## 4 Discussion
### Vegetation Species Parameters and Simulation of CO\({}_{2}\) Fluxes  
Simulations using the standard species parameters for pedunculate oak (_Q. robur_) resulted in annual GPP values approximately half those estimated by EC, indicating that customisation was required. However, if the model is to be applicable at more than one site, the number of parameters altered should be minimal. For PnET, the principal control on a species' C uptake is the parameter AMAXB (optimal photosynthetic rate), together with MFOLOPT (optimal foliage biomass), which determines LAI. These two parameters directly control the simulated GPP values and were changed incrementally until annual EC GPP was matched. Simulated autotrophic respiration is summed from the growth and maintenance of three C pools (leaves, wood and roots), and four parameters define the fractions of photosynthesis and biomass used to calculate respiration, together with a Q10 and three C allocation parameters. There is no parameter that controls heterotrophic soil respiration. Therefore, matching TER from EC data was less straightforward and required balancing with resultant NEP values derived from GPP and TER. Although sub-optimal GPP values may be necessary to ensure both TER and NEP are optimised, it is most likely that there will be a better fit for GPP than both TER and NEP when modelling with PnET. For PSIM, there are also two key parameters that control photosynthesis (VCMAX25 and MCFOLOPT), but only one key parameter that controls respiration (KM20), which simplifies the optimisation process. These gas exchange parameter values were selected to match averaged annual values for GPP, TER and NEP. Changing phenology-related parameters helped match seasonal variations but not inter-annual variations, particularly in PSIM data. PnET uses daily temperature, through cumulative growing degree days (GDD), to control the start and end of leaf unfolding, whereas PSIM only uses GDD at the start and a parameter-defined number of days to complete the process. Similarly, the timing and length of leaf fall remain constant for each year in PSIM, but in PnET these can vary according to conditions. Thus, PnET simulated a larger inter-annual range (205-252 days of non-zero C uptake) in growing season length (GSL) compared to PSIM canopy trees (210-248 days), which contributes to differences in their simulated inter-annual variations. PnET and PSIM both show a positive correlation between GSL and simulated annual GPP (PSIM, \(r\) = 0.78, \(p\) < 0.01; PnET, \(r\) = 0.52, \(p\) < 0.05). GSL has been reported by [PERSON] et al. [61] to be a controlling factor on annual GPP in a deciduous forest in New England. However, [PERSON] et al. [29] did not find any such correlation between observed GSL and EC GPP. The strongest correlation they found to explain inter-annual variation was between peak LAI and GPP in Straits data from 1999 to 2010. Conversely, simulated LAI shows very little annual variation in peak values (PnETrange: 4.85-5.79; PSIM canopy range: 5.47-5.86, understorey: 3.16-3.92, total: 8.64-9.69) and no correlation with simulated GPP or TER. Thus, it seems the inability to vary peak LAI appropriately in both models may cause the poor match in inter-annual variability. However, the exfoliating caterpillar infestations of 2009-2010 were known external factors that affected the forest LAI and therefore GPP, and there may have been other factors (e.g., disease, storm damage) that are not simulated but account for some of the mismatch. No correlation was found between annual rainfall and GPP in either PnET or PSIM, but drought stress affected PnET outputs more than those of PSIM in 2003, although EC measurements showed the Straits Inclosure was not adversely affected. The annual biomass increment for the Straits Inclosure between 1997 and 2011 was estimated as 347 g C m\({}^{-2}\) year\({}^{-1}\) from measurements of the canopy trees [29], although this was an underestimate of total stand NEP as it did not include the understorey.