Cancer is fundamentally a disease of ecology and evolution. Tumor cells forage for nutrients, evade immune predation, and adapt to changing microenvironments, giving rise to communities of phenotypically distinct clones. This intratumoral heterogeneity is a principal obstacle to durable therapy, as diverse tumors are more likely to harbor clones intrinsically resistant to any given treatment. Community ecology offers a complementary perspective: tumor clones form an ecological community whose diversity is structured by tradeoffs and interaction kernels, and eco-evolutionary simulation is the natural tool for understanding how this diversity arises. Here we will use EcoEvoSim, a Julia package I wrote that implements adaptive-dynamics-style simulations over user-specified ordinary differential equation models, to systematically explore how ecological mechanisms generate and maintain clonal diversity in tumors. Because the ODE-based approach yields deterministic, computationally cheap trajectories, systematic parameter scans become feasible at scale. The objectives are: (1) to map how the strength and width of the competition kernel, mutation step size, and founder trait values determine whether a founding clone remains evolutionarily stationary or undergoes repeated evolutionary branching into stable multiclonal assemblages; (2) to quantify how models such as the competition-proliferation tradeoff produce structured coexistence; and (3) to test the robustness of these community structures against model variants (multidimensional trait spaces, spatially structured habitats, and explicit nutrient compartments) identifying which assumptions actually drive the emergence of intratumoral heterogeneity. Expected outcomes include parameter-robustness maps of evolutionary branching in cancer competition models, open-source reproducible simulation configurations, and transferable insight into which interventions could collapse tumor clonal community structure. This should provide a first step toward ecologically informed, adaptive therapeutic design. No patient or sensitive data are involved; all computations are purely in silico.