Contrasting patterns of local adaptation along climatic gradients between a sympatric parasitic and autotrophic tree species

Genome-wide diversity analysis suggests divergence among Upper Guinea and the Dahomey Gap populations of the Sisrè berry (Syn: miracle fruit) plant (Synsepalum dulcificum [Schumach. & Thonn.] Daniell) in West Africa

Although Synsepalum dulcificum is viewed as one of the most economically promising orphan tree crops worldwide, its genetic improvement and sustainable conservation are hindered by a lack of understanding of its evolutionary history and current population structure. Here, we report for the first time the application of genome-wide single nucleotide polymorphism genotyping to a diverse panel of S. dulcificum accessions to depict the genetic diversity and population structure of the species in the Dahomey Gap (DG) and Upper Guinea (UG) regions to infer its evolutionary history. Our findings suggest low overall genetic diversity but strong population divergence within the species. Neighbor-joining analysis detected two genetic groups in the UG and DG regions, while STRUCTURE distinguished three genetic groups, corresponding to the UG, Western DG, and Central DG regions. Application of Monmonier's algorithm revealed the existence of a barrier disrupting connectivity between the UG and DG groups. The Western DG group consistently exhibited the highest levels of nucleotide and haplotype diversities, while that of the Central DG exhibited the lowest. Analyses of Tajima's D, Fu's Fs, and Achaz Y* statistics suggest that while both UG and Central DG groups likely experienced recent expansions, the Western DG group is at equilibrium. These findings suggest a geographical structuring of genetic variation which supports the conclusion of differential evolutionary histories among West African groups of S. dulcificum. These results provide foundational insights to guide informed breeding population development and design sustainable conservation strategies for this species.

Spatial variations of root-associated bacterial communities of alpine plants in the Qinghai-Tibet Plateau

Exploring the geospatial variation of root-associated microbiomes is critical for understanding plant-microbe-environment interactions and plant environmental adaptability. Root-associated bacterial communities from the three compartments [rhizosphere surrounding soil (RSS), rhizosphere soil (rhizosphere), and root endosphere (endophytic)] are influenced by multiple factors, including plant species and geographical locations. Nonetheless, these communities remain poorly understood under harsh conditions. In this study, we selected four dominant alpine plants on the Qinghai-Tibet Plateau (i.e., Elymus nutans, Festuca sinensis, Kobresia pygmaea, and Kobresia humilis) to investigate their root-associated bacterial communities across 11 geographical locations and determine the factors driving spatial variation. The results showed that the microbiota of the three compartments had significantly different community compositions, with more Pseudomonadaceae and Enterobacteriaceae present in the endosphere. Spatial variations in root endophytic microbiota were mainly governed by stochastic processes, which were different from the deterministic processes in the other two compartments. Meanwhile, the geographical location had greater effects on bacterial communities than plant species, and the spatial variation of α-diversity in the endosphere was much higher than that in the RSS and rhizosphere. We further found that the differentiation of bacterial diversity in the endosphere among sympatric plant species was enhanced by higher annual precipitation, lower soil nutrients (carbon and nitrogen), and pH. For example, the coefficient of variation of endosphere Pseudomonadaceae abundance was positively correlated with annual mean precipitation, whereas that of Enterobacteriaceae abundance was negatively correlated with soil pH. The co-occurrence network analysis identified a higher proportion of bacterial coexistence in the endosphere (70.9%) than in the RSS (49.5%) and rhizosphere soil (50.9%). Finally, we revealed the relative convergence of endophytic communities among sympatric plant species in the alpine grasslands.

Landscape Genomics in Tree Conservation Under a Changing Environment

Understanding the genetic basis of how species respond to changing environments is essential to the conservation of species. However, the molecular mechanisms of adaptation remain largely unknown for long-lived tree species which always have large population sizes, long generation time, and extensive gene flow. Recent advances in landscape genomics can reveal the signals of adaptive selection linking genetic variations and landscape characteristics and therefore have created novel insights into tree conservation strategies. In this review article, we first summarized the methods of landscape genomics used in tree conservation and elucidated the advantages and disadvantages of these methods. We then highlighted the newly developed method "Risk of Non-adaptedness," which can predict the genetic offset or genomic vulnerability of species via allele frequency change under multiple scenarios of climate change. Finally, we provided prospects concerning how our introduced approaches of landscape genomics can assist policymaking and improve the existing conservation strategies for tree species under the ongoing global changes.

MrIML: Multi-response interpretable machine learning to model genomic landscapes

We introduce a new R package "MrIML" ("Mister iml"; Multi-response Interpretable Machine Learning). MrIML provides a powerful and interpretable framework that enables users to harness recent advances in machine learning to quantify multilocus genomic relationships, to identify loci of interest for future landscape genetics studies, and to gain new insights into adaptation across environmental gradients. Relationships between genetic variation and environment are often nonlinear and interactive; these characteristics have been challenging to address using traditional landscape genetic approaches. Our package helps capture this complexity and offers functions that fit and interpret a wide range of highly flexible models that are routinely used for single-locus landscape genetics studies but are rarely extended to estimate response functions for multiple loci. To demonstrate the package's broad functionality, we test its ability to recover landscape relationships from simulated genomic data. We also apply the package to two empirical case studies. In the first, we model genetic variation of North American balsam poplar (Populus balsamifera, Salicaceae) populations across environmental gradients. In the second case study, we recover the landscape and host drivers of feline immunodeficiency virus genetic variation in bobcats (Lynx rufus). The ability to model thousands of loci collectively and compare models from linear regression to extreme gradient boosting, within the same analytical framework, has the potential to be transformative. The MrIML framework is also extendable and not limited to modelling genetic variation; for example, it can quantify the environmental drivers of microbiomes and coinfection dynamics.

Association of putatively adaptive genetic variation with climatic variables differs between a parasite and its host

Parasitism is a pervasive phenomenon in nature with the relationship between species driving evolution in both parasite and host. Due to their host-dependent lifestyle, parasites may adapt to the abiotic environment in ways that differ from their hosts or from free-living relatives; yet rarely has this been assessed. Here, we test two competing hypotheses related to whether putatively adaptive genetic variation in a specialist mistletoe associates with the same, or different, climatic variables as its host species. We sampled 11 populations of the specialist mistletoe Amyema gibberula var. tatei (n = 154) and 10 populations of its associated host Hakea recurva subsp. recurva (n = 160). Reduced-representation sequencing was used to obtain genome-wide markers and putatively adaptive variation detected using genome scan methods. Climate associations were identified using generalized dissimilarity modelling, and these were mapped geographically to visualize the spatial patterns of genetic composition. Our results supported the hypothesis of parasites and host species responding differently to climatic variables. Temperature was relatively more important in predicting allelic turnover in the specialist mistletoe while precipitation was more important for the host. This suggests that parasitic plants and host species may respond differently to selective pressures, potentially as a result of differing nutrient acquisition strategies. Specifically, mistletoes acquire water from hosts (rather than the abiotic environment), which may provide a buffer to precipitation as a selective pressure. This work deepens and complements the physiological and other ecological studies of adaptation and provides a window into the evolutionary processes that underlie previously observed phenomena. Applying these methods to a comparative study in a host-parasite system has also highlighted factors that affect the study of selection pressure on nonmodel organisms, such as differing adaptation rates and lack of reference genomes.

Contrasting patterns of local adaptation along climatic gradients between a sympatric parasitic and autotrophic tree species

Sympatric tree species are subject to similar climatic drivers, posing a question as to whether they display comparable adaptive responses. However, no study has explicitly examined local adaptation of co-occurring parasitic and autotrophic plant species to the abiotic environment. Here we test the hypotheses that a generalist parasitic tree would display a weaker signal of selection and that genomic variation would associate with fewer climatic variables (particularly precipitation) but have similar spatial patterns to a sympatric autotrophic tree species. To test these hypotheses, we collected samples from 17 sites across the range of two tree species, the hemiparasite Nuytsia floribunda (n = 264) and sympatric autotroph Melaleuca rhaphiophylla (n = 272). We obtained 5,531 high-quality genome-wide single nucleotide polymorphisms (SNPs) for M. rhaphiophylla and 6,727 SNPs for N. floribunda using DArTseq genome scan technology. Population differentiation and environmental association approaches were used to identify signals of selection. Generalized dissimilarly modelling was used to detect climatic and spatial patterns of local adaptation across climatic gradients. Overall, 322 SNPs were identified as putatively adaptive for the autotroph, while only 57 SNPs were identified for the parasitic species. We found genomic variation to associate with different sets of bioclimatic variables for each species, with precipitation relatively less important for the parasite. Spatial patterns of predicted adaptive variability were different and indicate that co-occurring species with disparate life history traits may not respond equally to selective pressures (i.e., temperature and precipitation). Together, these findings provide insight into local adaptation of sympatric parasitic and autotrophic tree species to abiotic environments.