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      Hamilton's inclusive fitness maintains heritable altruism polymorphism throughrb=c

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      Proceedings of the National Academy of Sciences
      Proceedings of the National Academy of Sciences

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          Abstract

          <p id="d7476079e167">Why should some individuals help others at the expense of their own fitness? Hamilton’s elegant formula <i>rb</i> &gt; <i>c</i> resolves the major paradox in social evolution and has become a golden rule of sociobiology. However, <i>rb</i> &gt; <i>c</i> only tells part of the story, namely how altruistic genotypes expand. Theoretically, altruistic genotypes can persist by coexisting stably with nonaltruistic ones relying on <i>rb</i> = <i>c</i>, which may let both genotypes have equal inclusive fitness. We present evidence for this prediction using long-term data on a species of bird. Our work suggests that altruism should be understood beyond <i>rb</i> &gt; <i>c</i>, given that <i>rb</i> = <i>c</i> has the potential to explain widespread altruism polymorphisms in nature. </p><p class="first" id="d7476079e201">How can altruism evolve or be maintained in a selfish world? Hamilton’s rule shows that the former process will occur when <i>rb</i> &gt; <i>c</i>—the benefits to the recipients of an altruistic act <i>b</i>, weighted by the relatedness between the social partners <i>r</i>, exceed the costs to the altruists <i>c</i>—drives altruistic genotypes spreading against nonaltruistic ones. From this rule, we infer that altruistic genotypes will persist in a population by forming a stable heritable polymorphism with nonaltruistic genotypes if <i>rb</i> = <i>c</i> makes inclusive fitness of the two morphs equal. We test this prediction using the data of 12 years of study on a cooperatively breeding bird, the Tibetan ground tit <i>Pseudopodoces humilis</i>, where helping is performed by males only and kin-directed. Individual variation in ever acting as a helper was heritable ( <i>h</i> <sup>2</sup> = 0.47), and the resultant altruism polymorphism remained stable as indicated by low-level annual fluctuation of the percentage of helpers among all adult males (24–28%). Helpers’ indirect fitness gains from increased lifetime reproductive success of related breeders statistically fully compensated for their lifetime direct fitness losses, suggesting that <i>rb</i> = <i>c</i> holds. While our work provides a fundamental support for Hamilton’s idea, it highlights the equivalent inclusive fitness returns to altruists and nonaltruists mediated by <i>rb</i> = <i>c</i> as a theoretically and realistically important mechanism to maintain social polymorphism. </p>

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          Alternative reproductive strategies and tactics: diversity within sexes.

          Mart Gross (1996)
          Not all members of a sex behave in the same way. Frequency- and statusdependent selection have given rise to many alternative reproductive phenotypes within the sexes. The evolution and proximate control of these alternatives are only beginning to be understood. Although game theory has provided a theoretical framework, the concept of the mixed strategy has not been realized in nature, and alternative strategies are very rare. Recent findings suggest that almost all alternative reproductive phenotypes within the sexes are due to alternative tactics within a conditional strategy, and, as such, while the average fitnesses of the alternative phenotypes are unequal, the strategy is favoured in evolution. Proximate mechanisms that underlie alternative phenotypes may have many similarities with those operating between the sexes.
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            An ecologist's guide to the animal model.

            1. Efforts to understand the links between evolutionary and ecological dynamics hinge on our ability to measure and understand how genes influence phenotypes, fitness and population dynamics. Quantitative genetics provides a range of theoretical and empirical tools with which to achieve this when the relatedness between individuals within a population is known. 2. A number of recent studies have used a type of mixed-effects model, known as the animal model, to estimate the genetic component of phenotypic variation using data collected in the field. Here, we provide a practical guide for ecologists interested in exploring the potential to apply this quantitative genetic method in their research. 3. We begin by outlining, in simple terms, key concepts in quantitative genetics and how an animal model estimates relevant quantitative genetic parameters, such as heritabilities or genetic correlations. 4. We then provide three detailed example tutorials, for implementation in a variety of software packages, for some basic applications of the animal model. We discuss several important statistical issues relating to best practice when fitting different kinds of mixed models. 5. We conclude by briefly summarizing more complex applications of the animal model, and by highlighting key pitfalls and dangers for the researcher wanting to begin using quantitative genetic tools to address ecological and evolutionary questions.
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              Estimating genetic parameters in natural populations using the "animal model".

              Estimating the genetic basis of quantitative traits can be tricky for wild populations in natural environments, as environmental variation frequently obscures the underlying evolutionary patterns. I review the recent application of restricted maximum-likelihood "animal models" to multigenerational data from natural populations, and show how the estimation of variance components and prediction of breeding values using these methods offer a powerful means of tackling the potentially confounding effects of environmental variation, as well as generating a wealth of new areas of investigation.
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                Author and article information

                Journal
                Proceedings of the National Academy of Sciences
                Proc Natl Acad Sci USA
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                February 20 2018
                February 20 2018
                February 20 2018
                January 02 2018
                : 115
                : 8
                : 1860-1864
                Article
                10.1073/pnas.1710215115
                5828573
                29295937
                219e968c-c84f-4936-8b65-2c8420dfee36
                © 2018

                Free to read

                http://www.pnas.org/site/misc/userlicense.xhtml

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