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Abstract
Compartment models are often used to represent and study biological and ecologicalsystems. They are modeled as directed graphs, describing flows of a conserved quantity (eg.mass, energy, a specific molecule, etc.) among a set of compartments. Various measures havebeen defined to capture universal system-wide properties of these models. In this thesis, we introduce a method that eliminates these shortcomings. Using a stochasticindividual based algorithm called Network Particle Tracking (NPT), we come up withsimulation-based definitions for storage analysis. While both definitions agree for steady-statesystems, the NPT-based definition works for dynamic systems as well. We use the samemethodology to study other interesting properties of mass and energy distribution withinecological networks.An important system-wide property storage analysis quantifies how each environmentalinput gets shared among all compartments. Application of current storage analysis is restricted tosteady-state models. It cannot be utilized to study dynamic systems. This is a major limitation asmost ecosystems experience seasonal changes. Another property, residence time (RT), is awidely used concept representing the average time the flow material stays in a compartment (orthe system) at equilibrium. Residence time distribution (RTD) offers more detailed informationabout the behavior of the flow material within the system. These properties get quite difficult tocompute for large and complex networks. Tracing the movement of tagged flow material in thesystem, similar to tracer experiments, could be used study these essential properties.