Uncertainties Brought by Weight Assignment in Ecosystem Health Modelling

Weight assignment is the most important step in ecosystem health modelling. However, few researches were conducted to test the uncertainties brought by weighting methods in ecosystem health modelling. In this research, aimed to test the rationality and uncertainties brought by objective weighting methods, we made a comparison between different objective weighting methods (Entropy, Variation coefficient, Mean square error, Critic). We found that (1) the weights assigned by different objective method are quite different; (2) the variation of sample size does not exert significant influences on weight assignment. However, the weight of indicator has the tendency of increasing or decreasing with the increment of sample size; (3) the weights assigned by these four objective methods were not able to reflect the actual relative importance of indicators. Therefore, we don't advise to use objective weighting method as the sole approach to assign the weight of indicator in ecosystem health modelling

Habitat differentiation and conservation gap of Magnolia biondii, M. denudata, and M. sprengeri in China

The flower buds of Magnolia biondii, M. denudata, and M. sprengeri are the materials of Xinyi, a traditional Chinese medicine. The harvest of flower buds and habitat fragmentation caused by human disturbance heavily threatens the natural regeneration and survival of these three Magnolia species. With the aim to support the conservation and improve the effectiveness of conservation, we performed an assessment on habitat suitability, influences of environmental variables on habitat suitability, and the conservation gap of these three Magnolia species, based on the Maxent modeling method. The results indicated that: (1) altitude, annual mean temperature, extreme temperature, temperature fluctuation, annual precipitation, and extreme precipitation are the most influential environmental variables for the distribution of M. sprengeri, M. biondii, and M. denudata; (2) obvious habitat differentiations were observed among M. biondii, M. denudata, and M. sprengeri. M. sprengeri tends to be located in further northern areas with higher altitudes, lower temperatures, and lower precipitation compared to M. biondii and M. denudata; and (3) a large proportion of suitable habitats have been left without protection. Woodland and forest shared the largest area out of the suitable habitats. However, grassland, agricultural land, residential land, and mining and industry areas also occupied large areas of suitable habitats

Roadmap on commercialization of metal halide perovskite photovoltaics

Perovskite solar cells (PSCs) represent one of the most promising emerging photovoltaic technologies due to their high power conversion efficiency. However, despite the huge progress made not only in terms of the efficiency achieved, but also fundamental understanding of the relevant physics of the devices and issues which affect their efficiency and stability, there are still unresolved problems and obstacles on the path toward commercialization of this promising technology. In this roadmap, we aim to provide a concise and up to date summary of outstanding issues and challenges, and the progress made toward addressing these issues. While the format of this article is not meant to be a comprehensive review of the topic, it provides a collection of the viewpoints of the experts in the field, which covers a broad range of topics related to PSC commercialization, including those relevant for manufacturing (scaling up, different types of devices), operation and stability (various factors), and environmental issues (in particular the use of lead). We hope that the article will provide a useful resource for researchers in the field and that it will facilitate discussions and move forward toward addressing the outstanding challenges in this fast-developing field

Three Magnolia species distribution data

This dataset includes the spatial distribution information of three Magnolia species (Magnolia biondii, Magnolia denudata and Magnolia sprengeri). The recorded information includes species name, longitude and latitude. This dataset could be used as base data for the conservation of these three Magnolia species

Relationships between Plant Species Richness and Terrain in Middle Sub-Tropical Eastern China

The objective of this research was to study the relation between species richness and topography in the middle sub-tropical area of Eastern China. A species richness survey was conducted along altitude in Kaihua County, Zhejiang Province, Eastern China. Topographic variables, such as altitude, slope, aspect, terrain roughness, relief degree and the topographical wetness index, were extracted from the digital elevation model. The Generalized Additive Model ( GAM), the linear model and the quadratic model were used to fit response curves of species richness to topographic variables. The results indicated that altitude and the topographical wetness index have a significant relation to species richness. Species richness has a unimodal response to altitude and a linear response to the topographical wetness index. However, no significant correlations were observed between slope, aspect and species richness. The predicted species richness by GAM is significantly correlated with the observed species richness, whereas the prediction error tends to increase with the increment of species richness. This study furthered insights into the relationship between topography and plants' diversity in the middle sub-tropical area of Eastern China

Distribution of Magnolia officinalis

The recorded information includes species name, longitude and latitude. This dataset could be used as base data for the conservation of Magnolia officinalis

Relationships between Plant Species Richness and Terrain in Middle Sub-Tropical Eastern China

The objective of this research was to study the relation between species richness and topography in the middle sub-tropical area of Eastern China. A species richness survey was conducted along altitude in Kaihua County, Zhejiang Province, Eastern China. Topographic variables, such as altitude, slope, aspect, terrain roughness, relief degree and the topographical wetness index, were extracted from the digital elevation model. The Generalized Additive Model (GAM), the linear model and the quadratic model were used to fit response curves of species richness to topographic variables. The results indicated that altitude and the topographical wetness index have a significant relation to species richness. Species richness has a unimodal response to altitude and a linear response to the topographical wetness index. However, no significant correlations were observed between slope, aspect and species richness. The predicted species richness by GAM is significantly correlated with the observed species richness, whereas the prediction error tends to increase with the increment of species richness. This study furthered insights into the relationship between topography and plants’ diversity in the middle sub-tropical area of Eastern China

Estimating Soil Salinity in the Yellow River Delta, Eastern China - An Integrated Approach Using Spectral and Terrain Indices with the Generalized Additive Model

Soil salinity is one of the most severe environmental problems worldwide. It is necessary to develop a soil-salinity-estimation model to project the spatial distribution of soil salinity. The aims of this study were to use remote sensed images and digital elevation model (DEM) to develop quantitative models for estimating soil salinity and to investigate the influence of vegetation on soil salinity estimation. Digital bands of Landsat Thematic Mapper (TM) images, vegetation indices, and terrain indices were selected as predictive variables for the estimation. The generalized additive model (GAM) was used to analyze the quantitative relationship between soil salt content, spectral properties, and terrain indices. Akaike's information criterion (AIC) was used to select relevant predictive variables for fitted GAMs. A correlation analysis and root mean square error between predicted and observed soil salt contents were used to validate the fitted GAMs. A high ratio of explained deviance suggests that an integrated approach using spectral and terrain indices with GAM was practical and efficient for estimating soil salinity. The performance of the fitted GAMs varied with changes in vegetation cover. Salinity in sparsely vegetated areas was estimated better than in densely vegetated areas. Visible red and near-infrared bands, and the second and third components of the tasseled cap transformation were the most important spectral variables for the estimation. Variable combinations in the fitted GAMs and their contribution varied with changes in vegetation cover. The contribution of terrain indices was smaller than that of spectral indices, possibly due to the low spatial resolution of DEM. This research may provide some beneficial references for regional soil salinity estimation

Habitat preference and potential distribution of Magnolia officinalis subsp. officinalis and M. o. subsp. biloba in China

Magnolia officinalis subsp. officinalis and M. officinalis subsp. biloba are important medicinal plants in China. The hark of these two subspecies is commonly used in the production of a widely-used Chinese traditional medicine named 'Houpu'. In recent years, M. o. subsp. officinalis and M. a. subsp. biloba have become increasingly threatened owing to the over-harvesting of their bark and the fragmentation of their habitats. In this study, we aimed to support the conservation and cultivation of these two subspecies in China by: (I) assessing the relationship between numerous environmental variables and the geographical distributions of the subspecies; (2) analysing the environmental characteristics of suitable habitats for both subspecies and predicting the spatial distribution of these habitats in China: and (3) identifying conservation areas of both subspecies in China via overlay analysis. We also assessed the degree of human disturbance within suitable habitats. We found that temperature was a major determinant for the distribution of Al: o. subsp. officinalis. Conversely, the distribution of Al. a. subsp. biloba was primarily dependent on precipitation rather than temperature. Distinct habitat preferences were observed between M. o. subsp. officinalis and M. o. subsp. biloba. Suitable habitats of Al. a. subsp. officinalis were primarily distributed in the northern subtropical areas of China, with greater fluctuations in ambient temperature, lower extreme temperatures, less precipitation and greater fluctuations in precipitation. Habitats suitable for M. o. subsp. biloba were highly fragmented and were distributed in the central subtropical areas of China. We found that a large proportion of suitable habitats were not in the protected all= and that they were significantly disturbed by human activity. This analysis could provide useful information for the conservation of both Al. a. subsp. officinalis and Al. o. subsp. biloba and could aid in the selection of cultivation sites

Predictive vegetation mapping approach based on spectral data, DEM and generalized additive models

This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision