Contrasting visions of how fate and chance may affect history have inspired great works of literature and philosophy (e.g., Sophocles, Lucretius, Shakespeare, etc.) and puzzle scientists up to this very day. Albert Einstein famously claimed “God doesn’t play dice with the world (Hermanns and Einstein 1983, p. 58)” articulating the quintessential scientific attitude that history is predictable, with each instant in time governed by the state of the universe at the pre-existing moment. This mechanical worldview has the universe unfolding according to strict laws of cause-and-effect, from conditions at the moment of creation some 13.8 billion years ago. Such hard determinism was formally put to rest by 20th century physics, with quantum mechanics describing a probabilistic universe, and with Niels Bohr’s famous reply: “Einstein, stop telling God what to do (Isaacson 2008, p. 326).” A probabilistic or statistical interpretation of causality is now thought to be as true at the subatomic level (Heisenberg 1958) as at biological scales of organization (Monod 1974) and right up through to the cosmos as a whole (Prigogine and Stengers 1997). Despite that hard-won knowledge, deterministic perceptions have crept back into physics (e.g., Carroll 2017), and many biologists increasingly embrace a Bayesian perception of probability as an epistemological expectation of knowledge, rather than an ontological expectation of nature (Jaynes 1986). This debate has played out in evolutionary biology along a spectrum from population biologists focusing on the repeatability of evolutionary changes, to systematists (both neontological and paleontological) emphasizing the distinctive character of biological traits and clades. Population biologists and evolutionary ecologists are rightly fascinated with the power of natural selection to generate adaptations. They point to the numerous examples of convergent evolution across ecosystems and branches of the Tree of Life, as evidence for the predictable nature of evolutionary change. Given a certain problem (e.g., high-speed swimming, xeric landscapes) natural selection crafts similar structures with similar functions (e.g., a fusiform body-shape with a forked caudal fin; a thick cuticle with needle-shaped leaves). For systematists, of course, convergent evolution is literally noise in the phylogenetic system. A systematist’s job is to discover clades and the homologous traits by which clades can be diagnosed, dismissing superficial similarities (homoplasies) that may arise due to shared function or chance alone (Hennig and Davis 1969). In $$\quad$$ his new book Improbable Destinies, Jonathan Losos describes the recent history of this debate in evolutionary biology, drawing insights from the emerging field of experimental evolution. This area of research has not featured prominently in the pages of Systematic Biology (but see Krause and Whitaker 2015), yet many of the ideas are well within our ken. One problem is that the notion of experimental evolution itself may seem improbable, as we often think of evolutionary processes occurring over geological time periods. But a quick reference to peppered moths and Darwin’s finches reminds us that some populations can indeed evolve rapidly enough for experimental manipulation, if they have sufficiently short generation times. Improbable Destinies reviews the history of experimental evolution over the past 40 years, including the role of the author and his research group in developing Anolis lizards of the Caribbean as a model system in this area. The text has a breezy conversational style that will be an easy read for the intelligent non-specialist, requiring no advanced background in biology or evolutionary theory. The book is filled with amusing anecdotes from the field and lab, giving the reader a window into the more human side of science. However, these genial trappings envelope a serious study of the mechanisms by which biological system evolve through time. Improbable Destinies documents the roles of both fate and chance in studies using the experimental evolution approach and describes some of the conditions under which we can expect the evolution of repeated or convergent functional solutions versus unique one-offs. A central goal of experimental evolution is to ask what would happen if we could rewind and replay the proverbial “tape of Life”, as Stephen J. Gould posed in his book Wonderful Life (1989). Would we see the same forms evolve again and again, or would each replaying of the tape generate different and unpredictable organisms? Such questions have immediate practical relevance in the fields of conservation biology (evolution in response to local pollution, regional climate change, human predation, anthropogenic land-use, and water-use changes), invasion biology (evolution of responses to new predators or parasites), agriculture (evolution of pests or pesticide resistance), and infectious disease (evolution of antibiotic resistance). Many of these practical concerns are touched on in Chapter 12. In the absence of a time machine, experimental evolutionists rely on replicated natural experiments, and increasingly, on deliberately manipulated experiments in field and laboratory settings. Improbable Destinies reviews studies on plants adapted to toxic mine tailings, guppies in streams above and below waterfalls on Trinidad Island, lizards on Bahamian islands, and stickleback fishes in lakes of coastal British Columbia. A generality that stands out from all these case studies is that different species exposed to similar environments may or may not adapt convergently, depending on whether they evolve phenotypes that produce a similar functional response. The gold standard in experimental evolution is long-term studies of bacteria by Richard Lenski and colleagues on Escherichia coli, and Paul Rainey and colleagues on Pseudomonas fluorescens, $$\quad$$ tracking populations for several tens of thousands of generations. These projects are as laborious they are invaluable, yielding unique insights into the factors that contribute to evolutionary repeatability. The general take-home message from these long-term evolution experiments in bacteria is that replicate populations usually evolve the same way. Passages from pp. 214–215 provide a window into how Losos interprets the contrasting results obtained from field and laboratory settings: “From what we know now, the general results are clear. When an experiment is set in which multiple populations experience the same environmental treatment, the populations tends to evolve in very similar ways$$\ldots$$ Evolution is repeatable. This result should not be completely surprising. $$\ldots$$ closely related populations or species tend to evolve in the same way because they are similar genetically. $$\ldots$$ In contrast, distant relatives, starting from different initial genetic constitutions and phenotypes, are more likely to evolve different adaptive responses to the same environmental challenges. $$\ldots$$ As a result, the experiments are predisposed to generate parallel evolutionary responses$$\ldots$$ This is not to say the evolutionary change is identical from one experimental population to the next. Quite the contrary, there is always some degree of variation. $$\ldots$$ Such variation may indicate a degree of indeterminacy in evolutionary response. $$\ldots$$ Like most scientific research, experimental evolution studies focus on general trends, analyzed in a statistical framework. They tend to overlook the exceptions; the occasional aberrant population $$\ldots$$ adapting in a different way.” The Gouldian perspective holds that evolutionary history is contingent on the occurrence of rare or even unique phenomena, sometimes with outsized consequences; the so-called high-impact low-frequency events (HILF) of risk managers and policy makers. HILF events can be external to a system under study (e.g., hurricanes on Caribbean islands, asteroid impacts on the biosphere as a whole) or internal to organisms (e.g., genetic mutations or genomic revolutions like whole genome duplications). Regardless of their sources, HILF events are a bane to scientific inquiry because they difficult to study (they are rare), and yet strongly affect natural systems (have high impact). In the extreme they are unique, and therefore are as difficult to study as miracles. The fossil record suggests that certain rare and even unique events do indeed occur and have had strong impacts on the history of Life on Earth (e.g., Cambrian explosion, mass extinctions, and evolution of humans). Losos devotes the whole of Chapter 10 to the discovery of one such rare and important event in the long-term evolution experiment of E. coli: the origin of a Cit$$+$$ line that can metabolize citrate (Blount et al. 2008). “This single population that had lived in flasks for fourteen years in the Lenski Lab had made a major evolutionary leap. Somehow through the right combination of mutations and natural selection, the population has evolved an adaptation that, as far as anyone knows, this species had never been able to produce in the millions of years of existence in the wild. $$\ldots$$ Even now, more than a dozen years and thirty thousand generations later, the ability hasn’t evolved in any of the other (populations). So much for predictability and parallel evolution (Losos 2017, p. 252–253)!” In light of this remarkable Cit$$+$$ discovery, it worth noting that the field of experimental evolution is still mostly restricted to studying microevolutionary processes of natural selection and adaptation; that is evolution within populations and species (but see Rainey and Travisano 1998). However, these are by no means the only important phenomena in the larger field of evolution. Topics currently beyond the reach of experimental evolution in eukaryotes include speciation, adaptive radiation, mass extinction, and the origins of higher taxa and regional biotas. Further, presumably due to its focus on organismal evolution, Improbable Destinies does not treat some other fascinating areas where experimental evolution is being used, including experimental phylogenetics using viral evolution to test phylogenetic methods (e.g., Hillis and Bull 1992), studies of the RNA world and origin of Life (e.g., Joyce 2002), and synthetic biology using directed evolution (artificial selection) to engineer biological entities for targeted purposes (e.g., Nandagopal and Elowitz 2011). Improbable Destinies is $$\quad$$ not at all a rebuttal to Gould’s Wonderful Life. Gould certainly emphasized the unpredictable course of evolution, but never argued for “hard contingency” in the sense that evolution is utterly capricious and senseless. Gould published extensively on the role of allometry and heterochrony constraining and guiding evolutionary changes repeatedly in some directions and against others (e.g., Gould 1966, 1977). Gould and colleagues summarized this view in Raup et al. (1973, p. 526): “we test the possibility that some aspects of the evolutionary record behave as stochastic or random variables. We do not suggest that evolution be viewed as a haphazard process, independent of basic relations of cause and effect. Rather, we suggest that an evolutionary event may depend upon the joint occurrence of many underlying causes, each having a specific probability of occurrence at a given time, so that the event itself can be predicted only in a statistical sense—even though it does, in fact, have a conventional cause.” In science, the idea of determinism is intimately connected with the concepts of predictability and forecasting. These ideas trace back to the Ionian philosophers seeking immutable laws or principles of nature, which of course have contributed to the intellectual foundation of the modern world. But even if the major features of evolution were eventually found to be statistically predictable, there is a world of difference between a completely and an incompletely deterministic universe. The presence of any indeterminism at all in the natural world is an existential question; does chance exist, or does it call come down to fate? Hard determinism is problematic in several regards. It denies even the possibility that organisms make adaptive decisions and execute intentional behaviors, despite manifest neurobiological evidence to the contrary (e.g., Schultze-Kraft et al. 2016). Hard determinism poses an existential challenge to our intuitive personal experience of choice and denies the irreversibility of time (i.e., entropy; Prigogine and Stengers 1997). Hard determinism is not supported by quantum mechanics or neuroscience, and now, it seems, as effectively described in Improbable Destinies, by the field of experimental evolution. References Carroll S. ( 2017). The big picture: on the origins of life, meaning, and the universe itself. London: Penguin Books. Google Scholar CrossRef Search ADS Blount Z.D., Borland C.Z., Lenski R.E. 2008. Historical contingency and the evolution of a key innovation in an experimental population of Escherichia coli. Proc. Natl. Acad. Sci. 105: 7899– 7906. Google Scholar CrossRef Search ADS Isaacson W. 2008. Einstein: his life and universe. New York: Simon and Schuster, pp. 501. Gould S.J. 1966. Allometry and size in ontogeny and phylogeny. Biol. Rev. 41( 4): 587– 638. Google Scholar CrossRef Search ADS Gould S.J. 1977. Ontogeny and phylogeny. Cambridge (MA): Harvard University Press. Gould S.J. 1990. Wonderful life: the burgess shale and the nature of history. New York (NY): WW Norton & Company. Heisenberg W. 1958. Physics and philosophy: the revolution in modern science. Amherst (NY): Prometheus Books. Hennig W., Davis D.D. 1999. Phylogenetic systematics. Chicago (IL): University of Illinois Press. Google Scholar CrossRef Search ADS Hermanns W., Einstein A. 1983. Einstein and the poet: in search of the cosmic Man. Wellesley (MA): Branden Publishing. Hillis D.M., Bull J.J. 1992. Experimental phylogenetics: generation of a known phylogeny. Science, 255: 589– 592. Google Scholar CrossRef Search ADS Jaynes E.T. 1986. Bayesian methods: general background. In: Justice J.H., editor. Maximum-entropy and Bayesian methods in applied statistics. Cambridge, UK: Cambridge University Press, pp. 279. Google Scholar CrossRef Search ADS Joyce G.F. 2002. The antiquity of RNA-based evolution. Nature 418: 214– 221. Google Scholar CrossRef Search ADS PubMed Krause D.J., Whitaker R.J. 2015. Inferring speciation processes from patterns of natural variation in microbial genomes. Syst. Biol. 64: 926– 935. Google Scholar CrossRef Search ADS PubMed Losos J.B. 2017. Improbable destinies: Fate, chance, and the future of evolution. New York (NY): Riverhead Books. Nandagopal N., Elowitz M.B. 2011. Synthetic biology: integrated gene circuits. Science 333: 1244– 1248. Google Scholar CrossRef Search ADS PubMed Monod J. 1974. On chance and necessity. In: Studies in the philosophy of biology. UK: Macmillan Education. p. 357– 375. Prigogine I., Stengers I. 1997. The end of certainty. New York (NY): Simon and Schuster. Rainey P.B., Travisano M. 1998. Adaptive radiation in a heterogeneous environment. Nature 394: 69– 72. Google Scholar CrossRef Search ADS PubMed Raup D.M., Gould S.J., Schopf T.J., Simberloff D.S. 1973. Stochastic models of phylogeny and the evolution of diversity. J. Geol. 81( 5): 525– 542. Google Scholar CrossRef Search ADS Schultze-Kraft M., Birman D., Rusconi M., Allefeld C., Görgen K., Dähne S., Blankertz B., Haynes J.D. 2016. The point of no return in vetoing self-initiated movements. Proc. Natl. Acad. Sci. 113: 1080– 1085. Google Scholar CrossRef Search ADS © The Author(s) 2017. 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Systematic Biology – Oxford University Press
Published: Mar 1, 2018
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