Support: National Science Foundation (NSF)

Period of Performance: September 1st, 2015 — August 31st, 2019

Budget: $500,000

Summary: The methodology developed in this research project will provide important computational tools, broadly applicable to biological modeling, to study population dynamics across many species. In particular, the modeling results will provide a deeper understanding of fundamental processes underlying population response of Daphnia magna to changes in the environment. The findings will have important implications for environmental sustainability, since D. magna is a toxicologically sensitive species that plays a vital role in freshwater ecosystems as feeders on phytoplankton and as a source of food for other invertebrates and fish. The investigators will hold interdisciplinary workshops to ensure broad application/adaptation of the computational tools developed in this research to other species, stressors, and biological scenarios. These workshops, designed to share the innovative efforts with graduate students, postdoctoral associates, faculty, and researchers in the ecology, toxicology, and mathematics communities, will be held at the National Institute for Mathematical and Biological Synthesis. The investigators will train graduate and undergraduate students, including those from underrepresented minority groups, in multi-disciplinary research involving population biology, toxicology, computer science, statistics, and mathematics.

Structured population models (SPMs) are well characterized for describing aggregate ecological data across a wide variety of species and have utility in estimating population-level responses to natural changes in the environment (e.g., climate change) as well as anthropomorphic influences on the environment (e.g., ecotoxicological risk assessments). Yet, the uncertainty involved in parameterizing SPMs using only population-level data (e.g., longitudinal size or age distributions) can be unreasonably high, thereby limiting the practical utility of such models to understand and predict future population change. A fundamental problem associated with this high uncertainty is that inter-individual variability can influence population-level dynamics and may be difficult to estimate from population level alone. The overall objectives in this research include three aims: (Aim 1) To test the ability of a novel parameter estimation framework (involving random differential equations and the Prohorov metric) to estimate inter-individual variability in demographic rates for SPMs from population-level data. (Aim 2) To develop a parameter estimation framework for estimating inter-individual variability in demographic rates for SPMs that utilizes both organismal-level and population-level data. The investigators will quantify the effect of using organismal-level data within this framework on estimating demographic rate distributions and reducing parameter uncertainty. (Aim 3) To extend optimal experimental design theory for application to SPMs within a statistical framework that estimates inter-individual variability. Using this extended theory, the investigators will test the effect of experimental design complexity on the reduction of parameter uncertainty for SPMs using organismal-level and population-level data. To validate these methods, the investigators will collect experimental data using a species of water flea, Daphnia magna, an ecologically important organism in the context of evolution, toxicology, ecology, and genomics. The investigators aim to develop a novel methodology that quantitatively connects and propagates the assessment of D. magna organismal responses (i.e., to environmental change, to the population level), thereby enabling the causal association of organismal responses to ecosystems adversity.