Early-life experiences have profound effects on the health, survival, and fitness of many mammalian species, including humans. These consequences are thought to emerge as a byproduct of adaptive processes during development (i.e., adaptive developmental plasticity). To date, two leading, non-mutually exclusive models of adaptive developmental plasticity have been proposed. The developmental constraints model posits that organisms experiencing poor early environments make tradeoffs that promote survival in early life. These tradeoffs lead to poor somatic quality in adulthood, reflected in delayed maturity, low fertility, and shorter lifespans. In contrast, the internal predictive adaptive response (iPAR) model posits that organisms use harsh early environments to predict their own poor somatic state in adulthood. Accordingly, they accelerate their life history to account for shorter life expectancies. Discovering which model dominates is critical to understanding evolved responses to early adversity in long-lived species, and how early-life environments shape individual life-history trajectories, health, aging, and fitness.
The objective of my dissertation is to test the evolutionary, developmental, and aging-related consequences of early-life adversity. To accomplish this objective, I utilize long-term, individual-based data from a wild population of baboons (\emph{Papio cynocephalus}) monitored by the Amboseli Baboon Research Project in Kenya. Specifically, I leverage demographic, environmental, social, reproductive, and hormonal data that have been collected for thousands of individuals over the last 52 years. For my first data chapter, I used these data to show that accelerating reproduction is not an adaptive response to early-life adversity. Instead, females only benefited from reproductive acceleration if they also led long lives. My results raise doubts that accelerated reproduction is an adaptive response to early adversity in baboons and other long-lived, slow-reproducing species. For my second data chapter, I applied machine learning to several long-term data sets on the Amboseli baboons to create a non-invasive physio-behavioral aging clock and tested the effects of early adversity on biological aging. I found that early-life adversity is associated with accelerated behavioral and physiological aging, and in turn, accelerated behavioral and physiological aging is associated with short lifespans. These results offer a new, non-invasive tool for measuring biological aging and provide a potential mechanism through which experiences early in life manifest in adulthood. For my third data chapter, I created a Bayesian survival model to estimate the impact the early-life adversity has on survival rates for adult males and females. I found no evidence that early-life adversity has stronger effects on male survival compared to female survival. These results suggest that males of polygynandrous species are not always more negatively impacted by early-life adversity, despite the fact that they have increased energy requirements during development. Together, my research furthers our understanding of the long-term consequences of early-life adversity and how evolution has shaped organismal responses to early-life adversity.