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Under rapid urbanization, the urban heat island (UHI) effect is increasing, which poses a serious threat to human settlements. Changes in neighborhood land surface temperature (LST) reflect the UHI effect at a finer scale, with implications for the thermal comfort of residents. Landsat images were used to analyze the distribution of the urban neighborhood heat/cool island (UNHI/UNCI) within the fourth ring area of Shenyang City. Three-dimensional buildings and the urban functional zones (UFZs) were combined to explore the relationships with the UNHI and UNCI. Using boosted regression trees to analyze the relative importance of UFZs in the UNHI and UNCI, the results showed a significant lowering effect on the neighborhood LST with increased building height, which may be due to the fact of more architectural shadows generated by higher buildings. As the size of the green space patches increased, the cooling amplitude and the influence distance had an increasing trend. Industrial and public service zones had the most significant effect on the UNHI, with influences of 30.46% and 19.35%, respectively. In comparison, green space zones and water contributed the most to the UNCI effect, with influences of 18.75% and 11.95%, respectively. These results will provide urban decision-makers with crucial information on mitigating UHI problems through urban planning.

Land use/cover change (LUCC) and climate change have important influences on ecosystem services (ESs) and their interactions, particularly in regions with rapid socioeconomic development. However, little research has distinguished the impacts of these 2 factors on ES interactions. Therefore, the impacts of LUCC and climate change on water-related ecosystem service (WES) trade-offs in the Yangtze River Economic Belt (YREB) were measured by combining spatial analysis with ES valuation. These results indicated that water yield, soil retention, and water purification presented varying degrees of increase from 1990 to 2020, with rates of 4.53%, 21.80% and 5.40%, respectively. The order of the mean WES trade-offs in the upstream, midstream, and downstream regions remained stable at the grid scale from 1990 to 2020, while there were important changes at the county scale. Climate change had a greater impact on WES trade-offs than LUCC, with climate change dominating 88.17% of the total area. The effect of climate change on WES trade-offs across areas was in the order of downstream > midstream > upstream, while the effect of LUCC was not obvious. The relative importance of drivers on WES trade-offs from 1990 to 2020 was dominated by climate dominance (71.50%), followed by LUCC to climate dominance (13.66%) and least by LUCC dominance (0.65%). This research emphasized the importance of LUCC and climate change on ES trade-offs in heavily disturbed areas, providing important guidance for multi-objective land management that sustainably provides ESs.

Urban flooding is an important natural hazard that affects urban security in the context of global climate change and urbanization. The simulation of stormwater inundation and risk assessment in urban environments is critical for the sustainable development of urban agglomerations. However, few studies have focused on rainstorm flooding at the urban agglomeration scale. In this study, the Central and Southern Liaoning Urban Agglomeration (CSLN) was selected a case study. A method was proposed to assess the urban flooding and potential exposure risk. The urban flooding of CSLN was characterized by the average flooding depth (AFD) of each sub-catchment, and exposure risk was indicated by potential regional and population exposure risk. The results illustrate that the AFD increased with an increase in the rainfall return period, especially in the urban core. The AFD of each city in the CSLN ranged from 10 to 80 mm, and there were strong differences among cities. The spatial distribution of urban flooding in the nine cities presented aggregation characteristics, and hotspots were mainly concentrated in the old urban districts. The potential regional exposure risk tended to increase as the rainfall return period increased from 10 to 100 years; however, there were significant differences among cities. Among them, the area of potential regional exposure risk in Shenyang was the largest, increasing from 365 km2 under a 10-year rainfall return period to 463 km2 under a 100-year rainfall return period. The largest proportion of potential regional exposure risk was in Anshan, which increased from 3% under 10-year to 13.8% under 100-year rainfall return periods. Most cities had some potential population exposure risk. More residents in populous cities, such as Shenyang and Dalian, were subject to higher exposure risks. However, Benxi, Anshan, and Tieling had a high proportion of severely affected areas, although the size of their cities is much smaller than that of Shenyang and Dalian. Urban flooding and potential exposure risk were apparently higher in the business and government zones than in the industrial and greenspace zones. These methods and results may provide references for rainstorm flooding mitigation through urban planning in urban agglomeration areas.

Accurate and timely urban boundaries can effectively quantify the spatial characteristics of urban evolution and are essential for understanding the impacts of urbanization processes and land-use changes on the environment and biodiversity. Currently, there is a lack of long time-series, high-resolution, nationally consistent Chinese urban boundary data for urban research. In this study, the city clustering algorithm was used to generate urban settlement boundaries in China based on the local density, size, and spatial relationships of impervious surfaces. The results showed that both the area and the number of urban settlements in China revealed an upward trend from 1985 to 2020, with East China (EC) being much higher than other regions and South China showing the most significant growth rate. The average area ratio of urban green space in China was 41.68%, with the average area ratio in EC being higher than in other regions. Meanwhile, Zipf’s law was used to verify the universality of urban settlement rank–size; the changes in the Zipf index from 1985 to 2020 also revealed that China’s urban size tended to be concentrated, and the development of large urban settlements was relatively prominent. The urban definition method we propose in this study can divide urban boundaries efficiently and accurately, identify urban expansion hotspots, and promote research on farmland loss and ecological land degradation, further exploring the impacts of urbanization on food security, biodiversity, and carbon sequestration. By coupling big data such as economy, energy, and population with urban evolution patterns, urban managers can analyze current and future problems in urban development, thereby providing scientific decision-making for urban sustainability.

Understanding the mechanisms that influence changes in ecosystem services (ESs) is critical to the sustainable management of ecosystems. However, existing studies ignore the different importance of influencing factors of ESs in different periods and do not consider the spatiotemporal heterogeneity of influence factors. In this study, we first quantified six ESs for the Yangtze River Delta (YRD) and Pearl River Delta (PRD) in 2000 and 2020 based on remote sensing data, including water yield, grain production, climate regulation, air purification, biodiversity, and recreation. Then, eight factors influencing ESs were selected from natural and human perspectives, and random forest was used to determine the importance level of factors influencing ESs. Finally, the GTWR model was used to explore the spatial and temporal differentiation of factors influencing ESs. The results showed that the spatial variation of the six ESs in the YRD and PRD was irregular from 2000 to 2020. In 2000, natural factors (forest, topography, climate) dominated the regional ESs, while in 2020 human factors (population, economy, human activities) gradually replaced the dominance of natural factors on ESs. The spatial and temporal heterogeneity of multiple influence factors on ESs in the YRD and PRD is significant, and we interpret the ecological implications in detail and propose a series of policy recommendations. The results of this study could provide an important reference for scientific guidance to enhance ecological sustainability in developed regions.

Our study focused on quantifying available forage and the conditions for avoiding predation provided within the home ranges of Siberian roe deer (Capreolus pygargus). We conducted transect surveys in both winter and summer–autumn home ranges of the Siberian roe deer in the Tieli Forestry Bureau of the Lesser Xing’an Mountains. Our results revealed significant differences: (1) In terms of the quantity and quality of available forage, the summer–autumn home range had substantially more available forage than the winter home range, with meadows and cornfields showing the highest edible biomass in each, respectively. In terms of forage quality, there were differences in hemicellulose, cellulose, and lignin content between the two ranges. (2) In terms of the conditions for avoiding predation, the winter home range had lower vegetation coverage and greater visibility, making escape strategies more viable. In contrast, the summer–autumn home range had denser vegetation and limited visibility, making hiding strategies more viable. Our study offers comprehensive insights into the available forage and the conditions for avoiding predation, which is crucial for wildlife conservation strategies and habitat management in the region, as it directly informs strategies that address the seasonal forage requirements and predation avoidance of these deer, ultimately enhancing their prospects for survival in the area.

Abstract Background Soil microorganisms in the thawing permafrost play key roles in the maintenance of ecosystem function and regulation of biogeochemical cycles. However, our knowledge of patterns and drivers of permafrost microbial communities is limited in northeastern China. Therefore, we investigated the community structure of soil bacteria in the active, transition and permafrost layers based on 90 soil samples collected from 10 sites across the continuous permafrost region using high-throughput Illumina sequencing. Results Proteobacteria (31.59%), Acidobacteria (18.63%), Bacteroidetes (9.74%), Chloroflexi (7.01%) and Actinobacteria (6.92%) were the predominant phyla of the bacterial community in all soil layers; however, the relative abundances of the dominant bacterial taxa varied with soil depth. The bacterial community alpha-diversity based on the Shannon index and the phylogenetic diversity index both decreased significantly with depth across the transition from active layer to permafrost layer. Nonmetric multidimensional scaling analysis and permutation multivariate analysis of variance revealed that microbial community structures were significantly different among layers. Redundancy analysis and Spearman’s correlation analysis showed that soil properties differed between layers such as soil nutrient content, temperature and moisture mainly drove the differentiation of bacterial communities. Conclusions Our results revealed significant differences in bacterial composition and diversity among soil layers. Our findings suggest that the heterogeneous environmental conditions between the three soil horizons had strong influences on microbial niche differentiation and further explained the variability of soil bacterial community structures. This effort to profile the vertical distribution of bacterial communities may enable better evaluations of changes in microbial dynamics in response to permafrost thaw, which would be beneficial to ecological conservation of permafrost ecosystems.

In recent years, China has suffered severe particulate matter pollution. Satellite-derived PM2.5 datasets and baseline mortality rate data were used to analyze the premature deaths (PDs) and economic losses (ELs) attributable to long-term PM2.5 exposure (LTE) and short-term PM2.5 exposure (STE) in mainland China. The results illustrated that the PDs attributable to LTE presented an increasing trend between 2000 and 2014 and a fluctuating decreasing trend after 2014. The number of peaked PDs in 2014 was 1.45 million, and it decreased to 1.43 million in 2019. From 2000 to 2019, ELs attributable to LTE increased from $93.83 billion to $523.02 billion. The PDs attributable to STE revealed a fluctuating upward trend from 2000 to 2013 and a downward trend after 2013. The number of peaked PDs in 2013 was 75.89 × 103 and decreased to 14.81 × 103 in 2019. ELs attributable to STE rose from $3.56 billion in 2000 to $22.96 billion in 2013, declining to $5.78 billion in 2019. The benefits of improving air quality in reducing PDs attributed to LTE were partially offset by the aging population. Therefore, the increase in PDs and ELs attributed to LTE slowed in recent years. Benefit from the decrease in PM2.5 concentration, PDs and ELs attributable to STE declined significantly. The results of this study could provide a better understanding of the hazards attributable to PM2.5 exposure and provide a reliable theoretical basis for regional air pollution control measures.

Urbanization has introduced a series of environmental problems worldwide, and particulate matter (PM) is one of the main threats to human health. Due to the lack of high-resolution, large-scale monitoring data, few studies have analyzed the intraurban spatial distribution pattern of PM at a fine scale. In this study, portable air monitors carried by five taxis were used to collect the concentrations of PM1, PM2.5 and PM10 for five months in Shenyang during the heating season. The results showed that high concentrations of PM were distributed in the suburbs, while relatively low concentration areas were found in the central area. Agricultural, industrial and development zones had higher concentration values among the eight observed types. The PM concentration exhibited strong spatial autocorrelation based on Moran’s I index analysis. Meteorological factors were the most important influencing factors of the three pollutants, and their total contribution rate accounted for more than 80% among the 13 factors according to boosted regression trees analysis. The taxi monitoring method we proposed was a more efficient and feasible method for monitoring urban air pollution and could obtain higher spatial-temporal resolution data at a lower cost to elucidate the region’s dynamic air pollution distribution patterns.

Urban forest is considered an effective strategy for mitigating urban air pollution via deposition, absorption and dispersion processes. However, previous studies had focused mainly on the deposition effect or removal capacity near the ground, while the net effect of the urban forest on air quality is rarely evaluated in the vertical dimension. In this study, PM2.5 (particulate matter with diameter less than 2.5), PM10 (particulate matter with diameter less than 10 μm), carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3) concentrations, air temperature, relative humidity, and atmospheric pressure at 0, 2.5, 5, 10, 15, 20, 30, 40, 60, 80, and 100 m in urban forest, street, and community areas were collected by unmanned aerial vehicles (UAVs) equipped with Sniffer4D V2 on overcast and sunny days. The PM, CO, NO2, and O3 concentrations increased with height below 20 m and then slightly decreased with height on an overcast day, whereas SO2 concentrations decreased with height within 20 m. The urban forest increased PM concentrations in the morning of an overcast day, whereas it decreased PM concentrations in the afternoon of the overcast day. The forest obstructed PM dispersion from the canopy when PM concentrations grew lower in the morning, but it hindered PM from deposition when PM concentrations grew higher in the afternoon.

Fires greatly threaten the grassland ecosystem, human life, and economic development. However, since limited research focuses on grassland fire prediction, it is necessary to find a better method to predict the probability of grassland-fire occurrence. Multiple environmental variables impact fire occurrence. After selecting natural variables based on remote sensing data and anthropogenic variables, we built regression models of grassland fire probability, taking into account historical fire points and variables in Inner Mongolia. We arrived at three methods to identify grassland fire drivers and predict fire probability: global logistic regression, geographically weighted logistic regression, and random forest. According to the results, the random forest model had the best predictive effect. Nine variables selected by a geographically weighted logistic regression model exercised a spatially unbalanced influence on grassland fires. The three models all showed that meteorological factors and a normalized difference vegetation index (NDVI) were of great importance to grassland fire occurrence. In Inner Mongolia, grassland fires occurring in different areas indicated varying responses to the influencing drivers, and areas that differed in their natural and geographical conditions had different fire-prevention periods. Thus, a grassland fire management strategy based on local conditions should be advocated, and existing fire-monitoring systems based on original meteorological factors should be improved by adding remote sensing data of grassland fuels to increase accuracy.

Abstract Vegetation community complexity is a critical factor influencing terrestrial ecosystem stability. China, the country leading the world in vegetation greening resulting from human activities, has experienced dramatic changes in vegetation community composition during the past 30 years. However, how China's vegetation community complexity varies spatially and temporally remains unclear. Here, we examined the spatial pattern of China's vegetation community complexity and its temporal changes from the 1980s to 2015 using two vegetation maps of China as well as more than half a million field samples. Spatially, China's vegetation community complexity distribution is primarily dominated by elevation, although temperature and precipitation can be locally more influential than elevation when they become the factors limiting plant growth. Temporally, China's vegetation community complexity shows a significant decreasing trend during the past 30 years, despite the observed vegetation greening trend. Prevailing climate warming across China exhibits a significant negative correlation with the decrease in vegetation community complexity, but this correlation varies with biogeographical regions. The intensity of human activities have an overall negative influence on vegetation community complexity, but vegetation conservation and restoration efforts can have a positive effect on maintaining vegetation composition complexity, informing the critical role of vegetation management policies in achieving the sustainable development goal.

Abstract Background Wetland loss is a global concern due to its enormous ecosystem services. Marshland, a typical natural wetland, which is concentrated in the Sanjiang Plain, has undergone dramatic loss in the last several decades. The spatiotemporal changes in marshland were studied based on Landsat images of the Sanjiang Plain from 1980 to 2016 with the land use maps in 1980, 1995, 2000, 2005, 2010, and 2016 using land use dynamic degree and landscape indices. The driving forces of marshland loss, including biophysical factors, socio-economic factors, and land management, were analyzed with boosted regression trees (BRTs) methods. Results The area of marshland loss was 7372 km2, which accounted for 65.7% of the area of marshland in 1980; however, the paddy field area was expanded by 22,313 km2. The lost marshland was mainly converted to dry farmland (47.5%) and paddy field (47.2%) during 1980–2016. Both the landscape pattern of the study area and marshland became increasingly fragmented. The relatively important factors responsible for marshland loss were biophysical factors, socio-economic factors, and land management, which accounted for 65.2%, 25.5%, and 8.4%, respectively. The most important driving forces with high “relative influence” were “distance to river,” which accounted for 20.6% of the total variance explained. The “relative influence” of potential crop yield and ditch density reached 20.2% and 8.4%, respectively. Conclusion Significant land use changes have occurred in the Sanjiang Plain over the past 37 years, with characteristics of rapid paddy field expansion and drastic marshland loss. Meanwhile, marshland fragmentation continued to increase. Reclamation was the main reason for the large-scale marshland loss. Biophysical factors influenced the decisions regarding the locations of marshland loss. Moreover, climate factors (i.e., average annual precipitation and average annual temperature) also played an important role in marshland loss. These results can provide helpful knowledge for understanding the patterns and reasons for marshland loss and protecting and managing strategies for wetlands restoration.

Wetlands are important to global ecosystems and are easily affected by changes in climate, soil properties, terrain, and human disturbances. To describe the effects quantitatively, structural equation modeling was applied to detect the impacts of these four latent variables on the distribution of wetlands in the Jing-Jin-Ji region in 1980, 1995, 2005, and 2015. The results of the models show that human disturbance and terrain conditions had negative effects on the distribution of wetlands, and their standard coefficients ranged from −0.084 to −0.011 and from −0.124 to −0.006, respectively. Climate and soil properties were beneficial for wetland distribution, and their standard coefficients ranged from 0.039 to 0.169 and from 0.015 to 0.290, respectively. The modeling results also indicate some differences in indirect influence among the four models, and 9 indirect pathways were detected in the four models. We also found that there was a significant negative correlation between the variation in the influencing factors and their impact on wetlands (p

The serious pollution of PM2.5 caused by rapid urbanization in recent years has become an urgent problem to be solved in China. Annual and daily satellite-derived PM2.5 datasets from 2001 to 2020 were used to analyze the temporal and spatial patterns of PM2.5 in China. The regional and population exposure risks of the nation and of urban agglomerations were evaluated by exceedance frequency and population weight. The results indicated that the PM2.5 concentrations of urban agglomerations decreased sharply from 2014 to 2020. The region with PM2.5 concentrations less than 35 μg·m−3 accounted for 80.27% in China, and the average PM2.5 concentrations in 8 urban agglomerations were less than 35 μg·m−3 in 2020. The spatial distribution pattern of PM2.5 concentrations in China revealed higher concentrations to the east of the Hu Line and lower concentrations to the west. The annual regional exposure risk (RER) in China was at a high level, with a national average of 0.75, while the average of 14 urban agglomerations was as high as 0.86. Among the 14 urban agglomerations, the average annual RER was the highest in the Shandong Peninsula (0.99) and lowest in the Northern Tianshan Mountains (0.76). The RER in China has obvious seasonality; the most serious was in winter, and the least serious was in summer. The population exposure risk (PER) east of the Hu Line was significantly higher than that west of the Hu Line. The average PER was the highest in Beijing-Tianjin-Hebei (4.09) and lowest in the Northern Tianshan Mountains (0.71). The analysis of air pollution patterns and exposure risks in China and urban agglomerations in this study could provide scientific guidance for cities seeking to alleviate air pollution and prevent residents’ exposure risks.

With the continuous development of urbanization, the urban heat island (UHI) phenomenon is becoming increasingly prominent. Especially with the development of various large urban agglomerations and the shrinking distance between cities, the regional thermal environment has attracted extensive attention. Therefore, we used Modis land surface temperature (LST) data and employed least squares, standard deviation and spatial autocorrelation analysis methods to analyze the spatiotemporal patterns and characteristics of summer daytime regional urban heat islands (RHI) in the Beijing–Tianjin–Hebei (BTH) urban agglomeration. Our results indicated that the relative land surface temperature (RLST) in the southeastern part of BTH with a relatively high level of urbanization showed a significant and continuous upward trend. With the continuous development of the level of urbanization in the southeast, the center of gravity (GC) of RHI gradually moved to the southeast, and the development direction of RHI changed from northwest–southeast to northeast–southwest. The area transfer of RHI was concentrated in no change and little change, indicating that the evolution trend of RHI was relatively stable. The high-high aggregation areas were mainly located in the more developed areas in the southeast. In addition, the methods and results of this study can provide reasonable and effective insights into the future development and planning of the BTH.

Habitat loss and fragmentation are serious threats to biodiversity worldwide. In order to promote animal movement and gene flow between habitat islands, it is important to identify and evaluate the ecological network of species. In Tieli Forestry Bureau (TFB) in Northeast China, we simulated and evaluated the ecological network of roe deer, aiming to provide theoretical support to population conservation. By using three sources of roe deer occurrence data and 15 environmental variables, we simulated the suitability of habitats using the Maximum Entropy Model (MaxEnt). Then, the corridor design model Linkage Mapper was used to identify and evaluate the linkages between habitat cores which are defined by the home range size of roe deer. The results showed that, of the terrain, land cover, vegetation, and anthropogenic interference factors, the anthropogenic interference factors played the most important and complex role in roe deer habitat selection, creating large-area suitable habitats by farming. Two innovative variables of farmland-forest edge length/patch perimeter (FFE:PP) and viewshed introduced in simulation contributed 8.5% and 2.3%, respectively. Surrounded by the unsuitable habitats, the suitable habitats mainly distributed along both sides of the river, covering 41.8% of the total area of TFB. The ecological network of roe deer was composed of 11 habitat cores and 17 linkages. Three linkages connecting the key habitat cores had low quality and linkage suitability ratios, which were probably due to barrier effect of the HeHa expressway intersecting with the linkages. Our study suggests the priority areas for protecting roe deer and the key locations where the wildlife road-crossing structures or animal guidance measures are needed.

Introduction One of the most striking features of urbanization is the replacement of the original natural land cover type by artificial impervious surface area (ISA). However, the extent of the contribution of various environmental factors, especially the growth of 3D space to ISA expansion, and the scope and mechanism of their influences in dramatically expanding cities, are yet to be determined. The boosted regression tree (BRT) model was adopted to analyze the main influencing factors and driving mechanisms of ISA change in Shenyang, China between 2010 and 2017. Outcomes The nearly complete-coverage ISA (≥0.7) increased from 42% in 2010 to 47% in 2017. The percentage of landscape with a high ISA fraction increased, while the landscape evenness and diversity of ISA decreased. The BRT analysis revealed that elevation, regional population density, and landscape class had the largest influences on the change of urban ISA, contributing 22.55%, 18.16%, and 11.18% to the model, respectively. Conclusion Overall, topographic and socioeconomic factors had the greatest influence on urban ISA change in Shenyang, followed by land use type and building pattern indices. The trend of high aggregation was strong in large commercial and residential areas. The 3D expansion of the city had an influence on its areal expansion.

Vegetation phenology does not only serve as a key index of terrestrial ecosystem response to worldwide climate change but also has a major influence on plant productivity and the carbon cycle. In the current research, the change of vegetation phenological parameters was studied and the impact exerted by climate change on phenological phases in northeast China for 1982–2014 was explored using the latest edition of the Global Inventory Modeling and Mapping Studies Normalized Difference Vegetation Index (GIMMS NDVI3g) dataset. The results showed that the start of the growing season (SOS) slightly advanced, the end of the growing season (EOS) showed a significant delay, and the length of the growing season (LOS) exhibited a significant prolonging at the regional scale. At the different vegetation types scale, there existed diverse responses of vegetation phenological phases to climate change for forest, grassland, and cultivated land. Significant decreasing trends in the SOS occupied 19.1% of the entire research area, whereas pixels with significantly increasing trends in the SOS accounted for 13.1%. The EOS was delayed in most of the study region (approximately 72.1%). As the result of the variations of SOS and EOS, the LOS was obviously enhanced (p < 0.05) in 29.7% of the research area. According to the correlation of vegetation phenology with climate factors, the SOS had a significant negative relationship with the average temperature in springtime, while the EOS was notably negatively connected to summer total precipitation at the regional scale. At the pixel scale, the correlation of phenological parameters with climate variables showed strong spatial heterogeneities. This study contributes to the comprehension of the responses of vegetation phenology to climate change.

Forest fires are a significant factor that affects the boreal forest carbon distribution which emits carbon into the atmosphere and leads to carbon redistribution among carbon pools. However, knowledge about how much carbon was transferred among pools and the immediate changes in soil nutrient contents in areas that were burned by fires of various severities are still limited. In this study, we surveyed eight wildfire sites that are located in northeast China within three months after the fires occurred. Our results indicate that the total soil nitrogen, phosphorus, and organic carbon contents significantly increased after moderate- and high-severity fires. The carbon emissions were 3.84, 5.14, and 12.86 Mg C/ha for low-, moderate-, and high-severity fires, respectively. The amount of carbon transferred among pools increased with fire severity except for the charcoal pool, storing the highest amounts of carbon in moderate-severity fires. Although the charcoal and ash pools accounted for a small proportion of the total ecosystem, they are important for biogeochemical cycles and are worthy of attention. The carbon redistribution information in our study is important for accurately estimating the forest carbon budget and providing crucial parameters for forest carbon cycling models to incorporate the carbon transfer process.

Identifying waterfowl habitat suitability under changing environments, especially land-use change, is crucial to make waterfowl habitat conservation planning. We took Wetland Nature Reserve of Liaohe Estuary, the largest breeding area of Saunders’s Gulls (Larus saundersi) in the world, as our study area, generated land-use-type maps through interpretation of satellite images from four different years (1988, 2000, 2009, 2017), and predicted the potential breeding habitat of Saunders’s Gulls by MaxEnt model based on the land-use map, along with other environmental variables (NDVI, distance to roads and artificial facilities, distance to rivers and water bodies, DEM and distance to shoreline) for the four years, respectively. The models were evaluated using the area under the curve (AUC). We analyzed the changes of the breeding habitat from 1988 to 2017 and utilized RDA to explore the relationships among the changes of suitable habitat of Larus saundersi and the dynamics of land uses. Our results showed that the most suitable habitat decreased by 1286.46 ha during 1988-2009 and increased by 363.51 ha from 2009 to 2017. The suitable habitat decreased by 582.48 ha from 1988 to 2009 and then increased to 1848.96 ha in 2017, while the unsuitable habitat increased by 2793.87 ha during 1988–2009 and then decreased by 178.83 ha from 2009 to 2017. We also found that land use, distance to the coastline, distance to artificial facilities, distance to rivers, distance to roads, and NDVI had certain degrees of impact on the Larus saundersi distribution. The contribution of land use ranged from 16.4% to 40.3%, distance to coastline from 34.7% to 48.0%, distance to artificial facilities from 5.9% to 11.1%, distance to rivers from 5.5% to 11.0%, distance to roads from 3.9% to 12.5%, and NDVI from 0.3% to 6.3%. The change in suitable habitat of Larus saundersi has a positive relationship with the change of seepweed marsh. Human-induced changes in seepweed marsh and coastline position are the main factors influencing the potential breeding habitat of Saunders’s Gulls. We suggest strict conservation of seepweed marsh and implementation of habitat management practices to better protect Saunders’ Gull’s breeding habitat.

Suaeda salsa (L.) Pall. (S. salsa) acts as a pioneer species in coastal wetlands due to its high salt tolerance. It has significant biodiversity maintenance, socioeconomic values (e.g., tourism) due to its vibrant color, and carbon sequestration (blue carbon). Bohai Bay region, the mainly distributed area of S. salsa, is an economic intensive region with the largest economic aggregate and population in northern China. The coastal wetland is one of the most vulnerable ecosystems with the urbanization and economic developments. S. salsa in Bohai Bay has been changed significantly due to several threats to its habitat in past decades. In this paper, we analyzed all available archived Landsat TM/ETM+/OLI images of the Bohai Bay region by using a decision tree algorithm method based on the Google Earth Engine (GEE) platform to generate annual maps of S. salsa from 1990 to 2020 at a 30-m spatial resolution. The temporal-spatial dynamic changes in S. salsa were studied by landscape metric analysis. The influencing factors of S. salsa changes were analyzed based on principal component analysis (PCA) and a logistic regression model (LRM). The results showed that S. salsa was mainly distributed in three regions: the Liao River Delta (Liaoning Province), Yellow River Delta (Shandong Province), and Hai River Estuary (Hebei Province, Tianjin). During the past 31 years, the total area of S. salsa has dramatically decreased from 692.93 km2 to 51.04 km2, which means that 92.63% of the area of S. salsa in the Bohai Bay region was lost. In the 641.89 km2 area of S. salsa that was lost, 348.80 km2 of this area was converted to other anthropic land use categories, while 293.09 km2 was degraded to bare land. The landscape fragmentation of S. salsa has gradually intensified since 1990. National Nature Reserves have played an important role in the restoration of suitable S. salsa habitats. The analysis results for the natural influencing factors indicated that precipitation, temperature, elevation, and distance to the coastline were considered to be the major influencing factors for S. salsa changes. The results are valuable for monitoring the dynamic changes of S. salsa and can be used as effective factors for the restoration of S. salsa in coastal wetlands.

Wetlands, as the most essential ecosystem, are degraded throughout the world. Wetlands in Zhenlai county, with the Momoge National Nature Reserve, which was included on the Ramsar list, have degraded by nearly 30%. Wetland degradation is a long-term continuous process with annual or interannual changes in water area, water level, or vegetation presence and growth. Therefore, it requires sufficiently frequent and high-spatial-resolution data to represent its dynamics. This study mapped yearly land-use maps with 30-m resolution from 1985 to 2018 using Landsat data in Google Earth Engine (GEE) to explore the wetland degradation process and mapped 12-day interval land-use maps with 15-m resolution using the Sentinel-1B and Sentinel-2 data in GEE and other assistant platforms to study the characteristics of wetland dynamics in 2018. Four sets of maps were generated using Sentinel-1B (S1), Sentinel-2 (S2), the combination of Sentinel-1B and Sentinel-2 (S12), and S12 with multitemporal remote sensing (S12’). All of the classifications were performed in the Random Forest Classification (RFC) method using remote sensing indicators. The results indicate that S12’ was the most accurate. Then, the impact of the historic land-use degradation process on current wetland change dynamics was discussed. Stable, degradation, and restoration periods were identified according to the annual changes in wetlands. The degraded, stable, restored, and vulnerable zones were assessed based on the transformation characteristics among wetlands and other land-use types. The impact of historical land-use trajectories on wetland change characteristics nowadays is diverse in land-use types and distributions, and the ecological environment quality is the comprehensive result of the effect of historical land-use trajectories and the amount of rainfall and receding water from paddy fields. This study offers a new method to map high-spatiotemporal-resolution land-use (S12’) and addresses the relationship between historic wetland change characteristics and its status quo. The findings are also applicable to wetland research in other regions. This study could provide more detailed scientific guidance for wetland managers by quickly detecting wetland changes at a finer spatiotemporal resolution.

Waterfowl surveys, especially for endangered waterfowl living in wetlands, are essential to protect endangered waterfowl and to create a management scenario of their habitats. Unmanned aerial vehicles (UAVs) are powerful new tools for waterfowl surveys. In this paper, we propose one method for a habitat survey and another for a waterfowl species distribution survey. The habitat survey method obtained the waterfowl’s habitat and spatial distribution with a UAV automatic flight plan in the aggregation area. The waterfowl species distribution survey was used to detect and identify waterfowl species with high-spatial-resolution images from a free UAV flight plan in the aggregation area or areas where individuals were suspected to be present. The UAV-based data showed not only the area where waterfowl were found, but also additional ground surveys. The results showed that the species and locations of the waterfowl were recorded more accurately and efficiently using the distribution method based on the images from the UAV. The waterfowl habitat type and the number of waterfowl were obtained in detail using the habitat survey method. UAV-derived counts of waterfowl were greater (+37%) than ground counts. The results indicated the feasibility and advantages of using a low-cost UAV survey of large waterfowl in wetland regions with complex vegetation. This study provides one case study of large waterfowl numbers and habitat surveys. The UAV-based methods also provide a feasible and scientific way to obtain basic data for the protection and management of waterfowl.

Urban agglomeration is a new characteristic of the Chinese urbanization process, and most of the urban agglomeration is located in the same watershed. Thus, urban non-point source (NPS) pollution, especially the characteristic pollutants in urban areas, aggravates NPS pollution at the watershed scale. Many agricultural studies have been performed at the watershed scale; however, few studies have provided a study framework for estimating the urban NPS pollution in an urban catchment. In this study, an integrated approach for estimating agricultural and urban NPS pollution in an urban agglomeration watershed was proposed by coupling the Soil and Water Assessment Tool (SWAT), the event mean concentration (EMC) method and the Storm Water Management Model (SWMM). The Hun-Taizi River watershed, which contains a typical urban agglomeration and is located in northeastern China, was chosen as the study case. The results indicated that the per unit areas of total nitrogen (TN) and total phosphorus (TP) in the built-up area simulated by the EMC method were 11.9% and 23 times higher than the values simulated by the SWAT. The SWAT greatly underestimated the nutrient yield in the built-up area. This integrated method could provide guidance for water environment management plans considering agricultural and urban NPS pollution in an urban catchment.

In this study, we analyzed microbial community composition and the functional capacities of degraded sites and restored/natural sites in two typical wetlands of Northeast China—the Phragmites marsh and the Carex marsh, respectively. The degradation of these wetlands, caused by grazing or land drainage for irrigation, alters microbial community components and functional structures, in addition to changing the aboveground vegetation and soil geochemical properties. Bacterial and fungal diversity at the degraded sites were significantly lower than those at restored/natural sites, indicating that soil microbial groups were sensitive to disturbances in wetland ecosystems. Further, a combined analysis using high-throughput sequencing and GeoChip arrays showed that the abundance of carbon fixation and degradation, and ~95% genes involved in nitrogen cycling were increased in abundance at grazed Phragmites sites, likely due to the stimulating impact of urine and dung deposition. In contrast, the abundance of genes involved in methane cycling was significantly increased in restored wetlands. Particularly, we found that microbial composition and activity gradually shifts according to the hierarchical marsh sites. Altogether, this study demonstrated that microbial communities as a whole could respond to wetland changes and revealed the functional potential of microbes in regulating biogeochemical cycles.

An estuarine wetland is an area of high ecological productivity and biodiversity, and it is also an anthropic activity hotspot area, which is of concern. The wetlands in estuarine areas have suffered declines, which have had remarkable ecological impacts. The land use changes, especially wetland loss, were studied based on Keyhole and Landsat images in the Liao River delta from 1962 to 2016. The dynamics of the ecosystem service values (ESVs), suitable habitat for birds, and soil heavy metal potential ecological risk were chosen to estimate the ecological effects with the benefit transfer method, synthetic overlaying method, and potential ecological risk index (RI) method, respectively. The driving factors of land use change and ecological effects were analyzed with redundancy analysis (RDA). The results showed that the built-up area increased from 95.98 km2 in 1962 to 591.49 km2 in 2016, and this large change was followed by changes in paddy fields (1351.30 to 1522.39 km2) and dry farmland (189.5 to 294.14 km2). The area of wetlands declined from 1823.16 km2 in 1962 to 1153.52 km2 in 2016, and this change was followed by a decrease in the water area (546.2 to 428.96 km2). The land use change was characterized by increasing built-up (516.25%), paddy fields (12.66%) and dry farmland (55.22%) areas and a decline in the wetland (36.73%) and water areas (21.47%) from 1962–2016. Wetlands decreased by 669.64 km2. The ESV values declined from 6.24 billion US$ to 4.46 billion US$ from 1962 to 2016, which means the ESVs were reduced by 19.26% due to wetlands being cultivated and the urbanization process. The area of suitable habitat for birds decreased by 1449.49 km2, or 61.42% of the total area available in 1962. Cd was the primary soil heavy metal pollutant based on its concentration, accumulation, and potential ecological risk contribution. The RDA showed that the driving factors of comprehensive ecological effects include wetland area, Cd and Cr concentration, river and oil well distributions. This study provides a comprehensive approach for estuarine wetland cultivation and scientific support for wetland conservation.

It is necessary to estimate the population abundance of deer for managing their populations. However, most estimates are from high-density populations inhabiting the forests of North America or Europe; there is currently a lack of necessary knowledge regarding low-density deer populations in different forest habitats. In this article, we used fecal DNA based on the capture-mark-recapture method to estimate the population abundance of Siberian roe deer (Capreolus pygargus) in Liangshui National Nature Reserve in the Lesser Xing’an Mountains, northeast China, where the deer population was found to be of a low density by limited studies. We used a robust survey design to collect 422 fecal pellet groups in 2016 and extracted DNA from those samples, generating 265 different genotypes; we thus identified 77 deer individuals based on six microsatellite markers (Roe1, Roe8, Roe9, BM757, MB25 and OarFCB304). With capture and recapture records of these 77 individuals, the abundance of roe deer was estimated to be 87 deer (80–112, 95% CI) using the Program CAPTURE. Using an effective sampling area which resulted from the mean maximum recapture distance (MMRD), we converted the population abundance to a density of 2.9 deer/km2 (2.7–3.7, 95% CI). Our study estimated the roe deer population abundance by a feces-based capture-mark-recapture approach in northeast China, successfully demonstrating the applicability of non-invasive genetic sampling in monitoring populations of deer in this area, which contributes to the development of low-density deer population ecology and management.

Fire frequency and size are two important parameters describing fire characteristics. Exploring the spatial variation of fire characteristics and understanding the environmental controls are indispensable to fire prediction and sustainable forest landscape management. To illustrate the spatial variation of forest fire characteristics over China and to quantitatively determine the relative contribution of each of the environmental controls to this variation, forest fire characteristic data (mean number of forest fires and mean burned forest area) and environmental data (climate, land use, vegetation type and topography) at provincial level were derived. These data sets can potentially serve as a foundation for future studies relating to fire risk assessment, carbon emission by forest fires, and the impact of climate change on fire characteristics. This data article contains data related to the research article entitled “Environmental controls on the characteristics of mean number of forest fires and mean forest area burned (1987–2007) in China” by chang et al. [1].

Background. Understanding species distribution, especially areas of overlapping habitat between sympatric species, is essential for informing conservation through natural habitat protection. New protection strategies should simultaneously consider conservation efforts for multiple species that exist within the same landscape, which requires studies that include habitat overlap analysis. Methods. We estimated the potential habitat of cervids, which are typical ungulates in northern China, using the present locations of red deer (Cervus elaphus; N = 90) and roe deer (Capreolus capreolus; N = 106) in a Maximum Entropy (MaxEnt) model. Our study area was a human-dominated landscape in the Tieli Forestry Bureau located at the southern slope of the Lesser Xing’an Mountains. We grouped 17 environmental predictor variables into five predictor classes (terrain, habitat accessibility, land cover, vegetation feature, and interference), which were used to build habitat suitability models. Results. Habitat accessibility and human interferences were found to have the strongest influence on habitat suitability among the five variable classes. Among the environmental factors, distance to farmland (26.8%), distance to bush-grass land (14.6%), elevation (13.5%), and distance to water source (12.2%) were most important for red deer, distance to farmland (22.9%), distance to settlement (21.4%), elevation (11.6%), and coverage of shrub-grass (8%) were most important for roe deer. Model accuracy was high for both species (mean area under the curve (AUC) = 0.936 for red deer and 0.924 for roe deer). The overlapping habitat comprised 89.93 km2 within the study area, which occupied 94% of potentially suitable habitat for red deer and 27% for roe deer. Conclusions. In terms of habitat suitability, roe deer showed greater selectivity than red deer. The overlapping habitat was mostly located in the eastern mountains. The southwestern plain was not a suitable habitat for deer because it was close to Tieli City. Regarding management measures, we suggest that priority protection should be given to the potential areas of overlapping deer habitats found in this study.

Black carbon (BC) from incomplete combustion of biomass and fossil fuel is widespread in sediments and soils because of its high stability in nature and is considered an important component of the global carbon sink. However, knowledge of BC stocks and influencing factors in forest ecosystems is currently limited. We investigated soil BC contents in burned boreal forests of the Great Khingan Mountains, northeast China. We collected soil samples from 14 sites with different fire severities, slope positions and aspects. The samples were analyzed by the chemo-thermal oxidation method to obtain their BC concentrations. The BC concentrations of the studied soils ranged from 0.03 to 36.91 mg C g−1, with a mean of 1.44 ± 0.11 mg C g−1. BC concentrations gradually decline with depth, and that was significantly less in the 20–30 cm layer compared to all shallower layers. Forests burned by moderate-severity fires had the highest soil BC, the shady aspect had higher soil BC than the sunny aspect. Our results provide some basic data for evaluating the soil BC sink in boreal forests, which is a useful amendment to current carbon budget and carbon cycle in boreal forest ecosystems.

Analyzing spatiotemporal characteristics of the historical urbanization process is essential in understanding the dynamics of urbanization and scientifically planned urban development. Based on historical urban area maps and remote sensing images, this study examined the urban expansion of Shenyang from 1910 to 2010 using area statistics, typology identification, and landscape metrics approaches. The population and gross domestic product were analyzed as driving factors. The results showed that the urban area of Shenyang increased 43.39-fold during the study period and that the growth rate has accelerated since the 1980s. Three urban growth types were distinguished: infilling, edge-expansion, and spontaneous growth. Edge-expansion was the primary growth type. Infilling growth became the main growth type in the periods 1946-70, 1988-97, and 2004-10. Spontaneous growth was concentrated in the period of 1997 to 2000. The results of landscape metrics indicate that the urban landscape of Shenyang originally was highly aggregated, but has become increasingly fragmented. The urban fringe area was the traditional hot zone of urbanization. Shenyang was mainly located north of the Hun River before 1980; however, the south side of the river has been the hot zone of urbanization since the 1980s. The increase of urban area strongly correlated with the growth of GDP and population. Over a long time scale, the urbanization process has been affected by major historical events.

The aboveground carbon sequestration rate (ACSR) reflects the influence of climate change on forest dynamics. To reveal the long-term effects of climate change on forest succession and carbon sequestration, a forest landscape succession and disturbance model (LANDIS Pro7.0) was used to simulate the ACSR of a temperate forest at the community and species levels in northeastern China based on both current and predicted climatic data. On the community level, the ACSR of mixed Korean pine hardwood forests and mixed larch hardwood forests, fluctuated during the entire simulation, while a large decline of ACSR emerged in interim of simulation in spruce-fir forest and aspen-white birch forests, respectively. On the species level, the ACSR of all conifers declined greatly around 2070s except for Korean pine. The ACSR of dominant hardwoods in the Lesser Khingan Mountains area, such as Manchurian ash, Amur cork, black elm, and ribbed birch fluctuated with broad ranges, respectively. Pioneer species experienced a sharp decline around 2080s, and they would finally disappear in the simulation. The differences of the ACSR among various climates were mainly identified in mixed Korean pine hardwood forests, in all conifers, and in a few hardwoods in the last quarter of simulation. These results indicate that climate warming can influence the ACSR in the Lesser Khingan Mountains area, and the largest impact commonly emerged in the A2 scenario. The ACSR of coniferous species experienced higher impact by climate change than that of deciduous species.