Most mammals depend entirely upon their mothers when they are born. In these species, losing a mother at a young age has dramatic consequences for survival. In cases where orphaned individuals do reach adulthood, they often suffer negative effects, like reduced reproductive success or lower social status. But this is not the case for humans. If a child loses their mother, relatives, friends and the wider community can take over. This does not tend to happen in nature. Even our closest relatives, chimpanzees, are much less likely to survive if their mothers die before they reach adolescence.

Although orphan survival is not the norm for mammals, humans may not be entirely unique. Mountain gorillas also live in stable family groups, usually with a dominant male and one or more females who care for their offspring for between 8 and 15 years. It is possible that gorillas may also be able to provide community support to orphans, which could buffer the costs of losing a mother, just as it does in humans.

To answer this question, Morrison et al. examined 53 years of data collected by the Dian Fossey Gorilla Fund to assess the effects of maternal loss in mountain gorillas. The analysis examined survival, reproduction and changes in social relationships. This revealed that, like humans, young gorillas that lose their mothers are not at a greater risk of dying. There is also no clear long-term effect on their ability to reproduce. In fact, gorillas who lost their mothers ended up with stronger social relationships, especially with the dominant male of the group and young gorillas around the same age. It seems that gorilla social groups, like human families, provide support to young group members that lose their mothers.

These findings suggest that the human ability to care for others in times of need may not be unique. It is possible that the tendency to care for orphaned young has its origins in our evolutionary past. Understanding this in more depth could provide clues into the social mechanisms that help to overcome early life adversity, and have a positive impact on future health and survival.

Maternal loss, along with a number of other indicators of early-life adversity, is one of the strongest predictors of lifespan in humans and other social mammals (Snyder-Mackler et al., 2020). In mammals, mothers are vital for the survival of young offspring, providing nutrition, thermoregulation, and protection (Clutton-Brock, 1991). In some species, particularly social species with slow life histories (Mitani et al., 2013), mothers continue to provide benefits to their co-residing offspring throughout immaturity and even into adulthood (Surbeck et al., 2019; Surbeck et al., 2011; Andres et al., 2013; Stanton et al., 2020). The active support of mothers can increase the rank of their offspring (Strauss et al., 2020; East et al., 2009; Maestripieri and Mateo, 2009; Lea et al., 2014) and improve their integration in the group (Tung et al., 2016), both of which are linked with greater survival (Snyder-Mackler et al., 2020; Archie et al., 2014). Maternal presence can also influence nutrition at these later stages of development by buffering against feeding competition (Samuni et al., 2020), providing access to valuable ecological knowledge (Stanton et al., 2020; Foley et al., 2008; Brent et al., 2015) or increasing opportunities for the social learning of complex feeding techniques (Lonsdorf et al., 2004; Estienne et al., 2019).

In social mammals maternal loss can therefore reduce the fitness of offspring across a broad age range through long-term effects on their social environment, negatively influencing their social integration and social status throughout their lives (Snyder-Mackler et al., 2020; Strauss et al., 2020; Tung et al., 2016). Multiple studies have now confirmed effects on survival for individuals orphaned well past the period of nutritional dependency, with these negative changes to their social environment, often termed social adversity, posited to be the key mechanism by which this occurs (Andres et al., 2013; Stanton et al., 2020; Tung et al., 2016; Watts et al., 2009; Foster et al., 2012). In social mammals, the social environment can have extreme consequences for health, fitness, and lifespan, mediated through pathways such as chronic stress, immune function, or environmental exposure (Snyder-Mackler et al., 2020).

The impact of maternal loss varies based on the loss in benefits relative to individuals with mothers present. In species with sex-biased dispersal, the consequences of maternal loss can differ between the sexes due to longer periods for potential investment in non-dispersing offspring (Clutton-Brock, 1991; Fairbanks, 2009; Altmann and Alberts, 2005; Greenwood, 1980). In female-philopatric red deer (Cervus elaphus), maternal loss increases mortality for males and females, but this effect is only detectable in males under 2 years of age, whilst for females, maternal benefits continue throughout their lives (Andres et al., 2013). In male-philopatric chimpanzees (Pan troglodytes), males that suffer maternal loss before reaching 15 years of age have lower survival, whilst females only show reduced survival from maternal loss under the age of 10 (Nakamura et al., 2014; Stanton et al., 2020). In killer whales (Orcinus orca), where neither sex disperses, maternal loss when offspring are over 30 years old reduces survival for both sexes, although considerably more so for males (Foster et al., 2012). However, maternal loss between 15 and 30 years appears to reduce male but not female survival. This is thought to be due to higher maternal investment in males which mate outside the group and whose offspring therefore do not increase within-group feeding competition.

As a result of the numerous benefits mothers can provide, it is not surprising that maternal loss not only influences offspring survival but can also impact other components of their offspring’s fitness, such as reproduction and the survival of grand-offspring. Male bonobos (Pan paniscus) residing in groups with their mothers sire three times the number of offspring (Surbeck et al., 2019), whilst maternal loss before weaning negatively affects antler development in male red deer – a trait found to correlate with reproductive success (Andres et al., 2013). In chimpanzees, females mature faster, first give birth younger (Walker et al., 2018) and enter the dominance hierarchy higher (Foerster et al., 2016) if their mothers are present which is expected to considerably increase their lifetime reproductive success. In savannah baboons (Papio cynocephalus), if mothers had themselves suffered maternal loss in the first 4 years of their life, their offspring had 48% higher mortality throughout the first 4 years of their life, suggesting an intergenerational effect of maternal loss driven by lifelong developmental constraints (Zipple et al., 2019). Maternal loss in social mammals with extended maternal care can therefore have long-term fitness consequences mediated through multiple pathways that detrimentally affect survival and reproduction.

Due to the extended periods of mother–offspring co-residence and the important social support mothers can provide, highly social species often have the most to lose from maternal loss. However, social groups also provide the potential for support from other group members following maternal loss. Both kin and non-kin group members have been suggested to compensate for the loss of close kin to varying extents (Hamilton et al., 1982; Engh et al., 2006; Goldenberg and Wittemyer, 2017; Reddy and Mitani, 2019) and the strengthening of relationships with remaining group members may buffer against changing social environments (Firth et al., 2017). In chacma baboons (Papio ursinus), social support from group members is thought to alleviate the stress of losing a close relative (Engh et al., 2006). Similar social support has been suggested in African savannah elephants (Loxodonta africana) which associate more with group members of a similar age and siblings in response to maternal loss (Goldenberg and Wittemyer, 2017). However, these orphaned elephants interact less with matriarchs which may decrease their access to key knowledge and high-quality resource patches. In chimpanzees, older siblings can ‘adopt’ younger siblings after maternal loss, increasing their social contact and showing heightened vigilance in dangerous situations (Reddy and Mitani, 2019; Hobaiter et al., 2014). But despite these compensatory social behaviours, the negative consequences of maternal loss post-weaning are well documented in all three genera (Stanton et al., 2020; Tung et al., 2016; Samuni et al., 2020; Nakamura et al., 2014; Goldenberg and Wittemyer, 2017; Goldenberg and Wittemyer, 2018).

Humans (Homo sapiens) are a rare example of a group-living mammal in which compensatory social behaviours have been suggested to have the capacity to consistently overcome these negative consequences of maternal loss. In a meta-analysis of historic and contemporary human populations, the death of a mother was associated with increased child mortality in all 28 populations studied (Sear and Mace, 2008). However, this effect appeared to decline substantially with age, disappearing for children that suffered maternal loss over 2 years of age in 5 of the 11 populations in which it was investigated (Sear and Mace, 2008). This reduced mortality was thought to be due to the care provided by other kin, particularly after weaning, suggesting that social buffering from other group members can overcome the negative effect of maternal loss on survival in certain circumstances. Whilst the effects of care from specific kin members varied across populations, at least one kin member significantly impacted child survival in all studies (Sear and Mace, 2008), and there is evidence for the importance of maternal grandmothers (Lahdenperä et al., 2004; Sear et al., 2000) and fathers (Hurtado and Hill, 1992; Hill and Hurtado, 2017) in particular. In killer whales, maternal grandmothers, especially those that are post-reproductive, are also known to improve grand-offspring survival (Nattrass et al., 2019). Whilst the specific effect of killer whale grandmothers on orphan survival has not been investigated, this finding suggests that killer whales may represent a further species in which care from other kin has the capacity to overcome the effects of maternal loss. In humans, there is also evidence for the benefits of care provided by non-kin such as step-mothers (Andersson et al., 1996; Campbell and Lee, 2002) and through the modern practices of non-kin adoption (Bentley and Mace, 2009).

Mountain gorillas (Gorilla beringei beringei) show extended maternal care with offspring remaining in their natal groups at least until sexual maturity and approximately half remaining beyond sexual maturity (48% of females [Robbins et al., 2009a] and 55% of males [Stoinski et al., 2009a]). Females that disperse from their natal group tend to do so earlier (mean age of 7.9 years [Robbins et al., 2009a]) than males (mean age of 15.3 years [Stoinski et al., 2009a]) and therefore have a shorter period of potential maternal investment. The complexity of gorilla social structure with numerous types of differentiated social relationship both within and among groups (Morrison et al., 2019; Mirville et al., 2018; Morrison et al., 2020a; Morrison et al., 2020b) suggests that detrimental long-term effects on individual gorillas’ social environments could have particularly negative fitness consequences. However, these stable, cohesive, social groups also have the potential to provide a social buffer to the negative consequences of maternal loss. Mountain gorilla groups either contain a single adult male (approximately 64% of groups) or multiple adult males (approximately 36% of groups) (Gray, 2010), at least one adult female, and their offspring (Robbins, 1995). Single male groups are polygynous whilst multimale groups have high reproductive skew towards the dominant male who sires the majority of offspring (Nsubuga et al., 2008; Stoinski, 2009b; Bradley et al., 2005). Infants (<4 years of age) are nutritionally dependent on their mothers until being weaned at a mean of 3.3 years (Eckardt et al., 2016) and are reliant on their mothers for thermoregulation and transport, being carried for prolonged periods (Breuer et al., 2009). Juveniles (4–6 years old) are nutritionally independent but remain in close proximity to their mothers the majority of the time (Breuer et al., 2009). Dominant males’ primary form of care is through protection from out-group males and potential predators (Harcourt and Greenberg, 2001). But they also show high levels of affiliative behaviour towards infants, grooming and resting in contact with them, with no evidence that they discriminate between infants based on paternity (Rosenbaum et al., 2018; Rosenbaum et al., 2015).

In this study, we use the long-term demographic records of the Dian Fossey Gorilla Fund’s Karisoke Research Center collected over 53 years (1967–2019) to (A) quantify the effects of maternal loss on multiple fitness measures: survival, female age at first birth, female survival of first offspring through infancy, and male dominance; and (B) investigate the social responses of group members to maternal loss by immature gorillas. We hypothesize that as demonstrated in chimpanzees (Stanton et al., 2020), gorillas may face greater fitness costs if they suffer maternal loss at an earlier age and that males may face greater costs from maternal loss than females due to their longer periods of mother–offspring co-residence. Alternatively, as observed in many human populations, the cohesive, stable social groups of mountain gorillas may enable social buffering from group members to compensate for the social costs of maternal loss with minimal fitness consequences to maternal loss. In particular, dominant males may take on crucial roles in buffering the social adversity faced by maternal orphans (hereafter, orphans), as past research has demonstrated the strong bond between dominant males and young orphans who may regularly share a nest at night (Robbins et al., 2005; Gatesire et al., 2016).

In contrast to many social mammals with extended periods of mother–offspring co-residence (Andres et al., 2013; Stanton et al., 2020; Foster et al., 2012), in mountain gorillas, we found no evidence for higher mortality in immature offspring of either sex following maternal loss. Whilst our sample size of 59 orphans between the ages of 2 and 8 years may limit our ability to detect relatively weak effects on survival, significant effects have been found in other species with comparable sample sizes (Stanton et al., 2020). This suggests that either maternal loss after offspring have reached 2 years of age does not reduce survival in mountain gorillas, or that the reduction in survival is considerably weaker than that observed in other species and therefore undetectable within our sample.

The effect of maternal loss on dispersal decisions differed depending on the age at which maternal loss occurred. Whilst no effect was found for those orphaned as infants, juvenile- or subadult-orphaned gorillas were more than twice as likely to disperse than non-orphans (although for females this effect was not quite significant). This suggests that there may be some benefits to co-residence with mothers for both sexes that do not directly translate to survival, but could influence lifetime reproductive success, like the faster maturation and higher dominance in female chimpanzees (Walker et al., 2018; Foerster et al., 2016) or greater mating opportunities in male bonobos (Surbeck et al., 2019). Alternatively, potential benefits may not be specific to the mother–offspring bond. Instead, the loss of a key social partner close to dispersal age may reduce the social benefits of remaining in a group. Without this strong social bond in the natal group, dispersal to a new group may lead to fewer social costs relative to remaining, whilst also providing considerable benefits for inbreeding avoidance (Vigilant et al., 2015). This might better explain the age-dependent influence of maternal loss, as by the time those suffering maternal loss as infants approach an age at which dispersal could occur, they may have compensated for the loss of such a key social partner through strengthening their relationships with other group members. Our analyses also suggest that the strengthening of relationships with group members post-maternal loss may be greatest for younger individuals, which could further reduce the likelihood that they disperse later in life.

We found no significant effect of maternal loss on the elements of female reproduction we investigated. Female orphans gave birth slightly younger, but despite this, their first offspring was marginally more likely to survive infancy. Whilst neither effect was significant, the direction of the effect, with earlier first birth, is consistent with the stress acceleration hypothesis: that those suffering from early caregiving adversity may show accelerated development as an adaptive strategy to low parental care (Ellis, 2004; Callaghan and Tottenham, 2016). Female dispersal has also not been found to cause any reproductive delays in mountain gorillas, suggesting that the potentially higher likelihood of those suffering maternal loss as juveniles or subadults to disperse is unlikely to reduce their reproductive capacity (Robbins et al., 2009b).

Male reproduction was harder to assess due to limited paternity data and later sexual maturity. However, as dominant males usually sire the majority of offspring, even within multi-male groups (Nsubuga et al., 2008; Stoinski, 2009b; Bradley et al., 2005), analyses of male dominance status provided some insights into the reproductive success of male orphans. Although only around half of males ever reached dominant male status, some males in both orphan age categories were able to do so, demonstrating that this is possible in the absence of maternal support. Small sample sizes limited the extent of analysis possible, but maternal loss appeared to have differing effects depending on the age of maternal loss, with infant-orphaned males more likely and juvenile- or subadult-orphaned males less likely to become the dominant male of a group relative to males that did not suffer maternal loss. This mirrors the effect of maternal loss on dispersal decisions. Modelling has suggested that males that disperse suffer a 50% reduction in lifetime reproductive success (Robbins and Robbins, 2005), as they lose the potential for mating opportunities as a subordinate male within their natal group or for eventually taking over as dominant male of that group and must instead attempt to attract females to form a group of their own (Stoinski et al., 2009a). It is therefore likely that males suffering maternal loss as juveniles or subadults may suffer from reduced siring opportunities throughout their lives due to their increased likelihood of dispersal. As discussed above, it is possible that the greater strengthening of relationships between group members and those orphaned at an earlier age may reduce the likelihood of these infant-orphaned gorillas dispersing relative to those suffering maternal loss as juveniles or subadults, ultimately influencing male orphans’ subsequent reproductive success.

Overall, these findings suggest there are no strong negative fitness consequences for maternal loss in female mountain gorillas over 2 years of age, although we cannot rule out longer-term effects on lifetime reproduction. In males, maternal loss after 2 years does not appear to influence survival but maternal loss as juveniles or subadults (4–8 years) could lower their future reproductive success. This lack of effect on survival reflects patterns found in human populations with natural fertility and mortality, where the negative effect of maternal loss on survival declines substantially with age and in many cases disappears entirely over the age of 2 (Sear and Mace, 2008). However, it is contrary to research in most other social mammals with long periods of mother–offspring co-residency (Snyder-Mackler et al., 2020; Andres et al., 2013; Stanton et al., 2020; East et al., 2009; Tung et al., 2016; Watts et al., 2009; Nakamura et al., 2014; Goldenberg and Wittemyer, 2017; Goldenberg and Wittemyer, 2018). In these species, one of the major mechanisms posited to link maternal loss with increased mortality is through the social adversity caused by mother absence. Specifically, mother absence is found to reduce social integration (Tung et al., 2016; Archie et al., 2014), competitivity (Surbeck et al., 2019; Samuni et al., 2020), dominance rank (Strauss et al., 2020; East et al., 2009; Maestripieri and Mateo, 2009; Lea et al., 2014), and opportunities for social learning (Stanton et al., 2020; Foley et al., 2008; Brent et al., 2015; Estienne et al., 2019). But the lack of increased mortality in gorillas and certain human populations suggests that mother absence does not always lead to social adversity in mammals with extended maternal care.

When immature mountain gorillas suffered maternal loss, we found that their social relationships with other group members estimated through proximity increased in strength (weighted degree), and their social integration within the group (eigenvector centrality) increased considerably. There was no increase in binary degree suggesting that orphans strengthen existing social bonds rather than forming new ones following maternal loss. The increase in orphan eigenvector centrality was especially large (Figure 2), which may be explained by the strengthening of the orphan-dominant male bond in particular, with dominant males typically being extremely well connected within the group network. The increase in proximity-based relationship strength with other group members was actually great enough to outweigh the loss of their mother, although this was not the case for relationships based on contact which did not change significantly more than non-orphans overall. In contrast to fission–fusion social systems such as in chimpanzees, where group composition regularly changes, mountain gorilla orphans remain in stable cohesive social groups in which their social integration is not dependent on their mother – as shown by the strengthening of relationships following maternal loss. Their increased social integration within the larger cohesive group immediately following maternal loss suggests that mountain gorilla orphans are unlikely to have fewer opportunities for social learning as they are regularly in close proximity to multiple group members. Gorillas also do not appear to require the complex feeding techniques such as nut-cracking or termite-fishing, for which close contact with mothers may be most beneficial (Lonsdorf et al., 2004; Estienne et al., 2019). Instead, common group-membership may be sufficient for learning the complex foraging behaviours observed in gorillas (Watts, 1984; Byrne et al., 2001) as groups travel and feed as a cohesive unit.

The greatest increases in relationship strength following maternal loss were found with the dominant male of the group, regardless of whether or not he was the genetic father. This suggests that unlike in elephants where maternal loss leads to weaker relationships with dominant group members, gorillas that suffer maternal loss instead have increased access to the most dominant member of their group. Whilst the cohesion of gorilla social groups already limits the extent to which orphans are likely to suffer from reduced access to resources or knowledge, the strengthening of social relationships, particularly with the dominant male, is likely to further buffer any potential reduction to their competitivity or future dominance rank within the group. Other cohesive social groups in which the effects of maternal loss have been studied have primarily been matriarchal, with groups led by older females, e.g. elephants (Goldenberg and Wittemyer, 2017; Goldenberg and Wittemyer, 2018), or where strong female dominance hierarchies are inherited, e.g. spotted hyenas (East et al., 2009; Watts et al., 2009) and cercopithecine species such as the savannah baboon (Archie et al., 2014). In contrast, males are the dominant sex in gorillas (Wright et al., 2019). Female dominance hierarchies are relatively weak, and the common dispersal of both sexes means maternal support is unlikely to provide considerable benefits for dominance to offspring of either sex. Orphaned gorillas are therefore unlikely to suffer costs in this regard. However, for males that remain in their natal group it is possible that stronger relationships with the dominant male, such as those developed post-maternal loss, could aid dominance acquisition. For males that lose their mothers at particularly early ages and show some of the strongest relationships with the dominant male as a result, it is possible that this could ultimately improve their chances of inheriting dominance of their natal group (Stoinski et al., 2009a; Robbins and Robbins, 2005). Although, due to the rarity of such events (three cases in 53 years) there may never be a large enough sample size to thoroughly investigate such a hypothesis.

The lack of paternity discrimination in the support provided by adult males after maternal loss is consistent with previous findings on paternal care in mountain gorillas, where the highest ranking males sire the majority of offspring and provide the most care, regardless of paternity (Rosenbaum et al., 2018; Rosenbaum et al., 2015). As observed in chimpanzees (Reddy and Mitani, 2019), support from some maternal siblings also appears to occur, with relationships between subordinate adult males and subadult females, and their younger siblings strengthening following maternal loss. In these two classes, caring for siblings may have additional benefits to those of inclusive fitness (Riedman, 1982). Care of infants by adult male mountain gorillas has been found to be linked with dramatically higher reproductive success. Males in the top tertile for showing affiliative behaviour towards infants were found to sire 5.5 times more infants than those in the bottom tertile, even after accounting for rank (Rosenbaum et al., 2018). This suggests that females may prefer males that demonstrate more caring behaviour towards infants, and that subordinate male kin that increase their care of younger maternal siblings following maternal loss may additionally increase their own reproductive success. Pre-reproductive female siblings, particularly subadult females, may also benefit through developing parental experience that may improve their own reproductive success (Riedman, 1982).

Our analyses of social relationship changes used undirected data on pairwise proximity and affiliative contact. Whilst both measures of affiliation indicate at a minimum, high tolerance for the other individual, we cannot determine who was responsible for initiating the relationship changes. Future research could investigate whether it is the orphans themselves that are strengthening their relationships by approaching other group members more often or whether it is the other group members responding to the maternal loss faced by these young gorillas. Our study also only investigates the immediate social response to maternal loss in the 6 months after the mother dies or leaves the group. Whilst this might be expected to be the period where individuals face the greatest social costs from maternal loss, our analyses cannot tell us whether the social buffering we detected during this period is maintained in the longer term. However, previous observations have suggested that the close associations that young gorillas develop following maternal loss continue into subadulthood (Robbins et al., 2005). It is therefore highly likely that the dramatic short-term changes we have detected are part of a longer-term social response to maternal loss with the potential to overcome the long-term fitness costs of maternal loss.

In many human populations, care from other family members is believed to buffer the negative consequences of maternal loss, but the identity of these carers can vary greatly between populations. In rural Gambia, elder sisters and maternal grandmothers increased offspring survival but not fathers, other grandparents, or brothers (Sear et al., 2003). In contrast, in the Ache of Paraguay the loss of fathers significantly impacted offspring survival but the loss of grandparents or adult siblings did not (Hill and Hurtado, 2017). Support from family members in rearing offspring is thought to be a human universal but the composition of those families and the specific family members involved in cooperative care appears to be flexible and responsive to ecological conditions (Sear and Mace, 2008). In killer whales, as observed in many human populations, grandmothers have been found to influence offspring survival and this additional support could in part contribute to the lack of reduced survival for younger female killer whales after maternal loss (Foster et al., 2012; Nattrass et al., 2019). In mountain gorillas, grandparents and their grand-offspring are rarely in the same social group, as females often transfer between groups multiple times within their lives (Robbins et al., 2009a). Instead, fathers (or dominant males with a high likelihood of paternity), siblings, and group members close in age seem to play a key role in buffering the detrimental effects of maternal loss. What appears to set humans, gorillas, and possibly killer whales apart from other species with extended maternal care where high fitness costs to maternal loss are observed is the potential for cooperative care from within the social group.

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Acceptance summary:

We were very pleased to see that the previous round of recommendations and suggestions were taken onboard and responded to in full. We believe the manuscript reads very clearly, and the methods are well explained throughout. As expressed in previous decision letters, we are highly impressed by this interesting work and believe it will be a valuable contribution to the literature that will be enjoyed by eLife's broad readership.

Decision letter after peer review:

Thank you for submitting your article "Social groups buffer maternal loss in mountain gorillas" for consideration by eLife. Your article has been reviewed by Christian Rutz as the Senior Editor, a Guest Editor, and three reviewers. The reviewers have opted to remain anonymous.

The reviewers have had the opportunity to discuss their reviews with one another, and the Guest Editor has drafted this decision letter to help you prepare a revised submission.

We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, we are asking editors to quickly process manuscripts, like yours, that they judge can potentially stand as eLife papers without additional data. Thus, the revisions requested below are aimed at: (a) addressing clarity and presentation of the manuscript; and (b) addressing further considerations about the current analyses and arising conclusions.

Summary:

This manuscript provides an in-depth assessment of the consequences of maternal loss for wild mountain gorillas, and its rigorous and detailed approach was appreciated by all reviewers. However, some concerns were raised by the reviewers in relation to the reported analyses, and the wording of certain text passages.

Essential revisions:

We judge that the manuscript would benefit from addressing the reviewers' comments point-by-point, and have therefore decided to append their full reports below. In relation to some of the comments detailed below, we would like to highlight the following three points:

1) The use of paired t-tests for the examination of social changes is a bit confusing. Specifically, we are unsure how these properly control for repeated sampling of individuals when considering changes in dyadic association scores. Also, the changes reported here appear to just assess how orphan behaviour altered between the pre- and the post-maternal loss period, but how do we know that such changes aren't expected anyway over time? Ideally, these analyses would directly compare orphaned to non-orphaned individuals. Note the comments from reviewer #1 about these paired t-tests that also need to be addressed.

2) Animal social networks (either dyadic association scores or individual-level metrics) are strongly influenced by observation. But it is not clear how these analyses controlled for, or considered, how individual-level differences in observation propensity might alter inferences of their social network position/association scores, and what this might mean for the arising conclusions. We think the manuscript would benefit from including a much more detailed report of how individuals differed in their likelihood of being observed in relation to the social behaviour considered here, and how this may shape the arising networks, and whether controlling for these effects might alter the results/conclusions.

3) We are unsure why only weighted degree was used as a network metric; reviewer #3 also raises a point about this metric. We suggest that this could be addressed through also considering mean average non-zero dyadic bond scores as a metric. Furthermore, you may want to re-run the network metric-based analyses, but with other measures of social behaviour (e.g., eigenvector centrality) to get an idea of how wider indirect centrality within the network may relate to maternal loss.

Reviewer #1:

This is an interesting paper about how the loss of a mother influences individuals future development, or rather the fact that it might not due to social connections with other group members. The paper examines a very comprehensive set of question, and on the whole I found it very well written, though I have some quibbles about how certain ideas are presented/supported. The dataset presented seems impressive. The methods used generally seem appropriate and robust, though I'd like to see some extra justifications for some of the approaches taken during network analysis. For more info, see detailed comments:

Introduction

I think an example of how the presence of the mother affects fitness in later life would be nice here, in order to lend weight to the statement about maternal loss having affects throughout an individual's lifetime. The third paragraph contains examples, and currently feels repetitive in terms of the points raised.

Similarly, I think "these negative changes" could be expanded on in the preceding sentences with an example of negative changes to the social environment.

I am not sure how convinced I am that the human example here adds to the authors' point. As mentioned above, I would prefer to see more general examples of these ideas/effects in social mammals (or animals in general). If the authors' really want to keep a human example, I'd like to see significantly more detail about the circumstances in which this result was obtained.

It feels slightly odd to discuss sex specific consequences before the general consequences.

As above, I am slightly unconvinced by the need/relevance to relate these results back to humans. This might be a result of me not having an anthropology background of course. Related to this, I feel this is making a rather large assumption about the familiarity a reader might have with this literature. Some small details about what these populations are might help convince that they are relevant to build the argument in the same paragraph as killer whales. There are a few other instances of this in the Discussion too, but I'll avoid repeating myself.

Any directional predictions going into the study? There are quite a few variables under investigation, and it would be nice to link them more strongly to the ideas articulated in the Introduction. Is social buffering more likely in certain group compositions of age/sex?

Results: The term age mates initially confused me a little, I thought it was a typo in the table. Perhaps "cohort" or similar accepted term?

Discussion: I feel you could just say "non-breeder" or "virgin" here.

Materials and methods:

I am also not quite sure about the use of simple paired t-tests to address a rather complex question, with many potential confounding variables. While I am all for using simple stats where possible, it seems like there would be more going on (age difference of a dyad, group size, age of orphan, year of study) that should be controlled for/investigated. Given the detailed models that follow, perhaps some clarity about why this is not necessary for the question currently being addressed would be useful?

Additionally, is there a citation for this permutation approach over permuting the networks themselves?

This suggests multiple models, but it seems only one model is described. I think I need to see these models/model formula laid out in a table for clarity. I'd also like to see a citation for the use of the random effects structure to control network independence.

Reviewer #2:

This is a novel, very well written and well-researched project, making use of a fantastic long-term dataset. It is interesting to a wide readership and of high scientific value. I do not really have any major concern but a few queries and suggestions in order improve the clarity in places.

List of slightly more substantial comments/queries:

1) Results – throughout – can you please include sample sizes when stats values are given?

2) Results – can you please also give us an idea of how much time they spent in proximity and contact? It’s good to see the stats but it would be nice to get a feel for who much time we are talking about (and how big the actual change is).

3) Discussion – I don't follow this argument – can you explain the reduced siring opportunities? Also, can you explain why infant-orphans are doing BETTER than older age orphans? This seems counter-intuitive and I am wondering why this would be the case?

4) Can you discuss a little who initiated the contact and proximity? Would that be on the initiative of the orphan or of the other group members?

5) Can you provide any thoughts on what happens after the 6 months? You are concluding that the strengthening of bonds for 6 months after the loss of maternal care is responsible for the lack of adverse effects – but do you have any evidence or indication that this strengthening is extending over a longer time period? I understand that you chose 6 months in order avoid other confounding variables in the analysis, such as changes to group composition – but in the discussion it reads like these bonds are strengthen “forever”. Some indicators that this indeed the case would be good to see if this generalization is warranted.

6) Materials and methods – a few more details about the data feeding into the networks would be good – which behaviours were included as affiliative behaviours? And how was the SRI calculated, ie did you use frequencies or durations of time in contact? More details on how focal sampling was carried out (length of focals and frequency of data recording) would be useful.

7) I would also like to see more details on number of groups, group sizes and compositions used in this study (maybe as supplementary material); all of this can go in supplementary material – but would be nice to have. Also, information on observation times for each social group and how stable they were would be helpful.

Reviewer #3:

The manuscript "Social groups buffer maternal loss in mountain gorillas" examines the potential consequences of maternal loss in mountain gorillas. The topic of early life adversity and consequences of maternal care that extends beyond weaning is of growing interest to researchers including behavioral ecologists and anthropologists. The results presented in the manuscript are an interesting and important contribution to that literature. Whereas most studies have found negative fitness consequences associated with maternal loss, the results reported here indicate that gorillas who experience early maternal loss do not face negative consequences in terms of survival, maturation (age at first birth), or an indicator of reproductive success (first offspring survival). Furthermore, rather than speculating the authors follow-up on social buffering as a potential explanation for this somewhat unexpected (given outcomes observed in other social species) result using social network analyses. My comments on the manuscript primarily concern organization and clarity to help the reader, particularly the reader of a broader journal such as eLife.

1) I realize that the structure of an eLife article has the methods at the end, but as currently presented most of the results are impossible to interpret without a lot of flipping back and forth searching for information. For example, the Results start out by briefly stating that survival was examined using a cox proportional hazards model, which was very helpful for interpreting the results. However, in the next section there is reference to model results without any information about what type of model it is. Later, there are t statistics and p values with no explanations and no indication they came from permutation-based tests.

2) Some of the abruptness of the transition from introduction to results might be helped by stating clearer predictions that help set up the outcomes you tested. I found the descriptions and expectations concerning the fitness outcomes clearer than the social network outcomes and suggests more information be given about the networks before the results of the analyses are presented.

3) I also have some questions concerning the social network analysis. One additional analysis that might be interesting is looking at binary degree along with weighted degree. High weighted degree can result from few strong connections or many weak connections and presenting both weighted and binary degree might indicate one strategy (find a strong buddy) versus another (cast a wide net). Furthermore, what was the variation in group size in these data? Did you take variation in group size into account when calculating network metrics. The maximum weighted degree of an individual in a group with 5 individuals is much lower than the maximum weighted degree of an individual in a group with 15 individuals.

4) Results: Regarding the results for dispersal – any reason to believe group size will influence likelihood of dispersal?

5) Materials and methods: Social buffering of maternal loss results: can you clarify whether the edge to mom included when weighted degree was calculated?

6) Table 5:

- How was age included in the GAMM models? Age in years?

- This table could be presented more clearly. Sometimes the bold is used to describe the two variables under it (Maternal orphan and Group member) and other times the bold itself is a variable with multiple levels under it. Maybe just write out "Orphan age" "Orphan Sex" and not include the extra bold rows?

- Should the age/sex class – sibling interaction results be relative to the dominant male who is or isn't a sibling?

- Where are the results of the smoothed term?

7) Figure 1: Curious about why each age category has its own line. Since maternal loss is a time varying covariate, shouldn't the survival probability of individuals who lost their mother at 7 (for example) be the same as non-orphans until age 7? Apologies if what I described above is the actually the case. It is tricky to tell where the dashed lines start.

8) Materials and methods: Can you explain why sampling variation warranted a smooth? Not necessarily questioning your decision – I find it interesting and looking for more information about it!

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for revising and resubmitting your article "Social groups buffer maternal loss in mountain gorillas" for consideration by eLife. Your article and response have been evaluated by a Guest Editor and Christian Rutz as the Senior Editor.

We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, we are asking Editors to quickly process manuscripts, like yours, that they judge can potentially stand as eLife papers without additional data. Thus, the revisions requested below are aimed at (a) addressing clarity and presentation of the manuscript, and (b) addressing further considerations about the current analyses and arising conclusions.

As outlined in the first decision letter, this manuscript provides an "in-depth assessment of the consequences of maternal loss for wild mountain gorillas" and was found to be a "rigorous and detailed examination" that was appreciated by the Editors and all the reviewers. During the first assessment by the reviewers and Editors, some potential problems with the wording of particular parts of the text were raised, and this revision appears to address all of these in full. However, the first assessment also raised some issues with the reported analyses, and unfortunately, there is still some lack-of-clarity and some issues that need to be resolved. To outline these issues in as clear a way as possible, the below text takes a 3-step approach for each issue: (A) it revisits the initial comment, (B) provides the authors' response (and associated manuscript text), and finally (C) describes why a problem still exists and what needs to be addressed.

1) Differences in social changes

1A) Revisiting the issue: "The use of paired t-tests for the examination of social changes is a bit confusing here. Specifically -on my part- I'm unsure how these properly control for repeated sampling of individuals when considering changes in dyadic association scores? Also, the changes reported here appear to just assess how orphan behaviour altered between the pre- and the post- maternal loss period, but how do we know that such changes aren't expected anyway over time? Ideally this analysis would directly compared orphaned to non-orphaned individuals. Note the comments from reviewer 1 about these paired t-tests need to be addressed too"

1B) Author response: "Yes, we entirely agree and have changed these analyses to instead compare whether network metrics (binary degree, weighted degree and eigenvector centrality) change differently in orphans relative to other immature gorillas within the same group during the same time period using node-based permutations constrained to swap only between individuals within the same incident of maternal loss (based on the same pair of networks). Described in the Materials and methods and Results."

1C) Current Issue:

While we appreciate that the authors have taken the first comment onboard and reconsidered the approach, there are two remaining problems with the current approach. Namely:

1Ci) In the Results, the results of these tests are reported. But, when reporting these results, it is important to not entirely focus on the p-value generated from the null models, but to also report the full results of the standard statistical test as well (i.e., the estimate, the SE, the t value, and the standard p-value). The p-value from the null model is simply used to 'double-check' the results of the standard statistical test, and shouldn't be fully relied upon for conclusions like this. For example, the first result reported here is "(t=1.721,p=0.029)" which isn't clear to the reader, as a t-value this low wouldn't generally result in a p-value this low. Instead, the text needs to report the actual observed statistical test results from the standard test (including the real p-value) and then also include a P_null value (the null value generated from the node permutations). If the primary standard statistical test shows no significant difference (as may be the case here), then this primary result should be relied upon for drawing conclusions (not the null model p-value, as this is mainly just for double-checking tests, rather than drawing direct conclusions from them in this particular way).

Also, as a small side-point, the method used here for actually calculating the node permutation p-value is quite a strange one. Specifically, it is stated in the revised text that "two-tailed p-values were calculated as two times the proportion of permutation t-values greater or smaller than the t-value from the real data set". But this is problematic and doesn't make clear sense? What is actually being done here?

1Cii) The text goes on to state that "within contact-based networks orphans did not show these same gains, with no significant increase in binary degree (t=0.099, p=0.730), weighted degree (t=1.110, p=0.397) or eigenvector centrality (t=0.129, p=0.741) relative to non-orphans". But it isn't clear where these results are actually derived from. We are assuming that they come from a subset of tests the authors refer to in their response, i.e., 'node-based permutations constrained to swap only between individuals within the same incident of maternal loss'. But, if the swaps are constrained between individuals who experience the same incident of maternal loss, and you subsequently try to look at the comparable difference between incident of maternal loss, then the tests will never find a difference because this is already controlled for earlier in the pipeline? Some clarity about what is actually going on here needs to be added to the manuscript (and also some thought about whether such tests are appropriate for testing the questions at hand).

2) Social networks and observations

2A) Revisiting the issue: The first assessment of the paper flagged up that animal social networks are strongly influenced by observation, and requested some analytical consideration of this in the revisions.

2B) Author Response: The authors responded by providing more details about the data collection methods, which is very much appreciated. However, the revision doesn't include any attempts to actually control for/integrate consideration of differences in observation number. Instead, the response states that the Simple Ratio Index includes this (stating "variation in the number of focal scans per individual which is accounted for within the simple ratio index" and "we did not believe that these extra data would bias the estimate of the specific relationships based on SRI (as these account for the total number of observations)".

2C) Current Issue: Unfortunately, the SRI does not control for differences in observation number in the way that the authors lay out here. While the SRI takes some of the difference in observation into account during the calculation of the dyadic values, it in no way fully accounts for this; it just uses it for the calculation. For instance, even in simulations where all individuals have the same social phenotype, those individuals which are observed more will have higher centrality (e.g., higher degree) than those which are observed less. This is simply a product of how these metrics are calculated and how these networks emerge. Therefore, the previous comments from the Editors and the reviewers that the analyses should directly consider 'observation number' still stands.

3) Outstanding analytical issues

3A/B) Revisiting the issue: In the reviewer comments, a few analytical (smaller) issues were pointed out, and in most cases the authors have addressed these fully and clearly. However, a couple of key points remain. Particularly, controlling for age and network differences within the models themselves, and also using 'change in metric' as the response.

3C) Current Issue: The revision didn't make any attempt to actually integrate age of individuals into the models, nor did it try to consider network groups, but the reviewers made solid points that these would be good to consider. We believe these should be integrated into the paper as supplementary analyses. Finally, the switch to considering “change in metric” as the response is certainly useful in lots of ways, but have the authors' considered problems with “regression to the mean” in this sense? Specifically, we can automatically expect this metric to be altered significantly by the initial value, whereby those which initially have a large SN metric value are likely to (just by chance) have large -ve change values, and whereby those which initially have a small SN metric value are likely to (just by chance) have large +ve change values. Have the authors assessed this as a driver/contributor to the results here? (Note this consideration relates to the above point about considering things like age, and network group, which might alter these initial values and thus affect the change values.)

Essential revisions:

We judge that the manuscript would benefit from addressing the reviewers' comments point-by-point, and have therefore decided to append their full reports below. In relation to some of the comments detailed below, we would like to highlight the following three points:

1) The use of paired t-tests for the examination of social changes is a bit confusing. Specifically, we are unsure how these properly control for repeated sampling of individuals when considering changes in dyadic association scores. Also, the changes reported here appear to just assess how orphan behaviour altered between the pre- and the post-maternal loss period, but how do we know that such changes aren't expected anyway over time? Ideally, these analyses would directly compare orphaned to non-orphaned individuals. Note the comments from reviewer #1 about these paired t-tests that also need to be addressed.

Yes, we entirely agree and have changed these analyses to instead compare whether network metrics (binary degree, weighted degree and eigenvector centrality) change differently in orphans relative to other immature gorillas within the same group during the same time period using node-based permutations constrained to swap only between individuals within the same incident of maternal loss (based on the same pair of networks). Described in the Materials and methods and Results.

2) Animal social networks (either dyadic association scores or individual-level metrics) are strongly influenced by observation. But it is not clear how these analyses controlled for, or considered, how individual-level differences in observation propensity might alter inferences of their social network position/association scores, and what this might mean for the arising conclusions. We think the manuscript would benefit from including a much more detailed report of how individuals differed in their likelihood of being observed in relation to the social behaviour considered here, and how this may shape the arising networks, and whether controlling for these effects might alter the results/conclusions.

We have added further details of the sampling methods to the manuscript. Data were collected on fully habituated mountain gorilla groups using focal sampling. Under this approach researchers systematically worked their way through a randomly ordered list of all individuals in the group. If an individual could not be observed, the researchers moved on to the next individual on the list and returned to them the subsequent day. During focals, researchers recorded all individuals within 2m and within physical contact of the focal individual every 10 minutes for an hour. This meant there was no bias in certain individuals being missed that were within 2m or in physical contact of the focal individual as with these small distances and extended periods of observation it is extremely unlikely any individuals at this close proximity were missed. We therefore do not believe there are any biases in the likelihood of certain individuals in a group being observed when participating in the social behaviour considered here, except the variation in the number of focal scans per individual which is accounted for within the simple ratio index.

During the 17 years of social data used in these analyses, some additional projects took place. For example, one project examining the relationships of dominant males and infants. This meant that additional focal data were available for certain individuals and led to some variation in the number of focals per individual within a group. However, we did not believe that these extra data would bias the estimate of the specific relationships based on SRI (as these account for the total number of observations) but would likely just lead to more accurate estimates of these relationships relative to others in the group. We therefore kept these extra focal data within the larger dataset but included the number of focals of either individual involved in a pairwise relationship as a smoothing term in the GAMMs in case there was some potential non-linear bias introduced by this sample size variation that we hadn’t thought of.

3) We are unsure why only weighted degree was used as a network metric; reviewer #3 also raises a point about this metric. We suggest that this could be addressed through also considering mean average non-zero dyadic bond scores as a metric. Furthermore, you may want to re-run the network metric-based analyses, but with other measures of social behaviour (e.g., eigenvector centrality) to get an idea of how wider indirect centrality within the network may relate to maternal loss.

Thanks so much for these suggestions, we think they add really useful information on the overall network changes faced by orphans. We have rerun the analyses looking at the change in binary degree, weighted degree and eigenvector centrality. We used mixed models to account for differences between networks and permuted only between individuals within the same network (as discussed for point 1). There was no relationship between change in weighted degree and network size so we therefore stuck with weighted degree as the more commonly used metric rather than changing to average non-zero dyadic bond scores.

Reviewer #1:

This is an interesting paper about how the loss of a mother influences individuals future development, or rather the fact that it might not due to social connections with other group members. The paper examines a very comprehensive set of question, and on the whole I found it very well written, though I have some quibbles about how certain ideas are presented/supported. The dataset presented seems impressive. The methods used generally seem appropriate and robust, though I'd like to see some extra justifications for some of the approaches taken during network analysis. For more info, see detailed comments:

Introduction

I think an example of how the presence of the mother affects fitness in later life would be nice here, in order to lend weight to the statement about maternal loss having affects throughout an individual's lifetime. The third paragraph contains examples, and currently feels repetitive in terms of the points raised.

Thank for this suggestion – we fully agree and have reorganised the Introduction considerably in an attempt to make it more streamlined and less repetitive

Similarly, I think "these negative changes" could be expanded on in the preceding sentences with an example of negative changes to the social environment.

This has now been addressed in the reorganization of the Introduction.

I am not sure how convinced I am that the human example here adds to the authors' point. As mentioned above, I would prefer to see more general examples of these ideas/effects in social mammals (or animals in general). If the authors' really want to keep a human example, I'd like to see significantly more detail about the circumstances in which this result was obtained.

This has been removed – we agree that it does not tie in very well here.

It feels slightly odd to discuss sex specific consequences before the general consequences.

Agreed – Introduction reorganized as discussed above.

As above, I am slightly unconvinced by the need/relevance to relate these results back to humans. This might be a result of me not having an anthropology background of course. Related to this, I feel this is making a rather large assumption about the familiarity a reader might have with this literature. Some small details about what these populations are might help convince that they are relevant to build the argument in the same paragraph as killer whales. There are a few other instances of this in the Discussion too, but I'll avoid repeating myself.

Thank you for pointing this issue out. We have now added further information to the Introduction to explain why humans are an important example – they are potentially the only social mammal where social buffering does overcome the social adversity of maternal loss.

Any directional predictions going into the study? There are quite a few variables under investigation, and it would be nice to link them more strongly to the ideas articulated in the Introduction. Is social buffering more likely in certain group compositions of age/sex?

We have now more clearly stated our hypotheses going into the study to the Introduction. We hypothesize that as demonstrated in chimpanzees, gorillas may face greater fitness costs if they suffer maternal loss at an earlier age and that males may face greater costs from maternal loss than females due to their longer periods of mother-offspring co-residence. Alternatively, as observed in many human populations, the cohesive, stable social groups of mountain gorillas may enable social buffering from group members to compensate for the social costs of maternal loss with minimal fitness consequences to maternal loss. In particular, dominant males may take on crucial roles in buffering the social adversity faced by maternal orphans, as past research has demonstrated the strong bond between dominant males and young orphans who may regularly share a nest at night.

Results: The term age mates initially confused me a little, I thought it was a typo in the table. Perhaps "cohort" or similar accepted term?

This term was used as it is the same term used in the elephant paper investigating social responses to maternal loss. It’s also quite a common term in the human literature although is slightly confusing as it has nothing to do with mating. We’ve therefore added in “those within 2 years age of the orphan” where it is first used.

Discussion: I feel you could just say "non-breeder" or "virgin" here.

We have changed it to “pre-reproductive”. We thought that non-breeder could be confused with infertile older females and virgin had too many human social connotations.

Materials and methods

I am also not quite sure about the use of simple paired t-tests to address a rather complex question, with many potential confounding variables. While I am all for using simple stats where possible, it seems like there would be more going on (age difference of a dyad, group size, age of orphan, year of study) that should be controlled for/investigated. Given the detailed models that follow, perhaps some clarity about why this is not necessary for the question currently being addressed would be useful?

Additionally, is there a citation for this permutation approach over permuting the networks themselves?

Yes, we agree there are some issues with this previous t-test approach. We have now altered this analysis to compare the change in network metric between the pre- and post-maternal loss periods, testing whether orphans and non-orphans show different changes in network metrics (as described above) using node-based permutations between immature gorillas (both orphans and non-orphans) within the same group (Described in the Materials and methods and Results.). This tests the observed pattern against a null model in which the network metrics of orphans and non-orphans within the same group and the same set of networks do not change differently after the maternal loss incident. This therefore controls for differences in group size, group composition, age-related changes over time, year of study etc. This does not control for the age of the immature gorilla themselves, however ages were distributed fairly evenly across both orphans and non-orphans (the mean age ± SD of orphans was 5.12 ±1.49 years and for non-orphans was 4.71 ±1.71 years) and there is therefore no reason to believe that age could be driving a difference between orphans and non-orphans.

This suggests multiple models, but it seems only one model is described. I think I need to see these models/model formula laid out in a table for clarity. I'd also like to see a citation for the use of the random effects structure to control network independence.

Model formulas have now been added to the table legends in which results are reported (Table 5 and Supplementary file 4 and Supplementary file 5).

The random effects structures are used to control for the non-independence of pairwise relationships involving the same individual but each pairwise relationship is only included in each model once. Apologies, this section was poorly worded and was been altered (subsection “Relationship changes following maternal loss”). We don’t believe that random effect structures would adequately account for non-independence of network metrics, hence the permutation approach used for degree and centrality metrics.

Reviewer #2:

This is a novel, very well written and well-researched project, making use of a fantastic long-term dataset. It is interesting to a wide readership and of high scientific value. I do not really have any major concern but a few queries and suggestions in order improve the clarity in places.

List of slightly more substantial comments/queries:

1) Results – throughout – can you please include sample sizes when stats values are given?

Apologies, this has now been added more consistently throughout.

2) Results – can you please also give us an idea of how much time they spent in proximity and contact? It’s good to see the stats but it would be nice to get a feel for who much time we are talking about (and how big the actual change is).

We have added in this information for mother-offspring dyads to give a feel for exactly what losing that bond means for their time in proximity and their affiliative contact. Changes overall for the group are harder to interpret as it’s the sum of the proportion of time spent in contact or proximity of another individual so a value of 1 could mean they are always in physical contact with only 1 other individual or that they are in physical contact with 2 other individuals simultaneously but only half the time. We have therefore included this information as a percentage increase or decrease compared to the pre-maternal loss period (subsection “Changes in network position following maternal loss”).

3) Discussion – I don't follow this argument – can you explain the reduced siring opportunities?

We have reworded this section to clarify – other studies have demonstrated that for males, those that remain in their natal group have greater siring opportunities than those that disperse. If they remain, they can potentially mate while they are subordinate (although far less so than the dominant male) and they can also wait it out for the dominant male to die or become weak so they can eventually take over. In contrast, males that disperse will not be accepted into another group and are solitary until they are able to attract females and form a group of their own. Many solitary males never succeed in attracting females and therefore have almost no siring opportunities.

Also, can you explain why infant-orphans are doing BETTER than older age orphans? This seems counter-intuitive and I am wondering why this would be the case?

Further details have been added (Discussion). We believe it may be due to two things. Firstly, that individuals that suffer maternal loss at a younger age show greater strengthening of proximity-based relationships with group members. Secondly, that there is a greater period of time between maternal loss and the age at which they could disperse. Both of these things mean that these infant-orphaned gorillas may have stronger social relationships within their group by the time they reach the age at which they may choose to disperse.

4) Can you discuss a little who initiated the contact and proximity? Would that be on the initiative of the orphan or of the other group members?

We cannot say from this data who initiated the contact or proximity, which is an important caveat to the study. This has now been discussed further (Materials and methods).

5) Can you provide any thoughts on what happens after the 6 months? You are concluding that the strengthening of bonds for 6 months after the loss of maternal care is responsible for the lack of adverse effects – but do you have any evidence or indication that this strengthening is extending over a longer time period? I understand that you chose 6 months in order avoid other confounding variables in the analysis, such as changes to group composition – but in the discussion it reads like these bonds are strengthen “forever”. Some indicators that this indeed the case would be good to see if this generalization is warranted.

Thank you for highlighting this important caveat, we have now added some discussion of this into the manuscript (Materials and methods).

6) Materials and methods – a few more details about the data feeding into the networks would be good – which behaviours were included as affiliative behaviours? And how was the SRI calculated, ie did you use frequencies or durations of time in contact? More details on how focal sampling was carried out (length of focals and frequency of data recording) would be useful.

More details on the behaviours included as affiliative contact have now been added to the Results section where they are first mentioned. Further details on the focal sampling approach and the use of frequencies rather than durations of association have now been added to the Materials and methods.

7) I would also like to see more details on number of groups, group sizes and compositions used in this study (maybe as supplementary material); all of this can go in supplementary material – but would be nice to have. Also, information on observation times for each social group and how stable they were would be helpful.

This information has been added to the supplementary material (Supplementary file 4).

Reviewer #3:

The manuscript "Social groups buffer maternal loss in mountain gorillas" examines the potential consequences of maternal loss in mountain gorillas. The topic of early life adversity and consequences of maternal care that extends beyond weaning is of growing interest to researchers including behavioral ecologists and anthropologists. The results presented in the manuscript are an interesting and important contribution to that literature. Whereas most studies have found negative fitness consequences associated with maternal loss, the results reported here indicate that gorillas who experience early maternal loss do not face negative consequences in terms of survival, maturation (age at first birth), or an indicator of reproductive success (first offspring survival). Furthermore, rather than speculating the authors follow-up on social buffering as a potential explanation for this somewhat unexpected (given outcomes observed in other social species) result using social network analyses. My comments on the manuscript primarily concern organization and clarity to help the reader, particularly the reader of a broader journal such as eLife.

1) I realize that the structure of an eLife article has the methods at the end, but as currently presented most of the results are impossible to interpret without a lot of flipping back and forth searching for information. For example, the Results start out by briefly stating that survival was examined using a cox proportional hazards model, which was very helpful for interpreting the results. However, in the next section there is reference to model results without any information about what type of model it is. Later, there are t statistics and p values with no explanations and no indication they came from permutation-based tests.

Thank you for pointing this out – this additional information has now been added throughout the Results section.

2) Some of the abruptness of the transition from introduction to results might be helped by stating clearer predictions that help set up the outcomes you tested. I found the descriptions and expectations concerning the fitness outcomes clearer than the social network outcomes and suggests more information be given about the networks before the results of the analyses are presented.

Agreed – thanks for this suggestion, we have now added this information at the end of the Introduction and prior to the network results (Results).

3) I also have some questions concerning the social network analysis. One additional analysis that might be interesting is looking at binary degree along with weighted degree. High weighted degree can result from few strong connections or many weak connections and presenting both weighted and binary degree might indicate one strategy (find a strong buddy) versus another (cast a wide net). Furthermore, what was the variation in group size in these data? Did you take variation in group size into account when calculating network metrics. The maximum weighted degree of an individual in a group with 5 individuals is much lower than the maximum weighted degree of an individual in a group with 15 individuals.

Thank you very much for these constructive suggestions. We have reanalyzed this section entirely incorporating binary degree and eigenvector centrality (Described in the Materials and methods and Results). By looking at the change in these metrics rather than the raw values we minimize the issues relating to variation in group size, but also control for these by including random effects for each network and only permuting between individuals within the same network to generate p-values. Data on group sizes are provided in Supplementary file 4 and the Figure 2—source data 1.

4) Results: Regarding the results for dispersal – any reason to believe group size will influence likelihood of dispersal?

No evidence of this was found in previous studies so we have not included it in the models in the interests of simplicity. Robbins, (1995). Stoinski et al., (2009).

5) Materials and methods: Social buffering of maternal loss results: can you clarify whether the edge to mom included when weighted degree was calculated?

This has now been reanalyzed, as described above. The edge to the mother was not included when looking at the overall change in network metrics and clarification of this has been added. Only broad descriptive statistics are used for the mother-offspring bond of orphans to give an idea of the importance of that relationship pre-maternal loss.

6) Table 5:

- How was age included in the GAMM models? Age in years?

Yes – added.

- This table could be presented more clearly. Sometimes the bold is used to describe the two variables under it (Maternal orphan and Group member) and other times the bold itself is a variable with multiple levels under it. Maybe just write out "Orphan age" "Orphan Sex" and not include the extra bold rows?

This has been reformatted.

- Should the age/sex class – sibling interaction results be relative to the dominant male who is or isn't a sibling?

Yes, this has now been added.

- Where are the results of the smoothed term?

These have now been added below the table.

7) Figure 1: Curious about why each age category has its own line. Since maternal loss is a time varying covariate shouldn't the survival probability of individuals who lost their mother at 7 (for example) be the same as non-orphans until age 7? Apologies if what I described above is the actually the case. It is tricky to tell where the dashed lines start.

You are correct that we used time varying covariates for the Cox-proportional hazards model, however we still distinguished between the three categories to make our study comparable to other studies (e.g., Stanton et al., 2020). The analysis shows two things, that there are no proportional differences between the three orphan classes and the non-orphans, therefore a proportional hazards is not the correct assumption, and second, when running the Bayesian model, we confirmed that there are in fact no differences between the orphan classes.

To make the points at which orphans and non-orphans split clearer we have added Figure 1—figure supplement 1 which plots each age category by sex separately and indicates the point from which their survival is modelled separately.

8) Materials and methods: Can you explain why sampling variation warranted a smooth? Not necessarily questioning your decision – I find it interesting and looking for more information about it!

We think it is probably unnecessary but accounts for any potential non-linear relationship between sampling and relationship strength. The SRI index takes into account any linear relationship, but we wanted to be extra cautious because of some of the uneven sampling introduced by different focal sampling protocols over the years (as described in more detail above).

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

As outlined in the first decision letter, this manuscript provides an "in-depth assessment of the consequences of maternal loss for wild mountain gorillas" and was found to be a "rigorous and detailed examination" that was appreciated by the Editors and all the reviewers. During the first assessment by the reviewers and Editors, some potential problems with the wording of particular parts of the text were raised, and this revision appears to address all of these in full. However, the first assessment also raised some issues with the reported analyses, and unfortunately, there is still some lack-of-clarity and some issues that need to be resolved. To outline these issues in as clear a way as possible, the below text takes a 3-step approach for each issue: (A) it revisits the initial comment, (B) provides the authors' response (and associated manuscript text), and finally (C) describes why a problem still exists and what needs to be addressed.

1) Differences in social changes

1A) Revisiting the issue: "The use of paired t-tests for the examination of social changes is a bit confusing here. Specifically -on my part- I'm unsure how these properly control for repeated sampling of individuals when considering changes in dyadic association scores? Also, the changes reported here appear to just assess how orphan behaviour altered between the pre- and the post- maternal loss period, but how do we know that such changes aren't expected anyway over time? Ideally this analysis would directly compared orphaned to non-orphaned individuals. Note the comments from reviewer 1 about these paired t-tests need to be addressed too"

1B) Author response: "Yes, we entirely agree and have changed these analyses to instead compare whether network metrics (binary degree, weighted degree and eigenvector centrality) change differently in orphans relative to other immature gorillas within the same group during the same time period using node-based permutations constrained to swap only between individuals within the same incident of maternal loss (based on the same pair of networks). Described in Materials and methods and Results."

1C) Current Issue:

While we appreciate that the authors have taken the first comment onboard and reconsidered the approach, there are two remaining problems with the current approach. Namely:

1Ci) In the Results, the results of these tests are reported. But, when reporting these results, it is important to not entirely focus on the p-value generated from the null models, but to also report the full results of the standard statistical test as well (i.e., the estimate, the SE, the t value, and the standard p-value). The p-value from the null model is simply used to 'double-check' the results of the standard statistical test and shouldn't be fully relied upon for conclusions like this. For example, the first result reported here is "(t=1.721,p=0.029)" which isn't clear to the reader, as a t-value this low wouldn't generally result in a p-value this low. Instead, the text needs to report the actual observed statistical test results from the standard test (including the real p-value) and then also include a P_null value (the null value generated from the node permutations). If the primary standard statistical test shows no significant difference (as may be the case here), then this primary result should be relied upon for drawing conclusions (not the null model p-value, as this is mainly just for double-checking tests, rather than drawing direct conclusions from them in this particular way).

Thank you for clarifying this point. We have now included the full model outputs as supplementary tables and reported the estimate, SE, t-value, standard p-value and P_null, and adjusted the wording to focus less on the P_null. (Results). Standard p-value and P_null values correspond closely overall. With changes in the overall model (described below in our response to sections 2 and 3) results remain very similar. Metrics from networks based on affiliative contact do not increase in orphans relative to non-orphans. The eigenvector centrality of orphans in networks based on proximity still shows a strong increase relative to non-orphans (Est=0.169±0.037, t=4.594, P<0.001, P_null<0.001), whilst weighted degree also increases but to a lesser extent (Est=0.075±0.032, t=2.337, P=0.021, P_null=0.024). Binary degree of orphans no longer increases significantly relative to non-orphans in these proximity-based networks.

Also, as a small side-point, the method used here for actually calculating the node permutation p-value is quite a strange one. Specifically, it is stated in the revised text that "two-tailed p-values were calculated as two times the proportion of permutation t-values greater or smaller than the t-value from the real data set". But this is problematic and doesn't make clear sense? What is actually being done here?

We have reworded this section to make the approach we have used clearer. We used a two-tailed approach since we were interested in both whether the observed test statistic was larger than those in the null models (ie strengthening of relationships between orphans and other group members after maternal loss lead to increased network metrics) or smaller than those in the null models (ie orphans relationships with other group members weakened after losing their mothers relative to other immature gorillas in the group leading to decreased network metrics). If the observed t-value was below the median of the null model t-values, P_null was calculated as:

2 x $\frac{numberofnulltvalueslowerthanobservedt-value}{\mathrm{\text{total null t values}}}$

If the observed t-value was above the median of the null model t-values, P_null was calculated as:

2 x $\frac{numberofnulltvaluesgreaterthanobservedt-value}{\mathrm{\text{total null t values}}}$

Such that a p-value of <0.05 would indicate the observed t-value was within the lowest 2.5% of null values or highest 2.5% of null values.

1Cii) The text goes on to state that "within contact-based networks orphans did not show these same gains, with no significant increase in binary degree (t=0.099, p=0.730), weighted degree (t=1.110, p=0.397) or eigenvector centrality (t=0.129, p=0.741) relative to non-orphans". But it isn't clear where these results are actually derived from. We are assuming that they come from a subset of tests the authors refer to in their response, i.e., 'node-based permutations constrained to swap only between individuals within the same incident of maternal loss'. But, if the swaps are constrained between individuals who experience the same incident of maternal loss, and you subsequently try to look at the comparable difference between incident of maternal loss, then the tests will never find a difference because this is already controlled for earlier in the pipeline? Some clarity about what is actually going on here needs to be added to the manuscript (and also some thought about whether such tests are appropriate for testing the questions at hand).

Apologies –this was not worded very clearly by us. When we refer to “the same incident of maternal loss” we are meaning individuals within the same group when a group-member suffered maternal loss (same group, same time period, same set of networks) rather than specifically only swapping between individuals that suffered maternal loss themselves. Ie if there were 5 immature gorillas within a group and one suffered maternal loss, our node-based permutations would permute the orphan/non-orphan labels associated with each of those immature gorillas in that network. We have tried to make this clearer in the manuscript. These values come from the same models discussed above which are run twice: once on networks based on 2m proximity and once on networks based on affiliative contact. This has now been clarified further in the manuscript.

2) Social networks and observations

2A) Revisiting the issue: The first assessment of the paper flagged up that animal social networks are strongly influenced by observation, and requested some analytical consideration of this in the revisions.

2B) Author Response: The authors responded by providing more details about the data collection methods, which is very much appreciated. However, the revision doesn't include any attempts to actually control for/integrate consideration of differences in observation number. Instead, the response states that the Simple Ratio Index includes this (stating "variation in the number of focal scans per individual which is accounted for within the simple ratio index" and "we did not believe that these extra data would bias the estimate of the specific relationships based on SRI (as these account for the total number of observations)".

2C) Current Issue: Unfortunately, the SRI does not control for differences in observation number in the way that the authors lay out here. While the SRI takes some of the difference in observation into account during the calculation of the dyadic values, it in no way fully accounts for this; it just uses it for the calculation. For instance, even in simulations where all individuals have the same social phenotype, those individuals which are observed more will have higher centrality (e.g., higher degree) than those which are observed less. This is simply a product of how these metrics are calculated and how these networks emerge. Therefore, the previous comments from the Editors and the reviewers that the analyses should directly consider 'observation number' still stands.

Thank you for pointing this out. All network metric models now include a smoothing term for the number of focal scans per individual to account for possible increases in network metric with number of observations of an individual (described in the Materials and methods). A smoothing term was chosen as these relationships may not be linear, particularly at higher numbers of observations. The models of pairwise relationships include the total number of focals of both individuals involved in the relationship as a smoothing term (as was the case in the previous draft).

3) Outstanding analytical issues

3A/B) Revisiting the issue: In the reviewer comments, a few analytical (smaller) issues were pointed out, and in most cases the authors have addressed these fully and clearly. However, a couple of key points remain. Particularly, controlling for age and network differences within the models themselves, and also using 'change in metric' as the response.

3C) Current Issue: The revision didn't make any attempt to actually integrate age of individuals into the models, nor did it try to consider network groups, but the reviewers made solid points that these would be good to consider. We believe these should be integrated into the paper as supplementary analyses. Finally, the switch to considering 'change in metric' as the response is certainly useful in lots of ways, but have the authors' considered problems with 'regression to the mean' in this sense? Specifically, we can automatically expect this metric to be altered significantly by the initial value, whereby those which initially have a large SN metric value are likely to (just by chance) have large -ve change values, and whereby those which initially have a small SN metric value are likely to (just by chance) have large +ve change values. Have the authors assessed this as a driver/contributor to the results here? (Note this consideration relates to the above point about considering things like age, and network group, which might alter these initial values and thus affect the change values.)

Thank you for these suggestions. We have rerun all models of network metric change before and after maternal loss incorporating these suggestions. Age has now been directly included in the models and the metrics themselves have been adjusted relative to the specific network so that binary and weighted degree of each individual is proportional to the maximum value of binary or weighted degree within the network (as is already the case for eigenvector centrality), described in the Materials and methods. This should make network metrics more comparable across networks, but we also still included the network (or incident of maternal loss as referred to in earlier drafts) as a random factor in all models. The potential for initial values that are far from the mean to be driving the results is also now accounted for by including the deviance of the initial network metric value from the group mean (separate mean values calculated for each network as this was included as a random factor).

New Model run as a GAMM to allow inclusion of a smoothing term for number of focal scans:

Metric Change ~ Orphan + Age + Deviance of initial value from group mean + s(focal scans), random=~(1|Network))

As described in our response to section 1, these model changes did not alter the overall findings except that the increase in binary degree of orphans in proximity-based networks went from being weakly significant to no longer significant. This however still supports our previous conclusions that orphans primarily strengthen existing social bonds following maternal loss rather than forming new ones.

Author details

1. Robin E Morrison

   1. Dian Fossey Gorilla Fund, Musanze, Rwanda
   2. Centre for Research in Animal Behaviour, University of Exeter, Exeter, United Kingdom

   Contribution

   Data curation, Formal analysis, Visualization, Writing - original draft, Writing - review and editing

   For correspondence

   rmorrison@gorillafund.org

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9161-4734

2. Winnie Eckardt

   Dian Fossey Gorilla Fund, Musanze, Rwanda

   Contribution

   Conceptualization, Data curation, Formal analysis, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

3. Fernando Colchero

   1. Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
   2. Interdisciplinary Center on Population Dynamics, University of Southern Denmark, Odense, Denmark

   Contribution

   Formal analysis, Visualization, Writing - review and editing

   Competing interests

   No competing interests declared

4. Veronica Vecellio

   Dian Fossey Gorilla Fund, Musanze, Rwanda

   Contribution

   Data curation, Project administration

   Competing interests

   No competing interests declared

5. Tara S Stoinski

   Dian Fossey Gorilla Fund, Musanze, Rwanda

   Contribution

   Conceptualization, Funding acquisition, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

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