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• Build a multi-objective method for improving LEN functional and structural connectivity. • Identify key locations for improving structural connectivity based on graph theory. • Introduce "integral index of probable connection" into network link additional technique. • Search corridor paths with maximum functional connectivity using greedy algorithm. • Use hierarchical rules with different priorities to set the greedy algorithm. By building connectivity between ecological spaces, the landscape ecology network (LEN) can effectively prevent further habitat fragmentation and loss in urbanizing areas. The existing LEN construction method fails to consider the multiple optimization goals of improving network structural and functional connectivity. This paper aimed to provide a novel method to improve connectivity for existing LENs in urbanizing areas. We took an urbanizing area in China as an example and extract existing LEN based on ecological priorities. On this basis, from the dimension of structural connectivity optimization, we used the network link additional technique based on "integral index of probable connection" to identify 50 optimal locations of additional corridors that could significantly improve the network structural connectivity. From the dimension of functional connectivity enhancement, we applied the greedy algorithm based on hierarchical rules with different priorities to search for corridor paths with maximum ecological function and low ecological and construction resistance. The optimized LEN contained 43 ecological sources and 74 ecological corridors. The dual dimension method proposed in this paper can improve the functional and structural connectivity of existing LEN, and greatly save the optimization time and efficiency of the network, to provide targeted ecological space protection schemes for urbanizing areas. [ABSTRACT FROM AUTHOR]

• The ecohydraulic modeling framework is a valuable tool for assessing fish ecology in dammed rivers. • Cascade dam construction could fragment spawning site habitats and reduce fish population abundance in the tailwater zone of the main stream. • Spawning site habitats would improve, leading to an increase in fish population abundance in the backwater zone of tributaries. • The effects of cascade dam construction on endemic fish habitat and population status depend on spawning site location, fish species, and life stages. Cascade dam constructions will alter the habitat of spawning sites and affect fish population in the Lancang River basin. This paper examines the impacts of cascade dam construction on four spawning sites, focusing on two co-distributed and endemic fish species: Schizothorax prenanti and Schizothorax davidi. An ecohydraulic modeling framework that combined hydro-morphodynamic, habitat, and population models was used to investigate changes in habitat quality and fish population under two scenarios: no-dam and with-dam. Fish habitat quality was evaluated using the habitat suitability index, and population fluctuations were assessed by the abundance and density of fish populations at all life stages. The simulation results indicated that cascade dam construction would fragment the spawning site habitat in the tailwater zone of the main stream and result in a decrease in fish population abundance and density. Conversely, spawning site habitats in backwater zone of tributaries would improve, and fish population abundance and density would increase. Additionally, the simulation results showed that the magnitude of fish population variations varied by species and life stages. These results highlight the different responses of spawning site location, fish species, and life stages to dam construction, which can help identify areas of the habitat with higher potential for finer-scale endemic fish conservation and recovery efforts. This study provides a comprehensive framework tool for fish ecological evaluation in dammed rivers, which has the potential to assist decision-makers involved in river ecosystem ecological conservation. [Display omitted] [ABSTRACT FROM AUTHOR]

• We compared the structural characteristics of 18 urban agglomerations in China. • Our framework contained analysis of 3 different scales based on 4 elements' flows. • Guangdong-Hong Kong-Macao urban Agglomeration had the greatest average flow (5.15). • Shandong Peninsula Urban Agglomeration had the best performance in overall network. • Cities with high centrality were in the bidirectional or main spillover subgroups. Flows of different elements among cities can affect urban organizational structures, leading to specific links between and roles for cities within urban agglomerations. Previous studies have mainly focused on flows produced by specific elements in a single urban agglomeration, and they cannot reveal differences in organizational structures affected by multiple elements among different urban agglomerations. Therefore we selected 18 typical urban agglomerations in China and explored their structural characteristics under comprehensive effects of four elements: economy, resources, population and technology, with gravity model and social network analysis. Guangdong-Hong Kong-Macao Urban Agglomeration had a maximum flow intensity of 5.15, this concentration range was the largest from 0.65 to 3.75, indicating that more elements flowed internally. Shandong Peninsula Urban Agglomeration had the largest network density at 0.412, the lowest efficiency at 0.567, and the lowest hierarchy at 0.000, showing that intercity relationships within SP were the tightest among the18 urban agglomerations. Geographical proximity and topological structure of subgroups proved that connections among subgroups were not complex, and cities of different provinces lacked close interactions. Furthermore, most of the key cities with high centrality indexes were municipalities directly under the Central Government or provincial capitals; these cities played important roles in networks and were distributed into "main spillover" and "bidirectional spillover" subgroups. This paper provides a unified analytical framework aimed at urban agglomerations that accounts for flows of multiple types of elements and explores urban organizational structures, and our results could provide scientific guidance for promoting the coordinated and sustainable development of urban agglomerations. [ABSTRACT FROM AUTHOR]

• An ABM for socio-ecological simulation in small-scale coupled human-nature systems. • A microeconomic basis for heterogeneous agents with partial rationality. • A rich set of mechanisms of two-way human-nature interactions. • Low data demand, and broad implementability in developing countries. • A case study in Wolong, China showing the validity of the model. The tension between economic development and ecological conservation has always been a hotly debated topic, especially in critical ecologically sensitive areas such as nature reserves in the developing world. Complexity modeling methods, such as Agent-based Models (ABM), are a feasible way to study such Coupled Human and Nature Systems (CHANS). This paper presents an ABM named Socio-Econ-Ecosystem Multipurpose Simulator (SEEMS) for small-scale CHANS, with particular focus on biological conservation hotspots areas under developmental pressures. The model highlights a microeconomic basis tailored for research objects of the kind featuring heterogeneous agents with partial rationality, and integration of various mechanisms of two-way human-nature interaction such as ecological succession, and farmland fallowing. These features, together with merits in low data and computational requirements and convenient extendibility, make SEEMS an easy-to-apply approach in CHANS modeling. A case study in China's Wolong National Reserve for Giant Pandas not only validates the model's capacity of accurately simulate the real-world demographic, social, economic, and ecological situations, but also demonstrates nonlinear outcomes that might not otherwise emerge in statistical models. Subsequent studies may benefit from these features of SEEMS to investigate theoretical problems in the human-nature relationship in biological conservation hotspots, or practical ones regarding conservative policy-making in such areas. [ABSTRACT FROM AUTHOR]

• Evaluated the static and dynamic urban eco-efficiency of the Beijing-Tianjin-Hebei region in China. • Described the spatial association network characteristics of the urban eco-efficiency. • Analyzed the impact of influencing factors on the spatial association network. • Suggestions for strengthening the spatial association of urban eco-efficiency. This paper uses the DEA-Malmquist model to measure the static and dynamic ecological efficiency of city and describes its spatial distribution characteristics. Then, we use social network analysis and the quadratic assignment procedure to analyze the spatial association network characteristics of ecological efficiency and its influencing factors. The results show that urban eco-efficiency is close being effective according to the DEA method but does not fully. The ecological total factor productivity of the city is increasing. The spatial distribution of technical progress is consistent with total factor productivity, which shows that the former is the main reason for the progress of the latter. The ecological efficiency spatial association network structure is obvious and shows the characteristics of multithreading and complexity. The changing trends of network density and associated relationships remain consistent. In general, there is an upward trend of volatility. Network connectedness, network hierarchy, and network efficiency show that the spatial association of ecological efficiency is inseparable. The overall network structure has better correlation and stability, but there are strict ecological efficiency spatial association network structural characteristics between cities. The analysis of individual network characteristics shows that the connection between cities is getting closer, but the spatial association needs strengthening. The correlation coefficients of green level, industrial structure, government financial support, and fixed asset investment are significantly positive, while the influence of economic level and opening up on the ecological efficiency spatial network are not significant. [ABSTRACT FROM AUTHOR]

• We propose a new forest prediction model and use actual deforestation data for optimization and verification. • Use the XGBoost method to reconstruct deforestation intensity data. • Deforestation hotspots in China are mainly concentrated in the southern and southeastern regions. • Our model provides certain reference information for forest disaster prevention and forest management. Forests are important for the global carbon cycle, hydrothermal balance, and climate change. Human activities can exert a significant impact on forest ecosystems, thereby having the potential to alter their physical and chemical properties and thus affecting carbon, water, and heat budgets, and climate change. The historical reconstruction of the disturbance of global forests can help us understand the processes and patterns of human activities and global change. In this paper, we construct a deforestation prediction model using a Spearman correlation coefficient and implement the XGBoost method, using Python 3.6, for the reconstruction of deforestation intensity data from 2000 to 2019. Secondly, the selection of the driver indicators is done by using extreme difference regularization to unify the magnitude, and the potential deforestation area risk index is calculated in the form of equal weights. Finally, the actual deforestation data were used for optimization and validation. The model shows that the deforestation hotspots are mainly concentrated in the southern and southeastern regions of China and that there are large differences in deforestation in different provinces. In the future, the fine spatial and temporal patterns of deforestation in China during the historical period can be quantitatively reconstructed, which can provide some reference information for forest disaster prevention and forest management in China. [ABSTRACT FROM AUTHOR]

Rapid economic growth and social development in China have led to serious water pollution problems and water resource shortages, limiting the sustainable and coordinated development that could support both the socio-economy and water environment (WE). However, the spatial heterogeneity and evolutionary characteristics of the coordination between economic development (ED) and WE have not been adequately explored in China's provinces. In this study, we use the spatial global (local) autocorrelation and geographically weighted regression (GWR) model to explore the relationship between ED and WE subsystems and its influencing factors in China's provinces from 2010 to 2019. The main results are as follows, (a) The coupling coordination degree (CCD) between the two subsystems in southern China is higher than that of northern China. Transitional development areas have gradually spread to the middle of China, and there has been an increase trend in the number of moderate and high coordination provinces. (b) The ED-led (WE lags behind ED) type is mainly concentrated in central and eastern China, and the WE-led (ED lags behind WE) type is concentrated in the northeastern and western regions. The areas with synchronous development are relatively small in number and are mainly concentrated in east coastal China. (c) There is an obvious spatial dependence of the coordination between ED and WE from the perspective of global correlation, but this dependence is weakened with increasing geographical distance. The local mode of agglomeration shows a trend towards the evolution of high-high clustering in the eastern region. (d) The GWR analysis indicates that the factors of population density, energy consumption, fixed investment, and research and development (R&D) expenditure significantly influence the coordination between ED and WE. Fixed investment and R&D expenditure are mainly positive driving factors, while the impacts of population density and energy consumption vary among different regions as time evolves. Changes in driving force in a region can extend spatial effects to adjacent regions through the spatial transmission mechanism. Finally, we close the paper by recommending the different policy implications according to the GWR results. [ABSTRACT FROM AUTHOR]