Ecologists have been fascinated by the factors influencing the length of food chains in natural communities since Elton introduced the concept of "food cycles" in the early 20th century. Proposed drivers for food chain length have included productivity, disturbance regime, and ecosystem size, among others. However, existing theories have primarily considered simple, two-dimensional habitat structures and may not be sufficient for predicting food chain length in ecosystems with complex branching architectures. In this study, we present a spatially explicit theoretical model that offers a framework for understanding the variability in food chain length within branching networks. Our findings reveal independent, positive impacts of both ecosystem size and complexity (as indicated by branching probability) on food chain length. Nevertheless, the effects of ecosystem size and complexity were dependent on other factors and were more pronounced in high-disturbance environments. Our model predictions provide testable hypotheses with stable isotope approaches; to this end, we have curated a global stable isotope database in rivers. We will present the preliminary results of stable isotope analysis for the relationship between food chain length and ecosystem geometries (size and complexity) at large spatial scales.