The relationship between niche breadth and range size of beech (Fagus) species worldwide

Aim: This work explores whether the commonly observed positive range size–niche breadth relationship exists for Fagus, one of the most dominant and widespread broad-leaved deciduous tree genera in temperate forests of the Northern Hemisphere. Additionally, we ask whether the 10 extant Fagus species’ niche breadths and climatic tolerances are under phylogenetic control. Location: Northern Hemisphere temperate forests. Taxon: Fagus L. Methods: Combining the global vegetation database sPlot with Chinese vegetation data, we extracted 107,758 relevés containing Fagus species. We estimated biotic and climatic niche breadths per species using plot-based co-occurrence data and a resource-based approach, respectively. We examined the relationships of these estimates with range size and tested for their phylogenetic signal, prior to which a Random Forest (RF) analysis was applied to test which climatic properties are most conserved across the Fagus species. Results: Neither biotic niche breadth nor climatic niche breadth was correlated with range size, and the two niche breadths were incongruent as well. Notably, the widespread North American F. grandifolia had a distinctly smaller biotic niche breadth than the Chinese Fagus species (F. engleriana, F. hayatae, F. longipetiolata and F. lucida) with restricted distributions in isolated mountains. The RF analysis revealed that cold tolerance did not differ among the 10 species, and thus may represent an ancestral, fixed trait. In addition, neither biotic nor climatic niche breadths are under phylogenetic control. Main Conclusions: We interpret the lack of a general positive range size–niche breadth relationship within the genus Fagus as a result of the widespread distribution, high among-region variation in available niche space, landscape heterogeneity and Quaternary history. The results hold when estimating niche sizes either by fine-scale co-occurrence data or coarse-scale climate data, suggesting a mechanistic link between factors operating across spatial scales. Besides, there was no evidence for diverging ecological specialization within the genus Fagus

Evaluation Method for Node Importance of Urban Rail Network Considering Traffic Characteristics

As a sustainable means of public transport, the safety of the urban rail transit is a significant section of public safety and is highly important in urban sustainable development. Research on the importance of urban rail stations plays an important role in improving the reliability of urban rail networks. This paper proposed an improved method for evaluating the importance of urban rail stations in a topology network, which was used to identify the key stations that affect the urban rail network performance. This method was based on complex network theory, considering the traffic characteristics of the urban rail network that runs on specific lines and integrating the structural characteristics and interrelationship of the lines where the stations are located. Hereafter, this method will be abbreviated as CLI. In order to verify that the high importance stations evaluated by this method were the key stations that had a great impact on the urban rail network performance, this paper designed a comparative attack experiment of betweenness centrality and CLI. The experiment was carried out by taking the Suzhou Rail Transit (SZRT) network as an example and the largest connected subgraph as well as the network efficiency as indicators to measure the network performance. The results showed that CLI had a greater impact on network performance and could better evaluate the key stations in the urban rail network than node degree and betweenness centrality

A novel PEM fuel cell remaining useful life prediction method based on singular spectrum analysis and deep Gaussian processes

International audienceAccurate remaining useful life (RUL) prediction of proton exchange membrane fuel cells (PEMFCs) can assess the reliability of fuel cells to determine the occurrence of failures and mitigate their operational risk. However, is it quite challenging to design a high-precision prediction method because the implicit degradation details of PEMFCs are difficult to learn well from the measurement data with high-frequency noise. Recognizing this, a novel RUL prediction method based on singular spectrum analysis (SSA) and deep Gaussian process (DGP) is proposed in this paper. The SSA-based method is firstly employed to preprocess the measurement data, which can strengthen the effective information of PEMFC degradation data at the same time remove the noise and spikes that interfere with degradation prediction. As a deep structural model, DGP has strong feature learning ability which can represent the nonlinear details of degradation data and give more accurate prediction results. At the same time, it serves as a probabilistic model that can provide the confidence interval to enhance reliability of RUL prediction. The effectiveness of the proposed method is evaluated by experimental data of the PEMFCs under steady-state conditions, and the results show that the SSA-DGP method has higher accuracy and reliability than conventional methods

Dynamic Evaluation Method for Mutation Degree of Passenger Flow in Urban Rail Transit

When urban rail transit is affected by interference, the fluctuation pattern of passenger flow undergoes mutation, which is not conducive to its operational safety and sustainable development. The more intense the mutation in the passenger flow, the greater the impact on the network and operations. Therefore, it is necessary to measure and evaluate the mutation degree of the urban rail transit passenger flow. In this study, we clarify the definition of the mutation degree of urban rail transit passenger flow and construct an evaluation index system for the mutation degree of passenger flow from two dimensions: horizontal mutation amplitude and vertical mutation amplitude. Based on the catastrophe theory, an evaluation model of the mutation degree was constructed. Using this evaluation method, abbreviated as CDCT, the level division of the mutation degree at different time intervals under different interference scenarios can be obtained, achieving a dynamic evaluation of the mutation degree of passenger flow. Finally, taking the passenger flow data of the Suzhou rail transit as an example, the mutational fluctuation of passenger flow affected by interference is analyzed, and the evaluation results of the mutation degree of passenger flow are obtained. The analysis results show that the CDCT evaluation method can better reflect the dynamic changes in the mutation degree throughout the process under the influence of the mutational passenger flow

Changes in the trends of vegetation net primary productivity in China between 1982 and 2015

China has been experiencing significant climate and land use changes over the past decades. The way in which these changes, particularly a warming hiatus and national ecological restoration projects that occurred almost concurrently in the late 1990s, have influenced vegetation net primary productivity (NPP), is not well documented. Here, we estimated annual and seasonal changes in China?s NPP between 1982 and 2015 using the Carnegie-Ames-Stanford Approach model and examined their shifting years (SHYs) caused by the switch in climatic factors and the restoration projects. Our analyses revealed that the growth of annual, spring and summer NPP stalled in 1997 or 1998, while the trend of autumn NPP increased in 1992 at the national scale. We also showed that the changes in the NPP trends were more sensitive to the warming hiatus in spring and autumn, as well as in the temperate monsoonal region and the Tibetan Plateau, while the larger trend of autumn NPP in eastern China after the SHY was strongly coupled with increased monsoonal precipitation. Although the starting years of the restoration projects were partially consistent with the SHYs of the NPP trends, the projects were likely playing minor roles in enhancing NPP increase. Our findings can be applied for ecological risk assessment and future management of ecological restoration projects in the context of global change

Pearson’s correlation coefficients (<i>r</i>) between elevation and biotic or abiotic factors in decomposing litter.

<p>An asterisk (*) indicates significant difference at <i>P</i><0.05.</p><p>Abbreviations: FTCs = numbers of soil freeze-thaw cycles, MBC = microbial biomass carbon, MBN = microbial biomass nitrogen, MBP = microbial biomass phosphorus, and ACPA = acid (pH 6.5) phosphatase (ACP) activity. An asterisk (*) indicates statistically significant (<i>P</i><0.05).</p

Concentrations of leaf litter microbial biology, activity of sucrase and acid phosphatase at different elevations after every sampling date.

<p>Abbreviations: MBC = microbial biomass carbon (A), MBN = microbial biomass nitrogen (B), MBP = microbial biomass phosphorous (C), bacterial biomass (D), fungal biomass (E) sucrase A = Sucrase activity (F) and ACPA = acid (pH 6.5) phosphatase activity (G).</p

Results of the two-way ANOVA for the effects of elevation and species treatments and their interactions on the leaf litter decomposition rate (<i>k</i>).

<p>Bold <i>P</i>-values indicate significant effects (<i>P</i><0.05).</p