Finding the Genomic Basis of Local Adaptation: Pitfalls, Practical Solutions, and Future Directions

The American Naturalist, Journal Year: 2016, Volume and Issue: 188(4), P. 379 - 397

Published: Aug. 15, 2016

Uncovering the genetic and evolutionary basis of local adaptation is a major focus biology. The recent development cost-effective methods for obtaining high-quality genome-scale data makes it possible to identify some loci responsible adaptive differences among populations. Two basic approaches identifying putatively locally have been developed are broadly used: one that identifies with unusually high differentiation populations (differentiation outlier methods) searches correlations between population allele frequencies environments (genetic-environment association methods). Here, we review promises challenges these genome scan methods, including correcting confounding influence species' demographic history, biases caused by missing aspects genome, matching scales environmental structure, other statistical considerations. In each case, make suggestions best practices maximizing accuracy efficiency scans detect underlying adaptation. With attention their current limitations, can be an important tool in finding change.

Language: Английский

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Genomic signals of selection predict climate-driven population declines in a migratory bird

Science, Journal Year: 2018, Volume and Issue: 359(6371), P. 83 - 86

Published: Jan. 5, 2018

Yellow warblers already in decline As the climate changes, species' ability to adapt changing conditions may relate directly their future persistence. Determining whether and when this will happen is challenging, however, because it difficult tease apart causes of or maintenance. Bay et al. looked at relationship between genomic variation environment North American populations yellow warbler (see Perspective by Fitzpatrick Edelsparre). Genes linked exploratory migratory behavior were important for successful adaptation. Furthermore, identified as “genetically vulnerable” limited climate-associated declining. Science , issue p. 83 ; see also 29

Language: Английский

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A decade of seascape genetics: contributions to basic and applied marine connectivity

Marine Ecology Progress Series, Journal Year: 2016, Volume and Issue: 554, P. 1 - 19

Published: June 7, 2016

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout JournalEditorsTheme Sections 554:1-19 (2016) - DOI: https://doi.org/10.3354/meps11792 FEATURE ARTICLE: REVIEW A decade of seascape genetics: contributions basic and applied marine connectivity Kimberly A. Selkoe1,2,3,*,**, Cassidy C. D'Aloia4,**, Eric D. Crandall5, Matthew Iacchei6, Libby Liggins7, Jonathan B. Puritz8, Sophie von der Heyden9, Robert J. Toonen1 1Hawai'i Institute Biology, University Hawai'i, Kāne'ohe, HI 97644, USA 2National Center for Ecological Analysis Synthesis, California, Santa Barbara, CA 93101, 3Bren School Environmental Science Management, 4Department & Evolutionary Toronto, ON M5S 3G5, Canada 5School Natural Sciences, California State University, Monterey Bay, 100 Campus Center, Seaside, 93955, 6Department Oceanography, Hawai'i at M-anoa, Honolulu, 96822, 7Institute Mathematical Massey Auckland 0745, New Zealand 8Marine Northeastern Nahant, MA 01945, 9Evolutionary Genomics Group, Department Botany Zoology, Stellenbosch, Private Bag X1, Matieland 7602, South Africa *Corresponding author: [email protected]**These authors contributed equally this work ABSTRACT: Seascape genetics, a term coined in 2006, is fast growing area population genetics that draws on ecology, oceanography geography address challenges understanding applications management. We provide an accessible overview latest developments merge exciting new ideas from field with statistical technical advances genetics. After summarizing historical context leading emergence we detail questions methodological approaches are evolving discipline, highlight conservation management, conclude summary field's transition genomics. From genetic studies, assess trends taxonomic geographic coverage, sampling design, dominant drivers. Notably, temperature, show equal prevalence influence spatial patterns, tests over 20 other factors suggest variety forces impact distinct spatio-temporal scales. level rigor analysis critical disentangling multiple drivers spurious effects. Coupled GIS data genomic scale sequencing methods, taking beyond initial focus identifying correlations hypothesis-driven insights into patterns processes adaptation. The studies illuminating differences between demographic, functional neutral connectivity, informing reserve fisheries science strategies resilience climate change anthropogenic impacts. KEY WORDS: · Connectivity Gene flow Dispersal Landscape Full text pdf format Supplementary material NextCite article as: Selkoe KA, D'Aloia CC, Crandall ED, Iacchei M others connectivity. Mar Ecol Prog Ser 554:1-19. Export citation Tweet linkedIn Cited by Published Vol. 554. Online publication date: July 28, 2016 Print ISSN: 0171-8630; 1616-1599 Copyright © Inter-Research.

Language: Английский

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Evolutionary genomics can improve prediction of species’ responses to climate change

Evolution Letters, Journal Year: 2020, Volume and Issue: 4(1), P. 4 - 18

Published: Jan. 14, 2020

Abstract Global climate change (GCC) increasingly threatens biodiversity through the loss of species, and transformation entire ecosystems. Many species are challenged by pace GCC because they might not be able to respond fast enough changing biotic abiotic conditions. Species can either shifting their range, or persisting in local habitat. If populations persist, tolerate climatic changes phenotypic plasticity, genetically adapt conditions depending on genetic variability census population size allow for de novo mutations. Otherwise, will experience demographic collapses may go extinct. Current approaches predicting responses begin combine ecological evolutionary information distribution modelling. Including an dimension substantially improve projections which have accounted key processes such as dispersal, adaptive change, demography, interactions. However, eco-evolutionary models require new data methods estimation a species' potential, so far only been available small number model species. To represent global biodiversity, we need devise large-scale collection strategies define ecology potential broad range especially keystone We also standardized replicable modelling that integrate these account when impact survival. Here, discuss different genomic used investigate predict GCC. This serve guidance researchers looking appropriate experimental setup particular system. furthermore highlight future directions moving forward field allocating resources more effectively, implement mitigation measures before extinct ecosystems lose important functions.

Language: Английский

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

Annual Review of Ecology Evolution and Systematics, Journal Year: 2020, Volume and Issue: 51(1), P. 245 - 269

Published: Aug. 10, 2020

Signals of local adaptation have been found in many plants and animals, highlighting the heterogeneity distribution adaptive genetic variation throughout species ranges. In coming decades, global climate change is expected to induce shifts selective pressures that shape this variation. These changes will likely result varying degrees maladaptation spatial reshuffling underlying distributions alleles. There a growing interest using population genomic data help predict future disruptions locally gene-environment associations. One motivation behind such work better understand how effects changing on populations’ short-term fitness could vary spatially across Here we review current use disruption climates. After assessing goals motivationsunderlying approach, main steps associated statistical methods currently explore our understanding limits potential genomics (mal)adaptation.

Language: Английский

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Environmental filtering bypHand soil nutrients drives community assembly in fungi at fine spatial scales

Molecular Ecology, Journal Year: 2017, Volume and Issue: 26(24), P. 6960 - 6973

Published: Nov. 8, 2017

Whether niche processes, like environmental filtering, or neutral dispersal limitation, are the primary forces driving community assembly is a central question in ecology. Here, we use natural experimental system of isolated tree "islands" to test whether environment geography primarily structures fungal composition at fine spatial scales. This consists pairs two distantly related, congeneric pine trees established varying distances from each other and forest edge, allowing us disentangle effects geographic distance vs. host edaphic on associated communities. We identified with Illumina sequencing ITS amplicons, measured all relevant parameters for tree-including age, size soil chemistry-and calculated others nearest edge. applied generalized dissimilarity modelling total ectomycorrhizal (EMF) communities were structured by filtering. Our results provide strong evidence that as many organisms, processes both contribute significantly turnover fungi, but filtering plays dominant role structuring free-living symbiotic In our study system, found pH organic matter drive cation exchange capacity-and, surprisingly, not species-were largest factors affecting EMF composition. These findings support an emerging paradigm may play soil-mediated systems.

Language: Английский

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Controlling false discoveries in genome scans for selection

Molecular Ecology, Journal Year: 2015, Volume and Issue: 25(2), P. 454 - 469

Published: Dec. 15, 2015

Abstract Population differentiation (PD) and ecological association (EA) tests have recently emerged as prominent statistical methods to investigate signatures of local adaptation using population genomic data. Based on models, these genomewide testing procedures attracted considerable attention tools identify loci potentially targeted by natural selection. An important issue with PD EA is that incorrect model specification can generate large numbers false‐positive associations. Spurious may indeed arise when shared demographic history, patterns isolation distance, cryptic relatedness or genetic background are ignored. Recent works widely focused improvements test corrections for those confounding effects. Despite significant algorithmic improvements, there still a number open questions how check false discoveries under control implement corrections, combine from multiple genome scan methods. This tutorial study provides detailed answer questions. It clarifies the relationships between traditional based allele frequency unified framework their underlying tests. We demonstrate techniques developed in area studies, such inflation factors linear mixed benefit provide guidelines good practice while conducting landscape applications. Finally, we highlight combination several well‐calibrated increase power reject neutrality, improving our ability infer data sets.

Language: Английский

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Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes

Molecular Ecology, Journal Year: 2015, Volume and Issue: 25(1), P. 104 - 120

Published: Nov. 18, 2015

Abstract The spatial structure of the environment (e.g. configuration habitat patches) may play an important role in determining strength local adaptation. However, previous studies heterogeneity and adaptation have largely been limited to simple landscapes, which poorly represent multiscale common nature. Here, we use simulations pursue two goals: (i) explore how landscape heterogeneity, dispersal ability selection affect adaptation, (ii) evaluate performance several genotype–environment association ( GEA ) methods for detecting loci involved We found that increased spatially aggregated regimes, but remained strong patchy landscapes when was moderate strong. Weak resulted weak relatively unaffected by heterogeneity. In general, power detection closely reflected levels False‐positive rates FPR s), however, showed distinct differences across based on population structure. univariate approach had high s (up 55%) under scenarios, due isolation distance. By contrast, multivariate, ordination‐based uniformly low (0–2%), suggesting these approaches can effectively control Specifically, constrained ordinations best balance will be a useful addition toolkit. Our results provide both theoretical practical insights into conditions shape impact our detect selection.

Language: Английский

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Redundancy analysis: A Swiss Army Knife for landscape genomics

Methods in Ecology and Evolution, Journal Year: 2021, Volume and Issue: 12(12), P. 2298 - 2309

Published: Sept. 20, 2021

Abstract Landscape genomics identifies how spatial and environmental factors structure the amount distribution of genetic variation among populations. genomic analyses have been applied across diverse taxonomic groups ecological settings, are increasingly used to analyse datasets composed large numbers markers multiple predictors. It is in this context that multivariate methods show their strengths. Redundancy analysis (RDA) a constrained ordination that, landscape framework, models linear relationships environment predictors variation, effectively identifying covarying allele frequencies associated with environment. RDA can be at both individual population levels, include covariates account for confounding directly infer genotype–environment associations on landscape. The modelling response explanatory variables allows accommodate complexity found nature, producing powerful efficient tool genomics. In review, we outline uses genomics, including variable selection, variance partitioning, associations, calculation adaptive indices offset. To illustrate these applications, use published dataset lodgepole pine includes genomic, phenotypic data. We provide an introduction statistical basis RDA, tutorial its interpretation discuss limitations guidelines avoid misuse. This review comprehensive resource community improve understanding as encourage appropriate applications. truly Swiss Army Knife genomics: multipurpose, adaptable versatile approach identifying, evaluating forecasting between variation.

Language: Английский

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Connecting Earth observation to high-throughput biodiversity data

Nature Ecology & Evolution, Journal Year: 2017, Volume and Issue: 1(7)

Published: June 22, 2017

Language: Английский

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