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Resources that are embedded in social relationships, such as shared knowledge, access to food, services, social support or cooperation, are all examples of social capital. Social capital is recognized as an important age-related mediator of health in humans and fitness-related traits in animals. A rich social capital in humans can slow down senescence and reverse age-related deficits. Some animals are able to adjust their social capital at different life stages (i.e., early, reproductive and post-reproductive life), which may promote individual fitness. However, the underlying biological mechanisms remain unknown. We suggest future research avenues to focus on social capital as a modifiable dimension to gain a better understanding of variations in senescence, and thereby provide new approaches to promote healthy ageing.
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Cédric Sueur, Martin Quque,
Alexandre Naud, Audrey
Bergouignan and François
Criscuolo (2021) Social capital:
an independent dimension of
healthy ageing, Peer
Community Journal, 1: e23.
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Social capital: an independent
dimension of healthy ageing
Cédric Sueur1,2,3, Martin Quque4,3, Alexandre
Naud5, Audrey Bergouignan6,3, and François
Volume 1(2021), article e23
Resources that are embedded in social relationships, such as shared knowledge, access
to food, services, social support or cooperation, are all examples of social capital. So-
cial capital is recognized as an important age-related mediator of health in humans and
fitness-related traits in animals. A rich social capital in humans can slow down senes-
cence and reverse age-related deficits. Some animals are able to adjust their social capi-
tal at different life stages (i.e., early, reproductive and post-reproductive life), which may
promote individual fitness. However, the underlying biological mechanisms remain un-
known. We suggest future research avenues to focus on social capital as a modifiable
dimension to gain a better understanding of variations in senescence, and thereby pro-
vide new approaches to promote healthy ageing.
1Anthropo-Lab, ETHICS EA7446, Lille Catholic University, Lille, France, 2Institut Universitaire de France,
Saint-Michel 103, 75005 Paris, France, 3Université de Strasbourg, CNRS, IPHC UMR 7178, F-67000 Stras-
bourg, France, 4Center for Nonlinear Phenomena and Complex Systems (Cenoli) - CP 231, Université libre
de Bruxelles (ULB), Campus Plaine, Boulevard du Triomphe, Building NO - level 5, B-1050, Bruxelles, Bel-
gium., 5Ecole de Sante Publique, Centre de recherche du Centre hospitalier, Université de Montréal, Canada,
6Division of Endocrinology, Metabolism, and Diabetes and Anschutz Health and Wellness Center, University
of Colorado, School of Medicine, Aurora, CO 80045, USA
The key role of social relationships in ageing
Humans are a social species. Any lack of social contact affects both mental and physical health (see
Glossary for definition of health) (Rattan 2013; Rook 2015). Poor social interactions are even known to be
a risk factor for all-cause mortality (Kawachi et al. 2008; Rook 2015; Snyder-Mackler et al. 2020). Although
numerous studies report associations between social interactions and health outcomes, the underlying
mechanisms are largely unknown. The number of animal studies on the physiological (e.g. stress) or
ecological (e.g. food access) determinants of ageing has risen sharply over the past ten years (Lucas,
Keller 2020; Snyder-Mackler et al. 2020). They suggest that complex and intertwined behavioural,
psychological and biological pathways are likely involved (Box 1) (Rook 2015; Nattrass et al. 2019).
However, these animal studies provided with contrasted results according to species traits (e.g. group size)
(Lucas, Keller 2020) or individual traits (e.g. social status) (Snyder-Mackler et al. 2020). A large part of the
ageing variations at both inter- and intra-specific levels is therefore still unexplained.
Resilience to stress and body energy homoeostasis is affected by social resources (i.e. the knowledge,
services, social support or cooperation (Lakon et al. 2008; Brent et al. 2011; Thoits 2011; Wittig et al. 2016;
Moscovice et al. 2020)) an individual has access to or has used (Lin et al. 2001), which is called social capital.
Individual social capital is a widely used concept in human healthy ageing literature, and recent research
on non-human animals seems to show that social capital represents a key set of components (see Box 2
and table 1) in adjusting senescence and influencing fitness. By adjusting, we mean that changes in social
capital are not random but made in a way to increase fitness (survival and/or reproduction) and/or healthy
ageing. This adjustment is the result of behavioural strategies (e.g., favouring, selecting or avoiding social
interactions). Although these strategies may or may not be ruled by intermediary mechanisms (e.g., stress,
genetics, mating system), the latter being most likely evolutionary selected (Sueur et al. 2019). Based on
the fact that social capital varies with individual age and social group characteristics, we propose that it is
the main factor that mediates the associations between sociality and healthy ageing. In this perspective,
we propose that the mechanisms linking social capital to healthy ageing can be better understood by
adopting an evolutionary and comparative approach within individuals and between humans and animals
(Chiou et al. 2020; Emery Thompson et al. 2020; Machanda, Rosati 2020), thus providing greater insight
into the observed variation in senescence rates and facilitating the identification of anti-ageing
Box 1: Biology of ageing, senescence and longevity in social animals
While an individual can have a long-life expectancy, it may not attain the same fitness as a conspecific
due to an accelerated senescence of the reproductive function (figure 1). The rate of senescence at the
individual level is expected to reflect the lifelong deleterious impact of costly traits such as growth,
immunity or reproduction (Hamilton 1966). Inter-individual variability in the age of senescence onset is also
a unique opportunity to investigate the genetic and socio-environmental factors that shape ageing trade-
offs within a given population. Social stress has been known to modulate ageing pathways for the last
decade (Blackburn, Epel 2012). However, interplay between social capital and age may highlight putative
loops of intertwined pathways that modulate reproductive success and survival rate in both negative and
positive ways ❶. In a resource-based explanation, an initial underlying mechanism relies on the impact of
social capital on energy resource acquisition (for instance via the acquisition of knowledge or friendly
relationships) ❷. However, variation in social capital may act indirectly through cellular and physiological
changes that strengthen resilience to stress ❸ or body energy homoeostasis ❹. These effects are
currently inferred from previous observations. Social isolation and interactions have been described as
having opposite effects on stress hormones (Wittig et al. 2016), with potentially negative consequences but
also adaptive responses observed at the physiological and cellular level (e.g. oxidative stress) (Katyare et
al. 2003). Another study suggests that social isolation has negative effects on stress and energy
balance (Koto et al. 2015). Inflammation is also an important biological mechanism that links social capital
to unhealthy states (Uchino et al. 2018). Indeed, various forms of social adversity are associated with
elevated expression of proinflammatory genes and decreased expression of genes related to innate
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immune responses in humans (Cole 2014) and rhesus macaques (Simons, Tung 2019). These altered
individual performances in the acquisition of energy from the environment will be reflected in the life-
history trade-offs for the allocation of energy to individual fitness traits. Social isolation triggers an
increased rate of telomere loss (a biological index of ageing) (Aydinonat et al. 2014) and disrupts energy
homoeostasis. Increased telomerase activity in socially stressed individuals has also been described in the
literature (Beery et al. 2012). This suggests that social variables do indeed impact cell-ageing proxies, as
previously suggested for social rank and telomere length (Lewin et al. 2015; Bateson, Nettle 2018).
However, as social capital likely varies over time and depends on individual physiological status, a feedback
of physiology is expected on sociality (❺, ❻). For instance, some authors suggest possible causal effects
of short telomeres on unhealthy behaviours as smoking in humans (Bateson, Nettle 2018). It means that
some physiological traits (short telomeres) can conduct to some bad aspects of sociality (here being
conformist with risky behaviours for health) enhancing the physiological traits (decreasing telomeres).
Another example is the accelerated death of ill flies (Drosophila melanogaster) who are isolated from their
conspecifics, likely because of reluctant physiological traits as cancer (Dawson et al. 2018). Because
individuals have cancer, they are isolated from others but this in turn accelerates cancer progression. These
studies confirm that the social capital fitness relationships have auto-regulating properties, a finding that
calls for dedicated studies to identify these causal links.
Figure 1: Schema of the proposed mutual influence of social capital, chronological age and biological
age, from the cell level to the network.
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Table 1: Components of social capital according to the studied species and the level of study.
Variations inside a same group/colony
Variations between
Interspecific variations
(e.g. ants,
bees, naked
mole rats)
Properties of social interactions including
number and duration of interactions, type of
interactions (e.g., trophallaxes (Bles et al. 2018),
antenna-to-antenna, grooming), intra-caste or inter-
caste interactions.
Spatial distribution of social interactions
(Planckaert et al. 2019) according to individual
mobility patterns
Individual positions within the social system
including, not exclusively, individual caste (e.g., male,
queen, nest worker) or centrality index (Stroeymeyt
et al. 2018)
Properties of the colony (Dornhaus
et al. 2009) including, not exclusively, its
size, the population distribution by caste
(e.g., mono/polygyny, the ratio of
individuals between caste), and the
colony age.
Properties of the whole system of
social interactions (Quque et al. 2021)
using network indicators such as
community separation and its resilience.
Properties of the colony, including,
among others, its size, the caste system
specific characteristic (e.g., number of
reproductive individuals, marked division
of labour, short or long-lived males,
worker dimorphism) (Jarvis 1981;
Robinson 1992; Burda et al. 2000;
Beshers, Fewell 2001)
Relation with other colonies
including the tolerance level and
belonging to supercolony (Sunamura et
al. 2011).
breeding species
Properties of social interactions including
number and duration of interactions, type of
interactions (e.g., grooming, aggression, reproductive
behaviour). (Drewe 2010; Dey et al. 2013)
Properties of social relationships which may
include kinship, sex, reproductive status and
dominance hierarchy. (Reber et al. 2013)
Individual positions within social structures
including, not exclusively, individual status (e.g.,
reproductive or helpers) and its position between
reproductive subgroups (Drewe et al. 2009; Madden
et al. 2009, 2011)
Properties of the colony, may
include its size, the number of helpers
and offspring and the system of
interactions between reproductive
subgroups. (Covas et al. 2008)
Properties of cooperative breeding,
including if its facultative or systematic
and the level of competition for
reproduction between helpers and male
breeder. (Kimball et al. 2003;
Hatchwell 2009; Riehl 2013; Griesser,
Suzuki 2016; Bebbington et al. 2018)
Comparing evolutionary
advantages of cooperative and not-
cooperative breeding, regarding e.g.
longevity, reproductive success, life
history. (Beauchamp 2014; Downing et
al. 2015, 2021)
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societies and
similar mammal
Properties of social interactions including
number and duration of interactions, type of
interactions (e.g., grooming, aggression, reproductive
behaviour, exchange of resources) (Bret et al. 2013;
Levé et al. 2016; Puga-Gonzalez et al. 2018; Sosa et
al. 2019; Ramos et al. 2019) and their spatial
distribution (Naud et al. 2016)
Properties of social relationships, including, not
exclusively, kinship (Best et al. 2014), dominance
(Wroblewski et al. 2009), direction and reciprocity in
conflicts and resources exchange (Puga-Gonzalez et
al. 2018).
Individual position within social structures,
including, among others, centrality (Sosa et al. 2020),
belonging to certain subgroups and dominance
(Balasubramaniam et al. 2012) relative to the whole
Properties of the group, including
their size, and their age/sex distribution
(Wrangham 1987; Kappeler,
Schaik 2002)
Cultural variation (Cronin et al.
2014; Borgeaud et al. 2016) including,
among others, tolerance in aggression
and exchange with non-kin, tool use
Properties of the interaction
network, including, among others, the
level of community division resulting
from non-kin interactions (Sueur et al.
Exchange with and tolerance of
other groups (between-group
Properties of the group, including,
their size, the mating system, the
hierarchical structures, and affiliation
between non-kin (Sueur et al. 2011).
Structure of the interaction
networks including their size, community
structure and efficacy in exchange of
information and ressources (Pasquaretta
et al. 2014; Romano et al. 2018)
Properties of social relationships which may
include, relation type (e.g., relatives, colleagues,
friends) (Pinquart, Sörensen 2000), relationship
diversity (Ali et al. 2018), marital status and quality
(Robles et al. 2014; Kiecolt-Glaser, Wilson 2017;
Kiecolt-Glaser et al. 2019), closeness and
intimacy (Kelley et al. 1983; Debra J. Mashek 2004),
homophily (Fowler et al. 2011; Montgomery et al.
2020) and their perceived valence (i.e., positive,
negative, ambivalent) (Uchino et al. 2012a).
Properties of social interactions, which may
include perceived and received support (Lyyra,
Heikkinen 2006; Nausheen et al. 2009; Gariepy et al.
2016), companionship (Rook 1987; Buunk,
Verhoeven 1991), negative interactions (Rook 2001;
International and intra-national
comparison of individual-level social
capital according, not exclusively, to
ethnic groups (Baron-Epel et al. 2008),
welfare regime (Kääriäinen,
Lehtonen 2006), regional economic
growth (Beugelsdijk, Van Schaik 2005),
or socio-economic status (Kim et al.
Community-level social properties
such as centrality (Strauss, Pollack 2003;
Christakis, Fowler 2007), clustering
(González et al. 2007; Christakis,
Fowler 2008; Frank et al. 2013) dyadic
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Akiyama et al. 2003) and their geospatial
distribution (van den Berg et al. 2015; Kestens et al.
Individual position within social structures such
as centrality and periphery (Shakya et al. 2015),
brokerage (Dekker 2006), and belonging to specific
subgroups (Hynie et al. 2011)
Indicators of complex processes such as social
isolation (Holt-Lunstad et al. 2015; Smith et al. 2020)
bonding and bridging capital (Kim et al. 2006;
Murayama et al. 2015), social participation
(Levasseur et al. 2010), social inclusion and exclusion
(Wright, Stickley 2013).
distances (Christakis, Fowler 2007,
2008), social connectedness (Entwisle et
al. 2007; Shakya et al. 2014) or
components and cyclical structures
(Helleringer, Kohler 2007)
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Social capital changes with chronological age
The social capital of an individual varies according to its life stage (i.e., early, reproductive or post-
reproductive life) (McDonald, Mair 2010). In humans, non-human mammals and other species with long-
lasting mother-offspring bonds, infants focus on a small number of strong relationships with their mother
and individuals who share common traits (e.g., gender, kin). As adolescents, the individuals then expand
the quantity and diversity of their social relationships, and become more selective upon reaching
adulthood (Field, Minkler 1988) in order to adjust social capital in favour of resource acquisition (box 1).
Elephants (Loxondota africana (McComb et al. 2001)), chimpanzees (Pan troglodytes (Rosati et al.
2020)) and macaques (Macaca sp. (Brent et al. 2011; Almeling et al. 2016)) show comparable patterns of
social changes with chronological age, even if they generally display higher interspecific than intraspecific
longevity variation. In elephants, social relationships such as dominance are age-based (Wittemyer,
Getz 2007). Matriarchs are the repositories of knowledge and manage relationships (McComb et al. 2001).
In chimpanzees, ageing males display more mutual, positive and selective relationships than younger
counterparts (Rosati et al. 2020; Silk 2020). Some authors proposed that the maintenance of social
relationships with elders may improve their health status and longevity. Almeiling et al. (2016) reported
that old Barbary macaques (Macaca sylvanus) appear to remain valuable alliances for young macaques,
who continue grooming them to obtain social resources. These alliances result in a richer social capital with
fewer injuries and better transmission of knowledge, all of which give access to resources for animals of all
ages (McComb et al. 2001; Almeling et al. 2016). In mammal societies and many native human societies
such as the Māori (Durie 1999), knowledge is a key resource provided by older group members. The fitness
of both older and younger members increases because of the expertise and leadership of the
elders (McComb et al. 2001; Nattrass et al. 2019; Migliano et al. 2020). Social capital also varies in eusocial
insects. Throughout ontogenesis, worker ants or bees change from one caste to another (Münch et al.
2008). This is associated with age-related cognitive decline (Baker et al. 2012) and changes in their social
capital; they no longer interact with the same individuals (Mersch et al. 2013; Richardson et al. 2020; Wild
et al. 2021).
Different theories offer contrasting arguments to explain this change in social capital throughout life,
based on ultimate (e.g. reproduction-life trade-off (Lahdenperä et al. 2004; Lemaître et al. 2020) and kin
selection (Abbot et al. 2011)) or proximate (e.g. cognitive (Aartsen et al. 2004; Carstensen 2006) or cellular
processes (Bateson, Nettle 2018)) approaches. Thus, comparing the age-specific changes in social capital
between different animal species may help to identify the associations between the timing of these changes
and the individual physiological markers of ageing.
Biological age changes with social capital
Social capital fluctuates according to the different stages of life (early-life, reproductive life, post-
reproductive life) and may therefore influence individual health and biological age through stress and body
energy homoeostasis. For example, early maternal loss leads to short, but not long-term stress increases in
wild chimpanzees (Girard-Buttoz et al. 2021). Social isolation itself, i.e. independent of the usually
associated increased risk of predation and lower feeding efficiency, causes death in carpenter ants
(Camponotus fellah (Koto et al. 2015)) by disrupting energy homoeostasis. In reproductive fruit flies
(Drosophila melanogaster), social isolation induces stress, significantly accelerates the progression of
tumour growth, and triggers rapid death (Dawson et al. 2018). Of course, usually social isolation increases
predation risks or decreases feeding efficiency, but the latter results were done in absence of predation
and with ad libitum food. In primates, males often disperse and this social isolation period is the most
dangerous for them (Campos et al. 2020). Conversely, helping (early-life stage in cooperative breeders) and
being helped by others (reproductive stage) increase social capital and positively influence individual
health, and ultimately fitness, in all age categories (Lemaître et al. 2015; Berger et al. 2018; Hammers et al.
2019). Of course, social capital can have a negative impact on fitness (Snyder-Mackler et al. 2020; Campos
et al. 2020; Anderson et al. 2021), but this is relative to other group members, and this negative impact of
social capital on fitness is still lower than the cost associated to solitary living (Krause, Ruxton 2002). Yet,
perception of ambivalent relationships in humans is related to shorter telomere length (Uchino et al. 2012b)
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which suggests that social capital could also negatively impact biological ageing. In a nutshell, social capital,
as early as infancy, could be one of the main determinants of individual long-term fitness prospects.
In old macaques, maintaining an active social life has been suggested to stimulate and maintain brain
activity through a good quality of life at both mental and physical levels (Almeling et al. 2016). Cognitive
decline is observed in many non-human primate species (Emery Thompson et al. 2020; Lacreuse et al.
2020), but the interplay with the components of social capital is underappreciated. For instance, young lab
animals who grow up alone may have difficulties developing good relationships when they become adults,
which in turn may trigger faster senescence. Remarkably, the longevity of eusocial insect workers ranges
from a few weeks to more than two years. This plasticity is largely controlled by social factors (Lucas,
Keller 2020). Although these individuals are closely related genetically, distinct life trajectories can emerge
as a result of variations in their social capital. Recent studies conducted in honeybees (Apis mellifera (Wild
et al. 2021)) and carpenter ants (Richardson et al. 2020) confirm that social capital predicts survival better
than chronological age. A high social demand exposes workers to an overload of social stimulations,
speeding up senescence and decreasing longevity. Richardson et al. (Richardson et al. 2020) went further
and concluded that the transition between castes is not hard wired or age dependent, but rather stochastic
and dependent on changes in social capital. Bees and ants are also able to return to their previous caste
and modify their interactions if a new demand appears in the colony (e.g., following a nest predation event).
This sole change in social capital results in molecular (Quque et al. 2019) and neuronal
modifications (Münch et al. 2008) associated with reversible age-related phenotypes (Baker et al. 2012)
(Box 1) and improved health, cognitive abilities and longevity. Social reprogramming in Harpegnathos
saltator ants (from workers to gamergates) conducts to longevity-associated brain remodelling (Sheng et
al. 2020). To sum up, social capital can reverse biological age.
Box 2: What are the components of social capital?
Although work on social capital abounds across disciplines, there is no consensus on its
conceptualisation and operationalization (Bourdieu 1980; Putnam 1993; Kawachi et al. 2008; Fine 2010).
Social capital can first be studied in terms of resources or services that are embedded in spatial associations
(e.g., proximities, being close to an individual can provide access to food) or social interactions (e.g.,
grooming). Although social resources that are embedded in social relationships cannot be directly
controlled using behavioural strategies, individuals can choose the individuals with whom they maintain
relationships (Snyder-Mackler et al. 2020; Moscovice et al. 2020). Food is primarily an ecological resource,
but access to it depends on the social capital of the individual (social support, cooperation, alliances,
Because social relationships are the basis on which social capital is managed, the notion of social capital
is often simplified to these social relationships, in which social resources are exchanged. These relationships
can be described from their compositional (e.g., hierarchical position of the individuals) or structural (e.g.,
distributions of social relationships) properties. In many studies, social network indices such as degree
(number of social relationships, see table 2 for metrics from social network analysis to measure the
components of social capital) are used as a proxy of social capital. Most of the past studies have focused on
the direct social relationships between individuals in a network (e.g., degree or strength (Sosa et al. 2020)),
yet indirect relationships (e.g., friend of our friend, betweenness or clustering coefficient (Sosa et al. 2020))
also influence social capital (Brent et al. 2011; Quque et al. 2021). These indirect connections affecting
information but also disease (Romano et al. 2020) transmission networks may strengthen the cognition and
longevity of species, in which cultural behaviour is important (Romano et al. 2020). Furthermore, cultural
differences influence social capital in humans (Mulder et al. 2009); few studies have been conducted to
date on this topic in non-human animals, and further studies should be carried out.
Lastly, social activities and geospatial locations can be studied in relation with social capital (Naud et al.
2020), but can also be integrated as components of the latter. Indeed, human social activities are linked to
specific locations and both elements can be combined to better understand covariation between social
capital and health (Naud et al. 2020). This covariation between social capital, location and task is obvious
in eusocial insects (Richardson et al. 2020; Wild et al. 2021), but evidence is lacking in other species. Both
Wild et al. (Wild et al. 2021) and Richardson et al. (Richardson et al. 2020) used information about social
interactions, proximities, social activities and location to calculate a social capital index.
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To summarise, the social capital components we need to identify are: resources embedded in social
relationships (Lin et al. 2001) such as information and services (Moscovice et al. 2020), the composition and
structure of social networks (individual but also group metrics), cultural differences, social activities and
geospatial locations. Table 1 summarises the currently considered components of social capital according
to the studied species and the level of studies. Table 2 summarises metrics from social network analysis to
measure the components of social capital.
Future perspectives: the interplay between social capital and biological age matters
Organic (e.g., food) and inorganic (e.g., social) resources influence survival, growth and reproduction.
Social resources alone define social capital. Individuals can act on social interactions or social activities to
modify social capital and thus decrease stress, balance homoeostasis, and ultimately improve health.
Because social capital is flexible and seems to be partly independent of chronological age, we suggest that
social capital should be considered as a modifiable dimension (as defined in mathematics, Figure 2) within
the health space (Sanromà, Adserà 2010), with its own regulatory processes and bidirectional effects on
individual senescence. As proposed by Richardson et al. (Richardson et al. 2020), social capital is not directly
linked to chronological age but can change with biological age. This modifiable characteristic involves large
intra- and inter-specific variations in social capital, which in turn influence individual ageing rate and fitness.
These statements (i.e., the presence of variations in social capital leading to variations in ageing rate
and fitness) give rise to future research directions that can be addressed in the three following questions:
1) What is the extent of our knowledge on social capital? Social capital is most certainly a complex
concept. This is illustrated by the large number of existing definitions in human sciences (Putnam 1993; Lin
et al. 2001; Rattan 2013) but also by the diversity of its potential components. Portes (Portes 2000) noted
that the point is approaching at which social capital comes to be applied to so many events and in so many
different contexts as to lose any distinct meaning. Because social capital seems to be important for
individual fitness and the evolution of sociality, it is crucial to acknowledge and apprehend its complexity.
First, although most of the attention has been focused on the health benefits of social capital so far, the
possible health risks associated with social capital also need to be considered, especially in terms of social
overloading (Richardson et al. 2020; Wild et al. 2021) or exposure to pathogens (Romano et al. 2020). Page
and collaborators (Page et al. 2017), for example, observed that mothers with higher betweenness and
closeness centrality show more frequent instances of sickness, which somewhat counteracts other positive
fitness effects. Other researchers have begun to acknowledge that social capital ranges across a large
spectrum spanning from positive to negative consequences (Wacquant 1998; Portes 2014), the latter being
associated with adverse health outcomes. Costs of sociality are important. For instance, high social status
males experience accelerated epigenetic aging in wild baboons (Anderson et al. 2021) and higher oxidative
damage but only during the mating season in mandrills (Beaulieu et al. 2014). We also need to consider
other positive resources that can be considered components of social capital. For example, it has been
shown that in addition to providing food (Quque et al. 2021), trophallaxes convey compounds that are
essential to individual health and growth in a conserved way across several taxa (LeBoeuf et al. 2016), which
seems to indicate a selection. Like eusocial insects, mammals share organic compounds through the social
transmission of gut microbiome, which is known to influence health outcomes (Sarkar et al. 2020). This field
of study extends to birds, in which the feeding of chicks may allow intergenerational transmission of such
compounds (Lecomte et al. 2006), and thus ensure rapid adaptations to environmental changes (Badyaev,
Uller 2009). Whether or not a richer social capital can improve adaptation in social species remains to be
evaluated. Finding new components of social capital is a research horizon that needs to be explored. Box 2
shows that social capital may simply be directly related to the number of relationships or could be evaluated
in a complex way with the inclusion of social activities and the locations in which these social activities are
performed. How social capital should be operationalised also depends on the studied species, the
conditions and the scales of the study (temporal scale and subject/social organisation scale, i.e.,
interspecific comparisons of individuals that are studied throughout their lifetime). Future research should
further explore the potential components of social capital and their independent or additive/synergistic
effects on ageing outcomes, in the laboratory but more importantly in natural settings to demonstrate
similar effects under natural variation of social relationships.
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Figure 2: Changes in biological age (③, curved line) according to chronological age (①, x-axis) and
social capital (②, y-axis). The dotted line represents variations observed in returning to a previous caste
and solicitations in eusocial insects, but may result from intervention on social parameters in humans and
other animals. The most recent research in animal species showed that biological age ③ is not only
dependent on chronological age ① but also on social capital ② with an interplay between ② and ③.
Interplay with ① cannot exist as chronological age cannot be altered.
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Table 2: Metrics from social network analysis to measure the components of social capital. We define usual metrics used in social network analysis and give
non-exhaustive example of their possible use in diverse systems to assess the components of social capital cited above in Table 1. We call network, the scales
greater than the individual. It can be groups, colonies, species. A path is the successive connections that are necessary to link an individual A to an individual B
within a given group. The shortest path is the one that minimises the number of necessary connections. We use the term 'resources' in a broad sense that can
include, depending on the model studied: information, food, sexual partners...
Social metrics
Practical examples of the social network metrics to study
social capital
* The number of connections (neighbours) of an
* This metric can be undirected or directed, in this
latter case we distinguish the case where individuals
emit interaction towards their neighbours (out-
degree) from the ones where individuals receive
interactions (in-degree).
* Studying the individual degree highlights social immunity in
eusocial insects. (Cremer et al. 2007)
* Chicks' degree in cooperative breeders is a proxy for the
intensity of parental care they can get. (Boheemen et al. 2019)
* Humans live a longer and healthier life when maintaining
numerous positive social relationships. (Umberson, Karas
Montez 2010; Yang et al. 2016)
The number of shortest paths passing through an
individual. Individuals with a high betweenness are
crucial nodes through which a large amount of
resource passes.
* Having a high betweenness may be an advantage regarding
the access to resources but a drawback regarding the exposition
to pathogens. (VanderWaal et al. 2014)
This metric adds the neighbour amount of an
individual to the neighbour amount of those
neighbours. It reflects the possibility to access
resources through direct and indirect connections.
* Chimpanzees with higher values of eigenvector centrality in
early adulthood have been found more likely to be high-ranked in
the hierarchy later in life. (Watts 2018)
Gives the average distance (number of
connections) necessary to reach all other members of
the groups. So, counter-intuitively, a high closeness
coefficient reflects social isolation.
* Closeness coefficients reliably predict hierarchy and
dominance patterns, e.g. in pigs. (Büttner et al. 2019)
* Social isolation is proved to be a major health issue in
humans and non-human animals. (House et al. 1988)
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and clustering
These two metrics are based on different
formula, but both assess whether individuals tend to
cluster into modules characterised by a strong intra-
module interaction but a weak inter-modules
* Such metrics highlight groups cooperating for access to
resources and thus increasing their social capital. (Assenza et al.
2008; Kuperman, Risau-Gusman 2012)
Gives the longest path of the network and thus a
clue about the speed all group members can access a
* Diameter and other network metrics have been used in ants
to measure the network plasticity in different ant colonies, and
have been linked to pathogen resistance. (Stroeymeyt et al. 2018)
The number of connections observed within the
group divided by all the possible connections.
* A density index may be used, for instance, to compare the
degree of selectivity (high selectivity implies low density) of
different groups of ravens to know if they share resources with
specific individuals or not. (Kulahci et al. 2016)
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2) How can we explain individual and species variations in health and longevity? This section is about
ultimate mechanisms linking social capital to ageing. Among species, environmental factors have differently
shaped age-specific trade-offs between growth, reproduction and survival. Some components of the social
capital can be influenced by environmental factors but can also attenuate the impact of the latter,
increasing or decreasing individual and species variations in health and longevity. Future studies should
therefore address the co-evolution of interspecific variances in social capital and senescence rate. Animal
species characterised by particular age-specific social capital can emerge as novel behavioural models to
address questions in current human ageing research (Lemaître et al. 2015; Lucas, Keller 2020; Lacreuse et
al. 2020). For instance, such studies may delineate how social capital modulates life period trade-offs (i.e,.
early-life growth and subsequent young and adult survival, and reproductive success) and how adult social
capital may have co-evolved with post-reproductive lifespan (Vágási et al. 2020). For example, female killer
whales (Orcinus orca) live twice longer than males, and post-reproductive females have greater knowledge
and lead the group, thus enhancing the survival of their grand-offspring (Nattrass et al. 2019). These old
females, like elephant matriarch (McComb et al. 2001), have a rich social capital, live longer and also
provide their offspring with a huge social capital. This grandmother hypothesis was primarily proposed in
humans (Lahdenperä et al. 2004). In line with these observations, one can hypothesise that variations in
social capital in different life stages influence variability in post-reproductive longevity (Figure 3b) and
indirectly modulate sex differences in senescence (Lemaître et al. 2020). This means that sex-related
differences in social capital could lead to sex-related differences in longevity because of health or because
of fitness benefits of social capital. However, such sex-related differences in longevity can be buffered when
males associate with females. For instance, male baboons who are more strongly bonded to females have
longer lifespans (Campos et al. 2020). The subject of age-related cognitive processes requires longitudinal
neurobiological studies focusing on the ageing brain within the context of social capital (Lacreuse et al.
2020). Finally, the interaction between social capital and life history traits has certainly been constrained
by environmental factors such as predation risks, parasite prevalence or local population density. It is also
important to note that non-social species like ctenophores or cnidarians have almost reached
immortality (Petralia et al. 2014), or may live for centuries like the Galapagos turtle or the Greenland shark.
This casts doubt on the incompressible limits of social benefits for longevity (Figure 3a and d). Multi-specific
and multigenerational studies will help to discover the mechanisms that underlie the relationships of social
capital with species life history and ecology.
3) How is social capital encoded to enhance fitness? This section is more about proximate mechanisms
linking social capital to ageing and fitness. Although we know that social capital is related to individual
fitness, little is known about the extent to which this relationship depends on species ecology and gender,
or whether it is restricted to certain life-history traits. The role of social capital in variations of senescence
onset or in senescence rate can be assessed in the context of evolutionary theories of ageing (Reznick et
al. 2005). For instance, this can be done by determining how social capital modulates the energy trade-offs
that can occur during the life trajectory of individuals (e.g. growth/reproduction and ageing trade-
offs (Williams 1957; Hamilton 1966; Lemaître et al. 2015)). Potential biological mechanisms such as
telomere rate of loss (Lewin et al. 2015), oxidative stress or mitochondrial dysfunction (Hood et al. 2018)
(Box 1) that are already suspected to play a major role in ageing would have to be tested in the light of the
social capital context. For example, extended sex-specific post-reproductive life in killer whales may have
been co-selected with specific social traits and anti-ageing mechanisms that have positive effects on female
fitness and their offspring (Kirkwood 1977; Lahdenperä et al. 2004). Age-related variations in social capital
in cooperative breeders have already been linked with the fitness traits of individuals (see Berger et al.
2018; Hammers et al. 2019). However, we have yet to elucidate the question of how eusocial reproducers
have acquired a specific social capital that probably enables them to successfully face higher reproduction
rates and attain a longer lifespan than non-reproducers. How is the impact of social capital on senescence
genetically or epigenetically encoded? For instance, personality, which is heritable, has an impact on
longevity and pace of life (Réale et al. 2010), and one of its bases is sociality. Social capital could be encoded
in this personality variable, a hypothesis that needs to be tested via the demonstration of a covariation at
the individual level among social capital, personality and longevity. Understanding the genetics and
epigenetics of sociality would be of help in unraveling mechanisms that link sociality to ageing outcomes
Cédric Sueur et al. 13
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and fitness. In this respect, we propose that the recent development of genomics and proteomics to study
ageing (Münch et al. 2008; Quque et al. 2019) should be extended to include the study of social capital.
These investigations will likely extend our knowledge on how evolution has co-selected sociality and
longevity (Lucas, Keller 2020; Vágási et al. 2020). Furthermore, these new findings could subsequently be
leveraged to promote healthy ageing.
Figure 3: Variation (y-axis) of social capital (blue), biological age (green) and health (orange) over
chronological age (x-axis) for an individual having access to (a.) life-long high social capital, (b.) only early-
life high social capital, (c.) late-life high social capital, and (d.) life-long low social capital. Curves are
theoretical and based on past research conducted in different species that are cited in the main text. They
represent the global trajectory of the dimensions over the lifetime of an individual. Health is a state of
physical, mental and social well-being that depends on internal (senescence) and external (pathogens,
pollutants, etc.) factors. Individuals die when health level reaches zero (dashed black line). Biological age
is a sum of intrinsic proxies and predicts health and survival prospects. These schematic representations
also raise questions pertaining to the limits of social capital influence (both positive and negative) on
longevity and health (❶ and ❹), or indeed on the programming of physiological and social processes in
early life that may counteract ageing even if social capital evaporates over age (❷, dashed orange and
green lines representing how biological age and health would change without these programming
effects). Finally, Figure 3 also highlights the reversible interaction with senescence (❸).
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Concluding remarks
The three points we developed indicate that working with social capital and markers of senescence
along life will prove to be more powerful than standing with chronological age. Making these comparisons
in animals is of paramount importance as animal studies allow (i) to reduce the number of confounding
factors by controlling experimental conditions; (ii) to carry out studies over several generations in a
relatively short timeframe, and study evolution through genetic and epigenetic effects; and (iii) to conduct
invasive and integrative experimental studies going from the cell to the group level, which is impossible in
humans. Experimental designs or observations of wild individuals throughout their lifespan and across
several generations will help to better understand the long-term consequences of social capital. This is
mainly possible through longitudinal studies (Clutton-Brock, Sheldon 2010) or multigenerational laboratory
studies with a controlled environment and small changes in the study design (i.e., systematic
heterogenization of study samples as group size, group composition, number of helpers) (Voelkl et al. 2018,
Taken together, currently available data suggest that focusing on social capital and markers of
senescence throughout lifespan may explain individual health and fitness better than chronological age.
The observation that mean lifespan is greater in eusocial than non-eusocial species leads us to question the
co-evolution of sociality with senescence (Lucas, Keller 2020). Social capital adjustment further suggests
that the basic assumptions that environmentally driven mortality shapes the selection of senescence may
be more complex than we initially thought. Although mean lifespan is influenced by a large number of
factors, the respective contribution of social capital versus other biological, ecological and environmental
factors in the regulation of senescence and longevity remains an open question. Time is finite for most living
animals, but social capital appears to be a promising tool to make senescence an adjustable parameter and
to slow down the rate of ageing (Colchero et al. 2021).
- Ageing: the only consensual definition is that it is a heterogeneous process of becoming older.
- Biological age: individual age as determined through different biological markers that change over
time, but not necessarily related to chronological age. Biological age is composed of different stages (e.g.,
ontogeny, reproductive life, and senescence, including post-reproductive life). Contrary to chronological
age, biological age considers the individual in relation to its date of death, while chronological age considers
it in relation to its date of birth.
- Cooperative breeding: social system characterised by alloparental care: offspring receives care not
only from their parents, but also from additional group members, often called helpers.
- Chronological age (or age): the age of an individual as measured from birth to a given date referring
to time, usually based on the Gregorian calendar.
- Eusociality: highest level of sociality defined by cooperative brood care, overlapping generations, and
division of labour into reproductive and non-reproductive groups.
- Evolutionary theories of ageing: proposals to explain the persistence of the deleterious process of
ageing over several generations, despite the action of natural selection.
- Fitness: defined here as the individual's ability to transmit its genes directly (with offspring) or
indirectly (by helping relatives, i.e., inclusive fitness) to future generations.
- Health: state of complete physical and mental independence in activities of daily living (Rattan 2013).
Being healthy, in practical terms, means having adequate physical and mental independence in activities of
daily living. The three main characteristics of the dynamic equilibrium between the occurrence of damage
and the processes of maintenance and repair are damage control, stress response and constant remodelling
and adaptation. These elements can be studied at different levels of the organism, as described in Box 1.
- Healthy ageing: process of maintaining functionality of a living system as age advances.
- Longevity: mean lifetime duration for a species.
- Ontogeny: development of an organism from fertilisation to the adult stage (reproductive stage).
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- Senescence: progressive decline of biological functions, eventually leading to death. In evolutionary
terms, senescence can be defined as the decrease in the age-specific contribution to fitness over lifetime.
- Social capital: resources embedded in a social structure which are accessed and/or mobilised in
purposive action. The resources of an individual vary during its life, meaning that social capital fluctuates
with age. In some studies, the number of partners or the connections an individual has within its
network (Brent et al. 2011) are a proxy to measure social capital. Differences in social capital implies that
group members have differentiated and contrasting relationships with each other (Moscovice et al. 2020),
as observed in cooperative breeding or eusocial species. This means that it is difficult to seek to identify
social capital components in communal breeding or gregarious species with few differentiated
relationships (Moscovice et al. 2020). However, in these cases it would be possible to start with the use of
simpler indices like group size or kinship size as social capital proxies.
- Social resources: Social resources are defined as any concrete or symbolic item that can be used as an
object of exchange among people. Foa and Foa classified social resources into six categories for humans:
love/affection, status, information, services, goods, and money (Foa, Foa 1980). Money can be replaced by
access to food in non-human animals.
CS was granted by the ANR-15-CE36-0005 (HANC) and INNOVEPHAD (ANR-Région Grand-Est, France).
FC participation to the present work was supported by the 80|PRIME 2020 program of the MITI-CNRS
(number 196025). A previous version of this article has been peer-reviewed and recommended by Peer
Community In Network Science (
Conflict of interest disclosure
The authors declare that they have no financial conflict of interest with the content of this article. CS is
one of the PCI Network Sci recommenders.
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... Glucocorticoid concentrations are also linked to social capital but this correlation-positive or negative-depends on the stability of the hierarchy and other parameters as the reproduction state [27][28][29][30]. Mainly, high-ranking individuals have lower concentrations of glucocorticoids when the hierarchy is stable but higher concentrations when it is unstable. ...
... In our different models (except Model 2, see below), age and dominance rank always showed a VIF > 4, meaning that they were highly correlated (adj r 2 = 0.79, F = 388, p < 0.0001, Figure 1a). Therefore, we decided to remove dominance rank as variable in the models 1, 3 and 4 as it is dependent on age, which is also shown in previous studies [30,31,38,67] but we tested it in a separate model. Subsequently, multifactorial linear models (LM) were applied as follows: ...
... We had to remove dominance rank from three of our models, because this factor is highly determined by age in European bison [38] but also in other bovines [30,31,67]. However, as shown in many studies, dominance rank is linked to glucocorticoid concentrations in one way [5][6][7], or the other [8,9]. ...
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Stress is the body’s response to cope with the environment and generally better survive unless too much chronic stress persists. While some studies suggest that it would be more stressful to be the dominant individual of the group, others support the opposite hypothesis. Several variables can actually affect this relationship, or even cancel it. This study therefore aims to make the link between social status and the basal level of stress of 14 wild European bison (Bison bonasus, L. 1758) living together. We collected faeces and measured the faecal glucocorticoid metabolites (FGM). We showed that FGM is linked to different variables of social status of European bison, specifically age, dominance rank, eigenvector centrality but also to interactions between the variables. Preferential leaders in bison, i.e., the older and more dominant individuals which are more central ones, are less stressed compared to other group members. Measurement of such variables could thus be a valuable tool to follow and improve the conservation of species by collecting data on FGM and other social variables and adapt group composition or environmental conditions (e.g., supplement in food) according to the FGM concentration of herd individuals.
... This was confirmed by analysing different drawings metrics [37]. A parallel could be made between humans and non-human apes for drawing as is done for other activities, such as the social ones [45,46]. ...
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This study analyses 749 drawings by five female Bornean orang-utans (Pongo pygmaeus) at Tama Zoological Park in Japan. We searched for differences between individuals but also tried to identify possible temporal changes among the drawings of one individual, Molly, who drew almost 1300 drawings from 2006 to 2011. An analysis of the drawings was carried out after collecting quantitative and qualitative variables. Our findings reveal evidence of differences in the drawing style of the five individuals as well as creative changes in Molly’s drawing style throughout her lifetime. Individuals differed in terms of the colours used, the space they filled, and the shapes (fan patterns, circles, or loops) they drew. Molly drew less and less as she grew older, and we found a significant difference between drawings produced in winter, when orang-utans were kept inside and had less activity, and those produced during other seasons. Our results suggest that the drawing behaviour of these five orang-utans is not random and that differences among individuals might reflect differences of styles, states of mind, and motivation to draw.
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The biological embedding model (BEM) suggests that fitness costs of maternal loss arise when early-life experience embeds long-term alterations to hypothalamic-pituitary-adrenal (HPA) axis activity. Alternatively, the adaptive calibration model (ACM) regards physiological changes during ontogeny as short-term adaptations. Both models have been tested in humans but rarely in wild, long-lived animals. We assessed whether, as in humans, maternal loss had short- and long-term impacts on orphan wild chimpanzee urinary cortisol levels and diurnal urinary cortisol slopes, both indicative of HPA axis functioning. Immature chimpanzees recently orphaned and/or orphaned early in life had diurnal cortisol slopes reflecting heightened activation of the HPA axis. However, these effects appeared short-term, with no consistent differences between orphan and non-orphan cortisol profiles in mature males, suggesting stronger support for the ACM than the BEM in wild chimpanzees. Compensatory mechanisms, such as adoption, may buffer against certain physiological effects of maternal loss in this species.
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Is it possible to slow the rate of ageing, or do biological constraints limit its plasticity? We test the ‘invariant rate of ageing’ hypothesis, which posits that the rate of ageing is relatively fixed within species, with a collection of 39 human and nonhuman primate datasets across seven genera. We first recapitulate, in nonhuman primates, the highly regular relationship between life expectancy and lifespan equality seen in humans. We next demonstrate that variation in the rate of ageing within genera is orders of magnitude smaller than variation in pre-adult and age-independent mortality. Finally, we demonstrate that changes in the rate of ageing, but not other mortality parameters, produce striking, species-atypical changes in mortality patterns. Our results support the invariant rate of ageing hypothesis, implying biological constraints on how much the human rate of ageing can be slowed. The ‘invariant rate of ageing’ hypothesis suggests that the rate of ageing tends to be constant within species. Here, Colchero et al. find support for the hypothesis across primates, including humans, suggesting biological constraints on the rate of ageing.
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Aging, for virtually all life, is inescapable. However, within populations, biological aging rates vary. Understanding sources of variation in this process is central to understanding the biodemography of natural populations. We constructed a DNA methylation-based age predictor for an intensively studied wild baboon population in Kenya. Consistent with findings in humans, the resulting 'epigenetic clock' closely tracks chronological age, but individuals are predicted to be somewhat older or younger than their known ages. Surprisingly, these deviations are not explained by the strongest predictors of lifespan in this population, early adversity and social integration. Instead, they are best predicted by male dominance rank: high-ranking males are predicted to be older than their true ages, and epigenetic age tracks changes in rank over time. Our results argue that achieving high rank for male baboons—the best predictor of reproductive success—imposes costs consistent with a 'live fast, die young' life history strategy.
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In complex societies, individuals’ roles are reflected by interactions with other conspecifics. Honey bees (Apis mellifera) generally change tasks as they age, but developmental trajectories of individuals can vary drastically due to physiological and environmental factors. We introduce a succinct descriptor of an individual’s social network that can be obtained without interfering with the colony. This ‘network age’ accurately predicts task allocation, survival, activity patterns, and future behavior. We analyze developmental trajectories of multiple cohorts of individuals in a natural setting and identify distinct developmental pathways and critical life changes. Our findings suggest a high stability in task allocation on an individual level. We show that our method is versatile and can extract different properties from social networks, opening up a broad range of future studies. Our approach highlights the relationship of social interactions and individual traits, and provides a scalable technique for understanding how complex social systems function.
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Our understanding on how widespread reproductive senescence is in the wild and how the onset and rate of reproductive senescence vary among species in relation to life histories and lifestyles is currently limited. More specifically, whether the species-specific degree of sociality is linked to the occurrence, onset and rate of reproductive senescence remains unknown. Here, we investigate these questions using phylogenetic comparative analyses across 36 bird and 101 mammal species encompassing a wide array of life histories, lifestyles and social traits. We found that female reproductive senescence (1) is widespread and occurs with similar frequency (about two thirds) in birds and mammals; (2) occurs later in life and is slower in birds than in similar-sized mammals; (3) occurs later in life and is slower with an increasingly slower pace of life in both vertebrate classes; and (4) is only weakly associated, if any, with the degree of sociality in both classes after accounting for the effect of body size and pace of life. However, when removing the effect of species differences in pace of life, a higher degree of sociality was associated with later and weaker reproductive senescence in females, which suggests that degree of sociality is either indirectly related to reproductive senescence via the pace of life or simply a direct outcome of the pace of life. This article is part of the theme issue ‘Ageing and sociality: why, when and how does sociality change ageing patterns?’
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Our understanding on how widespread reproductive senescence is in the wild and how the onset and rate of reproductive senescence vary among species in relation to life histories and lifestyles is currently limited. More specifically, whether the species-specific degree of sociality is linked to the occurrence, onset and rate of reproductive senescence remains unknown. Here, we investigate these questions using phylogenetic comparative analyses across 36 bird and 101 mammal species encompassing a wide array of life histories, lifestyles and social traits. We found that female reproductive senescence (1) is widespread and occurs with similar frequency (about two thirds) in birds and mammals; (2) occurs later in life and is slower in birds than in similar-sized mammals; (3) occurs later in life and is slower with an increasingly slower pace of life in both vertebrate classes; and (4) is only weakly associated, if any, with the degree of sociality in both classes after accounting for the effect of body size and pace of life. However, when allowing for species differences in pace of life, a higher degree of sociality was associated with later and weaker reproductive senescence in females, which suggests that degree of sociality is indirectly related to reproductive senescence via the pace of life. Subject Areas ecology, evolution
The remarkable ecological success of social insects is often attributed to their advanced division of labor, which is closely associated with temporal polyethism in which workers transition between different tasks as they age. Young nurses are typically found deep within the nest where they tend to the queen and the brood, whereas older foragers are found near the entrance and outside the nest.1-3 However, the individual-level maturation dynamics remain poorly understood because following individuals over relevant timescales is difficult; hence, previous experimental studies used same-age cohort designs.4-15 To address this, we used an automated tracking system to follow >500 individuals over >100 days and constructed networks of physical contacts to provide a continuous measure of worker social maturity. These analyses revealed that most workers occupied one of two steady states, namely a low-maturity nurse state and a high-maturity forager state, with the remaining workers rapidly transitioning between these states. There was considerable variation in the age at transition, and, surprisingly, the transition probability was age independent. This suggests that the transition is largely stochastic rather than a hard-wired age-dependent physiological change. Despite the variation in timing, the transition dynamics were highly stereotyped. Transitioning workers moved from the nurse to the forager state according to an S-shaped trajectory, and only began foraging after completing the transition. Stochastic switching, which occurs in many other biological systems, may provide ant colonies with robustness to extrinsic perturbations by allowing the colony to decouple its division of labor from its demography.
In many species that raise young in cooperative groups, breeders live an exceptionally long time despite high investment in offspring production. How is this possible given the expected trade-off between survival and reproduction? One possibility is that breeders extend their lifespans by outsourcing parental care to non-reproductive group members. Having help lightens breeder workloads and the energy that is saved can be allocated to survival instead. We tested this hypothesis using phylogenetic meta-analysis across 23 cooperatively breeding bird species. We found that breeders with helpers had higher rates of annual survival than those without helpers (8% on average). Increased breeder survival was correlated with reduced investment in feeding offspring, which in turn depended on the proportion of feeding provided by helpers. Helpers had similar effects on female and male breeder survival. Our results indicate that one of the secrets to a long life is reduced investment in parental care. This appears to be a unique feature of cooperative societies with hard-working helpers. This article is part of the theme issue ‘Ageing and sociality: why, when and how does sociality change ageing patterns?’
Chimpanzees, like humans, place a higher value on positive social relationships as they grow older