As a Nature-Based Solution, urban forests deliver a number of environmental ecosystem services (EESs). To quantify these EESs, well-defined, reliable, quantifiable and stable indicators are needed. With literature analysis and expert knowledge gathered within COST Action FP1204 GreenInUrbs, we proposed a classification of urban forest EESs into three categories: (A) regulation of air, water, soil and climate; (B) provisioning of habitat quality; and (C) provisioning of other goods and services. Each category is divided into EES types: (a) amelioration of air quality; restoration of soil and water; amelioration of the microclimate; removal of CO₂ from the air; (b) provision of habitat for biodiversity; support for resilient urban ecosystems; provision of genetic diversity; and (c) provision of energy and nutrients; provision of grey infrastructure resilience. Each EES type provides one or more benefits. For each of these 12 benefits, we propose a set of indicators to be used when analyzing the impacts on the identified EESs. Around half of the 36 indicators are relevant to more than one single benefit, which highlights complex interrelationships. The indicators of wider applicability are tree and stand characteristics, followed by leaf physical traits and tree species composition. This knowledge is needed for the optimization of the EESs delivered by urban forests, now and in the future.

Iris sangumea Dorm ex Horn. is a perennial cold-tolerant herbs of the Iris genus of Iridaceae, which has a well-formed, bright color and adaptability. Through understanding the flower bud differentiation, Sporogenesis, gametogenesis, fertilization and embryogenesis of I. sangumea, we developed flowering regulation and control breeding. I. sangumea flower bud differentiation was observed in April for 21 days; in this stage, the early differentiation process was slow, temperature was more than 5 °C, if the temperature is shorter than this, most flower bud will have no differentiation, and turn to vegetative growth; later differentiation is faster and required temperature is from 5 to 8 °C and gradually rose to 10‒12 °C. The temperature in April plays the definitely role in the number of bud differentiation. Three stamina contain tetrasporangiate anthers and a glandular tapetum. The anther wall is composed of four cell layers inclduding the epidermis, the endothecium, one middle layer and the secretory tapetum. The type of anther wall development the dicotyledonous type. After pollen maturation, the anther wall forms a double-layer containing only epidermis and endothecium. Simultaneous cytokinesis is of the continuous type during meiosis of microspore mother cell results in a zygomorphous or tetragonal tetrad. Mature pollen grains are two-celled. I. sangumea’s stylar canal is hollow, open type style, and the gynoecium has three carpels. The ovaries have axial placentas. The ovule is anatropous, bitegmic and crassinucellate. The primary sporogenous cell differentiates directly as the megaspore. The megaspore tetrads are linear, T-shaped or juxtaposed. Generally, the first of the tetrad megaspores at the chalaza becomes functional while the other three degenerate. A mature embryo sac with seven cells and eight nuclei embryo sac is of the polygonum type. For dichogamy, the male gametophyte matures about 2 days earlier than the female gametophyte. The pollen tube entered into the embryo sac for 22–24 h after pollination. The dormancy stage of fertilized ovules is 5‒6 days, and the fusion of sperm and egg nucleus is of the premitotic type. The primary endosperm cell has shorter dormancy of 4‒6 days, and endosperm formation is of the nuclear type.

Korean larch (Larix olgensis) is one of the main tree species for afforestation and timber production in northeast China. However, its timber quality and growth ability are largely influenced by crown size, structure and shape. The majority of crown models are static models based on tree size and stand characteristics from temporary sample plots, but crown dynamic models has seldom been constructed. Therefore, this study aimed to develop height to crown base (HCB) and crown length (CL) dynamic models using the branch mortality technique for a Korean larch plantation. The nonlinear mixed-effects model with random effects, variance functions and correlation structures, was used to build HCB and CL dynamic models. The data were obtained from 95 sample trees of 19 plots in Meng JiaGang forest farm in Northeast China. The results showed that HCB progressively increases as tree age, tree height growth (HT growth) and diameter at breast height growth (DBH growth). The CL was increased with tree age in 20 years ago, and subsequently stabilized. HT growth, DBH growth stand basal area (BAS) and crown competition factor (CCF) significantly influenced HCB and CL. The HCB was positively correlated with BAS, HT growth and DBH growth, but negatively correlated with CCF. The CL was positively correlated with BAS and CCF, but negatively correlated with DBH growth. Model fitting and validation confirmed that the mixed-effects model considering the stand and tree level random effects was accurate and reliable for predicting the HCB and CL dynamics. However, the models involving adding variance functions and time series correlation structure could not completely remove heterogeneity and autocorrelation, and the fitting precision of the models was reduced. Therefore, from the point of view of application, we should take care to avoid setting up over-complex models. The HCB and CL dynamic models in our study may also be incorporated into stand growth and yield model systems in China.

Forest fires are influenced by several factors, including forest location, species type, age and density, date of fire occurrence, temperatures, and wind speeds, among others. This study investigates the quantitative effects of these factors on the degree of forest fire disaster using nonparametric statistical methods to provide a theoretical basis and data support for forest fire management. Data on forest fire damage from 1969 to 2013 was analyzed. The results indicate that different forest locations and types, fire occurrence dates, temperatures, and wind speeds were statistically significant. The eastern regions of the study area experienced the highest fire occurrence, accounting for 85.0% of the total number of fires as well as the largest average forested area burned. April, May, and October had more frequent fires than other months, accounting for 78.9%, while September had the most extensive forested area burned (63.08 ha) and burnt area (106.34 ha). Hardwood mixed forest and oak forest had more frequent fires, accounting for 31.9% and 26.0%, respectively. Hardwood-conifer mixed forest had the most forested area burned (50.18 ha) and burnt area (65.09 ha). Temperatures, wind speeds, and their interaction had significant impacts on forested area burned and area burnt.

To understand the function of phosphoenolpyruvate carboxylase kinase, we introduced PtPEPCK1 gene under the control of 35S promoter into 84K poplar (Populus alba ×  P. glandulosa). PtPEPCK1 gene is well-known for its role in gluconeogenesis. However, our data confirmed that it has significant effects on amino acid biosynthesis and nitrogen metabolism. Immunohistochemistry and fluorescence microscopy indicate that PtPEPCK1 is specifically expressed in the cytoplasm of the spongy and palisade tissues. Overexpression of PtPEPCK1 was characterized through transcriptomics and metabolomics. The metabolites concentration of the ornithine cycle and its precursors also increased, of which N-acetylornithine was up-regulated almost 50-fold and ornithine 33.7-fold. These were accompanied by a massive increase in levels of several amino acids. Therefore, overexpression of PtPEPCK1 increases amino acid levels with urea cycle disorder.

Species of the Ericaceae or heath family are widely distribute in continental ecosystems and their special ericoid mycorrhizas (ERM) are considered beneficial to their survival and persistence in variable habitats. Currently, increasing anthropogenic disturbances and improper forest management are affecting subtropical forests of China where these native species located. These activities not only affect plant communities above-ground, but also impose pressures on microbial communities below- ground. In this study, root-associated fungal communities of Rhododendron simsii in four forest types under different anthropogenic disturbances were identified using an Illumina Miseq platform, i.e., old growth forests, secondary forests with one cutting (SEC I), secondary forests with two cuttings (SEC II), and Chinese-fir plantations (PLF). Intra- and inter-annual variations were analyzed by comparing samples taken in different seasons and years. The results show that: (1) over 1000 OTUs were found in hair roots with most from the division Ascomycota and Basidiomycota belonging to different functional groups; (2) while there were a few indicator OTUs specific to different forest types, seasons and years, the proportion of shared taxa was quite large, accounting for 44.9–79.4% of the total OTUs; (3) significantly positive correlations were found between disturbance sensitivity and temporal variations in common fungal orders, and both in major fungal orders were significantly different among fungal functional groups in which putative and possible ERM fungi were highly resistant to disturbances and low temporal variations. The high disturbance resistance and temporal persistence of putative ERM fungi may be essential for the successful adaptation of R. simsii in disturbed subtropical forests of China.

To improve the accuracy in recognizing defects on wood surfaces, a method fusing near infrared spectroscopy (NIR) and machine vision was examined. Larix gmelinii was selected as the raw material, and the experiments focused on the ability of the model to sort defects into four types: live knots, dead knots, pinholes, and cracks. Sample images were taken using an industrial camera, and a morphological algorithm was applied to locate the position of the defects. A portable near infrared spectrometer (900–1800 nm) collected the spectra of these positions. In addition, principal component analysis was utilized on these variables from spectral information and principal component vectors were extracted as the inputs of the model. The results show that a back propagation neural network model exhibited better discrimination accuracy of 92.7% for the training set and 92.0% for the test set. The research reveals that the NIR fusing machine vision is a feasible tool for detecting defects on board surfaces.