Estimating scale-specific and localized spatial patterns in allele frequency.

Characterizing spatial patterns in allele frequencies is fundamental to evolutionary biology because these patterns contain evidence of underlying processes. However, the spatial scales at which gene flow, changing selection, and drift act are often unknown. Many of these processes can operate inconsistently across space, causing non-stationary patterns. We present a wavelet approach to characterize spatial pattern in allele frequency that helps solve these problems. We show how our approach can characterize spatial patterns in relatedness at multiple spatial scales, i.e. a multi-locus wavelet genetic dissimilarity. We also develop wavelet tests of spatial differentiation in allele frequency and quantitative trait loci (QTL). With simulation we illustrate these methods under different scenarios. We also apply our approach to natural populations of Arabidopsis thaliana to characterize population structure and identify locally-adapted loci across scales. We find, for example, that Arabidopsis flowering time QTL show significantly elevated genetic differentiation at 300 to 1300 km scales. Wavelet transforms of allele frequencies offer a flexible way to reveal geographic patterns and underlying evolutionary processes.

Predicting gene expression responses to environment in Arabidopsis thaliana using natural variation in DNA sequence.

The evolution of gene expression responses are a critical component of adaptation to variable environments. Predicting how DNA sequence influences expression is challenging because the genotype to phenotype map is not well resolved for cis regulatory elements, transcription factor binding, regulatory interactions, and epigenetic features, not to mention how these factors respond to environment. We tested if flexible machine learning models could learn some of the underlying cis-regulatory genotype to phenotype map. We tested this approach using cold-responsive transcriptome profiles in 5 diverse Arabidopsis thaliana accessions. We first tested for evidence that cis regulation plays a role in environmental response, finding 14 and 15 motifs that were significantly enriched within the up- and down-stream regions of cold-responsive differentially regulated genes (DEGs). We next applied convolutional neural networks (CNNs), which learn de novo cis-regulatory motifs in DNA sequences to predict expression response to environment. We found that CNNs predicted differential expression with moderate accuracy, with evidence that predictions were hindered by biological complexity of regulation and the large potential regulatory code. Overall, DEGs between specific environments can be predicted based on variation in cis-regulatory sequences, although more information needs to be incorporated and better models may be required.

Climate biogeography of Arabidopsis thaliana: linking distribution models and individual variation.

AIM: Patterns of individual variation are key to testing hypotheses about the mechanisms underlying biogeographic patterns. If species distributions are determined by environmental constraints, then populations near range margins may have reduced performance and be adapted to harsher environments. Model organisms are potentially important systems for biogeographical studies, given the available range-wide natural history collections, and the importance of providing biogeographical context to their genetic and phenotypic diversity. LOCATION: Global. TAXON: Arabidopsis thaliana ("Arabidopsis"). METHODS: We fit occurrence records to climate data, and then projected the distribution of Arabidopsis under last glacial maximum, current, and future climates. We confronted model predictions with individual performance measured on 2,194 herbarium specimens, and we asked whether predicted suitability was associated with life-history and genomic variation measured on ~900 natural accessions. RESULTS: The most important climate variables constraining the Arabidopsis distribution were winter cold in northern and high elevation regions and summer heat in southern regions. Herbarium specimens from regions with lower habitat suitability in both northern and southern regions were smaller, supporting the hypothesis that the distribution of Arabidopsis is constrained by climate-associated factors. Climate anomalies partly explained interannual variation in herbarium specimen size, but these did not closely correspond to local limiting factors identified in the distribution model. Late-flowering genotypes were absent from the lowest suitability regions, suggesting slower life histories are only viable closer to the center of the realized niche. We identified glacial refugia farther north than previously recognized, as well as refugia concordant with previous population genetic findings. Lower latitude populations, known to be genetically distinct, are most threatened by future climate change. The recently colonized range of Arabidopsis was well-predicted by our native-range model applied to certain regions but not others, suggesting it has colonized novel climates. MAIN CONCLUSIONS: Integration of distribution models with performance data from vast natural history collections is a route forward for testing biogeographical hypotheses about species distributions and their relationship with evolutionary fitness across large scales.

Chromosome-scale genome assembly of Poa trivialis and population genomics reveal widespread gene flow in a cool-season grass seed production system.

Poa trivialis (L.) is a cool-season grass species found in various environments worldwide. In addition to being a desired turfgrass species, it is a common weed of agricultural systems and natural areas. As a weed, it is an important contaminant of commercial cool-season grass seed lots, resulting in widespread gene flow facilitated by human activities and causing significant economic losses to farmers. To better understand and manage infestations, we assembled and annotated a haploid genome of P. trivialis and studied troublesome field populations from Oregon, the largest cool-season grass seed producing region in the United States. The genome assembly resulted in 1.35 Gb of DNA sequence distributed among seven chromosome-scale scaffolds, revealing a high content of transposable elements, conserved synteny with Poa annua, and a close relationship with other C3 grasses. A reduced-representation sequencing analysis of field populations revealed limited genetic diversity and suggested potential gene flow and human-assisted dispersal in the region. The genetic resources and insights into P. trivialis provided by this study will improve weed management strategies and enable the development of molecular detection tests for contaminated seed lots to limit seed-mediated gene flow. These resources should also be beneficial for turfgrass breeders seeking to improve desirable traits of commercial P. trivialis varieties and help to guide breeding efforts in other crops to enhance the resiliency of agricultural ecosystems under climate change. Significance Statement: The chromosome-scale assembly of Poa trivialis and population genomic analyses provide crucial insights into the gene flow of weedy populations in agricultural systems and contribute a valuable genomic resource for the plant science community.

Evidence that variation in root anatomy contributes to local adaptation in Mexican native maize.

Mexican native maize (Zea mays ssp. mays) is adapted to a wide range of climatic and edaphic conditions. Here, we focus specifically on the potential role of root anatomical variation in this adaptation. Given the investment required to characterize root anatomy, we present a machine-learning approach using environmental descriptors to project trait variation from a relatively small training panel onto a larger panel of genotyped and georeferenced Mexican maize accessions. The resulting models defined potential biologically relevant clines across a complex environment that we used subsequently for genotype-environment association. We found evidence of systematic variation in maize root anatomy across Mexico, notably a prevalence of trait combinations favoring a reduction in axial hydraulic conductance in varieties sourced from cooler, drier highland areas. We discuss our results in the context of previously described water-banking strategies and present candidate genes that are associated with both root anatomical and environmental variation. Our strategy is a refinement of standard environmental genome-wide association analysis that is applicable whenever a training set of georeferenced phenotypic data is available.

A haplotype-resolved chromosome-scale genome for Quercus rubra L. provides insights into the genetics of adaptive traits for red oak species.

Northern red oak (Quercus rubra L.) is an ecologically and economically important forest tree native to North America. We present a chromosome-scale genome of Q. rubra generated by the combination of PacBio sequences and chromatin conformation capture (Hi-C) scaffolding. This is the first reference genome from the red oak clade (section Lobatae). The Q. rubra assembly spans 739 Mb with 95.27% of the genome in 12 chromosomes and 33,333 protein-coding genes. Comparisons to the genomes of Quercus lobata and Quercus mongolica revealed high collinearity, with intrachromosomal structural variants present. Orthologous gene family analysis with other tree species revealed that gene families associated with defense response were expanding and contracting simultaneously across the Q. rubra genome. Quercus rubra had the most CC-NBS-LRR and TIR-NBS-LRR resistance genes out of the 9 species analyzed. Terpene synthase gene family comparisons further reveal tandem gene duplications in TPS-b subfamily, similar to Quercus robur. Phylogenetic analysis also identified 4 subfamilies of the IGT/LAZY gene family in Q. rubra important for plant structure. Single major QTL regions were identified for vegetative bud break and marcescence, which contain candidate genes for further research, including a putative ortholog of the circadian clock constituent cryptochrome (CRY2) and 8 tandemly duplicated genes for serine protease inhibitors, respectively. Genome-environment associations across natural populations identified candidate abiotic stress tolerance genes and predicted performance in a common garden. This high-quality red oak genome represents an essential resource to the oak genomic community, which will expedite comparative genomics and biological studies in Quercus species.

Vegetative phase change causes age-dependent changes in phenotypic plasticity.

Phenotypic plasticity allows organisms to optimize traits for their environment. As organisms age, they experience diverse environments that benefit from varying degrees of phenotypic plasticity. Developmental transitions can control these age-dependent changes in plasticity, and as such, the timing of these transitions can determine when plasticity changes in an organism. Here, we investigate how the transition from juvenile-to adult-vegetative development known as vegetative phase change (VPC) contributes to age-dependent changes in phenotypic plasticity and how the timing of this transition responds to environment using both natural accessions and mutant lines in the model plant Arabidopsis thaliana. We found that the adult phase of vegetative development has greater plasticity in leaf morphology than the juvenile phase and confirmed that this difference in plasticity is caused by VPC using mutant lines. Furthermore, we found that the timing of VPC, and therefore the time when increased plasticity is acquired, varies significantly across genotypes and environments. The consistent age-dependent changes in plasticity caused by VPC suggest that VPC may be adaptive. This genetic and environmental variation in the timing of VPC indicates the potential for population-level adaptive evolution of VPC.

Multiscale phenological niches of seed fall in diverse Amazonian plant communities.

Phenology has long been hypothesized as an avenue for niche partitioning or interspecific facilitation, both promoting species coexistence. Tropical plant communities exhibit striking diversity in reproductive phenology, but many are also noted for large synchronous reproductive events. Here we study whether the phenology of seed fall in such communities is nonrandom, the temporal scales of phenological patterns, and ecological factors that drive reproductive phenology. We applied multivariate wavelet analysis to test for phenological synchrony versus compensatory dynamics (i.e., antisynchronous patterns where one species' decline is compensated by the rise of another) among species and across temporal scales. We used data from long-term seed rain monitoring of hyperdiverse plant communities in the western Amazon. We found significant synchronous whole-community phenology at multiple timescales, consistent with shared environmental responses or positive interactions among species. We also observed both compensatory and synchronous phenology within groups of species (confamilials) likely to share traits and seed dispersal mechanisms. Wind-dispersed species exhibited significant synchrony at ~6-month scales, suggesting these species might share phenological niches to match the seasonality of wind. Our results suggest that community phenology is shaped by shared environmental responses but that the diversity of tropical plant phenology may partly result from temporal niche partitioning. The scale-specificity and time-localized nature of community phenology patterns highlights the importance of multiple and shifting drivers of phenology.

Genotype-environment associations to reveal the molecular basis of environmental adaptation.

A fundamental goal in plant biology is to identify and understand the variation underlying plants' adaptation to their environment. Climate change has given new urgency to this goal, as society aims to accelerate adaptation of ecologically important plant species, endangered plant species, and crops to hotter, less predictable climates. In the pre-genomic era, identifying adaptive alleles was painstaking work, leveraging genetics, molecular biology, physiology, and ecology. Now, the rise of genomics and new computational approaches may facilitate this research. Genotype-environment associations (GEAs) use statistical associations between allele frequency and environment of origin to test the hypothesis that allelic variation at a given gene is adapted to local environments. Researchers may scan the genome for GEAs to generate hypotheses on adaptive genetic variants (environmental genome-wide association studies). Despite the rapid adoption of these methods, many important questions remain about the interpretation of GEA findings, which arise from fundamental unanswered questions on the genetic architecture of adaptation and limitations inherent to association-based analyses. We outline strategies to ground GEAs in the underlying hypotheses of genetic architecture and better test GEA-generated hypotheses using genetics and ecophysiology. We provide recommendations for new users who seek to learn about the molecular basis of adaptation. When combined with a rigorous hypothesis testing framework, GEAs may facilitate our understanding of the molecular basis of climate adaptation for plant improvement.

Natural variation identifies new effectors of water-use efficiency in Arabidopsis.

Water-use efficiency (WUE) is the ratio of biomass produced per unit of water consumed; thus, it can be altered by genetic factors that affect either side of the ratio. In the present study, we exploited natural variation for WUE to discover loci affecting either biomass accumulation or water use as factors affecting WUE. Genome-wide association studies (GWAS) using integrated WUE measured through carbon isotope discrimination (δ13C) of Arabidopsis thaliana accessions identified genomic regions associated with WUE. Reverse genetic analysis of 70 candidate genes selected based on the GWAS results and transcriptome data identified 25 genes affecting WUE as measured by gravimetric and δ13C analyses. Mutants of four genes had higher WUE than wild type, while mutants of the other 21 genes had lower WUE. The differences in WUE were caused by either altered biomass or water consumption (or both). Stomatal density (SD) was not a primary cause of altered WUE in these mutants. Leaf surface temperatures indicated that transpiration differed for mutants of 16 genes, but generally biomass accumulation had a greater effect on WUE. The genes we identified are involved in diverse cellular processes, including hormone and calcium signaling, meristematic activity, photosynthesis, flowering time, leaf/vasculature development, and cell wall composition; however, none of them had been previously linked to WUE. Thus, our study successfully identified effectors of WUE that can be used to understand the genetic basis of WUE and improve crop productivity.

Host genomic influence on bacterial composition in the switchgrass rhizosphere.

Host genetic variation can shape the diversity and composition of associated microbiomes, which may reciprocally influence host traits and performance. While the genetic basis of phenotypic diversity of plant populations in nature has been studied, comparatively little research has investigated the genetics of host effects on their associated microbiomes. Switchgrass (Panicum virgatum) is a highly outcrossing, perennial, grass species with substantial locally adaptive diversity across its native North American range. Here, we compared 383 switchgrass accessions in a common garden to determine the host genotypic influence on rhizosphere bacterial composition. We hypothesized that the composition and diversity of rhizosphere bacterial assemblages would differentiate due to genotypic differences between hosts (potentially due to root phenotypes and associated life history variation). We observed higher alpha diversity of bacteria associated with upland ecotypes and tetraploids, compared to lowland ecotypes and octoploids, respectively. Alpha diversity correlated negatively with flowering time and plant height, indicating that bacterial composition varies along switchgrass life history axes. Narrow-sense heritability (h2 ) of the relative abundance of 21 core bacterial families was observed. Overall compositional differences among tetraploids, due to genetic variation, supports widespread genotypic influence on the rhizosphere microbiome. Tetraploids were only considered due to complexities associated with the octoploid genomes. Lastly, a genome-wide association study identified 1861 single-nucleotide polymorphisms associated with 110 families and genes containing them related to potential regulatory functions. Our findings suggest that switchgrass genomic and life-history variation influences bacterial composition in the rhizosphere, potentially due to host adaptation to local environments.

Diversity and extinction risk are inversely related at a global scale.

Increases in biodiversity often lead to greater, and less variable, levels of ecosystem functioning. However, whether species are less likely to go extinct in more diverse ecosystems is unclear. We use comprehensive estimates of avian taxonomic, phylogenetic and functional diversity to characterise the global relationship between multiple dimensions of diversity and extinction risk in birds, focusing on contemporary threat status and latent extinction risk. We find that more diverse assemblages have lower mean IUCN threat status despite being composed of species with attributes that make them more vulnerable to extinction, such as large body size or small range size. Indeed, the reduction in current threat status associated with greater diversity far outweighs the increased risk associated with the accumulation of extinction-prone species in more diverse assemblages. Our results suggest that high diversity reduces extinction risk, and that species conservation targets may therefore best be achieved by maintaining high levels of overall biodiversity in natural ecosystems.

What processes must we understand to forecast regional-scale population dynamics?

An urgent challenge facing biologists is predicting the regional-scale population dynamics of species facing environmental change. Biologists suggest that we must move beyond predictions based on phenomenological models and instead base predictions on underlying processes. For example, population biologists, evolutionary biologists, community ecologists and ecophysiologists all argue that the respective processes they study are essential. Must our models include processes from all of these fields? We argue that answering this critical question is ultimately an empirical exercise requiring a substantial amount of data that have not been integrated for any system to date. To motivate and facilitate the necessary data collection and integration, we first review the potential importance of each mechanism for skilful prediction. We then develop a conceptual framework based on reaction norms, and propose a hierarchical Bayesian statistical framework to integrate processes affecting reaction norms at different scales. The ambitious research programme we advocate is rapidly becoming feasible due to novel collaborations, datasets and analytical tools.

Effects of genomic and functional diversity on stand-level productivity and performance of non-native Arabidopsis.

Biodiversity can affect the properties of groups of organisms, such as ecosystem function and the persistence of colonizing populations. Genomic data offer a newly available window to diversity, complementary to other measures like taxonomic or phenotypic diversity. We tested whether native genetic diversity in field experimental stands of Arabidopsis thaliana affected their aboveground biomass and fecundity in their colonized range. We constructed some stands of genotypes that we a priori predicted would differ in performance or show overyielding. We found no relationship between genetic diversity and stand total biomass. However, increasing stand genetic diversity increased fecundity in high-resource conditions. Polyculture (multiple genotype) stands consistently yielded less biomass than expected based on the yields of component genotypes in monoculture. This under-yielding was strongest in stands with late-flowering and high biomass genotypes, potentially due to interference competition by these genotypes. Using a new implementation of association mapping, we identified genetic loci whose diversity was associated with stand-level yield, revealing a major flowering time locus associated with under-yielding of polycultures. Our field experiment supports community ecology studies that find a range of diversity-function relationships. Nevertheless, our results suggest diversity in colonizing propagule pools can enhance population fitness. Furthermore, interference competition among genotypes differing in flowering time might limit the advantages of polyculture.

Genomics of natural history collections for understanding evolution in the wild.

A long-standing question in biology is how organisms change through time and space in response to their environment. This knowledge is of particular relevance to predicting how organisms might respond to future environmental changes caused by human-induced global change. Usually researchers make inferences about past events based on an understanding of current static genetic patterns, but these are limited in their capacity to inform on underlying past processes. Natural history collections (NHCs) represent a unique and critical source of information to provide temporally deep and spatially broad time-series of samples. By using NHC samples, researchers can directly observe genetic changes over time and space and link those changes with specific ecological/evolutionary events. Until recently, such genetic studies were hindered by the intrinsic challenges of NHC samples (i.e. low yield of highly fragmented DNA). However, recent methodological and technological developments have revolutionized the possibilities in the novel field of NHC genomics. In this Special Feature, we compile a range of studies spanning from methodological aspects to particular case studies which demonstrate the enormous potential of NHC samples for accessing large genomic data sets from the past to advance our knowledge on how populations and species respond to global change at multiple spatial-temporal scales. We also highlight possible limitations, recommendations and a few opportunities for future researchers aiming to study NHC genomics.

Genomic history and ecology of the geographic spread of rice.

Rice (Oryza sativa) is one of the world's most important food crops, and is comprised largely of japonica and indica subspecies. Here, we reconstruct the history of rice dispersal in Asia using whole-genome sequences of more than 1,400 landraces, coupled with geographic, environmental, archaeobotanical and paleoclimate data. Originating around 9,000 yr ago in the Yangtze Valley, rice diversified into temperate and tropical japonica rice during a global cooling event about 4,200 yr ago. Soon after, tropical japonica rice reached Southeast Asia, where it rapidly diversified, starting about 2,500 yr BP. The history of indica rice dispersal appears more complicated, moving into China around 2,000 yr BP. We also identify extrinsic factors that influence genome diversity, with temperature being a leading abiotic factor. Reconstructing the dispersal history of rice and its climatic correlates may help identify genetic adaptations associated with the spread of a key domesticated species.

Competition × drought interactions change phenotypic plasticity and the direction of selection on Arabidopsis traits.

Populations often exhibit genetic diversity in traits involved in responses to abiotic stressors, but what maintains this diversity is unclear. Arabidopsis thaliana exhibits high within-population variation in drought response. One hypothesis is that competition, varying at small scales, promotes diversity in resource use strategies. However, little is known about natural variation in competition effects on Arabidopsis physiology. We imposed drought and competition treatments on diverse genotypes. We measured resource economics traits, physiology, and fitness to characterize plasticity and selection in response to treatments. Plastic responses to competition differed depending on moisture availability. We observed genotype-drought-competition interactions for relative fitness: competition had little effect on relative fitness under well-watered conditions, whereas competition caused rank changes in fitness under drought. Early flowering was always selected. Higher δ13 C was selected only in the harshest treatment (drought and competition). Competitive context significantly changed the direction of selection on aboveground biomass and inflorescence height in well-watered environments. Our results highlight how local biotic conditions modify abiotic selection, in some cases promoting diversity in abiotic stress response. The ability of populations to adapt to environmental change may thus depend on small-scale biotic heterogeneity.

In the presence of population structure: From genomics to candidate genes underlying local adaptation.

Understanding the genomic signatures, genes, and traits underlying local adaptation of organisms to heterogeneous environments is of central importance to the field evolutionary biology. To identify loci underlying local adaptation, models that combine allelic and environmental variation while controlling for the effects of population structure have emerged as the method of choice. Despite being evaluated in simulation studies, there has not been a thorough investigation of empirical evidence supporting local adaptation across these alleles. To evaluate these methods, we use 875 Arabidopsis thaliana Eurasian accessions and two mixed models (GEMMA and LFMM) to identify candidate SNPs underlying local adaptation to climate. Subsequently, to assess evidence of local adaptation and function among significant SNPs, we examine allele frequency differentiation and recent selection across Eurasian populations, in addition to their distribution along quantitative trait loci (QTL) explaining fitness variation between Italy and Sweden populations and cis-regulatory/nonsynonymous sites showing significant selective constraint. Our results indicate that significant LFMM/GEMMA SNPs show low allele frequency differentiation and linkage disequilibrium across locally adapted Italy and Sweden populations, in addition to a poor association with fitness QTL peaks (highest logarithm of odds score). Furthermore, when examining derived allele frequencies across the Eurasian range, we find that these SNPs are enriched in low-frequency variants that show very large climatic differentiation but low levels of linkage disequilibrium. These results suggest that their enrichment along putative functional sites most likely represents deleterious variation that is independent of local adaptation. Among all the genomic signatures examined, only SNPs showing high absolute allele frequency differentiation (AFD) and linkage disequilibrium (LD) between Italy and Sweden populations showed a strong association with fitness QTL peaks and were enriched along selectively constrained cis-regulatory/nonsynonymous sites. Using these SNPs, we find strong evidence linking flowering time, freezing tolerance, and the abscisic-acid pathway to local adaptation.

Genomics of sorghum local adaptation to a parasitic plant.

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.