Abstract
In response to our chapter “The Organism in Evolutionary Explanation: From Early Twentieth Century to the Extended Evolutionary Synthesis,” population biologists T. N. C. Vidya, Sutirth Dey, N. G. Prasad, and Amitabh Joshi press for making an explicit distinction between the causes and the consequences of selection, and further suggest that such a distinction weighs in the three explanatory roles ascribed to organisms in past and present evolutionary research that we recounted in our work: (1) contextualizing parts in development; (2) drawing attention to reciprocal organism-environment interactions; and (3) underscoring the role of agency in evolution. Here, we first provide an overview of their arguments and then offer a rejoinder to their position which, we think, does not correctly apportion the evolutionary significance of organismal development and activities. We argue that organisms are relevant both for the “causes” (and sources) and the “consequences” of selection, and for evolutionary dynamics and trajectories in general. Evolutionary biology cannot dispense with the successful populational models built with the mathematical tools and assumptions of quantitative and population genetics, but, at the same time, it also needs new organismal models that take into account development, agency, and organism-environment reciprocal interactions.
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Notes
- 1.
In our commentary, we restrict our discussions to the points considered by Vidya and colleagues on the topic of the causes and consequences of selection, and how organisms intersect (or not) with those evolutionary processes. Nevertheless, we want to stress that they raise other important issues as well: we agree with them on the fact that, in many instances of the EES debate, the target of criticism has not been Modern Synthesis thinking per se (for example, the Fisherian view imbued in quantitative genetics), but rather the subsequent gene’s-eye view of evolution stemming from Williams and Dawkins, and cognate theses such as gene-centrism and -determinism in development that took prominence in the last decades of the twentieth century. More nuanced historical appraisals of what is covered by the EES-derived neologism “Standard Evolutionary Theory,” and when it misses its target, are surely needed and welcome. At the same time, it is important to stress that some EES proponents have waged specific critiques against the models of quantitative genetics, such as genetic variance–covariance matrices (see Pigliucci 2006), so it is not correct to claim that they only dispute population genetics. The general point of contention that underlines that what the EES is opposing or resisting to is not always clear in historiographic terms might still hold though.
- 2.
For most philosophers of biology, this would not be a common position to take in the opposition between “causalist” and “statisticalist” interpretations of natural selection, a debate that has raged in the last 20 years and is usually mounted in dichotomous terms: i.e., either one grants that natural selection can be effectively spelled out in causal terms all the way through and exerts causal influence over population change, or one concedes that natural selection is not really a process that exists in the world, but rather turns out to be a very convenient statistical summary of the genuinely causal processes taking a toll in the actual lives and deaths of individual organisms engaging and struggling with their conditions of existence (for steadfast statisticalists, the idea of “causes of selection,” as Vidya and colleagues frame them would seem odd, to say the least; for them, natural selection is an abstract, higher order effect). For a good overviews of this debate, see Otsuka (2016) and Pence (2021); see also Walsh et al. (2017).
- 3.
Even in the restricted issue of transmission fidelity there is space to disagree with Vidya and co-authors: using causal graph theory, Otsuka (2015) has shown that developmental processes can have an impact on evolutionary responses by affecting one or more of the four components of the Price equation, including transmission fidelity.
- 4.
On this point, Andrews (2021: 29) advances important general remarks: “Reification involves mistaking an aspect of a model—its structure, its construal, or the union of both—for an aspect of empirical data or the natural world; mistaking the math for the territory, so to speak. Reification also occurs when we take an analogical relationship to be a literal one, or when elements of a model’s construal in its original domain of application get brought along parasitically into a novel domain in model transfer.”
- 5.
In addition, “bookkeeping” about evolutionary change can, in principle, not only be provided by genes, but, for example, also through registering stable developmental or ecological niches that affect populations’ trait stability.
- 6.
For a discussion on the explanatory role of invariance in theoretical population genetics, see Walsh (2015).
- 7.
In their contribution, Vidya and colleagues impute to us the mistaken view according to which “organism-environment” and “gene-environment” are ontological comparable relata. They suggest that we “seem to be treating gene-environment interactions as a ‘thing’ belonging to the same logical category as organism-environment relations” (Vidya et al. 2023: 5). We certainly do not think in those terms and our discussion on idealization and abstraction in evolutionary models should make this contention more explicit: a gene-environment interaction has no ontic referent due to unbreachable scale-related and spatio-temporal discordances (e.g., a token gene inside the nucleus of any cell of a developing organism never interacts directly with the environment of said organism). What are featured in the models of quantitative genetics are the products of higher order abstraction (e.g., simplifications, surrogate variables, or mathematical identities) that can be analytically or explanatory useful for certain scientific tasks (gene-environment interactions being one among many abstractions featured in the models). Vidya and colleagues seem to be aware of this: “a gene-environment interaction is one way of statistically (not materially or causally) quantifying the effects of a real organism-environment relation on variation in a phenotype” (Idem). However, they assume that gene-environment interactions unproblematically apprehend (ontic) causal organism-environment interactions. One needs to do more work to show how organism-environment interactions get translated into, for instance, statistical gene-environment covariances. How can we link causal knowledge about the former to statistical models about the latter? These are not trivial questions that most evolutionary biologist simply take for granted in their praxis.
- 8.
We should clarify here that the notion of “model” in the philosophy of science not only encompasses mathematical models, but also different kinds of epistemic objects with differing virtues and aims: e.g., analogical models, mechanistic models, testing models, scale models, probing models, phenomenological models, heuristic models, didactic models, toy models, instrumental models, and a large etcetera (for a general introduction to models in science, see Frigg and Hartmann 2020).
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Fábregas-Tejeda, A., Baedke, J. (2023). Organisms and the Causes and Consequences of Selection: A Reply to Vidya et al.. In: Dickins, T.E., Dickins, B.J. (eds) Evolutionary Biology: Contemporary and Historical Reflections Upon Core Theory. Evolutionary Biology – New Perspectives on Its Development, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-031-22028-9_10
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