Allometry Unleashed: an Adaptationist Approach of Brain Scaling in Mammalian Evolution
Allometry unleashed: an adaptationist approach of brain scaling in mammalian evolution
Romain Willemet London, United Kingdom. Email: [email protected] ORCID iD: 0000-0003-4364-3420
Abstract
The idea that allometry in the context of brain evolution mainly result from constraints channelling the scaling of brain components is deeply embedded in the field of comparative neurobiology. Constraints, however, only prevent or limit changes, and cannot explain why these changes happen in the first place. In fact, considering allometry as a lack of change may be one of the reasons why, after more than a century of research, there is still no satisfactory explanatory framework for the understanding of species differences in brain size and composition in mammals. The present paper attempts to tackle this issue by adopting an adaptationist approach to examine the factors behind the evolution of brain components. In particular, the model presented here aims to explain the presence of patterns of covariation among brain components found within major taxa, and the differences between taxa. The key determinant of these patterns of covariation within a taxon-cerebrotype (groups of species whose brains present a number of similarities at the physiological and anatomical levels) seems to be the presence of taxon-specific patterns of selection pressures targeting the functional and structural properties of neural components or systems. Species within a taxon share most of the selection pressures, but their levels scale with a number of factors that are often related to body size. The size and composition of neural systems respond to these selection pressures via a number of evolutionary scenarios, which are discussed here. Adaptation, rather than, as generally assumed, developmental or functional constraints, thus appears to be the main factor behind the allometric scaling of brain components. The fact that the selection pressures acting on the size of brain components form a pattern that is specific to each taxon accounts for the peculiar relationship between body size, brain size and composition, and behavioural capabilities characterizing each taxon. While it is important to avoid repeating the errors of the “Panglossian paradigm”, the elements presented here suggests that an adaptationist approach may shed a new light on the factors underlying, and the functional consequences of, species differences in brain size and composition.
Key-words: allometry; behaviour; brain evolution; brain size; comparative approach
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Contents Introduction ...... 3 1. Comparing brains: the taxon-cerebrotype approach ...... 5 1.1. Taxon-specific differences...... 5 1.2. Taxon-cerebrotypes and phylogeny ...... 7 1.3. Taxon-cerebrotypes and allometry ...... 8 2. Interpretations of allometry: constraints and adaptations ...... 9 2.1. Developmental constraints account ...... 9 2.2. Functional constraints account ...... 11 2.3. A third view: allometry as a pattern of adaptations ...... 13 3. The evolution of functional neural systems ...... 14 3.1. Scenarios of concerted evolution within functional neural systems ...... 14 3.1.1. Balanced functional selection ...... 14 3.1.2. Unbalanced functional selection ...... 15 3.1.3. Adjustment effect ...... 16 3.2. Patterns of selection pressures ...... 19 4. Factors influencing the evolution of brain size and composition...... 20 4.1. Adaptations and allometry: body size required vs body size allowed adaptations ...... 21 4.2. Functional adaptations ...... 22 4.3. Structural adaptations ...... 28 4.3.1. Body size required adaptations...... 28 4.3.2. Body size allowed adaptations ...... 30 4.4. Selection pressures acting against an increase in brain component size ...... 31 4.4.1. Energetic constraints ...... 31 4.4.2. Developmental constraints ...... 32 4.5. Summary on the selection factors determining the size of neural components/systems ...... 32 5. A synthesis ...... 33 5.1. Understanding differences within and between taxa ...... 33 5.2. Approaches and methods ...... 40 5.2.1. Studying allometry: scaling ...... 41 5.2.2. Studying allometry: relative component size ...... 42 Conclusion ...... 45
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Introduction
The remarkable diversity of behaviours displayed by vertebrates has its roots in species differences in brain size and composition. No two species have the same brain, and it is a central goal of the fields of comparative psychology and evolutionary neuroscience to understand the factors behind these differences (Striedter 2005).
Overall, the vertebrate brain is composed of a number of anatomically and functionally distinct components (e.g. cortical regions, thalamic nuclei, etc.) that are connected to each other within functionally differentiated neural systems (Nieuwenhuys et al. 1998). Despite the global level of integration between brain components necessary for the brain as a whole to produce adaptive behaviours, individual brain components generally have particular functions that distinguish them from the others (Nieuwenhuys et al. 1998, Healy and Rowe 2007). The functional properties of individual components are determined by their particular physiology and internal structure (Doya 1999) and their pattern of connection to other components (e.g., Behrens et al. 2003, de Schotten et al. 2016).
Several factors are responsible for the differentiation of brain components and their evolution. One of them is the existence of selection pressures toward particular brain regions, progressively leading these regions to perform their (potentially new type of) computation at least partly independently from the other components (see Gahr (2000) for an example on the neural song control system in birds). Another factor is the presence of structural constraints, in particular those related to the issue of allowing a given level of connectivity between cells as their number increases, leading to the compartmentalization of neuronal computation as the distance between brain areas increases (Ringo 1991, Kaas 2000), and thereby to the differentiation of brain components. These changes happen in the context of a number of constraints limiting the range of possible outcomes. These are physiological constraints, for example regarding the regulation of energy (e.g. Herculano-Houzel 2014) and connectivity between cells (e.g. Wyatt et al. 2005, Perge et al. 2012), developmental constraints, such as the differential maturation of cells within the brain (e.g., Le Magueresse and Monyer 2013, Yang et al. 2013), and physical constraints, for example in the fact that the brain has to fit within the skull that itself has to be supported by the body (an obvious, but often overlooked factor (Striedter 2005)).
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Irrespective of how individual brain components evolved, their very existence as partially distinct entities suggests that they can be the target of selection pressures acting on their functional and/or structural properties (for an introduction to the concept of “quasi- independence”, see Lewontin 1978). Changes in the quantity of neurotransmitters and their receptors (e.g., Lim and Young 2006), or changes in a component’s internal connectivity (e.g., Illing 1996), for example, can modify the functional properties of a component. In most cases, however, these changes have little or no effect on the size of brain components. In contrast, changes in the number and size of the neuronal and non-neuronal cells lead to changes in the size of individual components, and, consequently, of brain size as a whole (Herculano-Houzel 2011a). In fact, changes in the number of cells and their connectivity (neurons, but not only, see for example Dallérac and Rouach 2016 & Oliveira et al. 2015 for evidence that nonneuronal cells also have a role on the processing capacity of a brain component) appears to have been amongst the most direct response to selection pressures targeting the processing capacity of a neural component (for a seminal example on the adaptation of the somatosensory cortex in the highly dextrous raccoon, see Welker and Campos 1963).
Considering the elements discussed in this succinct summary, the main objective of this paper is to discuss some of the main factors, and the mechanisms by which they act, behind the variation in brain size and composition in mammals, and to examine the functional consequences of species differences in brain anatomy, including in term of behavioural capabilities. The first section provides an overview of the taxon-cerebrotype approach (Willemet 2012) by discussing the necessity of taking into account the differences between clades when attempting to understand the diversity of brain size and composition (a taxon- cerebrotype designates a group of related species whose brains present a number of similarities at the physiological and anatomical levels). The following section presents the classical theoretical frameworks that have been used to explain the evolution of brain components, and, in particular, the patterns of covariations in the size of brain components. The theoretical framework defended here is introduced. The third section examines the different evolutionary scenarios behind the concerted scaling of brain components often seen within taxon- cerebrotypes that are predicted by this theoretical framework. The fourth section reviews the patterns of selection pressures needed for these evolutionary scenarios to take place. Adaptations at the level of the structural properties of brain components is argued to be an overlooked but potentially fundamental factor behind the evolution of brain size and composition. The final section examines how the theoretical framework presented here helps PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27872v1 | CC BY 4.0 Open Access | rec: 25 Jul 2019, publ: 25 Jul 2019 4
understand the anatomical and functional differences between taxa. Some of the methods used to find the neuroanatomical characteristics behind functional adaptations are discussed in light of the theoretical frameworks introduced here. Although for reasons of brevity this article focuses on mammals, the general lines of the framework discussed here should also apply to the other vertebrate groups, and, in the main lines, perhaps to invertebrates as well given the similarities between the vertebrate and invertebrate nervous systems (e.g. Farris 2008).
1. Comparing brains: the taxon-cerebrotype approach
Studies comparing the brains of different species have the potential to reveal information about the factors responsible for the variability in brain anatomy, and thus shed light on some aspects of the functioning of the brain (Lashley 1949, Striedter 2005). However, simply comparing two or more random species is unlikely to give precise information upon the causes and effects of the variability of a given brain feature, due to the many differences, at various anatomical and physiological levels, between their brains and the environment surrounding them (Nieuwenhuys et al. 1998). Therefore, it is necessary to be specific and focus on the most insightful comparisons, as discussed below.
1.1. Taxon-specific differences
Understanding the factors behind the diversity of brain size and composition in animals is a complex endeavour. There are more than six thousands living mammalian species (Burgin et al. 2018), with a large diversity of morphological and behavioural adaptations. The range of variation in brain size, for example, is remarkable; with the size of the mammalian brain ranging from less than a tenth of a gram in the Etruscan shrew Suncus etruscus (Naumann 2015) to close to ten kilograms in sperm whales Physeter macrocephalus (Oelschläger and Kemp 1998) (see also Maderspacher (2016) and DeFelipe (2011) for representative images of species differences in brain anatomy). A reasonable approach for simplifying such a complex problem is to try to determine groups of species that could be sorted based on objective criteria, and then examine the factors behind the variability within and between groups.
Interestingly, a fundamental observation in comparative neuroanatomy is the presence of taxon-specific differences at many levels of brain organization, such as cellular composition (Herculano-Houzel 2011a, Jacobs et al. 2018), cortical folding pattern (Zilles et al. 2013), and, PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27872v1 | CC BY 4.0 Open Access | rec: 25 Jul 2019, publ: 25 Jul 2019 5
as shown in Figure 1, the size and scaling of brain components (Willemet 2012, see also Halley and Krubitzer 2019). Species within particular taxa differ from each other in most of the above- mentioned aspects, but, as a whole, they are closer to each other than they are from other taxa, forming what has been called taxon-cerebrotypes (Willemet 2012, after Clark et al. 2001). Importantly, the appropriate taxonomic rank when determining a taxon-cerebrotype depends on the question asked and the level of resolution desired (see section 1.2). In all cases, brains, much like bodies, appear to have particular and recognizable forms shared by species within a taxon (compare, for example, the cats (Felidae) body form (from wildcats to tigers) with the deer (Cervidae) body form (from pudús to moose)). It is the variability of brain forms between taxa, and the homogeneity within a taxon that constitutes the basis of the taxon-cerebrotype approach (Willemet 2012).
Although class-wide (e.g., Finlay and Darlington 1995) and even broader (e.g., Jerison 1973) analyses have revealed fundamental patterns, or global tendencies, in brain evolution, the presence of taxa-specific differences in brain anatomy and physiology as well as in the scaling of brain components means that the level of resolution of these analyses is lower than the level of resolution needed to give a mechanistic and functional explanation of species differences in brain size and composition (Willemet 2013). Importantly, even studies focusing on single neuroanatomical variables, such as neuron number (e.g. Herculano-Houzel 2017), or a combination of variables (e.g. Dicke and Roth 2016), while less sensitive, are still expected to be subject to the taxon-cerebrotype effects due to taxon specific variations in the other variables. Thus, a consequence of these taxa-specific differences at many levels of brain organization is that comparative analyses should compare either species within taxon- cerebrotypes, or taxon-cerebrotypes themselves. The conclusions from studies having mixed several taxa should thus be taken with caution.
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Figure 1. The size (volume) of the telencephalon regressed onto the size of the medulla in “insectivores” (green, bottom), carnivorans (purple, middle), and haplorhine primates (yellow, top). Data for carnivorans are from Reep et al. (2007). Data for insectivores (this taxon is now obsolete (Stanhope et al. 1998) but is used here to facilitate comparisons with other analyses based on this dataset) and haplorhine primates are from Stephan et al. (1981).
1.2. Taxon-cerebrotypes and phylogeny
As can be seen in Figure 1, taxon-cerebrotypes are related to phylogenetic taxa, in such a way that there seems to be a primate cerebrotype, a carnivora cerebrotype, et cetera. Importantly, the scaling of brain components within a taxon-cerebrotype is not equivalent to the evolutionary history of the taxon. That is, the evolutionary tendency within a taxon is not always from small to big brains (Montgomery et al. 2010), and, as such, the scaling of brain components within a taxon cannot be automatically equated to an evolutionary progression in terms of brain size.
As mentioned in the section above, there is no privileged taxonomic level for conducting taxon- cerebrotype based analyses. Instead, the taxonomical level should be determined for each study, and the choice will affect the scope and precision of the analyses. In the absence of any other objective markers (note that analyses based on rate-shifting models might help provide such a marker (Rabosky 2014, Lewitus 2018)), the species chosen should constitute a coherent group in term of phylogenetic and ecological factors, with no obvious separations between sub- groups based on their allometric characteristics (Willemet 2012). ─“Allometry” is the central concept discussed in this paper. In its simplest, modern form, free from theoretical assumptions, it is a term invoked to describe a situation where the size (mass, volume, area, length or number of elements composing it) of one body component consistently varies with the size of one or several other component, or the organism as a whole, when comparing different biological entities (Butler and Hodos 2005).─ The primate cerebrotype, for example, hides some significant taxon-specific differences in the allometrical relationships between some brain components, so that using two distinct taxon-cerebrotypes (a “strepsirrhini cerebrotype” and a “haplorhini cerebrotype”) should often be preferred when conducting comparative brain studies (Willemet 2012, see also Vanier et al. 2019).
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1.3. Taxon-cerebrotypes and allometry
The second important observation from Figure 1 is that there seems to be a trend in the scaling of brain components between species within a taxon-cerebrotype, such that there is a pattern of covariation in the size of most brain components. This is illustrated in more detail for the major brain components in Figure 2, which shows that all the major components participate to the enlargement of brain size, albeit at different rates.
Figure 2. Representation of the size of the major brain components in several species of carnivorans (a) and haplorhine primates (b), sorted by their overall brain size. Data are from Reep et al. 2007 (carnivorans) and Stephan et al. 1981 (haplorhine primates). Humans and great apes data have been excluded to facilitate the visualization of species with smaller brains.
This predictable scaling is not restricted to the level of brain component size, but instead encompasses most of the anatomical and physiological characteristics of brain components, including, most notably, cell number (Herculano-Houzel 2011a). As such, a great deal of our understanding of the variability of brain size and composition, both within and between taxa, depends on our understanding of the factors behind these allometric patterns. PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27872v1 | CC BY 4.0 Open Access | rec: 25 Jul 2019, publ: 25 Jul 2019 8
2. Interpretations of allometry: constraints and adaptations
Traditionally, allometry between brain components has been interpreted as the result of constraints; either developmental (Finlay and Darlington 1995, Finlay et al. 2001) or functional (Barton and Harvey 2000, Montgomery et al. 2016). In both accounts, emphasis is placed on the apparent lack of variability around the allometric relationship, which is traditionally thought to reflect the presence of constraints (as summarized by Montgomery and Merrill (2017): “conserved scaling relationships are typically interpreted as indicating the presence of some constraint that results in covariance between variables. This constraint may arise from shared developmental mechanisms (or pleiotropy), or be due to selective covariance to maintain a constant level of functional integration”). In contrast, the present article defends an alternative interpretation of allometry in the context of brain evolution (see also Willemet 2012, 2013). In this account, the concerted patterns seen within a taxon are interpreted as being mainly the result of a directed set of selection pressures (i.e. adaptations), rather than constraints. These three accounts are discussed below.
2.1. Developmental constraints account
In view of the correlation between the scaling of the major brain components and the order of neurogenesis, Finlay and collaborators (Finlay and Darlington 1995, Finlay et al. 2001) hypothesised that the allometry between brain components was due to developmental constraints, such that selection on the functions of one component would lead to changes in the size of all the other (non-olfactory, see Finlay and Darlington 1995) components. However, the dataset used in these studies included data from various taxa, most particularly primates, insectivores, and bats, which impairs the ability to accurately determine the mechanisms behind the species differences in brain composition, as seen in section 1.2. Indeed, there are large differences in the number of neuronal and non-neuronal cells in the taxa included in Finlay and collaborators’ analyses, and these differences do not necessarily correlate with the size of brain components (Herculano-Houzel 2011a). Moreover, there is now strong evidence, at many levels of brain organization (Willemet 2012), including at the molecular level (e.g., Hager et al. 2012, Carlisle et al. 2017, Davis et al. 2019, and see review by Montgomery et al. 2016), against the hypothesis that strong developmental constraints limit the independent evolution of most brain components. These elements are incompatible with the hypothesis that developmental constraints are the main cause of allometry. PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27872v1 | CC BY 4.0 Open Access | rec: 25 Jul 2019, publ: 25 Jul 2019 9
Indeed, early proponents of the developmental constraints model latter reconsidered the strong version of this hypothesis. Finlay et al. (2010), in particular, explained: “in a pre-evo-devo context, when we first described the strong relationship between brain allometry and neurogenesis – which seemed to make certain structures disproportionately large by rule, independent of specific niches and behaviors – we struggled to account for it in terms of ‘developmental constraints’ and ‘spandrels’. Now, we attempt to understand the conserved pattern of allometric scaling as a substrate for ‘evolvability’”. “That is”, they continued, “we investigate what features such a conserved plan might afford for graceful scaling and facilitated variability (that is, genetic variation translated through conserved genetic and epigenetic contexts which coordinate and stabilize functionality)”.
Although using the concept of evolvability in the context of allometry in brain evolution as described by Finlay et al. (2010) is a useful approach, several problems remain. First, the conserved pattern of allometric scaling mentioned by the authors is not really one, due to the differences in the allometric scaling between taxa at the levels of component size (Willemet 2012) and cell number (Herculano-Houzel 2011a). Thus, although such facilitated variability may occur, it takes a different form in each major taxon. Second, the evolvability account advocated by Finlay and collaborators rests on a number of strong assumptions regarding the adaptive nature, or lack thereof, of changes in the size of brain components that happened independently of changes in other components (i.e. mosaic changes). Charvet and Finlay (2016) for example, noted: “the notion that neuron number in a particular cortical area, whether visual, social, or motor, is an important locus of control for the elaboration of that function is becoming increasingly untenable”. These assumptions are undermined, among other things, by evidence of specializations at the level of cortical area size (e.g. Welker and Campos 1963, Barton 2007, Krubitzer et al. 2011).
Although life experience can affect the size and physiology of brain components (e.g. Mehlhorn and Rehkämper 2010), most of the species differences in brain composition has its basis in development (the regulation of cell death appears to have only a limited effect, see Finlay et al. 2001). As such, the concerted pattern that is seen at the level of brain component size must necessarily be mirrored by developmental processes that also scale in a predictable manner, as observed by Finlay and Darlington (1995). While the developmental approach is necessary for understanding these processes (Charvet et al. 2011, Florio and Huttner 2014), consideration must be given to the evolutionary factors potentially behind the observed PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27872v1 | CC BY 4.0 Open Access | rec: 25 Jul 2019, publ: 25 Jul 2019 10
patterns. For example, the larger allometric increase of the late developing telencephalic regions in mammals and birds has been taken as evidence for the developmental “constraints” or “conservation” model (Finlay and Darlington 1995, Charvet and Finlay 2016, Moore and DeVoogd 2017). However, this positive allometry is also what would be expected if selection factors specifically targeted the functions of these telencephalic regions, as developed in the sections below.
2.2. Functional constraints account
The presence of differences between taxa in the relative size of brain components, and the correlated evolution of brain components within particular neural systems, both evidence of mosaic evolution of brain components, led Barton and Harvey (2000) to put forward the hypothesis that functional constraints, rather than developmental constraints, are responsible for the concerted scaling of brain components (for a more recent version of the hypothesis, see Montgomery et al. 2016). This hypothesis stipulates that selection on (the functions of) one component within a neural system would lead to the enlargement of the other components as well, in order to maintain the functional integrity of the functional neural system to which these components belong. A related argument is that the level of integration between brain components is so strong that nonconcerted changes within functional systems are unlikely to be adaptive, explaining the allometric patterns seen at the levels of brain component size and number of cells between species within a taxon. However, the claim that concerted changes in brain component size should systematically maintain functional correspondence within a system appears to be unwarranted. Instead, consider the case where selection pressures systematically favoured one component over the others, within a functional neural system (see section 3). That is, during the evolution of the species within this taxon, this component in particular changed its size at a higher rate than the others because it is the aspects of the functions supported by this component within the neural system that were particularly targeted by selection pressures. In that case, the change is clearly not concerted, and the functional integration between the components of the system is changed. Yet, if this scenario happens repeatedly within a taxon, this leads to a pattern that looks concerted, but that is formed by a succession of non-concerted (i.e. mosaic) changes (Figure 3). Thus, insofar as the direction of the changes is conserved among species within a taxon, mosaic changes in the selection threshold (defined here as the value that a character must take to perform to the magnitude set by natural selection) within a functional neural system can lead to a pattern of covariation in PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27872v1 | CC BY 4.0 Open Access | rec: 25 Jul 2019, publ: 25 Jul 2019 11
the size of the components of the system (i.e. allometry). Although for simplicity reasons Figure 3 suggests that allometry happens via changes from species to species, in reality allometry does not necessarily correlate with the evolutionary history of a taxon (see section 1.2). Instead, Figure 3 is best interpreted as if the brain of each species responded to a selection threshold whose value corresponds to the total area of the arrows leading to it (representing the amount of changes in the selection threshold), from the taxon’s last common ancestor.
Figure 3. Concerted pattern via mosaic evolution on a schematic brain comprising three components. For reasons of simplicity, it is assumed that the three components have similar connective properties, so that the changes in size are directly related to the threshold of the selection pressure (see main text for a discussion of the term) in a way that is comparable between components. The width of the arrows illustrates how strong the changes of the thresholds of the selection pressures varies from species to species. From species A to species F, the components yellow and green (bottom) increases their size by a factor 1.1 (that is, the threshold for selection pressures increases by 10% from species to species). The size of the orange component instead increases by a factor 1.6, betraying that, during the evolution of this group of species, the change in the threshold for selection pressures has been stronger for this component than for the two other ones (its functions have been selected to a greater extent than the functions carried out by the two bottom components).
Thus, while the functional constraints hypothesis argues that allometry is best conceived as “the product of selection to maintain functional correspondence” (Montgomery et al. 2016), this example suggests that this might not necessarily be the case. In fact, even relatively simple cases of allometry may hide changes in the functional relationships between components. For example, consider the changes, between species of different body sizes, in the proportion of body mass constituted by the skeleton in mammals. This example was recently used by Barton
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and Montgomery (2018) as a support for the claim that “a biological structure can differ across species as a proportion of overall size and yet be functionally equivalent”. However, although the primary function of the skeleton remains equivalent across variations in body size, the functional influence of the skeleton on the rest of the body does change with body size, most notably with regards to posture and locomotion ability (Biewener 1990, Christiansen 1999), since the mechanical stresses acting on bones and muscles vary with body size (Biewener 2005). Thus, allometry does not necessarily maintain the functional relationship between components. Indeed, concerted evolution could in theory be caused by a pattern of mosaic changes associated with “functional shifts”, to use Montgomery et al. 2016’s terminology. This hypothesis is an integral part of the theoretical account presented in this paper and developed in the next section.
2.3. A third view: allometry as a pattern of adaptations
The two accounts discussed above focus on the apparent lack of variability in allometry. In contrast, the present account considers instead allometry as being mostly a directed variability, resulting from the fact that the set of selection pressures within a given taxon changes the threshold of selection for most characters in a concerted way (for a discussion of the concept of selective covariance, see Felsenstein 1988)). In this adaptationist account, species are not just forced to evolve in a direction specified by developmental or functional constraints (that is, allometry does not arise from a “conserved pattern-formation mechanism”, Stevens 2009). Instead, the present account considers the allometric patterns to be at least partly indicative of the selection pressures having acted on individual species and taxa as a whole. At a developmental level, what is conserved between species within a taxon-cerebrotype is thus not necessarily the mechanism by itself (which would permit “the existence of allometric relations so that one mechanism would work for an individual of any size”, Stevens 2009), but rather the direction of the changes (note that variations in just a few mechanisms could be responsible for a large range of changes within a taxon, see Charvet et al. 2011). At the functional level, what is conserved is not necessarily the functional relationship between the components of a neural system (“more of the same” in Montgomery et al. 2016), but the direction of the changes within and between neural systems. Although both accounts have at their core mosaic evolution, the account developed here and the functional constraint hypothesis (Barton 2001, Montgomery 2016) differ in major ways. Most notably, as seen just above, the present account includes cases where allometry between brain structures can be caused by functional shifts. PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27872v1 | CC BY 4.0 Open Access | rec: 25 Jul 2019, publ: 25 Jul 2019 13
In the present framework, allometric patterns are thought to indicate the direction of adaptations within a taxon once the action of developmental and functional constraints have been considered. That is, part of allometry betrays the action of selection pressures in species within a taxon, and the brain’s response to it. The following two sections examine the two pillars of this theoretical framework. They are: (i) evolutionary scenarios about the mechanisms by which selection on individual components and neural systems can lead to concerted changes at the level of neural systems and major brain components (section 3), and (ii) a set of selection pressures on the size and composition of neural components/systems that correlates among each other (section 4).
3. The evolution of functional neural systems
3.1. Scenarios of concerted evolution within functional neural systems
Following the elements discussed in the introduction, the assumption made here is that an increase in the number of neuronal and nonneuronal cells and their connectivity, and, as such, an increase in the size of a brain component, is the most common way for a brain component to adapt to selection pressures targeting its processing capacity (how a given component respond to selection pressures is further determined by its particular pattern of connectivity). Indeed, there are at least three possible evolutionary scenarios by which selection pressures on the functions of at least one component within a functional neural system affect the size of the components of this system. These three scenarios are schematized in Figure 4, and described below.
3.1.1. Balanced functional selection
In the first scenario (Figure 4ad), called here balanced functional selection, all the components of a functional system are needed to carry out the function(s) under selection. In this case, the selection pressures target the (functions of the) neural system as a whole. However, because of the relative independence of most brain components in term of developmental mechanisms (see review by Montgomery et al. 2016), a single adaptive change is unlikely to affect all the components of the neural system. Therefore, even if selection pressures target the function(s) of the system as a whole, this could require multiple changes in the developmental pathways. The visual system, with its multiple parallel pathways required for representing the visual PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27872v1 | CC BY 4.0 Open Access | rec: 25 Jul 2019, publ: 25 Jul 2019 14
surroundings (Nassi and Callaway 2009), could correspond to the system typically evolving under this scenario. This example is also particularly interesting because it illustrates the possibility that changes of (supposedly) similar intensity in the selection pressures acting on the various components of a neural system do not necessarily lead to similar changes in the size of the components within the system (in Figure 4d, this is represented by the alternative grey line), due to specificities in the components’ internal composition. Indeed, Stevens (2001) suggested that the larger increase in size of the primary visual cortex compared to the thalamic components involved in visual processing (see also Collins et al. 2013) could be explained by relatively simple organizational principles of information processing (leading to the number of neurons in the primate primary visual cortex to increase as the 3/2 power of the number of neurons in the lateral geniculate nucleus). In this scenario of balanced functional selection, the function of the system is conserved throughout the scaling, although the absolute magnitude of the function changes (for the visual system, species with a larger visual system have better visual abilities (de Sousa and Proulx 2014)). The functional dependence between components of a neural system has two consequences on its evolution. Firstly, the larger the number of components involved in the function targeted by selection pressures is, the more complex the changes in the regulatory pathways may have to be (because, as noted above, the relative independence of most brain components in term of developmental mechanisms suggests that selection acts on multiple pathways). Secondly, the hierarchy of information processing between components within a neural system suggests that changes in component size are selected only if they allow the system as a whole to perform in a way that is evolutionarily advantageous. This suggests the possible existence of a hierarchical order in the evolution of the brain components within a system (which could help explain why some allometric patterns between brain structures seem to have been repeated across vertebrates, e.g. Yopak et al. 2010).
3.1.2. Unbalanced functional selection
In contrast to the first scenario, where a global functional property of a neural system is under selection, in some cases some aspects of the functional properties of a neural system, and therefore the particular component(s) involved in these aspects of the function(s), may have been preferentially targeted by selection. If this adaptation is shared within a taxon, albeit at a different scale between species, an allometric pattern between the brain components is formed (see Figure 3 for a simple example), associated with a constant change in the output of the individual brain components, and, therefore, of the system as a whole. This scenario, PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27872v1 | CC BY 4.0 Open Access | rec: 25 Jul 2019, publ: 25 Jul 2019 15
represented in Figure 4be and called here unbalanced functional selection (because the level and/or direction of the selection pressures differs between the components of a system), is different from the first scenario discussed above because, in this case, the allometric pattern at the level of the taxon results from an adaptive change in the functional properties of the neural system, rather than simply an increase in its functional capacity. Striedter (2005) discussed two examples that could correspond to this scenario of functional changes associated with allometry. Firstly, the structural complexity of the dorsal cochlear nucleus, a component of the mammalian medulla involved in auditory processing, appears to be inversely correlated with brain size in primates (Moore 1980). This led Striedter (2005, p. 323) to suggest that some of the functions carried out by this component were integrated by the neocortex as it became larger. In the context of the second scenario discussed here, this means that the increase in the size of the neocortex was partly due to selection on its capacity to incorporate the functions previously supported by the dorsal cochlear nucleus. Secondly, the degree of neocortical projections to the spinal cord is correlated with the size of the neocortex and, to some extent, to digital dexterity in primates (Heffner and Masterton 1983). This indicates that changes in neocortex size are associated with functional changes in the influence of the neocortex in limb control. While Striedter (2005, p.240), following Deacon’s (1990a) displacement hypothesis, suggested that it is a simple consequence of the neocortex becoming proportionally larger and thus better connected, the framework presented here considers instead that this “invasion” of neocortical axons in the spinal cord was one of the characters (among many) selected during primate brain evolution. That is, primates appear to have consistently been under selection for a better cortical control of the limbs, and this adaptation contributed to the enlargement of the neocortex, among other brain components (more on this in section 4.5.). This scenario goes against the widespread claim that allometry between two brain components necessarily represents a “conserved function” (Montgomery 2013).
3.1.3. Adjustment effect
Finally, in the third scenario (Figure 4cf), which has been described as the adjustment effect (Willemet 2015a), the functional dependence between brain components of a neural system is asymmetrical (for example, a component X may send all its outputs to another component Y, whereas the output of X constitutes just a fraction of the inputs of Y), and there is no direct selection on the functional capabilities of the component subject to the adjustment effect. For example, Willemet (2015a) noted that whereas the size of the olfactory bulb correlates with PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27872v1 | CC BY 4.0 Open Access | rec: 25 Jul 2019, publ: 25 Jul 2019 16
brain size in primates, there is little evidence for a correlated increase in olfactory ability. This suggests that part of the increase in the size of the olfactory bulb could result from selection to adjust the output of this component to an enlarged network (either by preventing the output of the olfactory bulb from being “diluted” if it had remained at the same size while the rest of the brain became larger, or by allowing the olfactory information to reach a larger number of targets as the brain became larger). The relatively large number of glomeruli in anthropoids' olfactory bulbs (Maresh et al. 2008, Moriya-Ito et al. 2015), despite primates having a relatively low number of functional olfactory receptor genes (Niimura and Nei 2007), is compatible with the kind of changes predicted by the adjustment effect (i.e. increasing the amount of output but not necessarily the complexity of the computation). Unlike the two preceding scenarios, in this scenario there is no selection on the functional properties of the component subject to the adjustment effect (in the example above, the newly evolved species does not have better olfactory abilities), and the change in the number of cells within the secondary selected component (and therefore its size) only happens because of functional constraints.
Figure 4. Three scenarios of the effects of selection pressures on one (adjustment effect) or two (balanced and unbalanced functional selection) component(s) of a neural system composed of two components. The square and disk represent two individual components of a functional system. The size of the black arrow heads represents the magnitude of the functional dependence of one component on the other. The variation in the size of the squares and disks illustrates the variation in the size of the brain components that they represent. Their computational ability is encoded as grey level (the darker components have higher processing capacity). The blue arrows represent the selection pressures acting on the functions of a PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27872v1 | CC BY 4.0 Open Access | rec: 25 Jul 2019, publ: 25 Jul 2019 17
particular component, and the size of the arrows represents the level (threshold) of this selection. The graphs in d., e., f. summarize the Figures in a.,b., c., respectively; the black lines represent the size of the square (dash line) and disk (dotted line) components, and the blue lines represent the level of the selection pressures acting on these components (same symbols used). The grey dotted line in d. represents an alternative to the black dotted line, where the size of one component (the disk) have, due to difference in its connectivity pattern, enlarged much more than the other component (the square), despite being subject to selection pressures having comparable thresholds. See main text for details.
3.1.4. Summary and limits
These three scenarios give very different predictions about the relationship between the size of a brain component and its functional capacity. In the scenario of balanced functional selection, there is a direct relationship between the size of the component and its functional capacity, when comparing species within a taxon-cerebrotype. In the unbalanced functional scenario, there is also a direct relationship between the size of a component and its functional capacity, but the functional properties of some individual components, and of the system as a whole, change with the size of the functional system. Finally, in the adjustment effect, there should be no or minimal (secondary) association between the size of at least one component of a neural system (the component that has to adjust to the variation in size of the other components) and its functional capabilities. It is only in this last scenario that the kind of secondary selection of one component following changes in another component that is described by Montgomery et al. (2016) to illustrate the effect of functional dependence can be found.
The discussion above is simplistic because many brain components are involved in more than one neural system. A consequence of this is that the three scenarios probably happen simultaneously in many components. Moreover, the discussion above ignores any kind of developmental constraints, even though such constraints should influence the general aspect of the different scenarios in certain neural systems (despite being generally not strong enough to prevent the independent evolution of individual brain component, as discussed above). Finally, it is important to note that the amount of change in the size of brain components is unlikely to be directly proportional to the strength of the selection pressures. That is, although selection pressures can, as seen above, affect the size of a component (e.g. Welker and Campos 1963), the strength of the selection pressures cannot be directly inferred from the amount of change between various brain components. This is because the differences in the connectivity properties between components make them react differently to selection pressures. As such,
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changes in the proportional size of a brain component should not be automatically interpreted as evidence for preferential selection on the functions of this component unless there is evidence of an increase in the weight of the functions of this component on the output of the system. Although these issues do not undermine the general elements discussed above, they complicate the analyses trying to examine the respective influence of these different scenarios on the evolution of a brain component (see section 5.2.).
3.2. Patterns of selection pressures
In the three scenarios discussed above, the overall size of the neural system becomes increasingly large from species to species because of a constant, but scaling (it has a lower level/threshold in species with the smallest brain components compared to the species with the largest brain components) selection on all (scenario 1) or parts of (scenarios 2 & 3) its functional properties. To put it otherwise, in the scenarios presented above, the concerted pattern between brain components (i.e. allometry) arises because, from the smallest to the largest brains, the same functional properties are selected, in at least one component of a functional system. Thus, allometry between the components of a neural system in species within a taxon reflects a pattern of adaptations that is shared from species to species, albeit at a different scale.
Functional dependence, whose effects on brain scaling are described by the three scenarios above, cannot alone explain the scaling of brain components belonging to functionally independent neural systems. That is, the scenarios described above apply within a functional system and therefore cannot explain the concerted evolution between components of different functional neural systems subject to selection pressures on their respective functions. However, the evolution of brain components belonging to different neural systems can create an allometric pattern when the thresholds of selection pressures targeting the different neural systems are correlated. In other words, the selection pressures targeting (individual components of) various functional systems, while independent to some extent, can vary concertedly, and this is because of the correlation between the thresholds of the selection pressures targeting different functional neural systems (i.e. selective covariance) that the systems that they are targeting enlarge in an allometric fashion.
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The idea that correlation between selection pressures can lead to the coevolution of multiple traits is not new (e.g. Felsenstein 1988, Endler 1995). Moreover, it has long been hypothesised that inter-correlations between brain components indicate functional relationships (e.g. Jolicoeur et al. 1984). The framework presented here extends these ideas by considering these patterns of selection pressures to be the main factor responsible for the very existence of the concerted scaling of brain components, rather than developmental (Finlay et al. 2001) or functional (Montgomery et al. 2016) constraints. More particularly, it is suggested here that perhaps the most important aspect of brain scaling within a taxon is that the direction of the selection pressures acting on the size of brain components/neural systems are conserved, albeit at a different scale, from species to species within a taxon (an important variant of this hypothesis, not discussed here for sake of simplicity, is that the direction of selection does shift with brain size, and that the direction of the shift varies with brain size). Importantly, this does not simply mean that more closely related species share more similar selection pressures; but instead that the selection threshold of the various selection pressures acting on the size of brain components/neural systems scales concertedly between species within a taxon. Indeed, directional selection can alter the patterns of phenotypic integration and create the facilitated variation towards particular allometric patterns (Assis et al. 2016, Penna et al. 2017, and see Armbruster et al. 2014). But what are the selection pressures acting on neural components/systems? And why are they correlated? Do they only target the functional properties of brain components/neural systems? These are the issues examined in the next section.
4. Factors influencing the evolution of brain size and composition
For the account defended here to be plausible, there must be a set of selection pressures that are correlated to each other within particular taxa and that target the whole set of brain components. This claim appears to be supported by the evidence presented below, which reviews some of the major selection factors at play in brain component evolution. Given the strong relationship between brain and body size (e.g. Dubois 1897), it is not surprising that most of the factors discussed in the next section are either directly or indirectly linked to body size. In fact, body size seems to be the major factor behind the correlation between the selection pressures acting on neural systems. Before discussing these selection factors, it is important to define what kind of adaptations can be expected in the context of allometric scaling.
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4.1. Adaptations and allometry: body size required vs body size allowed adaptations
It has been argued in the sections above that allometry may be mostly the result of concerted selection pressures, and that, as such, only a theoretical framework mainly based on adaptations, rather than constraints, can give a credible account of the origin and characteristics of allometry between brain components (adaptation, as defined by Lewontin (1978), is “the process of evolutionary change by which the organism provides a better and better "solution" to the "problem," and the end result is the state of being adapted”). Although this claim goes against a large fraction of the literature on this subject, where often only deviations from allometry are taken as evidence of adaptations (Lewontin 1978), the idea that allometric scaling involves adaptation can be found in past literature. Most notably, Stephen Jay Gould noted more than fifty years ago: “there has been, in my opinion, mistaken emphasis on the non- adaptive nature of simple allometric trends in phylogeny. Proportions produced by constant α values [i.e. the slopes of allometry] need not be viewed as by-products of size increase brought to expression without selective modification of genetic shape factors; constant α may, rather, reflect an ordered set of proportions specifically selected to accommodate absolute magnitude at each step of phyletic size increase” (Gould 1966, brackets added). For Gould, however, allometry was mostly the product of constraints, and, in what has become a classical paper on allometry, he and Lewontin argued that organisms are so “constrained by phyletic heritage, pathways of development, and general architecture”, that “the constraints themselves become more interesting and more important in delimiting pathways of change than the selective force that may mediate change when it occurs” (Gould and Lewontin 1979). However, in doing so, Gould had to revisit the negative connotation of the term “constraint” (Gould 1989), which suggests that the term was not appropriate in the first place (Arthur 2004). Evolutionary constraints (forces limiting or channelling the evolution of a character) and selection pressures (forces acting toward the modification of a particular character) act simultaneously on any given character at any given time during brain evolution, with constraints arising in part from the fact that neural components are integrated within functional systems. The difficulty is to assess the relative weight of constraints and adaptations in the final form of a character (Hallgr msson et al. 2009). What the elements presented here suggest is that considering adaptation, rather than constraints, as the main factor behind the allometry of brain components 횤 within a taxon allows for a better understanding of the diversity of brain composition between species.
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Gould (1966) considered adaptations in the context of allometry to consist either in “size- required allometry”, determined by body size, or in special adaptations, found instead in the deviations from allometry. His understanding of allometry as a pattern of adaptive changes made necessary by changes in size is particularly evident by his definition of allometry as “the study of size and its consequences” (Gould 1966). By contrast, in the present paper, allometry is as much the study of size and its consequences as the study of size and its drivers. More particularly, allometry between neural components is considered to be mainly due to two kinds of adaptations; body size required adaptations, and what could be called body size allowed adaptations. Body size required adaptations correspond to the kind of allometry discussed by Gould (1966). It is, for example, behind the relationship between the size of the heart and the size of the body (the heart has to be larger to pump the larger volume of blood present in a larger body, Holt et al. 1968), or, here, behind some aspects of the relationship between the size of the brain regions involved in motor control and body size (see below). Although the size of the body parts evolving due to body size required adaptations is partially determined by specific constraints, adaptation remains the main factor behind the resulting allometry, in such a way that, as Gould (1966) noted, characters “are specifically adapted to mode of life (hence to body size) for each form”.
Body size allowed adaptations consist in the kind of adaptations made possible by the physical, physiological and life-history characteristics associated with a given body size (explaining their correlations with body size). Although these adaptations are not exclusively related to body size, they are facilitated by the fact that, in terms of physical and energetic properties, a larger body can support larger brain components. Species within a taxon may take advantage of this to evolve in a particular direction as their bodies got larger. Examples of such adaptations, how they differ from body size required adaptation, and why they are fundamental to our understanding of species differences in brain size and composition, are discussed in the next section. Each set of selection pressures presented below is discussed in relation to the two categories discussed above: body size required and body size allowed adaptations.
4.2. Functional adaptations
Following the discussion above, the assumption is made here that selection for an increase in the processing capacity of at least one component of a neural system is mainly answered by an increase in the number of cells and connections constituting the component, leading to an PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27872v1 | CC BY 4.0 Open Access | rec: 25 Jul 2019, publ: 25 Jul 2019 22
increase in the size of the component, and, as such, an increase in the size of the neural system via one of the three scenarios described above (i.e. balanced or unbalanced functional selection and the adjustment effect). Importantly, however, this does not mean that the size and computational power of a brain component are systematically correlated. Indeed, there are a number of reasons why the size of a brain component can change without affecting its computational power (in particular, see section 4.3. on structural adaptations below).
4.2.1. Sensory-motor adaptations
In view of the evidence against the hypothesis of a strong developmental integration between brain and body size (see review by Montgomery 2017), consider the following scenario: due to selection for being stronger in some aspects of its interaction with the environment (it could also be selection for a larger interior volume, a relatively lower metabolic rate, or any other factor or combination of factors, see Blueweiss et al. (1978) for an early review of some of the factors associated with an increase in body size), an hypothetical species (B) evolved a larger body size than its ancestor species (A). In this example, species A and B have similar selection pressures targeting their cognitive and behavioural capabilities (that is, there is no “need” for species B to change its cognitive and behavioural capabilities, compared to species A), but they are subject to different selection pressures targeting the size of their body. This change in body size, in turn, affects the selection pressures targeting the functional properties (and therefore, most likely the size) of particular brain components in species B, and are thus the basis for sensory-motor adaptations.
4.2.1.1. Somatic factor and motor control
Body size required adaptations The brain of species B may require a larger number of nervous cells, and/or larger cells and axons, in order to control the larger body. As a result, the components directly involved in the innervations of body components and the processing of body information are expected to be larger in species B (Jerison 1973). Indeed, the number of neurons in the spinal cord increases with body size (length) in primates (Burish et al. 2010). At a finer level, the number of motor neurons in the facial nucleus, a component that innervates the superficial facial muscles, increases (albeit slowly) with body size in both primates and marsupials (Watson et al. 2012).
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Importantly, the hypothesis that the somatic factor is a fundamental factor in the evolution of the brain components involved in the treatment of body signal does not necessarily mean that the functional properties are maintained between small and large animals (see More et al. 2010, but also Schmitt et al. 2013, Heldstab et al. 2016). Indeed, the fact that axonal conduction velocity does not scale proportionally with body size might force larger species to rely more on predictions to control their body movements (More et al. 2010, 2013); thereby contributing to the enlargement of the brain components involved in this aspect of the somatic factor.
Because of the selection pressures associated with the fact that a larger body requires additional and/or larger nervous cells, all the brain regions involved in motor control, such as the motor cortex and parts of the cerebellum for example, are expected to scale with body size (note that body tissues differ in the extent to which they contribute to this somatic factor, since they are differently innervated, Schoenemann 2004). The detail of which components are affected is beyond the scope of this article. What is important is that even without selection on cognitive or behavioural capabilities, some brain components are expected to be larger in species B compared to species A, following selection on their function. The somatic factor is thus a fundamental aspect of brain scaling, at least partly responsible for both the concerted scaling of particular brain components and the correlation between brain and body size.
Body size allowed adaptations Irrespective of this body size related somatic factor, a finer motor control of the limbs can also be a factor leading to the selection of the brain components involved in motor control. The decreasing relative cost of brain tissue as bodies get larger, and the increased computational capacity of larger brains, may have favored neural adaptations toward finer sensory motor control in a way that correlates with body size. In particular, this appears to have been the case in primates, where species with larger brains have a larger number of corticospinal connections (Heffner and Masterton 1983) and are capable of more complex manipulations (Heldstab et al. 2016). In fact, while corticalization of motor control has been hypothesised to be a consequence of brain scaling (Striedter 2005, Herculano-Houzel et al. 2016) the framework presented here interprets the same data as evidence that corticalization of motor control was one of the factors leading to a larger brain in primates (i.e. one of the causes behind, rather than a consequence of, brain scaling).
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4.2.1.2. Sensory abilities
If the motor aspect of the somatic factor was the only selection factor varying with body size, the only components whose size would be expected to vary with body size are the ones involved in motor control (note that this is a radical simplification, as many cognitive processes are based on the processing of body signals, e.g. Garbarini and Adenzato 2004). Instead, there is an enlargement of virtually all the other brain components as well, albeit more or less strongly (Stephan et al. 1981). Developmental constraints may have a role in it, but as discussed above, these are unlikely to be the main factors. Thus other factors seem to be at play. As developed below, selection on sensory abilities appears to be one of them.
Body size required adaptations The quantity of sensory information needed to be transmitted to the brain increases as bodies get larger (Jerison 1973). Again, the detail of which components are affected is beyond the scope of this article. The important point is that this suggests that, alongside the motor aspect discussed in the preceding section, the sensory aspect of the somatic factor should participate to the correlation between brain and body size within a taxon.
Body size allowed adaptations The larger body of species B means that it can support larger sensory organs, with more receptors. It is likely that this opportunity has been exploited by many taxa, leading to selection on the neural substrates of sensory processing, and therefore to larger brain components associated with sensory processing (and thus larger brains), as bodies get larger. For example, body size, eye size, visual abilities and the size of brain components involved in visual processing all appear to be correlated in primates (Bush and Allman 2004a, Ross and Kirk 2007, de Sousa and Proulx 2014). In addition, larger bodies are generally associated with larger home ranges (e.g. Milton and May 1976). This may form the basis of selection pressures on larger sensory capacities (and thus larger sensory organs) that in turn require larger processing capacity (and therefore larger brain components).
Only the sensory systems that could benefit from an increase in computational ability should have exploited the possibilities offered by a larger body. For example, the size (number of axons) of the cochlear and trigeminal nerves varies with body and brain size in shrews and moles (with the star-nosed mole Condylura cristata being an interesting and predictable outlier PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27872v1 | CC BY 4.0 Open Access | rec: 25 Jul 2019, publ: 25 Jul 2019 25
for the trigeminal nerve), whereas no such pattern is seen for the optic nerves (Leitch et al. 2014). This is not particularly surprising, given that these animals are primarily nocturnal or live in burrows. In contrast, and although the comparison is imperfect because the range of brain size differs considerably, the number of optic nerve axons increases with brain size in New and Old World primates (Finlay et al. 2008).
4.2.2. Behavioural adaptations
The elements presented above indicate that, even without selection on behavioural capabilities, selection for a larger body leads to the enlargement of some brain components (and therefore, to a larger brain) in species B, compared to species A. However, selection on behavioural capabilities may also have been a fundamental factor in the evolution of brain size and composition, in some taxa at least, as described below.
Body size required adaptations While the cognitive adaptations required for changes in body size are not obvious; the relationship between body size and longevity (e.g. Barrickman et al. 2008) suggests that larger brain components, with more numerous cells and connections, may have been selected for what has been called “adaptive redundancy” (Chittka and Niven 2009). In particular, part of the selection for larger brain components might have been toward increasing the redundancy in the connections between cells, which might protect against interference or loss (physiological or following injury). Thus, cognitive robustness may be another important selection factor in favour of larger brain components, contributing to the correlation between brain and body size.
Body size allowed adaptations A larger body can support a larger amount of brain tissue, in term of mass and volume, but also because of the physiological costs of this tissue that decreases, in relative terms, with increasing body size. A small mammal with a very large brain would probably not have enough time in a day to find the energy necessary for its brain (Herculano-Houzel 2011b). Thus, in some taxa, an increase in body size may have facilitated selection for increased cognitive capabilities, broadly defined, and therefore larger brains (or inversely, selection for increased cognitive capabilities may have been one of the factors leading to an increase in body size as a means to support a larger brain).
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In haplorhine primates, several elements suggest that an important fraction of brain size enlargement has been caused by selection for increased cognitive capabilities. That is, the components known to be involved in complex cognition consistently increased their size more than other components, leading to an increase in their relative size, as brains became bigger. This is true for the neocortex and cerebellum, whose change in size mainly results from an increase in the number of the neuronal and nonneuronal cells composing them (Gabi et al. 2010). Inside the neocortex, the frontal lobes became relatively larger with brain size (Bush and Allman 2004b). Although this could be due to differences in connectivity between the frontal and nonfrontal lobes (leading the frontal lobe to increase more for a “comparable” change in its functional properties, see also section 5.2.1), the cerebellar lobules linked to the prefrontal cortex appear to have been particularly selected in primates (Balsters et al. 2010), suggesting that the functional properties of the cortico-cerebellar system changed with brain size (and see Smaers and Vanier 2019 for a more detailed analysis on the evolution of the cortico-cerebellar system).
Perhaps the strongest evidence that a large fraction of the enlargement of brain components in primates results from the selection for increased behavioural complexity is the correlation between various measures of cognitive abilities and absolute brain size in primates (Deaner et al. 2007, Reader et al. 2011). The selection pressures behind these adaptations towards increased cognitive capabilities (that is, a deeper understanding and control of the environment) have traditionally been classified as either ecological (for example, those related to the capacity to forage a temporally and geographically variable resource, e.g. Clutton-Brock and Harvey 1980) or social (better predictions of other individuals’ behaviours, e.g. Humphrey 1976, Byrne and Whiten 1988), although the distinction between the two is not always clear-cut. This topic is still receiving much attention (e.g.DeCasien et al. 2017, Street et al. 2017, Ashton et al. 2018, González-Forero and Gardner 2018, Kverková et al. 2018, Louail et al. 2019). It is beyond the scope of this paper to discuss this literature in detail (but see Dunbar and Shultz 2017 and Rosati 2017 for reviews), in part because these works have sometimes used inconsistent brain measures in their analyses or are based on inconsistent datasets (Willemet 2013, 2015b, Powell et al. 2017, Wartel et al. 2018). In fact, the elements presented here suggest that this question cannot be adequately addressed without considering allometry between brain and body size, as well as between brain components, within an adaptationist framework taking into account the many factors influencing brain size and composition. In any case, the important point here is that selection for increased cognitive abilities (broadly defined), has been an important factor PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27872v1 | CC BY 4.0 Open Access | rec: 25 Jul 2019, publ: 25 Jul 2019 27
in the evolution of larger brains in primates. Such body size-allowed direct selection on cognitive abilities may be rare among taxa, and perhaps even unique to haplorhine primates, at least in that form and extent (section 5).
4.3. Structural adaptations
With some exceptions (e.g. Manger 2006, Karbowski 2009) changes in brain component size is widely assumed to be exclusively the result of selection on their functional capacity, albeit influenced by the developmental and energetic/physiological costs of brain tissue (Montgomery 2017). Yet, selection pressures on brain components may also target their structural properties (Willemet 2013). This hypothesis, developed below, may help explain a large fraction of species differences in brain size and composition.
4.3.1. Body size required adaptations
Brain tissue is very sensitive to changes in metabolite and oxygen concentration in the blood (Hossmann 1994, Jessen et al. 2015), blood pressure (Leonardi-Bee et al. 2002), temperature (Mrozek et al. 2012) and, to a lesser extent, to acceleration forces (Jordan 2013), among other factors. Yet, changes in body size are associated with a modification of the physical constraints faced by the brain. As such, these changes could have been the basis of selection pressures targeting the structural properties of brain components. This hypothesis, if correct, could prove fundamental to our understanding of the diversity of brain and composition within and between taxa, because it predicts, among other things, a coordinated increase in the size of brain components, and between brain and body size.
4.3.1.1. Cardiovascular system
To function, the brain must precisely control blood flow in order to regulate the temperature, oxygen level, glucose level and intracranial pressure. This hypersensitivity of the brain to alterations of blood flow is partly explained by the combination of the high energy requirement and limited storage of glycogen of brain tissue (Brown and Ransom 2007), and the challenge of constantly adjusting blood flow within the fixed volume of the protective cranium without damaging the structural integrity of the brain tissue (for more details, see the so-called Monro- Kellie hypothesis, e.g. Mokri 2001). PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27872v1 | CC BY 4.0 Open Access | rec: 25 Jul 2019, publ: 25 Jul 2019 28
Several elements suggest that blood flow regulation could have been an important factor in the evolution of brain components, forcing the components to adapt, notably by increasing their size, as the cardiovascular system of a new species became adapted to its larger body. Indeed, a particular aspect of the cerebral vasculature is the role of the large arteries in controlling blood flow (Faraci and Heistad 1990); “serving the first-line defense such that the pial and cortical vessels experience minimal changes in pressure and can respond principally to prevailing systemic and local neural metabolism” (Willie et al. 2014). Since several physiological variables involved in the main characteristics of blood composition (Kjeld and Ólafsson 2008) and blood flow (Holt et al. 1968, White and Seymour 2015, but see Poulsen et al. 2018) appear to scale with body size, this may have forced the cerebral vasculature to adapt accordingly. Indeed, given the tight coupling between neuronal and non-neuronal cells and their blood supplies (leading to the concept of neurovascular units, see review by Muoio et al. 2014), these changes in blood flow related to changes in body size might have affected the brain as a whole (see also Karbowski 2011). The presence of region specific metabolic rates (Karbowski 2007) further suggests that this factor affected each brain component in a specific way. Indeed, brain components differ in their response to hypoxia (Cervos-Navarro and Diemer 1990). Although this selective vulnerability cannot be fully accounted for by vascular distribution (van der Knaap and Valk 2005), cortices and nuclei appear to be differently sensitive to changes in blood flow (Faraci et al. 1987). When considered in the context of the blood flow factor discussed here, these elements could perhaps help explain the hyperallometry of the cerebellum and neocortex in most mammalian taxa (Willemet 2012). This hypothesis predicts the existence of taxa specific different in the scaling relationships between encephalic flow and brain size. Although an analysis comparing cerebral blood flow rate in haplorhines and strepsirrhines did not find evidence for such differences (Boyer and Harrington 2018), it is interesting to note that the vascular tissue occupies a remarkably large part of the endocranial volume in some species of cetaceans (Ridgway et al. 2016), which are large bodied mammals with adaptations, among other things, to long periods of apnoea. Thus, an interesting area of research would be to examine how much, if any, of the correlation between brain and body size is due to adaptations of the brain vasculature to the circulatory system of larger animals.
4.3.1.2. Acceleration forces
Acceleration forces may be another structural factor having had a role in the evolution of brain components. Because of their mass, large brains are more sensitive to potential damage from PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27872v1 | CC BY 4.0 Open Access | rec: 25 Jul 2019, publ: 25 Jul 2019 29
acceleration forces than smaller brains (Margulies and Thibault 1989). Thus, mechanisms for coping with the stress from acceleration forces (potentially leading to diffuse axonal damage) may have evolved in parallel to the enlargement of brain components, perhaps contributing in some aspects to their enlargement. Interestingly, sheep brains have been found to be more resistant to percussive brain trauma than dog brains (Millen et al. 1985), perhaps because of special adaptations to repeated impact accelerations. Studying the possible structural adaptations behind these differences (either by comparing species or looking for sex differences) could thus potentially shed light on some aspects of the structural factors involved in the correlation between brain and body size.
4.3.2. Body size allowed adaptations
Having small brains involves a number of specific factors (in term of processing speed, mechanisms of heat dispersion, metabolite treatments, and other physiological factors) that do not equally apply to larger brains (Laughlin and Sejnowski 2003, Karbowski 2009). Therefore, in species with the smallest brains, a larger brain, perhaps initially made possible by a larger body size, could be partly the result of relaxed selection pressures on the constraints specific to smaller brains. For example, thinner axons in small brains have a smaller signal-to-noise ratio than thicker axons (Laughlin and Sejnowski 2003). Larger brains could also have been selected because they allow for relaxed constraints on the proportion of the time spent sleeping (Herculano-Houzel 2015), for example. Since in mammals most (but not all, see Altman 1962, Eriksson et al. 1998) neurons are produced during embryonic development, part of the enlargement of brain size in some species may be the result of adaptations to increase the longevity of neurons (perhaps larger neurons or larger space between them, or more neurons with less connection). The stronger correlation between the number of neurons in the neocortex and maximum lifespan, compared to the number of neurons in other brain structures (Herculano-Houzel 2018), suggests that the neocortex is critical in this respect. Some insight might be gained by studying bats, who have both a small brain and a long lifespan (Brunet- Rossinni and Austad 2004, and see Baudry et al. 1986 for some preliminary results). The remarkable longevity of some cetacean species (George et al. 1999, Robeck et al. 2015) also suggests that part of their large brain may be an adaptation to create the conditions that enable neurons to have a longer lifespan. This would help explain the large brain of some cetacean species, perhaps in addition to adaptation to complex cognitive abilities (Marino et al. 2007). Also, primate brains’ vulnerability to signs of neurodegenerative diseases (e.g. Perez et al. PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27872v1 | CC BY 4.0 Open Access | rec: 25 Jul 2019, publ: 25 Jul 2019 30
2016), and the possibility that the onset of the age related size correlates with brain size and differs between species (as the data from Cramer et al. (2018) seem to indicate) suggest that longevity is a fundamental factor in the evolution of the size of brain component in primates. Species living longer probably needed to evolve special characteristics to conserve the functional properties of their brain cells for a longer period of time. Thus, irrespective of their computational abilities, a global enlargement of brain components (and therefore, of brain size) with body size, may result from adaptations on the structural properties of brain components.
4.4. Selection pressures acting against an increase in brain component size
In the discussion above, and for the purpose of simplicity, little has been said of the evolutionary cost of brain tissue, except that larger bodies facilitate the selection of larger brain components because of the lower relative cost of brain tissue. However, such constraints are a fundamental aspect of brain evolution, and, as such, an integral part of the present framework.
4.4.1. Energetic constraints
Due to the high metabolic cost of brain tissue (Mink et al. 1981), evolutionary changes in the quantity of brain tissue must be met by a new balance between energy intake and expenditure. In other words, only the changes in the amount of brain tissue that can be supported energetically can be selected (Niven and Laughlin 2008). There are at least five ways by which mammalian species could have addressed these constraints. First, an increase in the quantity of brain tissue can be facilitated by a concomitant increase in body size, as larger bodies reduces the relative cost of brain tissue (see also Fonseca-Azevedo and Herculano-Houzel 2012). Second, the cost of changes in brain mass can be compensated by changes in one or several lifestyle variables, such as litter size or pace of maturation (Barrickman et al. 2008). Third, changes in brain size and composition can lead to behavioural changes that ensure an adequate intake of nutrients via a better monitoring of resources and/or an enhanced capacity to exploit new ones (Sol 2009). Fourth, the cost of larger brain components may be offset by changes in the size of other body components. Several hypotheses have been formulated in that direction, such as trade-offs between brain size and the size of intestines (Aiello and Wheeler 1995), testes (Pitnick et al. 2006), or the quantity of fat storage (Navarrete et al. 2011), for example. Finally, changes in brain component size may have been accompanied by the
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elaboration of behavioural strategies for increasing food reserve (for example, food caching in mammals and birds, Pravosudov and Roth II 2013, Delgado and Jacobs 2017) or reducing the energy expenditure (for example, by entering a state of torpor, see Ruf and Geiser 2015). Inversely, long periods with low energy intake could also have been a factor limiting brain size in species that hibernate (Heldstab et al. 2018).
Although there is a significant literature on this topic, reviewing it is beyond the scope of this article. Importantly, the brain’s energy expenditure is roughly dependent on the number of neurons (Herculano-Houzel 2011b). Therefore, a given increase in brain volume does not have the same metabolic cost for each taxon, given the differences in cell number scaling between taxa (Herculano-Houzel 2011a). Moreover, there are also differences between brain regions in their metabolic rate (Karbowski 2007), and the impact of these differences on the scaling of individual components via the selection factors detailed above deserves further consideration.
4.4.2. Developmental constraints
In addition to the developmental constraints that exist at the cellular level (e.g. Charvet et al. 2011, Le Magueresse and Monyer 2013), two important aspects of brain development is that larger brains take longer to develop in term of size and anatomy (Finlay and Darlington 1995), and also to mature functionally (Charvet and Finlay 2012). While in some species the longer time needed to develop a larger brain may have acted as a constraint, in others this period of maturation may have been exploited in order to organize more complex functional systems (Charvet and Finlay 2012). Although not developed further here, these developmental aspects are fundamental for our understanding of the factors responsible for the diversity of brain size and composition in mammals.
4.5. Summary on the selection factors determining the size of neural components/systems
The elements presented above indicate that changes in body size modify some of the selection pressures acting on the functional and structural properties of most neural systems, leading to body size required adaptations that are partly responsible for the allometric relationships both between brain components and between brain and body size. Above these body size required factors, there is also a number of selection factors that, taking advantage of the physical and physiological characteristics of larger bodies and crania, led to an enlargement of particular PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27872v1 | CC BY 4.0 Open Access | rec: 25 Jul 2019, publ: 25 Jul 2019 32
neural components/systems. There is in fact evidence, reviewed in the sections above, that motor (digital dexterity for example), sensory (vision), and cognitive factors have been selected in primates above what could be predicted by the body size-required factors alone.
All these factors correlate between each other to some extent, due to their primary or secondary correlation with body size. Thus, even if various neural systems are targeted by these factors, the fact that the selection pressures are correlated lead to a concerted evolution of all the neural systems, and, therefore, of all the major brain components. The relationship between brain and body size, a subject of intense research since the early days of comparative neuroanatomy (e.g. Dubois 1897, Tsuboi et al. 2018), should therefore not be viewed as primarily the result of constraints and passive responses. Instead, each taxon is subject to a different combination of the selection pressures reviewed above (and most probably others as well), explaining the taxa differences in brain scaling, as elaborated in the section below.
5. A synthesis
5.1. Understanding differences within and between taxa
Why, for example, tigers do not appear to display behaviours that are much more complex than wild cats, despite having a brain seven times larger (≈280 grams vs. ≈40 grams, data from Gittleman 1986), while, within primates, there seems to be a correlation between cognitive abilities and absolute brain size (Deaner et al. 2007), is a question that has puzzled generations of comparative neurobiologists. Despite additional results at many levels of brain organization, an answer to this question still appears out of reach (Montgomery 2017).
The theoretical framework developed here offers the beginning of an explanation. The key point is that taxa differ in the patterns of selection pressures acting on the size and constitution of brain components. Figure 5ab represents the factors responsible for the relationship between brain and body size, as well as between brain components, in two hypothetical taxa.
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Figure 5. a & b. Factors acting on the size of brain components in two hypothetical taxa. The areas of different colours represent the different factors (from top to bottom; yellow: “cognitive abilities”, broadly defined, green: sensory abilities, blue: somatic factor and motor control, orange: structural factors), and their level (right-hand scale) acting on the size of brain components, and therefore on brain size (left-hand scale). c. More realistic representation of the factors responsible for the size of the brain components (and therefore the size of the brain) in a sample of hypothetical species (black dots) from the taxon represented in (a). The black line represents brain size, and the grey dashed line represents the critical brain size for a given body size (see explanations in text).
Several elements are needed to understand Figure 5ab, which represents brain size and the level of the selection factors on the y-axes and body size on the x-axis, in two hypothetical taxa. In each of these figures, the black line represents brain size. The areas of different colours represent the different factors, and their level, responsible for the size of the brain (via selection on individual brain components/systems), for the whole range of body size. The blue area represents the somatic and motor factors. As discussed in section 4, the somatic factor varies with body size, leading to an enlargement of the brain components involved in treating body signal; and therefore of brain size as a whole, as represented in the Figure 5ab. The green area represents the sensory factor. As also discussed in section 4, one hypothesis is that selection for increased sensory motor function may have been facilitated by the fact that a bigger sensory system can be fitted on a larger body, as well as the fact that the overall cost for a given increase in brain tissue decreases with body size. For reasons of simplicity, and in order to focus more
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particularly on the differences in the two other factors (cognitive and structural), it is assumed here that the two hypothetical taxa represented in Figure 5ab are under similar selection pressures targeting their somatic, motor, and sensory systems, and thus have similar adaptations for these systems. The area in yellow represents the factors related to cognitive abilities, broadly defined. The level of the selection factor targeting cognitive abilities does not differ between species within the taxon represented in Figure 5a, so that the cognitive abilities remains constant between species. That is, selection on behavioural complexity was not involved in the changes in absolute brain size at the level of the taxon (see below for the species level, in particular Figure 6). In contrast, in the taxon represented in Figure 5b, there has been an important and increasing selection on cognitive abilities, so that a significant part of the increase in brain size was due to selection for larger brain components involved in cognition (represented here by the yellow area getting larger with brain size). Finally, the orange area represents the structural factor that allows brains to be functional for a given body size (see section 4.2.2.). This structural factor is determinant in the increase in brain size with body size in the taxon represented in Figure 5a, because it pushes for a brain that is at least as big as what could be conceived of as the “critical brain size for body size”, which is represented by the grey dashed line in Figure 5ab. This line symbolises the hypothetical minimal brain size that allows the brain to function in the particular physical and cardiovascular environment associated with a given body size, as discussed in section 4.3. In the taxon represented in Figure 5b, this factor has a limited role in the changes in brain size, because species already have a brain that is bigger than the critical size. However, this factor is nevertheless expected to have a role in this taxon, because, as suggested above, brains could have to adapt in specific ways to, among other factors, the cardiovascular conditions of bodies of different sizes. Note that although this factor is represented as a line in Figure 5abc, it is expected to vary between species due to physiological or lifestyle adaptations.
For simplicity reasons, the critical brain size is assumed here to be the same for the two taxa, which is unlikely to be realistic given that the brain and body composition, as well as the cardiovascular system, differ between taxa (e.g. Mitchell and Lust 2008, Navarrete et al. 2011). Also, the structural factors could have been selected for their effect on the functional properties of the neural systems, rather than simply to accommodate the brain architecture to the constraints associated with a given body size. Importantly, the distinction used between the factors in Figure 5 is schematic only, as all of these factors intertwine to some extent. Finally, it is important to note that the level of the selection pressures cannot be equated with their PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27872v1 | CC BY 4.0 Open Access | rec: 25 Jul 2019, publ: 25 Jul 2019 35
absolute effect on the size of the brain components. The two are however represented on the same graph in Figure 5ab for simplification purpose.
Although the pattern of selection pressures is concerted at the level of the taxon (Figure 5ab), in reality each species is under a particular set of selection pressures (Figure 5c). Such species- specific patterns of adaptation take the form of “deviations” from the allometric pattern of selection pressures, and therefore of brain component size (see also Figure 2). However, the term deviation in the context of allometry has traditionally been considered as designing a divergent mechanism (adaptation leading to changes in function) taking place over a more constrained, though still adaptive, mechanism (allometry, maintaining a particular function) (e.g. Montgomery 2017). The interpretation of allometric deviations within the present framework is different, as allometry itself is seen as being mainly the result of an active mechanism (adaptation) that is associated with functional and/or structural changes. Within a taxon, the concerted evolution of brain component size has to be expected given the general pattern of selection pressures acting on each species within a taxon (as discussed in section 4). However, species have particular values, above these general trends, in most if not all the selection pressures due to specificities in their ecology and life history. It is because of the phylogenetic and ecological similarities between species within a taxon that species are unlikely to diverge from the general direction of evolution within a particular taxon for the majority of the characters. Indeed, divergences are most likely to significantly affect (in the sense of leading to changes that are measurable by our methods of investigation) just a few characters, which explains why significant deviations happen above an otherwise generally concerted pattern.
As said above, the level of the selection factor (that determines the threshold value of the character under selection) and the size of the brain components involved in the character under selection are distinct things (that is, a given change in the level of selection factor is not necessarily met by a proportional change in the size of the component under selection). However, these two factors are linked to each other. By assuming, for simplicity reasons, that the two are closely related, it is possible to summarize the relationship between relative and absolute brain size and cognitive abilities in different taxa (Figure 6).
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Figure 6. Functional significance of variations in absolute and relative brain size. a and b) species-specific variations of the level of the selection factors on cognitive abilities leading to changes in the size of the brain components, and, as a consequence, of absolute and relative brain size, in two taxa. c) representation of brain size and the absolute level of the selection factor targeting cognitive abilities in case 1 (green; corresponds to hypothetical species from the taxon represented in Figure 5a) and case 2 (yellow; corresponds to hypothetical species from the taxon represented in Figure 5b). d) correlation between the level of the selection pressures (hypothesised to be correlated with the size of the brain components) targeting cognitive abilities and absolute and relative brain size in cases 1 and 2.
Figure 6ab is based on the same hypothetical taxa and colour code as in Figure 5ab. For simplicity reasons, variations in relative brain size are assumed to be caused uniquely by changes in absolute brain size, rather than by changes in body size (see Deacon 1990b, Montgomery et al. 2010 for why this assumption is limited). Moreover, the assumption is made that the variation in relative brain size (that is, the deviations from the allometric relationship between brain and body size within a taxon) is entirely due to changes in the selection factors targeting cognitive abilities. The consequence of this is that, beyond the general directions shared by the species within each of these two taxa, each species has smaller or larger brain components involved in cognition. The impact of these differences between taxa on the PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27872v1 | CC BY 4.0 Open Access | rec: 25 Jul 2019, publ: 25 Jul 2019 37
functional significance of relative brain size appears clearly in Figure 6cd. Figure 6c shows the significance of relative brain size in term of the value of the fraction of brain size most associated with cognitive abilities, in a taxon of the kind shown in Figure 6a (green, case 1) or 6b (yellow, case 2) (note that here for simplicity reasons the mean relative brain size is identical in the two taxa). As depicted in Figure 6d, if the level (threshold) of selection for cognitive capabilities has been constant within a taxon, as in the taxon represented in Figure 6a, a correlation between the relative size of the brain (compared to the body) and the level of the selection factor for the cognitive factor can be found. In contrast, when selection for increased cognitive capabilities has been a continuous feature within a taxon, as in the taxon represented in Figure 6b, a correlation between the absolute size of the brain and the fraction of the brain that is mainly dedicated to cognition can be found.
Together, these elements could help explain the differences mentioned above between haplorhine primates and carnivorans. The correlation between cognitive ability and absolute brain size in primates (e.g. Deaner et al. 2007) is evidence that the level of selection for cognitive abilities increased with brain size in this taxon. Assuming a correlation between component size and computational capacity, this indicates that larger brained primates evolved larger brain components involved in cognition compared to smaller primates. As such, (haplorhine) primates resemble the taxon represented in Figure 6b, where selection for increased cognitive ability has been a major driver of increasing brain size.
In contrast, there is no evidence that cognitive ability increases with absolute brain size in carnivorans (Benson-Amram et al. 2016). This suggests that the level of selection for cognitive ability did not consistently vary with brain size in this taxon, as in the taxon presented in Figure 6a. By assuming a correlation between component size and computational capacity, this would suggest that the brain components involved in cognition did not consistently change their size with brain size in carnivorans. Yet, all brain components, including those most involved in cognition, became larger with brain size in carnivorans (Willemet 2012). The theoretical framework presented here resolves this apparent discrepancy by suggesting that the increase in the size of the brain components has been caused by other factors, including structural ones (Figure 5a). In this kind of taxon, some species may nevertheless acquire bigger (or smaller) brain components in response to selection for increased cognitive ability. If this adaptation is shared by many species within the taxon, it can lead to a correlation between cognitive abilities and relative brain size (Figure 6acd). Indeed, Benson-Amram et al. (2016) recently suggested PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27872v1 | CC BY 4.0 Open Access | rec: 25 Jul 2019, publ: 25 Jul 2019 38
that relative brain size was correlated with cognitive abilities in carnivorans, albeit at a relatively crude level (their results might be affected by species-specific differences in the emotional, attentional and motivational processes that modulate cognition, see Willemet 2013).
Importantly, rather than increasing the size of brain components, selection on functional capabilities could theoretically lead to the adaptation of the fraction of the size of a brain component dedicated to the structural factor, perhaps by increasing the density of cells and connections. This is because the structural factor supposedly affects the size and overall structure, but not the computational capacity of a brain component; leaving room for adaptations changing the connectional properties of the components with minimal effects on their size. This hypothesis could help explain the results from a recent study examining the number of neuronal and non-neuronal cells in carnivorans. Jardim-Messeder et al. (2017) found that large carnivoran species generally do not have more neurons in their cerebral cortex compared to medium-sized species. Interpreted within the framework developed here, this suggests that the threshold of selection on the processing capacity of the cerebral cortex did not increase in species with brains larger than medium-size species. —Note that this hypothesis differs from, but is not necessarily incompatible with, the hypothesis put forward by the authors, namely that this pattern is due to metabolic constraints that would impose a trade-off between body size and number of cortical neurons (Jardim-Messeder et al. 2017).— Therefore, the other factors represented in Figure 5 should account for the majority of the change in the size of the cerebral cortex. Crucially, Jardim-Messeder et al. (2017) found that raccoons have a higher neuronal density in the brain structures tested by compared to carnivorans of similar brain size, and that dogs have the most neurons of all the carnivorans species tested. This could be two examples of the hypothesis discussed just above, namely that adaptations to functional factors (such as an increase in computational ability) can take place within the fraction of brain size that originally evolved due to structural factors.
Thus, whether absolute or relative brain size correlates with cognitive abilities, a topic that has been much debated since the origins of comparative brain studies, and that was still recently called “an open question in the field” (Navarrete et al. 2016), can be predicted by the impact the cognitive factor has had on the enlargement of the brain components (see also Willemet 2013). Indeed, the framework presented here helps explain why, for some taxa, “controlling for body mass would be equivalent to controlling for cognition” (Deaner et al. 2000). In these taxa, “controlling” for the size factor effectively cancels out the scaling of the cognitive factor. PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27872v1 | CC BY 4.0 Open Access | rec: 25 Jul 2019, publ: 25 Jul 2019 39
Importantly, the elements above suggest that the fact that some non-cognitive factors scale with body size, which suggests that the brain components concerned changed their size as well, does not mean, by itself, that brain size should be scaled to body size in comparative brain studies within these taxa. For example, as mentioned in section 4.2.1.1., sensorimotor delays (the time it takes for the brain to get sensory feedback) increase with brain size in terrestrial mammals (More and Donelan 2018). This suggests that particular brain components had to increase their size in order to maintain processing speed, and/or increase prediction ability. This would explain part of the enlargement of brain size with body size, but not all of it (note that the proportion of the brain constituted by these components might remain unchanged depending on how the size of the other components responding to their own selection pressures changed). Indeed, if part of the enlargement is due to the cognitive factor (as in the taxa represented in Figure 6b), then factoring out the effect of body size would have the side effect of removing the scaling of cognitive abilities with brain size, as shown in figure 6d. Yet, despite earlier calls for caution (Willemet 2013), the idea that the effect of body size should systematically be taken into account when considering the factors behind the evolution of brain size and composition is still widespread nowadays (e.g. Heldstab et al. 2016, Fristoe et al. 2017). The elements presented above further suggest that this practise often leads to results that are difficult to interpret. Besides, while both absolute and relative brain size may, depending on the taxon, correlate with species differences in cognitive abilities, as schematized in Figure 6, this level of analysis is very crude, and finer levels of analyses should be preferred, as discussed below.
5.2. Approaches and methods
Fundamental to the theoretical framework discussed here is the notion that it is the sum of the brain parts that determines brain size (Willemet 2013). The variable brain size is thus simply a (often bad) proxy for more significant variables and, as such, analyses at finer levels of organization should generally be favored. This interpretation is preferred to the traditional alternative, which argues instead that the size of the brain components is determined by allometric scaling. As such, species do not “adhere”, “maintain” or inversely “deviate” from a given allometric pattern that is itself understood to represent a “scaling rule”. Instead, it is the adaptations of the species within a taxon that create the allometric pattern, and what appears to be deviations from it. Both allometric patterns and the deviations from them are due to “mosaic” evolution on individual components/neural systems, as are changes between taxa. Note that there has been several other accounts aiming to divide brain size into several PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27872v1 | CC BY 4.0 Open Access | rec: 25 Jul 2019, publ: 25 Jul 2019 40
components, most often a somatic and a nonsomatic factor (e.g. Jerison 1973, Hofman 1982, Fox and Wilczynski 1986). Some of the major differences between these accounts and the one presented here are i) the hypothesis that these factors are not fixed and instead can vary with brain size, ii) the inclusion of the structural factor and iii) the fact that these questions can only be understood in a taxon-cerebrotype approach. All of this has profound consequences on the methods that can be used to study the presence and significance of adaptations in the mammalian brain, as briefly discussed below.
5.2.1. Studying allometry: scaling
In the functional constraints hypothesis, evidence of preferential selection is thought to be seen only in deviations from allometry at the species level, and in the presence of grade-shifts at the taxon level (Barton and Montgomery 2018). This is because in this hypothesis, allometry is thought to be the result of selection to maintain the functional correspondence between neural components (Montgomery et al. 2016). In contrast, in the adaptationist framework discussed here, preferential selection on the function of one component or one neural system (leading to a larger increase in size of this component/neural system compared to other components/neural systems) can be part of allometry itself if this preferential selection has been shared by most species within a taxon. A consequence of this is that the direction of the selection pressures within a taxon could, in some cases and to a certain extent (see section 3), be estimated by observing the scaling of the functional properties of a given brain component or neural system between species. For example, the sensory capacities of many species within a taxon can be measured, and this information can be used to determine what features of the brain components are associated with, and therefore potentially responsible for, the functional differences between species. This approach requires precise anatomical and functional data on many species, which are unavailable today (although the use of modern imagery tool could help address this issue, e.g. Mars et al. 2018, Navarrete et al. 2018). For example, while the structure of the olfactory bulb is relatively well known, little effort has been made to obtain precise data on species differences in olfactory abilities (with some notable exceptions, e.g. Laska and Salazar 2015). The same holds for the visual system in primates, for which the data on species differences in visual ability do not match our understanding of the visual system, limiting the analyses to the study of general relationships between variables (e.g. de Sousa and Proulx 2014). Yet, the olfactory bulb and visual system are perhaps the best choices to start with for improving, within a comparative approach, our understanding of the relationship between PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27872v1 | CC BY 4.0 Open Access | rec: 25 Jul 2019, publ: 25 Jul 2019 41
structure and function in a brain component (see for example Srinivasan and Stevens 2019). Besides, these analyses, both within and between taxa cerebrotypes, could allow researchers to examine the potential effects of the structural factors discussed in section 4.3.
As discussed in section 3.1.4., most changes taken individually are not straightforward to interpret. However, what the elements discussed above suggest is that adaptationist hypotheses should be considered before potentially dismissing them in favour of a constraint-based account. For example, consider the fact that while the proportional size of the frontal cortex increases with brain size in primates, this region exhibits a stronger decline in neuron density than more posterior regions of the cortex (Gabi et al. 2016). Interpreted within the functional constraints account discussed earlier that is based on the postulate that allometry mostly maintains the relationship between brain components, this pattern has been hypothesised to represent “a trade-off between volume and neuron densities, with steeper declines in frontal neuron density with increasing overall size compensated by steeper increases in volume” (Montgomery et al. 2016). In an adaptationist view such as the one discussed here, the same data is interpreted as suggesting that the functional properties of the frontal cortex depends both on its number of neurons and the space between them (for reasons of connectivity, e.g. Elston 2000, and/or slower maturation, e.g. Teffer et al. 2013). In the primates species that could increase their investment in neural tissue, these two traits evolved in some way that would optimize (after accounting for the structural and other non cognitive factors as well as the functional and developmental constraints) the functional properties of the frontal cortex, leading to a larger component with a lower neuronal density.
5.2.2. Studying allometry: relative component size
The patterns of selection pressures described above imply that the general direction of selection pressures is comparable between species of a taxon. However, each species is under the influence of a particular matrix of selection pressures. When the direction and/or level of the selection pressures differ from the general pattern at the level of the taxon, this leads to species- specific deviations from allometry (Figure 5 c and see for example Aristide et al. 2016). Deviations from allometry are thus indicative of species-specific adaptations differing from the general pattern of adaptations at the level of the taxon (again, in the framework discussed here, both the allometry and the deviations from it are primarily the products of adaptation).
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Even though, in some cases, the factors responsible for species-specific deviations from allometry may be particular to individual species, thus making generalizations difficult, deviations from allometry can be an essential tool to examine the selection pressures acting not only on the species of interest, but also on the taxon-cerebrotype as a whole. Indeed, by studying the relative importance of the absolute and relative size of a brain component, for example, it is possible to examine in more details the role of a particular anatomical feature on a functional system. Willemet (2015b) presented an example of such an analysis using data on the song system and repertoire size in songbirds. The analysis suggested that both the absolute and relative size of the brain components involved in song production influence repertoire size, which is what would be expected given the framework discussed here. Interestingly, a recent analysis on a related research question, the neuroanatomical basis of vocal repertoire in primates, reported seemingly incompatible results. Dunn and Smaers (2018) tested the hypothesis that a larger repertoire size in primates would require an increased voluntary control over behavioural output. They further hypothesised that such an increased voluntary control would be visible in the relative size of the association areas of the brain, compared to the sensory areas. In what appears to be support for their hypothesis, they found that the relative size of the cortical association areas, but not the absolute size, correlates with vocal repertoire size in primates. If confirmed, this result would suggest that the increase in cognitive factor with brain size in primates is much less steep than what is schematically represented in Figure 5b. In that case, there would only be limited changes in the functional capacity of association areas with absolute component size at the level of the taxon, so that species-specific increases, such as those hypothesised to have occurred in species with large vocal repertoire, would be more significant. This is indeed what is suggested in Figure 7a, as the species on the isometric line have smaller repertoire size compared to the species above. However, several other factors should be considered. First, the dataset used by Dunn and Smaers (2018) is particularly small (12 species in their main analysis), making any conclusion tentative at best. Second, vocalisation is only one way by which primates species communicate (Liebal et al. 2013), and the intentionality component can be difficult to assess (Liebal and Oña 2018a). It would be interesting to see how gestural (Liebal and Oña 2018b) and facial (Waller and Micheletta 2013) communication, in particular, compare to Dunn and Smaers’s results. Finally, those species having the smallest repertoire size also have a larger striate cortex (Figure 7b). This may have conflated Dunn and Smaers’s results, as different species may rely on different strategies.
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Figure 7. Scaling of brain components in relation to vocal repertoire size in primates (data from Dunn and Smaers 2018). a. Size of the prefrontal cortex onto the size of the striate. b. Size of the striate onto the size of the medulla. The vocal repertoire size is indicated by the size of the symbols (which is proportional to the size of the vocal repertoire) and their colour (black if vocal repertoire is equal or inferior to ten and grey for the other species). Species: "Ag": Ateles geoffroyi, "Ca": Cercopithecus ascanius, "Cm": Cercopithecus mitis, "Gg": Gorilla gorilla, "Ll": Lagothrix lagotricha, "La": Lophocebus albigena, "Mt": Miopithecus talapoin, "Pt": Pan troglodytes, "Pa": Papio anubis, "Pb": Piliocolobus badius, "Pp": Pithecia pithecia.
An important aspect of the elements discussed above is that the extent to which the changes in size of a brain component affect absolute (and thus relative) brain size depends on the relative size of a brain component. That is, although a change in size of a small component leads to an increase in its proportional size, it has little effect on absolute brain size, whereas a similar increase (in term of proportion) in a larger component will have a more pronounced effect on absolute brain size. This idea was presented in Willemet (2013), and more recently discussed by Logan et al. (2017). However, Logan et al. (2017) discussed this phenomenon by referring to the presence of different “scaling rules” between brain components, which is problematic under the framework presented here (see section 4.5.). Moreover, Logan et al. (2017), noted that “when a behavior generated by a specific brain structure is targeted by selection, the effect on total brain size will depend on the scaling relationship between that brain structure and total brain size”. In contrast, in the present framework (for simplicity reasons, the structural factors discussed in section 4.3. are not considered here), the effect that the selection of a brain component (via selection on its functions) has on total brain size depends on the importance of the change in the selection threshold and the connectional properties of this particular component (in terms of the proportions of long range, or inter-regional connections, for example). The “scaling relationship between that brain structure and total brain size” is thus PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27872v1 | CC BY 4.0 Open Access | rec: 25 Jul 2019, publ: 25 Jul 2019 44
not a factor in itself, but rather a consequence of the above-mentioned factors. In other words, the evolution of a brain component is not determined by the parameters (intercept and slope) of the allometric equations between this component and overall brain size (or another component). Instead, how a brain component evolves (determined by the strength and direction of the selection pressures acting on its functional and structural properties, as well as the intra and inter component patterns of connectivity that determine how the component responds to the selection pressures) determines the rate of changes for this component in absolute and relative terms, which can then be measured, at the level of the taxon, in the form of allometric relationships. This is one of the reasons, as developed in the sections above, why features that have the relative size expected for a given allometric pattern can sometimes be considered as adaptations.
Conclusion
“Such a vast subject could, and should, be approached in many ways, but coherence demands a synthetic theme. The theme must be accurate and fruitful; it must not be twisted to encompass more than it can explain; it must not claim exclusive rights as a unifying approach”, Gould (1966), on allometry.
Twenty years after the modern version of this debate was initiated (Finlay and Darlington 1995, Barton and Harvey 2000), and despite regular attempts to settle the issue (e.g. Striedter 2005, Herculano-Houzel et al. 2014), there is still no consensus on the factors behind the scaling of brain components in mammals. In the absence of the “synthetic theme” mentioned by Gould, researchers are compelled to integrate their results within a binary view of evolution consisting, on the one hand, of the functional constraints model (which is abusively called the mosaic model, since, as the model presented here, it is only one of the models that include mosaic evolution as its core), and, on the other hand, of the strong version of the developmental constraints model (e.g. Smaers et al. 2018, Sukhum et al. 2018, Halley and Krubitzer 2019). There seems to be multiple factors behind this lack of consensus. On the one hand, the strong version of the developmental constraints hypothesis appears to be incompatible with the evidence, found at many levels of brain organization, that the size and composition of brain components can evolve independently of changes in other components. On the other hand, the functional constraints hypothesis appears unable to fully explain the selection factors behind
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the concerted evolution of functionally differentiated neural systems. Attempts to reconcile the two views have been hampered by the assumption that concerted evolution necessarily implies either developmental constraints or the existence of “scaling rules”, and by considering mosaic evolution mostly in term of deviations from an otherwise concerted pattern (e.g. Herculano- Houzel et al. 2014, Moore and DeVoogd 2017).
In order to illustrate the new perspectives given by the adaptationist framework presented here, the discussion above has focused on the limits of the constraint-based models. However, there can be no doubt that both the developmental and functional constraints approaches are needed to get a better understanding of the factors behind the diversity of brain size and composition in mammals. Indeed, not everything in brain evolution can be considered to be adaptations, as functional and developmental constraints, as well as trade-offs between characters, are at play. However, as noted by Gould, there must be a guiding theme around which individual approaches can be articulated. In this regard, it has been suggested here that models mainly based on developmental or functional constraints, or a combination of the two, are fundamentally limited when it comes to explaining the variability of brain size and composition, and in particular the presence of the patterns of covariations between brain components seen in most taxa, and the differences between taxa. Instead, the alternative view proposed here, where functional and structural adaptations are considered to be responsible not only for the deviations from allometry, but for most of allometry itself, is worth considering. Indeed, it appears to be compatible with the data accumulated over decades of research, and it can be used to design future studies. Moreover, it does not seem to have some of the limitations of the previous accounts discussed here. Most importantly, it does not require different mechanisms for inter- and intra-taxa changes in the size of brain components, other than the existence of changes in the direction of selection pressures. Finally, it allows to make unique predictions with regard to the variability of brain size and composition, and the differences between major taxa, offering a tentative answer to a number of issues that have remained unresolved despite years of research.
Although the adaptationist framework proposed here might bring us a step closer to the synthetic theme mentioned by Gould, how much so remains to be determined. For example, strong empirical evidence regarding some elements of the framework, such as the role of selection on the structural factors of brain components, is currently lacking. Thus, much remains to be done before a comprehensive understanding of the factors underlying species PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27872v1 | CC BY 4.0 Open Access | rec: 25 Jul 2019, publ: 25 Jul 2019 46
differences in brain anatomy can be achieved. The topic is so complex, whether in the collection of the data or its interpretation, that a comprehensive answer to this fundamental question requires a dramatic increase in the frequency and scope of the collaborations between comparative neurobiologists and comparative psychologists. Two recently proposed collaborative projects (one on non-human primate magnetic resonance imaging datasets (Milham et al. 2018, and see Schotten et al. 2018), and the other on primate cognition research (Bohn et al. 2019)) represent a potentially important step in that direction. Only through a concerted effort from researchers with different backgrounds will a synthetic theme be developed, allowing us to study and better understand, the factors behind the variability of brain size and composition in extant and extinct species.
Acknowledgements: The author would like to thank the anonymous reviewers who made useful suggestions on previous drafts of this paper.
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