Genomic Prediction of (Mal)Adaptation Across Current and Future Climatic Landscapes

Capblancq, Thibaut; Fitzpatrick, Matthew C.; Bay, Rachael A.; Expósito-Alonso, Moisés; Keller, Stephen R.

doi:10.1146/annurev-ecolsys-020720-042553

Cited by 191 publications

(280 citation statements)

References 122 publications

(186 reference statements)

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“…Thus, denser sampling of wild populations globally is needed to model GD at finer scales and better inform conservation planning and management of the species genetic substrate. Population‐specific genomic information is starting to unlock our potential to understand evolutionary responses of species and ecological assemblages to environmental change, including the ability to estimate relevant conservation metrics, such as the magnitude of mutation load in wild populations or the adaptive genetic variation revealed using landscape genomic analyses (Fitzpatrick and Keller 2015, Capblancq et al 2020). While the coarse spatial grain used in our assessment cannot directly inform conservation strategies for GD at local population levels, it contributes towards understanding of global adaptive capacity and the potential evolutionary responses of mammal assemblages to climate and land‐use change.…”

Section: Discussionmentioning

confidence: 99%

Exposure of mammal genetic diversity to mid‐21st century global change

2021

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Accelerating climate and land‐use change are rapidly transforming Earth's biodiversity. While there is substantial evidence on the exposure and vulnerability of biodiversity to global change at the species level, the global exposure of intraspecific genetic diversity (GD) is still unknown. Here, we assess the exposure of mitochondrial GD to mid‐21st century climate and land‐use change in terrestrial mammal assemblages at grid‐cell and bioclimatic region scales under alternative narratives of future societal development. We used global predictions of mammal GD distribution based on thousands of georeferenced mitochondrial genes for hundreds of mammal species, the latest generation of global climate models from the ongoing sixth phase of the Coupled Model Intercomparison Project (CMIP6), and global future projections of land‐use prepared for CMIP6. We found that more than 50% of the genetically poorest geographic areas (grid‐cells), primarily distributed in tundra, boreal forests/taiga and temperate bioclimatic regions, will be exposed to mean annual temperature rise that exceeds 2°C compared to the baseline period under all considered future scenarios. We also show that at least 30% of the most genetically rich areas in tropical, subtropical and montane regions will be exposed to an increase of mean annual temperature > 2°C under less optimal scenarios. Genetic diversity in these rich regions is also predicted to be exposed to severe reductions of primary vegetation area and increasing human activities (an average loss of 5–10% of their total area under the less sustainable land‐use scenarios). Our findings reveal a substantial exposure of mammal GD to the combined effects of global climate and land‐use change. Meanwhile the post‐2020 conservation goals are overlooking genetic diversity, our study identifies both genetically poor and highly diverse areas severely exposed to global change, paving the road to better estimate the geography of biodiversity vulnerability to global change.

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Section: Discussionmentioning

confidence: 99%

Exposure of mammal genetic diversity to mid‐21st century global change

2021

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“…ML has long ago been used for ecological niche modeling [129,130] and functional genomics [131]. However, ML has started permeating, until very recently, other approaches more relevant to this review such as GWSS [128,132] and GP [133][134][135]. In this latter example, ML techniques (i.e., deep learning) outperformed GP's predictive ability for single traits in multi-environment trials (Figure 1k).…”

Section: A Way Forward Via Machine Learningmentioning

confidence: 94%

Harnessing Crop Wild Diversity for Climate Change Adaptation

Cortés

López-Hernández

2021

Genes

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Warming and drought are reducing global crop production with a potential to substantially worsen global malnutrition. As with the green revolution in the last century, plant genetics may offer concrete opportunities to increase yield and crop adaptability. However, the rate at which the threat is happening requires powering new strategies in order to meet the global food demand. In this review, we highlight major recent ‘big data’ developments from both empirical and theoretical genomics that may speed up the identification, conservation, and breeding of exotic and elite crop varieties with the potential to feed humans. We first emphasize the major bottlenecks to capture and utilize novel sources of variation in abiotic stress (i.e., heat and drought) tolerance. We argue that adaptation of crop wild relatives to dry environments could be informative on how plant phenotypes may react to a drier climate because natural selection has already tested more options than humans ever will. Because isolated pockets of cryptic diversity may still persist in remote semi-arid regions, we encourage new habitat-based population-guided collections for genebanks. We continue discussing how to systematically study abiotic stress tolerance in these crop collections of wild and landraces using geo-referencing and extensive environmental data. By uncovering the genes that underlie the tolerance adaptive trait, natural variation has the potential to be introgressed into elite cultivars. However, unlocking adaptive genetic variation hidden in related wild species and early landraces remains a major challenge for complex traits that, as abiotic stress tolerance, are polygenic (i.e., regulated by many low-effect genes). Therefore, we finish prospecting modern analytical approaches that will serve to overcome this issue. Concretely, genomic prediction, machine learning, and multi-trait gene editing, all offer innovative alternatives to speed up more accurate pre- and breeding efforts toward the increase in crop adaptability and yield, while matching future global food demands in the face of increased heat and drought. In order for these ‘big data’ approaches to succeed, we advocate for a trans-disciplinary approach with open-source data and long-term funding. The recent developments and perspectives discussed throughout this review ultimately aim to contribute to increased crop adaptability and yield in the face of heat waves and drought events.

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Section: Overviewmentioning

confidence: 99%

What processes must we understand to forecast regional-scale population dynamics?

2020

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An urgent challenge facing biologists is predicting the regional-scale population dynamics of species facing environmental change. Biologists suggest that we must move beyond predictions based on phenomenological models and instead base predictions on underlying processes. For example, population biologists, evolutionary biologists, community ecologists and ecophysiologists all argue that the respective processes they study are essential. Must our models include processes from all of these fields? We argue that answering this critical question is ultimately an empirical exercise requiring a substantial amount of data that have not been integrated for any system to date. To motivate and facilitate the necessary data collection and integration, we first review the potential importance of each mechanism for skilful prediction. We then develop a conceptual framework based on reaction norms, and propose a hierarchical Bayesian statistical framework to integrate processes affecting reaction norms at different scales. The ambitious research programme we advocate is rapidly becoming feasible due to novel collaborations, datasets and analytical tools.

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Genomic Prediction of (Mal)Adaptation Across Current and Future Climatic Landscapes

Cited by 191 publications

References 122 publications

Exposure of mammal genetic diversity to mid‐21st century global change

Exposure of mammal genetic diversity to mid‐21st century global change

Harnessing Crop Wild Diversity for Climate Change Adaptation

What processes must we understand to forecast regional-scale population dynamics?

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