Experiences early in life can have lasting effects on the health and survival of humans and other creatures. Whether early hardships can also influence the wellbeing of the next generation is less clear. One previous study with captive hamsters suggested that adversity early in the life of a mother may indeed shorten how long her offspring will live. But hamsters only live for a few years and much less is known about the possibility for intergenerational effects in animals with longer lifespans. This is partly because such studies are time-consuming and thus more difficult to complete.

Over the past 45 years, scientists have collected data on generations of baboons living in and around the Amboseli National Park in southern Kenya. Baboons live in social groups with a strict hierarchy, and individuals can live for up to 30 years in the wild. Previous research has shown that early life adversity – such as being orphaned or simply having a low-ranking mother – can shorten the lifespan of female baboons even if they make it to adulthood. It was unclear, however, whether these ill effects could be passed on to the next generation. Now, Zipple et al. have used the wealth of data about the Amboseli baboons to find the answer.

After taking into account any adversity that each baboon experienced directly, Zipple et al. showed that juvenile baboons whose mothers were orphaned before reaching adulthood were 44% more likely to die young than juveniles whose grandmothers survived during their mother’s early years. Baboons whose mothers had a close-in-age younger sibling were also 42% more likely to die early as compared to those whose mothers did not, perhaps because the younger sibling competed with the mother for access to maternal care.

The analysis suggests that early life adversity in female baboons can have intergenerational effects. More studies are needed to determine if this is also true of humans. If it is, such a result may help explain the persistence of poor health outcomes across generations and shed light on how best to intervene to interrupt this transmission.

An individual’s health, survival, and fertility can be profoundly shaped by its early life environment (Uller et al., 2013). For example, in humans, low early life socioeconomic status predicts increased risk of mortality and many measures of poor health in adulthood (Naess et al., 2004; Beebe-Dimmer et al., 2004; Kittleson et al., 2006; Smith et al., 1998; Frankel et al., 1999; Lidfeldt et al., 2007; van de Mheen et al., 1998; Kuh et al., 2002; Galobardes et al., 2004). Similarly, several studies of wild mammals and birds (Lea et al., 2015; Douhard et al., 2014; Nussey et al., 2007; Pigeon and Pelletier, 2018; Herfindal et al., 2015; Balbontín and Møller, 2015; Millon et al., 2011) find that adult fecundity is reduced in animals that experienced adverse early life environments, and some have also found an effect of early life adversity on adult survival (Nussey et al., 2007; Pigeon and Pelletier, 2018; Herfindal et al., 2015; Tung et al., 2016).

If the effects of early adversity extend to the descendants of exposed individuals, the epidemiological and evolutionary impact of these effects would be further amplified. However, in humans, evidence that intergenerational effects stem directly from parental experience is mixed, as studies have produced somewhat contradictory results (Veenendaal et al., 2013; Painter et al., 2008; Kaati et al., 2007; Pembrey et al., 2006). For example, a study of the historical Överkalix population in Sweden identified strong, contrasting effects of grandparents’ exposure to early-life food scarcity on grand-offspring survival, depending on small differences in the age at which the grandparent was exposed to scarcity (Pembrey et al., 2006). Similarly, two studies of a population that was exposed in utero to the Dutch hunger winter (a famine that resulted from a German blockade of the Netherlands during the winter of 1944–1945) found contradictory, sex-specific intergenerational effects, in one case suggesting an intergenerational effect that depended only upon the mother’s early experience (Painter et al., 2008), and in the other case an effect that depended only upon the father’s early experience (Veenendaal et al., 2013).

Compelling evidence for intergenerational effects of early adversity faced only in the parental generation comes from numerous laboratory studies of short-lived animals, which find strong relationships between a female’s early life environment and the body size of her offspring (Huck et al., 1986; Alonso-Alvarez et al., 2007; Helle et al., 2012; Goerlich et al., 2012; Taborsky, 2006; Beckerman et al., 2003; Saastamoinen et al., 2013; Vijendravarma et al., 2010; Fischer et al., 2003; Jobson et al., 2015; reviewed in Burton and Metcalfe (2014); but see Bowers et al., 2017 for a rare example in the wild on house wrens). These findings provide important evidence that intergenerational effects of early adversity can occur. However, these studies do not address whether intergenerational effects of early adversity, independent of parent-offspring environmental correlations, occur in natural populations of long-lived animals. And while a few studies of short-lived captive animals have demonstrated a relationship between a female’s early environment and her offspring’s survival or reproduction (Huck et al., 1987; Naguib et al., 2006; Marcil-Ferland et al., 2013), the ecological validity of these findings has yet to be verified by studying intergenerational fitness effects in a population of wild and/or long-lived animals. In wild populations, animals are exposed only to natural, unmanipulated levels of early adversity, and are also subject to any social factors which might mitigate or aggravate the influence of those early adverse events.

Addressing whether the effects of early adversity in one generation affect reproduction or survival in the next is challenging because of the difficulties of linking high-quality data on early adversity in one generation to health and survival outcomes in the next. Here, we overcome these challenges by taking advantage of a prospective longitudinal dataset from a natural primate population: the baboons of the Amboseli ecosystem in southern Kenya (Alberts and Altmann, 2012). This dataset includes 45 years of individual-based data on early adversity, and real-time observations of later-life survival outcomes for hundreds of subjects with known maternities and grand maternities. Moreover, unlike many human populations, we do not observe inter-generational transmission of adverse conditions; that is, offspring of females who experienced early life adversity are not more likely to experience early life adversity themselves (except in the case of inheritance of low social rank, see below), allowing us to avoid this common confound in human societies.

To test for intergenerational effects of early adversity, we focused on early adversity experienced by female baboons who later became mothers, and whose offspring were also in our dataset. We asked whether the early adversity experienced by these females (‘maternal early adversity’) predicted the survival of their juvenile offspring in the next generation, after controlling for the early adversity directly experienced by the offspring themselves.

We considered five types of early adverse conditions (Table 1), based on previous work in our study population that demonstrated effects of these conditions on a female baboon’s own adult survival (Tung et al., 2016). These included: (i) maternal death during development (0–4 years of age), which indicates the loss of an important source of social support, physical protection, and nutrition (Altmann, 1980; Lea et al., 2014), (ii) being born to a low-ranking mother, which influences growth rates and age at maturation (Charpentier et al., 2008; Altmann and Alberts, 2003a; Altmann et al., 1988) (iii) being born into a large social group (and thus experiencing high density conditions and high levels of within-group competition) (Lea et al., 2015; Charpentier et al., 2008; Altmann and Alberts, 2003b) (iv) being born during a drought, which reduces fertility in adulthood (Lea et al., 2015; Beehner et al., 2006), and (v) experiencing the birth of a close-in-age younger sibling, which may reduce maternal investment received during development (Altmann et al., 1978). Importantly—and in contrast to research on humans (Felitti et al., 1998)—sources of early adversity are not strongly correlated in our population, which allows us to measure the independent effects of different sources of adversity (Supplementary file 1 Table S1).

We built a mixed effects Cox proportional hazards model of offspring survival during the juvenile period that included early adversity measures present in the mother’s and the offspring’s early life as binary fixed effects. We defined the juvenile period based on survival until age 4, near the age of menarche for females and earliest dispersal for males in this population (Charpentier et al., 2008). We included data on maternal early adversity for all five adverse early life conditions, and we included data on offspring early adversity for four of the five conditions. We excluded the birth of a close-in-age younger sibling for the offspring generation to avoid including a potential reverse-causal factor in our model. Specifically, the closest-in-age siblings in our dataset tend to occur as a result of the focal offspring’s death, because female baboons (who are not seasonal reproducers) quickly conceive again if they lose a dependent offspring. In these cases, early mortality would be correlated with short interbirth intervals, but only because the offspring’s death predicts subsequent production of another infant, not because short interbirth intervals constitute a form of adversity. We included maternal and grandmaternal ID as random effects. In total, we used data collected from 1976 to 2017 to analyze the survival of 687 offspring (46.5% males) born to 169 females (mean 4.1 offspring per female, range 1–12) for whom we had data on all five adverse conditions in the mother’s early life, and all four adverse conditions in the offspring’s early life.

Each adverse condition was scored as present or absent for each subject, and each one affected a minority of our study subjects (range 6–34%). Mothers and offspring had similar chances of experiencing adverse conditions, except for social density: offspring were more likely than mothers to be born into large social groups because of population growth over the 5-decade study period (Table 1). Unlike typical patterns of early adversity in human populations (Felitti et al., 1998), different sources of early life adversity in our population were not strongly correlated with each other: no adverse condition explained more than 4% of the variance in any other condition, either within or between generations, with the exception of maternal rank in the mother and offsprng’s generation (p<0.0001, r² = 0.16) (Supplementary file 1 Table S1).

Maternal Early Life Adversity and Offspring Survival

Our full multivariate Cox proportional hazards model for offspring survival (Supplementary file 1 Table S2) included all nine early adverse conditions (five for mothers and four for offspring). We found strong negative effects of two characteristics of the mother’s early life environment on their offspring’s survival during the first 4 years of life: maternal loss (hazard ratio = 1.48, p=0.006) and presence of a close-in-age younger sibling (HR = 1.39, p=0.03). Following backwards model selection (performed by removing the parameters with the highest p values until only predictors with a p-value<0.05 remained), these two characteristics remained the only significant maternal early life predictors of offspring survival (Table 2, Figure 1, along with two conditions in the offspring’s early life environment: see below). Adding maternal age, offspring sex or interactions between maternal age or offspring sex and sources of maternal adversity did not improve the fit of the model (Supplementary file 1 Tables S3-S5).

Figure 1

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Table 2

Generation	Parameter*	Coefficient	Hazard ratio (95% CI)	P value	Interpretation
Maternal	Maternal Loss	0.37	1.44 (1.10–1.90)	0.009	Offspring survive less well if their mother experienced maternal loss during her early life.
	Close-in-age Younger Sibling	0.35	1.42 (1.06–1.90)	0.018	Offspring survive less well if their mother had a close-in-age younger sibling during her early life.
Offspring	Maternal Loss	0.68	1.98 (1.53–2.56)	3 × 10−7	Offspring survive less well if they experienced maternal loss within four years of their birth.
	Low Maternal Rank	0.43	1.54 (1.17–2.01)	0.002	Offspring survive less well if they were born to a low-ranking mother.

1. ^*An alternative model that considered cumulative maternal adversity was not a better or worse fit than the reduced multivariate maternal adversity model (see Supplementary file 1 Table S6. For both the model presented here and that in Supplementary file 1 Table S6, R² = 0.07, log likelihood = −1598).

In sum, offspring whose mothers experienced early maternal loss experienced a 48% higher probability of dying throughout the first four years of life than unaffected offspring, and offspring whose mothers had a close-in-age sibling experienced a 39% higher probability of dying than unaffected offspring. This effect is striking especially considering that a median of 7.0 and 8.0 years separated the offspring’s own birth from the mother’s experience of maternal loss or birth of a close-in-age sibling, respectively. A similar pattern holds if mothers, rather than offspring, are treated as the unit of analysis: mothers who experienced early adversity have lower average offspring survival than mothers who did not (see Figure 1c).

Notably, previous work in our population found that these two sources of adversity—maternal loss and the presence of a close-in-age younger sibling during early life—are also sources of mortality risk once females reach adulthood, and in fact are the two strongest predictors of adult survival among six different early-life conditions considered (Tung et al., 2016). Hence, early-life conditions that are especially adverse for females when they reach adulthood also negatively affect the survival of their offspring.

Both the full and reduced models of offspring survival also included two conditions in the offspring’s early life environment as significant predictors of juvenile survival. Specifically, maternal loss experienced by the offspring and low maternal rank during the offspring’s juvenile period had strong negative effects on offspring survival (Supplementary file 1 Table S2, maternal death: Hazard Ratio = 1.95 [1.51–2.54], p=5×10⁻⁷, low maternal rank: Hazard Ratio = 1.43 [1.05–1.94], p=0.025). Thus, maternal loss in the offspring’s generation had a stronger effect on offspring survival (nearly doubling offspring mortality risk) than maternal loss in the mother’s generation. In contrast, the effect of having a low-ranking mother, which was associated with a 43% increase in offspring mortality risk, was comparable in its effect size to the two significant predictors from the maternal generation (maternal loss and close-in-age sibling for the mother, 48% and 39% increase in offspring mortality, respectively). Thus, two adverse conditions in a mother’s early life had as large or larger of an impact on her offspring’s survival than all but one adverse condition experienced by the offspring directly.

Maternal Viability and Offspring Survival

The strong effect of the mother’s death on offspring survival prior to four years (Table 2) is unsurprising at first consideration: the most obvious explanation for this effect is that offspring depend upon their mothers, so that if the mother dies the offspring is also likely to die at the same time or die subsequently. Indeed, this sequence of events does occur in our population: of the 32 offspring that were alive and less than eight months old when their mother died, 31 (97%) died before reaching one year of age.

However, offspring death could also precede maternal death if it acts as a harbinger of the mother’s death, as opposed to a consequence of it. In this scenario, offspring mortality risk is increased because their mothers are in poor condition and hence unable to provide adequate care or resources to the offspring. This hypothesis therefore proposes an alternative causal chain from poor maternal health to offspring death, which would occur while the mother is still alive.

To examine whether this phenomenon occurs in our study population, we modeled offspring survival to age 2 years (halfway through the juvenile period) as a function of maternal death during years 2–4 after an offspring’s birth (i.e., the two years that followed the offspring survival period modeled in the response variable). In this analysis, we considered only the subset of offspring in our dataset whose mothers survived the entire first two years of the offspring’s life, and for whom we were able to evaluate the four significant predictors of offspring survival identified above and in Table 2 (N = 671). Our results were striking: offspring were less likely to survive during the first two years of life if their mothers died 2–4 years after their birth. In other words, these offspring were more likely to die even when their mother was still present (hazard ratio = 1.50 [1.01–2.23], p=0.045).

To test for a role of maternal early adversity in this effect, we next partitioned our analysis of offspring survival to age two based on whether the mother experienced either maternal loss or a close-in-age younger sibling (i.e., either or both of the two maternal early life conditions that significantly predicted their offspring’s survival; Table 2). We found that, among offspring whose mothers experienced either or both of these two adverse events (N = 247), maternal death in years 2–4 after the offspring’s birth significantly predicted reduced offspring survival to age 2 years (Figure 2a, hazards ratio = 1.78, 95% CI = [1.05–3.01], p=0.034). Maternal death in the same period did not, however, predict reduced offspring survival when mothers had not experienced maternal loss or a close-in-age younger sibling (N = 424; Figure 2b, hazard ratio = 1.21, 95% CI = [0.7–2.2], p=0.53). Hence, the pattern we observed when analyzing the full data set of offspring that survived to age 2 (N = 671) is completely driven by the offspring of mothers who experienced substantial early adversity. This finding is consistent with the hypothesis that maternal early life adversity results in compromised maternal condition in adulthood, which in turn results in both earlier death for adult females and a reduction in their ability to successfully raise offspring towards the end of their lives (i.e., a maternal effect on the offspring generation).

Figure 2

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We have demonstrated that adverse environmental conditions during the early life of a female baboon, which are already known to negatively affect both her survival (Tung et al., 2016) and her reproduction (Lea et al., 2015) in adulthood, also reduce the survival of her offspring. Importantly, this effect is independent of the environment experienced by those offspring themselves (Figure 1). The reduction in offspring survival is likely linked to reductions in maternal viability: mothers that experienced early life adversity are significantly less able to successfully raise offspring born near the ends of their lives, while the same is not true for mothers that did not experience early life adversity (Figure 2). Together, these findings support the hypothesis that early life adversity produces constraints during development that lead not only to reduced adult survival and lifetime reproductive success (Tung et al., 2016) but also to a reduced ability to successfully raise those offspring that are produced (Figure 2a). We did not identify any sex-specific intergenerational effects of maternal early adversity.

The results reported here help to fill a key gap in the literature concerning the intergenerational effects of early life adversity on survival. Human studies have yielded inconsistent results on this topic thus far when maternal and offspring environments are not correlated: different studies on the same populations have reported contradictory sex-specific effects on health (Veenendaal et al., 2013; Painter et al., 2008) or have found that small differences in the age at which subjects’ parents or grandparents were exposed to adversity can lead to a reversal in the direction of these effects (Kaati et al., 2007; Pembrey et al., 2006). Among studies in non-human animals, several studies in fish (Donelson et al., 2008; Venturelli et al., 2010), reptiles (Warner and Lovern, 2014), birds (Blomqvist et al., 1997; Ridley, 2007), and ungulates (Cameron et al., 1987; Théoret-Gosselin et al., 2015; Keech et al., 2000; Clutton-Brock et al., 1987; Clutton-Brock et al., 1984) have found that parental body condition at the time of offspring birth influences offspring survival, but none have linked parents’ early adverse experiences to offspring survival. Additionally, while previous studies have identified effects of parental early adversity on offspring traits in a limited number of captive, short-lived systems (Burton and Metcalfe, 2014; Huck et al., 1987; Naguib et al., 2006), ours is the first to link parental early adversity to an important component of offspring fitness in a wild, long-lived animal.

Our findings help to explain the persistence of health deficits across generations (Aizer and Currie, 2014; Kane et al., 2018; Cnattingius et al., 2012), by revealing that in long-lived primates, the early life experiences of mothers have important implications for offspring health and survival. Recent studies in humans have demonstrated that conditions experienced by mothers during pregnancy (e.g., low SES, psychosocial stress, mood dysregulation, prenatal smoking) can affect HPA axis regulation (Thayer and Kuzawa, 2014; Entringer et al., 2009) and birthweight (Aizer and Currie, 2014; Kane et al., 2018) in her offspring. These and other maternal characteristics present during pregnancy are influenced not only by mothers’ experiences in adulthood, but also by the long-term effects of environmental conditions experienced in mothers’ early lives (Kane et al., 2018; Kuzawa, 2005). Our findings therefore motivate future work to test for comparable intergenerational fitness effects of early adversity in humans and other non-human animals.

Our findings are consistent with the hypothesis that early adversity results in intergenerational developmental constraints (Lea et al., 2015; Grafen, 1988; Monaghan, 2008; Lea et al., 2017a) and are not consistent with an intergenerational predictive adaptive response hypothesis (Monaghan, 2008; Gluckman et al., 2005; Herman et al., 2014). Rather than being buffered against the effects of maternal loss, those offspring that experienced maternal loss and whose mothers had also experienced maternal loss were more likely, not less likely, to die, as compared to offspring that experienced maternal loss but whose mothers did not. Thus, individuals in the offspring generation experience constraints not only as a result of their own early environment, but also as a result of their mothers’ developmental histories, including events that occurred years before their own conception. Our results are consistent with the hypothesis that a female’s condition at the time of her offspring’s conception and/or birth reflects her previous experiences, and that her condition thereby influences the development and survival of her offspring (Kuzawa, 2005; Kuzawa, 2017; Lea et al., 2017b).

Our study is unable to definitively identify the mechanism by which effects of early adversity may be transmitted from parent to offspring. However, our finding that reduced offspring survival appears to be partially mediated by reduced maternal viability suggests that the mode of transmission is most readily explained as a classic parental effect, in which early life adversity affects the phenotypic quality of the mother during adulthood, and in turn affects her offspring’s development (Mousseau and Fox, 1998a; Mousseau and Fox, 1998b; Russell and Lummaa, 2009; Badyaev and Uller, 2009). Recently, intergenerational transmission of adversity has been discussed as a potential consequence of inherited epigenetic changes (Heard and Martienssen, 2014). While we cannot exclude this possibility, our results are a reminder that simpler mechanisms—in this case, a classic maternal effect—may be a more parsimonious (albeit non-mutually exclusive) explanation.

Notably, the importance of both maternal death and a close-in-age younger sibling suggest that maternal investment may be key to understanding the intergenerational developmental constraints we observed. Both maternal death and the presence of a close-in-age sibling suggest a possible reduction in the amount of maternal investment that the mothers in our analysis received during their early life. Maternal loss, even after weaning, may affect a developing primate’s ability to learn to forage, to avoid social harassment, and to receive social benefits, such as grooming, that are linked to health (King, 1994; Janson and van Schaik, 1993; Akinyi et al., 2013; Walters, 1987; Altmann, 1998; Ezenwa et al., 2016). Having a close-in-age sibling likely predicts a relatively early weaning event, which may reflect less maternal provisioning than would occur with more delayed weaning and a longer birth interval (Hinde and Milligan, 2011; Mattison et al., 2015; Silk, 1988). Thus, we hypothesize that mothers who lost their own mothers or had a close-in-age sibling suffered reduced energetic and social input from their mothers, which subsequently led to lifelong developmental constraints. Additionally, these females may not have had adequate time to learn from their mothers how to provide high quality maternal care later in life. While we do not routinely collect detailed data on maternal care as part of long-term monitoring, the results reported here motivate targeted analyses of how maternal adversity relates to differences in maternal care and style in future work.

All data used in analyses have been anonymized and made available on Dryad (https://doi.org/10.5061/dryad.4hc8k1r).

1. 1. Zipple MN
   2. Archie EA
   3. Tung J
   4. Altmann J
   5. Alberts SC

   (2019) Dryad Digitial Repository

   Data from: Intergenerational effects of early adversity on survival in wild baboons.

   https://doi.org/10.5061/dryad.4hc8k1r

1. 1. Aizer A
   2. Currie J

   (2014) The intergenerational transmission of inequality: maternal disadvantage and health at birth

   Science 344:856–861.

   https://doi.org/10.1126/science.1251872

   • PubMed
   • Google Scholar
2. 1. Akinyi MY
   2. Tung J
   3. Jeneby M
   4. Patel NB
   5. Altmann J
   6. Alberts SC

   (2013) Role of grooming in reducing tick load in wild baboons (Papio cynocephalus)

   Animal Behaviour 85:559–568.

   https://doi.org/10.1016/j.anbehav.2012.12.012

   • PubMed
   • Google Scholar
3. Book

   1. Alberts SC
   2. Altmann J

   (2012) The amboseli baboon research project: 40 years of continuity and change

   In: Kappeler P, Watts D, editors. Long-Term Field Studies of Primates. Berlin: Springer. pp. 261–287.

   https://doi.org/10.1007/978-3-642-22514-7_12

   • Google Scholar
4. 1. Alonso-Alvarez C
   2. Bertrand S
   3. Sorci G

   (2007) Sex-specific transgenerational effects of early developmental conditions in a passerine

   Biological Journal of the Linnean Society 91:469–474.

   https://doi.org/10.1111/j.1095-8312.2007.00811.x

   • Google Scholar
5. 1. Altmann J
   2. Altmann SA
   3. Hausfater G

   (1978) Primate infant's effects on mother's future reproduction

   Science 201:1028–1030.

   https://doi.org/10.1126/science.98844

   • PubMed
   • Google Scholar
6. Book

   1. Altmann J

   (1980)

   Baboon Mothers and Infants

   Cambridge, Massachussets: Harvard University Press.

   • Google Scholar
7. 1. Altmann J
   2. Hausfater G
   3. Altmann SA

   (1988)

   Reproductive Success

   Determinants of reproductive success in Savannah baboons, Papio cynocephalus, Reproductive Success, Chicago, IL, University of Chicago Press.

   • Google Scholar
8. Book

   1. Altmann S

   (1998)

   Foraging for Survival: Yearling Baboons in Africa

   Chicago, IL: University of Chicago Press.

   • Google Scholar
9. 1. Altmann J
   2. Alberts SC

   (2003)

   Offspring: The Biodemography of Fertility and Family Behavior

   Intraspecific variability in fertility and offspring survival in a nonhuman primate: behavioral control of ecological and social sources, Offspring: The Biodemography of Fertility and Family Behavior, Washington, DC, National Academy Press.

   • Google Scholar
10. 1. Altmann J
    2. Alberts SC

    (2003b) Variability in reproductive success viewed from a life-history perspective in baboons

    American Journal of Human Biology 15:401–409.

    https://doi.org/10.1002/ajhb.10157

    • PubMed
    • Google Scholar
11. 1. Badyaev AV
    2. Uller T

    (2009) Parental effects in ecology and evolution: mechanisms, processes and implications

    Philosophical Transactions of the Royal Society B: Biological Sciences 364:1169–1177.

    https://doi.org/10.1098/rstb.2008.0302

    • Google Scholar
12. 1. Balbontín J
    2. Møller AP

    (2015) Environmental conditions during early life accelerate the rate of senescence in a short-lived passerine bird

    Ecology 96:948–959.

    https://doi.org/10.1890/14-1274.1

    • PubMed
    • Google Scholar
13. 1. Beckerman AP
    2. Benton TG
    3. Lapsley CT
    4. Koesters N

    (2003) Talkin' 'bout my generation: environmental variability and cohort effects

    The American Naturalist 162:754–767.

    https://doi.org/10.1086/381056

    • PubMed
    • Google Scholar
14. 1. Beebe-Dimmer J
    2. Lynch JW
    3. Turrell G
    4. Lustgarten S
    5. Raghunathan T
    6. Kaplan GA

    (2004) Childhood and adult socioeconomic conditions and 31-year mortality risk in women

    American Journal of Epidemiology 159:481–490.

    https://doi.org/10.1093/aje/kwh057

    • PubMed
    • Google Scholar
15. 1. Beehner JC
    2. Onderdonk DA
    3. Alberts SC
    4. Altmann J

    (2006) The ecology of conception and pregnancy failure in wild baboons

    Behavioral Ecology 17:741–750.

    https://doi.org/10.1093/beheco/arl006

    • Google Scholar
16. 1. Blomqvist D
    2. Johansson OC
    3. Götmark F

    (1997) Parental quality and egg size affect chick survival in a precocial bird, the lapwing Vanellus vanellus

    Oecologia 110:18–24.

    https://doi.org/10.1007/s004420050128

    • PubMed
    • Google Scholar
17. 1. Bowers EK
    2. Thompson CF
    3. Sakaluk SK

    (2017) Maternal natal environment and breeding territory predict the condition and sex ratio of offspring

    Evolutionary Biology 44:11–20.

    https://doi.org/10.1007/s11692-016-9380-9

    • PubMed
    • Google Scholar
18. 1. Burton T
    2. Metcalfe NB

    (2014) Can environmental conditions experienced in early life influence future generations?

    Proceedings of the Royal Society B: Biological Sciences 281:20140311.

    https://doi.org/10.1098/rspb.2014.0311

    • Google Scholar
19. 1. Cameron RD
    2. Smith WT
    3. Fancy SG
    4. Gerhart K
    5. White R

    (1987) Calving success of female caribou in relation to body weight

    Journal of Zoology 71:480–486.

    https://doi.org/10.1139/z93-069

    • Google Scholar
20. 1. Charpentier MJ
    2. Tung J
    3. Altmann J
    4. Alberts SC

    (2008) Age at maturity in wild baboons: genetic, environmental and demographic influences

    Molecular Ecology 17:2026–2040.

    https://doi.org/10.1111/j.1365-294X.2008.03724.x

    • PubMed
    • Google Scholar
21. 1. Clutton-Brock TH
    2. Albon SD
    3. Guinness FE

    (1984) Maternal dominance, breeding success and birth sex ratios in red deer

    Nature 308:358–360.

    https://doi.org/10.1038/308358a0

    • Google Scholar
22. 1. Clutton-Brock TH
    2. Major M
    3. Albon SD
    4. Guinness FE

    (1987) Early development and population dynamics in red deer. I. Density-Dependent effects on juvenile survival

    The Journal of Animal Ecology 56:53–67.

    https://doi.org/10.2307/4799

    • Google Scholar
23. 1. Cnattingius S
    2. Villamor E
    3. Lagerros YT
    4. Wikström A-K
    5. Granath F

    (2012) High birth weight and obesity—a vicious circle across generations

    International Journal of Obesity 36:1320–1324.

    https://doi.org/10.1038/ijo.2011.248

    • Google Scholar
24. 1. Donelson JM
    2. McCormick MI
    3. Munday PL

    (2008) Parental condition affects early life-history of a coral reef fish

    Journal of Experimental Marine Biology and Ecology 360:109–116.

    https://doi.org/10.1016/j.jembe.2008.04.007

    • Google Scholar
25. 1. Douhard M
    2. Plard F
    3. Gaillard J-M
    4. Capron G
    5. Delorme D
    6. Klein F
    7. Duncan P
    8. Loe LE
    9. Bonenfant C

    (2014) Fitness consequences of environmental conditions at different life stages in a long-lived vertebrate

    Proceedings of the Royal Society B: Biological Sciences 281:20140276.

    https://doi.org/10.1098/rspb.2014.0276

    • Google Scholar
26. 1. Entringer S
    2. Kumsta R
    3. Hellhammer DH
    4. Wadhwa PD
    5. Wüst S

    (2009) Prenatal exposure to maternal psychosocial stress and HPA Axis regulation in young adults

    Hormones and Behavior 55:292–298.

    https://doi.org/10.1016/j.yhbeh.2008.11.006

    • PubMed
    • Google Scholar
27. 1. Ezenwa VO
    2. Ghai RR
    3. McKay AF
    4. Williams AE

    (2016) Group living and pathogen infection revisited

    Current Opinion in Behavioral Sciences 12:66–72.

    https://doi.org/10.1016/j.cobeha.2016.09.006

    • Google Scholar
28. 1. Felitti VJ
    2. Anda RF
    3. Nordenberg D
    4. Williamson DF
    5. Spitz AM
    6. Edwards V
    7. Koss MP
    8. Marks JS

    (1998) Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults. The adverse childhood experiences (ACE) Study

    American Journal of Preventive Medicine 14:245–258.

    https://doi.org/10.1016/S0749-3797(98)00017-8

    • PubMed
    • Google Scholar
29. 1. Fischer K
    2. Eenhoorn E
    3. Bot ANM
    4. Brakefield PM
    5. Zwaan BJ

    (2003) Cooler butterflies lay larger eggs: developmental plasticity versus acclimation

    Proceedings of the Royal Society of London. Series B: Biological Sciences 270:2051–2056.

    https://doi.org/10.1098/rspb.2003.2470

    • Google Scholar
30. 1. Frankel S
    2. Smith GD
    3. Gunnell D

    (1999) Childhood socioeconomic position and adult cardiovascular mortality: the boyd orr cohort

    American Journal of Epidemiology 150:1081–1084.

    https://doi.org/10.1093/oxfordjournals.aje.a009932

    • PubMed
    • Google Scholar
31. 1. Galobardes B
    2. Lynch JW
    3. Davey Smith G

    (2004) Childhood socioeconomic circumstances and cause-specific mortality in adulthood: systematic review and interpretation

    Epidemiologic Reviews 26:7–21.

    https://doi.org/10.1093/epirev/mxh008

    • PubMed
    • Google Scholar
32. 1. Gluckman PD
    2. Hanson MA
    3. Spencer HG

    (2005) Predictive adaptive responses and human evolution

    Trends in Ecology & Evolution 20:527–533.

    https://doi.org/10.1016/j.tree.2005.08.001

    • PubMed
    • Google Scholar
33. 1. Goerlich VC
    2. Nätt D
    3. Elfwing M
    4. Macdonald B
    5. Jensen P

    (2012) Transgenerational effects of early experience on behavioral, hormonal and gene expression responses to acute stress in the precocial chicken

    Hormones and Behavior 61:711–718.

    https://doi.org/10.1016/j.yhbeh.2012.03.006

    • PubMed
    • Google Scholar
34. 1. Grafen A

    (1988)

    Reproductive Success

    On the uses of data on lifetime reproductive success, Reproductive Success, Chicago,IL, University of Chicago Press.

    • Google Scholar
35. 1. Hausfater G

    (1975)

    Dominance and reproduction in baboons (Papio cynocephalus)

    Contributions to Primatology 7:1–150.

    • PubMed
    • Google Scholar
36. 1. Heard E
    2. Martienssen RA

    (2014) Transgenerational epigenetic inheritance: myths and mechanisms

    Cell 157:95–109.

    https://doi.org/10.1016/j.cell.2014.02.045

    • PubMed
    • Google Scholar
37. 1. Helle H
    2. Koskela E
    3. Mappes T

    (2012) Life in varying environments: experimental evidence for delayed effects of juvenile environment on adult life history

    Journal of Animal Ecology 81:573–582.

    https://doi.org/10.1111/j.1365-2656.2011.01937.x

    • PubMed
    • Google Scholar
38. 1. Herfindal I
    2. van de Pol M
    3. Nielsen JT
    4. Saether BE
    5. Møller AP

    (2015) Climatic conditions cause complex patterns of covariation between demographic traits in a long-lived raptor

    Journal of Animal Ecology 84:702–711.

    https://doi.org/10.1111/1365-2656.12318

    • PubMed
    • Google Scholar
39. 1. Herman JJ
    2. Spencer HG
    3. Donohue K
    4. Sultan SE

    (2014) How stable ‘SHOULD’ EPIGENETIC MODIFICATIONS BE? INSIGHTS FROM ADAPTIVE PLASTICITY AND BET HEDGING

    Evolution 68:632–643.

    https://doi.org/10.1111/evo.12324

    • Google Scholar
40. 1. Hinde K
    2. Milligan LA

    (2011) Primate milk: proximate mechanisms and ultimate perspectives

    Evolutionary Anthropology: Issues, News, and Reviews 20:9–23.

    https://doi.org/10.1002/evan.20289

    • PubMed
    • Google Scholar
41. 1. Huck UW
    2. Labov JB
    3. Lisk RD

    (1986) Food restricting young hamsters (Mesocricetus auratus) affects sex ratio and growth of subsequent offspring

    Biology of Reproduction 35:592–598.

    https://doi.org/10.1095/biolreprod35.3.592

    • PubMed
    • Google Scholar
42. 1. Huck UW
    2. Labov JB
    3. Lisk RD

    (1987) Food-restricting first generation juvenile female hamsters (Mesocricetus auratus) affects sex ratio and growth of third generation offspring

    Biology of Reproduction 37:612–617.

    https://doi.org/10.1095/biolreprod37.3.612

    • PubMed
    • Google Scholar
43. Book

    1. Janson C
    2. van Schaik C

    (1993)

    Ecological risk aversion in juvenile primates: slow and steady wins the race

    In: Pereira M. E, Fairbanks L. A, editors. Juvenile Primates: Life History, Development, and Behavior. Chicago, IL: University of Chicago Press. pp. 57–74.

    • Google Scholar
44. 1. Jobson MA
    2. Jordan JM
    3. Sandrof MA
    4. Hibshman JD
    5. Lennox AL
    6. Baugh LR

    (2015) Transgenerational effects of early life starvation on growth, reproduction, and stress resistance in Caenorhabditis elegans

    Genetics 201:201–212.

    https://doi.org/10.1534/genetics.115.178699

    • PubMed
    • Google Scholar
45. 1. Kaati G
    2. Bygren LO
    3. Pembrey M
    4. Sjöström M

    (2007) Transgenerational response to nutrition, early life circumstances and longevity

    European Journal of Human Genetics 15:784–790.

    https://doi.org/10.1038/sj.ejhg.5201832

    • PubMed
    • Google Scholar
46. 1. Kane JB
    2. Harris KM
    3. Siega-Riz AM

    (2018) Intergenerational pathways linking maternal early life adversity to offspring birthweight

    Social Science & Medicine 207:89–96.

    https://doi.org/10.1016/j.socscimed.2018.04.049

    • PubMed
    • Google Scholar
47. 1. Keech MA
    2. Bowyer RT
    3. Hoef JMV
    4. Boertje RD
    5. Dale BW
    6. Stephenson TR

    (2000) Life-History consequences of maternal condition in alaskan moose

    The Journal of Wildlife Management 64:450.

    https://doi.org/10.2307/3803243

    • Google Scholar
48. Book

    1. King B

    (1994)

    The Information Continuum: Evolution of Social Information Transfer in Monkeys, Apes, and Hominids

    Santa Fe, NM: School for Advanced Research Press.

    • Google Scholar
49. 1. Kittleson MM
    2. Meoni LA
    3. Wang NY
    4. Chu AY
    5. Ford DE
    6. Klag MJ

    (2006) Association of childhood socioeconomic status with subsequent coronary heart disease in physicians

    Archives of Internal Medicine 166:2356.

    https://doi.org/10.1001/archinte.166.21.2356

    • PubMed
    • Google Scholar
50. 1. Kuh D
    2. Hardy R
    3. Langenberg C
    4. Richards M
    5. Wadsworth ME

    (2002) Mortality in adults aged 26-54 years related to socioeconomic conditions in childhood and adulthood: post war birth cohort study

    BMJ 325:1076–1080.

    https://doi.org/10.1136/bmj.325.7372.1076

    • PubMed
    • Google Scholar
51. 1. Kuzawa CW

    (2005) Fetal origins of developmental plasticity: are fetal cues reliable predictors of future nutritional environments?

    American Journal of Human Biology 17:5–21.

    https://doi.org/10.1002/ajhb.20091

    • PubMed
    • Google Scholar
52. 1. Kuzawa CW

    (2017) Which environments matter in studies of early life developmental plasticity?

    Evolution, Medicine, and Public Health 2017:188–190.

    https://doi.org/10.1093/emph/eox024

    • PubMed
    • Google Scholar
53. 1. Lea AJ
    2. Learn NH
    3. Theus MJ
    4. Altmann J
    5. Alberts SC

    (2014) Complex sources of variance in female dominance rank in a nepotistic society

    Animal Behaviour 94:87–99.

    https://doi.org/10.1016/j.anbehav.2014.05.019

    • PubMed
    • Google Scholar
54. 1. Lea AJ
    2. Altmann J
    3. Alberts SC
    4. Tung J

    (2015) Developmental constraints in a wild primate

    The American Naturalist 185:809–821.

    https://doi.org/10.1086/681016

    • PubMed
    • Google Scholar
55. 1. Lea AJ
    2. Tung J
    3. Archie EA
    4. Alberts SC

    (2017a) Developmental plasticity: bridging research in evolution and human health

    Evol Med Public Heal 2017:162–175.

    https://doi.org/10.1093/emph/eox019

    • Google Scholar
56. 1. Lea AJ
    2. Tung J
    3. Archie EA
    4. Alberts SC

    (2017b) Developmental plasticity research in evolution and human health: response to commentaries

    Evolution, Medicine, and Public Health 2017:201–205.

    https://doi.org/10.1093/emph/eoy007

    • PubMed
    • Google Scholar
57. 1. Lidfeldt J
    2. Li TY
    3. Hu FB
    4. Manson JE
    5. Kawachi I

    (2007) A prospective study of childhood and adult socioeconomic status and incidence of type 2 diabetes in women

    American Journal of Epidemiology 165:882–889.

    https://doi.org/10.1093/aje/kwk078

    • PubMed
    • Google Scholar
58. 1. Marcil-Ferland D
    2. Festa-Bianchet M
    3. Martin AM
    4. Pelletier F

    (2013) Despite catch-up, prolonged growth has detrimental fitness consequences in a long-lived vertebrate

    The American Naturalist 182:775–785.

    https://doi.org/10.1086/673534

    • PubMed
    • Google Scholar
59. 1. Mattison SM
    2. Wander K
    3. Hinde K

    (2015) Breastfeeding over two years is associated with longer birth intervals, but not measures of growth or health, among children in Kilimanjaro, TZ

    American Journal of Human Biology 27:807–815.

    https://doi.org/10.1002/ajhb.22729

    • PubMed
    • Google Scholar
60. 1. Millon A
    2. Petty SJ
    3. Little B
    4. Lambin X

    (2011) Natal conditions alter age-specific reproduction but not survival or senescence in a long-lived bird of prey

    Journal of Animal Ecology 80:968–975.

    https://doi.org/10.1111/j.1365-2656.2011.01842.x

    • PubMed
    • Google Scholar
61. 1. Monaghan P

    (2008) Early growth conditions, phenotypic development and environmental change

    Philosophical Transactions of the Royal Society B: Biological Sciences 363:1635–1645.

    https://doi.org/10.1098/rstb.2007.0011

    • Google Scholar
62. Book

    1. Mousseau TA
    2. Fox CW

    (1998a)

    Maternal Effects as Adaptations

    Oxford: Oxford University Press.

    • Google Scholar
63. 1. Mousseau TA
    2. Fox CW

    (1998b) The adaptive significance of maternal effects

    Trends in Ecology & Evolution 13:403–407.

    https://doi.org/10.1016/S0169-5347(98)01472-4

    • PubMed
    • Google Scholar
64. 1. Naess Ø
    2. Claussen B
    3. Davey Smith G

    (2004) Relative impact of childhood and adulthood socioeconomic conditions on cause specific mortality in men

    Journal of Epidemiology and Community Health 58:597–598.

    https://doi.org/10.1136/jech.2003.012229

    • PubMed
    • Google Scholar
65. 1. Naguib M
    2. Nemitz A
    3. Gil D

    (2006) Maternal developmental stress reduces reproductive success of female offspring in zebra finches

    Proceedings of the Royal Society B: Biological Sciences 273:1901–1905.

    https://doi.org/10.1098/rspb.2006.3526

    • Google Scholar
66. 1. Nussey DH
    2. Kruuk LE
    3. Morris A
    4. Clutton-Brock TH

    (2007) Environmental conditions in early life influence ageing rates in a wild population of red deer

    Current Biology 17:R1000–R1001.

    https://doi.org/10.1016/j.cub.2007.10.005

    • PubMed
    • Google Scholar
67. 1. Painter RC
    2. Osmond C
    3. Gluckman P
    4. Hanson M
    5. Phillips DIW
    6. Roseboom TJ

    (2008) Transgenerational effects of prenatal exposure to the dutch famine on neonatal adiposity and health in later life

    BJOG: An International Journal of Obstetrics & Gynaecology 115:1243–1249.

    https://doi.org/10.1111/j.1471-0528.2008.01822.x

    • Google Scholar
68. 1. Pembrey ME
    2. Bygren LO
    3. Kaati G
    4. Edvinsson S
    5. Northstone K
    6. Sjöström M
    7. Golding J

    (2006) Sex-specific, male-line transgenerational responses in humans

    European Journal of Human Genetics 14:159–166.

    https://doi.org/10.1038/sj.ejhg.5201538

    • Google Scholar
69. 1. Pigeon G
    2. Pelletier F

    (2018) Direct and indirect effects of early-life environment on lifetime fitness of bighorn ewes

    Proceedings of the Royal Society B: Biological Sciences 285:20171935.

    https://doi.org/10.1098/rspb.2017.1935

    • Google Scholar
70. 1. R Development Core Team

    (2018)

    R: A Language and Environment for Statistical Computing

    R: A Language and Environment for Statistical Computing, Vienna, Austria.

    • Google Scholar
71. 1. Ridley AR

    (2007) Factors affecting offspring survival and development in a cooperative bird: social, maternal and environmental effects

    Journal of Animal Ecology 76:750–760.

    https://doi.org/10.1111/j.1365-2656.2007.01248.x

    • PubMed
    • Google Scholar
72. 1. Russell AF
    2. Lummaa V

    (2009) Maternal effects in cooperative breeders: from hymenopterans to humans

    Philosophical Transactions of the Royal Society B: Biological Sciences 364:1143–1167.

    https://doi.org/10.1098/rstb.2008.0298

    • Google Scholar
73. 1. Saastamoinen M
    2. Hirai N
    3. van Nouhuys S

    (2013) Direct and trans-generational responses to food deprivation during development in the glanville fritillary butterfly

    Oecologia 171:93–104.

    https://doi.org/10.1007/s00442-012-2412-y

    • PubMed
    • Google Scholar
74. 1. Silk JB

    (1988) Maternal investment in captive bonnet macaques (Macaca radiata)

    The American Naturalist 132:1–19.

    https://doi.org/10.1086/284834

    • Google Scholar
75. 1. Silk JB
    2. Alberts SC
    3. Altmann J

    (2003) Social bonds of female baboons enhance infant survival

    Science 302:1231–1234.

    https://doi.org/10.1126/science.1088580

    • PubMed
    • Google Scholar
76. 1. Smith GD
    2. Hart C
    3. Blane D
    4. Hole D

    (1998) Adverse socioeconomic conditions in childhood and cause specific adult mortality: prospective observational study

    BMJ 316:1631–1635.

    https://doi.org/10.1136/bmj.316.7145.1631

    • PubMed
    • Google Scholar
77. 1. Taborsky B

    (2006) Mothers determine offspring size in response to own juvenile growth conditions

    Biology Letters 2:225–228.

    https://doi.org/10.1098/rsbl.2005.0422

    • PubMed
    • Google Scholar
78. 1. Thayer ZM
    2. Kuzawa CW

    (2014) Early origins of health disparities: material deprivation predicts maternal evening cortisol in pregnancy and offspring cortisol reactivity in the first few weeks of life

    American Journal of Human Biology 26:723–730.

    https://doi.org/10.1002/ajhb.22532

    • PubMed
    • Google Scholar
79. 1. Théoret-Gosselin R
    2. Hamel S
    3. Côté SD

    (2015) The role of maternal behavior and offspring development in the survival of mountain goat kids

    Oecologia 178:175–186.

    https://doi.org/10.1007/s00442-014-3198-x

    • PubMed
    • Google Scholar
80. Software

    1. Therneau TM

    (2012)

    Coxme: Mixed Effects Cox Models

    R Package Version 2.3-3.
81. 1. Tung J
    2. Archie EA
    3. Altmann J
    4. Alberts SC

    (2016) Cumulative early life adversity predicts longevity in wild baboons

    Nature Communications 7:11181.

    https://doi.org/10.1038/ncomms11181

    • PubMed
    • Google Scholar
82. 1. Uller T
    2. Nakagawa S
    3. English S

    (2013) Weak evidence for anticipatory parental effects in plants and animals

    Journal of Evolutionary Biology 26:2161–2170.

    https://doi.org/10.1111/jeb.12212

    • PubMed
    • Google Scholar
83. 1. van de Mheen H
    2. Stronks K
    3. Looman CW
    4. Mackenbach JP

    (1998) Does childhood socioeconomic status influence adult health through behavioural factors?

    International Journal of Epidemiology 27:431–437.

    https://doi.org/10.1093/ije/27.3.431

    • PubMed
    • Google Scholar
84. 1. Veenendaal MVE
    2. Painter RC
    3. de Rooij SR
    4. Bossuyt PMM
    5. van der Post JAM
    6. Gluckman PD
    7. Hanson MA
    8. Roseboom TJ

    (2013) Transgenerational effects of prenatal exposure to the 1944-45 dutch famine

    BJOG: An International Journal of Obstetrics & Gynaecology 120:548–554.

    https://doi.org/10.1111/1471-0528.12136

    • Google Scholar
85. 1. Venturelli PA
    2. Murphy CA
    3. Shuter BJ
    4. Johnston TA
    5. Van Coeverden de Groot PJ
    6. Boag PT
    7. Casselman JM
    8. Montgomerie R
    9. Wiegand MD
    10. Leggett WC

    (2010) Maternal influences on population dynamics: evidence from an exploited freshwater fish

    Ecology 91:2003–2012.

    https://doi.org/10.1890/09-1218.1

    • PubMed
    • Google Scholar
86. 1. Vijendravarma RK
    2. Narasimha S
    3. Kawecki TJ

    (2010) Effects of parental larval diet on egg size and offspring traits in Drosophila

    Biology Letters 6:238–241.

    https://doi.org/10.1098/rsbl.2009.0754

    • PubMed
    • Google Scholar
87. 1. Walters JR

    (1987)

    Primate Societies

    Transition to adulthood, Primate Societies, Chicago, IL, University of Chicago Press.

    • Google Scholar
88. 1. Warner DA
    2. Lovern MB

    (2014) The maternal environment affects offspring viability via an indirect effect of yolk investment on offspring size

    Physiological and Biochemical Zoology 87:276–287.

    https://doi.org/10.1086/674454

    • PubMed
    • Google Scholar
89. Preprint

    1. Zipple MN
    2. Archie EA
    3. Tung J
    4. Altmann J
    5. Alberts SC

    (2019) Data from: intergenerational effects of early adversity on survival in wild baboons

    bioRxiv.

    https://doi.org/10.1101/591248

    • Google Scholar

Author details

1. Matthew N Zipple

   Department of Biology, Duke University, Durham, United States

   Contribution

   Conceptualization, Formal analysis, Visualization, Methodology, Writing—original draft, Writing—review and editing

   For correspondence

   matthew.zipple@duke.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3451-2103

2. Elizabeth A Archie

   1. Department of Biological Sciences, University of Notre Dame, South Bend, United States
   2. Institute of Primate Research, National Museums of Kenya, Nairobi, Kenya

   Contribution

   Resources, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

3. Jenny Tung

   1. Department of Biology, Duke University, Durham, United States
   2. Institute of Primate Research, National Museums of Kenya, Nairobi, Kenya
   3. Department of Evolutionary Anthropology, Duke University, Durham, United States
   4. Duke Population Research Institute, Duke University, Durham, United States

   Contribution

   Resources, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0416-2958

4. Jeanne Altmann

   1. Institute of Primate Research, National Museums of Kenya, Nairobi, Kenya
   2. Department of Ecology and Evolutionary Biology, Princeton University, Princeton, United States

   Contribution

   Resources, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

5. Susan C Alberts

   1. Department of Biology, Duke University, Durham, United States
   2. Institute of Primate Research, National Museums of Kenya, Nairobi, Kenya
   3. Department of Evolutionary Anthropology, Duke University, Durham, United States
   4. Duke Population Research Institute, Duke University, Durham, United States

   Contribution

   Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Investigation, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   alberts@duke.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1313-488X

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