The evolutionary genomics of species’ responses to climate change

Aguirre‐Liguori, Jonás A.; Ramírez‐Barahona, Santiago; Gaut, Brandon S.

doi:10.1038/s41559-021-01526-9

Cited by 110 publications

(121 citation statements)

References 108 publications

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“…Machine learning approaches, such as RF, are also increasingly recognized as important tools to untangle how environmental and landscape factors shape genomic change (Fitzpatrick & Keller, 2015;Fountain-Jones et al, 2017). Landscape and ecological genomics attempt to understand individual, population and community responses and adaptation to abiotic and biotic factors (e.g., Aguirre-Liguori et al, 2021;Allen & Banfield, 2005;Fitzpatrick & Keller, 2015;Steane et al, 2014;Ungerer et al, 2008). These responses are, by definition, complex, nonlinear and impacted by a variety of confounding factors (Ungerer et al, 2008).…”

Section: Ecological Genomicsmentioning

confidence: 99%

Machine learning in molecular ecology

Fountain-Jones

Smith

Austerlitz

2021

Molecular Ecology Resources

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Section: Ecological Genomicsmentioning

confidence: 99%

Machine learning in molecular ecology

Fountain-Jones

Smith

Austerlitz

2021

Molecular Ecology Resources

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“…To assess the potential effects of climate change, we employed the FOLDS model, a conceptual framework that considers distinct layers of information to evaluate how populations will respond to climate change (Jonas A. Aguirre-Liguori et al, 2021). The FOLDS model was applied based on six layers of genomic and climate information.…”

Section: Evaluating the Persistence Of Wild Vitis Under Predicted Climate Changementioning

confidence: 99%

“…We applied the FOLDS (Jonas A. Aguirre-Liguori et al, 2021) approach considering two factors: the number of layers and the threshold on each layer. For example, we first filtered accessions based on all six layers, with thresholds defined by: PD resistance (CFU < 13), SDM persistence until 2070, among the lowest 25% of local offset and load within a species, and among the highest 25% of Ne and Sa within a species.…”

Section: Species Distribution Modelsmentioning

confidence: 99%

“…It is worth noting, however, that genomic data contains information about additional processes that may help inform about the evolutionary fate of species (Jonas A. Aguirre-Liguori et al, 2021;Waldvogel et al, 2019). Such information includes the history of genetic migration among populations, insights into the potential effects of genetic drift and the impact of genetic load, which correlates inversely with fitness (Frankham, 2005).…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the persistence and utility of five wildVitisspecies in the context of climate change

Aguirre‐Liguori

Morales‐Cruz

Gaut

2021

Preprint

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Crop wild relatives (CWRs) have the capacity to contribute novel traits to agriculture. Given climate change, these contributions may be especially vital for perennial crops, because perennials are often clonally propagated and consequently do not evolve rapidly. By studying the landscape genomics of five Vitis CWRs (V. arizonica, V. mustangensis, V. riparia, V. berlandieri and V. girdiana) in the context of projected climate change, we addressed two goals. The first was assessing the relative potential of different CWRs to persist in the context of climate change. By integrating species distribution models with adaptive genetic variation, additional genetic features such as genomic load, and a phenotype (resistance to Pierce Disease), we predicted that accessions of V. mustangensis are particularly well-suited to persist. The second goal was identifying candidate CWRs to contribute to bioclimatic adaptation for grapevine (V. vinifera) cultivation. To do so, we first estimated that ~40% of current viticulture sites in the United States will be vulnerable to climate change, based on species distribution models projected to 2070. We then predicted which CWRs have the genomic profile to contribute to bioclimatic adaptation at these vulnerable sites. We identified rootstock candidates from V. mustangensis, V. riparia and V. girdiana and hypothesized that they may prove useful for mitigating climate impacts on viticulture. By identifying candidate germplasm, this work takes a conceptual step toward mitigating the climate impacts on crops by utilizing the genomic and bioclimatic characteristics of CWRs.

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“…Genomic tools allow us to rapidly characterize large germplasm collections and to identify genetic factors responsible for traits of agronomic interest 9,10 , unlocking desirable agrobiodiversity to breeding 11 and enabling genomic selection to accelerate genetic gains 12 . Genomic data can be put in relation with increasingly precise current and projected climatic data and derive information not only on locus-specific adaptation 13 , but also on genomic vulnerability under climate change scenarios [14][15][16] . Data-driven methods can also be applied to characterize the socioeconomic contexts in which crops are grown 17 , generating information that is critical to understand cropping dynamics in smallholder farming systems 18,19 .…”

mentioning

confidence: 99%

Data-driven, participatory characterization of farmer varieties discloses teff breeding potential under current and future climates

Woldeyohannes

Iohannes

Miculan

et al. 2021

Preprint

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In smallholder farming systems, traditional farmer varieties of neglected and underutilized crops species (NUS) support the livelihoods of millions of growers and consumers. NUS combine cultural and agronomic value with local adaptation, and call for transdisciplinary methods to evaluate their breeding potential. Here, we combined farmers' traditional knowledge, genomics, and climate science to characterize 366 Ethiopian teff (Eragrostis tef) farmer varieties and breeding materials. We found that teff genetic diversity in Ethiopia could be organized in six genetic clusters associated to climate variation on the landscape. A participatory evaluation conducted in collaboration with local farmers could consistently identify best performing varieties and inform a genome wide association study to identify candidate genes for farmers' appreciation, phenology, yield, and local adaptation. By modelling the genomic adaptation of teff to current and projected climates, we identified an area around lake Tana where teff cropping will be most vulnerable to climate change. Our results show that transdisciplinary approaches may efficiently propel untapped NUS farmer varieties into modern breeding to foster more resilient and sustainable cropping systems

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The evolutionary genomics of species’ responses to climate change

Cited by 110 publications

References 108 publications

Machine learning in molecular ecology

Machine learning in molecular ecology

Evaluating the persistence and utility of five wildVitisspecies in the context of climate change

Data-driven, participatory characterization of farmer varieties discloses teff breeding potential under current and future climates

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