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      Bet-hedging strategies in expanding populations

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          Abstract

          In ecology, species can mitigate their extinction risks in uncertain environments by diversifying individual phenotypes. This observation is quantified by the theory of bet-hedging, which provides a reason for the degree of phenotypic diversity observed even in clonal populations. Bet-hedging in well-mixed populations is rather well understood. However, many species underwent range expansions during their evolutionary history, and the importance of phenotypic diversity in such scenarios still needs to be understood. In this paper, we develop a theory of bet-hedging for populations colonizing new, unknown environments that fluctuate either in space or time. In this case, we find that bet-hedging is a more favorable strategy than in well-mixed populations. For slow rates of variation, temporal and spatial fluctuations lead to different outcomes. In spatially fluctuating environments, bet-hedging is favored compared to temporally fluctuating environments. In the limit of frequent environmental variation, no opportunity for bet-hedging exists, regardless of the nature of the environmental fluctuations. For the same model, bet-hedging is never an advantageous strategy in the well-mixed case, supporting the view that range expansions strongly promote diversification. These conclusions are robust against stochasticity induced by finite population sizes. Our findings shed light on the importance of phenotypic heterogeneity in range expansions, paving the way to novel approaches to understand how biodiversity emerges and is maintained.

          Author summary

          Ecological populations are often exposed to unpredictable and variable environmental conditions. A number of strategies have evolved to cope with such uncertainty. One of them is stochastic phenotypic switching, by which some individuals in the community are enabled to tackle adverse conditions, even at the price of reducing overall growth in the short term. In this paper, we study the effectiveness of these “bet-hedging” strategies for a population in the process of colonizing new territory. We show that bet-hedging is more advantageous when the environment varies spatially rather than temporally, and infrequently rather than frequently.

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          Most cited references60

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          THE WAVE OF ADVANCE OF ADVANTAGEOUS GENES

          R Fisher (1937)
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            Phenotypic diversity, population growth, and information in fluctuating environments.

            Organisms in fluctuating environments must constantly adapt their behavior to survive. In clonal populations, this may be achieved through sensing followed by response or through the generation of diversity by stochastic phenotype switching. Here we show that stochastic switching can be favored over sensing when the environment changes infrequently. The optimal switching rates then mimic the statistics of environmental changes. We derive a relation between the long-term growth rate of the organism and the information available about its fluctuating environment.
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              Animal personalities: consequences for ecology and evolution.

              Personality differences are a widespread phenomenon throughout the animal kingdom. Past research has focused on the characterization of such differences and a quest for their proximate and ultimate causation. However, the consequences of these differences for ecology and evolution received much less attention. Here, we strive to fill this gap by providing a comprehensive inventory of the potential implications of personality differences, ranging from population growth and persistence to species interactions and community dynamics, and covering issues such as social evolution, the speed of evolution, evolvability, and speciation. The emerging picture strongly suggests that personality differences matter for ecological and evolutionary processes (and their interaction) and, thus, should be considered a key dimension of ecologically and evolutionarily relevant intraspecific variation. Copyright © 2012 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: MethodologyRole: SoftwareRole: Writing – original draft
                Role: ConceptualizationRole: MethodologyRole: Writing – original draft
                Role: ConceptualizationRole: MethodologyRole: SoftwareRole: Writing – original draft
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                April 2019
                18 April 2019
                : 15
                : 4
                : e1006529
                Affiliations
                [1 ] Biological Complexity Unit, Okinawa Institute for Science and Technology Graduate University, Onna, Okinawa 904-0495, Japan
                [2 ] Departamento de Electromagnetismo y Física de la Materia and Instituto Carlos I de Física Teórica y Computacional, Universidad de Granada, Granada, Spain
                Spanish Institution for Scientific Research (CSIC), SPAIN
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-6157-6906
                Article
                PCOMPBIOL-D-18-01636
                10.1371/journal.pcbi.1006529
                6490941
                30998676
                bc8307da-18c9-4ed2-bebc-33b6d599e2cd
                © 2019 Villa Martín et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 21 September 2018
                : 26 March 2019
                Page count
                Figures: 5, Tables: 0, Pages: 17
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100010198, Ministerio de Economía, Industria y Competitividad, Gobierno de España;
                Award ID: FIS2017-84256-P
                Award Recipient :
                This study has been partially financed by the Consejería de Conocimiento, Investigación y Universidad, Junta de Andalucía and European Regional Development Fund (ERDF), ref. SOMM17/6105/UGR (to MAM). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Ecology
                Ecological Metrics
                Species Diversity
                Ecology and Environmental Sciences
                Ecology
                Ecological Metrics
                Species Diversity
                Physical Sciences
                Mathematics
                Numerical Analysis
                Numerical Integration
                Biology and Life Sciences
                Evolutionary Biology
                Population Genetics
                Biology and Life Sciences
                Genetics
                Population Genetics
                Biology and Life Sciences
                Population Biology
                Population Genetics
                Biology and Life Sciences
                Population Biology
                Population Metrics
                Population Size
                Biology and Life Sciences
                Microbiology
                Bacteriology
                Bacterial Evolution
                Biology and Life Sciences
                Microbiology
                Microbial Evolution
                Bacterial Evolution
                Biology and Life Sciences
                Evolutionary Biology
                Organismal Evolution
                Microbial Evolution
                Bacterial Evolution
                Ecology and Environmental Sciences
                Species Colonization
                Invasive Species
                Biology and Life Sciences
                Evolutionary Biology
                Evolutionary Theory
                Physical Sciences
                Mathematics
                Applied Mathematics
                Game Theory
                Custom metadata
                vor-update-to-uncorrected-proof
                2019-04-30
                All relevant data are within the manuscript and its Supporting Information files.

                Quantitative & Systems biology
                Quantitative & Systems biology

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