SELECTION EXPERIMENTS AS A TOOL IN EVOLUTIONARY AND COMPARATIVE PHYSIOLOGY: INSIGHTS INTO COMPLEX TRAITS-A physiological perspective on the response of body size and development time to (original) (raw)
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Species inhabit complex environments and respond to selection imposed by numerous abiotic and biotic conditions that vary in both space and time. Environmental heterogeneity strongly influences trait evolution and patterns of adaptive population differentiation. For example, heterogeneity can favor local adaptation, or can promote the evolution of plastic genotypes that alter their phenotypes based on the conditions they encounter. Different abiotic and biotic agents of selection can act synergistically to either accelerate or constrain trait evolution. The environmental context has profound effects on quantitative genetic parameters. For instance, heritabilities measured in controlled conditions often exceed those measured in the field; thus, laboratory experiments could overestimate the potential for a population to respond to selection. Nevertheless, most studies of the genetic basis of ecologically relevant traits are conducted in simplified laboratory environments, which do not...
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A great deal is known about the evolutionary significance of body size and development time. They are determined by the nonlinear interaction of three physiological traits: two hormonal events and growth rate (GR). In this study we investigate how the genetic architecture of the underlying three physiological traits affects the simultaneous response to selection on the two life-history traits in the hawkmoth Manduca sexta. The genetic architecture suggests that when the two life-history traits are both selected in the same direction (to increase or decrease) the response to selection is primarily determined by the hormonal mechanism. When the life-history traits are selected in opposite directions (one to increase and one to decrease) the response to selection is primarily determined by factors that affect the GR. To determine how the physiological traits affect the response to selection of the life-history traits, we simulated the predicted response to 10 generations of selection. A total of 83% of our predictions were supported by the simulation. The main components of this physiological framework also exist in unicellular organisms, vertebrates, and plants and can thus provide a robust framework for understanding how underlying physiology can determine the simultaneous evolution of life-history traits.
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Numerous studies have documented evolution by natural selection in natural populations, but few are genuine selection experiments that are designed and then executed in nature. We will focus on these few cases to illustrate what can be learned from field selection experiments alone or field and laboratory selection experiments together that cannot be learned from laboratory experiments alone. Both types of study allow us to evaluate cause and effect relationships because a planned experiment can be accompanied by a more direct evaluation of the factors that cause evolution. A unique benefit of field experiments is that they give us the opportunity to measure the rate and magnitude of selection in nature. We have found that this rate is far greater than one might imagine based on observations of the fossil record. A combination of field and laboratory selection experiments has revealed the importance of population size and structure in shaping the genetics of adaptation. For example, laboratory selection experiments on insecticide resistance tend to attain resistance though polygenic inheritance. The evolution of insecticide resistance in nature often eventually yields to single genes of large effect that are rare but, once they arise, represent a higher fitness solution to resistance and spread among populations. Finally, field studies enable us to test evolutionary theory in a context in which all of the tradeoffs associated with a trait are realized; in the laboratory, organisms may be shielded from the fitness tradeoffs associated with the evolution of a trait. For example, we have compared the patterns of senescence in guppies from high and low mortality rate environments in the laboratory and in the field. In the laboratory, guppies from high predation environments had delayed senescence relative to those from low predation environments. In the field the apparent relationship is the opposite. One hypothesis for this difference is that a tradeoff associated with the evolution of the high predation life history is a decrease in the investment in the immune system. Such a sacrifice would be evident in nature where there is exposure to disease and parasites but less so in the laboratory, which is relatively disease and parasite free.
Selection and phenotypic plasticity in evolutionary biology and animal breeding
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This paper reviews models for phenotypic plasticity in evolutionary genetics and animal breeding and show how those 21 models are connected. Environmental differences often lead to systematic phenotypic differences: phenotypic plasticity. 22 42 6 254-2219. 7 E-mail address: g.dejong@bio.uu.nl (G. de Jong). ent phenotypes in different environments. Phenotypic 43 1 0301-6226 / 02 / $ -see front matter 105 61 breeding is to include fixed effects, e.g., herd-year-composed from probability of survival, number of 106 62 season effects, into the model for breeding value offspring, mating success, longevity, and other so-107 63 estimation, and subsequently treat performance in called life-history characters. An individual's fitness 108 64 different environments (e.g., herds) as the same trait. depends upon its phenotype and the genotypic 109 65 128 84 Here, we shall first give an overview of the existence of a covariance between phenotype and 129 85 models of the evolution of phenotypically plastic fitness can be expressed in a selection gradient. A 130 86 quantitative traits as found within evolutionary biolo-selection gradient equals the partial derivative of 131 87 gy. The emphasis here will be on the different fitness with respect to the trait-that is, how fast 132 88 representation by the various models, as the models fitness changes with any change in trait value. A fast 133 89 themselves are straightforward applications of the change of fitness with trait value, i.e., a steep 134 90 general model for simultaneous selection on a num-selection gradient, implies strong selection. 135 91 ber of quantitative traits. Secondly, we will connect In accordance with the usage in evolutionary 136 1296 1259 converts into the product of the genetic variance and that S5cov(w ,z)5E(w z)5(12p)E(w zuz,t)1 1297 z z z 21 1260 the selection gradient of mean fitness towards mean pE(w zuz.t)501pis p , giving 1298 z z 1261 genotypic value. This yields
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