Abstract: Experimental evolution is the study of evolutionary processes occurring in experimental populations in response to conditions imposed by the experimenter. This research approach is increasingly used to study adaptation, estimate evolutionary parameters, and test diverse evolutionary hypotheses. Long applied in vaccine development, experimental evolution also finds new applications in biotechnology. Recent technological developments provide a path towards detailed understanding of the genomic and molecular basis of experimental evolutionary change, while new findings raise new questions that can be addressed with this approach. However, experimental evolution has important limitations, and the interpretation of results is subject to caveats resulting from small population sizes, limited timescales, the simplified nature of laboratory environments, and, in some cases, the potential to misinterpret the selective forces and other processes at work. Experimental evolution is the study of evolutionary processes occurring in experimental populations in response to conditions imposed by the experimenter. This research approach is increasingly used to study adaptation, estimate evolutionary parameters, and test diverse evolutionary hypotheses. Long applied in vaccine development, experimental evolution also finds new applications in biotechnology. Recent technological developments provide a path towards detailed understanding of the genomic and molecular basis of experimental evolutionary change, while new findings raise new questions that can be addressed with this approach. However, experimental evolution has important limitations, and the interpretation of results is subject to caveats resulting from small population sizes, limited timescales, the simplified nature of laboratory environments, and, in some cases, the potential to misinterpret the selective forces and other processes at work. Evolutionary theories are usually inspired and tested by studying patterns of, for example, phylogeny, divergence between species or populations, variation within populations, genome structure, and genome sequence, which all reflect past evolution. Experimental evolution is an alternative research framework that offers the opportunity to study evolutionary processes experimentally in real time. The past decade has seen the fast growth of studies that tap into this potential, fuelled both by an increasing awareness of the power of this approach and by technological advances that facilitate analysis of the genetic and molecular basis of experimental evolution. We define experimental evolution as the study of evolutionary changes occurring in experimental populations as a consequence of conditions (environmental, demographic, genetic, social, and so forth) imposed by the experimenter (Figure 1). Thus, we do not consider cases of evolution in action that do not result from a planned and designed experiment. The above definition also excludes artificial selection (see [1Garland T. Rose M.R. Experimental Evolution. University of California Press, 2009Google Scholar]), where breeding individuals are chosen explicitly by the investigator based on phenotypic values of defined traits or genotypes (e.g., at specific marker loci), thus enforcing a predetermined relation between those traits or genotypes and fitness. By contrast, in experimental evolution, selection can act on any and all traits and genes relevant to fitness under the environmental regimes of interest. Experimental evolution is sometimes called ‘laboratory natural selection’; however, some experimental evolution studies have been conducted in the field [2Ebert D. et al.A selective advantage to immigrant genes in a Daphnia metapopulation.Science. 2002; 295: 485-488Crossref PubMed Scopus (142) Google Scholar, 3Zbinden M. et al.Experimental evolution of field populations of Daphnia magna in response to parasite treatment.J. Evol. Biol. 2008; 21: 1068-1078Crossref PubMed Scopus (25) Google Scholar, 4Reznick D.A. et al.Experimentally induced life-history evolution in a natural population.Nature. 1990; 346: 357-359Crossref Scopus (468) Google Scholar] and, moreover, others have explicitly focused on other evolutionary forces, including mutation, genetic drift, and gene flow (e.g., [5Rundle H.D. Divergent environments and population bottlenecks fail to generate premating isolation in Drosophila pseudoobscura.Evolution. 2003; 57: 2557-2565Crossref PubMed Google Scholar, 6Halligan D.L. Keightley P.D. Spontaneous mutation accumulation studies in evolutionary genetics.Annu. Rev. Ecol. Evol. Syst. 2009; 40: 151-172Crossref Scopus (50) Google Scholar]). Indeed, these other forces almost invariably act along with selection during experimental evolution, just as they do in nature. Here, we provide an introduction to experimental evolution as a research approach, not only illustrating its power and versatility, but also highlighting its limitations and caveats. We discuss major aspects of study systems and experimental design, and we summarize recent technological advances that are revolutionizing the study of the genetic and molecular basis of experimental evolutionary change. Experimental evolution has been used to address diverse questions in many areas of evolutionary biology. Here, we discuss several major types of question, keeping in mind that different questions are often addressed in a single experiment. We also address the advantages of long-term experiments and some practical applications of experimental evolution. Many evolution experiments seek to understand how populations adapt to particular environmental conditions, usually defined in terms of a particular factor, such as temperature [7Bennett A.F. Lenski R.E. Evolutionary adaptation to temperature. II. Thermal niches of experimental lines of Escherichia coli.Evolution. 1993; 47: 1-12Crossref Google Scholar], nutrition [8Kolss M. et al.Life history consequences of adaptation to larval nutritional stress in Drosophila.Evolution. 2009; 63: 2389-2401Crossref PubMed Scopus (18) Google Scholar], other environmental stressors [9Dhar R. et al.Adaptation of Saccharomyces cerevisiae to saline stress through laboratory evolution.J. Evol. Biol. 2011; 24: 1135-1153Crossref PubMed Scopus (18) Google Scholar], parasites [3Zbinden M. et al.Experimental evolution of field populations of Daphnia magna in response to parasite treatment.J. Evol. Biol. 2008; 21: 1068-1078Crossref PubMed Scopus (25) Google Scholar], or competition [10terHorst C.P. Experimental evolution of protozoan traits in response to interspecific competition.J. Evol. Biol. 2011; 24: 36-46Crossref PubMed Scopus (7) Google Scholar, 11Santos M. et al.Density-dependent natural selection in Drosophila: evolution of growth rate and body size.Evolution. 1997; 51: 420-432Crossref Google Scholar]. A few of these studies are specifically designed to test hypothetical links between particular polymorphisms and fitness: they start from a gene pool constructed to be polymorphic at the focal locus or loci and then measure the response in terms of changes in allele frequency (e.g., [12Fitzpatrick M.J. et al.Maintaining a behaviour polymorphism by frequency-dependent selection on a single gene.Nature. 2007; 447: 210-212Crossref PubMed Scopus (62) Google Scholar, 13Murray R.L. Cutter A.D. Experimental evolution of sperm count in protandrous self-fertilizing hermaphrodites.J. Exp. Biol. 2011; 214: 1740-1747Crossref PubMed Scopus (8) Google Scholar]). By contrast, most studies rely on natural (i.e., uncontrolled) genetic variation sampled from a base population or generated de novo by random mutations. Although these studies are often motivated by specific hypotheses about traits presumed to be relevant for adaptation (inspired, e.g., by patterns of interpopulation variation in nature), other traits may evolve and provide additional and unexpected insights. Therefore, what one can learn from experimental evolution is relatively unconstrained by preconceptions about what traits and evolutionary processes are most important. The traditional focus on phenotypic aspects of adaptation has been increasingly combined with genomic data, facilitated by technological advances (Box 1).Box 1Genomics and experimental evolutionThe first complete genome sequence was for the phage ΦX174, and its 5375-bp sequence appeared in 1982. A draft of the approximately 3000-Mb human genome was published in 2001. These achievements were remarkable in their day but now, thanks to technical advances, whole-genome resequencing is accessible for experimental evolution studies. In 1997, Bull et al. [104Bull J.J. et al.Exceptional convergent evolution in a virus.Genetics. 1997; 147: 1497-1507PubMed Google Scholar] sequenced nine ΦX174 isolates that had evolved on two hosts. In 2007, Velicer and colleagues [105Velicer G.J. et al.Comprehensive mutation identification in an evolved bacterial cooperator and its cheating ancestor.Proc. Natl. Acad. Sci. U.S.A. 2006; 103: 8107-8112Crossref PubMed Scopus (73) Google Scholar] sequenced the genome of a Myxococcus xanthus derivative that had evolved from socially cooperative to cheating and back to cooperative. In 2009, Barrick and Lenski [49Barrick J.E. Lenski R.E. Genome-wide mutational diversity in an evolving population of Escherichia coli.Cold Spring Harb. Symp. Quant. Biol. 2009; 74: 119-129Crossref PubMed Scopus (35) Google Scholar] deeply sequenced seven whole-population samples that spanned 40 000 generations from an evolving Escherichia coli population to find genetic polymorphisms. In 2010, genomics was extended to experimentally evolved eukaryotes: Saccharomyces cerevisiae [106Araya C.L. et al.Whole-genome sequencing of a laboratory-evolved yeast strain.BMC Genomics. 2010; 11: 88Crossref PubMed Scopus (30) Google Scholar] and Drosophila melanogaster [54Burke M.K. et al.Genome-wide analysis of a long-term evolution experiment with Drosophila.Nature. 2010; 467: 587-590Crossref PubMed Scopus (84) Google Scholar]. The application of genomics to experimental evolution may soon be limited only by the imagination of the investigator and the quality of the study design. Other high-throughput approaches are also increasingly useful for experimental evolution, including characterizing the capacity of an organism to use diverse resources (e.g., [43Cooper V.S. Lenski R.E. The population genetics of ecological specialization in evolving Escherichia coli populations.Nature. 2000; 407: 736-739Crossref PubMed Scopus (220) Google Scholar]) as well as proteomic (e.g., [107Knight C.G. et al.Unraveling adaptive evolution: how a single point mutation affects the protein coregulation network.Nat. Genet. 2006; 38: 1015-1022Crossref PubMed Scopus (40) Google Scholar]), transcriptional (e.g., [40Cooper T.F. et al.Parallel changes in qene expression after 20,000 generations of evolution in Escherichia coli.Proc. Natl. Acad. Sci. U.S.A. 2003; 100: 1072-1077Crossref PubMed Scopus (200) Google Scholar]), and metabolic profiling (e.g., [108Ibarra R.U. et al.Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth.Nature. 2002; 420: 186-189Crossref PubMed Scopus (369) Google Scholar]).To date, studies at the interface of genomics and experimental evolution have ranged from descriptive ones that demonstrate new technologies [109Albert T.J. et al.Mutation discovery in bacterial genomes: metronidazole resistance in Helicobacter pylori.Nat. Methods. 2005; 2: 951-953Crossref PubMed Scopus (100) Google Scholar, 110Shendure J. et al.Accurate multiplex polony sequencing of an evolved bacterial genome.Science. 2005; 309: 1728-1732Crossref PubMed Scopus (589) Google Scholar] or find genes of interest [105Velicer G.J. et al.Comprehensive mutation identification in an evolved bacterial cooperator and its cheating ancestor.Proc. Natl. Acad. Sci. U.S.A. 2006; 103: 8107-8112Crossref PubMed Scopus (73) Google Scholar, 106Araya C.L. et al.Whole-genome sequencing of a laboratory-evolved yeast strain.BMC Genomics. 2010; 11: 88Crossref PubMed Scopus (30) Google Scholar, 111Turner T.L. et al.Population-based resequencing of experimentally evolved populations reveals the genetic basis of body size variation in Drosophila melanogaster.PLoS Genet. 2011; 7: e1001336Crossref PubMed Scopus (27) Google Scholar] to quantitative analyses of diverse conceptual issues. How repeatable is evolution at the levels of nucleotides, genes, and pathways [27Meyer J.R. et al.Repeatability and contingency in the evolution of a key innovation in phage Lambda.Science. 2012; 335: 428-432Crossref PubMed Scopus (41) Google Scholar, 51Barrick J.E. et al.Genome evolution and adaptation in a long-term experiment with Escherichia coli.Nature. 2009; 461: 1243-1247Crossref PubMed Scopus (294) Google Scholar, 104Bull J.J. et al.Exceptional convergent evolution in a virus.Genetics. 1997; 147: 1497-1507PubMed Google Scholar, 112Herring C.D. et al.Comparative genome sequencing of Escherichia coli allows observation of bacterial evolution on a laboratory timescale.Nat. Genet. 2006; 38: 1406-1412Crossref PubMed Scopus (154) Google Scholar, 113Woods R.J. et al.Second-order selection for evolvability in a large Escherichia coli population.Science. 2011; 331: 1433-1436Crossref PubMed Scopus (80) Google Scholar]? How do epistatic interactions and the order of mutations affect evolvability, marginal fitness effects, and the origin of new functions [26Khan A.I. et al.Negative epistasis between beneficial mutations in an evolving bacterial population.Science. 2011; 332: 1193-1196Crossref PubMed Scopus (85) Google Scholar, 27Meyer J.R. et al.Repeatability and contingency in the evolution of a key innovation in phage Lambda.Science. 2012; 335: 428-432Crossref PubMed Scopus (41) Google Scholar, 113Woods R.J. et al.Second-order selection for evolvability in a large Escherichia coli population.Science. 2011; 331: 1433-1436Crossref PubMed Scopus (80) Google Scholar, 114Lee D.H. Palsson B.O. Adaptive evolution of Escherichia coli K-12 MG1655 during growth on a nonnative carbon cource, L-1,2-propanediol.Appl. Environ. Microbiol. 2010; 76: 4158-4168Crossref PubMed Scopus (31) Google Scholar, 115Chou H.H. et al.Diminishing returns epistasis among beneficial mutations decelerates adaptation.Science. 2011; 332: 1190-1192Crossref PubMed Scopus (80) Google Scholar]? What are genomic mutation rates and the spectrum of mutational types, and how do they evolve [49Barrick J.E. Lenski R.E. Genome-wide mutational diversity in an evolving population of Escherichia coli.Cold Spring Harb. Symp. Quant. Biol. 2009; 74: 119-129Crossref PubMed Scopus (35) Google Scholar, 51Barrick J.E. et al.Genome evolution and adaptation in a long-term experiment with Escherichia coli.Nature. 2009; 461: 1243-1247Crossref PubMed Scopus (294) Google Scholar, 116Wielgoss S. et al.Mutation rate inferred from synonymous substitutions in a long-term evolution experiment with Escherichia coli.G3 (Bethesda). 2011; 1: 183-186Crossref PubMed Scopus (13) Google Scholar, 117Haag-Liautard C. et al.Direct estimation of per nucleotide and genomic deleterious mutation rates in Drosophila.Nature. 2007; 445: 82-85Crossref PubMed Scopus (167) Google Scholar, 118Denver D.R. et al.A genome-wide view of Caenorhabditis elegans base-substitution mutation processes.Proc. Natl. Acad. Sci. U.S.A. 2009; 106: 16310-16314Crossref PubMed Scopus (62) Google Scholar]? What are the dynamics of genome evolution in relation to phenotypic change and in terms of hard versus soft selective sweeps [49Barrick J.E. Lenski R.E. Genome-wide mutational diversity in an evolving population of Escherichia coli.Cold Spring Harb. Symp. Quant. Biol. 2009; 74: 119-129Crossref PubMed Scopus (35) Google Scholar, 51Barrick J.E. et al.Genome evolution and adaptation in a long-term experiment with Escherichia coli.Nature. 2009; 461: 1243-1247Crossref PubMed Scopus (294) Google Scholar, 54Burke M.K. et al.Genome-wide analysis of a long-term evolution experiment with Drosophila.Nature. 2010; 467: 587-590Crossref PubMed Scopus (84) Google Scholar, 104Bull J.J. et al.Exceptional convergent evolution in a virus.Genetics. 1997; 147: 1497-1507PubMed Google Scholar, 111Turner T.L. et al.Population-based resequencing of experimentally evolved populations reveals the genetic basis of body size variation in Drosophila melanogaster.PLoS Genet. 2011; 7: e1001336Crossref PubMed Scopus (27) Google Scholar, 113Woods R.J. et al.Second-order selection for evolvability in a large Escherichia coli population.Science. 2011; 331: 1433-1436Crossref PubMed Scopus (80) Google Scholar]?Although these high-throughput methods provide new opportunities, they can also be difficult to analyze and interpret. In particular, demonstrating causal links between specific changes at the genomic or transcriptional level with divergence in morphology, physiology, behavior, or life history remains challenging, especially in non-microbial systems. These methods often identify divergence in allele frequencies at hundreds of loci [54Burke M.K. et al.Genome-wide analysis of a long-term evolution experiment with Drosophila.Nature. 2010; 467: 587-590Crossref PubMed Scopus (84) Google Scholar, 111Turner T.L. et al.Population-based resequencing of experimentally evolved populations reveals the genetic basis of body size variation in Drosophila melanogaster.PLoS Genet. 2011; 7: e1001336Crossref PubMed Scopus (27) Google Scholar] or in expression of hundreds of transcripts [119Sorensen J.G. et al.Gene expression profile analysis of Drosophila melanogaster selected for resistance to environmental stressors.J. Evol. Biol. 2007; 20: 1624-1636Crossref PubMed Scopus (59) Google Scholar]. Owing to linkage, drift, and statistical false-positives, not all of these differences will have been caused by adaptation. Therefore, such data must be interpreted with caution. The first complete genome sequence was for the phage ΦX174, and its 5375-bp sequence appeared in 1982. A draft of the approximately 3000-Mb human genome was published in 2001. These achievements were remarkable in their day but now, thanks to technical advances, whole-genome resequencing is accessible for experimental evolution studies. In 1997, Bull et al. [104Bull J.J. et al.Exceptional convergent evolution in a virus.Genetics. 1997; 147: 1497-1507PubMed Google Scholar] sequenced nine ΦX174 isolates that had evolved on two hosts. In 2007, Velicer and colleagues [105Velicer G.J. et al.Comprehensive mutation identification in an evolved bacterial cooperator and its cheating ancestor.Proc. Natl. Acad. Sci. U.S.A. 2006; 103: 8107-8112Crossref PubMed Scopus (73) Google Scholar] sequenced the genome of a Myxococcus xanthus derivative that had evolved from socially cooperative to cheating and back to cooperative. In 2009, Barrick and Lenski [49Barrick J.E. Lenski R.E. Genome-wide mutational diversity in an evolving population of Escherichia coli.Cold Spring Harb. Symp. Quant. Biol. 2009; 74: 119-129Crossref PubMed Scopus (35) Google Scholar] deeply sequenced seven whole-population samples that spanned 40 000 generations from an evolving Escherichia coli population to find genetic polymorphisms. In 2010, genomics was extended to experimentally evolved eukaryotes: Saccharomyces cerevisiae [106Araya C.L. et al.Whole-genome sequencing of a laboratory-evolved yeast strain.BMC Genomics. 2010; 11: 88Crossref PubMed Scopus (30) Google Scholar] and Drosophila melanogaster [54Burke M.K. et al.Genome-wide analysis of a long-term evolution experiment with Drosophila.Nature. 2010; 467: 587-590Crossref PubMed Scopus (84) Google Scholar]. The application of genomics to experimental evolution may soon be limited only by the imagination of the investigator and the quality of the study design. Other high-throughput approaches are also increasingly useful for experimental evolution, including characterizing the capacity of an organism to use diverse resources (e.g., [43Cooper V.S. Lenski R.E. The population genetics of ecological specialization in evolving Escherichia coli populations.Nature. 2000; 407: 736-739Crossref PubMed Scopus (220) Google Scholar]) as well as proteomic (e.g., [107Knight C.G. et al.Unraveling adaptive evolution: how a single point mutation affects the protein coregulation network.Nat. Genet. 2006; 38: 1015-1022Crossref PubMed Scopus (40) Google Scholar]), transcriptional (e.g., [40Cooper T.F. et al.Parallel changes in qene expression after 20,000 generations of evolution in Escherichia coli.Proc. Natl. Acad. Sci. U.S.A. 2003; 100: 1072-1077Crossref PubMed Scopus (200) Google Scholar]), and metabolic profiling (e.g., [108Ibarra R.U. et al.Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth.Nature. 2002; 420: 186-189Crossref PubMed Scopus (369) Google Scholar]). To date, studies at the interface of genomics and experimental evolution have ranged from descriptive ones that demonstrate new technologies [109Albert T.J. et al.Mutation discovery in bacterial genomes: metronidazole resistance in Helicobacter pylori.Nat. Methods. 2005; 2: 951-953Crossref PubMed Scopus (100) Google Scholar, 110Shendure J. et al.Accurate multiplex polony sequencing of an evolved bacterial genome.Science. 2005; 309: 1728-1732Crossref PubMed Scopus (589) Google Scholar] or find genes of interest [105Velicer G.J. et al.Comprehensive mutation identification in an evolved bacterial cooperator and its cheating ancestor.Proc. Natl. Acad. Sci. U.S.A. 2006; 103: 8107-8112Crossref PubMed Scopus (73) Google Scholar, 106Araya C.L. et al.Whole-genome sequencing of a laboratory-evolved yeast strain.BMC Genomics. 2010; 11: 88Crossref PubMed Scopus (30) Google Scholar, 111Turner T.L. et al.Population-based resequencing of experimentally evolved populations reveals the genetic basis of body size variation in Drosophila melanogaster.PLoS Genet. 2011; 7: e1001336Crossref PubMed Scopus (27) Google Scholar] to quantitative analyses of diverse conceptual issues. How repeatable is evolution at the levels of nucleotides, genes, and pathways [27Meyer J.R. et al.Repeatability and contingency in the evolution of a key innovation in phage Lambda.Science. 2012; 335: 428-432Crossref PubMed Scopus (41) Google Scholar, 51Barrick J.E. et al.Genome evolution and adaptation in a long-term experiment with Escherichia coli.Nature. 2009; 461: 1243-1247Crossref PubMed Scopus (294) Google Scholar, 104Bull J.J. et al.Exceptional convergent evolution in a virus.Genetics. 1997; 147: 1497-1507PubMed Google Scholar, 112Herring C.D. et al.Comparative genome sequencing of Escherichia coli allows observation of bacterial evolution on a laboratory timescale.Nat. Genet. 2006; 38: 1406-1412Crossref PubMed Scopus (154) Google Scholar, 113Woods R.J. et al.Second-order selection for evolvability in a large Escherichia coli population.Science. 2011; 331: 1433-1436Crossref PubMed Scopus (80) Google Scholar]? How do epistatic interactions and the order of mutations affect evolvability, marginal fitness effects, and the origin of new functions [26Khan A.I. et al.Negative epistasis between beneficial mutations in an evolving bacterial population.Science. 2011; 332: 1193-1196Crossref PubMed Scopus (85) Google Scholar, 27Meyer J.R. et al.Repeatability and contingency in the evolution of a key innovation in phage Lambda.Science. 2012; 335: 428-432Crossref PubMed Scopus (41) Google Scholar, 113Woods R.J. et al.Second-order selection for evolvability in a large Escherichia coli population.Science. 2011; 331: 1433-1436Crossref PubMed Scopus (80) Google Scholar, 114Lee D.H. Palsson B.O. Adaptive evolution of Escherichia coli K-12 MG1655 during growth on a nonnative carbon cource, L-1,2-propanediol.Appl. Environ. Microbiol. 2010; 76: 4158-4168Crossref PubMed Scopus (31) Google Scholar, 115Chou H.H. et al.Diminishing returns epistasis among beneficial mutations decelerates adaptation.Science. 2011; 332: 1190-1192Crossref PubMed Scopus (80) Google Scholar]? What are genomic mutation rates and the spectrum of mutational types, and how do they evolve [49Barrick J.E. Lenski R.E. Genome-wide mutational diversity in an evolving population of Escherichia coli.Cold Spring Harb. Symp. Quant. Biol. 2009; 74: 119-129Crossref PubMed Scopus (35) Google Scholar, 51Barrick J.E. et al.Genome evolution and adaptation in a long-term experiment with Escherichia coli.Nature. 2009; 461: 1243-1247Crossref PubMed Scopus (294) Google Scholar, 116Wielgoss S. et al.Mutation rate inferred from synonymous substitutions in a long-term evolution experiment with Escherichia coli.G3 (Bethesda). 2011; 1: 183-186Crossref PubMed Scopus (13) Google Scholar, 117Haag-Liautard C. et al.Direct estimation of per nucleotide and genomic deleterious mutation rates in Drosophila.Nature. 2007; 445: 82-85Crossref PubMed Scopus (167) Google Scholar, 118Denver D.R. et al.A genome-wide view of Caenorhabditis elegans base-substitution mutation processes.Proc. Natl. Acad. Sci. U.S.A. 2009; 106: 16310-16314Crossref PubMed Scopus (62) Google Scholar]? What are the dynamics of genome evolution in relation to phenotypic change and in terms of hard versus soft selective sweeps [49Barrick J.E. Lenski R.E. Genome-wide mutational diversity in an evolving population of Escherichia coli.Cold Spring Harb. Symp. Quant. Biol. 2009; 74: 119-129Crossref PubMed Scopus (35) Google Scholar, 51Barrick J.E. et al.Genome evolution and adaptation in a long-term experiment with Escherichia coli.Nature. 2009; 461: 1243-1247Crossref PubMed Scopus (294) Google Scholar, 54Burke M.K. et al.Genome-wide analysis of a long-term evolution experiment with Drosophila.Nature. 2010; 467: 587-590Crossref PubMed Scopus (84) Google Scholar, 104Bull J.J. et al.Exceptional convergent evolution in a virus.Genetics. 1997; 147: 1497-1507PubMed Google Scholar, 111Turner T.L. et al.Population-based resequencing of experimentally evolved populations reveals the genetic basis of body size variation in Drosophila melanogaster.PLoS Genet. 2011; 7: e1001336Crossref PubMed Scopus (27) Google Scholar, 113Woods R.J. et al.Second-order selection for evolvability in a large Escherichia coli population.Science. 2011; 331: 1433-1436Crossref PubMed Scopus (80) Google Scholar]? Although these high-throughput methods provide new opportunities, they can also be difficult to analyze and interpret. In particular, demonstrating causal links between specific changes at the genomic or transcriptional level with divergence in morphology, physiology, behavior, or life history remains challenging, especially in non-microbial systems. These methods often identify divergence in allele frequencies at hundreds of loci [54Burke M.K. et al.Genome-wide analysis of a long-term evolution experiment with Drosophila.Nature. 2010; 467: 587-590Crossref PubMed Scopus (84) Google Scholar, 111Turner T.L. et al.Population-based resequencing of experimentally evolved populations reveals the genetic basis of body size variation in Drosophila melanogaster.PLoS Genet. 2011; 7: e1001336Crossref PubMed Scopus (27) Google Scholar] or in expression of hundreds of transcripts [119Sorensen J.G. et al.Gene expression profile analysis of Drosophila melanogaster selected for resistance to environmental stressors.J. Evol. Biol. 2007; 20: 1624-1636Crossref PubMed Scopus (59) Google Scholar]. Owing to linkage, drift, and statistical false-positives, not all of these differences will have been caused by adaptation. Therefore, such data must be interpreted with caution. It is widely assumed that many or most adaptations are associated with trade-offs, such that changes in traits that increase fitness in some environments or situations are deleterious in some other environments or situations. Experimental evolution provides ample evidence for widespread (although not universal) trade-offs in general and insights into their mechanisms in specific cases (e.g., [14Lenski R.E. Experimental studies of pleiotropy and epistasis in Escherichia coli. I. Variation in competitive fitness among mutants resistant to virus T4.Evolution. 1988; 42: 425-432Crossref Google Scholar]); the evidence has been reviewed elsewhere [15Fry J.D. Detecting ecological trade-offs using selection experiments.Ecology. 2003; 84: 1672-1678Crossref Google Scholar, 16Roff D.A. Fairbairn D.J. The evolution of trade-offs: where are we?.J. Evol. Biol. 2007; 20: 433-447Crossref PubMed Scopus (170) Google Scholar]. As one example, experimental populations of Drosophila melanogaster that evolved postponed aging showed a decline in their early fecundity relative to populations that were allowed to breed immediately after emerging as adults [17Rose M.R. Laboratory evolution of postponed senescence in Drosophila melanogaster.Evolution. 1984; 38: 1004-1010Crossref PubMed Google Scholar]. This experiment and several similar ones were pivotal in the broader acceptance of an evolutionary explanation for aging [18Williams G.C. Pleiotropy, natural selection and the evolution of senescence.Evolution. 1957; 11: 398-411Crossref Google Scholar, 19Hamilton W.D. The moulding of senescence by natural selection.J. Theor. Biol. 1966; 12: 12-45Crossref PubMed Google Scholar]. Experimental evolution has also been used to study constraints imposed by a lack of standing genetic variation for specific adaptation [20Hoffmann A.A. et al.Low potential for climatic stress adaptation in a rainforest Drosophila species.Science. 2003; 301: 100-102Crossref PubMed Scopus (167) Google Scholar] and to address the notion that certain adaptations are unattainable by mutation. In this latter category, evolution experiments have falsified the hypotheses that bacteria cannot evolve resistance to amphipathic antimicrobial peptides [21Perron G.G. et al.Experimental evolution of resistance to an antimicrobial peptide.Proc. R. Soc. B. 2006; 273: 251-256Crossref PubMed Scopus (75) Google Scholar] and that Escherich