RAMOS-FERNÁNDEZ Gabriel's profile
avatar

RAMOS-FERNÁNDEZ Gabriel

  • Mathematical Modelling of Social Systems, Institute for Research on Applied Mathematics and Systems, National Autonomous University of Mexico, Mexico City, Mexico
  • Animal networks, Biological Networks, Contact networks, Emergence in complex networks, Multilayer, multiplex or multilevel Networks, Network intelligence, Network measures, Networks in arts and humanities, Self-organization in complex networks, Sensor networks, Social networks, Structural network properties
  • recommender

Recommendations:  2

Reviews:  0

Educational and work
B.Sc in Basic Biomedical Research, National Autonomous University of Mexico (1994). PhD in Biology, University of Pennsylvania (2001). Worked as academic advisor to a conservation organization (2002-2004) before joining Instituto Politécnico Nacional as assistant professor (2004-2017). Joined the Institute for Research on Applied Mathematics and Systems at the National Autonomous University of Mexico as associate professor in 2018. Visiting professor at Center for Complexity Sciences at the same university.

Recommendations:  2

04 May 2022
article picture

Long term analysis of social structure: evidence of age-based consistent associations in male Alpine ibex

A social network of bucks

Recommended by based on reviews by Brenda McCowan and Sandra Smith Aguilar

How do social networks change over the long term? What features are more stable? Are there individuals that maintain their position? What factors determine this? These are the questions that Brambilla et al. (2022) successfully address in their manuscript, for a network of male Alpine ibex (Capra ibex) in the northwestern Italian Alps. 

While it is widely acknowledged that animal social networks are dynamic (Pinter-Wollman et al. 2014) not often can we see analyses of this temporal variation using data sets collected on the same individuals for long periods of time. Brambilla et al. (2022) collected such a data set on individually identified bucks from a wild-ranging population for ten years. Alpine ibex populations are sexually segregated except for the rutting period, which justifies focusing a social network analysis on each of the sexes separately. They also present a low degree of fission-fusion dynamics, forming cohesive groups or spreading over larger areas depending, presumably, on the resource heterogeneity. Taking advantage of the fact that temporary subgroups can be observed, Brambilla et al. (2022) measured the degree of association between individual bucks by the time they spent in the same subgroup. Building yearly networks with links thus defined, the authors were able to analyze the changes and stability of networks across the years. 

In all yearly networks, all bucks are connected in a single, giant component, which implies either that subgroups were sufficiently fluid in composition to include all possible pairs of individuals at least once, or that bucks formed temporarily large subgroups that included all of them, at least sometimes. This connectedness of the networks, as well as their high link density, prevailed over the whole study and can be said to characterize buck social networks. Other features, like the degree of centralization, differed between summer and spring networks, but in a consistent fashion across years, suggesting that the degree of resource heterogeneity (which is higher in the spring, when the snow melts only at low altitude) influences the association patterns between bucks. 

When analyzing the social network metrics at the node level, Brambilla et al. (2022) found a very clear effect of age, with individual degree and eigenvector centrality increasing and then decreasing as bucks aged. In fact, bucks showed mostly peripheral positions in the network of the year before their death. These results add to the accumulating evidence that age and social position are intricately linked (Sueur et al. 2021). The yearly networks also showed strong homophily by age, with bucks of similar age showing stronger bonds than those of different age, and an opposite effect of dominance rank, with bucks of similar rank showing weaker bonds than those of dissimilar rank.

In addition to the obvious integration of these results to those of the female social networks, including the rutting period, it remains to be studied what mechanisms at the individual and behavioral levels could lie behind these patterns: are individuals of similar age also similar in their nutritional requirements? Are they more familiar with each other because of spending time together since young? Are older individuals unable to invest in maintaining social relationships and therefore displaced from more central positions in the network? Are similarly ranked individuals more likely to enter into conflict and therefore avoid one another? Does personality influence patterns, beyond dominance rank or age?

These are open questions that result from a solid study, which counts as its strengths the longitudinal data set, rigorous methods for analyzing networks at the global and node levels and for statistically testing differences and similarities between networks at different points in time and a nicely written literature review with a broad taxonomic scope.  

References

Brambilla A, Hardenberg A von, Canedoli C, Brivio F, Sueur C, Stanley CR (2022) Long term analysis of social structure: evidence of age-based consistent associations in male Alpine ibex. bioRxiv, 2021.12.02.470954, ver. 3 peer-reviewed and recommended by Peer Community in Network Science. https://doi.org/10.1101/2021.12.02.470954

Pinter-Wollman N, Hobson EA, Smith JE, Edelman AJ, Shizuka D, de Silva S, Waters JS, Prager SD, Sasaki T, Wittemyer G, Fewell J, McDonald DB (2014) The dynamics of animal social networks: analytical, conceptual, and theoretical advances. Behavioral Ecology, 25, 242–255. https://doi.org/10.1093/beheco/art047

Sueur C, Quque M, Naud A, Bergouignan A, Criscuolo F (2021) Social capital: an independent dimension of healthy ageing. HAL, hal-03299528,  ver. 3 peer-reviewed and recommended by Peer Community in Network Science. https://hal.archives-ouvertes.fr/hal-03299528

19 Oct 2021
article picture

Social capital: an independent dimension of healthy ageing

How to age happily in a healthy network

Recommended by based on reviews by 2 anonymous reviewers

What is the relationship between social capital and healthy ageing? This is the simple yet ambitious question that Sueur et al. (2021) tackle in their review. The relationship between social capital (understood as the resources an individual has access to by virtue of belonging to a social group) and health has been the subject of discussion at least since the work of Émile Durkheim (1897) who emphasized the social roots of individual health problems, such as stress and its extreme form, reflected in suicidal tendencies. The discipline of medical sociology studies the social determinants of health, partly by focusing on those components of the social capital of individuals that directly influence their health (Cockerham 2017). 

Using a comparative approach and focusing more on senescence than chronological ageing, Sueur et al. (2021) provide ample evidence that social capital has a positive relationship with fitness in many animal species, while stressing the plastic nature of senescence and therefore, pointing at the possibility that one way of improving health over an individual’s life span could be to improve its social capital. This dynamic view of the relationship between social capital and health, as a determinant of healthy ageing as a process, is one of the main conceptual contributions of this work. Another important contribution is the multi-level framework used by the authors in their review. Taking into account the cellular, endocrine, behavioral, individual and social network levels into the same conceptual scheme is a welcome attempt in view of the traditional reductionistic approaches taken in biomedicine. Another strength of the paper is the use of clearly explained boxes to tackle complicated and long-debated terms like social capital or display a full glossary with all the important terms introduced in the paper.

The authors point at the potential mechanisms by which social capital could affect senescence. Here, it is worth pointing out the contemporary context in which one mechanism identified by the authors, takes place in human communities. Since the work of Seyle (1970) it is well known that stress hormones produce a kind of premature ageing process due to a continued stress response. Clearly, socially determined stressful conditions such as racism in modern society, can lead to the activation of coping mechanisms that may be related to premature ageing (e.g. Geronimus et al. 2006). 

A word of caution is particularly relevant: social capital can also have negative effects on health, the most obvious in the context of a pandemic like COVID-19’s being a higher risk of contagion from social exposure. It remains to be seen whether the way in which the human population has adapted as individuals and societies to this risk has necessarily implied a sharp, and probably costly, decrease in social capital.

Overall, this paper should be a good introduction to the intricate relationships between healthy ageing and social capital, hopefully inspiring further research using both animals and humans to understand the social component of ageing.

References

Cockerham WC (2017) Medical Sociology. Routledge, New York. https://doi.org/10.4324/9781315618692

Durkheim É (1951) Suicide: a study in sociology. Free Press, Glencoe, Illinois.

Geronimus AT, Hicken M, Keene D, Bound J (2006) “Weathering” and Age Patterns of Allostatic Load Scores Among Blacks and Whites in the United States. American Journal of Public Health, 96, 826–833. https://doi.org/10.2105/AJPH.2004.060749

Selye H (1970) Stress and Aging. Journal of the American Geriatrics Society, 18, 669–680. https://doi.org/10.1111/j.1532-5415.1970.tb02813.x

Sueur C, Quque M, Naud A, Bergouignan A, Criscuolo F (2021) Social capital: an independent dimension of healthy ageing. HAL, hal-03299528,  ver. 3 peer-reviewed and recommended by Peer Community in Network Science. https://hal.archives-ouvertes.fr/hal-03299528

avatar

RAMOS-FERNÁNDEZ Gabriel

  • Mathematical Modelling of Social Systems, Institute for Research on Applied Mathematics and Systems, National Autonomous University of Mexico, Mexico City, Mexico
  • Animal networks, Biological Networks, Contact networks, Emergence in complex networks, Multilayer, multiplex or multilevel Networks, Network intelligence, Network measures, Networks in arts and humanities, Self-organization in complex networks, Sensor networks, Social networks, Structural network properties
  • recommender

Recommendations:  2

Reviews:  0

Educational and work
B.Sc in Basic Biomedical Research, National Autonomous University of Mexico (1994). PhD in Biology, University of Pennsylvania (2001). Worked as academic advisor to a conservation organization (2002-2004) before joining Instituto Politécnico Nacional as assistant professor (2004-2017). Joined the Institute for Research on Applied Mathematics and Systems at the National Autonomous University of Mexico as associate professor in 2018. Visiting professor at Center for Complexity Sciences at the same university.