Parasitism by Cuscuta chinensis is associated with changes in leaf functional traits and hyperspectral characteristics of Eunymus japonicas.

Cuscuta chinensis have a significant regulatory effect on plant growth, but the response mechanism of functional traits to the parasitism of C. chinensis and the trade-off relationship between traits and hyperspectral characteristics are not clear. We investigated the functional trait response and hyperspectral characteristics of Euonymus japonicus, the most common urban hedge plant in China, to the parasitism of C. chinensis. The results showed that the parasitism of C. chinensis led to the difference of leaf functional traits: the leaf thickness, stomatal density, and leaf dry matter content were significantly increased, whereas the leaf area, leaf weight, specific leaf area, chlorophyll content index, and leaf tissue density were significantly decreased. Notably, the parasitism of C. chinensis changed the spatial distribution pattern of stomata and promoted the stomata to be evenly distributed. Furthermore, the spectral reflectance of leaves treated with the parasitism of C. chinensis tended to increase. The parasitism of C. chinensis led to the "blue shift" of hyperspectral reflectance of leaves. There was a significant correlation between spectral parameters and leaf functional traits, and leaf biomass accounted for 83% of the variation in reflectance of the water stress band. In general, the parasitism of C. chinensis determines the strategic way of plant utilization of resources and affects the change of plant strategy by affecting the difference of traits. Urban plants were more inclined to invest resources in nutrient storage capacity at the expense of resources investment in photosynthetic capacity and defense mechanism. The plant ecological strategy changed from resource acquisition to resource conservation. This finding comes up with a new strategy that urban tree species can modify the plasticity of functional traits for survival and growth under the interference of parasitic plants.

Homogenization characteristics and regional effects in the diversity pattern of woody plants in 101 cities in China.

Influenced by the interplay of global climate change and urbanization, urban plants have become increasingly homogenized in China. However, regional effects of biotic homogenization cannot be clearly explained due to the lack of continuous large-scale data. Thus, we explored the characteristics and regional effects of biotic homogenization, which not only contributes to the improvement of urban biodiversity, but also has important value for human well-being. Here, we analyzed the woody plants of 101 cities in 8 major urban agglomerations in China. The diversity patterns and influencing factors were explored using generalized additive, geographically weighted regression, and structural equation models. The main results were as follows: (1) The issue of woody plant homogenization is primarily manifested in urban greening species in China. (2) The characteristics of woody plant homogenization exhibit notable regional effects at a large scale. (3) Latitude, urban area, altitude and climatic factors all impact the woody plant homogenization. Thus, we found that the homogenization characteristics of urban greening species exhibit regional variations, influenced by both natural and anthropogenic factors. Finally, we suggested that urban biodiversity management should be considered specific regional environmental, both to meet the needs of residents.

Nitrogen addition and drought affect nitrogen uptake patterns and biomass production of four urban greening tree species in North China.

Nitrogen (N) is an essential nutrient element limiting plant growth and production, and plant N uptake capacity varies with environmental change. Recently, global climate changes such as N deposition and drought have important impacts on the terrestrial ecosystems, especially for urban greening trees. However, it's still unclear how N deposition and drought affect plant N uptake and biomass production and the underlying relationship between them. Therefore, we conducted a 15N isotope labeling experiment on four common tree species of urban green spaces in North China, including Pinus tabulaeformnis, Fraxinus chinensis, Juniperus chinensis, and Rhus typhina in pots. Three N addition treatments (0, 3.5, and 10.5 gN m -2 year -1; "no", "low", and "high" N treatments, respectively) and two water addition treatments (300 and 600 mm year-1; "drought" and "normal water", respectively) were set up in a greenhouse. Our results showed that N and drought significantly affected tree biomass production and N uptake rates, and the relationship between them depended on the species specificity. Trees could transform their N uptake preference to adapt to the changing environment, from ammonium to nitrate or vice versa, which was also reflected in total biomass. Furthermore, the variation of N uptake patterns was also related to distinct functional traits, including aboveground (specific leaf area and leaf dry matter content) or belowground (specific root length, specific root area, and root tissue density) traits. There was a transformation of plant resource acquisitive strategy in a high N and drought environment. In general, there were tight connections among N uptake rates, functional traits, and biomass production of each target species. This finding comes up with a new strategy that tree species can modify their functional traits and plasticity of the N uptake forms for survival and growth in the context of high N deposition and drought.

Leaf reflectance and functional traits as environmental indicators of urban dust deposition.

BACKGROUND: How to quickly predict and evaluate urban dust deposition is the key to the control of urban atmospheric environment. Here, we focus on changes of plant reflectance and plant functional traits due to dust deposition, and develop a prediction model of dust deposition based on these traits. RESULTS: The results showed that (1) The average dust deposition per unit area of Ligustrum quihoui leaves was significantly different among urban environments (street (18.1001 g/m2), community (14.5597 g/m2) and park (9.7661 g/m2)). Among different urban environments, leaf reflectance curves tends to be consistent, but there were significant differences in leaf reflectance values (park (0.052-0.585) > community (0.028-0.477) > street (0.025-0.203)). (2) There were five major reflection peaks and five major absorption valleys. (3) The spectral reflectances before and after dust removal were significantly different (clean leaves > dust-stagnant leaves). 695 ~ 1400 nm was the sensitive range of spectral response. (4) Dust deposition has significant influence on slope and position of red edge. Red edge slope was park > community > street. After dust deposition, the red edge position has obviously "blue shift". The moving distance of the red edge position increases with the increase of dust deposition. The forecast model of dust deposition amount established by simple ratio index (y = 2.517x + 0.381, R2 = 0.787, RMSE (root-mean-square error) = 0.187. In the model, y refers to dust retention, x refers to simple ratio index.) has an average accuracy of 99.98%. (5) With the increase of dust deposition, the specific leaf area and chlorophyll content index decreased gradually. The leaf dry matter content, leaf tissue density and leaf thickness increased gradually. CONCLUSION: In the dust-polluted environment, L. quihoui generally presents a combination of characters with lower specific leaf area, chlorophyll content index, and higher leaf dry matter content, leaf tissue density and leaf thickness. Leaf reflectance spectroscopy and functional traits have been proved to be effective in evaluating the changes of urban dust deposition.

Leaf functional traits differentiation in relation to covering materials of urban tree pits.

BACKGROUND: Understanding the ecological strategies of urban trees to the urban environment is crucial to the selection and management of urban trees. However, it is still unclear whether urban tree pit cover will affect plant functional traits. Here, we study the response of urban trees to different tree pit covers, analyzed the effects of different cover types on soil properties and their trade-off strategies based on leaf functional traits. RESULTS: We found that there were obvious differences in the physical properties of the soil in different tree pit covers. Under the different tree pit cover types, soil bulk density and soil porosity reached the maximum under cement cover and turf cover, respectively. We found that tree pit cover significantly affected the leaf properties of urban trees. Leaf thickness, chlorophyll content index and stomatal density were mainly affected by soil bulk density and non-capillary porosity in a positive direction, and were affected by soil total porosity and capillary porosity in a negative direction. Leaf dry matter content and stomata area were mainly negatively affected by soil bulk density and non-capillary porosity, and positively affected by soil total porosity and capillary porosity. Covering materials of tree pits promoted the functional adjustment of plants and form the best combination of functions. CONCLUSION: Under the influence of tree pit cover, plant have low specific leaf area, stomata density, high leaf thickness, chlorophyll content index, leaf dry matter content, leaf tissue density and stomata area, which belong to "quick investment-return" type in the leaf economics spectrum.

Intraspecific differences in plant functional traits are related to urban atmospheric particulate matter.

BACKGROUND: Functional trait-based ecological research has been instrumental in advancing our understanding of environmental changes. It is still, however, unclear how the functional traits of urban plants respond to atmospheric particulate matter, and which trade-off strategies are shown. In order to explore the variation of plant functional traits with the gradient of urban atmospheric particulate matter, we divided atmospheric particulate matter into three levels according to road distance, and measured the variation of six essential leaf functional traits and their trade-off strategies. RESULTS: Here, we show that the functional traits of plants can be used as predictors of plant response to urban atmospheric particulate matter. Within the study, leaf thickness, leaf dry matter content, leaf tissue density, stomatal density were positively correlated with atmospheric particulate matter. On the contrary, chlorophyll content index and specific leaf area were negatively correlated with atmospheric particulate matter. Plants can improve the efficiency of gas exchange by optimizing the spatial distribution of leaf stomata. Under the atmospheric particulate matter environment, urban plants show a trade-off relationship of economics spectrum traits at the intraspecific level. CONCLUSION: Under the influence of urban atmospheric particulate matter, urban plant shows a "slow investment-return" type in the leaf economics spectrum at the intraspecific level, with lower specific leaf area, lower chlorophyll content index, ticker leaves, higher leaf dry matter content, higher leaf tissue density and higher stomatal density. This finding provides a new perspective for understanding the resource trades-off strategy of plants adapting to atmospheric particulate matter.

Estimation model and its trade-off strategy of Mangifera persiciforma Colletotrichum gloeosporioides degree based on leaf reflection spectrum.

Colletotrichum gloeosporioides is one of the most common and serious fungal diseases of the tree Mangifera persiciforma. Yet we lack an effective method to evaluate this ecological interaction accurately. Here, we measured the functional traits and leaf reflectance spectrum of the host plants under different disease degrees. The findings provide a fast and efficient method for large-scale and high-precision monitoring of C. gloeosporioides in M. persiciforma stands. Using the collected leaf reflection data, we set up a prediction model of the optimal disease degree. Firstly, we found that leaf functional traits of M. persiciforma generally consisted of low leaf thickness, low relative chlorophyll content, small specific leaf area, high leaf tissue density, high dry matter content, low stomatal density, and large stomatal area. Secondly, leaf reflectivity increases with damage of C. gloeosporioides, which corresponds to five main reflection peaks and five absorption valleys in the spectral reflectance curve of leaves at the same positions (350-1800 nm). Thirdly, with the increase of infection degree, red edge slope and yellow edge slope decrease, while green peak reflectance, red valley reflectance, and blue edge slope all increase. Blue shift was detected in the red edge, green peak, and red valley, while red shift appeared at the blue edge and yellow edge. Finally, the best predictive model was that based on green peak reflectance (y=3.6396-0.0693x, R2=0.5149, RMSE [root-mean-square error] =0.2735), with an R2=0.92 and RMSE=0.0042 between its predicted vs. observed values. Because of its high inversion accuracy, the model can be used to predict the invasion conditions of M. persiciforma by C. gloeosporioides. Our study demonstrated that when plants are infected by C. gloeosporioides, there was a strong trade-off relationship between leaf functional traits. On the global leaf economics spectrum, the leaves tended toward the "slow investment-return" end when infected by C. gloeosporioides.

Response of plant reflectance spectrum to simulated dust deposition and its estimation model.

To quantitatively reflect the relationship between dust and plant spectral reflectance. Dust from different sources in the city were selected to simulate the spectral characteristics of leaf dust. Taking Euonymus japonicus as the research object. Prediction model of leaf dust deposition was established based on spectral parameters. Results showed that among the three different dust pollutants, the reflection spectrum has 6 main reflection peaks and 7 main absorption valleys in 350-2500 nm. A steep reflection platform appears in the 692-763 nm band. In 760-1400 nm, the spectral reflectance gradually decreases with the increase of leaf dust coverage, and the variation range was coal dust > cement dust > pure soil dust. The spectral reflectance in 680-740 nm gradually decreases with the increase of leaf dust coverage. In the near infrared band, the fluctuation amplitude and slope of its first derivative spectrum gradually decrease with the increase of leaf dust. The biggest amplitude of variation was cement dust. With the increase of dust retention, the red edge position generally moves towards short wave direction, and the red edge slope generally decreases. The blue edge position moved to the short wave direction first and then to the long side direction, while the blue edge slope generally shows a decreasing trend. The yellow edge position moved to the long wave direction first and then to the short wave direction (coal dust, cement dust), and generally moved to the long side direction (pure soil dust). The yellow edge slope increases first and then decreases. The R2 values of the determination coefficients of the dust deposition prediction model have reached significant levels, which indicated that there was a relatively stable correlation between the spectral reflectance and dust deposition. The best prediction model of leaf dust deposition was leaf water content index model (y = 1.5019x - 1.4791, R2 = 0.7091, RMSE = 0.9725).

A Fast and Automatic Method for Leaf Vein Network Extraction and Vein Density Measurement Based on Object-Oriented Classification.

Rapidly determining leaf vein network patterns and vein densities is biologically important and technically challenging. Current methods, however, are limited to vein contour extraction. Further image processing is difficult, and some leaf vein traits of interest therefore cannot be quantified. In this study, we proposed a novel method for the fast and accurate determination of leaf vein network patterns and vein density. Nine tree species with different leaf characteristics and vein types were applied to verify this method. To overcome the image processing difficulties at the microscopic scale, we adopted the remote object-oriented classification method applied comprehensively in the field of remote sensing research. The key to this approach is to determine the universally applicable leaf vein extraction threshold values (scale parameter, shape parameter, compactness parameter, brightness feature, spectral feature and geometric feature). Based on our analysis, the following recommended threshold values were determined: the scale parameter was 250, the shape parameter was 0.7, the compactness parameter was 0.3, the brightness feature value was 230∼280, the spectral feature value was 180∼230, and the geometric feature value was less than 2. With the optimal extraction parameters applied, the extraction precision was above 96.40% on average for the nine species studied. The leaf vein density calculation rate increased by more than 87.3% compared to that of the traditional methods. The results showed that this method is accurate, fast, flexible and complementary to existing technologies. It is an effective tool for the fast extraction of vein networks and the exploration of leaf vein characteristics, particularly for large-scale studies in plant vein physiology.

Effect of simulated warming on leaf functional traits of urban greening plants.

BACKGROUND: Response and adaptation strategies of plants to the environment have always been the core issues in ecological research. So far, relatively little study exists on its functional traits responses to warming, especially in an urban environment. This information is the key to help understand plant responses and trade-off strategy to urban warming. RESULTS: We chose the common greening trees of mature age in Beijing (Fraxinus pennsylvanica, Koelreuteria paniculata, and Sophora japonica) as the research subjects, and used infrared heaters to simulate warming for three gradients of natural temperature (CK), moderate warming (T1) and severe warming (T2). Results showed that:(1) Leaf dry matter content (LDMC), chlorophyll content (CHL), leaf tissue density (LTD), and stomatal density (SD) all increased with temperature warming. Specific leaf area (SLA), stomatal size (SS), and stomatal aperture (SA) decreased with simulated warming. (2) SLA was extremely significantly negatively correlated with CHL, LDMC, LTD and SD (P < 0.01), and was extremely significantly positively correlated with SS (P < 0.01). SA was extremely negatively correlated with SD (P < 0.01), and was extremely significantly positively correlated with SS (P < 0.01). There was a significant positive correlation between LDMC and LTD (P < 0.01). This showed that urban greening trees adapted to the environment by coordinating adjustment among leaf functional traits. (3) Under the T1 treatment, the R2 and slope among the leaf traits were higher than CK, and the significance was also enhanced. The correlation between leaf traits was strengthened in this warming environment. Conversely, it will weaken the correlation between leaf traits under the T2 treatment. CONCLUSION: Our study demonstrated that there was a strong trade-off between leaf functional traits in the urban warming environment. Plants in the warming environment have adopted relatively consistent trade-offs and adaptation strategies. Moderate warming was more conducive to strengthening their trade-off potential. It is further verified that the global leaf economics spectrum also exists in urban ecosystems, which is generally tend to a quick-investment return type with the characteristics of thick leaves, strong photosynthetic capacity, low transpiration efficiency and long life in urban environments.

Response of dust particle pollution and construction of a leaf dust deposition prediction model based on leaf reflection spectrum characteristics.

Urban plants can improve several environmental pollution problems in cities, especially dust prevention, noise reduction, purification of the atmosphere, etc. To explore the influence of dust deposition on the spectral characteristics of the leaf, a foliar dust deposition prediction model based on high-spectrum data was established. Taking Euonymus japonicus L., the common greening tree species in Beijing, as the research object, high (T1), medium (T2), and low (T3) dust pollution gradients were set and hyperspectral data were collected. Results showed that: (1) in the dust-contaminated environment with different concentrations, the trend of the reflectance curve of the leaves of Euonymus japonicus L. was generally consistent. The spectral reflectance of the leaf surface was positively correlated with the amount of leaf dust. (2) There were five obvious reflection peaks and five main absorption valleys with the same positions and ranges in the 350-2500 nm range. (3) The spectral reflectance of leaf flour dust particles of Euonymus japonicus L. was significantly different before and after dusting, and its size was generally clean leaves > dust-depositing leaves. The sensitive range of its spectral response was 695-1400 nm. (4) The overall trend of the first derivative spectrum was basically the same. The red edge slope and the blue edge slope appeared as T3 > T2 > T1, the red edge position and the blue edge position appeared as T1 < T2 < T3. The red edge position of the leaf surface after dust deposition had an obvious "blueshift", and the moving distance increases with the increase of dust retention on leaf surface. (5) The leaf water index (y = - 1.18x2 + 0.5424x + 0.9917, R2 = 0.8030, RMSE = 0.187) had the highest accuracy in the regression model of leaf surface dust deposition using spectral parameters. The test showed that the R2 reached 0.9019, which indicated that the model has a good fitting effect. This prediction model can effectively estimate the dust deposition of the leaf surface of Euonymus japonicus L.