Matrilateral bias of grandparental investment in grandchildren persists despite the grandchildren's adverse early life experiences.
Samuli HelleAntti O TanskanenDavid Antony CoallMirkka DanielsbackaPublished in: Proceedings. Biological sciences (2022)
Evolutionary theory predicts a downward flow of investment from older to younger generations, representing individual efforts to maximize inclusive fitness. Maternal grandparents and maternal grandmothers (MGMs) in particular consistently show the highest levels of investment (e.g. time, care and resources) in their grandchildren. Grandparental investment overall may depend on social and environmental conditions that affect the development of children and modify the benefits and costs of investment. Currently, the responses of grandparents to adverse early life experiences (AELEs) in their grandchildren are assessed from a perspective of increased investment to meet increased need. Here, we formulate an alternative prediction that AELEs may be associated with reduced grandparental investment, as they can reduce the reproductive value of the grandchildren. Moreover, we predicted that paternal grandparents react more strongly to AELEs compared to maternal grandparents because maternal kin should expend extra effort to invest in their descendants. Using population-based survey data for English and Welsh adolescents, we found evidence that the investment of maternal grandparents (MGMs in particular) in their grandchildren was unrelated to the grandchildren's AELEs, while paternal grandparents invested less in grandchildren who had experienced more AELEs. These findings seemed robust to measurement errors in AELEs and confounding due to omitted shared causes.
Keyphrases
- early life
- birth weight
- pregnancy outcomes
- physical activity
- healthcare
- young adults
- mental health
- palliative care
- body composition
- quality improvement
- gestational age
- pregnant women
- genome wide
- adverse drug
- big data
- cross sectional
- gene expression
- artificial intelligence
- climate change
- weight loss
- deep learning
- human health
- data analysis