Probing the dark matter of environmental associations yields novel insights into the architecture of adaptation

Eckert, Andrew J.; Neale, David B.

doi:10.1111/nph.18639

Cited by 7 publications

(5 citation statements)

References 15 publications

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“…To date, no consensus has been reached on how to account for population genetic structure and demographic history in landscape genomics. This also applies to GO approaches in which the first step is to identify a set of loci potentially involved in climate adaptation (Capblancq et al 2023, Eckert and Neale 2023). In species for which climatic gradients that drive local adaptation covary with neutral genetic variation, correcting for population structure in GEA reduces the number of false positives but at the expense of a higher number of false negatives (Lotterhos and Whitlock 2015).…”

Section: Discussionmentioning

confidence: 99%

Evaluating genomic offset predictions in a forest tree with high population genetic structure

Archambeau,

Benito-Garzón,

de-Miguel

et al. 2024

Preprint

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Predicting how tree populations will respond to climate change is an urgent societal concern. An increasingly popular way to make such predictions is the genomic offset (GO) approach, which aims to use genomic and climate data to identify populations that may experience climate maladaptation in the near future. More precisely, GO tries to represent the change in allele frequencies required to maintain the current gene-climate relationships under climate change. However, the GO approach has major limitations and, despite promising validation of its predictions using height data from common gardens, it still lacks broad empirical testing. In the present study, we evaluated the consistency and empirical validity of GO predictions in maritime pine (Pinus pinaster Ait.), a tree species from southwestern Europe and North Africa with a marked population genetic structure. First, gene-climate relationships were estimated using 9,817 SNPs genotyped in 454 trees from 34 populations; and candidate SNPs potentially involved in climate adaptation were identified. Second, GO was predicted using four methods, namely Gradient Forest (GF), Redundancy Analysis (RDA), latent factor mixed model (LFMM) and Generalised Dissimilarity Modeling (GDM), two sets of SNPs (candidate and control SNPs) and five climate general circulation models (GCMs) to account for uncertainty in future climate predictions. Last, the empirical validity of GO predictions was evaluated within a Bayesian framework by estimating the associations between GO predictions and two independent data sources: mortality data from National Forest Inventories (NFI), and mortality and height data from five common gardens in contrasting environments. We found high variability in GO predictions across methods, SNP sets and GCMs. Regarding validation, GO predictions with GDM and GF (and to a lesser extent RDA) based on the candidate SNPs showed the strongest and most consistent associations with mortality rates in common gardens and NFI plots. We found almost no association between GO predictions and tree height in common gardens, most likely due to the overwhelming effect of population genetic structure on tree height in this species. Our study demonstrates the imperative to validate GO predictions with a range of independent data sources before they can be used as informative and reliable metrics in conservation or management strategies.

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Section: Discussionmentioning

confidence: 99%

Evaluating genomic offset predictions in a forest tree with high population genetic structure

Archambeau,

Benito-Garzón,

de-Miguel

et al. 2024

Preprint

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“…The need to approach this result cautiously is further emphasized by the common garden results (particularly for NC) where differences in genomic offsets did not always match performance differences between genomic clusters (see above). Moreover, despite our substantial efforts to identify putatively adaptive SNP loci while also minimizing the influence of neutral demographic processes (Capblancq et al, 2023; Eckert & Neale, 2022), the low latitude allele frequencies might have been influenced by processes other than adaptation. For instance, fixation of alleles through genetic drift is likely to occur in small, isolated populations like those of low latitude red spruce populations, resulting in reduced standing genetic variation that is the basis of adaptive differentiation (Willi & Van Buskirk, 2022).…”

Section: Discussionmentioning

confidence: 99%

Novel genomic offset metrics integrate local adaptation into habitat suitability forecasts and inform assisted migration

Lachmuth,

Capblancq,

Prakash

et al. 2023

Ecological Monographs

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Genomic data is increasingly being integrated into macroecological forecasting, offering an evolutionary perspective that has been largely missing from global change biogeography. Genomic offset, which quantifies the disruption of genotype‐environment associations under environmental change, allows for the incorporation of intra‐specific climate‐associated genomic differentiation into forecasts of habitat suitability. Gradient forest (GF) is a commonly used approach to estimate genomic offset; however, major hurdles in the application of GF‐derived genomic offsets are (1) an inability to interpret their absolute magnitude in an ecologically meaningful way and (2) uncertainty in how their implications compare to those of species‐level approaches like Ecological Niche Models (ENMs). Here, we assess the climate change vulnerability of red spruce (Picea rubens), a cool‐temperate tree species endemic to eastern North America, using both ENMs and GF modeling of genomic variation along climatic gradients. To gain better insights into climate change risks, we derive and apply two new threshold‐based genomic offset metrics ‐ Donor and Recipient Importance ‐ that quantify the transferability of propagules between donor populations and recipient localities while minimizing disruption of genotype‐environment associations. We also propose and test a method for scaling genomic offsets relative to contemporary genomic variation across the landscape. In three common gardens, we found a significant negative relationship between (scaled) genomic offsets and red spruce growth and higher explanatory power for scaled offsets than climate transfer distances. However, the garden results also revealed the potential effects of spatial extrapolation and neutral genomic differentiation that can compromise the degree to which genomic offsets represent mal‐adaptation and highlight the necessity of using common‐garden data to evaluate offset‐based predictions. ENMs and our novel genomic offset metrics forecasted drastic northward range shifts in suitable habitat. Combining inferences from our offset‐based metrics, we show that a northward shift mainly will be required for populations in the central and northern parts of red spruce's current range, whereas southern populations might persist in situ owing to climate‐associated variation with less offset under future climate. These new genomic offset metrics thus yield refined, region‐specific prognoses for local persistence and show how management could be improved by considering assisted migration.

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“…For the corresponding geographic maps see Figures 3, 6; Supplementary Figures S5-S8. 10.3389/fevo.2023.1155783 Frontiers in Ecology and Evolution 11 frontiersin.org history and the environmental gradients driving adaptation was extensively explored when identifying our Candidate Loci set (Capblancq et al, 2023) and represents an important challenge for landscape genomics studies seeking to capture the signal of adaptation along environmental gradients while also minimizing the influence of neutral demographic processes (Eckert and Neale, 2022). While the alignment between genome-wide diversity and environmental gradients is particularly strong in red spruce, this general phenomenon could also apply to many other temperate species with North-South recolonization histories that parallel climate gradients.…”

Section: Local Offsetmentioning

confidence: 99%

Assessing uncertainty in genomic offset forecasts from landscape genomic models (and implications for restoration and assisted migration)

et al. 2023

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IntroductionEcological genomic models are increasingly used to guide climate-conscious restoration and conservation practices in the light of accelerating environmental change. Genomic offsets that quantify the disruption of existing genotype–environment associations under environmental change are a promising model-based tool to inform such measures. With recent advances, potential applications of genomic offset predictions include but are not restricted to: (1) assessing in situ climate risks, (2) mapping future habitat suitability while accounting for local adaptations, or (3) selecting donor populations and recipient areas that maximize genomic diversity and minimize maladaptation to future environments in assisted migration planning. As for any model-based approach, it is crucial to understand how arbitrary decisions made during the modeling process affect predictions and induce uncertainty.MethodsHere, we present a sensitivity analysis of how various modeling components influence forecasts of genomic offset-based metrics, using red spruce (Picea rubens), a cool-temperate tree species endemic to eastern North America, as a case study. We assess the effects of genomic marker set, climatic predictor set, climate change scenario, and “not-to-exceed” offset threshold and evaluate how uncertainty in predictions varies across space.ResultsClimate change scenario induced by far the largest uncertainty to our forecasts; however, the choice of predictor set was also important in regions of the Southern and Central Appalachians that are of high relevance for conservation and restoration efforts. While much effort is often expended in identifying candidate loci, we found that genomic marker set was of minor importance. The choice of a maximum offset threshold to limit transfers between potential donor and recipient locations in assisted migration programs has mostly affected the magnitude rather than geographic variation in our predictions.DiscussionOverall, our model forecasts suggest high climate change risks across the entire distributional range of red spruce and strongly underscore the potential for assisted migration to help ameliorate these risks. In that regard, populations in the Southern and Central Appalachians as well as along the US and Canadian east coast seem the best candidates for both in situ conservation and relocation.

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Probing the dark matter of environmental associations yields novel insights into the architecture of adaptation

Abstract: This article is a Commentary on Capblancq et al. (2023), 237: 1590–1605.

Cited by 7 publications

References 15 publications

Evaluating genomic offset predictions in a forest tree with high population genetic structure

Evaluating genomic offset predictions in a forest tree with high population genetic structure

Novel genomic offset metrics integrate local adaptation into habitat suitability forecasts and inform assisted migration

Assessing uncertainty in genomic offset forecasts from landscape genomic models (and implications for restoration and assisted migration)

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