Improving the accuracy of estimates of forest carbon exchange is a central priority for understanding ecosystem response to increased atmospheric CO levels and improving carbon cycle modelling. However, the spatially continuous parameterization of photosynthetic capacity (Vcmax) at global scales and appropriate temporal intervals within terrestrial biosphere models (TBMs) remains unresolved. This research investigates the use of biochemical parameters for modelling leaf photosynthetic capacity within a deciduous forest. Particular attention is given to the impacts of seasonality on both leaf biophysical variables and physiological processes, and their interdependent relationships. Four deciduous tree species were sampled across three growing seasons (2013-2015), approximately every 10 days for leaf chlorophyll content (Chl ) and canopy structure. Leaf nitrogen (N ) was also measured during 2014. Leaf photosynthesis was measured during 2014-2015 using a Li-6400 gas-exchange system, with A-Ci curves to model Vcmax. Results showed that seasonality and variations between species resulted in weak relationships between Vcmax normalized to 25°C (Vcmax25) and N (R = 0.62, P < 0.001), whereas Chl demonstrated a much stronger correlation with Vcmax25 (R = 0.78, P < 0.001). The relationship between Chl and N was also weak (R = 0.47, P < 0.001), possibly due to the dynamic partitioning of nitrogen, between and within photosynthetic and nonphotosynthetic fractions. The spatial and temporal variability of Vcmax25 was mapped using Landsat TM/ETM satellite data across the forest site, using physical models to derive Chl . TBMs largely treat photosynthetic parameters as either fixed constants or varying according to leaf nitrogen content. This research challenges assumptions that simple N -Vcmax25 relationships can reliably be used to constrain photosynthetic capacity in TBMs, even within the same plant functional type. It is suggested that Chl provides a more accurate, direct proxy for Vcmax25 and is also more easily retrievable from satellite data. These results have important implications for carbon modelling within deciduous ecosystems.

Aim  This study aimed to detect distribution patterns and identify diversity hotspots for Chinese endemic woody seed plant species (CEWSPS). Location  China. Methods  Presence of 6885 CEWSPS throughout China was mapped by taking the Chinese administrative county as the basic spatial analysis unit. The diversity was measured with five indices: endemic richness (ER), weighted endemism (WE), phylogenetic diversity (PD), phylogenetic endemism (PE) and biogeographically weighted evolutionary distinctiveness (BED). Three levels of area (i.e. 1, 5 and 10% of China’s total land area) were used to identify hotspots, but the 5% level was preferred when both the total area of the hotspots identified and the diversity of CEWSPS reached by the hotspots were considered. Results  Distribution patterns of CEWSPS calculated with the five indices are consistent with each other over the national extent. However, the hotspots do not show a high degree of consistency among the results derived from the five indices. Those identified with ER and PD are very similar, and so are those with WE and BED. In total, 20 hotspots covering 7.9% of China’s total land area were identified, among which 11 were identified with all the five indices, including the Hengduan Mountains, Xishuangbanna Region, Hainan Island, and eight mountainous areas located in east Chongqing and west Hubei, in east Yunnan and west Guangxi, in north Guangxi, south‐east Guizhou and south‐west Hunan, in north Guangdong and south Hunan, in south‐east Tibet, and in south‐east Hubei and north‐west Jiangxi. Taiwan Island was also identified as a major hotspot with WE, PE and BED. Main conclusions  Hotspots of CEWSPS were identified with five indices considering both distributional and phylogenetic information. They cover most of the key areas of biodiversity defined by previous researchers using other approaches. This further verifies the importance of these areas for China’s biodiversity conservation.