Reduced within-population quantitative genetic variation is associated with climate harshness in maritime pine

Archambeau, Juliette; Garzón, Marta Benito; Miguel, Marina de; Brachi, Benjamin; Barraquand, Frédéric; González‐Martínez, Santiago C.

doi:10.1038/s41437-023-00622-9

Cited by 4 publications

(3 citation statements)

References 108 publications

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“…This holds for all three metrics, with exceptions occurring mostly near the edges of either the current range (LO, DI) or of the geographical areas likely to be particularly suitable in the future (RI). These findings are in contrast with other studies that used various marker sets to predict Local Offsets (Nielsen et al, 2021;Archambeau, 2022) and may therefore be specific to the study species or genomic dataset and not represent a broad generality. Moreover, when we used common garden data to compare the predictive power of genomic offsets based on All vs.…”

Section: Local Offsetcontrasting

confidence: 89%

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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Section: Local Offsetcontrasting

confidence: 89%

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

et al. 2023

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“…Several studies recently evaluated GO-based methods by comparing predicted maladaptation (quantified with GO) against fitness-related traits. This was done through in silico simulations (Láruson et al, 2022;Gain et al, 2023;Lotterhos, 2023) or by studying populations in common gardens (Rhoné et al, 2020;Archambeau, 2022;Fitzpatrick et al, 2021). Encouragingly, these studies often found that higher predicted maladaptation aligned with reduced realized fitness.…”

Section: Introductionmentioning

confidence: 99%

Predicting species invasiveness with genomic data: is Genomic Offset related to establishment probability?

Camus,

Gautier,

Boitard

2024

Preprint

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Predicting the risk of establishment and spread of populations outside their native range represents a major challenge in evolutionary biology. Various methods have recently been developed to estimate population (mal)adaptation to a new environment with genomic data via so-called Genomic Offset (GO) statistics. These approaches are particularly promising for studying invasive species, but have still rarely been used in this context. Here, we evaluated the relationship between GO and the estab-lishment probability of a population in a new environment using both in silico and empirical data. First, we designed invasion simulations to evaluate the ability to predict establishment probability of two GO computation methods (Geometric GO and Gradient Forest) under several conditions. Additionally, we aimed to evaluate the interpretability of absolute Geometric GO values, which the-oretically represent the adaptive genetic distance between populations from distinct environments. Second, utilizing public real data from the crop pest speciesBactrocera tryoni, a fruit fly native from Northern Australia, we computed GO between “source” populations and a diverse range of locations within invaded areas. This practical application of GO within the context of a biological invasion underscores its potential in providing insights and guiding recommendations for future invasion risk assessment. Overall, our results suggest that GO statistics represent good predictors of the estab-lishment probability and may thus inform invasion risk, although the influence of several factors on prediction performance (e.g. propagule pressure or admixture) will need further investigation.

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“…Several studies recently evaluated GO‐based methods by comparing predicted maladaptation (quantified with GO) against fitness‐related traits. This was done through in silico simulations (Gain et al., 2023 ; Láruson et al., 2022 ; Lotterhos, 2023 ) or by studying populations in common gardens (Archambeau, 2022 ; Fitzpatrick et al., 2021 ; Rhoné et al., 2020 ). Encouragingly, these studies often found that higher predicted maladaptation aligned with reduced realized fitness.…”

Section: Introductionmentioning

confidence: 99%

Predicting species invasiveness with genomic data: Is genomic offset related to establishment probability?

Camus,

Gautier,

Boitard

2024

Evolutionary Applications

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Predicting the risk of establishment and spread of populations outside their native range represents a major challenge in evolutionary biology. Various methods have recently been developed to estimate population (mal)adaptation to a new environment with genomic data via so‐called Genomic Offset (GO) statistics. These approaches are particularly promising for studying invasive species but have still rarely been used in this context. Here, we evaluated the relationship between GO and the establishment probability of a population in a new environment using both in silico and empirical data. First, we designed invasion simulations to evaluate the ability to predict establishment probability of two GO computation methods (Geometric GO and Gradient Forest) under several conditions. Additionally, we aimed to evaluate the interpretability of absolute Geometric GO values, which theoretically represent the adaptive genetic distance between populations from distinct environments. Second, utilizing public empirical data from the crop pest species Bactrocera tryoni, a fruit fly native from Northern Australia, we computed GO between “source” populations and a diverse range of locations within invaded areas. This practical application of GO within the context of a biological invasion underscores its potential in providing insights and guiding recommendations for future invasion risk assessment. Overall, our results suggest that GO statistics represent good predictors of the establishment probability and may thus inform invasion risk, although the influence of several factors on prediction performance (e.g., propagule pressure or admixture) will need further investigation.

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Reduced within-population quantitative genetic variation is associated with climate harshness in maritime pine

Cited by 4 publications

References 108 publications

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

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

Predicting species invasiveness with genomic data: is Genomic Offset related to establishment probability?

Predicting species invasiveness with genomic data: Is genomic offset related to establishment probability?

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