Abstract
In the recent literature on explanation in biology, increasing attention is being paid to the connection between design explanation and mechanistic explanation, viz. the role of design principles and heuristics for mechanism discovery and mechanistic explanation. In this paper we extend the connection between design explanation and mechanism discovery by prizing apart two different types of design explanation and by elaborating novel heuristics that one specific type offers for mechanism discovery across species. We illustrate our claims in terms of two lines of biological research on the biological advantages of organismal traits, one on the eye-size of giant squid, the other on foraging habits of specific bat species. We argue that this research illustrates useful heuristics for mechanism discovery across species, viz. reasoning strategies to infer likely mechanisms for a certain biological role based on assessments of the environmental conditions in which the role is performed efficiently (i.e., offers a biological advantage) and less or in-efficiently. We bring out the novel features of our analysis in terms of a comparison with mechanistic approaches to mechanism discovery, amongst which graph-theoretical ones, and by comparing the different types of design explanation and the discovery heuristics they support.
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Notes
Note that Bechtel (2015a) recently argued that not all mechanistic explanations begin with identification of phenomena. He gives examples where researchers start with investigating how an activity of an entity might figure—play a role—in a mechanism.
The precise lingo differs; some speak about ‘entities’ and ‘activities’, others ‘working parts’ and ‘operations’, yet others ‘capacities’. These differences need not concern us here.
The mechanistic concept of role function is heavily inspired by Cummins’ (1975) account of function and explanation by functional analysis. The main difference is that the notion of ‘organization’ figures much more prominently in mechanistic role function ascription and mechanistic explanation.
To be sure, this is not to suggest that mechanist philosophers deny the importance of the environment in which a mechanism functions. Our claim is that biological research on biological advantages gives additional useful heuristics for mechanism discovery that are not part of the current store of mechanism discovery strategies available in the literature.
BA-design explanations are thus also different from what Thagard (2003, 240) calls “biological function explanation schemas”, which have a historical selection signature. Such schemas address the type of question “Why does an organism have a mechanism”—e.g., “why do cells have a glycolytic pathway” (Thagard 2003, 251)—in terms of biological role and selected effect-function ascriptions:
The mechanism enables [via biological role functions of the mechanism’ component entities and activities] the organism to accomplish some [selected effect] function that is important for its survival and reproduction. So the organism has developed the mechanism as the result of natural selection. (Thagard 2003, 240).
Though traits can also be formulated quantitatively, in the context of BA-design explanations, they are generally formulated in qualitative fashion. (For example: ‘why do giant squid have large eyes’ instead of ‘why is the diameter of the eyes of giant squid about 27 cm’?). In the quoted statement that motivates Nilsson et al.’s (2012) research they do phrase their explanation-seeking question in quantitative terms (‘nearly three times’). However, in their paper they mainly phrase their questions qualitatively (“giant eyes”, “huge eyes”, “uniquely large eyes”, 683).
Nilsson et al. (2012) assessed in which pelagic conditions giant eyes confer an advantage in terms of a mathematical/computational model. This is the core of their (a-historical) BA-design explanation. And they furthermore conjectured that when these conditions were present in the evolutionary past of the species—which seems a plausible assumption—selection pressures would favor the evolution of extremely large eyes. They also reason by analogy that similar conditions might have been operative in the evolutionary past of now extinct species of marine reptiles, due to which these species might have had such large eyes (their eye size is a known fact). However, it is important to realize that the explanation offered by Nilsson et al. (2012) does not spell out how evolutionary forces might have led to extremely large eyes in squid (or other animals). And neither do they provide an explanation for why the actual size of the eye squid is the optimal one and expected to emerge given certain constraints and tradeoffs acted on by natural selection, as optimality explanations do [cf. “our theory does not point to any specific optimal or maximal eye size” (Nilsson et al. 2012, 685)]. So we are the first to acknowledge that Nilsson et al. (2012)—and they are certainly not the only biologists that do so—tie their explanations and modeling to hypotheses about the emergence of traits, here the having of very large eyes, but they do not back these conjectures up with evolutionary (optimality) explanations. That is a different explanatory project. We thank an anonymous referee for urging us to clarify this matter.
SRBA-design explanation and DP-design explanation are clearly not to be confused with one another. Explaining the presence of specific design principles across species or systems in terms of the advantages they confer on the systems-species to which these principles apply, is a SRBA-design explanation in which the presence of a design principle is the explanandum trait. In DP-design explanation, design principles figure in the explanans and are invoked to explain the presence of another explanandum trait. Moreover, in the explanans of DP-design explanations, advantage ascriptions are not specified alongside design principles (although it is entirely conceivable that they do confer an advantage, but spelling this out is a different explanatory project, to wit: SRBA-design explanation).
DP-design explanations via design principles seem similar to the graph-theoretical analysis discussed by Bechtel (2015a, b), but there’s an important difference. The graph-theoretical analysis drives mechanism discovery, investigating which other classes of mechanisms also have a certain organizational structure. DP-design explanation targets a different question, viz., why do certain systems embody the same organizational principles? In other words, these organizational features are already known, whereas in the graph-theoretical analysis the explanatory target is precisely to uncover which systems-mechanisms share a common organizational structure.
A guild is a functional group used to classify bat species, based on their habitat type and foraging mode. Bat species belonging to different guilds do not compete for food resources since they differ in foraging modes and the environmental resources they use. Bats belonging to the same guild do use the same food resources and thus may have to compete for food. So they have to differ in niche dimensions in order to avoid competition for food (Denzinger and Schnitzler 2013). The research by Siemers and Schnitzler (2004) was aimed at investigating whether this indeed was the case, and if so, how this works as regards the five species of Myotis bats belonging to the “edge space aerial/trawling foragers” guild. Bats that forage in ‘edge space’ hunt prey near edges of buildings and vegetation, in gaps, and above ground or water surfaces. Bats that forage in ‘edge space’, such as the five species of Myotis bats, thus do not forage in ‘open spaces’, which are spaces up in the air far removed from background targets. The ‘aerial’ and ‘trawling’ modes refer to the mode of foraging behavior, i.e., the foraging for airborne prey and the foraging for prey on water surfaces, respectively. Importantly, the two trawling Myotis species also hunt for prey in the aerial mode, thus potentially competing for food with the other three Myotis species.
In the 2001 study, Siemers et al. consistently speak about the role of prey perception. In the 2004 study, Siemers and Schnitzler speak about the role(s) of prey perception and catching, the efficiency of which they assess in terms of the number of prey caught (whereas efficiency in the 2001 study was assessed in terms of capture attempts). Therefore, we speak about ‘prey perception and capture’ when discussing the 2004 study.
Siemers et al. (2001) did not further differentiate between echolocation signal structures relative to environmental resources exploited. They showed that echolocation is advantageous when trawling Myotis forage on smooth surfaces, but did not consider possible between-species differences in signal structure and the detection of prey at different locations on these smooth surfaces.
The overall functional architecture of echolocation mechanisms in bats is quite well known, with most research done on the brown bat, mustached bat, and horseshoe bat (Suga 1990; Moss and Sinha 2003). In a nutshell, sound waves vibrate the eardrum, this vibration is then conducted to the basilar membrane, producing neural excitatory signals that are transmitted along auditory nerve fibers to the central auditory system, which is comprised of inter alia, the lateral lemniscus, inferior colliculus, the medial geniculate body and auditory cortex, where the specifics of the sound waves are processed. These regions show enormous specialization as regards the processing of very specific features of sound waves such as frequencies, amplitudes of and time intervals between echoes, and the processing of Doppler shifted signals, i.e., echo signals that change due to the decreasing distance between bat and target. There are also comparative studies on species-specific differences in echolocation mechanisms, but these seem to focus on quite general differences; e.g., some bats use only Frequency Modulated (FM) signal frequencies in echolocation, whilst others use both FM and Constant Frequency (CF) signals. This difference relates to differences in foraging habits and habitats; e.g., calls with both FM and CF components are useful to detect prey in dense foliage. The mustached bat and horseshoe bat, which both use FM and CF components in their signal structure, have cochlear specializations that enable expanded representations of behaviorally relevant frequency ranges, such as the frequency of the second harmonic (FM2) of the FM component of the echolocation call (Covey 2005). The more fine-grained differences in echolocation signal structure across the five bat species that we discussed in the text seem not to have been investigated yet from a comparative neurobiological point of view. More generally, there are few neurobiological investigations that investigate aspects of the neural machinery involved in echolocation behavior when bats actually engage in natural tasks. Most is currently known from laboratory settings and experiments. Neurobiologists acknowledge that much more neurobiological research is needed on ‘echolocation the wild’ (Ulanovsky and Moss 2008).
The 2004 study by Siemers and Schnitzler can in turn be seen, by differentiating functional dependencies between echolocation signal structures and environmental conditions, as a differentiation of the SRBA-design explanation into several OSBA-design explanations.
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We thank Phyllis Illari and our reviewers for very helpful comments.
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van Eck, D., Mennes, J. Mechanism Discovery and Design Explanation: Where Role Function Meets Biological Advantage Function. J Gen Philos Sci 49, 413–434 (2018). https://doi.org/10.1007/s10838-017-9383-y
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DOI: https://doi.org/10.1007/s10838-017-9383-y