## 1 Introduction  
The long-lived GHGs that contribute most to global warming with high radiative forcing values are carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) [1]. Forests are net sinks for CO2 and are estimated to store 861 \(\pm\) 66 Pg C globally, of which 44% resides in the soil to a depth of 1 m [2]. Temperate forests occupy an area of 767 Mha and contribute 0.8 \(\pm\) 0.1 Pg C year\({}^{-1}\) to global C sinks [2]. Studies suggest that continuing or increasing C sequestration from temperate forests is likely in future decades unless major disturbances by management increase in severity or frequency [3]. In European forests, C density is higher than in other continents, possibly as a result of management with low harvest rates [4]. Soil microbiological processes that result in fluxes of GHGs are reasonably well understood. CO2 is produced by autotrophic respiration of roots and heterotrophic respiration of soil-dwelling macro- and micro-organisms that decompose soil organic material. Denitrification under anaerobic conditions and nitrification in aerobic conditions are the main soil processes that produce N\({}_{2}\)O. Many laboratory studies have contributed to the understanding and quantification of these soil processes (e.g., [5; 6; 7]), and field measurements have helped to understand their spatial and temporal variability, and differences between forest types (e.g., [8; 9]). Soil environmental conditions of temperature and moisture and nutrient levels are the primary controls of microbiological processes that lead to GHG fluxes, and are influenced directly and indirectly by secondary factors such as tree shading and density. Process-based biogeochemical models such as LandscapeDNDC [10] have been developed to study how soil processes, vegetation type and their controlling factors interact to influence the C and N cycles and related ecosystem and soil gas fluxes at local and regional scales, and to consider the effects of future climate scenarios [11]. LandscapeDNDC combines a choice of vegetation modules with microclimate and soil process modules in a framework that behaves functionally as a single model. In the case of forest vegetation, two modules are available: Photosynthesis-Evaportranspiration (PnET) [12; 13] and Physiological Simulation Model (PSIM) [14; 15]). PnET has been used in numerous studies, including many in which it was combined with the soil process model DNDC (DeNitrification-DeComposition [16; 17; 18]), but is limited in only being able to describe a single vegetation species type for a given site. There is a silvicultural trend away from forest monocultures, and in Europe 70% of forests are now dominated by two or more tree species [19], partly due to a move away from a management focus on yield, resulting from reduced demand for timber products [20], towards conservation, biodiversity and recreation [21]. Many forests naturally develop an understorey of smaller trees (including canopy offspring) and/or shrubs. Modelling these intermixed and understorey species, together with local environmental factors, requires compromises in the choice of values of parameters originally designed to model a single canopy species [22]. [PERSON] et al. [23] compared statistically derived site-specific and general parameter sets for three tree species in continental Europe, beech, spruce and pine, which typically have no understorey. It was concluded that the use of site-specific parameters consistently simulated measured CO\({}_{2}\) fluxes better than generic parameters, but the latter can reproduce C uptake reasonably accurately when averaged monthly over several years. The current PnET parameters defined for oak species are mainly derived from oak in North America (Harvard Forest) or continental Europe (Matra Mountains, Hungary) [8]; the milder, maritime UK climate merits evaluation of GHG models for these conditions. Furthermore, oak has been identified as a temperate species likely to adapt well to warming climates [24].