Patch Density Research Articles

Landscape structure influences the eukaryome of a folivorous-frugivorous primate

ABSTRACT Eukaryotes are important components of primate gut communities. Despite their role in the diversity and structure of the gut ecosystem, microbiome research has focused on the prokaryotic component of the gut community. While gut bacteria are shaped by host phylogeny and diet, these factors are known to have negligible effects on eukaryotic diversity, which is expected to be modulated by the characteristics of the habitat. We assess the influence of landscape composition and configuration on the eukaryome of black and gold howler monkeys (Alouatta caraya). We collected fecal samples from 10 independent social groups inhabiting small forest fragments or orchards enriched with native tree species and applied an 18S rRNA gene fragment metabarcoding approach to describe their eukaryotic communities. We used generalized linear models to assess the power of landscape metrics in predicting the richness, diversity, evenness, and phylogenetic diversity of the eukaryome. Most communities were dominated by Ascomycota, and it is likely that many of the reads had an environmental origin. Forest cover affected eukaryome richness positively and patch density showed a positive relationship with the Simpson’s diversity and Pielou’s evenness indexes. Howler monkeys living in landscapes with lower habitat coverage had a lower eukaryotic alpha-diversity in the fecal microbiome.

Spatial Distributions and Edge Relationships of Plant Communities in Coastal Barrens in Nova Scotia, Canada

Coastal barrens support varied vegetation that includes wetlands, dwarf shrublands, and small patches of forest. Forest expansion, sea-level rise and recreational trails affect plant communities but spatial vegetation patterns within barrens are unknown. Using high-resolution multispectral aerial imagery, we classified plant communities and other land cover types using 500m x 500m landcover maps at three coastal barrens sites on the Atlantic coast of Nova Scotia, Canada. Community patches were compared using size and shape metrics; shared edge length identified adjacent communities. Community distributions were modelled using environmental variables such as elevation and distance to coast. Forty distinct plant communities were detected, with shrublands (37.5% total area), dwarf shrublands (23.3%) and bog wetlands (13.9%) the most abundant. Average patch size was 9.2 m2; average patch density was 951 patches/ha, indicating fine scale community variability. Recreational vehicle trails occurred primarily in bog wetlands. Dwarf shrublands and some wetland types were closest to the coastline; taller shrublands and tree islands occurred further from the coast. Edge relationships revealed a vegetation height gradient across the forest-barren ecotone: tree islands were mostly adjacent to tall shrub communities, followed by progressively shorter vegetation. Topographic variability and distance to coast were important predictors of community distribution. Edge relationships among communities allowed identification of those most at risk from trail disturbance, forest expansion and coastal squeeze.

Developing Site-Specific Prescription Maps for Sugarcane Weed Control Using High-Spatial-Resolution Images and Light Detection and Ranging (LiDAR)

Sugarcane is a perennial grass species mainly for sugar production and one of the significant crops in Costa Rica, where ideal growing conditions support its cultivation. Weed control is a critical aspect of sugarcane farming, traditionally managed through preventive or corrective mechanical and chemical methods. However, these methods can be time-consuming and costly. This study aimed to develop site-specific, variable rate prescription maps for weed control using remote sensing. High-spatial-resolution images (5 cm) and Light Detection And Ranging (LiDAR) were acquired using a Micasense Rededge-P camera and a DJI L1 sensor mounted on a drone. Precise locations of weeds were collected for calibration and validation. Normalized Difference Vegetation Index derived from multispectral images separated vegetation coverage and soil. A deep learning (DL) algorithm further classified vegetation coverage into sugarcane and weeds. The DL model performed well without overfitting. The classification accuracy was 87% compared to validation samples. The density and average heights of weed patches were extracted from the canopy height model (LiDAR). They were used to derive site-specific prescription maps for weed control. This efficient and precise alternative to traditional methods could optimize weed control, reduce herbicide usage and provide more profitable yield.

Analysis of the Spatial Pattern and Driving Forces of Linpan in Western Sichuan

Abstract. This study aims to explore the landscape pattern and its driving forces of Linpan in western Sichuan, aiming to gain a deeper understanding of the spatial distribution of this unique rural settlement form. The landscape indices and redundancy analysis method were employed to investigate the landscape pattern and driving forces of Linpan in the western Sichuan region. By extracting the spatial information of Linpan in the study area, quantitative analysis of their landscape pattern was conducted using landscape indices. The results reveal certain characteristics of the landscape pattern of Linpan in the study area, where Longquanyi exhibits the highest degree of landscape fragmentation, with the highest patch density and smaller average patch size, yet the highest landscape diversity, while Wenjiang shows the lowest diversity, and Xinjin exhibits a more dispersed distribution of patches, and Dujiangyan has the highest number of patches. Using redundancy analysis, this study reveals the influence of natural and social driving forces on the landscape pattern of Linpan in Western Sichuan. The results indicate that urbanization rate, patch density, mean patch size, and average elevation are significant factors influencing the distribution and morphology of Linpan. These factors affect the spatial layout and connectivity of Linpan, driving changes in the land use structure, and profoundly impacting the landscape pattern of Linpan. To achieve the ecological sustainability, it is essential to comprehensively consider the impacts of both natural and human factors, scientifically plan and manage Linpan, and promote their harmonious coexistence with the surrounding environment.

Impact of green space patterns on PM2.5 levels: A local climate zone perspective

Green spaces have been demonstrated to significantly decrease PM2.5 levels. However, the impact of green spaces on PM2.5 levels in different spatial forms of urban blocks is not yet fully understood. This research utilized ensemble machine learning algorithms to investigate the impact of green space spatial patterns, assessed through landscape pattern analysis, to PM2.5 concentrations during summer and winter based on local climate zones (LCZ). The results revealed significant differences in PM2.5 concentration across the seven types of primary LCZs, primarily influenced by meteorological factors. In winter, eight green space indicators exhibited a more substantial contribution to PM2.5 levels in comparison to the summer season. Significant discrepancies were noted in the contributions of these indicators across different LCZs. Patch density (PD) and landscape shape index (LSI) made a more substantial contribution, while relative patch richness (RPR) and Shannon's evenness index (SHEI) showed a less significant contribution. The spatial pattern of green spaces was significantly related to their contributions. Among the seven predominant LCZs, the five key indicators included PD, LSI, Shannon's diversity index (SHDI), area-weighted mean patch area (AREA_AM), and connectance index (CONNECT). Spatial heterogeneity was further observed in the positive and negative contributions of each green space indicator. This study enhances understanding of the impact of green spaces on PM2.5 across LCZs and provides valuable insights for urban management and planning.

Quantitative Evaluation of Soil Erosion in Loess Hilly Area of Western Henan Based on Sampling Approach

The terrain in the loess hilly area of western Henan is fragmented, with steep slopes and weak soil erosion resistance. The substantial soil erosion in this region results in plenty of problems, including decreased soil productivity and ecological degradation. These problems significantly hinder the social and economic development in the region. Soil conservation planning and ecological development require accurate soil erosion surveys. However, the studies of spatio-temporal patterns, evolution, and the driving force of soil erosion in this region are insufficient. Therefore, based on a multi-stage, unequal probability, systematic area sampling method and field investigation, the soil erosion of the loess hilly area of western Henan was quantitatively evaluated by the Chinese Soil Loss Equation (CSLE) in 2022. The impact forces of soil erosion were analyzed by means of a geographic detector and multiple linear regression analysis, and the key driving factors of the spatio-temporal evolution of soil erosion in this region were revealed. The results were as follows. (1) The average soil erosion rate of the loess hilly area in western Henan in 2022 was 5.94 t⋅ha−1⋅a−1, with a percentage of soil erosion area of 29.10%. (2) High soil erosion rates mainly appeared in the west of Shangjie, Xingyang, and Jiyuan, which are related to the development of production and construction projects in these areas. The areas with a high percentage of soil erosion area were in the north (Xinan and Yima), west (Lushi), and southeast (Songxian and Ruyang) of the study area. Moreover, areas with the most erosion were found in forest land, cultivated land, and areas with a slope above 25°. (3) At the landscape level, the number and density of patches of all land types, except orchard land, increased significantly, and the boundary perimeter, landscape pattern segmentation, and degree of fragmentation increased. (4) The geographical detector and multiple linear regression analysis indicated that the driving forces of soil erosion are mainly topographic and climatic (slope length, elevation, precipitation, and temperature). Soil erosion was significantly influenced by the density of landscape patches. These maps and factors influencing soil erosion can serve as valuable sources of information for regional soil conservation plans and ecological environment improvements.

Effects of Bt-cotton cultivation on Helicoverpa armigera activity-density in agricultural landscapes in northwestern China.

Cotton bollworm, Helicoverpa armigera (Hübner), threatens many crops. Bacillus thuringiensis (Bt) cotton has been planted to control this severe pest in northern Xinjiang, China. In 2021 and 2022, we monitored the activity densities of H. armigera males using sex pheromone traps in Bt cotton and non-Bt maize fields. We assessed how much of the population variation of cotton bollworm in the fields within the Bt cotton planting area could be explained by (i) landscape composition [including the proportion of Bt cotton among total area of host crops (cotton, maize, wheat and vegetables)], (ii) landscape configuration (patch density - i.e. the number of patches within a given area) across 0.5-2.0 km scales, or (iii) the population density of the previous pest generation. Cotton bollworm activity-density exhibited two distinct peaks annually (mid- to late May and mid-July each year), with the number of males caught during the second peak significantly and positively correlated with the first peak's numbers. The suppressive effect of the proportion of Bt cotton in the landscape on bollworms was more pronounced at larger scales, and patch density had a significant positive effect on bollworm activity density. These findings support (i) the promotion of Bt cotton in northwestern China has reduced and suppressed the occurrence of cotton bollworms at the landscape scale and (ii) the importance of controlling spring populations for effective summer outbreak management, and (iii) that cotton bollworm control should be coordinated at a large scale across multiple crop fields. © 2024 Society of Chemical Industry.

Health effects of greenspace morphology: Large, irregular-shaped, well-connected, and close-clustered greenspaces may reduce mortality risks, especially for neighborhoods with higher aging levels

The healthcare burden has intensified with urbanization and aging populations in many global cities. While the health effects of urban greenspaces have been well documented, little is known about the associations between greenspace morphological features and health, especially in a high-density city with significant aging populations. Drawing on land use data with 10-m resolution, we assessed seven greenspace morphological metrics in terms of size (the percentage of greenspace, the largest pixel index, the average greenspace area), fragmentation (the patch density), shape (the average weighted shape index), connectedness (the cohesion index), and proximity (the aggregation index). We further conducted an ecological study to examine their associations with all-cause and three cause-specific (cardiovascular disease, respiratory disease, and cancer) mortality. Results from the negative binomial regression models revealed protective effects of five greenspace morphology metrics, including the percentage of greenspace, the largest pixel index, the average weighted shape index, the cohesion index, and the aggregation index, on mortality. The shape index showed the greatest effects, with every 1 Standard Deviation (SD) increase in the shape index linked to a reduction of 22.1% (95% CI: 22.0%–31.0%) in all-cause mortality, 22.1% (12.2%–30.8%) in mortality from cardiovascular diseases, 25.0% (14.0%–34.6%) in mortality from respiratory diseases, and 22.0% (12.3%–30.6%) in mortality from cancers. Moreover, stratified analyses revealed that the health effects of the cohesion index and the aggregation index were significantly more pronounced in neighborhoods with higher aging levels. Our findings highlight the significance of greenspace morphology features, beyond greenspace quantity, in improving residents’ health, particularly for societies with high aging populations.

Monitoring urban growth and landscape fragmentation in Kaduna, Nigeria, using remote sensing approach

This study assessed urban growth in Kaduna using remote sensing indices, landscape metrics, and Landsat images (2003, 2013, and 2023). Land use/land cover was carried out using the decision tree algorithm, while the transition matrix was computed using ArcGIS 10.8. Landscape fragmentation was assessed using Fragstat 4.3. The results revealed that between 2003 and 2023, bare land, built-up areas, and water bodies increased by 596 km2, 121.04 km2, and 0.22 km2, respectively, while cultivated land and tree cover decreased by 525.54 km2 and 191.91 km2, respectively. For the rate of change, bare lands, built-up areas, and water bodies increased by 29.81 km2, 6.05 km2, and 0.01 km2 annually, respectively, while cultivated lands and water bodies decreased by 26.28 km2 and 9.60 km2, respectively. For the land-use transition, 112.71 km2 of the built-up areas remained persistent. Cultivated lands, tree cover, and water bodies transitioned to built-up areas, losing 85.43 km2, 13.91 km2, and 0.91 km2, respectively. Landscape fragmentation analysis revealed that the number of patches increased from 405 in 2003 to 614 in 2023. Patch density, largest patch index, total edge, edge density, area-weighted mean shape index, and fractal dimension index decreased between 2003 and 2013 but increased between 2013 and 2023, indicating urban sprawl. However, the area-weighted mean contiguity index consistently increased between 2003 and 2023. The study concluded that urbanization is the leading driver of landscape fragmentation. This study recommends that development should be controlled to reduce encroachment on agricultural lands and habitats, thereby reducing landscape fragmentation and land degradation.

Spatial pattern and landscape change prediction of blowouts in sandy grassland

Blowouts are a common type of wind-eroded landform found in sandy and desertified areas. They also represent a major degradative surface process affecting grassland ecosystems. Blowouts exacerbate changes in surface morphology through their effects on other surface phenomena including vegetation. In this paper, Xilingol League sandy grassland blowouts are taken as the research object, and the U.S. Keyhole satellite data and China’s Gaofen-1 satellite data are used as the data source, and the blowouts are extracted based on the 3 S technology for a total of six periods of high-resolution remote sensing image data in the study area from 1962 to 2023. The Landscape Pattern Index method and Fuzzy Land Use Simulation (FLUS) modelling applied to changes over the last six decades provided spatial evolution parameters for predicting future blowout distributions. Results showed that blowouts affecting the Xilingol grassland area increased by 16.81% over the past 60 years. The patch density (PD) increased by 0.9 per hectare. The mean proximity index (PROX_MN) and mean Euclidean nearest neighbour distance (ENN_MN) showed a tendency to decrease and then increase indicating initial expansion and then merging of adjacent blowouts to create the present landscape. The FLUS model used ten factors to predict changes in blowout distributions from 2023 to 2033. Factors included digital elevation model (DEM), slope, aspect, normalized difference vegetation index (NDVI), mean annual temperature, mean annual precipitation, population density, real GDP, distance to water, and distance to impervious surfaces. It was found that grassland area decreased by 6217.12 hm2 and blowout area decreased by 102.91 hm2. Results of this study can expand understanding of blowout morphodynamics in ecologically sensitive areas.

How many parameters are needed to represent polar sea ice surface patterns and heterogeneity?

Abstract. Sea ice surface patterns encode more information than can be represented solely by the ice fraction. The aim of this paper is thus to establish the importance of using a broader set of surface characterization metrics and to identify a minimal set of such metrics that may be useful for representing sea ice in Earth system models. Large-eddy simulations of the atmospheric boundary layer over various idealized sea ice patterns, with equivalent ice fractions and average floe areas, demonstrate that the spatial organization of ice and water can play a crucial role in determining boundary layer structures. Thus, various methods used to quantify heterogeneity in categorical lattice-based spatial data, such as those used in landscape ecology and Geographic Information System (GIS) studies, are employed here on a set of recently declassified high-resolution sea ice surface images. It is found that, in conjunction with ice fraction, patch density (representing the fragmentation of the surface), the splitting index (representing variability in patch size), and the perimeter–area fractal dimension (representing the tortuosity of the interface) are all required to describe the two-dimensional pattern exhibited by a sea ice surface. For surfaces with anisotropic patterns, the orientation of the surface relative to the mean wind is also needed. Finally, scaling laws are derived for these relevant landscape metrics, allowing for their estimation using aggregated spatial sea ice surface data at any resolution. The methods used in and the results gained from this study represent a first step toward developing further methods for quantifying variability in polar sea ice surfaces and for parameterizing mixed ice–water surfaces in coarse geophysical models.

Using surface runoff to reveal the mechanisms of landscape patterns driving on various forms of nitrogen in non-point source pollution

Non-point source (NPS) pollution directly threatens river water quality, constrains sustainable economic development, and poses hazards to human health. Comprehension of the impact factors on NPS pollution is essential for scientific river water quality management. Despite the landscape pattern being considered to have a significant impact on NPS pollution, the driving mechanism of landscape patterns on NPS pollution remains unclear. Therefore, this study coupled multi-models including the Soil and Water Assessment Tool (SWAT), Random Forest, and Partial Least Squares Structural Equation Modeling (PLS-SEM) to construct the connection between landscape patterns, NPS pollution, and surface runoff. The results suggested that increased runoff during the wet season enhances the link between landscape patterns and NPS pollution, and the explained NPS pollution variation by landscape pattern increased from 59.6 % (dry season) to 84.9 % (wet season). Furthermore, from the impact pathways, we find that the sink landscape pattern can significantly and indirectly influence NPS pollution by regulating surface runoff during the wet season (0.301*). Meanwhile, the sink and source landscape patterns significantly and directly impact NPS pollution during different seasons. Moreover, we further find that the percentage of paddy land use (Pad_PLAND) and grassland patch density (Gra_PD) metrics can significantly predict the dissolved total nitrogen (DTN) and nitrate nitrogen (NO3−-N) variation. Thus, controlling the runoff migration process by guiding the rational evolution of watershed landscape patterns is an important development direction for watershed NPS pollution management.

Study on temperature regulation function of green spaces at community scale in high-density urban areas and planning design strategies

In recent years, the frequency of extreme high-temperature events has gradually increased. To better understand the impact of urban green space coverage's spatial pattern on land surface temperature (LST), this study selected three sample areas along the urban-rural gradient in Shanghai. Using LST inversion and resampling methods, LST data for different grid sizes were obtained for spring, summer, and winter in the 2010s, 2015s, and 2020 s. A boosting regression tree model was employed to determine the key indicators affecting LST and effective cooling thresholds. The impact of green quality, structure, and pattern at the community scale on LST was discussed, providing feasible suggestions for green space planning and design in different urban spaces. The study found significant differences in the spatial patterns of green areas and LST among the three sample regions. The quantity and integrity of green spaces in the Huangpu, Minhang, and Songjiang sample areas have progressively improved, with overall green coverages of 19 %, 38 %, and 43 %, respectively. As grid size increases, the relative influence of fractional vegetation coverage (FVC) on LST generally decreases, whereas the relative influence of green structure and patterns gradually increases. Taking the summer of the 2020 s as an example, the influence of FVC on the three study sample areas was 80.79, 83.36, and 87.18 at a 30 m grid size, which decreased to 48.87, 40.59, and 47.64 at a 120 m grid size. The green structure's impact rose from 13.09, 15.22, and 10.6–30.01, 51.82, and 38.5; the influence of green patterns increased from 6.12, 1.42, and 2.21–21.13, 7.6, and 13.86. Key indicators affecting LST include FVC (Fractional Vegetation Coverage), AREA (Green Patch Area), PD (Patch Density), COHESION (Patch Cohesion Index), and ED (Edge Density). High temperatures in summer are one of the ecological issues that need special attention in Shanghai's green space design. Setting the green space proportion to 35 % while avoiding fragmentation and low vegetation coverage can achieve effective cooling. This study's main advancement lies in utilizing machine learning algorithms to identify the principal green spatial pattern impact factors and key thresholds influencing LST at the community scale in Shanghai. The related results and proposed strategies provide a research framework and strong basis for special regulations in urban green space system planning concerning urban thermal environments. They offer references for urban renewal and new town planning to address climate change and urban high-temperature issues, such as clear requirements for optimal green space area at the community scale, community park spatial layout and quantity regulations, and urban park planning design suggestions. This study also highlights obstacles in the practical application of planning and design strategies, which can help avoid difficulties in implementing planning policies. It attempts to set goals for community-scale vegetation planning in China from a policy perspective and provides relevant recommendations.

Spatio-Temporal Heterogeneity of the Urban Heat Effect and Its Socio-Ecological Drivers in Yangzhou City, China

Rapid urbanization and land-use changes may affect the intensity of urban heat islands (UHIs). However, research on the eastern Chinese city of Yangzhou is lacking. Using land cover data and the InVest Urban Cooling model, this study evaluated the spatiotemporal heterogeneity of the UHI effect from 1990 to 2020 and its socioecological drivers in Yangzhou City. Landscape pattern indices such as patch area (CA), percentage of landscape (PLAND), number of patches, patch density, and aggregation index were created using Fragstats 4.2 software. Several social indicators, such as gross domestic product (GDP), night-light index, and population density, were considered to explore their correlation with UHI indicators. During the past three decades, rapid urbanization in Yangzhou has intensified the UHI effect, with the cooling capacity (cc park) and heat mitigation index (HMI) decreasing by ~9.6%; however, the mixed air temperature (T air) has increased by 0.14 °C. The main heat island areas are concentrated in southern Yangzhou, including the Hanjiang and Guangling districts, and have expanded over time. T air was positively correlated with GDP, night-light index, and population density. Moreover, for the impervious land use type, cc park and HMI were negatively correlated with CA and PLAND (p < 0.01). This study contributes to a deeper understanding of the dynamics of UHIs and provides valuable insights for policymakers, urban planners, and researchers striving to create sustainable and climate-resilient cities in Yangzhou.

Evaluating habitat use and relative abundance of Iowa's river otter with harvest data

AbstractThe North American river otter (Lontra canadensis) was extirpated from much of the United States in the early 20th century due to habitat loss, pollution of waterways, and overharvesting. The Iowa Department of Natural Resources began a river otter reintroduction effort in 1985, which placed otters in 14 sites across the state. Otters have since been known to occur in every county in Iowa and appear to have successfully repopulated their former range throughout the state. Our objective was to relate land cover characteristics and otter abundance using harvest data. We used data collected by agency staff to map the locations of otter harvest in Iowa from 2006 to 2016. We mapped otter harvest locations at the subwatershed level (also called 12‐digit Hydrologic Unit Code or HUC‐12). We related otter harvest to land cover variables and predicted otter abundance by land cover type. We found that roads, forests, larger waterways, and Ictaluridae (catfish) presence were negatively correlated with otter harvest. Variables positively correlated with otter harvest were areas with greater land cover diversity, wetland patch density, average stream density, and waterway and wetland areas. The land cover model predicted otters in equal or greater numbers than the harvest data in 62.8% of HUC‐12s. The areas of greatest otter abundance estimates were located near recreation areas and urban areas, indicating the underutilization of these heavy‐trafficked areas by trappers. Areas of fewer predicted otters were not concentrated in a single area of the state but occurred along the Interstate 80 corridor.

Exploring the relationship between preferred routes for walking exercise and green space layout in a dense urban area: a series of poisson regression models

ABSTRACT The literature considers urban green spaces as a key to promoting physical activity and health. Despite the increasingly recognized walking exercise promotion benefits of urban green spaces, the relationship between walking exercise preferences and green space layout remains unclear. We extracted preferred routes for walking exercise and measured the route heat using a one-year shared data of walking exercise from a mobile outdoor assistant app in the old city of Fuzhou, China. We then evaluated green space layout features within a 500-m buffer zone along the preferred routes in dimensions of scale, form, and function. The relationship between the route heat and green space layout features was analyzed using a series of poisson regression models, with the influence of urban morphology and the built environment allocation being controlled. Results showed that there existed a significant association between preferred routes for walking exercise and the combination of green space layout, urban morphology, and the built environment allocation; the route heat had positive associations with NDVI and comprehensive park area density, and showed negative associations with patch shape index and patch density. This study illustrates the necessity for targeted intervention strategies to promote the development of walkable and healthy urban areas.

Spatial Correlation of Landscape Fragmentation and Ecological Sensitivity in China’s Giant Panda National Park and Surrounding Areas

The Giant Panda National Park (GPNP) and its surrounding areas constitute a comprehensive ecosystem aimed at protecting the natural habitat of giant pandas, maintaining biodiversity, and ensuring ecological balance. Investigating the spatial correlation between landscape pattern indices and ecological sensitivity (ES) in this area is a crucial step in the construction of ecological civilization and contributes significantly to ecological conservation, restoration, and environmental management. This study utilized Geographic Information Systems (GIS) and Fragstats software to select nine ecological evaluation factors and four landscape pattern indices to comprehensively evaluate the ES and landscape patterns of the GPNP and its surrounding areas. This study discovered that the ecological sensitivity of the GPNP is substantially higher than that of the neighboring areas, with the northern Qionglai Mountain area and the western Minshan area showing the highest concentrations of exceptionally high sensitivity. Highly sensitive areas account for 35.22% of the study region, concentrated in areas within the national park, except the Qinling area, as well as the western and southern surrounding areas. The distribution of moderately sensitive areas is more uniform, while low and insensitive areas are found primarily in the northern and eastern areas, along with the national park’s environs. Patch density (PD) within the GPNP is lower than in surrounding areas, with higher PD in the northern and central parts. The landscape division index (DIVISION) decreases from west to east, and the landscape disturbance index (LDI) is significantly lower within the national park than in surrounding areas, with small areas of high LDI in the entire study region. Moran’s index analysis of the GPNP and its surrounding areas shows that ecological sensitivity is positively correlated with landscape pattern indices (PD, SPLIT, DIVISION, and LDI), with the strongest correlation between DIVISION and ecological sensitivity. Spatially, the internal areas of the national park have lower landscape pattern indices but higher ecological sensitivity, while the eastern region exhibits severe landscape fragmentation. Major clusters of high and low values are found around the Qionglai-Daxiangling area, indicating that these areas of high ecological sensitivity have complex landscape structures, numerous habitat edges, and significant impacts on biodiversity and ecological processes. Overall, the areas surrounding the GPNP exhibit lower ecological sensitivity levels and higher landscape fragmentation, emphasizing the need for focused ecological protection in the northern part of the QLS region and the western part of the MS region. Additionally, attention should be given to the impact of landscape fragmentation in the surrounding areas in the interior of the GPNP. These results provide scientific evidence for the sustainable development of the GPNP and its surrounding areas.

Response of Surface Runoff Evolution to Landscape Patterns in Karst Areas: A Case Study of Yun–Gui Plateau

To control and improve the phenomena of rocky desertification and soil erosion in karst landform areas, which are caused by a series of human factors that include social and economic development and human activities, China has successively introduced many policies, resulting in spatial and temporal changes in the landscape pattern of the southern karst area. In this study, land use transfer intensity maps, the grid method, the sample line method, the semivariogram method, and the Spearman analysis method are used to explore the spatial and temporal evolutions in surface runoff as responses to landscape pattern and policy factors in karst landform area. Therefore, this study provides theoretical and policy support for improving the regional landscape structure, optimizing the landscape layout, introducing regional policies, reducing surface runoff, and alleviating soil erosion. The results show that the best scale for the study of landscape patterns in the southern karst area is 3000 m. Forests are the land type that make up the highest proportion in the southern karst area, and they have the strongest interception capacity for surface runoff. The spatial and temporal distributions of the surface runoff are significantly different, and urban expansion has led to an increase in impervious runoff year over year. Runoff is positively correlated with the Shannon diversity index (SHDI), patch density (PD), and landscape shape index (LSI). The stronger the landscape heterogeneity, the more runoff. DIVISION is positively correlated with forest runoff and negatively correlated with other land types. The higher is the degree of aggregation of impervious patches, the higher the regional runoff rate. The more dispersed the forest patches are, the smaller the area proportion, and the greater the runoff. In addition, policy factors have a significant impact on surface runoff.

Multi-Dimensional Landscape Connectivity Index for Prioritizing Forest Cover Change Scenarios: A Case Study of Southeast China

Predicting forest cover change (FCC) and screening development scenarios are crucial for ecological resilience. However, quantitative evaluations of prioritizing forest change scenarios are limited. Here, we took five shared socio-economic pathways (SSPs) representing potential global changes, namely SSP1: sustainability, SSP2: middle of the road, SSP3: regional rivalry, SSP4: inequality, and SSP5: fossil-fueled development, which were constructed by integrated assessment and climate models. We modeled them with the patch-generating land use simulation (PLUS) and constructed a multi-dimensional landscape connectivity index (MLCI) employing forest landscape connectivity (FLC) indices to assess forest development in Fujian Province, Southeast China. The MLCI visualized by radar charts was based on five metrics, including forest patch size (class area (CA), number (patch density (PD), isolation (landscape division index (DIVISION), aggregation (mean nearest-neighbor index (ENN_MN), and connectance index, (CONNECT). The results indicate that FC will remain above 61.4% until 2030, with growth observed in SSP1 and SSP4. Particularly, FC in SSP4 substantially increased, converted from cropland (1140.809 km2) and grassland (645.741 km2). SSP4 has the largest MLCI values and demonstrates significant enhancements in forest landscape integrity, with CA, ENN_MN and CONNECT increasing greatly. Our study offers valuable approaches to and insights into forest protection and restoration.

Effects of the main media of wind and water on the biodiversity pattern of grassland and its driving mechanism in Poyang Lake

AbstractAs important media for species diffusion, water and wind are two important factors for grassland biodiversity conservation in lake areas. Exploring their driving mechanism on grassland biodiversity is crucial for maintaining the lake ecosystem's equilibrium. Our study utilizes the data of wind velocity and water level, which are significant performance factors for wind and water respectively in Poyang Lake from 2000 to 2020 to reveal their interannual time‐series fluctuation characteristics and their influence mechanism on the grassland biodiversity pattern. Landscape pattern indices and biodiversity indicators, such as the number of patches (NP), patch density (PD), landscape spreading index (CI), landscape fragmentation index (LSD), landscape aggregation index (AI), Simpson diversity index (MSIDI) and Simpson evenness index (MSIEI) were analysed, trend analysis, redundancy analysis and structural equation modelling were applied in this study. The main results were: (1) From 2000 to 2020, Poyang Lake's wind velocity decreased gradually, and the water level first decreased and then rose. NP in Poyang Lake fluctuated substantially, LSD fluctuated frequently and obvious temporal heterogeneity existed. (2) CI and AI increased from low to high value, facilitating species dispersal and migration. The dominant species with high aggregation gradually established stronger connectivity. Moderate spreading degree and aggregation degree maintained high biodiversity and evenness, whereas excessive spreading degree and aggregation degree led to homogenization of species, decrease in biodiversity, reduction in species evenness, and increase in dominance. (3) As the landscape transformed from having no obvious dominant species to being dominated by several dominant species, MSIEI and landscape dominance changed from high to low and low to high respectively. Moreover, the biodiversity shifted from high to low, and species distribution in the landscape shifted from balanced to unbalanced. (4) The effect of water level on PD, AI, LSD, MSIDI and MSIET was significantly higher than that of wind velocity. LSD was mainly regulated by the minimum wind velocity affecting the maximum and average water levels. MSIDI and MSIET were primarily governed by the minimum wind velocity affecting the minimum water level. The minimum water level decreased as the minimum wind velocity increased, and MSIDI and MSIET weakened as the minimum water level decreased.