1
|
Lion S, Sasaki A, Boots M. Extending eco-evolutionary theory with oligomorphic dynamics. Ecol Lett 2023; 26 Suppl 1:S22-S46. [PMID: 36814412 DOI: 10.1111/ele.14183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 01/30/2023] [Accepted: 01/30/2023] [Indexed: 02/24/2023]
Abstract
Understanding the interplay between ecological processes and the evolutionary dynamics of quantitative traits in natural systems remains a major challenge. Two main theoretical frameworks are used to address this question, adaptive dynamics and quantitative genetics, both of which have strengths and limitations and are often used by distinct research communities to address different questions. In order to make progress, new theoretical developments are needed that integrate these approaches and strengthen the link to empirical data. Here, we discuss a novel theoretical framework that bridges the gap between quantitative genetics and adaptive dynamics approaches. 'Oligomorphic dynamics' can be used to analyse eco-evolutionary dynamics across different time scales and extends quantitative genetics theory to account for multimodal trait distributions, the dynamical nature of genetic variance, the potential for disruptive selection due to ecological feedbacks, and the non-normal or skewed trait distributions encountered in nature. Oligomorphic dynamics explicitly takes into account the effect of environmental feedback, such as frequency- and density-dependent selection, on the dynamics of multi-modal trait distributions and we argue it has the potential to facilitate a much tighter integration between eco-evolutionary theory and empirical data.
Collapse
Affiliation(s)
| | - Akira Sasaki
- Research Center for Integrative Evolutionary Science, The Graduate University for Advanced Studies, SOKENDAI, Hayama, Japan
- Evolution and Ecology Program, International Institute for Applied Systems Analysis, Laxenburg, Austria
| | - Mike Boots
- Integrative Biology, University of California, Berkeley, California, USA
- Department of Ecology and Conservation, University of Exeter, Penryn, UK
| |
Collapse
|
2
|
Rolland J, Henao-Diaz LF, Doebeli M, Germain R, Harmon LJ, Knowles LL, Liow LH, Mank JE, Machac A, Otto SP, Pennell M, Salamin N, Silvestro D, Sugawara M, Uyeda J, Wagner CE, Schluter D. Conceptual and empirical bridges between micro- and macroevolution. Nat Ecol Evol 2023; 7:1181-1193. [PMID: 37429904 DOI: 10.1038/s41559-023-02116-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 06/13/2023] [Indexed: 07/12/2023]
Abstract
Explaining broad molecular, phenotypic and species biodiversity patterns necessitates a unifying framework spanning multiple evolutionary scales. Here we argue that although substantial effort has been made to reconcile microevolution and macroevolution, much work remains to identify the links between biological processes at play. We highlight four major questions of evolutionary biology whose solutions require conceptual bridges between micro and macroevolution. We review potential avenues for future research to establish how mechanisms at one scale (drift, mutation, migration, selection) translate to processes at the other scale (speciation, extinction, biogeographic dispersal) and vice versa. We propose ways in which current comparative methods to infer molecular evolution, phenotypic evolution and species diversification could be improved to specifically address these questions. We conclude that researchers are in a better position than ever before to build a synthesis to understand how microevolutionary dynamics unfold over millions of years.
Collapse
Affiliation(s)
- Jonathan Rolland
- CNRS, UMR5174, Laboratoire Evolution et Diversité Biologique, Université Toulouse 3 Paul Sabatier, Toulouse, France.
| | - L Francisco Henao-Diaz
- Department of Zoology, and Biodiversity Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
| | - Michael Doebeli
- Department of Zoology, and Department of Mathematics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Rachel Germain
- Department of Zoology, and Biodiversity Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Luke J Harmon
- Dept. of Biological Sciences, University of Idaho, Moscow, ID, USA
| | - L Lacey Knowles
- Department of Ecology and Evolutionary Biology, Museum of Zoology, University of Michigan, Ann Arbor, MI, USA
| | | | - Judith E Mank
- Department of Zoology, and Biodiversity Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Antonin Machac
- Department of Zoology, and Biodiversity Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
- Laboratory of Environmental Microbiology, Institute of Microbiology of the CAS, Prague, Czech Republic
| | - Sarah P Otto
- Department of Zoology, and Biodiversity Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Matt Pennell
- Departments of Quantitative and Computational Biology and Biological Sciences, University of Southern California, Los Angeles, CA, USA
| | - Nicolas Salamin
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Daniele Silvestro
- Department of Biology, University of Fribourg, Fribourg, Switzerland
- Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden
- Gothenburg Global Biodiversity Centre, University of Gothenburg, Gothenburg, Sweden
| | - Mauro Sugawara
- Department of Zoology, and Biodiversity Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
- Mário Schenberg Institute, São Paulo, Brazil
| | - Josef Uyeda
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA, USA
| | - Catherine E Wagner
- Department of Botany, and Program in Ecology and Evolution, University of Wyoming, Laramie, WY, USA
| | - Dolph Schluter
- Department of Zoology, and Biodiversity Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| |
Collapse
|
3
|
Ergon R. Dynamical BLUP modeling of reaction norm evolution, accommodating changing environments, overlapping generations, and multivariate data. Ecol Evol 2023; 13:e10194. [PMID: 37424936 PMCID: PMC10323147 DOI: 10.1002/ece3.10194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 05/28/2023] [Accepted: 05/30/2023] [Indexed: 07/11/2023] Open
Abstract
For theoretical studies, reaction norm evolution in a changing environment can be modeled by means of the multivariate breeder's equation, with the reaction norm parameters treated as traits in their own right. This is, however, not a feasible approach for the use of field data, where the intercept and slope values are not available. An alternative approach is to use infinite-dimensional characters and smooth covariance function estimates found by, e.g., random regression. This is difficult because of the need to find, for example, polynomial basis functions that fit the data reasonably well over time, and because reaction norms in multivariate cases are correlated, such that they cannot be modeled independently. Here, I present an alternative approach based on a multivariate linear mixed model of any order, with dynamical incidence and residual covariance matrices that reflect the changing environment. From such a mixed model follows a dynamical BLUP model for the estimation of the individual reaction norm parameter values at any given parent generation, and for updating of the mean reaction norm parameter values from generation to generation by means of Robertson's secondary theorem of natural selection. This will, for example, make it possible to disentangle the microevolutionary and plasticity components in climate change responses. The BLUP model incorporates the additive genetic relationship matrix in the usual way, and overlapping generations can easily be accommodated. Additive genetic and environmental model parameters are assumed to be known and constant, but it is discussed how they can be estimated by means of a prediction error method. The identifiability by the use of field or laboratory data containing environmental, phenotypic, fitness, and additive genetic relationship data is an important feature of the proposed model.
Collapse
Affiliation(s)
- Rolf Ergon
- University of South‐Eastern NorwayPorsgrunnNorway
| |
Collapse
|
4
|
de Souza Silva CC, Cirne D, Freitas O, Campos PRA. Phenotypic evolution as an Ornstein-Uhlenbeck process: The effect of environmental variation and phenotypic plasticity. Phys Rev E 2023; 107:024417. [PMID: 36932534 DOI: 10.1103/physreve.107.024417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 02/13/2023] [Indexed: 03/19/2023]
Abstract
Here we investigate phenotypic evolution from the perspective of the Ornstein-Uhlenbeck (OU) process. Evolutionarily speaking, the model assumes the existence of stabilizing selection toward a phenotypic optimum. The standard (OU) model is modified to include environmental variation by taking a moving phenotypic optimum and endowing organisms with phenotypic plasticity. These two processes lead to an effective fitness landscape, which deforms the original. We observe that the simultaneous occurrence of environmental variation and phenotypic plasticity leads to skewed phenotypic distributions. The skewness of the resulting phenotypic distributions strongly depends on the rate of environmental variation and strength of selection. When generalized to more than one trait, the phenotypic distributions are not only affected by the magnitude of the rate of environmental variation but also by its direction. A remarkable feature of our predictions is the existence of an upper bound for the critical rate of environmental variation to allow population persistence, even if there is no cost associated with phenotypic plasticity.
Collapse
Affiliation(s)
| | - Diego Cirne
- Departamento de Física, Universidade Federal de Pernambuco, 50740-560 Recife-PE, Brazil
| | - Osmar Freitas
- Departamento de Física, Universidade Federal de Pernambuco, 50740-560 Recife-PE, Brazil
| | - Paulo R A Campos
- Departamento de Física, Universidade Federal de Pernambuco, 50740-560 Recife-PE, Brazil
| |
Collapse
|
5
|
Decomposing phenotypic skew and its effects on the predicted response to strong selection. Nat Ecol Evol 2022; 6:774-785. [PMID: 35422480 DOI: 10.1038/s41559-022-01694-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 02/08/2022] [Indexed: 12/29/2022]
Abstract
The major frameworks for predicting evolutionary change assume that a phenotype's underlying genetic and environmental components are normally distributed. However, the predictions of these frameworks may no longer hold if distributions are skewed. Despite this, phenotypic skew has never been decomposed, meaning the fundamental assumptions of quantitative genetics remain untested. Here we demonstrate that the substantial phenotypic skew in the body size of juvenile blue tits (Cyanistes caeruleus) is driven by environmental factors. Although skew had little impact on our predictions of selection response in this case, our results highlight the impact of skew on the estimation of inheritance and selection. Specifically, the nonlinear parent-offspring regressions induced by skew, alongside selective disappearance, can strongly bias estimates of heritability. The ubiquity of skew and strong directional selection on juvenile body size imply that heritability is commonly overestimated, which may in part explain the discrepancy between predicted and observed trait evolution.
Collapse
|
6
|
Lion S, Boots M, Sasaki A. Multi-morph eco-evolutionary dynamics in structured populations. Am Nat 2022; 200:345-372. [DOI: 10.1086/720439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
|
7
|
Reid JM, Acker P. Conceptualizing the evolutionary quantitative genetics of phenological life‐history events: Breeding time as a plastic threshold trait. Evol Lett 2022; 6:220-233. [PMID: 35784452 PMCID: PMC9233176 DOI: 10.1002/evl3.278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 01/22/2022] [Accepted: 01/30/2022] [Indexed: 11/06/2022] Open
Affiliation(s)
- Jane M. Reid
- Centre for Biodiversity Dynamics NTNU Trondheim 7491 Norway
- School of Biological Sciences University of Aberdeen Aberdeen AB24 2TZ United Kingdom
| | - Paul Acker
- Centre for Biodiversity Dynamics NTNU Trondheim 7491 Norway
| |
Collapse
|
8
|
Hadfield JD, Reed TE. Directional selection and the evolution of breeding date in birds, revisited: Hard selection and the evolution of plasticity. Evol Lett 2022; 6:178-188. [PMID: 35386830 PMCID: PMC8966488 DOI: 10.1002/evl3.279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 12/30/2021] [Accepted: 01/07/2022] [Indexed: 11/23/2022] Open
Abstract
The mismatch between when individuals breed and when we think they should breed has been a long-standing problem in evolutionary ecology. Price et al. is a classic theory paper in this field and is mainly cited for its most obvious result: if individuals with high nutritional condition breed early, then the advantage of breeding early may be overestimated when information on nutritional condition is absent. Price at al.'s less obvious result is that individuals, on average, are expected to breed later than the optimum. Here, we provide an explanation of their non-intuitive result in terms of hard selection, and go on to show that neither of their results are expected to hold if the relationship between breeding date and nutrition is allowed to evolve. By introducing the assumption that the advantage of breeding early is greater for individuals in high nutritional condition, we show that their most cited result can be salvaged. However, individuals, on average, are expected to breed earlier than the optimum, not later. More generally, we also show that the hard selection mechanisms that underpin these results have major implications for the evolution of plasticity: when environmental heterogeneity becomes too great, plasticity is selected against, prohibiting the evolution of generalists.
Collapse
Affiliation(s)
- Jarrod D. Hadfield
- Institute of Evolutionary Biology, School of Biological SciencesUniversity of EdinburghEdinburghEH9 3JTUK
| | - Thomas E. Reed
- School of Biological, Earth and Environmental SciencesUniversity College Cork, Distillery FieldsNorth MallCorkT23 N73KIreland
| |
Collapse
|
9
|
Mattila ALK, Jiggins CD, Opedal ØH, Montejo-Kovacevich G, Pinheiro de Castro ÉC, McMillan WO, Bacquet C, Saastamoinen M. Evolutionary and ecological processes influencing chemical defense variation in an aposematic and mimetic Heliconius butterfly. PeerJ 2021; 9:e11523. [PMID: 34178447 PMCID: PMC8216171 DOI: 10.7717/peerj.11523] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 05/05/2021] [Indexed: 02/01/2023] Open
Abstract
Chemical defences against predators underlie the evolution of aposematic coloration and mimicry, which are classic examples of adaptive evolution. Surprisingly little is known about the roles of ecological and evolutionary processes maintaining defence variation, and how they may feedback to shape the evolutionary dynamics of species. Cyanogenic Heliconius butterflies exhibit diverse warning color patterns and mimicry, thus providing a useful framework for investigating these questions. We studied intraspecific variation in de novo biosynthesized cyanogenic toxicity and its potential ecological and evolutionary sources in wild populations of Heliconius erato along environmental gradients, in common-garden broods and with feeding treatments. Our results demonstrate substantial intraspecific variation, including detectable variation among broods reared in a common garden. The latter estimate suggests considerable evolutionary potential in this trait, although predicting the response to selection is likely complicated due to the observed skewed distribution of toxicity values and the signatures of maternal contributions to the inheritance of toxicity. Larval diet contributed little to toxicity variation. Furthermore, toxicity profiles were similar along steep rainfall and altitudinal gradients, providing little evidence for these factors explaining variation in biosynthesized toxicity in natural populations. In contrast, there were striking differences in the chemical profiles of H. erato from geographically distant populations, implying potential local adaptation in the acquisition mechanisms and levels of defensive compounds. The results highlight the extensive variation and potential for adaptive evolution in defense traits for aposematic and mimetic species, which may contribute to the high diversity often found in these systems.
Collapse
Affiliation(s)
- Anniina L K Mattila
- Research Centre for Ecological Change, Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki, Finland.,Helsinki Life Science Institute, University of Helsinki, Helsinki, Finland.,Current affiliation: Finnish Museum of Natural History (LUOMUS), University of Helsinki, Helsinki, Finland
| | - Chris D Jiggins
- Department of Zoology, University of Cambridge, Cambridge, United Kingdom
| | | | | | | | | | | | - Marjo Saastamoinen
- Research Centre for Ecological Change, Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki, Finland.,Helsinki Life Science Institute, University of Helsinki, Helsinki, Finland
| |
Collapse
|
10
|
O'Brien AM, Jack CN, Friesen ML, Frederickson ME. Whose trait is it anyways? Coevolution of joint phenotypes and genetic architecture in mutualisms. Proc Biol Sci 2021; 288:20202483. [PMID: 33434463 DOI: 10.1098/rspb.2020.2483] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Evolutionary biologists typically envision a trait's genetic basis and fitness effects occurring within a single species. However, traits can be determined by and have fitness consequences for interacting species, thus evolving in multiple genomes. This is especially likely in mutualisms, where species exchange fitness benefits and can associate over long periods of time. Partners may experience evolutionary conflict over the value of a multi-genomic trait, but such conflicts may be ameliorated by mutualism's positive fitness feedbacks. Here, we develop a simulation model of a host-microbe mutualism to explore the evolution of a multi-genomic trait. Coevolutionary outcomes depend on whether hosts and microbes have similar or different optimal trait values, strengths of selection and fitness feedbacks. We show that genome-wide association studies can map joint traits to loci in multiple genomes and describe how fitness conflict and fitness feedback generate different multi-genomic architectures with distinct signals around segregating loci. Partner fitnesses can be positively correlated even when partners are in conflict over the value of a multi-genomic trait, and conflict can generate strong mutualistic dependency. While fitness alignment facilitates rapid adaptation to a new optimum, conflict maintains genetic variation and evolvability, with implications for applied microbiome science.
Collapse
Affiliation(s)
- Anna M O'Brien
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Canada
| | - Chandra N Jack
- Department of Plant Pathology, Washington State University, Pullman, Washington, USA
| | - Maren L Friesen
- Department of Plant Pathology, Washington State University, Pullman, Washington, USA.,Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, USA
| | - Megan E Frederickson
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Canada
| |
Collapse
|
11
|
Milocco L, Salazar‐Ciudad I. Is evolution predictable? Quantitative genetics under complex genotype‐phenotype maps. Evolution 2020; 74:230-244. [DOI: 10.1111/evo.13907] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Revised: 11/22/2019] [Accepted: 11/27/2019] [Indexed: 12/28/2022]
Affiliation(s)
- Lisandro Milocco
- Institute of BiotechnologyUniversity of Helsinki 00014 Helsinki Finland
| | - Isaac Salazar‐Ciudad
- Institute of BiotechnologyUniversity of Helsinki 00014 Helsinki Finland
- Centre de Recerca Matemàtica 08193 Barcelona Spain
- Genomics, Bioinformatics and Evolution. Departament de Genètica i MicrobiologiaUniversitat Autònoma de Barcelona 08193 Barcelona Spain
| |
Collapse
|
12
|
Hunter DC, Pemberton JM, Pilkington JG, Morrissey MB. Quantification and decomposition of environment-selection relationships. Evolution 2019. [PMID: 29518255 DOI: 10.1111/evo.13461] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
In nature, selection varies across time in most environments, but we lack an understanding of how specific ecological changes drive this variation. Ecological factors can alter phenotypic selection coefficients through changes in trait distributions or individual mean fitness, even when the trait-absolute fitness relationship remains constant. We apply and extend a regression-based approach in a population of Soay sheep (Ovis aries) and suggest metrics of environment-selection relationships that can be compared across studies. We then introduce a novel method that constructs an environmentally structured fitness function. This allows calculation of full (as in existing approaches) and partial (acting separately through the absolute fitness function slope, mean fitness, and phenotype distribution) sensitivities of selection to an ecological variable. Both approaches show positive overall effects of density on viability selection of lamb mass. However, the second approach demonstrates that this relationship is largely driven by effects of density on mean fitness, rather than on the trait-fitness relationship slope. If such mechanisms of environmental dependence of selection are common, this could have important implications regarding the frequency of fluctuating selection, and how previous selection inferences relate to longer term evolutionary dynamics.
Collapse
Affiliation(s)
- Darren C Hunter
- School of Biology, University of St Andrews, St Andrews, Fife KY16 9TH, United Kingdom
| | - Josephine M Pemberton
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3FL, United Kingdom
| | - Jill G Pilkington
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3FL, United Kingdom
| | - Michael B Morrissey
- School of Biology, University of St Andrews, St Andrews, Fife KY16 9TH, United Kingdom
| |
Collapse
|
13
|
Regan CE, Tuke LA, Colpitts J, McLoughlin PD, Wilson AJ, Poissant J. Evolutionary quantitative genetics of juvenile body size in a population of feral horses reveals sexually antagonistic selection. Evol Ecol 2019. [DOI: 10.1007/s10682-019-09988-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
|
14
|
Pujol B, Blanchet S, Charmantier A, Danchin E, Facon B, Marrot P, Roux F, Scotti I, Teplitsky C, Thomson CE, Winney I. The Missing Response to Selection in the Wild. Trends Ecol Evol 2018; 33:337-346. [PMID: 29628266 PMCID: PMC5937857 DOI: 10.1016/j.tree.2018.02.007] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Revised: 02/26/2018] [Accepted: 02/28/2018] [Indexed: 01/28/2023]
Abstract
Although there are many examples of contemporary directional selection, evidence for responses to selection that match predictions are often missing in quantitative genetic studies of wild populations. This is despite the presence of genetic variation and selection pressures – theoretical prerequisites for the response to selection. This conundrum can be explained by statistical issues with accurate parameter estimation, and by biological mechanisms that interfere with the response to selection. These biological mechanisms can accelerate or constrain this response. These mechanisms are generally studied independently but might act simultaneously. We therefore integrated these mechanisms to explore their potential combined effect. This has implications for explaining the apparent evolutionary stasis of wild populations and the conservation of wildlife. Recent discoveries at the intersection of quantitative genetics and evolutionary ecology are challenging our views on the potential of wild populations to respond to selection. Multiple biological mechanisms can disconnect genetic variation from the response to selection in the wild. We highlight areas for future research. We provide an integrative framework that can be used to qualitatively assess the combined influence of these mechanisms on the response to selection.
Collapse
Affiliation(s)
- Benoit Pujol
- Laboratoire Évolution & Diversité Biologique (EDB UMR 5174), Université Fédérale de Toulouse Midi-Pyrénées, CNRS, IRD, UPS, 31062 Toulouse, France; Groupement de Recherche de l'Institut Ecologie et Environnement 6448, Génétique Quantitative dans les Populations Naturelles (GQPN), c/o EDB, 31062 Toulouse, France.
| | - Simon Blanchet
- Laboratoire Évolution & Diversité Biologique (EDB UMR 5174), Université Fédérale de Toulouse Midi-Pyrénées, CNRS, IRD, UPS, 31062 Toulouse, France; Station d'Ecologie Théorique Expérimentale (SETE), CNRS UMR 5321, Université Paul Sabatier, 09200 Moulis, France; Groupement de Recherche de l'Institut Ecologie et Environnement 6448, Génétique Quantitative dans les Populations Naturelles (GQPN), c/o EDB, 31062 Toulouse, France
| | - Anne Charmantier
- Centre d'Ecologie Fonctionnelle et Evolutive (CEFE), CNRS UMR 5175, 34293 Montpellier, France; Département des Sciences Biologiques, Université du Québec à Montréal, CP 888 Succursale Centre-Ville, H3P 3P8 QC, Canada; Groupement de Recherche de l'Institut Ecologie et Environnement 6448, Génétique Quantitative dans les Populations Naturelles (GQPN), c/o EDB, 31062 Toulouse, France
| | - Etienne Danchin
- Laboratoire Évolution & Diversité Biologique (EDB UMR 5174), Université Fédérale de Toulouse Midi-Pyrénées, CNRS, IRD, UPS, 31062 Toulouse, France; Groupement de Recherche de l'Institut Ecologie et Environnement 6448, Génétique Quantitative dans les Populations Naturelles (GQPN), c/o EDB, 31062 Toulouse, France
| | - Benoit Facon
- UMR Peuplements Végétaux et Bioagresseurs en Milieu Tropical (PVBMT), Institut National de la Recherche Agronomique (INRA), Saint Pierre, Réunion, France; Groupement de Recherche de l'Institut Ecologie et Environnement 6448, Génétique Quantitative dans les Populations Naturelles (GQPN), c/o EDB, 31062 Toulouse, France
| | - Pascal Marrot
- Laboratoire Évolution & Diversité Biologique (EDB UMR 5174), Université Fédérale de Toulouse Midi-Pyrénées, CNRS, IRD, UPS, 31062 Toulouse, France; Groupement de Recherche de l'Institut Ecologie et Environnement 6448, Génétique Quantitative dans les Populations Naturelles (GQPN), c/o EDB, 31062 Toulouse, France
| | - Fabrice Roux
- Laboratoire des Interactions Plantes-Microorganismes (LIPM), INRA, CNRS, Université de Toulouse, 31326 Castanet-Tolosan, France; Groupement de Recherche de l'Institut Ecologie et Environnement 6448, Génétique Quantitative dans les Populations Naturelles (GQPN), c/o EDB, 31062 Toulouse, France
| | - Ivan Scotti
- INRA Unité de Recherche 0629 Ecologie des Forêts Méditerranéennes, 84914 Avignon, France; Groupement de Recherche de l'Institut Ecologie et Environnement 6448, Génétique Quantitative dans les Populations Naturelles (GQPN), c/o EDB, 31062 Toulouse, France
| | - Céline Teplitsky
- Centre d'Ecologie Fonctionnelle et Evolutive (CEFE), CNRS UMR 5175, 34293 Montpellier, France; Muséum National d'Histoire Naturelle, CNRS UMR 7204 Centre d'Écologie et des Sciences de la Conservation (CESCO), 75005 Paris, France; Groupement de Recherche de l'Institut Ecologie et Environnement 6448, Génétique Quantitative dans les Populations Naturelles (GQPN), c/o EDB, 31062 Toulouse, France
| | - Caroline E Thomson
- Laboratoire Évolution & Diversité Biologique (EDB UMR 5174), Université Fédérale de Toulouse Midi-Pyrénées, CNRS, IRD, UPS, 31062 Toulouse, France; Groupement de Recherche de l'Institut Ecologie et Environnement 6448, Génétique Quantitative dans les Populations Naturelles (GQPN), c/o EDB, 31062 Toulouse, France
| | - Isabel Winney
- Laboratoire Évolution & Diversité Biologique (EDB UMR 5174), Université Fédérale de Toulouse Midi-Pyrénées, CNRS, IRD, UPS, 31062 Toulouse, France; Groupement de Recherche de l'Institut Ecologie et Environnement 6448, Génétique Quantitative dans les Populations Naturelles (GQPN), c/o EDB, 31062 Toulouse, France
| |
Collapse
|
15
|
Morrissey MB, Janeiro MJ, Sparks AM, White S, Pigeon G, Teplitsky C, Réale D, Milot E. Into the wild-WAMBAM goes to Canada. Mol Ecol 2018; 27:1098-1102. [PMID: 29411456 DOI: 10.1111/mec.14510] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Revised: 01/18/2018] [Accepted: 01/22/2018] [Indexed: 11/28/2022]
Abstract
The sixth Wild Animal Models Bi-Annual Meeting was held in July 2017 in Québec, with 42 participants. This report documents the evolution of questions asked and approaches used in evolutionary quantitative genetic studies of wild populations in recent decades, and how these questions and approaches were represented at the recent meeting. We explore how ideas from previous meetings in this series have developed to their present states, and consider how the format of the meetings may be particularly useful at fostering the rapid development and proliferation of ideas and approaches.
Collapse
Affiliation(s)
| | - Maria João Janeiro
- School of Biology, University of St Andrews, St Andrews, UK.,CESAM, Department of Biology, University of Aveiro, Aveiro, Portugal
| | - Alexandra M Sparks
- Institutes of Evolutionary Biology, Immunology and Infection Research, School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | - Stephen White
- Centre for Ecology and Conservation, University of Exeter (Penryn Campus), Cornwall, UK
| | - Gabriel Pigeon
- Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway
| | - Céline Teplitsky
- CEFE, CNRS, Univ Montpellier, EPHE, IRD, Univ Paul Valéry Montpellier 3, Montpellier, France
| | - Denis Réale
- Département des Sciences Biologiques, Université du Québec À Montréal, Montréal, QC, Canada
| | - Emmanuel Milot
- Department of chemistry, biochemistry and physics, Université du Québec à Trois-Rivières, Trois-Rivières, QC, Canada
| |
Collapse
|