Cerebrotypes in Cephalopods: Brain Diversity and Its Correlation With Species Habits, Life History, and Physiological Adaptations

Here we analyze existing quantitative data available for cephalopod brains based on classical contributions by J.Z. Young and colleagues, to cite some. We relate the relative brain size of selected regions (area and/or lobe), with behavior, life history, ecology and distribution of several cephalopod species here considered. After hierarchical clustering we identify and describe ten clusters grouping 52 cephalopod species. This allows us to describe cerebrotypes, i.e., differences of brain composition in different species, as a sign of their adaptation to specific niches and/or clades in cephalopod molluscs for the first time. Similarity reflecting niche type has been found in vertebrates, and it is reasonable to assume that it could also occur in Cephalopoda. We also attempted a phylogenetic PCA using data by Lindgren et al. (2012) as input tree. However, due to the limited overlap in species considered, the final analysis was carried out on <30 species, thus reducing the impact of this approach. Nevertheless, our analysis suggests that the phylogenetic signal alone cannot be a justification for the grouping of species, although biased by the limited set of data available to us. Based on these preliminary findings, we can only hypothesize that brains evolved in cephalopods on the basis of different factors including phylogeny, possible development, and the third factor, i.e., life-style adaptations. Our results support the working hypothesis that the taxon evolved different sensorial and computational strategies to cope with the various environments (niches) occupied in the oceans. This study is novel for invertebrates, to the best of our knowledge.

-Supplementary information to: Cerebrotypes in cephalopods: brain diversity and its correlation with species habits, life history and physiological adaptations Page 4 of 61 these bands together with the absence of the fusion of the ventral structures in a single suboesophageal mass that are considered primitive features of the cephalopod brain.
However, a closer examination of the assemblage of the three bands allows one to identify various differentiated lobes that closely resemble similar structures in coleoids (Young, 1965;Nixon and Young, 2003).
During the course of its evolution, the brain of cephalopods increased its complexity becoming completely surrounded by a cartilaginous capsule in coleoids 2 . It reached the maximum agglomeration of the neural masses by being fused in a supra-and suboesophageal part and two large optic lobes (one on each side) 3 , extending laterally from the supraoesophageal mass. This occurred as a result of the addition or loss of ganglia that brought about their change in position and relative volume. It is outside the aims of this work to provide details of the structure of cephalopod nerve cells, lobes and ganglia. A number of monumental works are available from J.Z. Young and coworkers (Young, 1971;1974;1976;1977b;Messenger, 1979;Young, 1979); reviews are provided by Bullock (1965) and Nixon and Young (2003), to cite some. Supplementary Table 1 provides a schematic overview of the major divisions of the "brain" of dibranchiate cephalopods using the terminology adopted by Young and co-workers.
The nervous system of cephalopods shows a series of features that are considered to be unusual to molluscan, and invertebrate or even vertebrate standards (Budelmann, 1995;Hochner et al., 2006). These are: i. the highest degree of centralization compared with any other mollusc or invertebrate (insects excluded), achieved by the shortening of the connectives; ii. the presence of very small neurons (3-5 micron of nuclear size) acting as local interneurons; iii. the absence of somatotopy (except for the chromatophore lobes) contrary to what appears to be the case for the insect or vertebrate brain (Plän, 1987;Zullo, 2004); iv. a blood-brain barrier (an exception for molluscs; Abbott and Pichon, 1987); v. compound field potentials (similar to those of vertebrate brains); vi. an elevated efferent innervation of the receptors (e.g., the retina, the equilibrium receptor with species habits, life history and physiological adaptations Page 5 of 61 organs); vii. peripheral first order afferent neurons 4 ; viii. a large variety of putative transmitters (review in : Messenger, 1996;Ponte, 2012;Ponte and Fiorito, 2015).
Such a sophisticated central nervous system coupled with a battery of well-developed sense organs is the by-product of cephalopods' life style as voracious marine predators.
In the words of J.Z. Young: «It is perhaps even yet not realized what an enormous variety the cephalopods exhibit, inhabiting every part of the ocean, its surface, midwaters and depths, its shores and sea bottoms. Each habitat requires different behaviour. ... By examining the details of the organization of the brain appropriate to each habitat we can draw conclusions about the significance of the patterns of connectivity. … At the same time the brain provides much evidence about evolution» (Young, 1977a, p. 378).
Young and co-workers spent more than three decades collecting and sectioning samples of the brains of as many species of cephalopods as possible with the goal to produce a book describing the central nervous system, sense organs and life history of octopuses, squids, cuttlefish and their allies. Unfortunately, the impressive result of this enterprise only found light after Young's death (Nixon and Young, 2003).

Comparative Information
Nixon and Young's enormous effort (lasting 30 years) to collect and compare the "brains and lives" of cephalopods stimulated interest in this field of research. However, previous studies provided a considerable amount of quantitative data on cephalopod brains (Wirz, 1959;Frösch, 1971;Maddock and Young, 1987).
The three aforementioned publications are somewhat complementary to each other. In her "Étude biométrique du système nerveux des Céphalopodes", Katharina Wirz (1959) was the first to compare quantitative data of the brain of 34 species of cephalopods. Her pioneering study was restricted however to cephalopods of the Mediterranean Sea and, with a few exceptions (juvenile individuals were considered by the Author for: Chtenopteryx sicula, Onychoteuthis banksii, Octopoteuthis sicula, and Brachioteuthis riisei), to sub-adult and adult individuals. Frösch (1971) extended Wirz's work by with species habits, life history and physiological adaptations Page 6 of 61 calculating the volumes of the brain lobes in "Schlüpfstadien" (i.e. hatchlings) of ten species of Mediterranean cephalopods. For the species in common with Wirz, Frösch was able to comment about the variations in brain size that occur during growth. Such variations are mainly due to changes in the allometric rate of the different parts of the brain in respect to body (Young, 1963;Packard and Albergoni, 1970;Giuditta et al., 1971), but also due to modifications in the neural "structures" and in the relationship between lobes because of changes in life style with growth (Frösch, 1971). This has been also found in the following years by studying species such as Sepia officinalis (Messenger, 1973;Dickel et al., 1997;Dickel et al., 2006), Idiosepius paradoxus (Yamamoto et al., 2003), Todarodes pacificus (Shigeno et al., 2001), Amphioctopus fangsiao (at the time of the study reported as Octopus ocellatus, Yamazaki et al., 2002). Frösch (1971) further suggested that the changes in the reciprocal organization of the lobes could reflect Haeckel's theory of recapitulation. For example, in octopods the palliovisceral lobe and the higher motor centers are all relatively larger in juveniles than in adults. This, as suggested by Frösch, may be related to the planktonic (early postembryonic) phase of these species (e.g., Octopus vulgaris, Eledone cirrhosa, Argonauta argo). In addition, the larvae of A. argo have an extremely large inferior frontal lobe at the moment of hatching that is enormously reduced in the adult; again a case of a 'structural' recapitulation of an ancestral pattern. Maddock and Young (1987) assembled the largest data set available on quantitative information of the brain in cephalopods, determining the volumes of the lobes of the brain for 63 cephalopod species. In analogy to Wirz (1959) and Frösch (1971), the values were expressed as percentages of brain volume. Contrary to the two studies, Maddock and Young did not utilize only Mediterranean species. This allowed them to increase the number of families considered, almost tripling the original diversity encompassed by Wirz, and also included several deep-sea forms. Juveniles (or very young individuals) were not considered in their work. Maddock and Young (1987) depicted the brain of cephalopods as having: i. distinct degrees of complexity between species that ii. parallel the richness in behavior, and iii.
the variety of environments and niches occupied within the marine realm (for review see also Nixon and Young, 2003).
For example, their comparative overview of the quantitative measurements of the lobes revealed that benthic forms of Octopodidae differ from pelagic ones whereby brachial and inferior frontal lobes appear smaller in the latter.
Despite minor differences, the three data sets can be considered as a unicum since the quantitative measurements they provide are deduced by applying similar methods. In addition, we assume that the criteria to assess the limits and attributions of the lobes within each histological section were the same for the three studies. Finally, problems due to shrinkage are not pertinent because the relative size of the different lobes is estimated in percentages and because, if shrinkage did occur, one would not expect different parts of the brain to shrink differentially (Maddock and Young, 1987).
Supplementary Table 1 -Outline of cephalopod central nervous system A schematic outline of the central nervous system of dibranchiate cephalopods with its possible analogies to the ancestral molluscan ganglia, and the main functions ascribed to the major lobes. The set of lobes are listed in the table from anterior to posterior, dorsal to ventral essentially following Young (1971). The optic lobes are here considered as extensions of the supraoesophageal mass and not as separate lobes of the 'proper brain' (as in Wirz, 1959;Frösch, 1971;Maddock and Young, 1987), following the indications provided by Young (1971, p. 443). For the different lobes we also refer to 'functional sets' following Maddock and Young (1987; see also Supplementary Table 2). The presence/absence of lobes in decapods and octopods has been deduced from the works of Young (1971;1974;1976;1977b; and also from the information reviewed by Nixon and Young (2003). The origin of the different parts has been attributed following Bullock (1965). Finally, the function/s of the lobes is derived from Young (1971), Wells (1978), Nixon and Young (2003), and as reviewed by Shigeno et al. (2018).

Pitfalls and workaround
As mentioned in the main text, data from Frösch (1971) are not included in this study since that work was focused only on the brain volumes of hatchlings, thus not comparable to the other two papers (Wirz, 1959;Maddock and Young, 1987), which provided information on mature (?) individuals.
In the two studies, the values of brain size and relative proportions of lobe-areas appear not attributable to a given body size. In fact, neither of the two works provided clear indication of the number of individuals per species utilized in the data sets and from which the brain and body size had been deduced. In more simple terms, was the value assigned to the vertical lobe, for example, derived from a single or 100 individuals of a given species? Maddock and Young (1987) were contradictory on this point; the values of the brain volume and body size included in their data set referred to a single individual although these values were not necessarily «the same as those used in the analyses which were sometimes averaged over several individuals» (Maddock and Young, 1987, p. 741). Nixon and Young (2003) did not help in clearing up the point or in adding information, apart from making a few corrections to the percentages provided for some species.
In Wirz's (1959) data the values of the total volumes of the brain (excluding the optic lobes) and mantle lengths were reported as ranges of the smallest and largest specimen of her sample.
Due to the above, Wirz (1959) and Maddock and Young (1987) data appear to be representative of a given cephalopod species, and any attempt of an estimation of the 'average' brain in reference to a given body size for each species is not possible.
As mentioned in the main text, in cephalopods the volume of the brain (and of the single lobes within it) varies with the size and age of the individual (e.g., Packard and Albergoni, 1970;Frösch, 1971;Dickel et al., 1997;Shigeno et al., 2001;Dickel et al., 2006). In addition, there is general consensus that cephalopods do not present a 'reference' or 'type' body size at maturity, as occurs instead in many vertebrate species such as fish (e.g., Huber et al., 1997;York et al., 2019), birds (e.g., Portmann, 1947), mammals: (e.g., Stephan and Pirlot, 1970;Marino, 1998). The size of individual cephalopods is traditionally indicated as maximum length (or weight; Jereb and Roper, 2005;2010;Jereb et al., 2016); however, although their growth is assumed to be better described by a slow asymptotic function based on a von Bertalanffy equation (Guerra, 1979; see also Lipinski and Roeleveld, 1990), it is commonly accepted that the observed scattering of points from the predicted function is an index of the variability in the relationship between growth (body size) and age.
In fact, as reviewed by Forsythe and Van Heukelem (1987), a series of biotic (age, size, sex, shape, food, activity, inter-and intra-specific interactions, populational and geographical effects) and abiotic (temperature, light, salinity, water quality) factors appear to affect cephalopods' growth. Among them, temperature and food availability (and quality) appear to be the most important. Changes in temperature are important not only when seasonal changes or latitudinal variations are considered: monthly or even weekly temperature changes may have significant effects on the life history of animals (for review see Forsythe and Van Heukelem, 1987; for details see Forsythe and Hanlon, 1988). Following Dr John W. Forsythe (1993) cephalopod individuals that hatch during periods of warmer seawater temperatures (e.g., as in late spring) produce cohorts that grow faster and become larger than individuals that are born during periods of relatively colder temperatures (as in early spring). At its extreme, hatchlings born at the end of spring could even probably surpass the size of older cohorts that are hatched at the beginning of spring. This hypothesis ("Forsythe Hypothesis" or Forsythe Effect), has been validated during the last years by a series of studies (Arkhipkin, 2004;Moltschaniwskyj, 2004;Pecl et al., 2004;Semmens et al., 2004), providing strong evidence in support of the view that cuttlefish, squid and octopuses, or more generally molluscs (e.g., Lazareth et al., 2006;Killam and Clapham, 2018), are good archives of environmental changes (Richardson, 2001;Forsythe, 2004). Maddock and Young (1987) could not be aware of this problem. Notwithstanding, the authors noticed some discrepancies when comparing their data with that of Wirz (1959). For example, the volumes of the optic lobes, for the species in common between works, found correspondence for eight but not for 13 genera. Regarding two genera (Eledone and Bathypolypus) the authors noticed "serious discrepancies": Wirz calculated the optic lobes to be about five times larger for Eledone and slightly more than two times for Bathypolypus. In the words of Maddock and Young: «We have checked our figures and, finding no reason to doubt their accuracy, conclude that the differences may be due to differences in the sizes of the animals. We intend to undertake a study of brain/body sizes in Eledone and other cephalopods, which should help to clear up this point» (1987, p. 763). We found that: i. Wirz utilized individuals of E. moschata with mantle lengths ranging between 66 and 107 mm and with corresponding volumes of the brain ranging between 65.9 and 121.2 mm 3 (see p. 90 of Wirz, 1959); ii. Maddock and Young reported the brain volume without listing mantle lengths for all the species of their data set, and referring to their "reference" exemplar with a mantle length of 43.8 mm 2 (see p. 741 of Maddock and Young, 1987).
Because of the Forsythe Effect, these differences are not only ascribable to differences in size, but more importantly to differences in age (and maybe population) and to the consequent allometric changes of the cerebral masses during post-hatchling development and growth (for a general review see Gould, 1966; for cephalopods see for example: Packard and Albergoni, 1970;Giuditta et al., 1971), as noticed by Frösch (1971) and as clearly pointed out by Maddock and Young (1987; see also Nixon and Young, 2003).
The study of the evolution of the cephalopod brain and of its problematics due to allometry (for a discussion in other taxa see for example: Clark et al., 2001;Mares et al., 2005) still remains little explored. A renewed effort in this sense should be made in the light of modern approaches.
To circumvent and workaround these problems we utilize only percentages of the different parts of the brain of the cephalopod species included in both papers (Wirz, 1959;Maddock and Young, 1987) with the final aim to search for a potential correspondence between brain size (considered as cerebrotypes) and life adaptations within cephalopods (see also main text).

Building the cephalopod brain data set
In order to merge data provided by the two studies and to construct a species-brain database the following considerations were made.
'Functional' brain sets Maddock and Young (1987) grouped single brain lobes in functional sets; this resulted to be advantageous for allowing comparisons between species belonging to different taxa within the class. For example, the vertical lobe system (VERT, Supplementary Table 2) includes the superior frontal, vertical, subvertical and precommissural lobes (sensu Young, 1971;Maddock and Young, 1987). Wirz (1959), by contrast, did not group the lobes of the brain into functional sets.
We adopted the functional sets approach of Maddock and Young (1987) and determined the correspondence between the two works (Supplementary Table 2) and then the corresponding brain values (see Supplementary Table 3).
Supplementary Table 2 -Functional sets of the cephalopod brain List of the eight functional sets and corresponding cephalopod brain lobes as compiled for the two data sets (Wirz, 1959;Maddock and Young, 1987). The work of Maddock and Young (1987) is here considered prior to Wirz (1959) since the former authors introduced the concept of "functional sets" to the taxon, to represent the various lobes of the brain in various species of cephalopods. 1. The numbers in parentheses reported in this column refer to the original footnotes described by the author (Wirz, 1959) and are included herein to explain which lobes we utilized in the construction of this brain data set. 2. Wirz (1959) also lists a further column (Lobes verticaux) that reports the sum of the lobes of the vertical system, probably also including the precommissural lobe. 3. The measurements of the two papers were here combined by summing up the values of the three columns of Table VI of Maddock and Young (1987; see also Table 2.6 of Nixon and Young, 2003) with the "Lobes basaux" of Wirz (1959) to produce a single functional set: BASAL. 4. The optic gland is listed in Table II of Maddock and Young (1987), but is not included in the equivalent list of Nixon and Young (2003). 5. Following Wirz (1959), the values of the chromatophore and fin lobes were summed up together in the same functional set (here called CHRF) and not considered separately as CHROM and FINL (sensu: Maddock and Young, 1987;Nixon and Young, 2003). 6. As reported at p. 758 of Maddock and Young (1987), it should be the posterior chromatophore lobe. 7. As reported in Tableau III of Wirz (1959) includes the fin lobe in the column of the posterior chromatophore lobe (L. chrom. post.).

Calculation of the relative proportion of 'functional' brain sets
Both Wirz and Maddock and Young calculated the size of each lobe of the brain (for a given species) as percentage relative to the volume of the whole brain (i.e. the sum of the supra, peri-and suboesophageal masses). The optic lobes were, instead, considered separately and quantified, again as percentage, by comparing their size to the whole brain (taken this time only as reference). By doing so, Wirz (1959) quantified the optic lobes of the different species to be within a range between 47 and 202% (in the original work, Eledone moschata and Pyroteuthis margaritifera, respectively). In a similar way, Maddock and Young (1987) measured the optic lobes as ranging between 13 and 610% (Cirrothauma murrayi and Cranchia scabra, respectively).
We recalculated the values of the different functional sets as proportions relative to the volume of the whole brain, id est as the sum of both the masses and optic lobes.
In this way, we did not alter the order of magnitude of the data within and between functional sets and provided values that prevented overemphasizing certain variables (e.g., the volume of the optic lobes) in the standardization procedures required by the assumptions of the clustering technique (Everitt, 1993;Everitt et al., 2001).
Moreover, in order to circumvent the intrinsic differences in the brain size values of the two data sets (see 'Pitfalls and workaround' above), we arbitrarily chose the data of Maddock and Young (1987) for species in common between papers instead of calculating the average of the percentages given by the two works, as is done in common practice with other taxa.

Cephalopods' life adaptations descriptors
The variables chosen to correlate life adaptations to the cephalopods cerebrotypes were deduced from the literature following Borrelli (2007). We considered: way of locomotion, feeding habits, development, reproduction (mating/spawning), habitat (i.e. vertical and horizontal distribution). We do not consider common ancestry, molecular phylogeny, and fossil record. A future study may attempt to combine more type of data including cephalopods cerebrotypes.
We are aware that further knowledge has been acquired over the last few years (see for example: Annex 11 in ICES, 2019; Young et al., 2019), but to be consistent with the original analysis we preferred to largely refer to most of the information included by Borrelli (2007).
A brief description of each variable included in the final cephalopod species-brain database (Supplementary Table 3) and the source of information included is provided below. In the following list, we also indicate the way characters or character states were coded (binary/multistate variables).

Way of Locomotion
Buoyancy and fin morphology were considered to describe cephalopods' way of locomotion.
Fin morphology: Two descriptors of fin morphology (Fin_1, Fin_2) were here considered, following several reviews (Roper et al., 1984;Clarke, 1988; see also: Hanlon and Messenger, 1996;Jereb and Roper, 2005;2010;Jereb et al., 2016;Hanlon and Messenger, 2018). Fins (Fin_1) were coded as either absent (0) or present (1). The list of scores (numbered from 1 to 9) of the multistate variable describing the morphology of the fins (Fin_2) was arranged on the basis of fins' origin and evolution and following Clarke (1988). In brief, the fringing fins of Sepia (coded as 1) are considered the most ancestral while the elongated and flapping fins of loliginids (coded as 9) are the most recent (see Clarke, 1988 for details).

Feeding Habits
Diet breadth and beak morphology were both taken into account to describe cephalopods' feeding habits.
Diet breadth -As far as diet richness is concerned the PhD Thesis of Dr Borrelli accurately reviewed available source of data available. For the sake of this work, we deliberately avoided to extend original data.
Following Borrelli (2007), up to ten different prey categories resulted from the literature and from the CephBase database that was still fully available at that time (Nixon and Dilly, 1977;Nixon, 1987;Kear, 1992;Lu and Williams, 1994;Lordan et al., 1998;Wood and Day, 2003;. These 'prey categories' (from zooplankton, to molluscs -including cephalopods -, polychaetae worms, crustaceans, chaetognaths, sea urchins, or fish) appear to be consumed by the cephalopod species included in our database. A small number of prey species (e.g., algae, jellyfish) figured only occasionally and were lumped in the general category 'Diet_other taxa'. Since each prey item was scored following a binary coding (present/absent), the estimation of the relative diet variety of each cephalopod species was here accomplished by summing up the number of prey items that resulted per species (V_Diet in Supplementary Table 3). Thus, low scores resulted for species that are reported to have a diet restricted to certain prey items (specialists, e.g., Spirula spirula) as opposed to more opportunistic species (generalists, e.g., Sepia officinalis) that scored higher values.
As clearly stated by Borrelli (2007), it should be noted that this estimation of diet richness may have been biased by research effort, i.e. by the fact that research in cephalopods has been mainly focused on certain species, and only very recently new information has been added to current knowledge. As originally discussed by Dr Borrelli, the bias is linked to large use of the common cuttlefish (Sepia officinalis 93), the common octopus (Octopus vulgaris 91) and some squids (e.g., Doryteuthis pealeii 25) in respect to other species 8 . Whether our knowledge of diet richness of a given cephalopod species in the wild is affected or not by research effort remains to be explored (see also Villanueva et al., 2017).
Beak morphology -The buccal mass of cephalopods is a complex (roughly spherical) muscular "organ" lying just anterior to the brain and located within a sinus at the base of the arms. It is made of a pair of strong chitinous "U-shaped troughs" that are commonly termed "beak" because of their resemblance to that of parrots. The two beaks (upper and lower) are attached to one another by a series of mandibular muscles that allow their opening and closure. When fitted into each other, their arrangement creates an internal buccal cavity that comprises several parts including the radula, another typical molluscan feature (for review see: Nixon, 1998;Messenger and Young, 1999). The general plan and shape of the buccal complex is well conserved through phylogeny as results from the oldest fossil traces dated back to the Carboniferous (Uyeno and Kier, 2005). However, some differences emerge between decapods and octopods. In the words of Clarke and Maddock: «It might be expected that beak shape would be closely related to the kind of food eaten, but although little detail of cephalopod food has been published … no great differences in the food are known which would seem likely to account for the differences in lower beak shape. In absence of any clear relationship with function, one would expect their form to reflect evolutionary relationships» (Clarke and Maddock, 1988, p. 123). A systematic analysis between the food eaten by cephalopods and the specific adaptations linked to feeding (i.e. morphology of the upper and lower beaks, and radula) should be carried out. Unfortunately, a large bulk of knowledge is to date only available on the morphology of the lower beak due to the effort of Dr M.R. Clarke who edited the results of a workshop focused on the identification and description of this structure in 40 cephalopod families, thus providing information for most genera. For the aims of this study and following Borrelli (2007) we considered some of the most distinguishing characters concerning lower beak identification as deduced from Dr M.R. Clarke reference work (Clarke, 1986; for review see also Clarke and Maddock, 1988). The following ten characters were included in the database and coded as described below: i. Rostrum (R_code): very short (1), short (2), medium (3), long (4); ii. Rostrum aperture (R_width): narrow (1), moderately broad (2)

'Development'
The three variables included in the database were chosen as an index of the ecological adaptability (i.e. flexibility) of each cephalopod taxon. Species were distinguished in those developing via a paralarval stage (Onto_1 = 1) and those having a direct development (Onto_1 = 2); in the latter case (e.g., Sepia officinalis), the two other variables (Onto_2 and Onto_3) were both coded as 0. In addition, species whose paralarval stage underwent metamorphosis (e.g., Architeuthis dux) were scored as 0 for the third variable. The variables were nested and coded (see below) following the classification of Young and Harman (1988) and Nesis (1995; although other sources were also taken into consideration (e.g., Hochberg et al., 1992;O'Shea et al., 2006).
Numbers in parenthesis refer to coding adopted

Reproduction
Differences in cephalopods' mating and spawning habits were assessed by considering: the presence/absence of the hectocotylus and/or penis, the spermatophore attachment, the size, grouping and 'habit' of the eggs, the reproductive strategy and the spawning pattern. The information compiled regarding the presence/absence of the hectocotylus/penis and the way spermatophores were attached to the female was found in Nesis (1995).

Eggs -we considered:
Eggs' size (Eg_1): Cephalopods' egg size was here considered as eggs' length following the classification proposed by Hochberg et al. (1992). In particular, eggs' size was ranked into three classes: small (0.2-4 mm, coded as '1'), intermediate Numbers in parenthesis refer to coding adopted Eg_3 was scored as 0 for cephalopods that laid eggs in clusters or collective capsules.

Eggs' habit:
Finally, the habitat of eggs' deposition (Eg_4, Eg_5) was also considered among the different species 9 of the data set following the reviews of Nesis (1995) and Boletzky (1998):

Habitat
Vertical distribution -Cephalopods' distribution in the water column was coded (binary coding: 1 present, 0 absent) in the database by considering the classification proposed by Nesis (2003 Horizontal distribution -We also considered the geographical distribution as a measure of cephalopods relative dispersal and flexibility to adapt to different temperature zones. Data on distribution were derived by Borrelli (2007) from several sources (Nesis, 1987;Young and Mangold, 1996a;1999b;a;Tsuchiya, 2000;Young and Vecchione, 2001;Nesis, 2003), including the Distribution Range feature of available from the CephBase Species Database (Wood and Day, 2003;

Number of species included in the dendrogram and reasons for exclusions
Supplementary Table 3 includes data of 78 cephalopod species belonging to 41 Families and 11 Subfamilies (see Table 1 for reference). The table is aimed at collecting and making fully available for the scientific community the largest available set of data belonging to cephalopods including 'brain' and 'life-adaptation' descriptors.
The database is organized in a table counting 10764 elements: 624 values related to the 'brains' (eight brain functional sets × 78 species); one hundred-thirty rows (variables and their states) represent descriptors of life-adaptations of the species considered (see 'Cephalopods' life adaptations descriptors' above).
As thoroughly illustrated, our work is based on previous studies. It provides for the first time a representation of brain diversity in cephalopods with the attempt to find a correlation with their life-style adaptations.
Despite some limitations, the database offers a platform to explore new avenues for a comparative analysis in the study of cephalopod adaptations and novelties by focusing on brain organization, life styles and other 'ecological' variables.
An update of its content, based on recent findings and knowledge currently available, is desirable.
Based on the data available to us (see Borrelli, 2007) a few missing data were encountered: 233 'null' values are counted in the final database, corresponding to 2.3% of the whole dataset.
The cluster analysis excludes cases (i.e. species = rows of the matrix) in the occasion of missing (even) a single value in a given row and the following computations eliminate relevant case(s). Solutions are available to fill-in the missing values (imputation) or adopting marginalization, i.e. ignoring the missing data (e.g., Wagstaff, 2004). In addition, strategies have been developed for the search of the most appropriate method for handling missing data (e.g., Wagstaff, 2004;Basagaña et al., 2013;Manly and Wells, 2015;Fiero et al., 2016;Boluki et al., 2019;Hughes et al., 2019).
Despite CLUSTAN and SPSS offer the possibility of missing data estimation and/or prediction, we preferred to not adopt this strategy. Our choice was based on the fact that the data included in the set of variables here considered and the diversity of species represent a large source of variability, that is difficult to control for in cases of imputation of null values.
Thus, our decision was to adopt the most conservative approach and do not use data/species where null values occurred.
As a consequence of the above mentioned 2.3% of missing data, 26 species (out of the total number of cephalopod species included, n = 78) had missing data. This appeared restricted in a limited set (32) Table   1 and Supplementary Table 3].
In the other cases a single species, member of a given Family and/or Subfamily, resulted with missing data and excluded from the analysis.
Based on the data available, the missing species belonging to taxa included in the twenty-six Families retained in the final analysis represent only a very limited set of cases, and we assume have not biased the final outcome. In all instances a single species attributed to a given taxon was excluded because of missing data, leaving other representatives with enough numerosity (see Table 1 and Supplementary Table   3) for the grouping to be considered consistent.
In addition, the diversity of the brain functional sets belonging to these six species We cannot rule out the fact that the 26 species excluded from the cluster analysis might have provided a different grouping or the identification of additional clusters, Ponte et al. (2021) -Supplementary information to: Cerebrotypes in cephalopods: brain diversity and its correlation with species habits, life history and physiological adaptations Page 30 of 61 perhaps suggesting that the divergence in cerebrotypes of cephalopods is more marked than the one we can estimate with the data available to us.
However, our analysis suggests that in terms of completeness of species -and possible inter-species variability -the final loss appeared limited and seem to have only partially influenced the final outcome (clusters). Furthermore, the loss in terms of families/subfamilies (and related species) accounts for about 40% of the total estimated species diversity, and we have no other approaches available to fill it, apart from suggesting future studies.

Supplementary Table 3 -Species-brain and life-adaptation descriptors database
In the following pages, the species-brain database is presented in tabular form together with relative brain areas size (proportions), ecology, life history, feeding, reproductive strategies, and distribution provided for each of the 78 cephalopod species included in this study. Data are included for species (N = 52) included in the cluster analysis (cluster grouping is also included: 1-10; see also Figure 3) and for those (N = 26) excluded for lack of information (in light blue following cluster 10). For each species (rows), we report: -the percentages of each of the eight functional sets of lobes (see also Figure 1); -the way of locomotion (buoyancy and fin morphology); -the feeding habits (diet breadth and beak morphology); -the development (ontogenesis); -the reproduction (hectocotylus/penis; spermatophore attachments; size, grouping and habit of the eggs; reproductive strategy/spawning pattern); -the habitat (vertical and horizontal distribution).
For definitions, describing single characters or states of a given character, and coding (binary or multistate variables) see 'Cephalopods' life adaptations descriptors' in this Supplementary Information. In the table, Northeast Australian Shelf/Great Barrier Reef = Northeast Australian Shelf/GB Reef.  Cephalopod species and their factor scores (rounded to two digits), after regression (Gorsuch, 1983), colors (Orders) and symbols (taxa; see Table 1) coding utilized in Figure 2. Symbols for taxa in most cases refer to a given Family; in some cases, and depending on the numerosity, a given symbol is assigned to more than a single Family.

Supplementary Table 5 -Differences between 'cerebrotypes' identified by clusters
Kruskal-Wallis one-way analysis of variance and post hoc pairwise comparisons (Dunn, 1964, as adopted in the calculation procedure by SPSS) for the relative proportions of each of the eight brain regions (i.e. brain-functional sets; Supraesophageal mass: INFF, VERT, BASAL; Suboesophageal mass: BRAC, PEDAL, PALL, CHRF; Optic Lobes: OPTIC) for species grouped in ten clusters identified after hierarchical cluster analysis (species N = 52; see also Table 3 and Figure 3). In addition to the asymptotic p, we also utilized the Monte Carlo exact module available in SPSS providing a Monte Carlo (MCp) corrected probability and a 95% CI of the MCp (CIMC). In all instances MCp resulted < 0.001 (data not shown).

Supplementary Table 5.2 -Post hoc pairwise comparisons of distribution of
proportions of each of the eight brain regions considered belonging to different species grouped into clusters (Fig. 3), after Kruskal-Wallis tests. Test statistic and p values are reported for each comparison; SE and standardized test statistics are omitted (after Dunn, 1964, as adopted in the calculation procedure by SPSS). The corrected p value is indicated (dark blue) whenever necessary; significance level = 0.05 (significant p values are in boldface). Symmetrical values of pairwise comparisons are given in italics. Data resulted not-normally distributed, after Shapiro-Wilk test (Zar, 1999), in a number of cases: INFF, clusters 3 and 6; VERT, clusters 3 and 5; BRAC, cluster 5; PEDAL, cluster 3; PALL, cluster 9 (clusters 2 and 5 only marginally significant); CHRF, clusters 3 and 4 (cluster 9 only marginally significant). In all other cases Shapiro-Wilk test confirmed the normal distribution of data.

Considerations taken for the phylogenetic PCA and subsequent analysis
To attempt to control for phylogenetic dependence/independence of traits here considered (brains' diversity) and possibly ruling out bias in detecting relationships and inaccurate estimates of correlations (Rezende and Diniz-Filho, 2012), we ran phylogenetic principal component analysis (pPCA, Revell, 2009) to account for the phylogenetic relationship between species. We utilized as reference tree the multigene phylogeny based on maximum likelihood analysis published by Lindgren et al. (2012). The original tree comprises 188 taxa (see Fig. 1 in Lindgren et al., 2012), however only 38 over 78 species matched in our dataset (see Supplementary Table 6).
To derive the phylogeny to input in the "phyloPCA" (pPCA, Revell, 2009) we pruned the tree including exclusively the 38 matching species (Supplementary Figure 1).
Data for the brain functional sets were included, for the corresponding species. Following phylogenetic PCA we ran again the cluster analysis, but utilizing a further reduced dataset. In fact, from the 38 species utilized in the pPCA we excluded the ones not included in the original cluster analysis (Figure 3). The final number of species used to perform the further cluster analysis resulted to be 24. The cluster (see Supplementary Figure 2) was obtained using "pvclust" (Clustering method Ward's minimum variance method, with dissimilarities squared before clustering; Ward.D2 in "pvclust" package; number of bootstraps=100,000).

Supplementary Figure 2.
Hierarchical cluster (Ward's Method) of the 24 species found as corresponding to the list of organisms included in this study after pruning Lindgren et al. (2012) tree (see Supplementary Figure 1) and the ones included in the final dendrogram of Figure 3. The topology of the resulting dendrogram is different from the one produced by our original cluster analysis (see Figure 3), but also from the phylogenetic tree (refer to Supplementary Figure 1) and the original by Lindgren et al. (2012). See main text for details.