PRINCIPALES RIESGOS DEL PROYECTO

After this very short and incomplete introduction to causal modelling, let us go back to our problem, namely to understand why with no biological mechanism involving the phenotype ‘colour’ leading to differences in reproductive output in EC between W and B, it is not possible to separate natural selection from drift. We can see from the DAG in Figure 1.3, that with no more information than reproductive outputs, there are five possible variables that could have a different distribution between the two different types of organisms and explain a difference in reproductive output between them (all the variables except the variable ‘number of offspring’).

Imagine now that we get some evidence, or that you have no reason to doubt, that the variable ‘other parameters conditioning reproduction’ has the same probability distribution in the population between the two types, as well as the variables ‘reproduction’ once the variable ‘survival’ is held constant and the variable ‘other parameters conditioning number of offspring’. With this supplementary information, the only difference maker remaining in the graph that can explain why the type B has less offspring than W is a difference in the distribution with respect

to the variable ‘parameters conditioning survival’. Yet, establishing that a difference in ‘parameters conditioning survival’ between the two types is causally involved in a difference in reproductive output between them, is not sufficient to determine whether the evolutionary change observed in the population is the result of natural selection. In fact, the difference in these parameters between the two types could, following the distinction made in the previous section, either be due to some invariable properties of organisms (a trait) and attributable to natural selection or due to variable properties of organisms and attributable to drift. Without more information on whether and to what extent these two parameters contribute to the reproductive output of the two types of organisms, there is no possible way of knowing whether the difference in number of offspring produced in the population is due to natural selection or drift.

In order to distinguish between natural selection and drift, we thus need to have more information on whether and to what extent there is a difference in the distribution with respect to the intrinsic-invariable or extrinsic /intrinsic-variable parameters between the two types. To do so, the first step is to decompose the variable ‘parameters conditioning survival’ into ‘intrinsic- invariable parameters conditioning survival’ and ‘extrinsic and intrinsic-variable parameters conditioning survival’ such as represented in Figure 1.5. Following the distinctions made earlier, differences in intrinsic-invariable parameters conditioning survival between organisms of different types should be associated with natural selection while differences in extrinsic and intrinsic-variable parameters conditioning survival between organisms of different should be associated with drift.

Figure 1.5. DAG representing the variables involved in the reproductive output of an organism in the case EC and allowing the distinction between ‘difference in reproductive output due to drift’ and ‘difference in reproductive output due to natural selection’.

(‘variable’ should be read as ‘intrinsic-variable’ and ‘invariable’ should be read as ‘intrinsic-invariable’); parameters in green are extrinsic and variables(-intrinsic) parameters; Parameters in red are invariable(- intrinsic) parameters.)

One might think at that point that one way to make the distinction between natural selection and drift is merely to ensure that by controlling that the probability distribution of all the variable parameters is the same between the two types, the only possible causal explanation of the difference in reproductive output between the two types is that there exists a difference in distribution between the two types on some invariable parameter (albeit) unknown to us. Proponents of this solution could argue from that point that having a biological mechanism explaining the downstream difference in reproductive output between the two types is thus non- obligatory. Although ideally perfectly valid this solution encounters two problems. First, arguably a substantial part of the research program in evolutionary biology is to identify adaptations, which is impossible with the solution proposed above. In fact, when we say that the type W is fitter

than B we mean “W is fitter than B in virtue of a difference in colour between the two types.” Without a mechanism explaining why a difference in colour leads to a difference in reproductive output, the latter claim can hardly be substantiated and is prone to be confounded with another factor. The problem of hidden confounds is the reason why Fisher (1970) regarded controlled experiments as an inferior method to infer causation than randomised experiments which partially deals with this problem (see Shipley 2002, chap. 1 for more details). The solution allows only making the claim that a difference in some invariable property or combination of invariable properties is involved in the difference in reproductive output and thus that there is some natural selection in our population with no special trait in mind. The distinction between our hypotheses

correlated response and natural selection presented earlier will be unsuccessful with this solution.

Second, it is impossible to know what variable parameters to attribute to drift without first having in mind potential mechanisms that would lead some individual parameters to remain constant while others vary, since those extrinsic and/or intrinsic-variable parameters might be involved in a mechanism explaining the difference in reproductive output between the two types (remember the parameter ‘location’ in Section 1.3 which could be under the causal influence of either intrinsic-invariable property or another extrinsic or intrinsic-variable properties). This is one major reason why bringing causal modelling into the philosophical literature on the causes of evolutionary change represents a genuine step forward in this literature.

This naturally brings me to a second solution, the one endorsed by Bouchard and Rosenberg and myself, and consists in independently identifying a causal mechanism (if such mechanism exists) involving the phenotype colour and explaining why in the population, the type W has a higher reproductive output than B as well as eliminating as much as possible any known confound. Imagine for example that we independently gain the knowledge that predators of the

organisms of our population in EC use sight to catch their prey and that because organisms of the two types spend a lot of time on the trunk of birch (white) that makes the Ws harder to distinguish and increases their probability of survival. This is a causal mechanism similar to the classical one described in the peppered moth by Kettlewell (1955). This solution, although not perfect because not immune to potential confounds, is nevertheless superior to the previous one for it renders the claims that the type W is fitter than B because it is white more likely to be true. Once such mechanism has been found, it is less likely that a confound variable, through an alternative mechanism would explain the exact same pattern. And if it does one will most likely be able to make further predictions in order to separate the two hypotheses.

In order to make causal claims about the role of selection for the trait colour, it is thus safer to modify the causal graph represented in Figure 1.5 into the one represented in Figure 1.6. This allows to reflect that one cause of survival for organisms is that they are not always visible to predators due in part to their colour which causally depends on the phenotype ‘colour’,39 _to

other invariable properties (such as for example whether there are specific patterns on the body of a given organism), due to some variable parameters (such as the amount of energy each organism has to escape predation) and finally some variable properties (such as location). With the causal graph represented in Figure 1.5 we have all the information necessary to infer from statistical data, by applying causal modelling techniques, whether and to what extant the trait ‘colour’ is adaptive (the result of natural selection for colour), a correlated response of one or some other adaptive trait(s) (the result of natural selection of colour)40 _{or again the result of}

difference of distributions in variable properties between the two types of organisms (drift).

39 _{I assume that the trait ‘colour’ is intrinsic-invariable in E.}

Figure 1.6. DAG representing the variables involved in the case EC allowing to establish whether the trait ‘colour’ is selected for.

(‘variable’ should be read as ‘intrinsic-variable’ and ‘invariable’ should be read as ‘intrinsic-invariable’); parameters in green are extrinsic and variables(-intrinsic) variables; Parameters in red are invariable(- intrinsic) variables.)

Although applying causal modelling techniques to the graph in Figure 1.6 allows us distinguishing neatly between natural selection, drift and the correlated response for the phenotype colour, one should bear in mind that it would not allow an observer to make this distinction in practice without having information on the distributions on all the extrinsic, intrinsic-variable and intrinsic-invariable properties of the organisms in the population. Evolutionary biologists, in many cases, only have a record of the reproductive outputs of the organisms they study and some evidence of a mechanism involving an intrinsic-invariable parameter. The only practical solution given those epistemic constraints is to suppose that the mechanism hypothesised is responsible for the evolutionary change observed, while considering

that the distribution for the other variables is the same between the two types. Yet, this solution is plagued with the problem of confounding variables potentially explaining the same data. Thus, because of the inherent epistemological problems it comes with, this solution should be used with great care and updated as one obtains more information on the different variables affecting reproductive outputs of populations under consideration. As poor as this solution is, this is the best available and these practical difficulties should not be confused with the theoretical apparatus underpinning the notion of natural selection and drift I have proposed.

So far this chapter has shown that there is an alternative to the concept of fitness as a propensity. I have argued for a concept of fitness as relational properties between two or more individual entities forming a population. I have shown, using causal graphs that distinguishing between extrinsic and intrinsic-variable properties on the one hand and intrinsic-invariable properties of entities on the other hand could be the basis for distinguishing natural selection from drift. Because those properties are properties of individual entities and not properties of populations, this demonstrates the plausibility of the ILC view. But as noted in the introduction there is another causal approach to natural selection and some might be tempted at that point to use causal modelling from a population level perspective, that is, to use population level variables instead individual level variables in a DAG and claim that it is better than the ILC view. In the next section, I show that although this is possible, the ILC view, if not misrepresented, is nevertheless superior for it leads to more invariant relations between variables.