Nonlinearities in carbon allocation and vegetation functioning Vegetation one of the major C sinks with respect to the atmosphere has an indispensable role in the global carbon cycle; it assimilates atmospheric CO2 via photosynthesis and distributes the assimilated C among plant parts with different cycling rates. These processes can be generalized into one main concept: carbon allocation (CA). Since vegetation has different CA strategies to survive changing climate and disturbance regimes the efforts towards a better understanding of CA in vegetation have increased particularly through the development of Dynamic Vegetation Models (DVMs). However there is no consensus in the model representation of CA i.e. there are discrepancies among predicted trajectories as well as between model predictions and empirical observations. This lack of consensus goes in hand with the quest to find a balance between precise predictions and a good level of abstraction; detailed models require high computer power whereas simpler models may fail to capture complex dynamics such as nonlinearities. Additionally not all processes that are relevant for ecosystem dynamics take place at the same space time resolution and they are not understood with the same level of detail. Thus upscaling and simplification are another source of uncertainty. These issues have motivated several model intercomparisons and conceptual reviews yet none of them have focused on their mathematical formulation. In this PhD thesis three fundamental aspects of the CA component in ecosystem models are assessed. First common mathematical representations of CA are evaluated and synthesized into a new framework that benefits from concepts of dynamical systems theory; second this framework is used to select pertinent diagnostics of model performance such as the distributions of carbon ages and transit times based on model properties (e.g. autonomy and linearity); and third the long standing puzzle of source and sink limitation on CA is approached from the dynamical systems perspective of the proposed framework using a simple nonlinear model. The implications that these nonlinearities have for the model behaviour are also discussed.