1
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Uller T, Milocco L, Isanta-Navarro J, Cornwallis CK, Feiner N. Twenty years on from Developmental Plasticity and Evolution: middle-range theories and how to test them. J Exp Biol 2024; 227:jeb246375. [PMID: 38449333 DOI: 10.1242/jeb.246375] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
In Developmental Plasticity and Evolution, Mary-Jane West-Eberhard argued that the developmental mechanisms that enable organisms to respond to their environment are fundamental causes of adaptation and diversification. Twenty years after publication of this book, this once so highly controversial claim appears to have been assimilated by a wealth of studies on 'plasticity-led' evolution. However, we suggest that the role of development in explanations for adaptive evolution remains underappreciated in this body of work. By combining concepts of evolvability from evolutionary developmental biology and quantitative genetics, we outline a framework that is more appropriate to identify developmental causes of adaptive evolution. This framework demonstrates how experimental and comparative developmental biology and physiology can be leveraged to put the role of plasticity in evolution to the test.
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Affiliation(s)
- Tobias Uller
- Department of Biology, Lund University, 223 62 Lund, Sweden
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2
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Snell-Rood EC, Ehlman SM. Developing the genotype-to-phenotype relationship in evolutionary theory: A primer of developmental features. Evol Dev 2023; 25:393-409. [PMID: 37026670 DOI: 10.1111/ede.12434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 02/09/2023] [Accepted: 03/16/2023] [Indexed: 04/08/2023]
Abstract
For decades, there have been repeated calls for more integration across evolutionary and developmental biology. However, critiques in the literature and recent funding initiatives suggest this integration remains incomplete. We suggest one way forward is to consider how we elaborate the most basic concept of development, the relationship between genotype and phenotype, in traditional models of evolutionary processes. For some questions, when more complex features of development are accounted for, predictions of evolutionary processes shift. We present a primer on concepts of development to clarify confusion in the literature and fuel new questions and approaches. The basic features of development involve expanding a base model of genotype-to-phenotype to include the genome, space, and time. A layer of complexity is added by incorporating developmental systems, including signal-response systems and networks of interactions. The developmental emergence of function, which captures developmental feedbacks and phenotypic performance, offers further model elaborations that explicitly link fitness with developmental systems. Finally, developmental features such as plasticity and developmental niche construction conceptualize the link between a developing phenotype and the external environment, allowing for a fuller inclusion of ecology in evolutionary models. Incorporating aspects of developmental complexity into evolutionary models also accommodates a more pluralistic focus on the causal importance of developmental systems, individual organisms, or agents in generating evolutionary patterns. Thus, by laying out existing concepts of development, and considering how they are used across different fields, we can gain clarity in existing debates around the extended evolutionary synthesis and pursue new directions in evolutionary developmental biology. Finally, we consider how nesting developmental features in traditional models of evolution can highlight areas of evolutionary biology that need more theoretical attention.
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Affiliation(s)
- Emilie C Snell-Rood
- Department of Ecology, Evolution and Behavior, University of Minnesota, St Paul, Minnesota, USA
| | - Sean M Ehlman
- Department of Ecology, Evolution and Behavior, University of Minnesota, St Paul, Minnesota, USA
- SCIoI Excellence Cluster, Leibniz Institute of Freshwater Ecology and Inland Fisheries, Humboldt University, Berlin, Germany
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3
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Yubero P, Lavin AA, Poyatos JF. The limitations of phenotype prediction in metabolism. PLoS Comput Biol 2023; 19:e1011631. [PMID: 37948461 PMCID: PMC10664875 DOI: 10.1371/journal.pcbi.1011631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 11/22/2023] [Accepted: 10/24/2023] [Indexed: 11/12/2023] Open
Abstract
Phenotype prediction is at the center of many questions in biology. Prediction is often achieved by determining statistical associations between genetic and phenotypic variation, ignoring the exact processes that cause the phenotype. Here, we present a framework based on genome-scale metabolic reconstructions to reveal the mechanisms behind the associations. We calculated a polygenic score (PGS) that identifies a set of enzymes as predictors of growth, the phenotype. This set arises from the synergy of the functional mode of metabolism in a particular setting and its evolutionary history, and is suitable to infer the phenotype across a variety of conditions. We also find that there is optimal genetic variation for predictability and demonstrate how the linear PGS can still explain phenotypes generated by the underlying nonlinear biochemistry. Therefore, the explicit model interprets the black box statistical associations of the genotype-to-phenotype map and helps to discover what limits the prediction in metabolism.
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Affiliation(s)
- Pablo Yubero
- Logic of Genomic Systems Lab, CNB-CSIC, Madrid, Spain
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4
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Zimm R, Berio F, Debiais-Thibaud M, Goudemand N. A shark-inspired general model of tooth morphogenesis unveils developmental asymmetries in phenotype transitions. Proc Natl Acad Sci U S A 2023; 120:e2216959120. [PMID: 37027430 PMCID: PMC10104537 DOI: 10.1073/pnas.2216959120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 02/07/2023] [Indexed: 04/08/2023] Open
Abstract
Developmental complexity stemming from the dynamic interplay between genetic and biomechanic factors canalizes the ways genotypes and phenotypes can change in evolution. As a paradigmatic system, we explore how changes in developmental factors generate typical tooth shape transitions. Since tooth development has mainly been researched in mammals, we contribute to a more general understanding by studying the development of tooth diversity in sharks. To this end, we build a general, but realistic, mathematical model of odontogenesis. We show that it reproduces key shark-specific features of tooth development as well as real tooth shape variation in small-spotted catsharks Scyliorhinus canicula. We validate our model by comparison with experiments in vivo. Strikingly, we observe that developmental transitions between tooth shapes tend to be highly degenerate, even for complex phenotypes. We also discover that the sets of developmental parameters involved in tooth shape transitions tend to depend asymmetrically on the direction of that transition. Together, our findings provide a valuable base for furthering our understanding of how developmental changes can lead to both adaptive phenotypic change and trait convergence in complex, phenotypically highly diverse, structures.
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Affiliation(s)
- Roland Zimm
- Institut de Génomique Fonctionnelle de Lyon, Ecole Normale Supérieure de Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5242, Lyon Cedex07 69364, France
| | - Fidji Berio
- Institut de Génomique Fonctionnelle de Lyon, Ecole Normale Supérieure de Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5242, Lyon Cedex07 69364, France
- Institut des Sciences de l’Evolution de Montpellier, University of Montpellier, CNRS, Institut de la Recherche pour le Développement, Montpellier34095, France
| | - Mélanie Debiais-Thibaud
- Institut des Sciences de l’Evolution de Montpellier, University of Montpellier, CNRS, Institut de la Recherche pour le Développement, Montpellier34095, France
| | - Nicolas Goudemand
- Institut de Génomique Fonctionnelle de Lyon, Ecole Normale Supérieure de Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5242, Lyon Cedex07 69364, France
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5
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Salazar-Ciudad I, Cano-Fernández H. Evo-devo beyond development: Generalizing evo-devo to all levels of the phenotypic evolution. Bioessays 2023; 45:e2200205. [PMID: 36739577 DOI: 10.1002/bies.202200205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/25/2022] [Accepted: 01/12/2023] [Indexed: 02/06/2023]
Abstract
A foundational idea of evo-devo is that morphological variation is not isotropic, that is, it does not occur in all directions. Instead, some directions of morphological variation are more likely than others from DNA-level variation and these largely depend on development. We argue that this evo-devo perspective should apply not only to morphology but to evolution at all phenotypic levels. At other phenotypic levels there is no development, but there are processes that can be seen, in analogy to development, as constructing the phenotype (e.g., protein folding, learning for behavior, etc.). We argue that to explain the direction of evolution two types of arguments need to be combined: generative arguments about which phenotypic variation arises in each generation and selective arguments about which of it passes to the next generation. We explain how a full consideration of the two types of arguments improves the explanatory power of evolutionary theory. Also see the video abstract here: https://youtu.be/Egbvma_uaKc.
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Affiliation(s)
- Isaac Salazar-Ciudad
- Centre de Recerca Matemàtica, Cerdanyola del Vallès, Spain.,Genomics, Bioinformatics and Evolution, Departament de Genètica i Microbiologia, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Hugo Cano-Fernández
- Genomics, Bioinformatics and Evolution, Departament de Genètica i Microbiologia, Universitat Autònoma de Barcelona, Barcelona, Spain
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6
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Wortel MT, Agashe D, Bailey SF, Bank C, Bisschop K, Blankers T, Cairns J, Colizzi ES, Cusseddu D, Desai MM, van Dijk B, Egas M, Ellers J, Groot AT, Heckel DG, Johnson ML, Kraaijeveld K, Krug J, Laan L, Lässig M, Lind PA, Meijer J, Noble LM, Okasha S, Rainey PB, Rozen DE, Shitut S, Tans SJ, Tenaillon O, Teotónio H, de Visser JAGM, Visser ME, Vroomans RMA, Werner GDA, Wertheim B, Pennings PS. Towards evolutionary predictions: Current promises and challenges. Evol Appl 2023; 16:3-21. [PMID: 36699126 PMCID: PMC9850016 DOI: 10.1111/eva.13513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 11/11/2022] [Accepted: 11/14/2022] [Indexed: 12/14/2022] Open
Abstract
Evolution has traditionally been a historical and descriptive science, and predicting future evolutionary processes has long been considered impossible. However, evolutionary predictions are increasingly being developed and used in medicine, agriculture, biotechnology and conservation biology. Evolutionary predictions may be used for different purposes, such as to prepare for the future, to try and change the course of evolution or to determine how well we understand evolutionary processes. Similarly, the exact aspect of the evolved population that we want to predict may also differ. For example, we could try to predict which genotype will dominate, the fitness of the population or the extinction probability of a population. In addition, there are many uses of evolutionary predictions that may not always be recognized as such. The main goal of this review is to increase awareness of methods and data in different research fields by showing the breadth of situations in which evolutionary predictions are made. We describe how diverse evolutionary predictions share a common structure described by the predictive scope, time scale and precision. Then, by using examples ranging from SARS-CoV2 and influenza to CRISPR-based gene drives and sustainable product formation in biotechnology, we discuss the methods for predicting evolution, the factors that affect predictability and how predictions can be used to prevent evolution in undesirable directions or to promote beneficial evolution (i.e. evolutionary control). We hope that this review will stimulate collaboration between fields by establishing a common language for evolutionary predictions.
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Affiliation(s)
- Meike T. Wortel
- Swammerdam Institute for Life SciencesUniversity of AmsterdamAmsterdamThe Netherlands
| | - Deepa Agashe
- National Centre for Biological SciencesBangaloreIndia
| | | | - Claudia Bank
- Institute of Ecology and EvolutionUniversity of BernBernSwitzerland
- Swiss Institute of BioinformaticsLausanneSwitzerland
- Gulbenkian Science InstituteOeirasPortugal
| | - Karen Bisschop
- Institute for Biodiversity and Ecosystem DynamicsUniversity of AmsterdamAmsterdamThe Netherlands
- Origins CenterGroningenThe Netherlands
- Laboratory of Aquatic Biology, KU Leuven KulakKortrijkBelgium
| | - Thomas Blankers
- Institute for Biodiversity and Ecosystem DynamicsUniversity of AmsterdamAmsterdamThe Netherlands
- Origins CenterGroningenThe Netherlands
| | | | - Enrico Sandro Colizzi
- Origins CenterGroningenThe Netherlands
- Mathematical InstituteLeiden UniversityLeidenThe Netherlands
| | | | | | - Bram van Dijk
- Max Planck Institute for Evolutionary BiologyPlönGermany
| | - Martijn Egas
- Institute for Biodiversity and Ecosystem DynamicsUniversity of AmsterdamAmsterdamThe Netherlands
| | - Jacintha Ellers
- Department of Ecological ScienceVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Astrid T. Groot
- Institute for Biodiversity and Ecosystem DynamicsUniversity of AmsterdamAmsterdamThe Netherlands
| | | | | | - Ken Kraaijeveld
- Leiden Centre for Applied BioscienceUniversity of Applied Sciences LeidenLeidenThe Netherlands
| | - Joachim Krug
- Institute for Biological PhysicsUniversity of CologneCologneGermany
| | - Liedewij Laan
- Department of Bionanoscience, Kavli Institute of NanoscienceTU DelftDelftThe Netherlands
| | - Michael Lässig
- Institute for Biological PhysicsUniversity of CologneCologneGermany
| | - Peter A. Lind
- Department Molecular BiologyUmeå UniversityUmeåSweden
| | - Jeroen Meijer
- Theoretical Biology and Bioinformatics, Department of BiologyUtrecht UniversityUtrechtThe Netherlands
| | - Luke M. Noble
- Institute de Biologie, École Normale Supérieure, CNRS, InsermParisFrance
| | | | - Paul B. Rainey
- Department of Microbial Population BiologyMax Planck Institute for Evolutionary BiologyPlönGermany
- Laboratoire Biophysique et Évolution, CBI, ESPCI Paris, Université PSL, CNRSParisFrance
| | - Daniel E. Rozen
- Institute of Biology, Leiden UniversityLeidenThe Netherlands
| | - Shraddha Shitut
- Origins CenterGroningenThe Netherlands
- Institute of Biology, Leiden UniversityLeidenThe Netherlands
| | | | | | | | | | - Marcel E. Visser
- Department of Animal EcologyNetherlands Institute of Ecology (NIOO‐KNAW)WageningenThe Netherlands
| | - Renske M. A. Vroomans
- Origins CenterGroningenThe Netherlands
- Informatics InstituteUniversity of AmsterdamAmsterdamThe Netherlands
| | | | - Bregje Wertheim
- Groningen Institute for Evolutionary Life SciencesUniversity of GroningenGroningenThe Netherlands
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7
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Schroeder L, Ackermann RR. Moving beyond the adaptationist paradigm for human evolution, and why it matters. J Hum Evol 2023; 174:103296. [PMID: 36527977 DOI: 10.1016/j.jhevol.2022.103296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 11/12/2022] [Accepted: 11/12/2022] [Indexed: 12/23/2022]
Abstract
The Journal of Human Evolution (JHE) was founded 50 years ago when much of the foundation for how we think about human evolution was in place or being put in place, providing the main framework for how we consider our origins today. Here, we will explore historical developments, including early JHE outputs, as they relate to our understanding of the relationship between phenotypic variation and evolutionary process, and use that as a springboard for considering our current understanding of these links as applied to human evolution. We will focus specifically on how the study of variation itself has shifted us away from taxonomic and adaptationist perspectives toward a richer understanding of the processes shaping human evolutionary history, using literature searches and specific test cases to highlight this. We argue that natural selection, gene exchange, genetic drift, and mutation should not be considered individually when considering the production of hominin diversity. In this context, we offer suggestions for future research directions and reflect on this more complex understanding of human evolution and its broader relevance to society. Finally, we end by considering authorship demographics and practices in the last 50 years within JHE and how a shift in these demographics has the potential to reshape the science of human evolution going forward.
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Affiliation(s)
- Lauren Schroeder
- Department of Anthropology, University of Toronto Mississauga, Mississauga, ON, L5L 1C6, Canada; Human Evolution Research Institute, University of Cape Town, Rondebosch, 7701, South Africa.
| | - Rebecca Rogers Ackermann
- Human Evolution Research Institute, University of Cape Town, Rondebosch, 7701, South Africa; Department of Archaeology, University of Cape Town, Rondebosch, 7701, South Africa.
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8
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Colizzi ES, Hogeweg P, Vroomans RMA. Modelling the evolution of novelty: a review. Essays Biochem 2022; 66:727-735. [PMID: 36468669 PMCID: PMC9750852 DOI: 10.1042/ebc20220069] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 11/04/2022] [Accepted: 11/07/2022] [Indexed: 12/12/2022]
Abstract
Evolution has been an inventive process since its inception, about 4 billion years ago. It has generated an astounding diversity of novel mechanisms and structures for adaptation to the environment, for competition and cooperation, and for organisation of the internal and external dynamics of the organism. How does this novelty come about? Evolution builds with the tools available, and on top of what it has already built - therefore, much novelty consists in repurposing old functions in a different context. In the process, the tools themselves evolve, allowing yet more novelty to arise. Despite evolutionary novelty being the most striking observable of evolution, it is not accounted for in classical evolutionary theory. Nevertheless, mathematical and computational models that illustrate mechanisms of evolutionary innovation have been developed. In the present review, we present and compare several examples of computational evo-devo models that capture two aspects of novelty: 'between-level novelty' and 'constructive novelty.' Novelty can evolve between predefined levels of organisation to dynamically transcode biological information across these levels - as occurs during development. Constructive novelty instead generates a level of biological organisation by exploiting the lower level as an informational scaffold to open a new space of possibilities - an example being the evolution of multicellularity. We propose that the field of computational evo-devo is well-poised to reveal many more exciting mechanisms for the evolution of novelty. A broader theory of evolutionary novelty may well be attainable in the near future.
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Affiliation(s)
- Enrico Sandro Colizzi
- Sainsbury Laboratory, University of Cambridge, 47 Bateman Street, CB2 1LR, Cambridge, U.K
| | - Paulien Hogeweg
- Theoretical Biology and Bioinformatics, Universiteit Utrecht, Padualaan 8, 3584 CH, Utrecht, Netherlands
| | - Renske M A Vroomans
- Sainsbury Laboratory, University of Cambridge, 47 Bateman Street, CB2 1LR, Cambridge, U.K
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9
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Souquet L, Guenser P, Girard C, Mazza M, Rigo M, Goudemand N. Temperature-driven heterochrony as a main evolutionary response to climate changes in conodonts. Proc Biol Sci 2022; 289:20220614. [PMID: 36259210 PMCID: PMC9579755 DOI: 10.1098/rspb.2022.0614] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 09/27/2022] [Indexed: 01/17/2023] Open
Abstract
Can we predict the evolutionary response of organisms to climate changes? The direction of greatest intraspecific phenotypic variance is thought to correspond to an 'evolutionary line of least resistance', i.e. a taxon's phenotype is expected to evolve along that general direction, if not constrained otherwise. In particular, heterochrony, whereby the timing or rate of developmental processes are modified, has often been invoked to describe evolutionary trajectories and it may be advantageous to organisms when rapid adaptation is critical. Yet, to date, little is known empirically as to which covariation patterns, whether static allometry, as measured in adult forms only, or ontogenetic allometry, the basis for heterochrony, may be prevalent in what circumstances. Here, we quantify the morphology of segminiplanate conodont elements during two distinct time intervals separated by more than 130 Myr: the Devonian-Carboniferous boundary and the Carnian-Norian boundary (Late Triassic). We evidence that the corresponding species share similar patterns of intraspecific static allometry. Yet, during both crises, conodont evolution was decoupled from this common evolutionary line of least resistance. Instead, it followed heterochrony-like trajectories that furthermore appear as driven by ocean temperature. This may have implications for our interpretation of conodonts' and past marine ecosystems' response to environmental perturbations.
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Affiliation(s)
- Louise Souquet
- Ecole Normale Supérieure de Lyon, IGFL, CNRS UMR 5242, UCBL, 46 Allée d'Italie, F-69364 Lyon Cedex 07, France
| | - Pauline Guenser
- Ecole Normale Supérieure de Lyon, IGFL, CNRS UMR 5242, UCBL, 46 Allée d'Italie, F-69364 Lyon Cedex 07, France
- Univ. Lyon, Université Claude Bernard Lyon 1, LEHNA, CNRS UMR 5023, 3-6 rue Raphaël Dubois – Bâtiments Forel, 69622 Villeurbanne Cedex 43
| | | | | | - Manuel Rigo
- Department of Geosciences, University of Padova, Via G. Gradenigo 6, 35131 Padova, Italy
| | - Nicolas Goudemand
- Ecole Normale Supérieure de Lyon, IGFL, CNRS UMR 5242, UCBL, 46 Allée d'Italie, F-69364 Lyon Cedex 07, France
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10
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Dubied M, Montuire S, Navarro N. Functional constraints channel mandible shape ontogenies in rodents. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220352. [PMID: 36300135 PMCID: PMC9579770 DOI: 10.1098/rsos.220352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 08/31/2022] [Indexed: 06/16/2023]
Abstract
In mammals, postnatal growth plays an essential role in the acquisition of the adult shape. During this period, the mandible undergoes many changing functional constraints, leading to spatialization of bone formation and remodelling to accommodate various dietary and behavioural changes. The interactions between the bone, muscles and teeth drive this developmental plasticity, which, in turn, could lead to convergences in the developmental processes constraining the directionality of ontogenies, their evolution and thus the adult shape variation. To test the importance of the interactions between tissues in shaping the ontogenetic trajectories, we compared the mandible shape at five postnatal stages on three rodents: the house mouse, the Mongolian gerbil and the golden hamster, using geometric morphometrics. After an early shape differentiation, by both longer gestation and allometric scaling in gerbils or early divergence of postnatal ontogeny in hamsters in comparison with the mouse, the ontogenetic trajectories appear more similar around weaning. The changes in muscle load associated with new food processing and new behaviours at weaning seem to impose similar physical constraints on the mandible, driving the convergences of the ontogeny at that stage despite an early anatomical differentiation. Nonetheless, mice present a rather different timing compared with gerbils or hamsters.
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Affiliation(s)
- Morgane Dubied
- Biogéosciences, UMR 6282 CNRS, EPHE, Université Bourgogne Franche-Comté, 6 bd Gabriel, 21000 Dijon, France
| | - Sophie Montuire
- Biogéosciences, UMR 6282 CNRS, EPHE, Université Bourgogne Franche-Comté, 6 bd Gabriel, 21000 Dijon, France
- EPHE, PSL University, 75014 Paris, France
| | - Nicolas Navarro
- Biogéosciences, UMR 6282 CNRS, EPHE, Université Bourgogne Franche-Comté, 6 bd Gabriel, 21000 Dijon, France
- EPHE, PSL University, 75014 Paris, France
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11
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Brun-Usan M, Zimm R, Uller T. Beyond genotype-phenotype maps: Toward a phenotype-centered perspective on evolution. Bioessays 2022; 44:e2100225. [PMID: 35863907 DOI: 10.1002/bies.202100225] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 06/30/2022] [Accepted: 07/04/2022] [Indexed: 11/08/2022]
Abstract
Evolutionary biology is paying increasing attention to the mechanisms that enable phenotypic plasticity, evolvability, and extra-genetic inheritance. Yet, there is a concern that these phenomena remain insufficiently integrated within evolutionary theory. Understanding their evolutionary implications would require focusing on phenotypes and their variation, but this does not always fit well with the prevalent genetic representation of evolution that screens off developmental mechanisms. Here, we instead use development as a starting point, and represent it in a way that allows genetic, environmental and epigenetic sources of phenotypic variation to be independent. We show why this representation helps to understand the evolutionary consequences of both genetic and non-genetic phenotype determinants, and discuss how this approach can instigate future areas of empirical and theoretical research.
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Affiliation(s)
- Miguel Brun-Usan
- Department of Biology, Lund University, 22362, Lund, Sweden.,Institute for Life Sciences/Electronics and Computer Science, University of Southampton, SO17 1BJ, Southampton, UK
| | - Roland Zimm
- Ecole Normale Supérieure de Lyon, Institute de Génomique Fonctionnelle de Lyon, Lyon, France
| | - Tobias Uller
- Institute for Life Sciences/Electronics and Computer Science, University of Southampton, SO17 1BJ, Southampton, UK
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12
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A method to predict the response to directional selection using a Kalman filter. Proc Natl Acad Sci U S A 2022; 119:e2117916119. [PMID: 35867739 DOI: 10.1073/pnas.2117916119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Predicting evolution remains challenging. The field of quantitative genetics provides predictions for the response to directional selection through the breeder's equation, but these predictions can have errors. The sources of these errors include omission of traits under selection, inaccurate estimates of genetic variance, and nonlinearities in the relationship between genetic and phenotypic variation. Previous research showed that the expected value of these prediction errors is often not zero, so predictions are systematically biased. Here, we propose that this bias, rather than being a nuisance, can be used to improve the predictions. We use this to develop a method to predict evolution, which is built on three key innovations. First, the method predicts change as the breeder's equation plus a bias term. Second, the method combines information from the breeder's equation and from the record of past changes in the mean to predict change using a Kalman filter. Third, the parameters of the filter are fitted in each generation using a learning algorithm on the record of past changes. We compare the method to the breeder's equation in two artificial selection experiments, one using the wing of the fruit fly and another using simulations that include a complex mapping of genotypes to phenotypes. The proposed method outperforms the breeder's equation, particularly when traits under selection are omitted from the analysis, when data are noisy, and when additive genetic variance is estimated inaccurately or not estimated at all. The proposed method is easy to apply, requiring only the trait means over past generations.
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13
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Zelditch ML. Extending microevolutionary theory to a macroevolutionary theory of complex adaptations. Evolution 2022; 76:1062-1063. [PMID: 35170045 DOI: 10.1111/evo.14450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 01/19/2022] [Indexed: 01/21/2023]
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14
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Milocco L, Salazar-Ciudad I. Evolution of the G Matrix under Nonlinear Genotype-Phenotype Maps. Am Nat 2022; 199:420-435. [DOI: 10.1086/717814] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Lisandro Milocco
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Isaac Salazar-Ciudad
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
- Centre de Recerca Matemàtica, Barcelona, Spain; and Genomics, Bioinformatics, and Evolution, Departament de Genètica i Microbiologia, Universitat Autònoma de Barcelona, Barcelona, Spain
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15
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Hansen TF, Pélabon C. Evolvability: A Quantitative-Genetics Perspective. ANNUAL REVIEW OF ECOLOGY, EVOLUTION, AND SYSTEMATICS 2021. [DOI: 10.1146/annurev-ecolsys-011121-021241] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The concept of evolvability emerged in the early 1990s and soon became fashionable as a label for different streams of research in evolutionary biology. In evolutionary quantitative genetics, evolvability is defined as the ability of a population to respond to directional selection. This differs from other fields by treating evolvability as a property of populations rather than organisms or lineages and in being focused on quantification and short-term prediction rather than on macroevolution. While the term evolvability is new to quantitative genetics, many of the associated ideas and research questions have been with the field from its inception as biometry. Recent research on evolvability is more than a relabeling of old questions, however. New operational measures of evolvability have opened possibilities for understanding adaptation to rapid environmental change, assessing genetic constraints, and linking micro- and macroevolution.
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Affiliation(s)
- Thomas F. Hansen
- Department of Biosciences, University of Oslo, 0316 Oslo, Norway
| | - Christophe Pélabon
- Center for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway
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16
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Smallegange IM. Integrating developmental plasticity into eco-evolutionary population dynamics. Trends Ecol Evol 2021; 37:129-137. [PMID: 34635340 DOI: 10.1016/j.tree.2021.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 09/16/2021] [Accepted: 09/21/2021] [Indexed: 10/20/2022]
Abstract
There are increasing calls to incorporate developmental plasticity into the framework of eco-evolutionary dynamics. The current way is via genotype-specified reaction norms in which inheritance and phenotype expression are gene-based. I propose a developmental system perspective in which phenotypes are formed during individual development in a process comprising a complex set of interactions that involve genes, biochemistry, somatic state, and the (a)biotic environment, and where the developmental system is the unit of phenotype evolution. I explain how the two perspectives differ in assumptions and predictions, which can be contrasted using cue-and-response systems of anticipatory or mitigating developmental plasticity. This can lead to new ways of eco-evolutionary thinking, and deliver important explanations of how populations respond to environmental change through evolved developmental plasticity.
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Affiliation(s)
- Isabel M Smallegange
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, PO Box 94240, 1090, GE, Amsterdam, The Netherlands; School of Natural & Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK.
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17
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Chevin LM, Leung C, Le Rouzic A, Uller T. Using phenotypic plasticity to understand the structure and evolution of the genotype-phenotype map. Genetica 2021; 150:209-221. [PMID: 34617196 DOI: 10.1007/s10709-021-00135-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 09/22/2021] [Indexed: 10/20/2022]
Abstract
Deciphering the genotype-phenotype map necessitates relating variation at the genetic level to variation at the phenotypic level. This endeavour is inherently limited by the availability of standing genetic variation, the rate of spontaneous mutation to novo genetic variants, and possible biases associated with induced mutagenesis. An interesting alternative is to instead rely on the environment as a source of variation. Many phenotypic traits change plastically in response to the environment, and these changes are generally underlain by changes in gene expression. Relating gene expression plasticity to the phenotypic plasticity of more integrated organismal traits thus provides useful information about which genes influence the development and expression of which traits, even in the absence of genetic variation. We here appraise the prospects and limits of such an environment-for-gene substitution for investigating the genotype-phenotype map. We review models of gene regulatory networks, and discuss the different ways in which they can incorporate the environment to mechanistically model phenotypic plasticity and its evolution. We suggest that substantial progress can be made in deciphering this genotype-environment-phenotype map, by connecting theory on gene regulatory network to empirical patterns of gene co-expression, and by more explicitly relating gene expression to the expression and development of phenotypes, both theoretically and empirically.
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Affiliation(s)
- Luis-Miguel Chevin
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Univ Paul Valéry Montpellier 3, Montpellier, France.
| | - Christelle Leung
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Univ Paul Valéry Montpellier 3, Montpellier, France
| | - Arnaud Le Rouzic
- Laboratoire Évolution, Génomes, Comportement, Écologie, CNRS, IRD, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Tobias Uller
- Department of Biology, Lund University, Lund, Sweden
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18
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Zelditch ML, Goswami A. What does modularity mean? Evol Dev 2021; 23:377-403. [PMID: 34464501 DOI: 10.1111/ede.12390] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 06/25/2021] [Accepted: 08/09/2021] [Indexed: 01/03/2023]
Abstract
Modularity is now generally recognized as a fundamental feature of organisms, one that may have profound consequences for evolution. Modularity has recently become a major focus of research in organismal biology across multiple disciplines including genetics, developmental biology, functional morphology, population and evolutionary biology. While the wealth of new data, and also new theory, has provided exciting and novel insights, the concept of modularity has become increasingly ambiguous. That ambiguity is underlain by diverse intuitions about what modularity means, and the ambiguity is not merely about the meaning of the word-the metrics of modularity are measuring different properties and the methods for delimiting modules delimit them by different, sometimes conflicting criteria. The many definitions, metrics and methods can lead to substantial confusion not just about what modularity means as a word but also about what it means for evolution. Here we review various concepts, using graphical depictions of modules. We then review some of the metrics and methods for analyzing modularity at different levels. To place these in theoretical context, we briefly review theories about the origins and evolutionary consequences of modularity. Finally, we show how mismatches between concepts, metrics and methods can produce theoretical confusion, and how potentially illogical interpretations can be made sensible by a better match between definitions, metrics, and methods.
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Affiliation(s)
- Miriam L Zelditch
- Museum of Paleontology, University of Michigan, Ann Arbor, Michigan, USA
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Wittman TN, Robinson CD, McGlothlin JW, Cox RM. Hormonal pleiotropy structures genetic covariance. Evol Lett 2021; 5:397-407. [PMID: 34367664 PMCID: PMC8327939 DOI: 10.1002/evl3.240] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 05/03/2021] [Accepted: 05/17/2021] [Indexed: 11/08/2022] Open
Abstract
Quantitative genetic theory proposes that phenotypic evolution is shaped by G, the matrix of genetic variances and covariances among traits. In species with separate sexes, the evolution of sexual dimorphism is also shaped by B, the matrix of between‐sex genetic variances and covariances. Despite considerable focus on estimating these matrices, their underlying biological mechanisms are largely speculative. We experimentally tested the hypothesis that G and B are structured by hormonal pleiotropy, which occurs when one hormone influences multiple phenotypes. Using juvenile brown anole lizards (Anolis sagrei) bred in a paternal half‐sibling design, we elevated the steroid hormone testosterone with slow‐release implants while administering empty implants to siblings as a control. We quantified the effects of this manipulation on the genetic architecture of a suite of sexually dimorphic traits, including body size (males are larger than females) and the area, hue, saturation, and brightness of the dewlap (a colorful ornament that is larger in males than in females). Testosterone masculinized females by increasing body size and dewlap area, hue, and saturation, while reducing dewlap brightness. Control females and males differed significantly in G, but treatment of females with testosterone rendered G statistically indistinguishable from males. Whereas B was characterized by low between‐sex genetic correlations when estimated between control females and males, these same correlations increased significantly when estimated between testosterone females and either control or testosterone males. The full G matrix (including B) for testosterone females and either control or testosterone males was significantly less permissive of sexually dimorphic evolution than was G estimated between control females and males, suggesting that natural sex differences in testosterone help decouple genetic variance between the sexes. Our results confirm that hormonal pleiotropy structures genetic covariance, implying that hormones play an important yet overlooked role in mediating evolutionary responses to selection.
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Affiliation(s)
- Tyler N Wittman
- Department of Biology University of Virginia Charlottesville Virginia 22904
| | | | - Joel W McGlothlin
- Department of Biological Sciences Virginia Tech Blacksburg Virginia 24061
| | - Robert M Cox
- Department of Biology University of Virginia Charlottesville Virginia 22904
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Manrubia S, Cuesta JA, Aguirre J, Ahnert SE, Altenberg L, Cano AV, Catalán P, Diaz-Uriarte R, Elena SF, García-Martín JA, Hogeweg P, Khatri BS, Krug J, Louis AA, Martin NS, Payne JL, Tarnowski MJ, Weiß M. From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics. Phys Life Rev 2021; 38:55-106. [PMID: 34088608 DOI: 10.1016/j.plrev.2021.03.004] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 03/01/2021] [Indexed: 12/21/2022]
Abstract
Understanding how genotypes map onto phenotypes, fitness, and eventually organisms is arguably the next major missing piece in a fully predictive theory of evolution. We refer to this generally as the problem of the genotype-phenotype map. Though we are still far from achieving a complete picture of these relationships, our current understanding of simpler questions, such as the structure induced in the space of genotypes by sequences mapped to molecular structures, has revealed important facts that deeply affect the dynamical description of evolutionary processes. Empirical evidence supporting the fundamental relevance of features such as phenotypic bias is mounting as well, while the synthesis of conceptual and experimental progress leads to questioning current assumptions on the nature of evolutionary dynamics-cancer progression models or synthetic biology approaches being notable examples. This work delves with a critical and constructive attitude into our current knowledge of how genotypes map onto molecular phenotypes and organismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension. As a final goal, this community should aim at deriving an updated picture of evolutionary processes soundly relying on the structural properties of genotype spaces, as revealed by modern techniques of molecular and functional analysis.
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Affiliation(s)
- Susanna Manrubia
- Department of Systems Biology, Centro Nacional de Biotecnología (CSIC), Madrid, Spain; Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain.
| | - José A Cuesta
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain; Departamento de Matemáticas, Universidad Carlos III de Madrid, Leganés, Spain; Instituto de Biocomputación y Física de Sistemas Complejos (BiFi), Universidad de Zaragoza, Spain; UC3M-Santander Big Data Institute (IBiDat), Getafe, Madrid, Spain
| | - Jacobo Aguirre
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain; Centro de Astrobiología, CSIC-INTA, ctra. de Ajalvir km 4, 28850 Torrejón de Ardoz, Madrid, Spain
| | - Sebastian E Ahnert
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, UK; The Alan Turing Institute, British Library, 96 Euston Road, London NW1 2DB, UK
| | | | - Alejandro V Cano
- Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Pablo Catalán
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain; Departamento de Matemáticas, Universidad Carlos III de Madrid, Leganés, Spain
| | - Ramon Diaz-Uriarte
- Department of Biochemistry, Universidad Autónoma de Madrid, Madrid, Spain; Instituto de Investigaciones Biomédicas "Alberto Sols" (UAM-CSIC), Madrid, Spain
| | - Santiago F Elena
- Instituto de Biología Integrativa de Sistemas, I(2)SysBio (CSIC-UV), València, Spain; The Santa Fe Institute, Santa Fe, NM, USA
| | | | - Paulien Hogeweg
- Theoretical Biology and Bioinformatics Group, Utrecht University, the Netherlands
| | - Bhavin S Khatri
- The Francis Crick Institute, London, UK; Department of Life Sciences, Imperial College London, London, UK
| | - Joachim Krug
- Institute for Biological Physics, University of Cologne, Köln, Germany
| | - Ard A Louis
- Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford, UK
| | - Nora S Martin
- Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, Cambridge, UK; Sainsbury Laboratory, University of Cambridge, Cambridge, UK
| | - Joshua L Payne
- Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | - Marcel Weiß
- Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, Cambridge, UK; Sainsbury Laboratory, University of Cambridge, Cambridge, UK
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21
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Feiner N, Brun-Usan M, Uller T. Evolvability and evolutionary rescue. Evol Dev 2021; 23:308-319. [PMID: 33528902 DOI: 10.1111/ede.12374] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 11/22/2020] [Accepted: 01/13/2021] [Indexed: 11/29/2022]
Abstract
The survival prospects of threatened species or populations can sometimes be improved by adaptive change. Such evolutionary rescue is particularly relevant when the threat comes from changing environments, or when long-term population persistence requires range expansion into new habitats. Conservation biologists are therefore often interested in whether or not populations or lineages show a disposition for adaptive evolution, that is, if they are evolvable. Here, we discuss four alternative perspectives that target different causes of evolvability and outline some of the key challenges those perspectives are designed to address. Standing genetic variation provides one familiar estimate of evolvability. Yet, the mere presence of genetic variation is often insufficient to predict if a population will adapt, or how it will adapt. The reason is that adaptive change not only depends on genetic variation, but also on the extent to which this genetic variation can be realized as adaptive phenotypic variation. This requires attention to developmental systems and how plasticity influences evolutionary potential. Finally, we discuss how a better understanding of the different factors that contribute to evolvability can be exploited in conservation practice.
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Affiliation(s)
| | | | - Tobias Uller
- Department of Biology, Lund University, Lund, Sweden
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22
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Increasing our ability to predict contemporary evolution. Nat Commun 2020; 11:5592. [PMID: 33154385 PMCID: PMC7645684 DOI: 10.1038/s41467-020-19437-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 10/15/2020] [Indexed: 12/21/2022] Open
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23
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Klausmeier CA, Osmond MM, Kremer CT, Litchman E. Ecological limits to evolutionary rescue. Philos Trans R Soc Lond B Biol Sci 2020; 375:20190453. [PMID: 33131439 DOI: 10.1098/rstb.2019.0453] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Environments change, for both natural and anthropogenic reasons, which can threaten species persistence. Evolutionary adaptation is a potentially powerful mechanism to allow species to persist in these changing environments. To determine the conditions under which adaptation will prevent extinction (evolutionary rescue), classic quantitative genetics models have assumed a constantly changing environment. They predict that species traits will track a moving environmental optimum with a lag that approaches a constant. If fitness is negative at this lag, the species will go extinct. There have been many elaborations of these models incorporating increased genetic realism. Here, we review and explore the consequences of four ecological complications: non-quadratic fitness functions, interacting density- and trait-dependence, species interactions and fundamental limits to adaptation. We show that non-quadratic fitness functions can result in evolutionary tipping points and existential crises, as can the interaction between density- and trait-dependent mortality. We then review the literature on how interspecific interactions affect adaptation and persistence. Finally, we suggest an alternative theoretical framework that considers bounded environmental change and fundamental limits to adaptation. A research programme that combines theory and experiments and integrates across organizational scales will be needed to predict whether adaptation will prevent species extinction in changing environments. This article is part of the theme issue 'Integrative research perspectives on marine conservation'.
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Affiliation(s)
- Christopher A Klausmeier
- W. K. Kellogg Biological Station, Michigan State University, 3700 East Gull Lake Drive, Hickory Corners, MI 49060, USA.,Department of Plant Biology, Michigan State University, East Lansing, MI, USA.,Department of Integrative Biology, Michigan State University, East Lansing, MI, USA.,Program in Ecology, Evolution and Behavior, Michigan State University, East Lansing, MI, USA
| | - Matthew M Osmond
- Center for Population Biology, University of California - Davis, Davis, CA, USA
| | - Colin T Kremer
- W. K. Kellogg Biological Station, Michigan State University, 3700 East Gull Lake Drive, Hickory Corners, MI 49060, USA
| | - Elena Litchman
- W. K. Kellogg Biological Station, Michigan State University, 3700 East Gull Lake Drive, Hickory Corners, MI 49060, USA.,Department of Integrative Biology, Michigan State University, East Lansing, MI, USA.,Program in Ecology, Evolution and Behavior, Michigan State University, East Lansing, MI, USA
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24
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Le Rouzic A, Renneville C, Millot A, Agostini S, Carmignac D, Édeline É. Unidirectional response to bidirectional selection on body size II. Quantitative genetics. Ecol Evol 2020; 10:11453-11466. [PMID: 33144977 PMCID: PMC7593195 DOI: 10.1002/ece3.6783] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 07/18/2020] [Accepted: 07/19/2020] [Indexed: 12/13/2022] Open
Abstract
Anticipating the genetic and phenotypic changes induced by natural or artificial selection requires reliable estimates of trait evolvabilities (genetic variances and covariances). However, whether or not multivariate quantitative genetics models are able to predict precisely the evolution of traits of interest, especially fitness-related, life history traits, remains an open empirical question. Here, we assessed to what extent the response to bivariate artificial selection on both body size and maturity in the medaka Oryzias latipes, a model fish species, fits the theoretical predictions. Three lines (Large, Small, and Control lines) were differentially selected for body length at 75 days of age, conditional on maturity. As maturity and body size were phenotypically correlated, this selection procedure generated a bi-dimensional selection pattern on two life history traits. After removal of nonheritable trends and noise with a random effect ("animal") model, the observed selection response did not match the expected bidirectional response. For body size, Large and Control lines responded along selection gradients (larger body size and stasis, respectively), but, surprisingly, the Small did not evolve a smaller body length and remained identical to the Control line throughout the experiment. The magnitude of the empirical response was smaller than the theoretical prediction in both selected directions. For maturity, the response was opposite to the expectation (the Large line evolved late maturity compared to the Control line, while the Small line evolved early maturity, while the opposite pattern was predicted due to the strong positive genetic correlation between both traits). The mismatch between predicted and observed response was substantial and could not be explained by usual sources of uncertainties (including sampling effects, genetic drift, and error in G matrix estimates).
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Affiliation(s)
- Arnaud Le Rouzic
- Laboratoire Évolution, Génomes, Comportement, ÉcologieCNRS, IRD, Université Paris‐SaclayGif‐sur‐YvetteFrance
| | - Clémentine Renneville
- Institut d'Écologie et des Sciences de l'Environnement de Paris (iEES‐Paris)Sorbonne Université, Université Paris Diderot, UPEC, CNRS, INRAE, IRDParisFrance
| | - Alexis Millot
- Centre de Recherche en Écologie Expérimentale et Prédictive (CEREEP‐Ecotron Ile‐de‐France), UMS 3194École normale supérieure, PSL Research University, CNRSSaint‐Pierre‐lès‐NemoursFrance
| | - Simon Agostini
- Centre de Recherche en Écologie Expérimentale et Prédictive (CEREEP‐Ecotron Ile‐de‐France), UMS 3194École normale supérieure, PSL Research University, CNRSSaint‐Pierre‐lès‐NemoursFrance
| | - David Carmignac
- Institut d'Écologie et des Sciences de l'Environnement de Paris (iEES‐Paris)Sorbonne Université, Université Paris Diderot, UPEC, CNRS, INRAE, IRDParisFrance
| | - Éric Édeline
- Institut d'Écologie et des Sciences de l'Environnement de Paris (iEES‐Paris)Sorbonne Université, Université Paris Diderot, UPEC, CNRS, INRAE, IRDParisFrance
- ESE Ecology and Ecosystem HealthINRAEAgrocampus OuestRennesFrance
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