How Our Ancestors Broke through the Gray Ceiling Comparative Evidence for Cooperative Breeding in Early Homo
Abstract
The “expensive brain” framework proposes that the costs of an increase in brain size can be met by any combination of increasing the total energy turnover or reducing energy allocation to other expensive functions, such as maintenance (digestion), locomotion, or production (growth and reproduction). Here, we explore its implications for human evolution. Using both comparative data on extant mammals and life-table simulations from wild extant apes, we show that primates with a hominoid lifestyle face a gray ceiling that limits their brain size, with larger values leading to demographic nonviability. We argue that cooperative care provides the most plausible exaptation for the increase in brain size in the Homo lineage.
For a change in any character to be adaptive, it must bring a net fitness benefit relative to the ancestral state. To explain the evolution of larger brains, many hypotheses have been devised that focus on the adaptive benefits without considering the costs (e.g., Dunbar 1998). Here, following the early proponents of an energetic viewpoint (e.g., Aiello and Wheeler 1995; Martin 1981), we argue that the high costs of brain tissue relative to those of other organs (Rolfe and Brown 1997) should also be considered because they may limit the net benefits to those situations where the survival benefits of larger brains outweigh the demographic consequences of the increased allocation of energy. Indeed, given that absolute brain size is tightly correlated with overall cognitive performance (Deaner et al. 2007; Reader, Hager, and Laland 2011), most lineages would be able to derive a great variety of cognitive benefits from larger brains (e.g., Shettleworth 2010), suggesting the possibility that the ability to overcome the costs may in fact be limiting and thus may explain most of the brain-size variation in homeothermic vertebrates.
The “expensive brain” framework notes that evolutionary increases in brain size can be paid for in two complementary but nonexclusive ways (fig. 1): (i) by increasing energy turnover or (ii) by reducing allocation to other targets, such as maintenance, locomotion, and production (Isler and van Schaik 2009a). This framework can be applied to hominin evolution. Early Homo is associated with the first increase in brain size among hominins outside the range of brain sizes found among great apes (Schoenemann 2006). This increase in brain size has no doubt brought various cognitive benefits, perhaps to do with tool use or cooperative hunting or other forms of cooperation. The question pursued here, however, is how the increasing encephalization could be afforded (Aiello and Key 2002; Aiello and Wells 2002; Leonard et al. 2003).
Expensive brain framework. From an ultimate perspective, any increase in brain tissue must be paid for either by any combination of increased energy turnover or by reduced energy allocation to other expensive body functions.
Thus, following the first pathway, a part of the brain-size increase in early Homo may be attributed to an increase in metabolic turnover. Supportive evidence comes from the finding that the positive correlation between basal metabolic rate (BMR) and brain size is most pronounced in primates (Isler and van Schaik 2006b). Although the BMRs of humans and chimpanzees are similar and near the value predicted from the Kleiber line for their respective body mass (Kleiber 1961), humans exhibit a higher percentage of body fat compared with most primates (reviewed in Wells 2006), and thus BMR relative to lean body mass is likely to be higher than in chimpanzees (Aiello and Wells 2002). In addition, there is growing evidence for a pronounced difference in daily energy expenditure between humans and great apes (Pontzer 2012; Pontzer et al. 2010).
Environmental conditions should affect the potential reaction space for stabilizing the energy throughput on a higher level. Increased metabolic turnover may only be possible in habitats that allow for a continuous food supply. Thus, when periods of unavoidable food scarcity recur, we expect most species to be forced to evolve smaller brains than their sister taxa in less seasonal environments. Indeed, we found that seasonality in food (and hence energy) intake is negatively correlated with brain size in strepsirrhine and catarrhine primates (van Woerden, van Schaik, and Isler 2010; van Woerden et al. 2012) as predicted by the expensive brain framework. Work on birds, however, had earlier suggested that habitat seasonality imposes selection on increased brain size (e.g., Sol 2009), a view known as the “cognitive buffer” hypothesis. This effect was also found among catarrhine primates in that relatively large-brained species show a larger difference between the seasonality of their habitat and the annual variation in food intake (van Woerden et al. 2012). Nevertheless, the relationship between relative brain size and habitat seasonality is neutral, indicating that the cognitive buffering may at best level out the energetic constraint (van Woerden et al. 2012).
The habitats invaded by early Homo were clearly more seasonal than the gallery forests, lacustrine edges, and woodlands inhabited by their ancestors (Potts 1998; Reed 1997). From the comparative evidence, we tentatively conclude that the increasing habitat seasonality was an important selective force in the early Homo lineage, although the primate data suggest that at this point it had not yet led to an increase in brain size. Rather, increasing habitat seasonality may have shaped the unique human combination of storing body fat in combination with cognitive solutions to survive irregular starvation periods (Navarrete, van Schaik, and Isler 2011; see also Kuzawa 1998; Wells 2010).
Turning to the second pathway, are there trade-offs between the brain and other expensive body functions that may explain early human encephalization? In a classic study, Leslie Aiello and coworkers proposed that energetic effects on human brain size were mainly linked to reduced allocation to intestinal tissues because of increased meat eating (Aiello and Key 2002; Aiello and Wheeler 1995). However, comparative support for this “expensive tissue” hypothesis is limited. Early studies had found no evidence for it in bats or birds (Isler and van Schaik 2006a; Jones and MacLarnon 2004). A recent study of a large sample of mammals, including 23 species of primates, with matching brain and organ mass data (Navarrete, van Schaik, and Isler 2011) also failed to support it. These results put the general validity of this hypothesis in doubt. Moreover, for the specific case of humans, we argue that the currently available data on great-ape digestive tract anatomy (Chivers and Hladik 1980) are not sufficiently clear to claim reduction of the gut in the human lineage (Hladik, Chivers, and Pasquet 1999).
Another trade-off, that between the energy used for locomotion and for the brain as shown in birds (Isler and van Schaik 2006a), may also have played a role in human evolution when in early Homo an energetically less efficient, australopithecine-like form of bipedalism evolved into a modern striding gait. The abandonment of the energetically very expensive climbing also freed these hominins from the anatomical compromise between climbing and walking (Isler and van Schaik 2006a). Apart from reducing costs of locomotion, this change in the locomotor habits may also have induced a reduction of maintenance costs during rest, as humans are reported to have relatively less muscle mass than great apes (Leonard et al. 2003; Snodgrass, Leonard, and Robertson 2009). However, this seeming difference could arise because of the higher amount of fat stores in humans. At present, hypotheses explaining increased encephalization in the human lineage with metabolic trade-offs through a shift in body composition are only weakly supported by empirical data (Muchlinski, Snodgrass, and Terranova 2012).
In this paper, we explore the trade-off between brain size and production, which includes growth and reproduction. This effect is well established among birds (e.g., Iwaniuk and Nelson 2003) and mammals (Isler and van Schaik 2009a, 2009b). Here, we will use correlations between life history characteristics and thus reproductive capacity and brain size in extant primates to shed light on the evolutionary history of early hominins. Briefly, we will argue that great apes have brain sizes that are close to the maximum achievable with their lifestyle and that our hominin ancestors could only break through this so-called gray ceiling after they had adopted cooperative breeding.
Brain Size and Life History Traits: How Do Humans Differ?
From the expensive brain framework it follows that an increase in relative brain size could be paid for by reduced investment in production (i.e., slowing down growth, reducing reproduction, or both). We have shown that relatively large-brained precocial mammals exhibit a reduced fertility rate by producing much larger offspring after longer interbirth intervals (Isler and van Schaik 2009a). This is probably because relatively large-brained immatures are highly vulnerable to temporary shortfalls in energy supply (the “brain malnutrition risk” hypothesis of Deaner, Barton, and van Schaik 2003), so that a relatively large neonatal body mass is needed to buffer this risk. In addition to larger newborns, the reduced allocation to production slows down development and delays the age at first reproduction in relatively large-brained precocial mammals and especially in primates (table 1). Indeed, a recent analysis for a carefully compiled data set of wild primates showed that brain size is the best predictor of the duration of all stages of developmental life history except the (poorly delineated) lactational period and that taking body size into account does not improve the fit (Barrickman et al. 2008).
Phylogenetic regressions of life history traits versus female brain and body mass data in nonhuman primates (
species)
| Female brain mass | Female body mass | ||||
| Life history parameter | λ | P | Effect | P | Effect |
| Neonate body mass | .959 | <.0001 | .674 | .014 | .209 |
| Gestation | .996 | .0005 | .071 | .095 | −.084 |
| Lactation | .737 | <.0001 | .811 | .396 | −.12 |
| Interbirth interval | .925 | .013 | .461 | .688 | −.054 |
| Litter size | .999 | .017 | −.242 | .166 | .097 |
| Annual fertility | .955 | .002 | −.702 | .355 | .148 |
| Age at first reproduction | .848 | .0009 | .573 | .111 | −.198 |
| Maximum life span | .864 | .0004 | .425 | .051 | −.167 |
| Maximum reproductive life span | .815 | .001 | .412 | .064 | −.171 |
| rmax | .950 | .0004 | −.688 | .172 | .186 |
To assess to what extent this effect of brain size on life history also characterizes humans, we should look at human life history traits in relation to relative brain size. Although some have questioned whether extant human foragers represent the “natural” condition for our species, they are certainly situated at the lower end of the spectrum of human reproductive capacity and can thus serve as a conservative estimate for comparison with extant ape species.
If we plot life history traits versus relative brain size in nonhuman primates (fig. 2) and assess the values of human foragers and horticulturalists based on those expected for other primates, the main human characteristic is a distinctly shortened period to weaning, and thus an increased annual fertility rate, for its brain size. On the other hand, humans exhibit considerably smaller neonates but only a slight decrease in gestation length and a perfectly normal age at first reproduction for their brain size.
Residuals of life history traits versus residuals of endocranial volume in primates (
species; Homo sapiens was excluded while calculating the regressions). Residuals were obtained from least squares regressions of the respective trait versus female body mass.
Thus, the main deviation from expectation is that humans manage to have much higher investment in reproduction (both pre- and postnatally) than expected for their brain and body size. The same conclusion is reached when we compare the life history of human foragers directly with that of extant nonhuman hominoids (table 2). This difference points to major changes in lifestyles adopted by hominins, which will be explored after we determine that a given lineage has a maximum brain size it can achieve.
Life history parameters of humans and other great apes
| Parameter | Gorilla gorilla | Pan troglodytes | Pan paniscus | Pongo pygmaeus | Pongo abelii | Human mean 14 |
| Female body mass (kg) | 71.5 | 40.4 | 33.2 | 36.9 | 41.1 | 45.26 |
| Female brain size (cm3) | 434 | 357 | 326 | 337 | 346 | 1,213 |
| Gestation length (m) | 8.45 | 7.73 | 7.6 | 8.22 | 8 | 8.9 |
| Neonate body mass (g) | 2,124 | 1,846 | 1,447 | 1,968 | 1,969 | 3,319 |
| Twinning rate | 1/100 | 2.8/100 | ? | ? | ? | 1/100 |
| Interbirth interval (years) | 5 | 5.43 | 4.8 | 7.35 | 9.3 | 3.331 |
| Weaning age (years) | 3.5 | 4 | 3 | 5.3 | 5.5? | 2.83 |
| Female age at first reproduction (years) | 10.2 | 13.25 | 14.2 | 15.7 | 15.4 | 18.84 |
| Maximum life span | 55 | 59.4 | 54.5 | 56.3 | 59 | 85 |
Brain Size and Maximum Population Growth Rates
In large-brained mammals and primates, the developmental slowdown and reduced reproductive rate are accompanied by an increased adult life span (Isler and van Schaik 2009a), but the question arises whether the increased life span can continue to fully compensate the reduced production per unit time as brain size increases. On average, females of every species leave roughly two viable adult offspring per lifetime, but species vary dramatically in their maximum reproductive capacity under ideal conditions. We need a measure of reproductive capacity that represents maximum possible lifetime reproductive success. The net reproductive rate (R(0)) of extant populations is derived from life tables and will hardly ever represent optimal conditions. A far better estimate of maximum reproductive capacity is maximum population growth rate (rmax). Additionally, in contrast to a rough product of average fertility and maximum fertile life span, rmax takes generation time into account. To give a stark example, if two otherwise identical species differed because in one, females start to reproduce at age 1 and die at age 21, whereas in the other, females start to breed at age 21 and die at age 41, the first species would soon outcompete the second (Lewontin 1978). The value of rmax is defined as
We have shown previously (Isler and van Schaik 2009b) that this rmax shows a very strong negative correlation with brain mass in mammals and precocial birds, and indeed, that brain mass is a better predictor of rmax than is body mass. The same is found within primates as a group (fig. 3) and if we control for phylogenetic nonindependence (table 1). This finding is not a statistical artifact because brain mass might be a better estimate of body size than body mass itself by being less prone to error variance (Economos 1980) and because the relationship is found only for brain mass and not for the mass of other organs, which also show a low degree of variation (see Isler and van Schaik 2009b, app.). What is especially striking is that great apes, in particular orangutans, show the lowest possible rmax, quite possibly close to what is minimally viable demographically.
Maximum population growth rate rmax as a function of (A) endocranial volume (ECV) and (B) body mass in nonhuman primates (
species; Homo is shown for comparison but is not included in the calculation). Strepsirrhines and tarsiers are blue, platyrrhines orange, cercopithecines green, and hominoids red.
Similarly, although rmax is based on an average annual fertility rate, we may expect that using a maximum fertility rate would only strengthen the observed relationship, as small-brained species probably exhibit a higher plasticity of reproduction in response to ecological conditions. In this case, rmax would underestimate the maximum reproductive capacity mostly in small-brained species, yielding an even stronger negative correlation between maximum reproductive capacity and brain size. Using rmax is therefore a conservative approach for our purpose.
The Gray Ceiling in Primates
The negative relationship between rmax and brain mass, controlling for body mass, indicates that as brain size increases, the increase in life span is increasingly unable to fully compensate for the costs incurred by long developmental periods and lower reproductive rates. The most likely reason is that there is a realistic minimum mortality rate set by freak accidents and freak environmental events (droughts, floods, fires, epidemics, lightning strikes, etc.) that are truly unavoidable regardless of niche or behavior. Thus, as this minimum mortality level is approached, further increases in brain size will of course continue to yield lower production but will inevitably lead to only a modest improvement in survival and thus maximum life span. As a result, rmax declines.
It is likely that such a low reproductive potential as found in great apes compromises demographic viability for two reasons. First, where survival must be near perfect just to maintain population stability, there is virtually no room for selective mortality. This means that drastic changes in the environment must be met with phenotypically plastic responses (including individual learning and innovativeness and socially learned innovations, i.e., culture) rather than selective mortality and that populations are almost certainly at higher risk of local extinction in such conditions. Indeed, a population’s maximum reproductive capacity directly affects the maximum rate of environmental change that it can adapt to without going extinct (Lynch and Lande 1993). Second, low reproductive potential even under perfect conditions also implies a limited ability of a species to recover from population crashes and thus a species that is less likely to build up enough individuals to colonize new areas or habitats until the next crisis period. We can therefore use this reproductive potential as an estimate of the ability to stave off population or species extinction.
A major consequence of this rule is that ever-lower rmax with increasing brain size should lead us to expect a particular maximum brain size, which we call the gray ceiling. As brains exceed this size, population extinction becomes increasingly likely, leading eventually to the extinction of the population or species whenever major changes in habitat (e.g., due to climate change) take place. Given that among primates, great apes are at the minimum of demographic viability, we must conclude that in this lineage no major increase in brain size should be possible. Nonetheless, humans, of course, achieved exactly this, raising the question how this was possible.
Calculating rmax of extant humans is complicated by the existence of midlife menopause, which is unique among primates. If instead of maximum life span we use maximum observed age at last reproduction (for females) and do the same thing for great apes, the rmax of humans lies between the values of gorillas and chimpanzees (table 3) instead of far lower, as one would expect based on the brain-size effect on rmax. Notice that Sumatran orangutans have a potential DTmin of over 25 years, which may well be the lowest value observed for all extant mammals (the actual value itself is not to be taken too seriously because it refers to a theoretical construct; it is only meant to be used for comparative purposes). In the more seasonal African environments, such a value may not be realistic, and the observed values of the African great apes (between 13.6 and 16.1 years) suggest a realistic value of the potential DTmin of around 20 years.
Maximum population growth rates of humans and other great apes
| Rate | Gorilla gorilla | Pan troglodytes | Pan paniscus | Pongo pygmaeus | Pongo abelii | Human mean 14 |
| Interbirth interval (years) | 5 | 5.43 | 4.8 | 7.35 | 9.3 | 3.331 |
| Female age at first reproduction (years) | 10.2 | 13.25 | 14.2 | 15.7 | 15.4 | 18.84 |
| Maximum life span | 55 | 59.4 | 54.5 | 56.3 | 59 | 85 |
| rmax | .054 | .049 | .047 | .031 | .025 | * |
| DTmin (years) | 12.8 | 14.1 | 14.7 | 22.4 | 27.7 | * |
| Maximum age at last birth | 45? | 45 | 45 | 45? | 45 | 47 |
| rmax** (using maximum age at last birth) | .051 | .044 | .043 | .025 | .017 | .047 |
| DTmin** (years) | 13.6 | 15.8 | 16.1 | 27.7 | 40.8 | 14.7 |
Predicting Human rmax
In comparison with other hominoids, humans exhibit a much larger rmax than expected for our extremely large brain size (fig. 3A). But what value of rmax would be predicted for a typical hominoid of humanlike brain and body mass? To answer this question, we must consider possible correlates of either brain size or rmax to construct a multivariate linear model that explains as much variation in primate rmax as possible.
In a multivariate analysis within nonhuman primates (
species; table 4), rmax is affected by arboreality (species that are at least partly terrestrial have a higher rmax; fig. 4A) and by nocturnality (nocturnal species have a lower rmax than diurnal or cathemeral species; fig. 4B) but not by diet (percentage of leaves or fruit or animal matter in the diet). But even if these covariates are controlled for, hominoid species exhibit a lower rmax than other primates (
in a multiple regression; table 4).
Multiple regression of variables affecting rmax simultaneously in nonhuman primates (
species, 
)
| Variable | Estimate | t ratio | P |
| Intercept | .083 | .22 | .829 |
| ln female endocranial volume | −.663 | −5.24 | <.0001 |
| ln female body mass | .067 | .69 | .495 |
| Terrestriality | .168 | 3.91 | .0002 |
| Nocturnality | −.131 | −2.40 | .019 |
| Hominoidea vs. others | −.232 | −3.53 | .0007 |
Relationship between maximum population growth rate rmax versus endocranial volume (ECV) in nonhuman primates as affected by (A) terrestriality and (B) nocturnality. To illustrate the magnitude of differences, slopes of the regression lines were forced to be identical in both groups. Symbols as in figure 3; multivariate statistics in table 5.
If humans followed the general primate trend, their rmax would be estimated as 0.027 (predicted from a multivariate model including terrestriality, nocturnality, and female body mass and endocranial volume [ECV]; table 5). If we take into consideration that we are hominoids too, the predicted rmax would be even lower, about 0.022. This means that the DTmin under optimum conditions would be around 30 years, which would almost certainly not lead to demographically viable populations under the unstable African conditions in which humans evolved.
Hypothetical life history traits of Homo sapiens predicted from primate and hominoid trends
| Trait | Model A: primate | Model B: hominoid | Actual values: mean 14 |
| Litter size | <1 | <1 | 1.011 |
| Neonate mass (g) | 7,377 | 6,865 | 3,319 |
| Gestation length (months) | 10.2 | 10.9 | 8.9 |
| Lactation length (years) | 5.46 | 7.57 | 2.83 |
| Interbirth interval (years) | 5.89 | 7.89 | 3.33 |
| Age at first reproduction (years) | 17.3 | 22.6 | 18.8 |
| Maximum life span (years) | 68.7 | 79.1 | 85 |
| rmax | .027 | .022 | .047 |
| DTmin (years) | 25.4 | 32.3 | 14.5 |
These hypothetical human rmax values, assuming a lifestyle like that of other primates, are lower than those found for any extant mammalian species. The lowest observed rmax values are found in species that experience very low adult mortality rates (i.e., live in extremely stable habitats and hardly suffer from predation), such as orangutans: 0.025 (Pongo abelii) and 0.031 (Pongo pygmaeus); killer whales: 0.028 (Pseudorca crassidens) and 0.046 (Orcinus orca); chimpanzees: 0.049 (Pan troglodytes) and 0.047 (Pan paniscus); gorillas and African elephants: 0.054; and dugongs: 0.058. In conclusion, regardless of which model we use, a species with human brain and body mass would not be able to survive if it otherwise adheres to a primate or hominoid lifestyle let alone whether it was not completely arboreal and living in African woodland or savanna.
Why Could Humans Break through the Gray Ceiling?
Up to this point, we have shown that a human brain–body size relationship would not be demographically feasible in a primate following a typical hominoid lifestyle even if we take differences in diet and locomotor patterns into account. The main distinction affecting interbirth intervals and weaning age is our system of cooperative care for infants and mothers (Burkart, Hrdy, and van Schaik 2009; Burkart and van Schaik 2010; Hrdy 2005). Callitrichids are the only other primates that exhibit cooperative breeding to a similar extent. Indeed, the maximum reproductive rate of callitrichines is, because of twinning, on roughly the same grade as Homo sapiens (fig. 5).
Maximum population growth rate rmax versus brain size (endocranial volume [ECV]) in nonhuman primates for species that exhibit cooperative breeding (red; Homo sapiens is excluded from the calculation); species that show at least some amount of allomaternal care such as paternal care, communal nursing, or babysitting (blue); and species that show no allomaternal care at all (green). To illustrate the magnitude of differences, slopes of the regression lines were forced to be identical in the three groups. Symbols as in figure 3; multivariate statistics are given in table 7.
A multivariate regression yields a clear additional effect of this very rough measure of the extent of allomaternal care in nonhuman primates taking into account the known covariates such as terrestriality, nocturnality, and diet (table 6). A more quantitative measurement of the extent and dimensions of allomaternal help confirms this relationship (van Schaik and Isler 2012).
Multiple regression of variables affecting rmax simultaneously in nonhuman primates including a rough measure of allomaternal care (
species, 
)
| Variable | Estimate | t ratio | P |
| Intercept | −.580 | −1.39 | .168 |
| ln female endocranial volume | −.540 | −4.32 | <.0001 |
| ln female body mass | .100 | 1.01 | .317 |
| Terrestriality | .106 | 2.40 | .019 |
| Nocturnality | −.001 | −.01 | .989 |
| Hominoidea vs. others | −.316 | −4.86 | <.0001 |
| Cooperative breeding: | |||
| Cooperative vs. some allomaternal care | .385 | 4.07 | .0001 |
| Some vs. no allomaternal care | .301 | 4.41 | <.0001 |
The inclusion of allomaternal care in the model to predict hypothetical human life history traits yields values that are much closer to the actual values of extant human subsistence populations (table 7). It is not clear a priori which of the two models (general primate [C] or hominoid [D] in table 7) provides the most accurate answer.
Hypothetical life history traits of Homo sapiens predicted from a primate trend including cooperative breeding
| Trait | Model C: primate and help | Model D: hominoid and help | Actual values: mean 14 |
| Litter size | <1 | 1.036 | 1.011 |
| Neonate mass (g) | 6,824 | 6,476 | 3,319 |
| Gestation length (months) | 11.1 | 11.8 | 8.9 |
| Lactation length (years) | 3.78 | 5.28 | 2.83 |
| Interbirth interval (years) | 3.16 | 4.41 | 3.33 |
| Age at first reproduction (years) | 16.6 | 20.9 | 18.8 |
| Maximum life span (years) | 67.5 | 76.7 | 85 |
| rmax | .057 | .042 | .047 |
| DTmin (years) | 12.2 | 16.5 | 14.5 |
Southeast Asian hominoids (gibbons, orangutans) live in regions that were at least in part affected less by the series of Pleistocene glaciations than Africa (Whitmore 1984). This relative stability may have allowed for slower viable rmax (perhaps in part achieved through lower BMRs; Pontzer et al. 2010), and they may pull the estimates for humans down. On the other hand, the heavily terrestrial gorillas may bias the estimates in the opposite direction, and the chimpanzee values are actually predicted quite well by the hominoid model. For now, therefore, we present both sets of results and expect that the true values may be intermediate.
Table 7 shows that the predicted age at first reproduction, fertility rates, interbirth intervals, and rmax are fairly accurate in both models. Note that in both models C and D, interbirth intervals are anomalously shorter than lactation periods, which is due to the result that in nonhuman primates, allomaternal care reduces interbirth intervals more than it shortens lactation periods. We are thus confident that cooperative care is indeed responsible for the observed differences between human and ape life history traits. This interpretation is supported by another result in table 7. Human life span is somewhat longer than predicted, which may be linked to our tendency to support the sick and injured, which should improve survival relative to the baseline situation of no support, as in great apes, and thus over time maximum life span.
There is one major discrepancy between model and observation that may therefore reflect another effect than cooperative breeding. Neonate mass is much smaller and gestation length somewhat shorter than the very large values predicted (largely due to our very large brain size). This discrepancy may be linked to the obstetrical dilemma, caused by the narrowing of the pelvic canal as a result of bipedalism (Montagu 1961; Trevathan 1987; Washburn 1960), which at some point has become limiting for the size of the human neonate. It is certainly consistent with the secondary altriciality of human neonates. Note, however, that a more altricial state at birth can explain only this one minor difference between the life histories of humans and great apes, whereas the overall difference can be attributed to the extensive allomaternal care in humans.
When Did Humans Break through the Gray Ceiling?
In this section, we aim to predict maximum potential population growth rates of extinct hominins from the primate model to find out when they would have reached the region of demographic nonviability without a change in the breeding system. In a first attempt, we plot DTmin of nonhuman primates versus their ECVs (fig. 6). To get a reasonable estimate of a threshold value, we conservatively assume that a doubling time beyond 30 years (
) would not yield viable populations. This is a very conservative estimate, as no other living mammal exhibits such a low maximum reproductive rate. From the relationship between DTmin and ECV, we conclude that this value would be reached with an ECV of about 650 cm3. If terrestriality is included in the model, which is rather likely for all early hominins (remember we do not require a high percentage of terrestrial locomotion here), the threshold would be even lower, about 610 cm3. This crude first attempt suggests that the first species to break through the gray ceiling was early Homo, which must therefore have had extensive allomaternal care. Using the more sophisticated model D—which was specific for the hominoids and included not only brain size but also body mass, terrestriality, and the level of allomaternal care—the effect of a change in breeding system can be specified in greater detail.
Minimum population doubling time versus endocranial volume (ECV) in nonhuman primates (Homo sapiens is excluded from the calculation). The vertical line represents an ECV of 655 cm3. Note that values are not log transformed here.
Table 8 lists predicted interbirth intervals and the corresponding DTmin for fossil hominin taxa groups. If we assume no allomaternal help, the predicted age at first reproduction (AFR) ranges from 12.6 years in Australopithecus afarensis to 26.1 years in the very large-brained Qafzeh Homo sapiens, and the predicted interbirth interval (IBI) is from 6 to 8.4 years (table 8). If we assume cooperative breeding, the predicted AFR is between 10.9 and 22.6 years, while the predicted IBI is 3.4 for A. afarensis and 4.7 for Qafzeh H. sapiens. Estimating twinning rate from our models is not feasible because the twinning callitrichines introduce a strong body-mass dependency of twinning rates. To estimate population growth rates, we therefore set litter size to 1.01; that is, twinning occurs in 1% of births. The results of the model are illustrated in figure 7.
Predicted life history and demographic parameters of early hominins
| Allomaternal care during predicted interbirth interval (years) | Allomaternal care during predicted DTmin (years) | ||||||||
| Species and sample | Time (Ma) | Female endocranial volume | Female body mass | No | Some | CB | No | Some | CB |
| Australopithecus afarensis: | |||||||||
| A.L. 333-105 | 3.2 | 343 | 29.3 | 6.04 | 4.59 | 3.43 | 17.7 | 14.0 | 10.3 |
| A.L. 444-2 | 3.2 | 550 | 51.3* | 6.88 | 5.24 | 3.91 | 23.4 | 17.7 | 12.6 |
| Australopithecus africanus: | |||||||||
| STS 71 | 2.75 | 428 | 26.6 | 6.21 | 4.73 | 3.53 | 20.1 | 15.6 | 11.4 |
| STW 505 | 2.5 | 560 | 46.8 | 6.85 | 5.21 | 3.89 | 23.8 | 18.0 | 12.7 |
| Australopithecus boisei: | |||||||||
| KNM-ER 732 female | 1.7 | 500 | 32.0 | 6.48 | 4.93 | 3.68 | 22.1 | 17.0 | 12.2 |
| OH 5 male | 1.8 | 530 | 57.6 | 6.91 | 5.26 | 3.93 | 22.8 | 17.3 | 12.4 |
| Early Homo: | |||||||||
| KNM-ER 1813 | 1.89 | 509 | 34.9 | 6.56 | 4.99 | 3.72 | 22.4 | 17.1 | 12.3 |
| KNM-ER 1805 | 1.89 | 580 | 30.3* | 6.61 | 5.03 | 3.76 | 24.6 | 18.6 | 13.1 |
| KNM-ER 1470 | 1.89 | 752 | 45.6 | 7.18 | 5.46 | 4.07 | 30.0 | 21.8 | 14.8 |
| Homo erectus: | |||||||||
| Africa: | |||||||||
| KNM-ER 42700 (Ileret) | 1.55 | 690 | 45* | 7.06 | 5.38 | 4.01 | 27.9 | 20.5 | 14.2 |
| KNM-ER 3733 | 1.8 | 850 | 59.2 | 7.50 | 5.71 | 4.26 | 33.4 | 23.7 | 15.7 |
| Georgia: | |||||||||
| D3444 | 1.77 | 638 | 47* | 7.00 | 5.33 | 3.97 | 26.2 | 19.5 | 13.6 |
| D2280 | 1.77 | 775 | 52.6* | 7.31 | 5.56 | 4.15 | 30.7 | 22.2 | 15.0 |
| Asia: | |||||||||
| Zhoukoudian XI female | .42 | 1,015 | 51.8 | 7.64 | 5.81 | 4.34 | 41.2 | 27.9 | 17.6 |
| Zhoukoudian X male | .42 | 1,225 | 65.6* | 8.06 | 6.13 | 4.57 | 53.2 | 33.3 | 19.8 |
| Archaic Homo sapiens: | |||||||||
| Steinheim | .25 | 1,110 | 60.5 | 7.86 | 5.98 | 4.47 | 45.9 | 30.1 | 18.6 |
| Jebel Irhoud | .09 | 1,305 | 80.5 | 8.3 | 6.31 | 4.71 | 58.0 | 35.2 | 20.5 |
| Homo neanderthalensis: | |||||||||
| Saccopastore female | .12 | 1,245 | 66.6 | 8.09 | 6.16 | 4.59 | 54.6 | 33.9 | 20.0 |
| Le Moustier male | .041 | 1,565 | 81.2 | 8.56 | 6.52 | 4.86 | 89.3 | 45.8 | 23.8 |
| H. sapiens: | |||||||||
| Zhoukoudian 102 female | .015 | 1,380 | 43.2 | 7.91 | 6.02 | 4.49 | 74.5 | 41.7 | 22.5 |
| Qafzeh 9 female | .1 | 1,531 | 64.6 | 8.35 | 6.36 | 4.74 | 89.5 | 46.1 | 23.8 |
| Extant females | 0 | 1,213 | 45.3 | 7.79 | 5.93 | 4.42 | 55.1 | 34.3 | 20.1 |
Minimum population doubling time (DTmin; years) of fossil hominins predicted from model D (individual values listed in table 8). We assume that a DTmin of somewhere between 15 years (extant chimpanzees) and 20 years would still be feasible (shaded bar). The lack of smoothness results from the inclusion of body mass in the model.
We assume that a DTmin of somewhere between 15 years (extant chimpanzees) and 20 years would still be feasible. It is apparent that no help for mothers (as in orangutans) results in a very steep relationship between population doubling times and brain size. Species are included in the category of “some help” even if they exhibit minimal helping behaviors, such as passive food sharing or babysitting, with only minimal frequency. Extant African apes (gorillas and chimpanzees) are at this lower end of the spectrum. From our model, it seems that an ECV of more than 700 cm3 would not yield sustainable populations with such an intermediate system of allomaternal care. Only with full cooperative breeding (as in extant humans or callitrichines) would fossil hominins have been able to provide sufficient energy for a sustainable population growth rate and support a brain that is larger than 700 cm3.
In conclusion, a gradual change in lifestyle toward a substantial increase in allomaternal help (including provisioning of mothers and weaned offspring) may have evolved early in, or even before, the genus Homo. (For the challenge of allocating the earliest Homo fossils to meaningful clusters, see Antón 2012.)
Were Early Homo Cooperative Breeders?
We believe that Homo erectus (= ergaster), as it emerged at around 1.8 Ma, was a good candidate for having extensive allomaternal care for two major reasons. First, they were likely the first systematic hunters of large game (Foley and Lee 1991; Pobiner et al. 2008). Large-game hunting requires cooperation during the hunt, cooperative defense against other dangerous carnivores, extremely high tolerance around kills, and frequent food sharing, perhaps even to the point of provisioning. These features are all more likely among cooperative breeders (van Schaik and Burkart 2010). Indeed, among mammals, carnivores are more likely to be cooperative breeders (Smith et al. 2012; Solomon and French 1997; Spencer-Booth 1970).
Second, the weaned juveniles were less likely to make a living on their own and would have strongly benefited from allomaternal support. They lived on the savanna, where resources harvested as efficiently by juveniles as adults, such as soft fruits, are much scarcer than in forests (Hawkes et al. 1998), leading to reduced juvenile foraging efficiency. The latter is especially likely if they had already acquired a great reliance on meat (Domínguez-Rodrigo and Pickering 2003), because the difficulty of learning how to hunt means that provisioning meat has strong positive effects on the fitness prospects of the young. More seasonal habitats are more likely to contain cooperative breeders (Hatchwell 2007; Rubenstein and Lovette 2007). The argument is further supported by H. erectus (= ergaster) occupying a much larger geographic range than earlier hominin species. Hrdy (2005, 2009) has argued convincingly that colonizing hostile new habitats is facilitated by cooperative breeding.
Identifying the source of extensive allomaternal care in early Homo is difficult, as the defining feature of human caretaking seems to be its large flexibility (Hrdy 2009). In present-day human societies, grandmothers and males, but also not directly related adults (Hill and Hurtado 2009), play a major role. As midlife menopause is extremely rare in mammals (Packer, Tatar, and Collins 1998), we cannot apply comparative evidence to the evolution of grandmothering. However, males were almost certainly involved in meat sharing and thus allomaternal care as soon as confrontational scavenging or hunting of large game was present (Marlowe 2007). In sum, while the brain size of H. erectus and various other indicators suggest that females of this species received much allomaternal care, we assume that male-female pair bonds accompanied by selective food sharing were sources of this care, but we can make no conclusions about the role of grandmothers.
Discussion
The analyses reported here suggest that the inability of survival to keep up with reduced production as brain size increases leads to a reduction in rmax in larger-brained organisms. There comes a point where no further increases in brain size are possible because the long-term viability of populations is severely compromised. This point we call the “gray ceiling.” For great apes living a great-ape lifestyle, we put this conservatively at 600–700 cm3. This explains why extant great apes and extinct australopithecines seem to have converged on similar brain sizes, but it makes the “escape” from great-ape level brain sizes by Homo even more striking. Assigning a distinct boundary to a highly fragmentary fossil record is tricky, but Homo rudolfensis (i.e., KNM-ER 1470) is a likely candidate for such a change in lifestyle. The first well-documented hominin to show brains that exceed this size was Homo erectus (= ergaster), which arose in Africa at around 1.8 Ma, occupied savanna habitats, hunted large game, and rather quickly had moved into other geographic regions.
Allomaternal care tends to lead to higher female reproductive output in both primates (Mitani and Watts 1997; Ross and MacLarnon 2000) and carnivores (Isler and van Schaik 2009a). We propose that as in other mammals and birds, the adoption of cooperative breeding (Hrdy 2005, 2009) had allowed H. erectus (= ergaster) to increase its rmax, which, given its value near the gray ceiling, made possible an expansion of its brain size. As a conservative estimate, our gray ceiling value of 600–700 cm3 provides an upper boundary to brain size if a species is adhering to an apelike lifestyle. Of course, we cannot exclude the possibility that cooperative breeding predated a pronounced increase of encephalization by several million years, as suggested in Lovejoy’s (2009) scenario for the adaptive suite of characters assigned to Ardipithecus ramidus. In this case, however, another explanation would be needed for the long time lag between the onset of provisioning and increase in brain size. Australopithecines were adept bipeds without sectorial canine complexes, but there is no evidence for a shift in life history traits and developmental trajectories before Homo (Dean 2006; Dean and Lucas 2009; Schwartz 2012; but see DeSilva 2011).
In conclusion, if we rely on estimating the effect of evolutionary processes known to operate in primates or in vertebrates in general, there is evidence for several factors that allowed for brain-size expansion throughout the evolutionary history of the human lineage. A more seasonal environment, a change in diet toward higher-quality food sources, and more efficient locomotion all may have played a role (Potts 2011). Instead of a comprehensive but unique “adaptive suite” of human traits (Lovejoy 2009), however, we find broad comparative support for a decisive role of cooperative breeding as the initial trigger of many subsequent changes in human biology (Burkart, Hrdy, and van Schaik 2009; Burkart and van Schaik 2010). As such a redistribution of energy toward mothers and infants is possible without changing the overall energy budget, it may have facilitated subsequent changes that led to the relatively high energetic throughput of modern humans as compared with extant apes (Pontzer 2012; Pontzer et al. 2010).
We thank Susan Antón and Leslie Aiello for inviting us to the Wenner-Gren spring symposium 2011 and for editing this volume. Many people have contributed data to our compilations, which we gratefully acknowledge. Financial support for this study was provided through Swiss National Science Foundation grant 3100A0-117789, the A. H. Schultz Foundation, and the University of Zurich.
References Cited
Aiello, Leslie C., and Cathy Key. 2002. Energetic consequences of being a Homo erectus female. American Journal of Human Biology 14(5):551–565. Aiello, Leslie C., and Jonathan C. K. Wells. 2002. Energetics and the evolution of the genus Homo. Annual Review of Anthropology 31:323–338. Aiello, Leslie C., and Peter Wheeler. 1995. The expensive-tissue hypothesis: the brain and the digestive system in human and primate evolution. Current Anthropology 36(2):199–221. Antón, Susan C. 2012. Early Homo: who, when, and where. Current Anthropology 53(suppl. 6):S278–S298. Barrickman, Nancy L., Meredith L. Bastian, Karin Isler, and Carel P. van Schaik. 2008. Life history costs and benefits of encephalization: a comparative test using data from long-term studies of primates in the wild. Journal of Human Evolution 54(5):568–590. Berger, Lee R., Darryl J. de Ruiter, Steven E. Churchill, Peter Schmid, Kristian J. Carlson, Paul H. G. M. Dirks, and Job M. Kibii. 2010. Australopithecus sediba: a new species of Homo-like australopith from South Africa. Science 328:195–204. Burkart, Judith M., Sarah B. Hrdy, and Carel P. van Schaik. 2009. Cooperative breeding and human cognitive evolution. Evolutionary Anthropology 18:175–186. Burkart, Judith M., and Carel P. van Schaik. 2010. Cognitive consequences of cooperative breeding in primates? Animal Cognition 13(1):1–19. Chivers, David J., and Claude M. Hladik. 1980. Morphology of the gastrointestinal tract in primates: comparisons with other mammals in relation to diet. Journal of Morphology 166:337–386. Cole, Lamon C. 1954. The population consequences of life-history phenomena. Quarterly Review of Biology 29(2):103–137. Dean, M. Christopher. 2006. Tooth microstructure tracks the pace of human life-history evolution. Proceedings of the Royal Society B 273:2799–2808. Dean, M. Christopher, and Victoria S. Lucas. 2009. Dental and skeletal growth in early fossil hominins. Annals of Human Biology 36(5):545–561. Deaner, Robert O., Rob Barton, and Carel P. van Schaik. 2003. Primate brains and life histories: renewing the connection. In Primate life histories and socioecology. Peter Kappeler and Michael E. Pereira, eds. Pp. 233–265. Chicago: University of Chicago Press. Deaner, Robert O., Karin Isler, Judith M. Burkart, and Carel P. van Schaik. 2007. Overall brain size, and not encephalization quotient, best predicts cognitive ability across non-human primates. Brain, Behavior and Evolution 70:115–124. DeSilva, Jeremy M. 2011. A shift toward birthing relatively large infants early in human evolution. Proceedings of the National Academy of Sciences, U.S.A. 108(3):1022–1027. Domínguez-Rodrigo, Manuel, and Travis R. Pickering. 2003. Early hominid hunting and scavenging: a zooarcheological review. Evolutionary Anthropology 12(6):275–282. Dunbar, Robin I. M. 1998. The social brain hypothesis. Evolutionary Anthropology 6(5):178–190. Economos, Angelos C. 1980. Brain–life span conjecture: a re-evaluation of the evidence. Gerontology 26:82–89. Ely, John J., William I. Frels, Sue Howell, M. Kay Izard, Michale E. Keeling, and D. Rick Lee. 2006. Twinning and heteropaternity in chimpanzees ( Pan troglodytes). American Journal of Physical Anthropology 130(1):96–102. Emery Thompson, Melissa, James H. Jones, Anne E. Pusey, Stella Brewer-Marsden, Jane Goodall, David Marsden, Tetsuro Matsuzawa, et al. 2007. Aging and fertility patterns in wild chimpanzees provide insights into the evolution of menopause. Current Biology 17(24):2150–2156. Foley, Robert A., and Phyllis C. Lee. 1991. Ecology and energetics of encephalization in hominid evolution. Philosophical Transactions of the Royal Society B 334:223–232. Gabunia, Leo, Abesolom Vekua, David Lordkipanidze, Carl C. Swisher III, Reid Ferring, Antje Justus, Medea Nioradze, et al. 2000. Earliest Pleistocene hominid cranial remains from Dmanisi, Republic of Georgia: taxonomy, geological setting, and age. Science 288(5468):1019–1025. Garland, Theodore, Paul H. Harvey, and Anthony R. Ives. 1992. Procedures for the analysis of comparative data using phylogenetically independent contrasts. Systematic Biology 41(1):18–32. Hatchwell, Ben J. 2007. Avian reproduction: role of ecology in the evolution of cooperative breeding. Current Biology 17(19):R845–R847. Hawkes, Kristen, James F. O’Connell, Nicholas G. Blurton Jones, Helen Alvarez, and Eric L. Charnov. 1998. Grandmothering, menopause, and the evolution of human life histories. Proceedings of the National Academy of Sciences, U.S.A. 95(3):1336–1339. Hill, Kim R., and A. Magdalena Hurtado. 1996. Ache life history: the ecology and demography of a foraging people. New York: Aldine. ———. 2009. Cooperative breeding in South American hunter-gatherers. Proceedings of the Royal Society B 276(1674):3863–3870. Hladik, Claude M., David J. Chivers, and Patrick Pasquet. 1999. On diet and gut size in non-human primates and humans: is there a relationship to brain size? discussion and criticism. Current Anthropology 40:695–697. Howell, Nancy. 1979. Demography of the Dobe !Kung. New York: Aldine de Gruyter. Hrdy, Sarah B. 2005. Evolutionary context of human development: the cooperative breeding model. In Attachment and bonding: a new synthesis. C. Sue Carter, Lieselotte Ahnert, Klaus E. Grossmann, Sarah B. Hrdy, Michael E. Lamb, Stephen W. Porges, and Norbert Sachser, eds. Pp. 9–32. Cambridge, MA: MIT Press. ———. 2009. Mothers and others: the evolutionary origins of mutual understanding. Cambridge, MA: Belknap. Isler, Karin, and Carel P. van Schaik. 2006 a. Costs of encephalisation: the energy trade-off hypothesis tested on birds. Journal of Human Evolution 51(3):228–243. ———. 2006 b. Metabolic costs of brain size evolution. Biology Letters 2:557–560. ———. 2009 a. The expensive brain: a framework for explaining evolutionary changes in brain size. Journal of Human Evolution 57(4):392–400. ———. 2009 b. Why are there so few smart mammals (but so many smart birds)? Biology Letters 5(1):125–129. Iwaniuk, Andrew N., and John E. Nelson. 2003. Developmental differences are correlated with relative brain size in birds: a comparative analysis. Canadian Journal of Zoology 81:1913–1928. Jones, Kate E., and Ann M. MacLarnon. 2004. Affording larger brains: testing hypotheses of mammalian brain evolution on bats. American Naturalist 164(1):E20–E31. Kappelman, John. 1996. The evolution of body mass and relative brain size in fossil hominids. Journal of Human Evolution 30(3):243–276. Kleiber, Max. 1961. The fire of life: an introduction to animal energetics. New York: Wiley. Kuzawa, Christopher W. 1998. Adipose tissue in human infancy and childhood: an evolutionary perspective. Yearbook of Physical Anthropology 41:177–209. Leonard, William R., Marcia L. Robertson, J. Josh Snodgrass, and Christopher W. Kuzawa. 2003. Metabolic correlates of hominid brain evolution. Comparative Biochemistry and Physiology A 136(1):5–15. Lewontin, Richard C. 1978. Adaptation. Scientific American 239(3):156–169. Lovejoy, C. Owen. 2009. Reexamining human origins in light of Ardipithecus ramidus. Science 326(5949):74e71–74e78. Lynch, Michael, and Russell Lande. 1993. Evolution and extinction in response to environmental change. In Biotic interactions and global change. Peter M. Kareiva, Joel G. Kingsolver, and Raymond B. Huey, eds. Pp. 234–250. Sunderland, MA: Sinauer. Marlowe, Frank W. 2007. Hunting and gathering: the human sexual division of foraging labor. Cross-Cultural Research 41(2):170–195. Martin, Robert D. 1981. Relative brain size and basal metabolic rate in terrestrial vertebrates. Nature 293(5824):57–60. Mitani, John C., and David Watts. 1997. The evolution of non-maternal caretaking among anthropoid primates: do helpers help? Behavioral Ecology and Sociobiology 40(4):213–220. Montagu, Ashley. 1961. Neonatal and infant immaturity in man. Journal of the American Medical Association 178(1):56–57. Muchlinski, Magdalena N., J. Josh Snodgrass, and Carl J. Terranova. 2012. Muscle mass scaling in primates: an energetic and ecological perspective. American Journal of Primatology 74:395–407. Navarrete, Ana F., Carel P. van Schaik, and Karin Isler. 2011. Energetics and the evolution of human brain size. Nature 480:91–93. Orme, David, Rob P. Freckleton, Gavin Thomas, Thomas Petzoldt, and Susanne Fritz. 2010. CAIC: comparative analyses using independent contrasts. https://r-forge.r-project.org/projects/caic/ .Packer, Craig, Marc Tatar, and Anthony Collins. 1998. Reproductive cessation in female mammals. Nature 392(6678):807–811. Pobiner, Briana L., Michael J. Rogers, Christopher M. Monahan, and John W. K. Harris. 2008. New evidence for hominin carcass processing strategies at 1.5 Ma, Koobi Fora, Kenya. Journal of Human Evolution 55(1):103–130. Pontzer, Herman. 2012. Ecological energetics in early Homo. Current Anthropology 53(suppl. 6):S346–S358. Pontzer, Herman, David A. Raichlen, Robert W. Shumaker, Cara Ocobock, and Serge A. Wich. 2010. Metabolic adaptation for low energy throughput in orangutans. Proceedings of the National Academy of Sciences, U.S.A. 107(32):14048–14052. Potts, Richard. 1998. Environmental hypotheses of hominin evolution. Yearbook of Physical Anthropology 41:93–136. ———. 2011. Evolution: big brains explained. Nature 480:43–44. Purvis, Andy, and Andrew Rambaut. 1995. Comparative analysis by independent contrasts (CAIC): an Apple Macintosh application for analysing comparative data. Computer Applications in the Biosciences 11(3):247–251. R Development Core Team. 2010. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Reader, Simon M., Yfke Hager, and Kevin N. Laland. 2011. The evolution of primate general and cultural intelligence. Philosophical Transactions of the Royal Society B 366:1017–1027. Reed, Kaye E. 1997. Early hominid evolution and ecological change through the African Plio-Pleistocene. Journal of Human Evolution 32(2/3):289–322. Rolfe, David F. S., and Guy C. Brown. 1997. Cellular energy utilization and molecular origin of standard metabolic rate in mammals. Physiological Reviews 77(3):731–758. Ross, Caroline. 1988. The intrinsic rate of natural increase and reproductive effort in primates. Journal of Zoology (London) 214(2):199–219. ———. 1992. Environmental correlates of the intrinsic rate of natural increase in primates. Oecologia 90(3):383–390. Ross, Caroline, and Ann M. MacLarnon. 2000. The evolution of non-maternal care in anthropoid primates: a test of the hypotheses. Folia Primatologica 71(1/2):93–113. Rubenstein, Dustin R., and Irby J. Lovette. 2007. Temporal environmental variability drives the evolution of cooperative breeding in birds. Current Biology 17:1414–1419. Ruff, Christopher B. 2010. Body size and body shape in early hominins: implications of the Gona Pelvis. Journal of Human Evolution 58(2):166–178. Schoenemann, P. Thomas. 2006. Evolution of the size and functional areas of the human brain. Annual Review of Anthropology 35:379–406. Schwartz, Gary T. 2012. Growth, development, and life history throughout the evolution of Homo. Current Anthropology 53(suppl. 6):S395–S408. Shettleworth, Sara J. 2010. Clever animals and killjoy explanations in comparative psychology. Trends in Cognitive Sciences 14(11):477–481. Smith, Jennifer E., Eli M. Swanson, Daphna Reed, and Kay E. Holekamp. 2012. Evolution of cooperation among mammalian carnivores and its relevance to hominid evolution. Current Anthropology 53(suppl. 6):S436–S452. Snodgrass, J. Josh, William R. Leonard, and Marcia L. Robertson. 2009. The energetics of encephalization in early hominids. In The evolution of hominin diets. Jean-Jacques Hublin and Michael P. Richards, eds. Pp. 15–29. Vertebrate Paleobiology and Paleoanthropology Series. Berlin: Springer. Sol, Daniel. 2009. Revisiting the cognitive buffer hypothesis for the evolution of large brains. Biology Letters 5(1):130–133. Solomon, Nancy G., and Jeffrey A. French, eds. 1997. Cooperative breeding in mammals. Cambridge: Cambridge University Press. Spencer-Booth, Yvette. 1970. The relationships between mammalian young and conspecifics other than mothers and peers: a review. Advances in the Study of Behavior 3:119–194. Spocter, Muhammad A., and Paul R. Manger. 2007. The use of cranial variables for the estimation of body mass in fossil hominins. American Journal of Physical Anthropology 134(1):92–105. Spoor, Fred, Meave G. Leakey, Patrick N. Gathogo, Francis H. Brown, Susan C. Antón, Ian McDougall, Christopher Kiarie, Fredrick K. Manthi, and Louise N. Leakey. 2007. Implications of new early Homo fossils from Ileret, east of Lake Turkana, Kenya. Nature 448(7154):688–691. Trevathan, Wenda. 1987. Human birth: an evolutionary perspective. New York: Aldine. van Schaik, Carel P., and Judith M. Burkart. 2010. Mind the gap: cooperative breeding and the evolution of our unique features. In Mind the gap: tracing the origins of human universals. Peter M. Kappeler and Joan Silk, eds. Pp. 477–496. Berlin: Springer. van Schaik, Carel P., and Karin Isler. 2012. Life history evolution in primates. In The evolution of primate societies. Josep Call, Peter M. Kappeler, John Mitani, Ryne A. Palombit, and Joan Silk, eds. Pp. 220–244. Chicago: University of Chicago Press. van Woerden, Janneke T., Carel P. van Schaik, and Karin Isler. 2010. Effects of seasonality on brain size evolution: evidence from strepsirrhine primates. American Naturalist 176(6):758–767. van Woerden, Janneke T., Erik P. Willems, Carel P. van Schaik, and Karin Isler. 2012. Large brains buffer energetic effects of seasonal habitats in catarrhine primates. Evolution 66:191–199. Walker, Robert, Michael Gurven, Kim Hill, Andrea Migliano, Napoleon Chagnon, Roberta De Souza, Gradimir Djurovic, et al. 2006. Growth rates and life histories in twenty-two small-scale societies. American Journal of Human Biology 18(3):295–311. Washburn, Sherwood L. 1960. Tools and human evolution. Scientific American 203(3):63–75. Wells, Jonathan C. K. 2006. The evolution of human fatness and susceptibility to obesity: an ethological approach. Biological Reviews 81:183–205. ———. 2010. The evolutionary biology of human body fatness. Cambridge: Cambridge University Press. Whitmore, Timothy C. 1984. Tropical rain forests of the Far East. Oxford: Clarendon. Wich, Serge A., S. Suci Utami-Atmoko, Tatang Mitra Setia, Herman D. Rijksen, Chris L. Schürmann, Jan A. R. A. M. van Hooff, and Carel P. van Schaik. 2004. Life history of wild Sumatran orangutans ( Pongo abelii). Journal of Human Evolution 47(6):385–398.
Notes
Karin Isler is Senior Lecturer and Carel P. van Schaik is Professor and Director of Museum at the Anthropological Institute and Museum, University of Zurich (Winterthurerstrasse 190, CH-8057 Zurich, Switzerland [[email protected]]).
