Space‐for‐time substitutions in climate change ecology and evolution

Biological reviews/Biological reviews of the Cambridge Philosophical Society, Journal Year: 2023, Volume and Issue: 98(6), P. 2243 - 2270

Published: Aug. 9, 2023

ABSTRACT In an epoch of rapid environmental change, understanding and predicting how biodiversity will respond to a changing climate is urgent challenge. Since we seldom have sufficient long‐term biological data use the past anticipate future, spatial climate–biotic relationships are often used as proxy for biotic responses change over time. These ‘space‐for‐time substitutions’ (SFTS) become near ubiquitous in global biology, but with different subfields largely developing methods isolation. We review climate‐focussed SFTS four ecology evolution, each focussed on type variable – population phenotypes, genotypes, species' distributions, ecological communities. then examine similarities differences between terms methods, limitations opportunities. While wide range applications, two main approaches applied across subfields: situ gradient transplant experiments. find that share common relating ( i ) causality identified ii transferability these relationships, i.e. whether observed space equivalent those occurring Moreover, despite widespread application research, key assumptions remain untested. highlight opportunities enhance robustness by addressing limitations, particular emphasis where could be shared subfields.

Language: Английский

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Genotype–environment associations to reveal the molecular basis of environmental adaptation

The Plant Cell, Journal Year: 2022, Volume and Issue: 35(1), P. 125 - 138

Published: Aug. 25, 2022

A fundamental goal in plant biology is to identify and understand the variation underlying plants' adaptation their environment. Climate change has given new urgency this goal, as society aims accelerate of ecologically important species, endangered crops hotter, less predictable climates. In pre-genomic era, identifying adaptive alleles was painstaking work, leveraging genetics, molecular biology, physiology, ecology. Now, rise genomics computational approaches may facilitate research. Genotype-environment associations (GEAs) use statistical between allele frequency environment origin test hypothesis that allelic at a gene adapted local environments. Researchers scan genome for GEAs generate hypotheses on genetic variants (environmental genome-wide association studies). Despite rapid adoption these methods, many questions remain about interpretation GEA findings, which arise from unanswered architecture limitations inherent association-based analyses. We outline strategies ground better GEA-generated using genetics ecophysiology. provide recommendations users who seek learn basis adaptation. When combined with rigorous testing framework, our understanding climate improvement.

Language: Английский

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Deep Learning in Population Genetics

Genome Biology and Evolution, Journal Year: 2023, Volume and Issue: 15(2)

Published: Jan. 23, 2023

Abstract Population genetics is transitioning into a data-driven discipline thanks to the availability of large-scale genomic data and need study increasingly complex evolutionary scenarios. With likelihood Bayesian approaches becoming either intractable or computationally unfeasible, machine learning, in particular deep algorithms are emerging as popular techniques for population genetic inferences. These rely on that learn non-linear relationships between input model parameters being estimated through representation learning from training sets. Deep currently employed field comprise discriminative generative models with fully connected, convolutional, recurrent layers. Additionally, wide range powerful simulators generate under scenarios now available. The application empirical sets mostly replicates previous findings demography reconstruction signals natural selection organisms. To showcase feasibility tackle new challenges, we designed branched architecture detect recent balancing temporal haplotypic data, which exhibited good predictive performance simulated data. Investigations interpretability neural networks, their robustness uncertain creative will provide further opportunities technological advancements field.

Language: Английский

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The paradox of adaptive trait clines with nonclinal patterns in the underlying genes

Proceedings of the National Academy of Sciences, Journal Year: 2023, Volume and Issue: 120(12)

Published: March 14, 2023

Multivariate climate change presents an urgent need to understand how species adapt complex environments. Population genetic theory predicts that loci under selection will form monotonic allele frequency clines with their selective environment, which has led the wide use of genotype–environment associations (GEAs). This study used a set simulations elucidate conditions are more or less likely evolve as multiple quantitative traits multivariate Phenotypic evolved nonmonotonic (i.e., nonclinal) patterns in frequencies promoted unique combinations mutations achieve optimum different parts landscape. Such resulted from interactions among landscape, demography, pleiotropy, and architecture. GEA methods failed accurately infer basis adaptation range scenarios due first principles (clinal did not evolve) statistical issues but were detected overcorrection for structure). Despite limitations GEAs, this shows back-transformation ordination can predict individual genotype environmental data regardless whether inference GEAs was accurate. In addition, frameworks introduced be by empiricists quantify importance clinal alleles adaptation. research highlights trait prediction lead accurate underlying display patterns.

Language: Английский

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How useful is genomic data for predicting maladaptation to future climate?

Global Change Biology, Journal Year: 2024, Volume and Issue: 30(4)

Published: April 1, 2024

Abstract Methods using genomic information to forecast potential population maladaptation climate change or new environments are becoming increasingly common, yet the lack of model validation poses serious hurdles toward their incorporation into management and policy. Here, we compare estimates derived from two methods—Gradient Forests (GF offset ) risk non‐adaptedness (RONA)—using exome capture pool‐seq data 35 39 populations across three conifer taxa: Douglas‐fir varieties jack pine. We evaluate sensitivity these algorithms source input loci (markers selected genotype–environment associations [GEA] those at random). validate methods against 2‐ 52‐year growth mortality measured in independent transplant experiments. Overall, find that both often better predict performance than climatic geographic distances. also GF RONA models surprisingly not improved GEA candidates. Even with promising results, variation projections future climates makes it difficult identify most maladapted either method. Our work advances understanding applicability approaches, discuss recommendations for use.

Language: Английский

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Interpretation issues with “genomic vulnerability” arise from conceptual issues in local adaptation and maladaptation

Evolution Letters, Journal Year: 2024, Volume and Issue: 8(3), P. 331 - 339

Published: Feb. 8, 2024

Abstract As climate change causes the environment to shift away from local optimum that populations have adapted to, fitness declines are predicted occur. Recently, methods known as genomic offsets (GOs) become a popular tool predict population responses landscape data. Populations with high GO been interpreted “genomic vulnerability” change. GOs often implicitly offset, or in of an individual new compared reference. However, there several different types offset can be calculated, and appropriate choice depends on management goals. This study uses hypothetical empirical data explore situations which may not correlated each other GO. The examples reveal even when common garden experiment, this does necessarily validate their ability environmental Conceptual also used show how large arise under positive thus cannot vulnerability. These issues resolved robust validation experiments evaluate GOs.

Language: Английский

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Seeing the forest for the trees: Assessing genetic offset predictions from gradient forest

Evolutionary Applications, Journal Year: 2022, Volume and Issue: 15(3), P. 403 - 416

Published: Feb. 4, 2022

Gradient Forest (GF) is a machine learning algorithm designed to analyze spatial patterns of biodiversity as function environmental gradients. An offset measure between the GF-predicted association adapted alleles and new environment (GF Offset) increasingly being used predict loss environmentally under rapid change, but remains mostly untested for this purpose. Here, we explore robustness GF Offset assumption violations, its relationship measures fitness, using SLiM simulations with explicit genome architecture metapopulation. We evaluate in: (1) neutral model no adaptation; (2) monogenic "population genetic" single locus; (3) polygenic "quantitative two adaptive traits, each adapting different environment. found be broadly correlated fitness offsets both locus architectures. However, demography, genomic architecture, nature can all confound relationships fitness. promising tool, it important understand limitations underlying assumptions, especially when in context predicting maladaptation.

Language: Английский

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Current status and trends in forest genomics

Forestry Research, Journal Year: 2022, Volume and Issue: 2(1), P. 0 - 0

Published: Jan. 1, 2022

Forests are not only the most predominant of Earth's terrestrial ecosystems, but also core supply for essential products human use. However, global climate change and ongoing population explosion severely threatens health forest ecosystem aggravtes deforestation degradation. Forest genomics has great potential increasing productivity adaptation to changing climate. In last two decades, field advanced quickly owing advent multiple high-throughput sequencing technologies, single cell RNA-seq, clustered regularly interspaced short palindromic repeats (CRISPR)-mediated genome editing, spatial transcriptomes, as well bioinformatics analysis which have led generation multidimensional, multilayered, spatiotemporal gene expression data. These together with basic technologies routinely used in plant biotechnology, enable us tackle many important or unique issues biology, provide a panoramic view an integrative elucidation molecular regulatory mechanisms underlying phenotypic changes variations. this review, we recapitulated advancement current status 12 research branches genomics, then provided future directions focuses each area. Evidently, shift from simple biotechnology-based research, setup investigation interpretation development differentiation just begun emerge.

Language: Английский

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A Quantitative Theory for Genomic Offset Statistics

Molecular Biology and Evolution, Journal Year: 2023, Volume and Issue: 40(6)

Published: June 1, 2023

Abstract Genomic offset statistics predict the maladaptation of populations to rapid habitat alteration based on association genotypes with environmental variation. Despite substantial evidence for empirical validity, genomic have well-identified limitations, and lack a theory that would facilitate interpretations predicted values. Here, we clarified theoretical relationships between unobserved fitness traits controlled by environmentally selected loci proposed geometric measure after change in local environment. The predictions our were verified computer simulations data African pearl millet (Cenchrus americanus) obtained from common garden experiment. Our results unified perspective provided foundation necessary when considering their potential application conservation management face change.

Language: Английский

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The accuracy of predicting maladaptation to new environments with genomic data

Molecular Ecology Resources, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 30, 2024

Rapid environmental change poses unprecedented challenges to species persistence. To understand the extent that continued could have, genomic offset methods have been used forecast maladaptation of natural populations future change. However, while their use has become increasingly common, little is known regarding predictive performance across a wide array realistic and challenging scenarios. Here, we evaluate currently available (gradientForest, Risk-Of-Non-Adaptedness, redundancy analysis with without structure correction LFMM2) using an extensive set simulated data sets vary demography, adaptive architecture number spatial patterns environments. For each set, train models either all, or neutral marker in silico common gardens by correlating fitness projected offset. Using over 4,849,600 such evaluations, find (1) method largely due degree local adaptation metapopulation (LA), (2) provide minimal advantages, (3) within range variable declines when are trained additional non-adaptive environments (4) despite more rapidly globally novel climates (i.e. climate analogue range) for metapopulations greater LA than lesser LA. We discuss implications these results management, assisted gene flow migration.

Language: Английский

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