Photosynthetic capacity is one of the most sensitive parameters in vegetation models and its relationship to leaf nitrogen content links the carbon and nitrogen cycles. Process understanding for reliably predicting photosynthetic capacity is still missing. To advance this understanding we have tested across C(3) plant species the coordination hypothesis, which assumes nitrogen allocation to photosynthetic processes such that photosynthesis tends to be co-limited by ribulose-1,5-bisphosphate (RuBP) carboxylation and regeneration. The coordination hypothesis yields an analytical solution to predict photosynthetic capacity and calculate area-based leaf nitrogen content (N(a)). The resulting model linking leaf photosynthesis, stomata conductance and nitrogen investment provides testable hypotheses about the physiological regulation of these processes. Based on a dataset of 293 observations for 31 species grown under a range of environmental conditions, we confirm the coordination hypothesis: under mean environmental conditions experienced by leaves during the preceding month, RuBP carboxylation equals RuBP regeneration. We identify three key parameters for photosynthetic coordination: specific leaf area and two photosynthetic traits (k(3), which modulates N investment and is the ratio of RuBP carboxylation/oxygenation capacity (V(Cmax)) to leaf photosynthetic N content (N(pa)); and J(fac), which modulates photosynthesis for a given k(3) and is the ratio of RuBP regeneration capacity (J(max)) to V(Cmax)). With species-specific parameter values of SLA, k(3) and J(fac), our leaf photosynthesis coordination model accounts for 93% of the total variance in N(a) across species and environmental conditions. A calibration by plant functional type of k(3) and J(fac) still leads to accurate model prediction of N(a), while SLA calibration is essentially required at species level. Observed variations in k(3) and J(fac) are partly explained by environmental and phylogenetic constraints, while SLA variation is partly explained by phylogeny. These results open a new avenue for predicting photosynthetic capacity and leaf nitrogen content in vegetation models.