03). Ruminococcaceae was the third most abundant family in our samples, but it was not affected by RFI phenotype. No effect of stocking intensity was observed for bacterial taxa, but there was a tendency for alpha diversity and operational taxonomic unit richness to increase with lower stocking intensity. Results suggested the rumen microbiome of p-RFI Brahman bulls has greater levels of Prevotella, but the bacterial community composition was unaffected by stocking intensity.