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Symbiosis often occurs between partners with distinct life history characteristics and dispersal mechanisms. Many bacterial symbionts have genomes comprising multiple replicons with distinct rates of evolution and horizontal transmission. Such differences might drive differences in population structure between hosts and symbionts and among the elements of the divided genomes of bacterial symbionts. These differences might, in turn, shape the evolution of symbiotic interactions and bacterial evolution. Here we use whole genome resequencing of a hierarchically structured sample of 191 strains of Sinorhizobium meliloti collected from 21 locations in southern Europe to characterize population structures of this bacterial symbiont, which forms a root nodule symbiosis with the host plant Medicago truncatula. S. meliloti genomes showed high local (within-site) variation and little isolation by distance. This was particularly true for the two symbiosis elements, pSymA and pSymB, which have population structures that are similar to each other, but distinct from both the bacterial chromosome and the host plant. Given limited recombination on the chromosome, compared to the symbiosis elements, distinct population structures may result from differences in effective gene flow. Alternatively, positive or purifying selection, with little recombination, may explain distinct geographical patterns at the chromosome. Discordant population structure between hosts and symbionts indicates that geographically and genetically distinct host populations in different parts of the range might interact with genetically similar symbionts, potentially minimizing local specialization.

Spatial patterns of adaptation provide important insights into agents of selection and expected responses of populations to climate change. Robust inference into the spatial scale of adaptation can be gained through reciprocal transplant experiments that combine multiple source populations and common gardens. Here we examine the spatial scale of local adaptation of the North American annual plant common ragweed, Ambrosia artemisiifolia, using data from four common gardens with 22 source populations sampled from across a ∼1200 km latitudinal gradient within the native range. We found evidence of local adaptation at the northernmost common garden, but maladaptation at the two southern gardens, where more southern source populations outperformed local populations. Overall, the spatial scale of adaptation was large — at the three gardens where distance between source populations and gardens explained variation in fitness, it took an average of 820 km for fitness to decline to 50% of its predicted maximum. Taken together, these results suggest that climate change has already caused maladaptation, especially across the southern portion of the range, and may result in northward range contraction over time.

The mutualism between legumes and rhizobia is clearly the product of past coevolution. However, the nature of ongoing evolution between these partners is less clear. To characterize the nature of recent coevolution between legumes and rhizobia, we used population genomic analysis to characterize selection on functionally annotated symbiosis genes as well as on symbiosis gene candidates identified through a two-species association analysis. For the association analysis, we inoculated each of 202 accessions of the legume host Medicago truncatula with a community of 88 Sinorhizobia (Ensifer) meliloti strains. Multistrain inoculation, which better reflects the ecological reality of rhizobial selection in nature than single-strain inoculation, allows strains to compete for nodulation opportunities and host resources and for hosts to preferentially form nodules and provide resources to some strains. We found extensive host by symbiont, that is, genotype-by-genotype, effects on rhizobial fitness and some annotated rhizobial genes bear signatures of recent positive selection. However, neither genes responsible for this variation nor annotated host symbiosis genes are enriched for signatures of either positive or balancing selection. This result suggests that stabilizing selection dominates selection acting on symbiotic traits and that variation in these traits is under mutation-selection balance. Consistent with the lack of positive selection acting on host genes, we found that among-host variation in growth was similar whether plants were grown with rhizobia or N-fertilizer, suggesting that the symbiosis may not be a major driver of variation in plant growth in multistrain contexts.

Summary Background The WHO VISION 2020 global initiative against blindness, launched in 2000, prioritised childhood visual disability by aiming to end avoidable childhood blindness by 2020. However, progress has been hampered by the global paucity of epidemiological data concerning childhood visual disability. The British Childhood Visual Impairment and Blindness Study 2 (BCVIS2) was done to address this evidence gap. Methods BCVIS2 was a prospective UK-wide, cross-sectional, observational study to establish an inception cohort of children newly diagnosed with visual impairment. Ophthalmologists and paediatricians reported cases from 89 hospitals and community centres across the UK. We included children aged 18 years or younger who were newly diagnosed with any condition causing impaired visual acuity to a level of 0·5 logMAR or worse (worse than 6/18 Snellen) in each eye, or equivalent vision as assessed by standard qualitative measures, between Oct 1, 2015, and Nov 1, 2016. Eligible children were notified simultaneously but independently by their managing ophthalmologists and paediatricians via the two national active surveillance schemes, the British Ophthalmological Surveillance Unit and the British Paediatric Surveillance Unit. Standardised detailed demographic, socioeconomic, and clinical data about detection, management, and treatment were collected at diagnosis and 1 year later. We calculated incidence estimates and relative rates by key sociodemographic factors. We did descriptive analyses of underlying ophthalmic disorders and non-ophthalmic comorbidities. Findings 61 (7%) of 845 eligible children initially notified were ineligible at follow-up because of improved vision after treatment. Thus, the study sample comprised 784 children with permanent newly-diagnosed all-cause visual impairment, severe visual impairment, or blindness. 559 (72%) of 778 children had clinically significant non-ophthalmic impairments or conditions. 28 (4%) of 784 children died within a year after diagnosis of visual disability (all had underlying systemic disorders). Incidence of visual disability in the first year of life was 5·19 per 10 000 children (95% CI 4·71–5·72), almost ten times higher than among 1-to-4-year-olds and between 20 times and 100 times higher than in the older age groups. The overall cumulative incidence (or lifetime risk) of visual impairment, severe visual impairment, or blindness was 10·03 per 10 000 children (9·35–10·76). Incidence rates were higher for those from any ethnic minority group, the lowest quintile of socioeconomic status, and those born preterm or with low birthweight. 345 (44%) of 784 children had a single affected anatomical site. Disorders of the brain and visual pathways affected 378 (48%) of 784 children. Interpretation BCVIS2 provides a contemporary snapshot of the heterogeneity, multi-morbidity, and vulnerability associated with childhood visual disability in a high-income country. These findings could facilitate developing and delivering health care and planning of interventional research. Our findings highlight the importance of including childhood visual disability as a sentinel event and metric in global child health initiatives. Funding Fight for Sight, National Institute for Health Research, and Ulverscroft Foundation.

Symbiosis often occurs between partners with distinct life history characteristics and dispersal mechanisms. Bacterial symbionts often have genomes comprised of multiple replicons with distinct rates of evolution and horizontal transmission. Such differences might drive differences in population genetic structure between hosts and symbionts and among the elements of the divided genomes of bacterial symbionts. These differences might, in turn, shape the evolution of symbiotic interactions and bacterial evolution. Here we use whole genome resequencing of a hierarchically-structured sample of 191 strains of Ensifer meliloti collected from 21 locations in the native range to characterize population structure of this bacterial symbiont and its host plant Medicago truncatula. E. meliloti genomes showed high local (within-site) variation and little isolation by distance. This was particularly true for the two symbiosis elements pSymA and pSymB, which have population structures that are similar to each other, but distinct from both the bacterial chromosome and the host plant. The differences in population structure may result from differences in mobility or selection driving bacterial adaptation to life in the soil versus in association with plants. Discordant population structure between hosts and symbionts indicates that geographically and genetically distinct host populations in different parts of the range might interact with genetically similar symbionts, potentially minimizing the potential for local specialization.

Reduction of fitness due to deleterious mutations imposes a limit to adaptive evolution. By characterizing features that influence this genetic load we may better understand constraints on responses to both natural and human-mediated selection. Here, using whole-genome, transcriptome, and methylome data from >600 Arabidopsis thaliana individuals, we set out to identify important features influencing selective constraint. Our analyses reveal that multiple factors underlie the accumulation of maladaptive mutations, including gene expression level, gene network connectivity, and gene-body methylation. We then focus on a feature with major effect, nucleotide composition. The ancestral vs. derived status of segregating alleles suggests that GC-biased gene conversion, a recombination-associated process that increases the frequency of G and C nucleotides regardless of their fitness effects, shapes sequence patterns in A. thaliana. Through estimation of mutational effects, we present evidence that biased gene conversion hinders the purging of deleterious mutations and contributes to a genome-wide signal of decreased efficacy of selection. By comparing these results to two outcrossing relatives, Arabidopsis lyrata and Capsella grandiflora, we find that protein evolution in A. thaliana is as strongly affected by biased gene conversion as in the outcrossing species. Last, we perform simulations to show that natural levels of outcrossing in A. thaliana are sufficient to facilitate biased gene conversion despite increased homozygosity due to selfing. Together, our results show that even predominantly selfing taxa are susceptible to biased gene conversion, suggesting that it may constitute an important constraint to adaptation among plant species.

Understanding how spatially variable selection shapes adaptation is an area of long-standing interest in evolutionary ecology. Recent meta-analyses have quantified the extent of local adaptation, but the relative importance of abiotic and biotic factors in driving population divergence remains poorly understood. To address this gap, we combined a quantitative meta-analysis and a qualitative metasynthesis to (1) quantify the magnitude of local adaptation to abiotic and biotic factors and (2) characterize major themes that influence the motivation and design of experiments that seek to test for local adaptation. Using local-foreign contrasts as a metric of local adaptation (or maladaptation), we found that local adaptation was greater in the presence than in the absence of a biotic interactor, especially for plants. We also found that biotic environments had stronger effects on fitness than abiotic environments when ignoring whether those environments were local versus foreign. Finally, biotic effects were stronger at low latitudes, and abiotic effects were stronger at high latitudes. Our qualitative analysis revealed that the lens through which local adaptation has been examined differs for abiotic and biotic factors. It also revealed biases in the design and implementation of experiments that make quantitative results challenging to interpret and provided directions for future research.

As climate change threatens species' persistence, predicting the potential for species to adapt to rapidly changing environments is imperative for the development of effective conservation strategies. Eco-evolutionary individual-based models (IBMs) can be useful tools for achieving this objective. We performed a literature review to identify studies that apply these tools in marine systems. Our survey suggested that this is an emerging area of research fuelled in part by developments in modelling frameworks that allow simulation of increasingly complex ecological, genetic and demographic processes. The studies we identified illustrate the promise of this approach and advance our understanding of the capacity for adaptation to outpace climate change. These studies also identify limitations of current models and opportunities for further development. We discuss three main topics that emerged across studies: (i) effects of genetic architecture and non-genetic responses on adaptive potential; (ii) capacity for gene flow to facilitate rapid adaptation; and (iii) impacts of multiple stressors on persistence. Finally, we demonstrate the approach using simple simulations and provide a framework for users to explore eco-evolutionary IBMs as tools for understanding adaptation in changing seas.

Coevolutionary interactions, from the delicate co-dependency in mutualistic interactions to the antagonistic relationship of hosts and parasites, are a ubiquitous driver of adaptation. Surprisingly, little is known about the genomic processes underlying coevolution in an ecological context. However, species comprise genetically differentiated populations that interact with temporally variable abiotic and biotic environments. We discuss the recent advances in coevolutionary theory and genomics as well as shortcomings, to identify coevolving genes that take into account this spatial and temporal variability of coevolution, and propose a practical guide to understand the dynamic of coevolution using an ecological genomics lens.

Coevolution is predicted to depend on how the genetic diversity of interacting species is geographically structured. Plant-microbe symbioses such as the legume-rhizobium mutualism are ecologically and economically important, but distinct life history and dispersal mechanisms for these host and microbial partners, plus dynamic genome composition in bacteria, present challenges for understanding spatial genetic processes in these systems. Here we study the model rhizobium Ensifer meliloti using a hierarchically-structured sample of 191 strains from 21 sites in the native range and compare its population structure to that of its host plant Medicago truncatula. We find high local genomic variation and minimal isolation by distance across the rhizobium genome, particularly at the two symbiosis elements pSymA and pSymB, which have evolutionary histories and population structures that are similar to each other but distinct from both the chromosome and the host. While the chromosome displays weak isolation by distance, it is uncorrelated with hosts. Patterns of discordant population structure among elements with the bacterial genome has implications for bacterial adaptation to life in the soil versus symbiosis, while discordant population genetic structure of hosts and microbes might restrict local adaptation of species to each other and give rise to phenotypic mismatches in coevolutionary traits.

Genomic structural variants (SVs) can play important roles in adaptation and speciation. Yet the overall fitness effects of SVs are poorly understood, partly because accurate population-level identification of SVs requires multiple high-quality genome assemblies. Here, we use 31 chromosome-scale, haplotype-resolved genome assemblies of Theobroma cacao—an outcrossing, long-lived tree species that is the source of chocolate—to investigate the fitness consequences of SVs in natural populations. Among the 31 accessions, we find over 160,000 SVs, which together cover eight times more of the genome than single-nucleotide polymorphisms and short indels (125 versus 15 Mb). Our results indicate that a vast majority of these SVs are deleterious: they segregate at low frequencies and are depleted from functional regions of the genome. We show that SVs influence gene expression, which likely impairs gene function and contributes to the detrimental effects of SVs. We also provide empirical support for a theoretical prediction that SVs, particularly inversions, increase genetic load through the accumulation of deleterious nucleotide variants as a result of suppressed recombination. Despite the overall detrimental effects, we identify individual SVs bearing signatures of local adaptation, several of which are associated with genes differentially expressed between populations. Genes involved in pathogen resistance are strongly enriched among these candidates, highlighting the contribution of SVs to this important local adaptation trait. Beyond revealing empirical evidence for the evolutionary importance of SVs, these 31 de novo assemblies provide a valuable resource for genetic and breeding studies in T. cacao.

Coevolution is predicted to depend on how the genetic diversity of interacting species is geographically structured. Plant-microbe symbioses such as the legume-rhizobium mutualism are ecologically and economically important, but distinct life history and dispersal mechanisms for these host and microbial partners, plus dynamic genome composition in bacteria, present challenges for understanding spatial genetic processes in these systems. Here we study the model rhizobium Ensifer meliloti using a hierarchically-structured sample of 191 strains from 21 sites in the native range and compare its population structure to that of its host plant Medicago truncatula. We find high local genomic variation and minimal isolation by distance across the rhizobium genome, particularly at the two symbiosis elements pSymA and pSymB, which have evolutionary histories and population structures that are similar to each other but distinct from both the chromosome and the host. While the chromosome displays weak isolation by distance, it is uncorrelated with hosts. Patterns of discordant population structure among elements with the bacterial genome has implications for bacterial adaptation to life in the soil versus symbiosis, while discordant population genetic structure of hosts and microbes might restrict local adaptation of species to each other and give rise to phenotypic mismatches in coevolutionary traits.

Genetic studies of legume symbiosis with nitrogen-fixing rhizobial bacteria have traditionally focused on nodule and nitrogen-fixation phenotypes when hosts are inoculated with a single rhizobial strain. These approaches overlook the potential effect of host genes on rhizobial fitness (i.e. how many rhizobia are released from host nodules) and strain-specific effects of host genes (i.e. genome × genome interactions). Using Medicago truncatula mutants in the recently described nodule-specific PLAT domain (NPD) gene family, we show how inoculating plants with a mixed inoculum of 68 rhizobial strains (Ensifer meliloti) via a select-and-resequence approach can be used to efficiently assay host mutants for strain-specific effects of late-acting host genes on interacting bacteria. The deletion of a single NPD gene (npd2) or all five members of the NPD gene family (npd1-5) differentially altered the frequency of rhizobial strains in nodules even though npd2 mutants had no visible nodule morphology or N-fixation phenotype. Also, npd1-5 nodules were less diverse and had larger populations of colony-forming rhizobia despite their smaller size. Lastly, NPD mutations disrupt a positive correlation between strain fitness and wild-type host biomass. These changes indicate that the effects of NPD proteins are strain dependent and that NPD family members are not redundant with regard to their effects on rhizobial strains. Association analyses of the rhizobial strains in the mixed inoculation indicate that rhizobial genes involved in chromosome segregation, cell division, GABA metabolism, efflux systems, and stress tolerance play an important role in the strain-specific effects of NPD genes.

Horizontal transfer (HT) alters the repertoire of symbiosis genes in rhizobial genomes and may play an important role in the on-going evolution of the rhizobia–legume symbiosis. To gain insight into the extent of HT of symbiosis genes with different functional roles (nodulation, N-fixation, host benefit and rhizobial fitness), we conducted comparative genomic and selection analyses of the full-genome sequences from 27 rhizobial genomes. We find that symbiosis genes experience high rates of HT among rhizobial lineages but also bear signatures of purifying selection (low Ka : Ks). HT and purifying selection appear to be particularly strong in genes involved in initiating the symbiosis (e.g. nodulation) and in genome-wide association candidates for mediating benefits provided to the host. These patterns are consistent with rhizobia adapting to the host environment through the loss and gain of symbiosis genes, but not with host-imposed positive selection driving divergence of symbiosis genes through recurring bouts of positive selection.

The environmental context of the nitrogen-fixing mutualism between leguminous plants and rhizobial bacteria varies over space and time. Variation in resource availability, population density, and composition likely affect the ecology and evolution of rhizobia and their symbiotic interactions with hosts. We examined how host genotype, nitrogen addition, rhizobial density, and community complexity affected selection on 68 rhizobia strains in the Ensifer meliloti - Medicago truncatula mutualism. As expected, the host genotype had the most substantial effect on the size, number, and strain composition of root nodules (the symbiotic organ). The understudied environmental variable of rhizobial density had a more significant effect on strain frequency in nodules than the addition of low nitrogen levels. Higher inoculum density resulted in a nodule community that was less diverse and more beneficial but only in the context of the more selective host genotype. Higher density resulted in more diverse and less beneficial nodule communities with the less selective host. Density effects on strain composition deserve additional scrutiny as they can create eco-evolutionary feedback. Lastly, we found that relative strain rankings were stable across increasing community complexity (community complexity (2, 3, 8, or 68 strains). This unexpected result suggests that higher-order interactions between strains are rare in the context of host nodule formation and development. Taken together, our empirical work highlights the importance of developing new theoretical predictions that incorporate density dependence. Further, it has translational relevance for overcoming establishment barriers in bio-inoculants and motivating host breeding programs that maintain beneficial plant-microbe interactions across diverse agro-ecological contexts.IMPORTANCELegume cash, forage, and cover crops establish beneficial associations with rhizobial bacteria who perform biological nitrogen fixation (BNF)—providing Nitrogen (N) fertilizer to plants without the economic and greenhouse gas emission costs of chemical N inputs. Here, for the first time, we examine the relative influence of three environmental factors that vary in agricultural fields on strain relative fitness in nodules when scores rhizobial strains compete. In addition to manipulating Nitrogen, we also use two biotic variables that have rarely been examined: the rhizobial community’s density and complexity. Taken together, our results suggest 1) breeding legume varieties that select beneficial strains despite environmental variation are possible, 2) changes in rhizobial population densities that occur routinely in agricultural fields could drive evolutionary changes in rhizobia populations, and 3) the lack of higher-order interactions between strains will allow the high-throughput assessments of rhizobia winners and losers during plant interactions.

Many of the environmental factors that shape selection on plant life-history traits, including temperature, water availability, growing-season length, nutrient availability, and biotic community, differ between urban and rural environments, as well as within urban environments. Therefore, we might expect that plant life-history traits are of central importance to plant adaptation to urban environments. While the study of adaptive evolution of plant life-history traits in urban environments is in its early stages, those studies that have been conducted provide clear evidence for adaptive divergence between urban and rural plant populations in plant life-history traits related to phenology, fecundity, and dispersal. This chapter reviews the existing studies that provide a foundation for understanding the adaptation of plant life histories in urban environments, and also point to directions of potentially fruitful further research.

Human impacts on freshwater and marine ecosystems have long been of special concern due to water’s essential role in ecosystem functioning and human civilization. Urban development causes a large number of changes in all types of aquatic environments, from small ephemeral pools to rivers to great lakes to expansive coastal habitats. These changes can strongly influence evolution of life in the water by altering selection, gene flow, and genetic drift. Yet our understanding of the evolutionary consequences of urbanization on aquatic organisms is still in early stages. This chapter reviews the impacts of urbanization on aquatic taxa, examining the evolutionary consequences (known or likely) of four major types of urban-induced changes to ecosystems: biotic interactions, physical environment, temperature, and pollution. By drawing connections between literature on ecological and evolutionary impacts in aquatic urban environments, the chapter concludes that (1) several anthropogenic factors seem to commonly drive evolutionary and phenotypic change (organic-compound pollution, altered temperature, and hydrologic shifts), (2) predictability of evolutionary changes are often taxa specific, and may commonly depend on the focal ‘scale’ (e.g., whole-organism performance, morphology, or gene), and (3) there are a few key ‘frontier topics’ (altered biotic interactions, artificial light, sound pollution, and fragmentation) where additional research on phenotypic evolution would be particularly informative.

Lianas, or woody vines, are abundant throughout forests worldwide, but are especially common in the tropics. Their presence can strongly suppress tree wood production, and presumably also reduce the strength of the tropical forest carbon sink. In intact neotropical forests, liana presence has been increasing over the past few decades, though the mechanisms remain under debate. Vexingly, lianas are not represented at all in current-day climate models. Better knowledge of liana morphology and allocation is required to unravel the mechanisms of below- and aboveground liana-tree competition in tropical forests. Such knowledge is also an essential step toward incorporating lianas into mechanistic forest dynamics models. To address these liana knowledge gaps, we have initiated a new project that integrates empirical and modeling work. Our objectives in this presentation are to compare observed liana allocation patterns to allocation patterns predicted by theory, and then to demonstrate how these results can be integrated into a numerical model.Empirical measurements are being carried out in tropical dry forests in Guanacaste, Costa Rica. These measurements will eventually include excavations of ~80 entire trees and lianas, which will enable measurements of belowground and aboveground biomass of co-occurring trees and lianas, coarse and fine root vertical distribution, and lateral root spread. Also being measured are liana traits (including several critical hydraulic traits), above- and belowground productivity, and species-level fine root productivity. The modeling work includes the incorporation of lianas into the TROLL model, which is a mechanistic, individual-based forest dynamics model. The model will simulate the unique features of lianas, accounting for their structural parasitism and their different allocation strategies and morphology compared to trees. The simulated trees and lianas will compete aboveground for light and belowground for water. Thus, the model will integrate above- and belowground processes and couple the carbon and water cycles. Traits measured as part of this project are being used to parameterize the model.Thus far, 33 mature, canopy-exposed individuals (18 trees and 15 lianas) have been harvested and analyzed. For both trees and lianas, biomass partitioning to roots, stems, and leaves were consistent with the predictions of allometric biomass partitioning theory. This result thwarted our initial expectation that lianas, with their narrow-diameter stems, would allocate proportionally less to stems than trees. We also found that vertical root profiles varied across life forms: lianas had the shallowest roots, evergreen trees had the deepest roots, and deciduous trees had intermediate rooting depths. The liana root systems also had notably broader lateral extents than the tree root systems. These results run contrary to previous work that reported that lianas were relatively deeply-rooted.Our empirical results have helped to motivate model development. Each of our modeled liana individuals is assigned a laterally-widespread root system that can potentially extend beneath many trees. The liana root system is then permitted to put up aboveground shoots that associate with trees within the footprint of the root system. Comparisons of simulated and observed above- and belowground productivity are currently being conducted to help evaluate model assumptions.

Pleiotropy, the control of multiple phenotypes by a single locus, is expected to slow the rate of adaptation by increasing the chance that beneficial alleles also have deleterious effects. However, a prediction arising from classical theory of quantitative trait evolution states that pleiotropic alleles may have a selective advantage when phenotypes are distant from their selective optima. We examine the role of pleiotropy in regulating adaptive differentiation among populations of common ragweed (Ambrosia artemisiifolia); a species that has recently expanded its North American range due to human-mediated habitat change. We employ a phenotype-free approach by using connectivity in gene networks as a proxy for pleiotropy. First, we identify loci bearing footprints of local adaptation, and then use genotype-expression mapping and co-expression networks to infer the connectivity of the genes. Our results indicate that the putatively adaptive loci are highly pleiotropic, as they are more likely than expected to affect the expression of other genes, and they reside in central positions within the gene networks. We propose that the conditionally advantageous alleles at these loci avoid the cost of pleiotropy by having large phenotypic effects that are beneficial when populations are far from their selective optima. We further use evolutionary simulations to show that these patterns are in agreement with a model where populations face novel selective pressures, as expected during a range expansion. Overall, our results suggest that highly connected genes may be targets of positive selection during environmental change, even though they likely experience strong purifying selection in stable selective environments., Author summary Theoretical studies examining the genetic basis of adaptation often predict that loci controlling multiple traits are under strong negative selection, because they have an increased chance of deleterious effects. However, such loci also tend to have large effects on phenotypes, which might be beneficial when populations are adapting to new environments. We test this hypothesis by using a widely-distributed annual plant, common ragweed (Ambrosia artemisiifolia), as our study species. Climate change after the last ice-age and the spread of agriculture has led ragweed to expand its North American range, exposing populations to novel environment stressors. We use genetic variants and gene expression data to infer how likely are loci involved in climate adaptation to control multiple traits. Our results show that loci bearing signatures of local adaptation are situated in central positions within gene networks, from where they affect the expression of many other genes. This high connectivity likely means that these adaptive loci also affect multiple phenotypes. We therefore present an empirical case where adaptation to new environments has resulted in loci controlling multiple phenotypes to be subject to positive selection, even though the same loci would likely be under negative selection in stable environments.

Because sexual dimorphism in plants is often less morphologically conspicuous than in animals, studies of sex-biased gene expression may provide a quantitative metric to better address their commonality, molecular pathways, consistency across tissues and taxa, and evolution. The presence of sex-biased gene expression in tissues other than the androecium or gynoecium, termed secondary sexual characters, suggests that these traits arose after the initial evolution of dioecy. Patterns of sequence evolution may provide evidence of positive selection that drove sexual specialization. We compared gene expression in male and female flowers and leaves of Populus balsamifera to assess the extent of sex-biased expression, and tested whether sex-biased genes exhibit elevated rates of protein evolution. Sex-biased expression was pervasive in floral tissue, but nearly absent in leaf tissue. Female-biased genes in flowers were associated with photosynthesis, whereas male-biased genes were associated with mitochondrial function. Sex-biased genes did not exhibit elevated rates of protein evolution, contrary to results from other studies in animals and plants. Our results suggest that the ecological and physiological constraints associated with the energetics of flowering, rather than sexual conflict, have probably shaped the differences in male and female gene expression in P. balsamifera.

The tropical tree Theobroma cacao is the source of chocolate, and its seeds are a major export from many producing countries in Central and South America, Africa, and Asia. Every year, 30–40% of pre-harvest yield is lost due to disease damage. Host plant resistance is the most efficient and environmentally friendly approach for disease management. Historically, cacao germplasm resources have been underutilized in efforts to introduce novel sources of disease tolerance into breeding programs. Maintenance of cacao germplasm also relies on clonally propagated live collections, as cacao seeds do not exhibit dormancy and cannot be stored for more than a few weeks. In this study, we use a 90 SNP array to verify genetic identity of a set of clones in the International Cocoa Collection at CATIE, Costa Rica, and assign the clones into known genetic groups. We also used a detached leaf inoculation technique to measure the susceptibility of 60 genotypes to Phytophthora palmivora, a major cacao pathogen with global importance. We identified 24 genotypes with disease tolerance statistically similar to a standard tolerant variety (SCA6) and another 24 which performed similarly to a standard susceptible variety (ICS1). Our results indicate that each of the four included genetic groups show variability for quantitative resistance to P. palmivora. These results provide a foundation for future genomic and transcriptomic analysis of disease tolerance and susceptibility in the field at CATIE and provide guidelines for breeders searching for novel sources of tolerance that can be introduced into breeding programs.

Mutualistic interactions can be stabilized against invasion by noncooperative individuals by putting such "cheaters" at a selective disadvantage. Selection against cheaters should eliminate genetic variation in partner quality-yet such variation is often found in natural populations. One explanation for this paradox is that mutualism outcomes are determined not only by responses to partner performance but also by partner signals. Here, we build a model of coevolution in a symbiotic mutualism, in which hosts' ability to sanction noncooperative symbionts and recognition of symbiont signals are determined by separate loci, as are symbionts' cooperation and expression of signals. In the model, variation persists without destabilizing the interaction, in part because coevolution of symbiont signals and host recognition is altered by the coevolution of sanctions and cooperation, and vice versa. Individual-based simulations incorporating population structure strongly corroborate these results. The dual systems of sanctions and partner recognition converge toward conditions similar to some economic models of mutualistic symbiosis, in which hosts offering the right incentives to potential symbionts can initiate symbiosis without screening for partner quality. These results predict that mutualists can maintain variation in recognition of partner signals or in the ability to sanction noncooperators without destabilizing mutualism, and they reinforce the notion that studies of mutualism should consider communication between partners as well as the exchange of benefits.

Background Rapid generation of omics data in recent years have resulted in vast amounts of disconnected datasets without systemic integration and knowledge building, while individual groups have made customized, annotated datasets available on the web with few ways to link them to in-lab datasets. With so many research groups generating their own data, the ability to relate it to the larger genomic and comparative genomic context is becoming increasingly crucial to make full use of the data. Results The Omics Database Generator (ODG) allows users to create customized databases that utilize published genomics data integrated with experimental data which can be queried using a flexible graph database. When provided with omics and experimental data, ODG will create a comparative, multi-dimensional graph database. ODG can import definitions and annotations from other sources such as InterProScan, the Gene Ontology, ENZYME, UniPathway, and others. This annotation data can be especially useful for studying new or understudied species for which transcripts have only been predicted, and rapidly give additional layers of annotation to predicted genes. In better studied species, ODG can perform syntenic annotation translations or rapidly identify characteristics of a set of genes or nucleotide locations, such as hits from an association study. ODG provides a web-based user-interface for configuring the data import and for querying the database. Queries can also be run from the command-line and the database can be queried directly through programming language hooks available for most languages. ODG supports most common genomic formats as well as generic, easy to use tab-separated value format for user-provided annotations. Conclusions ODG is a user-friendly database generation and query tool that adapts to the supplied data to produce a comparative genomic database or multi-layered annotation database. ODG provides rapid comparative genomic annotation and is therefore particularly useful for non-model or understudied species. For species for which more data are available, ODG can be used to conduct complex multi-omics, pattern-matching queries. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1777-7) contains supplementary material, which is available to authorized users.

Genomic "scans" to identify loci that contribute to local adaptation are becoming increasingly common. Many methods used for such studies have assumed that local adaptation is created by loci experiencing antagonistic pleiotropy (AP) and that the selected locus itself is assayed, and few consider how signals of selection change through time. However, most empirical data sets have marker density too low to assume that a selected locus itself is assayed, researchers seldom know when selection was first imposed, and many locally adapted loci likely experience not AP but conditional neutrality (CN). We simulated data to evaluate how these factors affect the performance of tests for genotype-environment association (GEA). We found that 3 types of regression-based analyses (linear models, mixed linear models, and latent factor mixed models) and an implementation of BayEnv all performed well, with high rates of true positives and low rates of false positives, when the selected locus experienced AP, and when the selected locus was assayed directly. However, all tests had reduced power to detect loci experiencing CN, and the probability of detecting associations was sharply reduced when physically linked rather than causative loci were sampled. AP also maintained detectable GEAs much longer than CN. Our analyses suggest that if local adaptation is often driven by loci experiencing CN, genome-scan methods will have limited capacity to find loci responsible for local adaptation.

Genome-wide association studies (GWAS) have developed into a valuable approach for identifying the genetic basis of phenotypic variation. In this article, we provide an overview of the design, analysis, and interpretation of GWAS. First, we present results from simulations that explore key elements of experimental design as well as considerations for collecting the relevant genomic and phenotypic data. Next, we outline current statistical methods and tools used for GWA analyses and discuss the inclusion of covariates to account for population structure and the interpretation of results. Given that many false positive associations will occur in any GWA analysis, we highlight strategies for prioritizing GWA candidates for further statistical and empirical validation. While focused on plants, the material we cover is also applicable to other systems. © 2017 by John Wiley & Sons, Inc.

Background Previous studies exploring sequence variation in the model legume, Medicago truncatula, relied on mapping short reads to a single reference. However, read-mapping approaches are inadequate to examine large, diverse gene families or to probe variation in repeat-rich or highly divergent genome regions. De novo sequencing and assembly of M. truncatula genomes enables near-comprehensive discovery of structural variants (SVs), analysis of rapidly evolving gene families, and ultimately, construction of a pan-genome. Results Genome-wide synteny based on 15 de novo M. truncatula assemblies effectively detected different types of SVs indicating that as much as 22% of the genome is involved in large structural changes, altogether affecting 28% of gene models. A total of 63 million base pairs (Mbp) of novel sequence was discovered, expanding the reference genome space for Medicago by 16%. Pan-genome analysis revealed that 42% (180 Mbp) of genomic sequences is missing in one or more accession, while examination of de novo annotated genes identified 67% (50,700) of all ortholog groups as dispensable – estimates comparable to recent studies in rice, maize and soybean. Rapidly evolving gene families typically associated with biotic interactions and stress response were found to be enriched in the accession-specific gene pool. The nucleotide-binding site leucine-rich repeat (NBS-LRR) family, in particular, harbors the highest level of nucleotide diversity, large effect single nucleotide change, protein diversity, and presence/absence variation. However, the leucine-rich repeat (LRR) and heat shock gene families are disproportionately affected by large effect single nucleotide changes and even higher levels of copy number variation. Conclusions Analysis of multiple M. truncatula genomes illustrates the value of de novo assemblies to discover and describe structural variation, something that is often under-estimated when using read-mapping approaches. Comparisons among the de novo assemblies also indicate that different large gene families differ in the architecture of their structural variation. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-3654-1) contains supplementary material, which is available to authorized users.

Genome-wide association studies (GWAS) have developed into a valuable approach for identifying the genetic basis of phenotypic variation. In this article, we provide an overview of the design, analysis, and interpretation of GWAS. First, we present results from simulations that explore key elements of experimental design as well as considerations for collecting the relevant genomic and phenotypic data. Next, we outline current statistical methods and tools used for GWA analyses and discuss the inclusion of covariates to account for population structure and the interpretation of results. Given that many false positive associations will occur in any GWA analysis, we highlight strategies for prioritizing GWA candidates for further statistical and empirical validation. While focused on plants, the material we cover is also applicable to other systems. © 2017 by John WileySons, Inc.

Wild maize, commonly known as teosinte, has a wide distribution in central Mexico and inhabits a wide range of environmental conditions. According to previous studies, the environment is a determinant factor for the amount and distribution of genetic diversity. In this study, we used a set of neutral markers to explore the influence of contemporary factors and historical environmental shifts on genetic diversity, including present and three historical periods. Using a set of 22 nuclear microsatellite loci, we genotyped 527 individuals from 29 localities. We found highly variable levels of genetic diversity (Z. m. parviglumis HE= 0.3646–0.7699;Z. m. mexicana HE= 0.5885–0.7671) and significant genetic structure among localities (averageDEST= 0.4332). Also, we recovered significant values of heterozygote deficiency (averageFIS= 0.1796) and variable levels of selfing (sg2=0.0–0.3090). The Bayesian assignment analysis yielded four genetic clusters dividing the sample into subspecies, that in turn, were separated into two clusters. Environmental conditions played a strong influence in the distribution of genetic diversity, as demographic analysis and changes in species range revealed by modeling analyses were consistent. We conclude that current genetic diversity in teosinte is the result of a mixture of local adaptation and genetic isolation along with historical environmental fluctuations.

Separating footprints of adaptation from demography is challenging. When selection has acted on a single locus with major effect, this issue can be alleviated through signatures left by selective sweeps. However, as adaptation is often driven by small allele frequency shifts at many loci, studies focusing on single genes are able to identify only a small portion of genomic variants responsible for adaptation. In face of this challenge, we utilize coexpression information to search for signals of polygenetic adaptation in Theobroma cacao, a tropical tree species that is the source of chocolate. Using transcriptomics and a weighted correlation network analysis, we group genes with similar expression patterns into functional modules. We then ask whether modules enriched for specific biological processes exhibit cumulative effects of differential selection in the form of high FST and dXY between populations. Indeed, modules putatively involved in protein modification, flowering, and water transport show signs of polygenic adaptation even though individual genes that are members of those groups do not bear strong signatures of selection. Modeling of demography, background selection, and the effects of genomic features reveal that these patterns are unlikely to arise by chance. We also find that specific modules are enriched for signals of strong or relaxed purifying selection, with one module bearing signs of adaptive differentiation and an excess of deleterious mutations. Our results provide insight into polygenic adaptation and contribute to understanding of population structure, demographic history, and genome evolution in T. cacao.

Genetic studies of legume symbiosis with nitrogen-fixing rhizobial bacteria have traditionally focused on nodule and nitrogen-fixation phenotypes when hosts are inoculated with a single rhizobial strain. These approaches overlook the potential effect of host genes on rhizobial fitness (i.e. how many rhizobia are released from host nodules) and strain-specific effects of host genes (i.e. genome × genome interactions). Using Medicago truncatula mutants in the recently described nodule-specific PLAT domain (NPD) gene family, we show how inoculating plants with a mixed inoculum of 68 rhizobial strains (Ensifer meliloti) via a select-and-resequence approach can be used to efficiently assay host mutants for strain-specific effects of late-acting host genes on interacting bacteria. The deletion of a single NPD gene (npd2) or all five members of the NPD gene family (npd1–5) differentially altered the frequency of rhizobial strains in nodules even though npd2 mutants had no visible nodule morphology or N-fixation phenotype. Also, npd1–5 nodules were less diverse and had larger populations of colony-forming rhizobia despite their smaller size. Lastly, NPD mutations disrupt a positive correlation between strain fitness and wild-type host biomass. These changes indicate that the effects of NPD proteins are strain dependent and that NPD family members are not redundant with regard to their effects on rhizobial strains. Association analyses of the rhizobial strains in the mixed inoculation indicate that rhizobial genes involved in chromosome segregation, cell division, GABA metabolism, efflux systems, and stress tolerance play an important role in the strain-specific effects of NPD genes.

Climate change is one of the most important threats to biodiversity and crop sustainability. The impact of climate change is often evaluated on the basis of expected changes in species’ geographical distributions. Genomic diversity, local adaptation, and migration are seldom integrated into projections of species’ responses to climate change. Here we predict that climate change will impact populations of two wild relatives of maize, the teosintes Zea mays ssp. mexicana and Z. mays ssp. parviglumis, by altering patterns of local adaptation and decreasing migration probabilities. These alterations appear to be geographically heterogeneous across populations, suggesting that the possible impacts of climate change will vary considerably among populations. This in spite that most populations exhibit high levels of genetic diversity and are predicted to lie within future suitable areas. The heterogeneous patterns of local adaptation uncovered in teosintes are also evident across maize landraces, which suggests that climate change may give way to future maladaptation of several landraces within currently cultivated areas, possibly leading to increased chances of production shocks under increased temperatures. The predicted alterations to habitat distribution, migration potential, and patterns of local adaptation in wild and cultivated maize, raises a red flag for the future of populations. This underscores the need for continued integration of agronomical practices, genomic data, and climate models to better understand the impacts of a rapidly changing climate on cultivated and wild species.

Palmer amaranth (Amaranthus palmeri) is an annual plant native to the desert Southwest of the United States and Mexico and has become invasive and caused large economic losses across much of the United States. In order to examine the temporal and spatial dynamics of past invasion, and to predict future invasion, we developed a broad array of species distribution models (SDMs). In particular, we constructed sequential SDMs throughout the invasion history and asked how well those predicted future invasion (1970 to present). We showed that invasion occurred from a restricted set of environments in the native range to a diverse set in the invaded range. Spatial autocorrelation analyses indicated that rapid range expansion was facilitated by stochastic, long-distance dispersal events. Regardless of SDM approach, all SDMs built using datasets from early in the invasion (1970–2010) performed poorly and failed to predict most of the current invaded range. Together, these results suggest that climate is unlikely to have influenced early stages of range expansion. SDMs that incorporated data from the most recent sampling (2011–2017) performed considerably better, predicted high suitability in regions that have recently become invaded, and identified mean annual temperature as a key factor limiting northward range expansion. Under future climates, models predicted both further northward range expansion and significantly increased suitability across large portions of the U.S. Overall, our results indicate significant challenges for SDMs of invasive species far from climate equilibrium. However, our models based on recent data make more robust predictions for northward range expansion of A. palmeri with climate change.

Climate change is affecting both the volume and distribution of precipitation, which in turn is expected to affect the growth and reproduction of plant populations. The near ubiquity of local adaptation suggests that adaptive differentiation may have important consequences for how populations are affected by and respond to changing precipitation. Here, we manipulated rainfall in a common garden to examine how differentiation among populations of common ragweed, Ambrosia artemisiifolia (Asteraceae) affects responses to water availability expected under climate change. We collected seeds from 26 populations along gradients of historical rainfall and used event-based rainout shelters and watering additions to simulate drier summer conditions and more extreme rainfall events, respectively. Ambrosia artemisiifolia had higher fitness on average under reduced rainfall, suggesting it may spread and become more abundant in areas projected to become hotter and drier during the summer months. We also found strong evidence for phenotypic and fitness clines across both latitude and longitude, and that phenological responses and fitness effects of altered rainfall depended on seed source or historical climate. The effect of rainfall treatment on female fitness was highest in western and mid longitudes, but there was little effect on eastern populations. Across latitude, the effect of rainfall treatment on male fitness was highest in southern populations. These phenology and fitness clines suggest that adaptive differentiation across the species’ range has the potential to shape future responses of A. artemisiifolia populations to climate change, particularly altered patterns of rainfall.