Environment and pathogens shape local and regional adaptations to climate change in the chocolate tree, Theobroma cacao L

Climate change reshaping plant-fungal interaction

Plant diseases pose a severe threat to modern agriculture, necessitating effective and lasting control solutions. Environmental factors significantly shape plant ecology. Human-induced greenhouse gas emissions have led to global temperature rise, impacting various aspects, including carbon dioxide (CO2) concentration, temperature, ozone (O3), and ultraviolet-B, all of which influence plant diseases. Altered pathogen ranges can accelerate disease transmission. This review explores environmental effects on plant diseases, with climate change affecting fungal biogeography, disease incidence, and severity, as well as agricultural production. Moreover, we have discussed how climate change influences pathogen development, host-fungal interactions, the emergence of new races of fungi, and the dissemination of emerging fungal diseases across the globe. The discussion about environment-mediated impact on pattern-triggered immunity (PTI), effector-triggered immunity (ETI), and RNA interference (RNAi) is also part of this review. In conclusion, the review underscores the critical importance of understanding how climate change is reshaping plant-fungal interactions. It highlights the need for continuous research efforts to elucidate the mechanisms driving these changes and their ecological consequences. As the global climate continues to evolve, it is imperative to develop innovative strategies for mitigating the adverse effects of fungal pathogens on plant health and food security.

Genomic signals of local adaptation in Picea crassifolia

BACKGROUND

Global climate change poses a grave threat to biodiversity and underscores the importance of identifying the genes and corresponding environmental factors involved in the adaptation of tree species for the purposes of conservation and forestry. This holds particularly true for spruce species, given their pivotal role as key constituents of the montane, boreal, and sub-alpine forests in the Northern Hemisphere.

RESULTS

Here, we used transcriptomes, species occurrence records, and environmental data to investigate the spatial genetic distribution of and the climate-associated genetic variation in Picea crassifolia. Our comprehensive analysis employing ADMIXTURE, principal component analysis (PCA) and phylogenetic methodologies showed that the species has a complex population structure with obvious differentiation among populations in different regions. Concurrently, our investigations into isolation by distance (IBD), isolation by environment (IBE), and niche differentiation among populations collectively suggests that local adaptations are driven by environmental heterogeneity. By integrating population genomics and environmental data using redundancy analysis (RDA), we identified a set of climate-associated single-nucleotide polymorphisms (SNPs) and showed that environmental isolation had a more significant impact than geographic isolation in promoting genetic differentiation. We also found that the candidate genes associated with altitude, temperature seasonality (Bio4) and precipitation in the wettest month (Bio13) may be useful for forest tree breeding.

CONCLUSIONS

Our findings deepen our understanding of how species respond to climate change and highlight the importance of integrating genomic and environmental data in untangling local adaptations.

Genomic population structure and local adaptation of the wild strawberry Fragaria nilgerrensis

The crop wild relative, Fragaria nilgerrensis, is adapted to a variety of diverse habitats across its native range in China. Thus, discoveries made in this species could serve useful to guide the development of new superior strawberry cultivars that are resilient to new or variable environments. However, the genetic diversity and genetic architecture of traits in this species underlying important adaptive traits remain poorly understood. Here, we used whole-genome resequencing data from 193 F. nilgerrensis individuals spanning the distribution range in China to investigate the genetic diversity, population structure and the genomic basis of local adaptation. We identified four genetic groups, with the western group located in Hengduan Mountains exhibited the highest genetic diversity. Redundancy analysis suggests that both environment and geographic variables shaped a significant proportion of genomic variation. Our analyses revealed that the environmental difference explains more of the observed genetic variation than geographic distance. This suggests that adaptation to distinct habitats, unique combination of abiotic factors, likely drove genetic differentiation. Lastly, by implementing selective sweeps scans and genome-environment association analysis throughout the genome, we identified the genetic variation associated with local adaptation and investigated the functions of putative candidate genes in F. nilgerrensis.

AlleleShift: an R package to predict and visualize population-level changes in allele frequencies in response to climate change

Background

At any particular location, frequencies of alleles that are associated with adaptive traits are expected to change in future climates through local adaption and migration, including assisted migration (human-implemented when climate change is more rapid than natural migration rates). Making the assumption that the baseline frequencies of alleles across environmental gradients can act as a predictor of patterns in changed climates (typically future but possibly paleo-climates), a methodology is provided by AlleleShift of predicting changes in allele frequencies at the population level.

Methods

The prediction procedure involves a first calibration and prediction step through redundancy analysis (RDA), and a second calibration and prediction step through a generalized additive model (GAM) with a binomial family. As such, the procedure is fundamentally different to an alternative approach recently proposed to predict changes in allele frequencies from canonical correspondence analysis (CCA). The RDA step is based on the Euclidean distance that is also the typical distance used in Analysis of Molecular Variance (AMOVA). Because the RDA step or CCA approach sometimes predict negative allele frequencies, the GAM step ensures that allele frequencies are in the range of 0 to 1.

Results

AlleleShift provides data sets with predicted frequencies and several visualization methods to depict the predicted shifts in allele frequencies from baseline to changed climates. These visualizations include 'dot plot' graphics (function shift.dot.ggplot), pie diagrams (shift.pie.ggplot), moon diagrams (shift.moon.ggplot), 'waffle' diagrams (shift.waffle.ggplot) and smoothed surface diagrams of allele frequencies of baseline or future patterns in geographical space (shift.surf.ggplot). As these visualizations were generated through the ggplot2 package, methods of generating animations for a climate change time series are straightforward, as shown in the documentation of AlleleShift and in the supplemental videos.

Availability

AlleleShift is available as an open-source R package from https://cran.r-project.org/package=AlleleShift and https://github.com/RoelandKindt/AlleleShift. Genetic input data is expected to be in the adegenet::genpop format, which can be generated from the adegenet::genind format. Climate data is available from various resources such as WorldClim and Envirem.

Transcriptomic responses to drought stress among natural populations provide insights into local adaptation of weeping forsythia

BACKGROUND

Understanding the genetic mechanisms of local adaptation is an important emerging topic in molecular ecology and evolutionary biology.

RESULTS

Here, we identify the physiological changes and differential expression of genes among different weeping forsythia populations under drought stress in common garden experiments. Physiological results showed that HBWZ might have higher drought tolerance among four populations. RNA-seq results showed that significant differential expression in the genes responding to the synthesis of flavonoids, aromatic substances, aromatic amino acids, oxidation-reduction process, and transmembrane transport occured among four populations. By further reanalysis of results of previous studies, sequence differentiation was found in the genes related to the synthesis of aromatic substances among different weeping forsythia populations.

CONCLUSIONS

Overall, our study supports the hypothesis that the dual differentiation in gene efficiency and expression increases among populations in response to heterogeneous environments and is an important evolutionary process of local adaptation. Here, we proposed a new working model of local adaptation of weeping forsythia populations under different intensities of drought stress, which provides new insights for understanding the genetic mechanisms of local adaptation for non-model species.