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Considering the Value and Limits of Evolutionary Prediction*

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Considering the Value and Limits of Evolutionary Prediction* vol. 193, no. 1 the american naturalist january 2019 American Society of Naturalists Address From the Past to the Future: Considering the Value and Limits of Evolutionary Prediction* Ruth G. Shaw† Department of Ecology, Evolution, and Behavior, University of Minnesota, Twin Cities, Saint Paul, Minnesota 55108 Submitted April 27, 2018; Accepted June 22, 2018; Electronically published October 30, 2018 abstract: The complex interplay of the multiple genetic processes to characterize in detail. In addition, environment, which of evolution and the ecological contexts in which they proceed frus- mediates much selection, varies over time and space, often trates detailed identification of many of the states of populations, both idiosyncratically. The dependence of a population’sevolu- past and future, that may be of interest. Prediction of rates of adapta- tionary change on its history (e.g., Travisano et al. 1995) tion, in the sense of change in mean fitness, into the future would, compounds the challenge. Together, these characteristics however, valuably inform expectations for persistence of populations, of evolution obviateaccurateprediction of apopulation’sde- especially in our era of rapid environmental change. Heavy invest- gree of adaptation—that is, its mean absolute fitness many ment in genomics and other molecular tools has fueled belief that — those approaches can effectively predict adaptation into the future. I generations into the future (Gerrish and Sniegowski 2012) contest this view. Genome scans display the genomic footprints of and, even over the short term, frustrate prediction of many the effects of natural selection and the other evolutionary processes attributes of interest. over past generations, but it remains problematic to predict future All of these reasons for doubting the possibility of de- change in mean fitness via genomic approaches. Here, I advocate for a tailed evolutionary prediction raise two key questions: What direct approach to prediction of rates of ongoing adaptation. Following evolutionary changes, if any, are currently feasible to pre- an overview of relevant quantitative genetic approaches, I outline the dict? And even more pertinent: Would it be worthwhile to promise of the fundamental theorem of natural selection for the study have these predictions? It is important to be clear about of the adaptive process. Empirical implementation of this concept can “ ” productively guide efforts both to deepen scientific insight into the pro- the meaning of the word prediction. In this essay, I am us- cess of adaptation and to inform measures for conserving the biota in the ing “predict” in the common language sense of foretelling at- face of rapid environmental change. tributes of a population sometime in the future, an objective that must draw not only on theory but also on data from the Keywords: adaptation, experimental evolution, fundamental theorem of natural selection, quantitative genetics. population(s) of interest. I am not considering retrodiction or hindcasting, that is, inference of past states. Nor am I re- ferring to qualitative prediction, the province of evolutionary Introduction theory developed as proof-of-concept models (Servedio et al. 2014), nor yet to prediction in the statistical sense of values Evolutionary biologists recognize immense impediments to fi fi tted from data in accordance with a statistical model. Each speci c, quantitative evolutionary prediction about popula- of these kinds of effort is valuable in its own right, but they tions in nature. The challenge stems from the size and com- are distinct from predicting states of populations in the fu- plexity of genomes, along with the multiple processes that ture. One kind of evolutionary prediction for which all of can change the genetic composition of populations: natural the necessary approaches are well established is prediction selection, gene flow, mutation, and genetic drift. In most fi fi fi of change in mean tness, that is, the rate of adaptation. I here speci c cases, each of these processes is profoundly dif cult propose that this research program merits extensive imple- mentation. Beyond its intellectual interest, evaluating the rate of adaptation now looms in practical importance as environ- * Ruth Shaw received the 2017 Sewall Wright Award. The Sewall Wright mental change at extreme rates threatens the persistence of Award, established in 1991, is given annually and honors a senior but still ac- populations and species globally. tive investigator who is making fundamental contributions to the society’s goals, namely, promoting the conceptual unification of the biological sciences. † Email: [email protected]. Can Genomics Predict Change in Mean Fitness? Am. Nat. 2019. Vol. 193, pp. 1–10. q 2018 by The University of Chicago. 0003-0147/2019/19301-58432$15.00. All rights reserved. With current heavy investment in genomics and other mo- DOI: 10.1086/700565 lecular tools, some have opined that those approaches can This content downloaded from 128.101.134.127 on January 07, 2019 07:34:03 AM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). 2 The American Naturalist effectively yield predictions of evolutionary adaptation in covariance between fitness and each of the traits; and b, the sense of change in mean fitness. I challenge this claim. the selection gradient, as a measure of selection directly on Genome scans yield the genomic historical record of the ef- each trait, is the partial regression of fitness on the traits. In fects of natural selection, along with all of the other evolu- the single-trait case, h2 is the trait heritability, the ratio of tionary processes, accumulated over many generations. the additive genetic variance of a trait to its phenotypic vari- Distinguishing from this record the loci that contributed ance. Reliable prediction is here favored by considering in to past adaptation remains problematic because of history aggregate the many polymorphic loci that collectively con- that generates population structure and linkage disequilib- tribute to the standing genetic variation in traits and to the rium (see, e.g., Tiffin and Ross-Ibarra 2014; Schrider et al. genetic covariance between fitness and traits. These equa- 2015; Hoban et al. 2016). Moreover, loci whose effects are tions, built on the regularities of Mendelian transmission, individually small but that can collectively account for much yield predictions for the change in trait mean from one of the adaptive change are unlikely to be detected (e.g., Lau- generation to the next, a modest timescale. rie et al. 2004). Thus, loci or genomic regions that can be Artificial selection experiments in controlled conditions discerned as possibly subject to selection generally repre- have demonstrated the utility of the quantitative predic- sent a small fraction of those that actually contribute to se- tions, even though realized responses to selection vary among lection response, and even these may often be mistakenly replicate populations and generations, reflecting random- identified (Schrider et al. 2015). More importantly, genomic ness of population sampling and genetic transmission. Sher- approaches do not generally elucidate rates of change in mean idan (1988), in his review of selection in livestock and lab- fitness except, at best, extremely indirectly (see Bay et al. oratory populations of Drosophila and Tribolium, deemed 2017, especially the numerous caveats therein). The tempo- selection responses to differ substantially from predictions. ral scale of the genomic signal of adaptation is that of many However, Hill and Caballero (1992) offer a critique of how the generations, and the spatial scale of most genomic studies comparisons were made (see also Walsh and Lynch 2018, far exceeds that occupied by an interbreeding population. p. 607) and a more encouraging view of agreement between In contrast, adaptation of a population in nature proceeds predicted and realized responses to selection. Realized re- on the timescale of generations and can continue over the sponses often show high repeatability and accord well with vast sweeps of time into the future only in populations that predictions (e.g., Enfield et al. 1966; Carey 1983; Conner et al. adapt to the vagaries of environment that confront them 2011). Moreover, even though h2 is expected to change with generation by generation and thus maintain an absolute fit- the changes in allele frequencies implied by the observed ness sufficient to persist. Adaptation over this timescale war- response to selection, in practice the rate of change in a trait rants direct study prospectively. I propose that quantitative or traits under artificial selection sometimes holds far beyond genetic approaches offer evolutionary predictions that would the initial generation—for 10–30 generations (Yoo 1980) or be of great value, both in deepening scientific understand- many more for large populations (Weber 1990; Weber and ing of evolutionary process and in informing measures to Diggins 1990). Within highly inbred lines, in which the stand- address issues of pressing societal concern. This line of ing genetic variation is nil (or very nearly so), the response to study is challenging, but I urge that the payoffs warrant selection is, as expected, not detectable initially (López and the effort. López-Fanjul 1993; Mackay et al. 1994; Keightley 1998). Thus, in accord with the theory for predicting selection response, empirical studies in the laboratory and the green- house
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