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Diversity in the Brain Sizes of Newborn Mammals Author(s): Mark D. Pagel and Paul H. Harvey Source: BioScience, Vol. 40, No. 2 (Feb., 1990), pp. 116-122 Published by: University of California Press on behalf of the American Institute of Biological Sciences Stable URL: http://www.jstor.org/stable/1311344 Accessed: 08/11/2010 11:01 Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at http://www.jstor.org/action/showPublisher?publisherCode=aibs. Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission. JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected]. American Institute of Biological Sciences and University of California Press are collaborating with JSTOR to digitize, preserve and extend access to BioScience. http://www.jstor.org Diversity in the Brain Sizes of Newborn Mammals Allometry, energetics,or life history tactics? Mark D. Pagel and Paul H. Harvey omparetwo newborninfants. One has a brain that will fit comfortably into a sewing thimble, whereas the brain of the other is larger than your own. This extraordinaryrangeof neonatalbrain sizes is found in differentspecies belongingto that subclassof animalsto which humans also belong, the placental mammals.At the small end of the spectrumlie a numberof rodent species, includingthe rats and mice, whose brainsweigh only a fractionof a gram at birth. Newborn African elephants (Loxodonta africana), on the other hand, have brainsweighing approximately1600 grams,although the averagebrain of a human adult weighs between 1300 and 1500 grams. What explainsthis diversityof neonatalbrainsizes acrossthe mammals? Three categories of explanation are commonlyused. Allometricexplanations interpretthe size of the neonate and its brain size as inevitableconsequencesof maternalbody size. Energetic explanationssuggestthat maternal metabolicturnoversets an upper bound to the size of the developing infant's brain. A species' neonatal brainsize,fromthispointof view, is as largeas possiblegiventhe energythat Largeneonatal brains may reduce juvenilemortality the mothercan provideduringgestation. Adaptiveexplanationsattemptto understandthe reasonswhy having a smaller or larger brain size at birth improves an individual'schances of survival.We suggestthatdifferencesin neonatalbrain size can be viewed as one exampleof a class of responses, known as life history tactics, to the differingrisks of mortality brought about by selectiveforces such as prefluctuation,and dation,environmental competition. The allometry of neonatal brain size in mammals Allometry, which was described by Stephen Jay Gould (1966) as the study of size and its consequences, refersto the findingthat many traits associated with living organisms change in predictable ways with changesin body size. McMahon and Bonner(1983) have provideda fascinating introduction to the topic. Traits are broadlydefinedto include Mark Pagelis a postdoctoralresearch physicalstructures,suchas the antlers fellow in the Departmentof Zoology, of deer, or even events relatedto the Universityof Oxford, Oxford,United timingof life, such as the age at which Kingdom,and a lecturerin biologyat a speciesmaturesor how long it lives. MertonCollege,Oxford.PaulHarveyis a SirJulianHuxley (1932) in his claslecturerin biologyat the Uniuniversity sic work, The Problems of Relative versityof Oxfordanda fellowof Merton InstiGrowth, ? American 1990 arguedthat allometricrelaOxford. College, tuteof BiologicalSciences. tionshipswere found both within and 116 among species because of common growth mechanismsregulatinga set of traits.In Huxley's view, genes that control body size also have effectson the other traits. For example, genes that control the rate of productionof a growthhormone,which determines overall body size, also influencethe weight of constituentorgans such as the heart or liver. Huxley's conclusion was that although differencesin body size were adaptiveresponsesto naturalselection,it was not necessary to give an adaptive explanation for the correlatedchange in many other characters.This line of reasoningset the course for interpretingallometric relationshipsduring the decadesthat followed and is commonly used today. Our interest is in the allometryof neonatal brain size. As with many othertraits,neonatalbrainsize differs across species accordingto a power relationship: neonatal brain size a(adult body size)b Expressingthe relationshipin terms of the logarithmsof brain and body size yields a straightline of the form log(neonatalbrain size) + log(a) blog(adultbody size) where the log(a) is the weight of the neonatal brain of an animal of unit adultbody weight (the logarithmof 1 is zero), and b is the slope of the line. Figure1 displaysthis allometricrelation for 41 differentfamiliesof mammals representing 116 species. For BioScienceVol. 40 No. 2 c 1000 0 100 .T t. ". * 10 * C o . I .1.01 '.1 1 10 100 Adult Body Weight, kg 1000 10000 Figure1. The relationshipbetween adult body weight (in kilograms)and neonatal brain weight across 41 mammalfamilies representing13 orders.Both axes are logarithmicscales. All lines in this and succeedingfigureswere fitted by regression. The correlation coefficient r = 0.94, p < 0.001. clarity, we have plotted the mean valuesof brainand body size for each family,ratherthan the individualspecies values, which show the same relationship.Familymeans are derived from the species in each family. The most striking aspect of the relationshipis how closely differences in adult body size across mammal species are matchedby differencesin neonatal brain size. There is little scatter about the line in comparison with the rangesof neonatalbrainsize and adult body size. Therefore, we can predict the size of a neonate's brain quite well from knowledge only of the adult body size for the species. On the logarithmic plot, increases in adult body size across species are associated with a proportional increase in neonatal brain size across approximately seven orders of magnitude in body size. Although our interest is in neonatal brain size, a similar allometric relationship also holds between neonatal body size and maternal body size. Perhaps the genetic and developmental forces that lead to maternal size also regulate neonatal size. Huxley would have liked that. But it is dangerous to argue that there is no adaptive reason for a character just because it seems unnecessary to give one. This caveat applies to neonatal brain size, for reasons that become clear on closer inspection of the data in Figure 1. Mammal species are often categorized as being either precocial or altricial, depending on the maturity of their offspring at February1990 birth (Case 1978). Precocial species give birth to relatively mature offspring that have their eyes open and can move around on their own shortly after birth. Altricial species, on the other hand, are less well developed at birth, their eyes may not open for days, and they cannot move around on their own. We have redrawn the data in Figure 1, fitting separate allometric lines to the precocial and altricial families (Figure 2). As with Figure 1, neonatal brain size differs consistently with adult body size, but now there are two distinct lines, one describing precocial mammals and the other altricial mammals. Each line has a slope of approximately 0.75, but the line describing precocial neonates is elevated above that for altricial neonates: 10 of the 11 altricial families lie below the line for precocial families, and 26 of the 30 precocial families lie above the altricial line. Therefore, for a given adult size, the offspring of precocial species have larger brains than those of altricial species. Thus, there is systematic variation in neonatal brain size that cannot be explained by adult body size. The allometric explanation is not a sufficient account of variation in neonatal brain size. Even without this difference between precocial and altricial species, the allometric explanation on its own is ultimately unsatisfactory. Without demonstrating the mechanism responsible for the allometry, calling a relationship allometric does little more than name an observed phenomenon. Allometry provides a useful description but not a scientific explanation. Huxley's common growth mechanism could apply, but there is no evidence that it does. We now turn to the energetic explanations for neonatal brain size as possible resolutions to these weaknesses in the allometric approach. Neonatal brain size and maternalmetabolic turnover Energetic explanations for the relationship between neonatal brain size and maternal body size suggest that the maternal basal metabolic rate places an upper bound on fetal brain size. Basal metabolic rate is usually measured as the amount of oxygen consumed by a resting animal per 1000 C 100 ._c 1 ' 10 0 1 * Precocial 0 Altricial 0 z .01 .1 10 100 Adult Body Weight, kg 1000 10000oo Figure2. The relationshipbetween adult body weight and neonatal brain weight separatelyfor precocialand altricialfamilies. Both lines have slopes of approximately 0.75, but the y-axis intercept is higherfor precocialfamilies(p < 0.001). Thus precocial neonates have larger brains for a given maternalsize than do altricialneonates. minute, and it is taken as an indica- tionof the minimalenergeticrequirementsfor maintainingand repairing tissuesandkeepingthe bodywarm. Like neonatal brain size, overall metabolic rate also increases with adult body size across mammal species according to an allometricrelationship with a slope of approximately 0.75. The positive slope indicates, not surprisingly, that larger animals require more energy per unit time. But the fact that the slope is less than one indicates that larger animals consume somewhat less energy per unit body weight than do smaller animals, in part because larger animals have a much lower ratio of body surface area to body volume, and consequently they lose much less heat per unit body weight. Robert Martin (1981) was the first to recognize explicitly the implications of the fact that both neonatal brain size and maternal metabolic rate increase with adult body size at the same rate. Martin pointed out that this relationship implies that neonatal brain size and maternal metabolic rate change in direct proportion to each other: unit increases in metabolic rate across mammal species should be accompanied by unit increases in neonatal brain size. This observation suggested that the amount of energy the mother could supply to the fetus might put an upper bound on how big its brain could grow. Martin (1983) argued that "it is the mother's metabolic turnover 117 Table 1. Allometric relationships with metabolic rate across mammalian families. The slopes relating neonatal brain weight and litter brain mass to maternal metabolic rate are similar for precocial and altricial families. However, differences between precocial and altrical families in the y-axis intercept indicate that precocial families have bigger neonatal brain weights and a greater litter brain mass for a given maternal metabolic rate. Slopes for litter brain mass are less than 1.0. All estimates are based on 28 families, 18 precocial and 10 altricial. All variables are logarithmically transformed before fitting a line of the form log(Y) = log(a) + blog(X) by the method of major axis analysis. The allometric slope or exponent is b and the y-axis intercept is log(a). Upper and lower bounds for the slopes are estimated by the 95% confidence intervals. Variable Neonatal brain weight Precocial Altricial Litter brain mass Precocial Altricial Correlation Intercept 95% Confidence interval 0.923 0.834 -2.311 -2.871 1.069 1.071 0.849-1.351 0.459-2.643 0.969 0.945 -1.417 -1.819 .875 .867 0.759-0.987 0.567-1.293 which, both in direct terms (through the physiology of gestation) and in indirectterms (throughthe partitioning of resourcesbetweenmaintenance and reproduction), determines the size of the neonate'sbrain"(p. 14). A similar explanation was offered by Hofman (1983), who suggestedthat maternalmetabolic rate, ratherthan limiting neonatal brain size directly, instead "is the principallimitingfactor of gestationtime." The evidence that Martin (1981, 1983) and Hofman (1983) used in supportof theirideaswas indirect.It is importantto test their ideas directly, becausethe relationshipbetweenneonatal brain size and metabolic rate may arisesimplybecauseeach has an independent association with body size (i.e., neonatalbrainsizes may increasewith adultbody size acrossspecies for reasonsnot directlyrelatedto the increase in metabolic rate). For Martin's and Hofman's ideas to be correct,the relationshipmust hold after controllingfor the effectsof maternal size on both variables.It must be shown that, for a given body size, species with higher metabolic rates produceoffspringwith largerbrainsor have longergestationlengths. We tested these ideas with data collectedon neonatalbrainsize, metabolic rate, gestation length, and adult body size for a varietyof mammal species (Pageland Harvey1988). Across mammal families, Martin's (1981) inferencethat neonatal brain size increasesin direct proportionto the maternal metabolic rate was borneout: neonatalbrainsize had an allometricslope of approximately1.0 against metabolic rate (Table 1) for both precocial and altricial families. But Martin'sidea did not incorporate 118 Slope the fact that most altricial species have litter sizes greater than one, whereas most precocial species have only one offspringper litter. For the energeticargumentto be correct,the total brain mass of the litter must be regulatedby maternalmetabolicrate. We calculatedlitter brain mass for each speciesas the productof neonatal brain size and litter size, then examinedits relationshipto maternal metabolicrate. The allometricslopes now fall to approximately0.85 for both precocial and altricial species, well below the requiredvalue of 1.0 (Table 1). Litterbrain mass does not keep pace with changes in maternal metabolic rate. Further, there is a differencein elevation (y-axis intercepts) of the allometriclines fitted to precocial and altricial species separately. The differenceindicates that, just as with maternalbody size, precocial species have larger neonatal brain sizes and a greaterlitter brain mass for a given maternalmetabolic rate than do altricialspecies. These results argue quite strongly againstMartin'smetabolicconstraint idea. However, we need to know whether variation in metabolic rate for a given maternalsize is associated with larger neonatal brain size and litter brain mass. It could be that precocial species have higher metabolic rates for their body sizes than altricialspecies.To removethe effects of maternalsize, we calculatedwhat we referto as relativevalues. Reconsider Figure 1: some points lie above the line, others below. The difference betweenthe actualneonatalbrainsize and the value predictedfrom the line is a measureof the relativesize of the neonate's brain for a given maternal size. Positivedifferencesindicaterelatively large-brainedoffspring, negative differences indicate relatively small-brainedoffspring. The important qualityof these relativemeasures is that they are unrelatedto body size. We calculated relative values this way for neonatal brain size, litter brain mass, metabolic rate, and gestation length. We then comparedthe differencebetween precocial and altricial families on each of these relative measures(Table2). Relativeneonatal brain size and litter brain masses were approximatelytwice as large for a given maternal size in precocial than in altricial families. Similarly, relative gestation length was greaterin precocialfamilies.But, critically,relativematernalmetabolic rate does not differ between the two groups.These comparisonsof relative values, along with the allometricresults, contradictboth Martin's(1981, 1983) and Hofman's (1983) ideas: metabolic rate for a given size bears no relationshipto neonatalbrainsize or litter brain mass either directlyor indirectly(viathe lengthof gestation). The energeticargumentsfail, then, for much the same reasons as the Table 2. Differences between precocial and altricial families after adjusting for adult body size differences. Body weight effects were removed by calculating the relative value for each variable about its best-fitting line with adult body weight (see text). Table entries are the means of the relative values for the precocial and altricial families on each variable. Standard deviations are in parentheses. Significance of the difference between precocial and altricial families is indicated by the p value from a t-test. Mean of relative values (standard deviations) Variable Neonatal brain weight Litter brain mass Gestation length Basal metabolic rate Adult brain weight Precocial 0.20 0.10 0.08 -0.04 0.002 (0.29) (0.23) (0.15) (0.22) (0.25) Altricial p value -0.21 (0.35) -0.13 (0.30) -0.16 (0.17) 0.11 (0.17) -0.005 (0.18) <0.0006 0.0123 <0.0001 0.1223 0.9291 BioScienceVol. 40 No. 2 sizes. Examples include the Delphinidae and Phocoenidae (dolphins and porpoises), the Procaviidae (hyraxes), the Chinchillidae (chinchillas), and the primate families. Families with relatively small neonatal brain sizes and short gestations include the Fel- 1.0- m ?* 0.6- co 0.2e-i a 0C~ -0.2- z -0.6 - o ,4 Ca -1.0 -0.5 Altricial 0 -0.3 -0.1 0.1 Relative Gestation 0.3 0.5 Lengtth Figure 3. Neonatal brain size versus length of gestation in 41 famillies, after removing the effects of adult tbody size from both variables. r = 0.72, p < 0.001. Thus longer gestation for a given maternal size is associated with a larger neonatal brainsize. The one extremelowroutlying dae). point belongsto the bears (Ursi( idae (cats), the Canidae (dogs, includ- ing wolves, and foxes), the Suidae (pigs), and the Erinaceidae (hedgehogs). The bears (Ursus arctos and Ursus maritimus) are an exception. These large mammals are unusual in that they have a litter of two rather than one and give birth to undeveloped offspring before or during winter hibernation. In contrast to the energetic explanation, our results suggest that, among mammals, the way to have large-brained offspring is to gestate them for a relatively long time and to have only one offspring per litter. Twenty-three of the families in our data set have offspring with brains larger than expected from maternal size (that is, they have relative neonatal brain size greater than zero). Of these, 20 are precocial families, 20 have gestations longer than that predicted for their size, and 18 have litters of one. This result suggests that the reproductive effort needed to produce relatively large-brained offspring is high and hints that, for these families, there must be (or have been) strong adaptive value associated with precociality or perhaps with large neonatal brain size, of which precociality is a consequence. allometric argument. We need to find a set of forces that can give riise to the systematic deviations from a[llometry or from the energetic pre dictions. One clue lies in Table 2, whlere relative gestation length is seen to be much longer in precocial families 7e tested than in altricial families. AW whether differences across s]pecies in relative neonatal brain size were related to differences in relati've gestation length (Figure 3). Even ^when the effects of maternal size halve been removed from both variable,s, the relationship between them remains strongly positive. That is, thie length of gestation for a given mate:rnal size is a good predictor of wheth er a neonate will have a relatively large or small brain. Separating neonatal brain size from This variable predicts differences in neonatal body size. Martin's and Hofneonatal brain size for w hich the man's predictions link neonatal brain other explanations do not account. size to maternal metabolic rate, and The relatively small neonaital brain so it has been necessary to control for sizes of altricial mamnrlals are maternal body size in our analyses. matched by relatively short gestation This reservation leaves open the poslengths: when relative neonaltal brain sibility that all our results for neonasize is plotted against relative gesta- tal brain size apply equally well to tion length, altricial and precocial neonatal body size: mammals that families lie on the same line . In con- produce relatively large-brained offtrast, across families relative neonatal spring may also produce relatively brain size bears no relatio)nship to large-bodied offspring. And in fact relative metabolic rate (Fiigure 4). they do. Therefore, to control for offspring Similar results were found 4substitutanaling relative litter brain mass for rela- body size we repeated the above of neonatal values relative both yses in size brain tive neonatal using brain size, gestation length, and metgraphs. Families with relatively 1large neo- abolic rate against neonatal body size natal brains and long gestati<ons cover rather than adult body size. The anala range of species with diffeirent body yses showed that, just as before, relaFebruary 1990 1.0- ._, m - 0.2 rn -0.2 z U ?J 0 0 13 . ., co m * 0.6- 0 -0.6 QC .1 n . -0.5 O Relative . . . -0.3 I 0 Precocial 3 Altricial -0.1 Basal 0.1 0.3 . 0.5 Metabolism Figure4. Neonatal brain size versus maternal metabolic rate (data available for 28 families)after removingthe effectsof adult body size from both variables(see text). r = 0.23, p > 0.25. Thusthereis no tendencyfor higher metabolic rate for a given size to be associated with larger neonatalbrain size. tive neonatal brain size (for a given neonatal body size) is associated with relatively longer gestations but not relatively higher metabolic rate. However, mammals with large neonatal body sizes for their neonatal brain sizes do not have longer gestation lengths or higher metabolic rates. Relatively long gestation lengths, then, are associated with relatively large neonates, and in particular neonates with larger brain size. This result combined with the evidence for the higher investment per offspring (that is, longer gestations and smaller litters) in precocial families demands explanation. We now turn to what we will call the adaptive explanation for neonatal brain size. Adaptive explanations: mortality differences Darwin (1859) wrote, "every being, which during its lifetime produces several eggs or seeds, must suffer destruction during some period of its life . . . otherwise, on the principle of geometrical increase, its numbers would quickly become so inordinately great that no country could support that product. Hence as more individuals are produced than can possibly survive, there must in every case be a struggle for existence, either one individual with another of the same species, or with individuals of distinct species, or with the physical conditions of life." Darwin reports calculating that, in the absence of 119 mortality, even the slow reproduction of the elephant could result in 19 million descendants from one original pair over only 750 years (in fact, the sums were a little wrong, but it was rare for Darwin to report any mathematical calculations). The adaptive approach that we describe in this section looks to the struggle for existence as an explanation of why the world is not overrun with elephants. All individuals face risks imposed by such mortality factors as predation, environmental fluctuation, and competition. Some of the characteristics of species are evolutionary responses to these risks. We argue that relative neonatal brain size is one of a suite of traits that a species evolves to counter the threat of a relatively high rate of mortality among juveniles. The length of gestation is able to explain why some animals have relatively larger neonates than would be predicted for a given maternal size or metabolic rate. Gestation length is one of a group of variables concerned with the timing of life that evolutionary biologists refer to collectively as life history variables. Other life history variables include the age at which offspring are weaned, the ages of sexual maturity and first reproduction, the length of time between reproductive attempts, and life-span or longevity. Life history variables tend to correlate with each other across species, such that species can be arranged Proboscidea 10 Chiropterao 2i: Lagomorpha C?t. ,, . . . . . . . . 100 Gestation Length,days . . . . . . . 900 Figure5. One exampleof the fast-slowlife history continuum in mammals. Points correspondto the mean values of gestation length and age at maturity for 15 orders of mammals. Shorter gestation lengthsare associatedwith earlierages at maturity.r = 0.90, p < 0.001. The point in the upper right is elephants (Proboscidea). Rabbits and hares (Lagomorpha) have the shortest gestation. Bats (Chiroptera)are in the middle. 120 along a fast-slow continuum describing their pace of life. Species with short gestation lengths also typically have early ages of maturity, short intervals between reproductive attempts, and short life-spans. For example, many small rodents reach sexual maturity within weeks of birth, and they may produce several litters of up to ten offspring in a single year. Elephants, on the other hand, may not reach sexual maturity until 14 years of age, and thereafter females will have one offspring every four years at best. Figure 5 illustrates the fast-slow continuum using the relationship between the length of gestation and age at maturity for 15 orders of mammals (data from Read and Harvey 1989). The small rabbits and hares (Lagomorpha) are at the fast end; carnivores, primates, chiroptera (bats), and artiodactyls (grazing species such as deer, buffalo, and giraffes) are near the middle; and elephants (Proboscidea) are at the slow end. The fast-slow continuum in Figure 5 roughly corresponds to a body size continuum as well, and so the variation in life histories might be explained allometrically, that is, as a consequence of size. However, recent work by Read and Harvey (1989) shows that the fast-slow continuum holds independently of size. Figure 6 plots the same two life history variables, but this time corrected for body size using the method of relative values. Now the bats define the slow end and elephants are closer to the middle. This change indicates that forces operating somewhat independently of size are causing animals to lead their lives at a pace either relatively speeded up or relatively slowed down. Life history theory attributes the fast-slow continuum to the differing rates of mortality that different species suffer (Harvey et al. 1989). For example, individuals in a species with high mortality that delay reproduction may face death before they leave any offspring. Natural selection will favor individuals that reproduce early in such species. Species with much lower rates of mortality, on the other hand, can afford to slow down their pace of life. But why should they? That is, why shouldn't an elephant go right on producing tiny offspring just as quickly as does a rabbit? Apart from the fact that there are probably upper limits to how fast individuals can develop, individuals may pay high costs for fast reproduction. For example, there may be higher mortality rates of small offspring from large litters than of large offspring from small litters, because the parents are not able to invest as much time and energy in each individual. The efficiency of reproduction may also improve with age, meaning that younger individuals that reproduce early are somehow not as good at raising offspring as animals that delay reproducing to a later age (Partridge and Harvey 1988). Thus, when mortality rates are low, animals that slow down reproduction and invest heavily in fewer offspring may actually be at an advantage over those that do not slow down. The pattern of high maternal investment (slow reproduction, small litters) characterizing species with large neonatal brain sizes suggested to us that species differences in relative neonatal brain size might be viewed as the results of different life history tactics, rather than as consequences of metabolic constraints (Pagel and Harvey 1988). We are interested, then, in whether variation in relative gestation length and relative neonatal brain size relate to differences in the patterns of mortality among mammal species. We did not have sufficient data on mortality patterns in mammals at the 0.5>. 0.3' Chiroptera a3 Proboscidea 0.1 . : r) 5' 0 -0.1 Lagomorpha am U. Vi -0.3' -__.X -0.5 , -0.3 Relative . ... -0.1 . 0.1 Gestation . 0.3 .. 0.5 Length Figure6. The fast-slowlife historycontinuumfor 15 ordersof mammals(seeFigure 5) after removingthe effectsof body size from both variables. Even for a given adult body size, longer gestationsare associatedwith later ages of maturity.r = 0.60, p < 0.05. Now bats (Chiroptera) occupy the upper right or slow end, and elephants are closer to the middle. The one verylow outlyingpoint belongsto the orderScandentia,the tree shrews. BioScience Vol. 40 No. 2 0.6 : 0.4- . -J c ? 0.2 o C 0.0'- . 'm -0.2. ^\ . . * h -u.5 A -0.55 -0.35 -0.15 Relative 0.05 .025 '045 Mortality IRate Figure7. The relationshipbetweenrateof mortalityin a speciesand lerigth of gestation in 18 mammalfamiliesafter removing the effects of adult bo(dy size from both variables. Species suflfering higher rates of mortality for a givrensize have shortergestations.r = -0.7 1, p < 0.01. time of our earlier research to test this conjecture, but they have since become available for 18 of the families in our data set, representing 10 orders (Promislow and Harvey in press). Not surprisingly, larger species suffer lower rates of mortality. In fact, body size is a good predictor of the shape of mortality curves. We compared each species' actual mortality pattern with what we might expect for a typical species of that body size. The difference between a species' observed mortality over time and its expected mortality for its size constitutes a measure of relative mortality from which size effects have been removed. A positive difference indicates higher mortality than expected and a negative difference indicates lower mortality than expected. If variation among species in rates of mortality is the reason for the evolution of species' differences in gestation length, and ultimately of variation in neonatal brain size, then species with relatively high rates of mortality should have relatively short gestations and produce neonates with relatively small brains. We tested whether the length of gestation for a particular body size is related to the rate of mortality by correlating relative gestation length with relative rate of mortality (both measures corrected for body size). The relationship (Figure 7) was strongly negative, indicating that, for a given body size, species suffering higher rates of mortality had shorter gestations. Relative rates of mortality also correlate with relative February1990 neonatal brain size (Figure 8), such that higher mortality for a given adult size is associated with a smaller relative neonatal brain size. These results provide strong support for the adaptive explanation in terms of life history tactics: species have evolved relatively shorter gestations and smaller-brained neonates apparently in response to high rates of mortality, and other species have evolved longer gestation lengths with larger-brained neonates in response to lower rates of mortality. Patterns of mortality are able to explain variation in gestation length and neonatal brain size that cannot be accounted for by either body size or metabolic rate. .N C0 Co 0.75 U ._ EU 0.25' U * - o n~~~~~ -0.25' U a) z . -0.75 a I-1.25 -0.55 I. -0.35 . . -0.15 Relative ~ ~ 0.05 0.25 0.45 Mortality Rate Figure8. The relationshipbetweenrateof mortalityin a species and neonatalbrain size after removing the effects of adult body size from both variables. Species sufferinghigherratesof mortalityfor their size produceneonates with smallerbrain sizes. r = -0.60, p < 0.05. The one extremelow outlyingpoint belongsto the Why neonateswith largebrains?Life bears (Ursidae,see Figure3). history theory makes clear the advantage of faster reproduction in the face of increased mortality, but why among the precocial families we inshould slower reproduction result in vestigated. neonates with large brains for their If Horn is right, natural selection body size? Here we must speculate on will favor adaptations that improve alternative scenarios in the absence of juvenile survivalin these groups. Inempirical evidence to distinguish be- creasedparentalcare is one such adtween them. aptation, and we suggest that inThe first explanation is nonadap- creased neonatal brain size (and, tive. Fetal brain growth during gesta- more generally, precociality) is antion is faster relative to body growth other. It may be particularlyimporthan after birth. If longer gestation tant for an offspring of a precocial prolongs the period of fetal brain speciesto be able to get up and move growth, then neonates will have rela- aroundon its own shortly after birth tively large brains for their body sizes, to avoid predationor to stay near its merely as an allometric consequence mother. And such actions may reof longer gestations. This explanation quire a better-developed brain at might be correct but, if it is, it needs birththan is found in altricialspecies. also to show that there is no extra Furthersupport for this idea comes cost to the mother in producing neo- from the observation that it is only nates with larger brain sizes. If there earlyin life that precocialspecieshave is a cost and the larger brains have no largerbrainsfor their body sizes than specific function, then they should altricialspecies(Table2; see also Bennett and Harvey 1985). Altricialand disappear over evolutionary time. The second explanation views in- precocial mammal species of similar creased neonatal brain size as an body sizes do not differin their adult adaptive response to the same forces brain sizes. that lead to increased length of gestation. Horn (1978) argues that species Conclusions that delay reproduction to produce large offspring and small litters are Allometricand energeticexplanations typically those that experience a dis- for variationin neonatalbrainsize in proportionately greater part of their mammals lack a theoretical foundamortality as juveniles rather than as tion for explainingwhy some species adults. Horn was referring to the show systematicdeviations from the mortality that organisms suffer due to trendsthey predict. Our adaptiveexsuch external factors as predation. planationbased on life historytheory Although our data on juvenile versus suggests that the increasedgestation adult mortality are limited, we found lengths and larger neonatal brain relatively high juvenile mortality sizes of some mammal species func- 121 Call for Nominees for the 1990 AIBS Distinguished Service Award Since 1972 the AIBS Distinguished Service Award has been presented to individuals who have contributed significantly in the service of biology. The principal criteria for this award are that the recipients shall have made an outstanding contribution toward: * advancing and integrating the biological disciplines, * applying biological knowledge to the solution of world problems, and * introducing pertinent biological considerations that improve public policy and planning. Emphasis is placed on distinguished service. Scientific discovery per se is not included as a criterion for this award, although some nominees will carry this distinction as well. Previous recipients of the award have been: * 1972-Harve Carlson, George Miller, Detlev Bronk * 1973-Theodore Dobzhansky, Rene Dubos * 1974-James G. Horsfall * 1975-W. Frank Blair, Theodore Cooper * 1976-Paul B. Sears, Edward O. Wilson * 1977-Paul J. Kramer, Elvin C. Stakman, William C. Steere * 1978-Eugene P. Odum, Howard T. Odum, George Gaylord Simpson * 1979-Theodore C. Byerly, H. C. Chiang, Lee M. Talbot * 1980-Arthur D. Hasler, A. Starker Leopold, Ruth Patrick * 1981-Peter H. Raven 0 0 1982-George M. Woodwell 1983-Karl Maramorosch 1984-Arnold B. Grobman * 1985-Sayed Z. El-Sayed * 1986-Garrett Hardin * 1987-Perry L. Adkisson * 1988-Donald E. Stone * 1989-Alfred E. Harper * 1990-Gene E. Lilzens AIBS members are invited to submit nominations for this award, which will be presented at the 1991 Annual AIBS Meeting, San Antonio Convention Center, San Antonio, Texas. Each nomination must be accompanied by a complete curriculum vitae and a statement of the individual's service to the biology profession. In particular, the supporting statement should highlight the nominee's accomplishments in each of the three award criteria given above. Nominators should note that traditional academic vitae often omit contributions to public affairs. As this area is considered equally important in the overall consideration, care should be taken to bring out the nominee's relevant accomplishments. Since 1981, recipients have been limited to single individuals, but nominations will remain active for three consecutive years, e.g., for the 1991, 1992, and 1993 awards. Send nominations (with biographies) to the AIBS Executive Director, 730 11th Street, NW, Washington, DC 20001-4521, by 1 October 1990. tion to reduce the risk of mortality during the juvenile period. References cited Bennett,P. M., and P. H. Harvey.1985. Brain size, developmentand metabolismin birds and mammals.J. Zool. Lond. (A)207: 491509. Case, T. J. 1978. On the evolutionand adaptive significanceof postnaitalgrowthratesin terrestrialvertebrates. Q. Rev. Biol. 53: 243-282. Darwin, C. 1859. On the Origin of Species. Harvard University Press (1964 facsimile edition),Cambridge,MA. Gould,S. J. 1966. Allometryand size in ontogeny and phylogeny.Biol. Rev. 41: 587-640. Harvey,P. H., A. F. Read, and D. E. L. Promislow. 1989. Lifehistoryvariationin placental mammals:unifyingthe datawith theory. Oxf. Surv.Evol. Biol. 6: 13-31. Hofman,M. A. 1983. Evolutionof the brainin neonatal and adult placental mammals.J. Theor.Biol. 105: 317-322. Horn,H. S. 1978. Optimaltacticsof reproduction and life history.Pages272-294 in J. R. Krebs and N. B. Davies, eds. Behavioural Ecology:An EvolutionaryApproach.Blackwell, Oxford, UK. Huxley, J. 1932. The Problems of Relative Growth.Dial, New York. Martin, R. D. 1981. Relative brain size and metabolicrate in terrestrialvertebrates.Nature293: 57-60. . 1983. Human brain evolution in an ecologicalcontext. 52nd JamesArthurLecture on the Evolutionof the Human Brain. AmericanMuseumof NaturalHistory,New York. McMahon,T. A., and J. T. Bonner.1983. On Size and Life. Scientific American, New York. Pagel, M. D., and P. H. Harvey. 1988. How mammals produce large-brainedoffspring. Evolution42: 948-957. Partridge,L., and P. H. Harvey. 1988. The ecological context of life-historyevolution. Science241: 1449-1455. Promislow, D. E. L., and P. H. Harvey. In press. Livingfast and dying young: a comparative analysis of life history variation amongmammals.J. Zool. Read, A. F., and P. H. Harvey. 1989. Life historydifferencesamongthe eutherianradiations.J. Zool. 219: 329-353.