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Brain Weight and Life-Span in Primate Species

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Brain Weight and Life-Span in Primate Species Proc. Natl. Acad. Sci. USA Vol. 90, pp. 118-122, January 1993 Neurobiology Brain weight and life-span in primate species JOHN ALLMAN, TODD MCLAUGHLIN, AND ATIYA HAKEEM Division of Biology (216-76), California Institute of Technology, Pasadena, CA 91125 Communicated by Carver A. Mead, September 23, 1992 ABSTRACT In haplorhine primates (tarsiers, monkeys, Finally, we also sought to test a popular alternative to the apes, and humans), there is a significant correlation between brain-life-span conjecture, the rate-of-living hypothesis [see brain weight and maximum life-span when the effect of body Finch (7) for a review]. To test this theory, which is epito- size is removed. There is also a significant correlation in mized by the expression "live fast, die young," we sought to haplorhine primates between brain weight and female age at determine whether primates with basal metabolic rates that first reproduction. For strepsirhine primates (lorises and le- are higher than would be expected for their body size have murs), there are no significant correlations between brain shorter maximum life-spans than would be expected for their weight and either life-span or female reproductive age when the body size. effect of body size is removed. This lack of correlation in strepsirhine primates may be related to the fact that these MATERIALS AND METHODS primates are nocturnal and/or natives of the island of Mad- agascar, both of which conditions may reduce competition for We used maximum recorded life-span because it should resources and predation pressure. These rmdings suggest that measure, under ideal circumstances, the genetic potential for in haplorhine primates the genetic systems controlling brain longevity for each species. To obtain the maximum life-span growth are linked to the systems governing the life cycle so that data, we queried 190 zoos and research institutions through- species with longer cycles have larger brains. When the effect out the world. We received 138 responses to our queries. Our of body weight is removed, leaf-eating haplorhines have sig- queries were guided in large part by the International Species nificantly smaller brains and shorter lives than haplorhines Inventory System (ISIS), which lists primate holdings. ISIS with other diets. Harem-living haplorhines also have signifil- provides age information but unfortunately only up to 20 cantly smaller brains and shorter life-spans than troop-living years. Thus, we used ISIS locations of older primates to haplorhines when the effect ofbody weight is removed. We also direct our queries. We also obtained information from Marvin sought to test the rate-of-living hypothesis by determining Jones (registrar ofthe Zoological Society of San Diego), who whether primates with basal metabolic rates that are higher has made a long-standing practice ofrecording longevity data than would be expected for their body size have shorter from throughout the world. We sought the name, sex, date of maximum life-spans than would be expected for their body size. birth or acquisition, estimated age of wild-caught primates, Metabolic rate is not correlated with life-span or female age at and date of death for long-lived representatives of each first when the effect of size is species. We obtained maximum life-span data and brain and reproduction body removed. body weights of65 ofthese species, which are the basis ofthe data used in this paper. The quality of the life-span data is It has long been postulated that species with larger brains limited by two major factors. First, many of the long-lived tend to live longer (1-3). In a sample of 63 mammalian primates were born in the wild and their age at acquisition species, Sacher found a somewhat stronger correlation be- could only be estimated; we used only conservative estimates tween brain weight and maximum recorded life-span (r = for the age at acquisition. Second, because of improved 0.88) than between body weight and life-span (r = 0.77). husbandry, in 28 species the maximum life-span record is for Sacher's conclusion that brain weight was a better predictor a living animal. Thus, the maximum life-spans will be under- of life-span than body weight was challenged by Economos estimated for these species. All of the life-span data are for (4), who (using a different data set) found that liver and primates in captivity. There are not sufficient data to deter- adrenal weight predicted life-span about as well as brain mine the maximum life-spans for primates living under nat- weight. He concluded that the brain has no special relation- ural conditions because there are very few natural primate ship with life-span. Economos's criticism of the brain-life- populations that have been under continuous observation for span conjecture has been widely accepted (5-7). We decided long enough periods to obtain maximum life-span data. There to reexamine this issue in primate species by using a statis- also are not sufficient data to measure sexual differences in tical technique to remove the effect of body weight because life-span for a large number of species. Because of improved larger animals tend to live longer. We reasoned that if the primate husbandry and record keeping, the maximum life- brain-life-span conjecture were true, one would predict that spans we obtained were considerably higher than those primates with brains larger than would be expected for their previously reported (6, 9-11). Due to space limitations, the body size would also have longer life-spans than would be life-span records will be published separately. The maximum expected for their body size. Lindstedt and Calder (8) found recorded human life-span was obtained from the Guinness that, for a sample of mammals containing a wide range of Book of World Records (12). sizes, many life cycle parameters including maximum life- We suspected that because of small sample sizes the span in captivity and age at reproductive maturity scale with life-spans for rare species might be underreported. On ex- body weight at very similar slopes. We reasoned that if there amining this possibility, we found that there does exist a small were a relationship between relative brain size and maximum but significant correlation between life-span residuals and the life-span, there might be a similar relationship between number ofanimals ofa particular species in captivity (n = 60; relative brain size and the duration of parts of the life cycle. r = 0.289; P = 0.025), but the major axis regression slope was so close to 0 (s = 0.000120) that the effect on our data was The publication costs of this article were defrayed in part by page charge negligible. payment. This article must therefore be hereby marked "advertisement" We obtained data for adult and neonatal brain and body in accordance with 18 U.S.C. §1734 solely to indicate this fact. weights from a data base compiled by Bob Martin and 118 Downloaded by guest on September 28, 2021 Neurobiology: Allman et al. Proc. Natl. Acad. Sci. USA 90 (1993) 119 colleagues at the University of Zurich (personal communica- line and each data point was added to 1, giving a value >1 for tion). This was supplemented by brain and body weight data points that fall above the line and <1 for points that fall below from Stephan et al. (13) and from Brauer and Schober (14). the line. This value is the residual value for each species. The Data for brain structure volumes were obtained from Stephan addition to 1 was used to make all the residual values positive. et al. (13) and from Frahm et al. (15). For stages in the life We used the least-squares regression as the basis for calcu- cycle, we used gestation lengths obtained from Bob Martin's lating brain and life-span residuals because this procedure data base. We also used data on female average age at first removes the effect of body size plotted along the x axis (21). reproduction from a published list compiled by Ross (16). The For example, the human data point (cross) lies considerably weights of the heart, kidneys, liver, and adrenals were ob- above the regression for brain-body weight; this distance is tained from Altman and Dittmer (17). Data on diet and social the human brain residual. Similarly, there is a large residual organization were obtained from the book Primate Societies for human life-span relative to body weight. Thus, the (18) and from a paper by Clutton-Brock and Harvey (19). We residuals indicate that humans have both a larger brain and a obtained basal metabolic rates from an unpublished data base longer life-span than one would expect for a primate of the compiled by Bob Martin. This data set includes only those same body weight. We have sought to determine, by using a primate species in which basal metabolic rate measurements Pearson correlation, to what degree these residuals for pri- were conducted on a resting, postabsorptive adult primate in mate species are correlated. In the following discussion, N is the thermoneutral zone of ambient temperature (20). We used the sample size, r is the Pearson correlation coefficient, and SYSTAT 5.2 to assist us in the statistical analysis. P is the probability associated with the x2 test of the corre- RESULTS lation's significance. For haplorhine primates (tarsiers, monkeys, apes, and Fig. 1 illustrates the relationship between body and brain humans), the residuals for brain and life-span are correlated weight (Fig. 1A) and between body weight and life-span (Fig. (Table 1 and Fig. 2). By contrast, in strepsirhine primates 1B). The distance in the y dimension between the regression (lorises and lemurs), the brain and life-span residuals are not 4.0 r correlated.
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