Abstract
Natural selection is the result of organisms’ interactions with their environment, but environments vary in space and time, sometimes in extreme ways. Such variation is generally thought to play an important role in evolution by natural selection, maintaining genetic variation within and between populations, increasing the chance of speciation, selecting for plasticity of responses to the environment, and selecting for behaviors such as habitat selection and niche construction. Are there different roles that environmental variation plays in natural selection? When biologists make choices about how to divide up an environment for the sake of modeling or empirical research, are there any constraints on these choices? Since diverse evolutionary models relativize fitnesses to component environments within a larger environment, it would be useful to understand when such practices capture real aspects of evolutionary processes, and when they count as mere modeling conveniences. In this paper, I try to provide a general framework for thinking about how fitness and natural selection depend on environmental variation. I’ll give an account of how the roles of environmental conditions in natural selection differ depending the probability of being experienced repeatedly by organisms, and how environmental conditions combine probabilistically to help determine fitness. My view has implications for what fitness is, and suggests that some authors have misconceived its nature.
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Notes
- 1.
- 2.
For example, the intensity with which a house sparrow nestling begs, influencing parents’ feeding behavior and subsequent nestling survival, seems to be the result of an earlier gene-environment interaction (Dor and Lotem 2009).
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- 5.
The constrained arbitrariness of population definitions is illustrated by contemporary research using the Human Genome Diversity Panel (Li et al. 2008). For example, Thompson et al. (2004) divides this whole-genome data (from roughly 1,000 individuals) into 52 populations, while Moreno-Estrada et al. (2009) cluster the same data into 39 populations for some analyses, and seven populations for others.
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I use “token fitness” rather than “individual fitness” because some biologists use the latter for a property of heritable types. For example, Michod (1999, 9). writes that “…fitness is often defined as the expected reproductive success of a type …. I refer to this notion of fitness as individual fitness.” I avoid “organism fitness” for related reasons, although it made sense to include it in the title of the paper.
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I intend “evolutionary success” to be vague, capturing the idea of increase in frequency in future generations, or at least maintenance of a type in the population; this vague notion will be sufficient for my purposes here. See (Abrams 2009b) for relevant discussion.
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The “statistical”/“parametric” terminology is derived from the use of “statistic” and “parameter” in statistics, and is not directly related to the distinction between “statisticalist” and “causalist” views about evolutionary “forces.”
- 10.
My argument is related to some given by Sober (1984) and Hodge (1987), who argue that overall individual fitness is not causal, but my argument is different. My argument is also related to arguments in Ariew and Ernst (2009) but is more general, and makes it clear that it is not the propensity interpretation of fitness per se that is the problem.
- 11.
Lewontin suggests that these three conditions are necessary and sufficient for evolutionary change by natural selection. In fact they are neither necessary nor sufficient for natural selection (Godfrey-Smith 2009). However, they capture the core of the notion of natural selection sufficiently well for my purposes here.
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Actually, the notion of “allele” that Williams and Dawkins used was unusual, but this subtlety needn’t concern us.
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Wimsatt (1980b, 1981) argued that when there are nonlinear interactions between alleles, it’s inappropriate to treat alleles as units of selection. Analogously, one might argue that when there are nonlinear interactions between heritable types of any sort, its inappropriate to assign fitness values to each type as such. However, given a probability distribution over possible combinations of types, fitness values for any one type can in principle be computed (Abrams 2009b). This is in effect to treat those alternative types, which might be combined with a particular type whose fitness is to be calculated, as the alternative environmental states discussed in Sect. 3 (cf. Dawkins 1976; Sterelny and Kitcher 1988).
- 14.
In Brandon’s (1990, chapter 2) terms, I am arguing that his assumption that there are broad regions of the space of environmental conditions which are objectively homogeneous or which vary only gradually with respect to probabilities relevant to fitness is incorrect, when we consider environmental variation in sufficient detail.
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For example, suppose fitness is expected number of offspring O a for type a, i.e. \(\mathsf{F}\,(a) = \mathsf{E}\,(O_{a}) =\sum _{k}k\,\mathsf{P}(O_{a} = k)\). The probability of having k offspring is the average across subenvironments E j , weighted by probability of E j : \(\mathsf{P}(O_{a} = k) =\sum _{j}\mathsf{P}(O_{a} = k\vert E_{j})\mathsf{P}(E_{j})\). Together these equations imply that \(\mathsf{F}\,(a) = \mathsf{E}\,(\,\mathsf{F}\,(a\vert E_{\bullet }))\), as in the text.
- 18.
I read parts of chapter 1 of Brandon (1990) as concerned with token fitness, and chapter 2 seems to allude to token fitness, e.g. on page 47, when it mentions the environment of an individual. However, the primary focus of chapter 2 is on environments of populations of organisms and these environments’ effects on the fitnesses of types.
- 19.
Using the first, additive model in Sect. 3, b is fitter than a overall if
$$\displaystyle{\mathsf{F}\,(a\vert E_{1})P(a\mbox{ in }E_{1})+\mathsf{F}\,(a\vert E_{2})P(a\mbox{ in }E_{2})\;\; <\;\; \mathsf{F}\,(b\vert E_{1})P(b\mbox{ in }E_{1})+\mathsf{F}\,(b\vert E_{2})P(b\mbox{ in }E_{2})\;.}$$Suppose F(a | E 1) = 10, F(a | E 2) = 2, P(a in E 1) = . 1, F(b | E 1) = 5, F(b | E 2) = 1, and P(b in E 1) = . 9. Then \(\mathsf{F}\,(a) = 10 \times .1 + 2 \times .9 = 2.8\) and \(\mathsf{F}\,(b) = 5 \times .9 + 1 \times .1 = 4.6\).
- 20.
That is, b could be fitter in the sense that it has a greater probability of evolutionary success, increased frequency, etc., in either/both the short term and/or the long term.
- 21.
To be precise, Brandon gives an example in which organisms of different types always choose specific subenvironments, thus in effect creating a uniform “selective environment”—in that each type competes with the other relative to constant environmental conditions. However, it’s not difficult to see how to extend this generalization of the concept of a selective environment to cases in which types have different non-extremal probabilities of encountering various ecological environments.
- 22.
Note that much of Brandon’s discussion is driven by concerns other than those that are my focus.
- 23.
Recall that my focus in this paper is on tendential token fitness and parametric type fitness. It’s not necessarily a mistake to estimate parametric type fitness using the average of measurable token fitnesses of actual organisms (Abrams 2012c).
- 24.
For those interested in pursuing alternative interpretations of probability that may be relevant to evolutionary processes, I suggest if the complex system constituted by a biological population and its environment satisfied conditions required for what are known as mechanistic, microconstant, or natural range probabilities (Rosenthal 2010, 2012; Strevens 2011; Abrams 2012a,b) it could turn out that type fitnesses would not derive from token fitnesses. This is a topic better left for later work, but I mention the possibility here for interested readers.
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Acknowledgements
I’m very grateful for helpful feedback from the editors of this volume: Trevor Pearce, Gillian Barker, and Eric Desjardins; and for feedback, on related presentations in Groningen (2009), Toronto (2009), and London, Ontario (2010), from Denis Walsh, Jan-Willem Romeijn, Robert Makowsky, Yann Klimentidis, Bruce Glymour, Greg Cooper, John Beatty, André Ariew, and others who gave equally helpful comments. Some ideas in this paper grew from seeds planted by discussions with Bill Wimsatt many years ago. None of these individuals should be assumed to agree with my claims. Olivia Fanizza made early versions of the figures.
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Abrams, M. (2014). Environmental Grain, Organism Fitness, and Type Fitness. In: Barker, G., Desjardins, E., Pearce, T. (eds) Entangled Life. History, Philosophy and Theory of the Life Sciences, vol 4. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7067-6_7
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