Lead Graduate Student: Kevin Phung
Background: There are three major stages in the life cycle of a sexually reproducing organism: developmental, aging, and late life, from the evolutionary perspective. According to Hamilton’s (1966) forces of natural selection, there are age-specific impacts on life history based on the declining force of selection during adulthood. This intuitively suggests that there may be age-dependent adaptations to novel dietary environments that are specific to early life, but which fade with adult age. Therefore we believe that a population’s ability to adapt to a novel environment will occur much faster at younger ages as opposed to later ages where selection, genetic variation, and population size are not as favorable for adaptation.
Research Objectives: Develop a model to describe the effects of the evolution of populations in novel environments via computer simulations. Computer models enable us to perform simulations that are generally limited by the production of finite results based on specific parameter choices. Given this circumstance, the experiment shall be useful in providing a guided pattern for generalization to populations beyond the specific parameters that are examined. The advantage of an explicit simulation is the option to analyze a complex pattern that is otherwise not possible using normal mathematical analysis. Standard mathematical analysis often requires extremely simplified assumptions for the calculations to hold true—assumptions that often make the results unrealistic for generalization beyond the specific event studied.
Methods and Analysis: The evolution of a population in a novel environment is being studied via computer simulations. Calculations are performed using the R programming language and statistical computing software by the R Foundation (Vienna, Austria). A population with an effective population size of 10,000 is assumed to have a functional phenotype that declines linearly with age. Age-specific survival is affected by this phenotype through a form of normalizing selection. The further the population phenotype deviates from the “optimum” phenotype, the lower the Darwinian fitness. We will specifically assume that the population is moved to a new environment where the optimum phenotype has changed.
Intellectual Merits: Recently, anthropologists and physicians have focused on the growing incidence of age-related disorders (e.g. type II diabetes and cardiovascular disease) in populations that have transitioned to the agricultural diet within recent human history. These authors claim that regression to a diet we were better adapted to over our evolutionary history prior to the introduction of grains and dairy foods to our diet, known as the Paleolithic or hunter-gatherer diet, may help prevent these age-related onset diseases, more specifically cardiovascular conditions (e.g. Lindeberg, 2009). The evidence suggests that we are not well adapted to the agricultural diet and therefore it is harmful to our health (e.g. Lindeberg et al., 2007). Transitioning populations to novel dietary regimes will be studied in this project using model equations based on the life-cycle of Drosophila melanogaster; however, the theory is general in its import.
Broader Impacts: If we extrapolate these findings to the human condition, they suggest that we could be very well-adapted to the agricultural life-style at younger ages, but are much less adapted at later ages. Therefore, there may be very beneficial health effects that would follow from a reversion to a pre-agricultural diet for the older adults in modern society, as suggested by Lindberg (2009). Further theoretical, laboratory, and clinical research that studies the impact that novel diets can have on the different age classes could fundamentally change our understanding of both gerontology and nutrition.