The genomics of climate change

Integrating evolutionary genomics of forest trees to inform future tree breeding amid rapid climate change

Global climate change is leading to rapid and drastic shifts in environmental conditions, posing threats to biodiversity and nearly all life forms worldwide. Forest trees serve as foundational components of terrestrial ecosystems and play a crucial and leading role in combating and mitigating the adverse effects of extreme climate events, despite their own vulnerability to these threats. Therefore, understanding and monitoring how natural forests respond to rapid climate change is a key priority for biodiversity conservation. Recent progress in evolutionary genomics, driven primarily by cutting-edge multi-omics technologies, offers powerful new tools to address several key issues. These include precise delineation of species and evolutionary units, inference of past evolutionary histories and demographic fluctuations, identification of environmentally adaptive variants, and measurement of genetic load levels. As the urgency to deal with more extreme environmental stresses grows, understanding the genomics of evolutionary history, local adaptation, future responses to climate change, and conservation and restoration of natural forest trees will be critical for research at the nexus of global change, population genomics, and conservation biology. In this review, we explore the application of evolutionary genomics to assess the effects of global climate change using multi-omics approaches and discuss the outlook for breeding of climate-adapted trees.

Genomic Variation, Population History, and Long-Term Genetic Adaptation to High Altitudes in Tibetan Partridge (Perdix hodgsoniae)

Species residing across elevational gradients display adaptations in response to environmental changes such as oxygen availability, ultraviolet radiation, and temperature. Here, we study genomic variation, gene expression, and long-term adaptation in Tibetan Partridge (Perdix hodgsoniae) populations residing across the elevational gradient of the Tibetan Plateau. We generated a high-quality draft genome and used it to carry out downstream population genomic and transcriptomic analysis. The P. hodgsoniae populations residing across various elevations were genetically distinct, and their phylogenetic clustering was consistent with their geographic distribution. We identified possible evidence of gene flow between populations residing in <3,000 and >4,200 m elevation that is consistent with known habitat expansion of high-altitude populations of P. hodgsoniae to a lower elevation. We identified a 60 kb haplotype encompassing the Estrogen Receptor 1 (ESR1) gene, showing strong genetic divergence between populations of P. hodgsoniae. We identified six single nucleotide polymorphisms within the ESR1 gene fixed for derived alleles in high-altitude populations that are strongly conserved across vertebrates. We also compared blood transcriptome profiles and identified differentially expressed genes (such as GAPDH, LDHA, and ALDOC) that correlated with differences in altitude among populations of P. hodgsoniae. These candidate genes from population genomics and transcriptomics analysis were enriched for neutrophil degranulation and glycolysis pathways, which are known to respond to hypoxia and hence may contribute to long-term adaptation to high altitudes in P. hodgsoniae. Our results highlight Tibetan Partridges as a useful model to study molecular mechanisms underlying long-term adaptation to high altitudes.

Whole genome resequencing identifies local adaptation associated with environmental variation for redband trout

Aquatic ectotherms are predicted to harbour genomic signals of local adaptation resulting from selective pressures driven by the strong influence of climate conditions on body temperature. We investigated local adaptation in redband trout (Oncorhynchus mykiss gairdneri) using genome scans for 547 samples from 11 populations across a wide range of habitats and thermal gradients in the interior Columbia River. We estimated allele frequencies for millions of single nucleotide polymorphism loci (SNPs) across populations using low-coverage whole genome resequencing, and used population structure outlier analyses to identify genomic regions under divergent selection between populations. Twelve genomic regions showed signatures of local adaptation, including two regions associated with genes known to influence migration and developmental timing in salmonids (GREB1L, ROCK1, SIX6). Genotype-environment association analyses indicated that diurnal temperature variation was a strong driver of local adaptation, with signatures of selection driven primarily by divergence of two populations in the northern extreme of the subspecies range. We also found evidence for adaptive differences between high-elevation desert vs. montane habitats at a smaller geographical scale. Finally, we estimated vulnerability of redband trout to future climate change using ecological niche modelling and genetic offset analyses under two climate change scenarios. These analyses predicted substantial habitat loss and strong genetic shifts necessary for adaptation to future habitats, with the greatest vulnerability predicted for high-elevation desert populations. Our results provide new insight into the complexity of local adaptation in salmonids, and important predictions regarding future responses of redband trout to climate change.

Large-scale genome-wide study reveals climate adaptive variability in a cosmopolitan pest

Understanding the genetic basis of climatic adaptation is essential for predicting species' responses to climate change. However, intraspecific variation of these responses arising from local adaptation remains ambiguous for most species. Here, we analyze genomic data from diamondback moth (Plutella xylostella) collected from 75 sites spanning six continents to reveal that climate-associated adaptive variation exhibits a roughly latitudinal pattern. By developing an eco-genetic index that combines genetic variation and physiological responses, we predict that most P. xylostella populations have high tolerance to projected future climates. Using genome editing, a key gene, PxCad, emerged from our analysis as functionally temperature responsive. Our results demonstrate that P. xylostella is largely capable of tolerating future climates in most of the world and will remain a global pest beyond 2050. This work improves our understanding of adaptive variation along environmental gradients, and advances pest forecasting by highlighting the genetic basis for local climate adaptation.

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.

Genetic Diversity and Demographic History of the Shaggy Soft-Haired Mouse Abrothrix hirta (Cricetidae; Abrotrichini)

Genetic information on species can inform decision making regarding conservation of biodiversity since the response of organisms to changing environments depend, in part, on their genetic makeup. Territories of central-southern Chile and Argentina have undergone a varying degree of impact during the Quaternary, where the response of local fauna and flora was rather species-specific. Here, we focus on the sigmodontine rodent Abrothrix hirta, distributed from 35° S in Chile and Argentina to northern Tierra del Fuego. Based on 119,226 transcriptome-derived SNP loci from 46 individuals of A. hirta, we described the geographic distribution of the genetic diversity of this species using a maximum likelihood tree, principal component and admixture analyses. We also addressed the demographic history of the main intraspecific lineages of A. hirta using GADMA. We found that A. hirta exhibited four allopatric intraspecific lineages. Three main genetic groups were identified by a Principal Component Analysis and by Ancestry analysis. The demographic history of A. hirta was characterized by recent population stability for populations at the northernmost part of the range, while southern populations experienced a recent population expansion.

Evolutionary genomics can improve prediction of species' responses to climate change

Global climate change (GCC) increasingly threatens biodiversity through the loss of species, and the transformation of entire ecosystems. Many species are challenged by the pace of GCC because they might not be able to respond fast enough to changing biotic and abiotic conditions. Species can respond either by shifting their range, or by persisting in their local habitat. If populations persist, they can tolerate climatic changes through phenotypic plasticity, or genetically adapt to changing conditions depending on their genetic variability and census population size to allow for de novo mutations. Otherwise, populations will experience demographic collapses and species may go extinct. Current approaches to predicting species responses to GCC begin to combine ecological and evolutionary information for species distribution modelling. Including an evolutionary dimension will substantially improve species distribution projections which have not accounted for key processes such as dispersal, adaptive genetic change, demography, or species interactions. However, eco-evolutionary models require new data and methods for the estimation of a species' adaptive potential, which have so far only been available for a small number of model species. To represent global biodiversity, we need to devise large-scale data collection strategies to define the ecology and evolutionary potential of a broad range of species, especially of keystone species of ecosystems. We also need standardized and replicable modelling approaches that integrate these new data to account for eco-evolutionary processes when predicting the impact of GCC on species' survival. Here, we discuss different genomic approaches that can be used to investigate and predict species responses to GCC. This can serve as guidance for researchers looking for the appropriate experimental setup for their particular system. We furthermore highlight future directions for moving forward in the field and allocating available resources more effectively, to implement mitigation measures before species go extinct and ecosystems lose important functions.

Effectiveness of Genomic Selection by Response to Selection for Winter Wheat Variety Improvement

Prediction performance for winter wheat grain yield and end-use quality traits. Prediction accuracies evaluated by cross-validations are significantly overestimated. Nonparametric algorithms outperform the parametric alternatives in cross-year predictions. Strategically designing training population improves response to selection. Response to selection varies across growing seasons and environments. Considering the practicality of applying genomic selection (GS) in the line development stage of a hard red winter (HRW) wheat (Triticum aestivum L.) variety development program (VDP), the effectiveness of GS was evaluated by prediction accuracy and by the response to selection across field seasons that demonstrated challenges for crop improvement under significant climate variability. Important breeding targets for wheat improvement in the southern Great Plains of the United States, including grain yield, kernel weight, wheat protein content, and sodium dodecyl sulfate (SDS) sedimentation volume as a rapid test for predicting bread-making quality, were used to estimate the effectiveness of GS across harvest years from 2014 (drought) to 2016 (normal). In general, nonparametric algorithms reproducing kernel Hilbert space (RKHS) and random forest (RF) produced higher accuracies in both same-year cross-validations (CVs) and cross-year predictions for the purpose of line selection. Further, the stability of GS performance was greatest for SDS sedimentation volume but least for wheat protein content. To ensure long-term genetic gain, our study on selection response suggested that across this sample of environmental variability, and though there are cases where phenotypic selection (PS) might be still preferred, training conducted under drought or in suboptimal conditions could provide an encouraging prediction outcome when selection decisions were made in normal conditions. However, it is not advisable to use training information collected from a normal season to predict trait performance under drought conditions. Finally, the superiority of response to selection was most evident if the training population (TP) can be optimized.

Comment on “Genomic signals of selection predict climate-driven population declines in a migratory bird”

Bay et al . (Reports, 5 January 2018, p. 83) combine genomics, spatial modeling, and future climate scenarios to examine yellow warbler population trends in response to climate change, and they suggest that their methods can inform conservation. We discuss problems in their statistical analyses and explain why the concept of “genomic vulnerability” needs further validation before application to real-world conservation problems.