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Simon MN, Marroig G, Arnold SJ. Detecting patterns of correlational selection with sampling error: A simulation study. Evolution 2021; 76:207-224. [PMID: 34888853 DOI: 10.1111/evo.14412] [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: 06/19/2020] [Revised: 09/16/2021] [Accepted: 10/14/2021] [Indexed: 11/29/2022]
Abstract
The adoption of a multivariate perspective of selection implies the existence of multivariate adaptive peaks and pervasive correlational selection that promotes co-adaptation between traits. However, to test for the ubiquity of correlational selection in nature, we must first have a sense of how well can we estimate multivariate nonlinear selection (i.e., the γ-matrix) in the face of sampling error. To explore the sampling properties of estimated γ-matrices, we simulated inidividual traits and fitness under a wide range of sample sizes, using different strengths of correlational selection and of stabilizing selection, combined with different number of traits under selection, different amounts of residual variance in fitness, and distinct patterns of selection. We then ran nonlinear regressions with these simulated datasets to simulate γ-matrices after adding random error to individual fitness. To test how well could we detect the imposed pattern of correlational selection at different sample sizes, we measured the similarity between simulated and imposed γ-matrices. We show that detection of the pattern of correlational selection is highly dependent on the total strength of selection on traits and on the amount of residual variance in fitness. Minimum sample size needs to be at least 500 to precisely estimate the pattern of correlational selection. Furthermore, a pattern of selection in which different sets of traits contribute to different functions is the easiest to diagnose, even when using a large number of traits (10 traits), but with sample sizes in the order of 1000 individuals. Consequently, we recommend working with sets of traits from distinct functional complexes and fitness proxies less prone to effects of environmental and demographic stochasticity to test for correlational selection with lower sample sizes.
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Affiliation(s)
| | - Gabriel Marroig
- Department of Genetics and Evolutionary Biology, University of São Paulo, São Paulo, Brazil
| | - Stevan J Arnold
- Department of Integrative Biology, Oregon State University, Corvallis, Oregon, USA
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de Villemereuil P, Charmantier A, Arlt D, Bize P, Brekke P, Brouwer L, Cockburn A, Côté SD, Dobson FS, Evans SR, Festa-Bianchet M, Gamelon M, Hamel S, Hegelbach J, Jerstad K, Kempenaers B, Kruuk LEB, Kumpula J, Kvalnes T, McAdam AG, McFarlane SE, Morrissey MB, Pärt T, Pemberton JM, Qvarnström A, Røstad OW, Schroeder J, Senar JC, Sheldon BC, van de Pol M, Visser ME, Wheelwright NT, Tufto J, Chevin LM. Fluctuating optimum and temporally variable selection on breeding date in birds and mammals. Proc Natl Acad Sci U S A 2020; 117:31969-31978. [PMID: 33257553 PMCID: PMC7116484 DOI: 10.1073/pnas.2009003117] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 09/24/2020] [Indexed: 01/01/2023] Open
Abstract
Temporal variation in natural selection is predicted to strongly impact the evolution and demography of natural populations, with consequences for the rate of adaptation, evolution of plasticity, and extinction risk. Most of the theory underlying these predictions assumes a moving optimum phenotype, with predictions expressed in terms of the temporal variance and autocorrelation of this optimum. However, empirical studies seldom estimate patterns of fluctuations of an optimum phenotype, precluding further progress in connecting theory with observations. To bridge this gap, we assess the evidence for temporal variation in selection on breeding date by modeling a fitness function with a fluctuating optimum, across 39 populations of 21 wild animals, one of the largest compilations of long-term datasets with individual measurements of trait and fitness components. We find compelling evidence for fluctuations in the fitness function, causing temporal variation in the magnitude, but not the direction of selection. However, fluctuations of the optimum phenotype need not directly translate into variation in selection gradients, because their impact can be buffered by partial tracking of the optimum by the mean phenotype. Analyzing individuals that reproduce in consecutive years, we find that plastic changes track movements of the optimum phenotype across years, especially in bird species, reducing temporal variation in directional selection. This suggests that phenological plasticity has evolved to cope with fluctuations in the optimum, despite their currently modest contribution to variation in selection.
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Affiliation(s)
- Pierre de Villemereuil
- Centre d'Écologie Fonctionnelle et Évolutive, CNRS, Université de Montpellier, Université Paul Valéry Montpellier 3, École Pratique des Hautes Études | Paris Science et Lettres, Institut de Recherche pour le Développement, 34000 Montpellier, France;
- Institut de Systématique, Évolution, Biodiversité, École Pratique des Hautes Études | Paris Sciences et Lettres, Muséum National d'Histoire Naturelle, CNRS, Sorbonne Université, Université des Antilles, 75005 Paris, France
| | - Anne Charmantier
- Centre d'Écologie Fonctionnelle et Évolutive, CNRS, Université de Montpellier, Université Paul Valéry Montpellier 3, École Pratique des Hautes Études | Paris Science et Lettres, Institut de Recherche pour le Développement, 34000 Montpellier, France
| | - Debora Arlt
- Department of Ecology, Swedish University of Agricultural Sciences, 75007 Uppsala, Sweden
| | - Pierre Bize
- School of Biological Sciences, University of Aberdeen, AB24 2TZ Aberdeen, United Kingdom
| | - Patricia Brekke
- Institute of Zoology, Zoological Society of London, NW1 4RY London, United Kingdom
| | - Lyanne Brouwer
- Division of Ecology and Evolution, Research School of Biology, The Australian National University, Canberra, ACT 2600 Australia
- Department of Animal Ecology, Netherlands Institute of Ecology, 6700 AB Wageningen, The Netherlands
- Department of Animal Ecology and Physiology, Institute for Water and Wetland Research, Radboud University, 6500 GL Nijmegen, The Netherlands
| | - Andrew Cockburn
- Division of Ecology and Evolution, Research School of Biology, The Australian National University, Canberra, ACT 2600 Australia
| | - Steeve D Côté
- Département de Biologie and Centre d'Études Nordiques, Université Laval, Québec, G1V 0A6 QC, Canada
| | - F Stephen Dobson
- Department of Biological Sciences, Auburn University, Auburn, AL 36849
| | - Simon R Evans
- Edward Grey Institute, Department of Zoology, University of Oxford, Oxford OX1 3PS, United Kingdom
- Centre for Ecology and Conservation, University of Exeter, Penryn TR10 9FE, United Kingdom
| | - Marco Festa-Bianchet
- Département de biologie, Université de Sherbrooke, J1K 2R1 Sherbrooke, Québec, Canada
- Division of Ecology and Evolution, Research School of Biology, The Australian National University, Canberra, ACT 2600 Australia
| | - Marlène Gamelon
- Centre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Sandra Hamel
- Département de Biologie, Université Laval, Québec, G1V 0A6 QC, Canada
| | - Johann Hegelbach
- Institute of Evolutionary Biology and Environmental Studies, University of Zurich, CH-8057 Zurich, Switzerland
| | | | - Bart Kempenaers
- Department of Behavioural Ecology and Evolutionary Genetics, Max Planck Institute for Ornithology, 82319 Seewiesen, Germany
| | - Loeske E B Kruuk
- Division of Ecology and Evolution, Research School of Biology, The Australian National University, Canberra, ACT 2600 Australia
| | - Jouko Kumpula
- Terrestrial Population Dynamics, Natural Resources Institute Finland, FIN-999870, Inari, Finland
| | - Thomas Kvalnes
- Centre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Andrew G McAdam
- Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, CO 80309
| | - S Eryn McFarlane
- Department of Ecology and Genetics, Uppsala University, 75236 Uppsala, Sweden
- 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
| | - Tomas Pärt
- Department of Ecology, Swedish University of Agricultural Sciences, 75007 Uppsala, Sweden
| | - Josephine M Pemberton
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3FL, United Kingdom
| | - Anna Qvarnström
- Department of Ecology and Genetics, Uppsala University, 75236 Uppsala, Sweden
| | - Ole Wiggo Røstad
- Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, 1432 Ås, Norway
| | - Julia Schroeder
- Department of Life Sciences, Imperial College London, SL5 7PY Ascot, Berks,
| | - Juan Carlos Senar
- Behavioural and Evolutionary Ecology Research Unit, Museu de Ciències Naturals de Barcelona, E-08003 Barcelona, Spain
| | - Ben C Sheldon
- Edward Grey Institute, Department of Zoology, University of Oxford, Oxford OX1 3PS, United Kingdom
| | - Martijn van de Pol
- Department of Animal Ecology, Netherlands Institute of Ecology, 6700 AB Wageningen, The Netherlands
| | - Marcel E Visser
- Department of Animal Ecology, Netherlands Institute of Ecology, 6700 AB Wageningen, The Netherlands
| | | | - Jarle Tufto
- Centre for Biodiversity Dynamics, Department of Mathematics, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway
| | - Luis-Miguel Chevin
- Centre d'Écologie Fonctionnelle et Évolutive, CNRS, Université de Montpellier, Université Paul Valéry Montpellier 3, École Pratique des Hautes Études | Paris Science et Lettres, Institut de Recherche pour le Développement, 34000 Montpellier, France;
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