(1985) 207491-509 ! Brain Size, Development and Metabolism In

WZooLLond.(A) (1985) 207,491-509

Peter M. Bennett and Paul H. Harvey* School of Biological Sciences, University of Sussex, Palmer, Brighton BN1 9QG, U.K.

(Accepted 12 March 1985)

(With 11 ﬁgures in the text)

! BrainRecent size, hypotheses development that variation and in brain metabolism size among birds in and birds mammals and result mammals from differences

Indices of embryonic and post-embryonic brain growth are deﬁned. Precocial birds and mammals have high embryonic brain growth indices which arc compensated for by low post- iin metabolicembryonic allocation indices during(with the ontogeny exception of are Homo tested. sapiens). In contrast, altricial birds and mammals have low embryonic and high post-embryonic indices. Altricial birds have relatively small brains at hatching and develop relatively large brains as adults, but among mammals there is no equivalent correlation between variation in adult relative brain sizes and state of neonatal development. Compensatory brain development in both birds and mammals is associated with compensatory parental metabolic allocation. In comparison with altricial development, precocial development is characterized by higher levels of brain growth and parental metabolic allocation prior to hutching or birth and lower levels subsequently. Differences between degrees of postnatal investment by the parents in the young of precocial birds versus precocial mammals may result in the different patterns of adult brain size associated with precociality versus altriciality in the two groups. The allometric exponent scaling brain on body size dilTcrs among taxonomic levels in birds. The exponent is higher for some parts ofthe brain than others, irrespective of taxonomic level. Unlike mammals, the exponents for birds do not show a general increase with taxonomic level. These patterns call into question recent interpretations of the allometric exponent in birds, and the reason for changes in exponent with taxonomic level.

Contents

Introduction 4-'- H y p o t h e s e s a n d t e s t s 4 9 . M a t e r i a l s a n d m e t h o d s 4 9 - W e i g h t s a n d r a t e s 4 9 ' Developmental classiﬁcation 49 A n a l y s i s 4 9 R e s u l t s 4 9 Brain size and metabolism in birds 49 Brain size and development in birds 49 Brain size and development in mammals 49 I n t e r - o r d e r c o m p a r i s o n s 5 0 Discussion 50 R e f e r e n c e s 5 0 A p p e n d i x 5 0

Present address: Department of Zoology, University of Oxford. South Parks Road. Oxford OX1 3PS

492 P. M. BENNETT AND P. H. HARVEY

Introduction The selective forces responsible for the evolution of differences in brain size among vertebr remain poorly understood. Larger-bodied species have larger brains, and this is presum*!? necessary in order to integrate the activities of their various parts. After controlling for this gen i effect of body size, a number of authors have identiﬁed certain ecological and behavioui correlates of relative brain size among mammals. These differences have often been interpr id i to mean that species with more complex behaviour patterns are selected to have h brains (Bauchot & Stephan, 1966, 1969; Pirlot & Stephan, 1970; Jerison, 1973- Ksenber/* j Wilson, 1978, 1981; Clutton-Brock & Harvey, 1980; Mace, Harvey & Clutton'-Brock I98n 1981). However, elsewhere we have pointed out that these correlates of relative brain <••« i y little evidence

II state of development ofthe young at hatching was found to be a strong'correlate of ^nation in relative brain size among birds. In this paper, we investigate recent hypotheses that varia tion in relative brain size among birds and mammals may result from energetic constraint (Martin, 1981, 1983; Armstrong, 1982, 1983; Hofman, 1983) linked to developmental process, < (Martin, 1981, 1983). processes

Hypotheses and tests The idea that brain size is somehow linked to metabolism has been encouraged by the realization that the allometric exponent linking brain to body size is around 0-75 for mammals (Bauchot, 1978; Martin, 1981; Armstrong, 1983; Martin & Harvey, 1984) and 0-56 across species of birds or reptiles (Martin, 1981). Basal metabolic rates (measured as oxygen consump. tion or heat production per unit body weight per unit time) have been found to increase with approximately) the 0-75 power of body weight within each of these taxonomic groups (Hemmingsen, 1950, 1960; Kleiber, 1961; Lasiewski & Dawson, 1967). The similarity- of the bramrbody and the metabolism:body exponents of 0-75 for mammals suggests a possible relationship between brain size and metabolism. Two recent hypotheses have been proposed to account for the relationship. The ﬁrst hypothesis is due to Armstrong (1982, 1983) and Hofman (1983), who plotted brain size against adult body metabolism across species of mammals. They found a linear relationship with a slope of one, which accords with the interpretation that adult mammals have been selected to allocate a constant proportion of their bodies' basal metabolism to the brain. But there is some scatter around the line, with primates having larger brains for their body metabolism than have ! other mammals. Both authors attempt to explain this by arguing that primates have higher cerebral metabolic rates than other mammals. Primates allocate 9-20% of their body metabolism to the brain, compared with 5% for other mammals. However, if primates have relatively large brains for whatever reason) they will concomitantly require a higher proportion of body metabolism to maintain their brains. Consequently, this argument makes no predictions about why primates (or, likewise, Homo within the primates) have relatively large brains (see also Harvey & Bennett, 1983). A second hypothesis linking brain size to metabolism in mammals has been proposed by Martin (1981, 1983), who takes into account the ﬁnding that, while the brain:body exponent is 0-75 lor mammals, it is about 0-56 for birds and reptiles. Since the basal metabolism: body size exponent X

* -' iX ■■ 1 BRA,N SIZE. DEVELOPMENT AND METABOLISM IN BIRDS AND MAMMALS 493

WSheen e in founda direct to linkapproximate between 0-75the brain for all size three of classes,an adult heand argued its own.metabolic that 'there is rate no case (Martn for

El"stead.qgl). 'it insteau, is the mothers u» metabolic i nartitinn turnover which, na both of m resources d.rect terms (through between the

nrthc adult brain (Martin, i^oj>;. ima "j^i""'j *■• r — - ^1ftoto size should scale with the 0-75 power on mother's body size and. therefore, ft 3ad" TW It second brain size prediction should is scale necessary isometrically to produce (with the an 0-75 exponent scaling of adultone) brainon neonatal size on brainadult

b0vvyhenMartin (1981) attempted to test for the predicted relationships in mammals, he found that /results were complicated by variation in the state of development ofthe young at birth For tH ' Liv c- sequencesizes, precocial of prolonged mammals intrauterine tend to have growth, relatively neonates long ofgestation precocial periods. mammals Probably (e.g

nrimates ungulates and cetaceans) have relatively larger brains at birth than do neonates oi dear mammals (e.g. insectivores and most carnivores). When the two groups were taken irately Martin demonstrated the predicted relationships between neonatal brain size adult hran size and adult body size. However, precocial mammals seemed to have relatively larger ins, both as neonates and as adults, than altricial mammals. The relationship between large lit relative brain size and precocial development contrasts with Eisenberg s (1981) ﬁnding ,hat in a different sample of mammalian species, there was no correlation between adult elat'ive brain size and the state of development of the young at birth. This difference may result from > 'tin's sample being biased by unequal representation of species from different mammalian orders. A high proportion of the precocial species in his sample are primates and species belonging to this order tend to have relatively large brains as adults. But several other orders are underrepresented in the sample and species from some of these tend to be precocial and have relatively small brain sizes as adults (e.g. Artiodactyla or Penssodactyla- see Martin & Harvey, 1984). An assessment of the generality of the correlation, or lack of correlation between precociality and large neonatal and adult brain sizes is necessary for mammals. . The ontogeny of brain development has already been examined in primates (Martin, 1983; Martin - Harvey, 1984; Harvey, Martin &Clutton-Brock, In press). A negative relationship exists between indices of foetal and post-natal brain growth. Folivorous primates have low foetal brain growth indices. Folivorous mammals tend to have lower basal metabolic rates than mammals of other dietetic groups (McNab, 1978, 1980). The suggestion that low foetal brain growth is .. associated with low adult metabolic rates lends support to Martin's theory. Furthermore, adult folivorous primates have relatively small brains. If folivorous primates do, indeed, have low \ metabolic rates, this may account for the dietary correlation which had previously been seen as lendina support to the sensory complexity interpretation of variation in relative brain size i (Clutton-Brock & Harvey, 1980). Preliminary results are in the predicted direction (see Harvey I & Benr-tt, 1983). Similarly, a relationship between diet and relative brain size in bats (Eisen- berg & Wilson, 1978) can also be accounted for by a correlation between diet and metabolic rates and another between metabolic rates and brain size (Armstrong, 1983). Martin (1981) went on to argue that, since birds and most reptiles are oviparous, in these groups it is the metabolism of the egg that provides for embryonic brain development. This led him to I Propose the following set of relationships in which MM is basal metabolic rate of the mother, P. M. BENNETT AND P. H. HARVEY BRAIN S

IHnW3MK«3t weight, and EA is adult brain weight: MM = kx.P*">* W = k2.MAf=k2.k].P°"JS ME =k3.W°"ls EH = k4.ME BA = ks.E„ from which: E, =A-6.(P°-75)0" = A-6.P°-56. Thus the exponent linking maternal bu between mother's metabolism and egg size (0-75) and that between egg metabolism and neonatal brain size (0-75), which is about 0-56. Equations (1) to (3) are fairly well established (Lasiewsk & Dawson, 1967; Rahn, Paganelli & Ar, 1975), while equations (4) and (5) which have not been tested will be investigated below. We have already pointed out that there is a need to test for relationships between brain size and state of neonatal development in mammals. A similar assessment is clearly desirable for birds Portmann (1947a) investigated variation in adult brain size in birds and found that altricial birds have relatively larger brains than precocial birds. Bennett & Harvey (1985) confirmed Portmann's finding, and concluded that it did not result from the confounding effects of associations between measures of altriciality and birds' ecological or behavioural habits. (This is the reverse association between mode of development and adult relative brain size found by Martin (1981. 1983) in his sample of mammals.) But altricial birds have been reported as havinc relatively small brains at hatching (Portmann, 19476) despite developing relatively large brains as adults; this must mean that the brains of altricial birds grow much more after hatching than do those of precocial birds. Neff (1972) studied two precocial and five altricial species of birds and found that, indeed, the brains ofthe precocial species grew much more before and much less after hatching than those of the altricial species. We examine the generality of these results below. We also investigate whether these developmental patterns are associated with variation in maternal metabolic allocation as predicted by Martin (1981). In summary, the aims of this paper arc: (1) To investigate whether developmental correlates of variation in adult relative brain size in birds can be accounted for by confounding effects of differences in adult metabolic rates. (2) To examine the partitioning of brain erowth into pre-hatching and post-hatching stages in altricial and precocial birds, and to determine whether variation in metabolic parameters is associated with these developmental patterns. (3) To examine the partitioning of brain growth in precocial and altricial mammals. (4) To find whether the reverse association between patterns of development and brain size in birds vs. mammals is free from bias introduced by unequal representation of species from different taxonomic groups.

Materials and methods Weights and rates Our data sources were as follows: bird adult brain and body weights-Crilc & Quiring (1940). Portmann (1947a); mammal adult brain and body weights-sources listed in Martin & Harvey (1984): hire! hatchling

.■ " - A N D M E T A B O L . S M I N B I R D S A N D M A M M A L S 4 9 5

"' " m mamma, aeona*Ifa* -g-**5* ■ %%2Z22Z*

.awson (1967). "£*£*£* *,°4) metabolic mtes of birds' eggs- """ "rarev Rahn & Parisi (1980), Pettil. Whitlow & Grant (1WM). **'If T-,U1980). Snyder, Black & Birchard (.982).

Down Obtain all own food present + Precocial 1 + Semi-precocial Semi-altricial +

Altricial

FlG. ,. Criteria used for the developmental classiﬁcation of avian hatches.

Developmental classiﬁcation ferp=.^n"^SClassiﬁcation oi mammalian ~ bi«h neonates -,0 was d"ys on t . 0W f ^ ^^ "*" ^ Q.

triviality (as deﬁned above) in birds and mammals.

Analysis

«n»nd. genera, the .axonom.c leve usedfor an*■£-« 8 ^ of lysls was

£S?i^r ^SftSTJ^K —'* ,e particular se, oCdata being used (e.g. see Figs 7 and 8 below). metabolism in birds, a common major axis among the For the analysis of adult brain stze on adult bart ntfatarfwnun heterogeneity of slopes developmental types was ﬁtted through the mean on both axes,ft cr r S ^ ^^ ( s e e H a r v e v & M a c e . 1 9 8 2 ) . G e n e r i c d e v i a t i o n s f r o m t e m ^ j h n c ^ ^ between developmental states were investtgated using one-way analyses variances not assumed equal). „M-unpd as ceneric deviations from the linear 1 For birds, indices of pre-hatching brain growth were obtained as gener relationship: , . _. (7) log(£//)-log(fc7) + ^-loS(^ here£„lsha,chli„gbrainweig,1,and,isma,erna,bodyweigh..AposUivedeviationdeserib=sahatch,,a8 P. M. BENNETT AND P. H. HARVEY

growth. Indices of post-hatching brain growth were obtained as M^^\^^^^^^ ^

log(EA) = log(k5) + b2. log(E„) which is the logarithmic form of formula (5) above, where EA is adult brain weight. Here a positive H • "' describes a large adult brain relative to the hatchling brain size and, therefore, a C ?> w FS kT 8r°Wth- ndTu°f f°etal and P°StnataI brain Srowth for mammals were obtain* P°"' pointsZiff fl TT"1"for each and,b'rdS' developmental C°mm0n category. maj°r The aXJS common S'°PeS slopes Were for fitted mammals through were log dfrivefrZ tran^ ' tSnl?^ e I ** eparate y to the precocial and altricial genera, while the common slopes for birds wereXivedtf ^ slopes winch also included ones for semi-altricial and semi-precocial genera "" thc < To test for the generality of the correlation between relative brain sizes of adults and th, ■

"trKH975) for *! buds yr°T' and SPCCieS Corbet & Hill W6re (1980) daSSirodent species CnfT?'5 in developmental categories among families within this order. Common major S!!!m^S£S^ developmental categor.es after ﬁrst testing for heterogeneity of slopes The c 11 I T"1 through the log mean on both axes and deviations were then c-tlcukted » P" ^ M precocial forms were tested using tf tests of association D.ffe««ice. between altricial and

Results Fig. 2 lull brai developn. ntal types. Brain size and metabolism in birds

Precocial log(^) = 0-55. log(P)- 2-40 r = 0-98 n = 28 Semi-precocial log(£/I) = 0-64. log(P)-2-62 r = 0-97 « = 30 Semi-altricial logf/-,) = 0-50. log(f)-1-25 r = 0-94 ,, = 17 Altncml log(£J) = 0-70. log(/>)- 2-42 r = 0-95 ,, = 68

Thire' fsllll^ W7h,' °" adU" b"Sal metab0lic rale for bird &»™ * Siven in Fig. 3. *m^d££ZT% PCr am°ng devel°Pmental «yi» (sec Table I) but there arc p'g "h " d,ffe.rcncesJn dev'«'ons from the common major axis line (F, s8 = 42 73 P < 0001). rt/<'"Pa"-«; ""ween developmental types al, yielded differences'^, were Li lie J. rainf relative o ?H °aSe; ?nSequentl* lhe resu" 'hat the more altricial birds hZ larger mibo £m In h° J Tf'r" n<" dUe '° the c°nf°™ding effects of variation in adul, Trainsteams for fot,arh8 their basal J"*. metabolism °l f 'T devel°P°>««' than do the more types-the precocial more genera altricial genera have larger

(0 Wis'notstynifihe T!!7 S'OPre f°r '°g adU" brain Wei8ht °n ">8 adult basal metabolic rale spec.esK2S dfre". of mammals from one,(see above). a result Now, that brain has weight been scales demonstrated on metabolic rate in with similar an exponent plots for of developmental ^^^^^^■ ,—

BRAIN SIZE, DEVELOPMENT AND METABOLISM IN BIRDS AND MAMMALS 497

i ^ o-5 >: -: \

4 1 0 1 0 2 1 0 3 1 0 * 1 0 J Adult body weight (g)

FlG. 2 lult brain weight vs. adult body weight plotted on logarithmically scaled axes for bird genera of the four devclopn. ital types. A. Altricial; t, semi-altricial; A, precocial; v, semi-precocial.

..y.-

1000 3000 Basal metabolic rate (kcal/24h)

F.G. 3. Adult brain weight vs. adult basal metabolic rate plotted on logarithmically scaled axes for bird genera of the our developmental types. •, Altricial; a, semi-altricial; a, precocial; v, semi-precocial.

■ BRAIN SIZE. uout one, and bra (depending on deve Jo body weight witl pumber of genera fo that this is the case ,ccepte 0-75 value metabolic rate on c! 0f slope with taxoi (cl.) = 0-65-0-68). 1

Fig. 4. Indices of pre- A, precocial; v, scmi-prct

The ■ artitioning i ofthe four devclopi from equation 7 are while deviations fro growth. Precocial, s growth and low lev There is a clear nej growth as represent

The partitioning toammal genera is p —

BBRAIN ne and SIZE, brain DEVELOPMENTweight scales on ANDbody METABOLISMweight with an exponent IN BIRDS of ANDbetween MAMMALS 0-5 and 4990-7

'nd ng on developmental category-see above). This implies that metabolic rate al™ «,:d,< ^weight with an exponent of between 0-5 and 0-7. Examtnatton of the data for th r of eenera for which we have measures of brain size, body si; ' thica Q-75 is the value case: We the haveaverage tested exponent for the is generality0-65, which of is this appre lowei „„r~~

ohc rate on data from 230 genera of birds. Although there is r~ * > ,'le with taxonomic level, the value across all the genera is 0-6 ,0-65-0-68) These results will be discussed in detail elsewhere.

• • •

-0-5 u °-5 Pre-hatching brain growth index

F.G. 4. Indices of pre-hatching vs. post-hatching brain growth (see text) for bird genera. ., Altricial; o. semi-altricial; precocial; ~, semi-precocial.

Brain size and development in birds The partitioning of brain growth into pre-hatching and post-hatching stages for genera of birds A the four developmental types is presented in Fig. 4. Exponents are given in Table I. Deviations from equation 7 are plotted on the abscissa which describes an index of pre-hatching brain growth, while deviations from equation 8 are plotted on the ordinate as an index of post-hatching brain growth. Precocial, semi-precocial and semi-altricial birds have high levels of pre-hatching brain growth and low levels of post-hatching brain growth, but the reverse is true for altricial birds. There is a clear negative relationship between levels of pre-hatching and post-hatching brain growth as represented by our indices.

Brain size and development in mammals The partitioning of brain growth into foetal and post-natal stages for altricial and precocial mammal genera is presented in Fig. 5. Exponents are given in Table I. Foetal and post-natal brain

.■ - —

P. M. BENNETT AND P. H. HARVEY

A & A A A Aaa A A"

Foetal brain growth index Fig. 5. Indices of foetal vs. post-natal brain growth (see text) for mammal genera. The enclosed star », Altricial; a, precocial.

w15 17 + _ 11T 14VTA-- 7A 10 ....-■-•'A16 A P 1 6 v sp 1 1 4AaA.....vT'A-A13 **5 A 8 T S A 6 0 ▲ A 4 H 4=9-63 P< 005 0-4 '"1A

Adult body weight (g) common '™T ^* ***** ^^ *** Weight ^ 0rderS °f birds °f the four developmental types. The line is the oZs 4 Pi TaX'S amT dcVcl°Pmental ,ypeS' The °rders are: '-Apodiformes, 2-pL*riformes, 3-Caprimu.gi- orme ' W PdrT'-'i TT- 6-Coraciifo™es. 7-Psittaciformes, 8-Columbiformes, 9-Charadrii- 5 aJL Pr°Ce'ar,,^rmefs- '1-Stng.formes, 12-Podicipediformes, 13-Galliformcs, 14-Falcon.Tont.cs. 21-Casuaruformes,2^C sua Hf--S' 7?22-Struth.oniformes. ,aV"0rm? 17-Gruif^nies, a. Altricial; 18-Anseriformes,r, semi-altricial; a, precocial; 19-PeIecaniformes, v, semi-precocial. 20-Sphenisciform«. Fig. 7. Adult bra common major axis ;i

' IPM IK, rtrds AND MAMMALS 501 BRAIN,« SIZE SIZE, DEVELOPMENT D Precocial AND METABOLISM genera IN have BIRDS A high levels of , are presented in the same way f/or^™Sw^ Altricial genera show the reverse, owth and low levels of post-natal bra ng owth^ Alt g } ^^ ^ Sus scrofa, in common *^g**££% and higPh levels of post-natal • "owever.it has low levels of foetal Bran g be reiated to its large erefore lies among the altncal genera nBg. S.TbKt y ^ & ^

"'" rs of foetal^characteristic brain growth of without altricial a correspondmgly mammals. **^0S3 lowkvc brain Vo. P growth. titioning of There brain *? hZ ore, convergent patterns between b,rd «£»£™£relationship between indices of •fl! i" P-°f a"^ hX^S—• the relationship is skewed by the

P£^2$£SSS£S« «*^ offoetalbraingrowth.

Inter-order comparisons srrb^»P.otsofadultbrainweighto^ .nciality among orders but th.s "^^^ relative brain size and precocity

E^^K^?"—SaT^U be dne to bias introduced by over- Station of precocial species in some orders.

Proboscidea A ..•'* ..ACetacea

Pinnipedia A.--"' ..••APerissodactyla

..••AArtiodactyla

Carnivora a-"'

Primates A A--HyraCoidea

..-•"A Marsupialia rA^P__4l4l [a^A jj_2j **=0-24 NS ▲ Insectivora

L.--& Chiroptera

0-4-. 106 3-3X106 2x10' 102 Adult brain weight (g) E,o. , A.,, -. -*. «. ^oa, ^«--S£ ~ " mammaU' ^ "" ^ '" common major axis among the two developmental types. A, Altncia P. M. BENNETT AND P. H. HARVEY

7M^° - A p 6 5 [/X'AG 5 A A | 4 2 1-1 14- /] = 0-24 NS 102 103 Adult body weight (g) Fig. 8. Adult brain weight vs. adult body weight for altricial and precocial families within the order Rodentiu Th • I" the common major axis among the two developmental types. The families are: 1-Zapodidae. 2 Hcteromvlw 3—Dipodidae, 4-Octodontidae, 5—Muridae, 6—Geomyidae, 7-Sciuridae, 8—Echymidae, 9—Cavlidae II)' .

Discussion The analyses show that, in several respects, birds and mammals exhibit convergent patterns of brain size development. Precocial birds have relatively large brains at hatching but relatively small brains as adults. In contrast, altricial birds have relatively small brains at hatching, but develop relatively large brains as adults. The partitioning of brain size development betv\ een foetal and post-natal stages in precocial and altricial mammals shows a similar pattern. However, our inter-order comparisons show that, while the relatively small-brained hatchlings of altricial birds develop larger relative brain sizes as adults, no such relationship holds for mammals. Altricial mammals produce relatively small-brained young which go on to develop adult brains that arc of similar size to those possessed by precocial mammals of equivalent body size. The negative association between indices of brain size growth before and after hatching or birth for both birds and mammals adds generality to the pattern already reported among primates (Martin, 1983; Harvey et al., In press). The negative association suggests that brain sizes exhibit compensatory development. A high level of embryonic brain growth in precocial birds and mammals is compensated for by a low level of growth subsequently, and vice versa for altricial genera. Homo sapiens provides the major exception, with a pronounced level of foetal brain growth that is not compensated for by a low level of post-natal brain growth. The development of the human brain is exceptional among precocial mammals in that high levels of brain growth arc maintained up to 12 months after birth (Martin, 1983). It is interesting to note that Harvey et al. (In press) examined the development of brain size within the wider context of life-history variation. Under the hypothesis that high investments early in development will result in beneﬁts (or lower costs) later, they investigated whether life-history compensation and evolutionary trade offs were characteristic of primates. In fact, the development of brain size provided the only example of compensation. Primates with high foetal brain growth have low post-natal brain growth indices, and vice versa (with the exception of Homo sapiens). In no other case was an example of compensation found. For example, primates producing relatively large neonates tend to wean them relatively late. This may also be the case in birds where, among closely related forms, BRAHi ,es SIZE, that produce DEVELOPMENT relatively large AND eggs METABOLISM take longer INto ﬂedge BIRDS the AND young MAMMALS after hatching 503

I vfnossible to examine Martin's (1981) hypothesis that, in birds, it is variation in maternal tiSDiTnllocation to eggs that leads to variation in hatchling brain size and, if chick bran, •o Ustant proportion of adult brain weight whatever the species, then ultimately to ! n adult brain size. Martin did not investigate the significant ditlerences in re U?Mnd adult brain sizes that exist between precocial and altricial birds. The hypothe Tl relationship between adult and hatchling brain weights (equation 5 above) was t tinn 8 above) and the common exponent among developmental categories was fou lificantly greater than one (Table 1). The common exponent scaling hatchling brain w 51 body weight was significantly lower than the predicted value of 0-56 (Table I; Fig.

0 - 1 4 — , . 001 0-1 1 Hatchling brain weight (g)

eight vs. hatchling brain weight for bird genera of the four developmental types (symbols as

. • . . . : A ■ , , , „ m o l n h n i c m i n i > r i " . K f « m r n i > f i l l ■pattern of increase in metabolism differs between precocial and altricial species. Egg metabolism in precocial species increases until about 80% of the way through incubation and then levels oil, iihilc in altricial species egg metabolism increases continuously throughout incubation (Vleck, xHoyt & Vleck. 1979. 1980). Consequently, it is not clear how these patterns of increase in egg ^metabolism re functionally related to chick brain weight and we cannot test the prediction C'fatly. (We have, however, investigated the relationship between the log of egg metabolic rate •;(ml 02/day) just prior to the increase associated with internal pipping of the egg (used as a fboseline) and the log of hatchling brain weight (equation 4). There is an exponent of one across bird genera (r = 0-98, n = 9). Unfortunately, sample sizes are not large enough to investigate ^'differences between the six precocial and three altricial genera for which we have data. We IJmphasize* Two further that difﬁculties this does facenot constitute Martin's hypothesis. a test of Martin's The ﬁrst prediction). is that altricial birds have relatively

^jnall brains at hatching but then grow to have relatively larger brains as adults than do precocial ^birds. The hypothesis, as it stands, does not seem to account for that phenomenon. The second difﬁculty is that, at certain taxonomic levels, the exponent linking the whole brain and some of its I Parts to body weight is greater than 0-56 (see Appendix). We found that birds exhibit a similar Prfationship between adult brain size and basal metabolic rate to that which has been reported for L4«nammals. Therefore, similar metabolic constraints may be operating on brain size in the two P. M. BENNETT AND P. H. HARVEY BRAIN SIZE.

groups. We examined whether there are patterns in parental metabolic allocation to develon" metabolic allocatic young that correlate with the divergent patterns of brain size growth in altricial and precoc" 1 jnammals exhibit c forms. p0st-natal stages ii The divergent pattern of brain size development between precocial and altricial birds * cXhibi' a converge associated with divergent patterns of parental metabolic allocation. Precocial birds have hi i to be i --case. levels of pre-hatching brain growth and low levels of post-hatching brain growth, while altrici-1 Itismoredifficul birds show the reverse. Maternal investment in precocial eggs is far greater than that in altrici-1 jvlevertheless, neon eggs (Vleck, Vleck & Hoyt, 1980; Carey et al., 1980). Figure 10 gives the mean deviations of bird relative to materna genera belonging to the four developmental types from the common relationship between lo > foetal investment ii caloric density and log fresh mass of egg contents. The more precocial birds tend to produce precocial mammal relatively large eggs with proportionately more yolk and higher concentrations of nutrients than greater among altri the altricial birds (Rahn et al., 1975; Ricklefs, 1977; Carey et al., 1980). This high pre-hatching pig, which has low investment in precocial eggs may result in their relatively large chick brain weights. In contrast milk energy output direct post-hatching parental metabolic investment is far greater in the more altricial birds. By Mam lis diverge l definition, precocial chicks are relatively independent soon after hatching, leaving the nest and does not result in c either foraging for themselves or being led to food. They must allocate considerable amounts of forms. Precocial m. their available energy to feeding activity and thermoregulation (Ricklefs, 1979). Hatchlings ofthe be this difference tl more altricial species remain in the nest and depend upon being supplied with high quality food neonatal devclopm until they fledge. Furthermore, while altricial hatchlings have relatively small brains, they have The ontogeny ol well developed digestive organs (Portmann, 19476; Neff, 1972), which are presumably needed to after birth or hatel process the massive quantities of food provided by the parents. This high post-hatching parental adult relative brai metabolic investment may enable the young of altricial species to develop larger brains as adults compensatory pari than precocial species of similar size. preco '1 chicks of be tha. this differe precocial mammal developmental dill precocial birds pre same brain size as continued investm altricial adult mam

We t.uink R. D. N

Semi- Precocial Armstrong, E.( 1982).. precocial Armstrong, E. (1983). I Fig. 10. Relative energy content ofthe eggs (kcal) for genera of birds from each ofthe four developmental types. The Bauchot, R. (1978). En bars represent standard errors. The data are from 23 altricial genera, 11 semi-altricial genera, 5 semi-precocial genera indices. Brain. I and 18 precocial genera. Bauchot. R. & Stephan 30: 160-196. [Hi Bauch. R. & Stephai The compensatory development of brain size before and after hatching between precocial and summary.) Bennett, P. M. & Han altricial birds is matched by compensatory patterns of parental metabolic allocation. Precocial Carey, C, Rahn, H. & development is characterized by high levels of metabolic allocation and brain size growth before Clutton-Brock, T. H. & hatching and low levels after hatching. Altricial development is characterized by high levels of Corbet, G. B. & Hill. J BRAIN SIZE, DEVELOPMENT AND METABOLISM IN BIRDS AND MAMMALS 505 tabolic allocation and brain size growth after hatching and low levels prior to hatching. As mmals exhibit convergent patterns in the partitioning of brain size growth between foetal and Qnatal stages in altricial and precocial forms, we should predict that, like birds, they also Px°hibii a convergent pattern of compensatory maternal metabolic investment. This appears t0 his more difﬁcult to obtain estimates of maternal foetal investment for mammals than for birds, xwertheless, neonates of precocial mammals have larger brains after longer gestation periods Live to maternal weight than altricial mammals (Martin, 1981, 1983), suggesting much higher foetal investment in individual neonates. Post-natal maternal investment is higher in altricial than 'recocial mammals-total milk energy output at peak lactation relative to neonatal weight is ■reater among altricial mammals (Martin, 1984). Interestingly, the aberrant precocial species, the L which has low levels of foetal brain growth and high levels after birth, also has relatively hi] milk e rgy output (see Martin, 1984), presumably to support its considerable post-natal growth. Mammals diveree from birds, however, in that this high post-natal investment in altricial young does not result in consistent differences in adult relative brain size between altricial and precocial forms Precocial mammals, unlike precocial birds, are food-dependent until they wean, and it may be this difference that leads to a lack of association between relative adult brain size and state of neonatal development in mammals and the presence of this association in birds. The ontogeny of brain size and the partitioning of parental metabolic allocation before and after birth or hatching appear to be crucial for understanding the determinants of variation in adult relative brain size in vertebrates. Compensatory brain development is associated with comp. satory parental metabolic allocation in birds and mammals. However, the self-feeding precoc. i! chicks of birds contrast with the food-dependent precocial neonates of mammals. It may be that this difference in patterns of postnatal parental investment between precocial birds and precocial mammals results in the diﬁerences between patterns of adult brain size associated with developmental differences in the two groups. The lack of continued investment by the parents in precocial birds prevents the postnatal brain development that would allow adults to achieve the same brain size as their altricial relatives. Among precocial mammals, in contrast, limited but continued investment in the young may allow continued brain growth so that precocial and altricial adult mammals have similar relative brain sizes.

We thank R. D. Martin for useful discussions. P.M.B. was ﬁnanced by a S.E.R.C. Research Studentship.

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Vleck. C. M., Vleck. D. & Hoyt, D. F. (1980). Patterns of metabolism and growth in avian embryos. Am. Zool. 20:405-416.

Appendix Brain size allometry and taxonomic level It is;- nerally accepted that the exponent linking brain to body size increases with taxonomic level (see Gould, .975). Thus, for example, among mammals the exponent for species within genera is generally thought to lie between 0-2 and 0-4 (see Lande, 1979), while that among orders within the class is about 0-75. A recent quantitative analysis conﬁrmed the general increase in exponent with taxonomic level among the mammals (Martin & Harvey, 1984), although the range is rather less than generally thought, going from about 0-5 up to 0-75 (rather than 0-2 or 0-4 to 0-75). One interpretation of this phenomenon is that body size responds more readily to selection over evolutionary time, and that changes in brain size lag somewhat behind. If this is the case, metabolic interpretations of the 0-75 exponent (Martin, 1981, 1983) would predict that the exponent might increase up to, but should never exceed, this value. Martin's (1981) interpretation ofthe scaling of brain size on body size among bird species would likewise predict it the exponent should not exceed 0-56 in this group. Changes of scaling with taxonomic level have not, to o .ii" knowledge, been systematically investigated among bird species. Here we present a summary of an analysis aimed at displaying the relevant patterns. We use the data of Portmann (1947a), the taxonomy of Morony et al. (1975), and the methodology of Martin & Harvey (1984).

Tribe Family Suborder Class Taxonomic level Appendix Fig. 1. Common major axis slopes for the whole brain and the various brain parts at different taxonomic levels across birds: + = whole brain, ♦ = hemispheres, ■ = cerebellum, • ■= brain stem, * = optic lobes. BRAIN SI/ 508 P. M. BENNETT AND P. H. HARVEY A hierarchical analysis of covariance using a major axis model (Sokal & Rohlf, 1981) was performed on the data. Data were logarithmically transformed before analysis, so that slopes are estimates of the allometric exponent. The lowest level of analysis is among species within genera, and only genera containing more than two species provided additional degrees of freedom for analysis of covariance. However, even monospeciﬁc genera are used for higher level analyses. For example, if a tribe contains 15 species with seven in each of two genera and one in a third, then the two larger genera are used to calculate the common (or average I major axis slope. But three data points are used for calculating the appropriate tribe slope—the averages o\ the i0„. transformed brain and body weights for each ofthe two seven-species genera, and the single-species point for the monospeciﬁc genus. Analyses were performed in this way at each increasing taxonomic level, so that each common slope was calculated using only data from the taxonomic level immediately below it (species for genus, genera for tribe, and so on). Our choice of taxonomic levels was determined by the distribution of sample sizes present in each. A maximum likelihood ratio with an associated x2 statistic (Harvey & Mace 1982) was employed to test for heterogeneity of major axis slopes, which together make the common slope at each taxonomic level.

Appendix Tablf. I Results of statistical analyses used to test for changes with taxonomic level in the exponent relating brain to body size. What, for example, the tribe is the level of analysis, then generic points were used lo calculate best jit lines using logarithmically transformed data. Sample sizes are given in Appendix Table II (d.f. = degrees of freedom, cl. = conﬁdence limits of common major axis slope)

Common 95°,, Brain part Taxonomic level correlation major-axis r(df.) cl.

Whole brain genus 0-97 0-57 1-89(3) 0-50 M 65 tribe 0-97 0-65 1502(10) 0-61 0 69 family 0-96 0-73 2-95 (3) 0-63-0S3 suborder 0-96 0-69 2-08 (5) 0-62-0-76 class 0-94 0-52 — 0-45-0-61 Brain stem genus 0-98 0-47 0-87 (3) 0-41-0-52 tribe 0-97 0-57 10-46(10) 0-52-0-61 family 0-97 0-62 4-66 (3) 0-54-0-70 suborder 0-96 0-59 4-37(5) 0-53-0-65 class 0-97 0 51 — 0-45-0-56 Optic lobes genus 0-97 0-41 0-61 (3) 0-36 (i 46 tribe 0-94 0-48 1212(10) 0-43 0-53 family 0-95 0-57 2-61 (3) 0-48-0-67 suborder 0-86 0-53 2-21(5) 0-43-0-65 class 0-95 0-38 — 0-33-0-44 Cerebellum genus 0-93 0-49 400 (3) 0-39-0-60 tribe 0-97 0-62 12-43(10) 0-58-0-67 family 0-99 0-74 210(3) 0-67-0-80 suborder 0-96 0-66 1-85(5) 0-60-0-73 class 0-96 0-54 — 0-48-O-61 Hemispheres genus 0-98 0-63 116(3) 0-55 0-70 tribe 0-97 0-70 9-86(10) 0-65-0-75 family 0-95 0-78 2-83 (3) 0-66-0-91 suborder 0-93 0-75 2-62(5) 0-65-0-85 class 0-91 0-56 0-46-0-67 BRAIN SIZE, DEVELOPMENT AND METABOLISM IN BIRDS AND MAMMALS 509 . results are given in Appendix Tables I and II, and the major patterns are portrayed in Appendix Fig. 1. , e major results are as follows: (1) The exponent linking brain to body size differs among parts of the brain respective of taxonomic level, the exponent increases from the optic lobes up through the brain stem, : Cerebellum and the hemispheres. (2) For each part of the brain, there is a parallel increase in exponent •ifUonomic level from the genus through the tribe to the family, followed by a subsequent decrease t' „Uo • the suborder level of analysis to the class. (3) For the cerebellum, the hemispheres, and for the whole £ato exponents are so high that 0-56 is not embraced in their 95% confidence limits.

Appendix Table II Sample size at each taxonomic level used in the analyses described in Table I Body size, whole brain and brain part data were available for the same species in each case. The X2 test for heterogeneity of slopes was performed among families where sample sizes were greater than two. The table is interpreted, for example, thus: there were data on body size, size of the whole brain, brain stem, optic lobes, cerebellum and hemispheres for 24 tribes containing more than one genus, II of which contained more than two genera, and data were available on at least one species from 70 genera

Number of Number of taxa Number of Taxonomic level taxa with > 2 subtaxa subtaxa

genus ' tribe 24 family 13 suborder 9 class I

These results do not conﬁrm the phenomenon of a general increase in slope with taxonomiclevel; although they highlight the importance ot consiaenn_niTTniHwjii» they also demonstrate that the allometric relationships of the brain components show similar patterns of change with taxonomic level; and they falsify the prediction that exponents do not exceed the 0-56 level in birds.