Early-life environment and differences in costs of reproduction in a preindustrial human population.
Ilona NenkoAdam D HaywardMirre J P SimonsVirpi LummaaPublished in: PloS one (2018)
Reproduction is predicted to trade-off with long-term maternal survival, but the survival costs often vary between individuals, cohorts and populations, limiting our understanding of this trade-off, which is central to life-history theory. One potential factor generating variation in reproductive costs is variation in developmental conditions, but the role of early-life environment in modifying the reproduction-survival trade-off has rarely been investigated. We quantified the effect of early-life environment on the trade-off between female reproduction and survival in pre-industrial humans by analysing individual-based life-history data for >80 birth cohorts collected from Finnish church records, and between-year variation in local crop yields, annual spring temperature, and infant mortality as proxies of early-life environment. We predicted that women born during poor environmental conditions would show higher costs of reproduction in terms of survival compared to women born in better conditions. We found profound variation between the studied cohorts in the correlation between reproduction and longevity and in the early-life environment these cohorts were exposed to, but no evidence that differences in early-life environment or access to wealth affected the trade-off between reproduction and survival. Our results therefore do not support the hypothesis that differences in developmental conditions underlie the observed heterogeneity in reproduction-survival trade-off between individuals.
Keyphrases
- early life
- free survival
- endothelial cells
- pregnancy outcomes
- gestational age
- cardiovascular disease
- risk assessment
- coronary artery disease
- autism spectrum disorder
- machine learning
- wastewater treatment
- adipose tissue
- low birth weight
- skeletal muscle
- metabolic syndrome
- human health
- atomic force microscopy
- artificial intelligence
- deep learning
- preterm infants
- pluripotent stem cells