In 1966, Richard Levins argued that there are different strategies in model building in population biology. In this paper, I reply to Orzack and Sober's (1993) critiques of Levins and argue that his views on modeling strategies apply also in the context of evolutionary genetics. In particular, I argue that there are different ways in which models are used to ask and answer questions about the dynamics of evolutionary change, prospectively and retrospectively, in classical versus molecular evolutionary genetics. Further, I (...) argue that robustness analysis is a tool for, if not confirmation, then something near enough, in this discipline. (shrink)

“ The Origins of Genome Architecture ” by Michael Lynch (2007) may not immediately sound like a book that someone interested in the philosophy of biology would grab off the shelf. But there are three important reasons why you should read this book. Firstly, if you want to understand biological evolution, you should have at least a passing familiarity with evolutionary change at the level of the genome. This is not to say that everyone interested in evolution should be a (...) geneticist or a bioinformatician, but that a working knowledge of genetic change is an essential part of the intellectual toolkit of modern evolutionary biology, even if your primary focus is the evolution of behaviour or the diversity of communities. Secondly, this book provides excellent examples of another important tool in the biologist’s intellectual toolkit, but one that is rarely explained or illustrated to such an extent: null (or neutral) models. The role null models play in testing hypotheses in evolution is a central focus of this book. Thirdly, as an accomplished work of advocacy for a strictly microevolutionary view of evolution, this book provides grist for the mill for the important debate about whether population genetic processes are the sine qua non of evolutionary explanations. (shrink)

In the 1960s molecular population geneticists used Monte Carlo experiments to evaluate particular diffusion equation models. In this paper I examine the nature of this comparative evaluation and argue for three claims: first, Monte Carlo experiments are genuine experiments: second, Monte Carlo experiments can provide an important meansfor evaluating the adequacy of highly idealized theoretical models; and, third, the evaluation of the computational adequacy of a diffusion model with Monte Carlo experiments is significantlydifferent from the evaluation of the emperical adequacy (...) of the same diffusion model. (shrink)

Among philosophers, controversy over the notion of drift in population genetics is ongoing. This is at least partly because the notion of drift has an ambiguous usage among population geneticists. My goal in this paper is to explicate the causal dimension of drift, to say what causal influences are responsible for the stochasticity in population genetics models. It is commonplace for population genetics to oppose the influence of selection to that of drift, and to consider how the dynamics of populations (...) are altered when each has greater or lesser influence. I define the causes that are referred to as drift when researchers speak this way. Introduction Populations and Variant Types The Cause–Effect Ambiguity of Drift Non-directional Factors in Population Genetics How N ev Is Used in Population Genetics Causal Conceptions of Drift 6.1 The Millstein/Beatty conception of drift 6.2 Rosenberg and Bouchard: Drift as initial conditions NINPICs 7.1 Why drift is instituted by NINPICs 7.2 How NINPICS work 7.3 NINPICs and random sampling 7.4 Independent sampling and effective population size 7.5 Variance in progeny number 7.6 Population effects of NINPICs NINPICs and the Stochastic Character of Selection Theory Conclusion Appendix CiteULike Connotea Del.icio.us What's this? (shrink)

Since the introduction of mathematical population genetics, its machinery has shaped our fundamental understanding of natural selection. Selection is taken to occur when differential fitnesses produce differential rates of reproductive success, where fitnesses are understood as parameters in a population genetics model. To understand selection is to understand what these parameter values measure and how differences in them lead to frequency changes. I argue that this traditional view is mistaken. The descriptions of natural selection rendered by population genetics models are (...) in general neither predictive nor explanatory and introduce avoidable conceptual confusions. I conclude that a correct understanding of natural selection requires explicitly causal models of reproductive success. *Received May 2006; revised December 2006. †To contact the author, please write to: Department of Philosophy, Kansas State University, 201 Dickens Hall, Manhattan, KS 66506; e‐mail: glymour@ksu.edu . (shrink)

A precise formulation of the structure of modern evolutionary theory has proved elusive. In this paper, I introduce and develop a formal approach to the structure of population genetics, evolutionary theory's most developed sub-theory. Under the semantic approach, used as a framework in this paper, presenting a theory consists in presenting a related family of models. I offer general guidelines and examples for the classification of population genetics models; the defining features of the models are taken to be their state (...) spaces, parameters, and laws. The suggestions regarding the various aspects of the characterization of population genetics models provide an outline for further detailed research. (shrink)

A hitherto neglected form of explanation is explored, especially its role in population genetics. “Statistically abstractive explanation” (SA explanation) mandates the suppression of factors probabilistically relevant to an explanandum when these factors are extraneous to the theoretical project being pursued. When these factors are suppressed, the explanandum is rendered uncertain. But this uncertainty traces to the theoretically constrained character of SA explanation, not to any real indeterminacy. Random genetic drift is an artifact of such uncertainty, and it is therefore wrong (...) to reify it as a cause of evolution or as a process in its own right. *Received July 2009. †To contact the author, please write to: Department of Philosophy, University of Toronto, 170 St. George St., Toronto, ON M5R 2M8, Canada; e‐mail: mohan.matthen@utoronto.ca. (shrink)

We have argued elsewhere that: (A) Natural selection is not a cause of evolution. (B) A resolution-of-forces (or vector addition) model does not provide us with a proper understanding of how natural selection combines with other evolutionary influences. These propositions have come in for criticism recently, and here we clarify and defend them. We do so within the broad framework of our own “hierarchical realization model” of how evolutionary influences combine.

How do fitness and natural selection relate to other evolutionary factors like architectural constraint, mode of reproduction, and drift? In one way of thinking, drawn from Newtonian dynamics, fitness is one force driving evolutionary change and added to other factors. In another, drawn from statistical thermodynamics, it is a statistical trend that manifests itself in natural selection histories. It is argued that the first model is incoherent, the second appropriate; a hierarchical realization model is proposed as a basis for a (...) statistical treatment. It emerges that natural selection does not cause evolution; it just is evolution. The theory incorporates relations of statistical correlation, but not the kind of causation found in fundamental physical processes. (shrink)

Population genetics attempts to measure the influence of the causes of evolution, viz., mutation, migration, natural selection, and random genetic drift, by understanding the way those causes change the genetics of populations. But how does it accomplish this goal? After a short introduction, we begin in section (2) with a brief historical outline of the origins of population genetics. In section (3), we sketch the model theoretic structure of population genetics, providing the flavor of the ways in which population genetics (...) theory might be understood as incorporating causes. In sections (4) and (5) we discuss two specific problems concerning the relationship between population genetics and evolutionary causes, viz., the problem of conceptually distinguishing natural selection from random genetic drift, and the problem of interpreting fitness. In section (6), we briefly discuss the methodology and key epistemological problems faced by population geneticists in uncovering the causes of evolution. Section (7) of the essay contains concluding remarks. (shrink)

Ernst Mayr has criticised the methodology of population genetics for being essentialist: interested only in “types” as opposed to individuals. In fact, he goes so far as to claim that “he who does not understand the uniqueness of individuals is unable to understand the working of natural selection” (1982, 47). This is a strong claim indeed especially since many responsible for the development of population genetics (especially Fisher, Haldane, and Wright) were avid Darwinians. In order to unravel this apparent incompatibility (...) I want to examine the possible sources and implications of essentialism in this context and show why the kind of mathematical analysis found in Fisher's work is better seen as responsible for extending the theory of natural selection to a broader context rather than inhibiting its applicability. (shrink)

Evolutionary biology is a field currently animated by much discussion concerning its conceptual foundations. On the one hand, we have supporters of a classical view of evolutionary theory, whose backbone is provided by population genetics and the so-called Modern Synthesis (MS). On the other hand, a number of researchers are calling for an Extended Synthe- sis (ES) that takes seriously both the limitations of the MS (such as its inability to incorporate developmental biology) and recent empirical and theoretical research on (...) issues such as evolvability, modularity, and self-organization. In this article, I engage in an in-depth commentary of an influential paper by population geneticist Michael Lynch, which I take to be the best defense of the MS-population genetics position published so far. I show why I think that Lynch’s arguments are wanting and propose a modification of evolutionary theory that retains but greatly expands on population genetics. (shrink)

The recent literature in philosophy of biology has drawn attention to the different sorts of explanations proffered in the biological sciences—we have molecular, biomedical, and evolutionary explanations. Do these explanations all have a common structure or relation that they seek to capture? This paper will answer in the negative. I defend a pluralistic and pragmatic approach to explanation. Using examples from classical population genetics, I argue that formal demonstrations, and even strictly “mathematical truths,” may serve as explanatory in different historical (...) contexts. (shrink)

: This paper explores the calibration of laboratory models in population genetics as an experimental strategy for justifying experimental results and claims based upon them following Franklin (1986, 1990) and Rudge (1996, 1998). The analysis provided undermines Coyne et al.'s (1997) critique of Wade and Goodnight's (1991) experimental study of Wright's (1931, 1932) Shifting Balance Theory. The essay concludes by further demonstrating how this analysis bears on Diamond's (1986) claims regarding the weakness of laboratory experiments as evidence, and further how (...) the calibration strategy fits within Lloyd's (1987, 1988) account of the confirmation of ecological and evolutionary models. (shrink)

Although epistasis is at the center of the Fisher-Wright debate, biologists not involved in the controversy are often unaware that there are actually two different formal definitions of epistasis. We compare concepts of genetic independence in the two theoretical traditions of evolutionary genetics, population genetics and quantitative genetics, and show how independence of gene action (represented by the multiplicative model of population genetics) can be different from the absence of gene interaction (represented by the linear additive model of quantitative genetics). (...) The two formulations converge with weak selection but not with strong selection or, for multiple loci, when the aggregated interaction terms are not negligible. As a result of the different formulations of gene interaction, the presence or absence of linkage disequilibrium,/D/, does not necessarily indicate the presence or absence of fitness epistasis. Indeed, linkage disequilibrium is generated in ‘additive’ models in quantitative genetics whenever two (or more) loci experience simultaneous selection. As a research strategy, it is often practical, for theoretical or experimental reasons, to minimize gene interaction by assuming independence of gene action in regard to fitness, or by assuming linear additive effects of multiple loci on a phenotype. However, minimizing the role of epistasis in theoretical investigations hinders our understanding of the origins of diversity and the evolution of complex phenotypes. (shrink)

This paper suggests that many of the pressing dilemmas of bioethics are global and structural in nature. Accordingly, global ethical frameworks are required which recognize the ethically significant factors of all global actors. To this end, ethical frameworks must recognize the rights and interests of both individuals and groups (and the interrelation of these). The paper suggests that the current dominant bioethical framework is inadequate to this task as it is over-individualist and therefore unable to give significant weight to the (...) ethical demands of groups (and by extension communal and public goods). It will explore this theme by considering the inadequacy of informed consent (the ‘global standard’ of bioethics) to address two pressing global bioethical issues: medical tourism and population genetics.Using these examples it will show why consent is inadequate to address all the significant features of these ethical dilemmas. Four key failures will be explored, namely,• That the rights and interests of those related (and therefore affected) are neglected;• That consent fails to take account of the context and commitments of individuals which may constitute inducement and coercion;• That consent alone does not have the ethical weight to negate exploitation or make an unjust action just (‘the fallacy of sufficiency’);• That consent is a single one-off act which is inappropriate for the types of decision being made.It will conclude by suggesting that more appropriate models are emerging, particularly in population genetics, which can supplement consent. (shrink)