Habitat quality dynamics in China's first group of national parks in recent four decades: Evidence from land use and land cover changes

Habitat quality outweighs the human footprint in driving spatial patterns of Cetartiodactyla in the Kunlun-Pamir Plateau

The Human Footprint (HFP) and Habitat Quality (HQ) are critical factors influencing the species' distribution, yet their relation to biodiversity, particularly in mountainous regions, still remains inadequately understood. This study aims to identify the primary factor that affects the biodiversity by comparing the impact of the HFP and HQ on the species' richness of Cetartiodactyla in the Kunlun-Pamir Plateau and four protected areas: The Pamir Plateau Wetland Nature Reserve, Taxkorgan Wildlife Nature Reserve, Middle Kunlun Nature Reserve and Arjinshan Nature Reserve through multi-source satellite remote sensing product data. By integrating satellite data with the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST)HQ model and utilizing residual and linear regression analysis, we found that: (1) The Wildness Area (WA) predominantly underwent a transition to a Highly Modified Area (HMA) and Intact Area (IA), with a notable 12.02% rise in stable regions, while 58.51% rather experienced a negligible decrease. (2) From 1985 to 2020, the Kunlun-Pamir Plateau has seen increases in the forestland, water, cropland and shrubland, alongside declines in bare land and grassland, denoting considerable land cover changes. (3) The HQ degradation was significant, with 79.81% of the area showing degradation compared to a 10.65% improvement, varying across the nature reserves. (4) The species richness of Cetartiodactyla was better explained by HQ than by HFP on the Kunlun-Pamir Plateau (52.99% vs. 47.01%), as well as in the Arjinshan Nature Reserve (81.57%) and Middle Kunlun Nature Reserve (56.41%). In contrast, HFP was more explanatory in the Pamir Plateau Wetland Nature Reserve (88.89%) and the Taxkorgan Wildlife Nature Reserve (54.55%). Prioritizing the restoration of degraded habitats areas of the Kunlun Pamir Plateau could enhance Cetartiodactyla species richness. These findings provide valuable insights for the biodiversity management and conservation strategies in the mountainous regions.

A two-dimensional four-quadrant assessment method to explore the spatiotemporal coupling and coordination relationship of human activities and ecological environment

Human activities that involve diverse behaviors and feature a variety of participations and collaborations usually lead to varying and dynamic impacts on the ecological environment. Quantitative analysis of the dynamic changes and complex relationships between human activities and the ecological environment (eco-environment) can provide crucial insights for ecological protecting and balance maintaining. We proposed a two-dimensional four-quadrant assessment method based on the dynamic changes in Human Activity Index (HAI) - Environmental Ecological Condition Index (EECI) to analyze the dynamic trends and coupling coordination degree (CCD) between HAI and EECI. This approach was applied in an empirical study of Hainan Province. A comprehensive HAI at a resolution of 1 km × 1 km is established to measure human activities, while an EECI is developed to evaluate ecological environment quality. The eco-environment showed continuous improvement, with the HAI initially rising and then declining. Analysis of coupling coordination revealed a ratio of 6:1 between coordinated development regions and conflict regions, indicating a gradual improvement in overall coupling coordination. The interaction between the HAI and EECI is strengthening, though variations exist across different locations. Using the geodetector method, we identified Net Primary Productivity (NPP), Land use and land cover (LULC), and Particulate Matter (PM) as the primary factors influencing changes in coupling coordination between HAI and EECI. These factors indirectly affect the stability and carrying capacity of the ecological environment. This method facilitates a quantitative examination of the dynamic relationship between HAI and EECI in different regions, offering insights into ecosystem functionality, biodiversity maintenance, and the effect of HAI on the region.

Landscape fragmentation and regularity lead to decreased carbon stocks in basins: Evidence from century-scale research

Landscapes evolution have significantly altered the Earth's energy balance and biogeochemical cycles, thereby exacerbating climate change. This, in turn, affects surface characteristics and the provision of ecosystem services, especially carbon storage. While recent centuries have witnessed unprecedented landscape changes, limited long-term studies have offered insights into the comparison between present-day features and historical conditions. This study utilized historical reconstruction data and remote sensing imagery to assess landscape evolution and its consequences for carbon stocks over 300 years. Employing multiple regression and random forest models were selected to quantify the influence of key landscape metrics on carbon stocks in the Dongting Lake basin, allowing for a thorough analysis across different sub-basins and land types. The results revealed that intensified human disturbances led to increased landscape fragmentation (+82%), regularity (+56%), and diversity (+37%) within the basin. Moreover, carbon stocks decreased from 4.13 Gt to 3.66 Gt, representing an 11.4% loss, with soil carbon stock experiencing the most considerable reduction (0.24 Gt, 51%). These changes in carbon stock metrics corresponded to shifts in landscape patterns, both undergoing significant transitions at the turn of the 21st century. Meanwhile, fragmentation and regularity played a vital role in explaining carbon stock changes, as their increase contributes to greater carbon losses. Likewise, an increase in landscape diversity correlated with decreased carbon stocks, challenging the prevailing notion that enhanced diversity promotes carbon stocks. The influence of landscape patterns on carbon stocks varies notably across distinct land types. An increase in the dominance of farmland and built-up land led to decreased carbon stocks, while the opposite holds true for forestland. Similarly, a decrease in regularity for farmland, forestland, and built-up land benefits carbon storage, while grassland demonstrates the opposite trend. These findings offer insights for countries and regions in the early stages of development or approaching development, suggesting improvements in land use practices and strategies to address climate change. This involves offsetting land-based carbon emissions through changes in landscape spatial configuration.

Trends in habitat quality and habitat degradation in terrestrial protected areas

Protected areas are typically considered a cornerstone of conservation programs and play a fundamental role in protecting natural areas and biodiversity. Human-driven land-use and land-cover (LULC) changes lead to habitat loss and biodiversity loss inside protected areas, impairing their effectiveness. However, the global dynamics of habitat quality and habitat degradation in protected areas remain unclear. We used the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model based on global annual remotely sensed data to examine the spatial and temporal trends in habitat quality and degradation in global terrestrial protected areas. Habitat quality represented the ability of habitats to provide suitable conditions for the persistence of individuals and populations, and habitat degradation represented the impacts on habitats from human-driven LULC changes in the surrounding landscape. Based on a linear mixed-effects modeling method, we also explored the relationship between habitat degradation trends and protected area characteristics, biophysical factors, and socioeconomic factors. Habitat quality declined by 0.005 (0.6%) and habitat degradation increased by 0.002 (11%) from 1992 to 2020 globally, and similar trends occurred even in remote or restrictively managed protected areas. Habitat degradation was attributed primarily to nonirrigated cropland (62%) and urbanization (27%) in 2020. Increases in elevation, gross domestic production per capita, and human population density and decreases in agricultural suitability were associated with accelerated habitat degradation. Our results suggest that human-induced LULC changes have expanded from already-exploited areas into relatively undisturbed areas, and that in wealthy countries in particular, degradation is related to rapid urbanization and increasing demand for agricultural products.

Examining the rationality of Giant Panda National Park's zoning designations and management measures for habitat conservation: Insights from interpretable machine learning methods

Examining the rationality of zoning designations and management measures in the initial establishment of national parks in China is of great significance for supporting decision-making regarding habitat conservation. There exists a research gap in exploring the threshold effects of both environmental and human-related factors on habitat distribution in the context of national parks. However, it may be a challenge because of the limited species distribution data. Our study aims to put forward an analytical framework that integrates species distribution models (SDMs) with interpretable machine learning methods. A case study was performed in the Sichuan region of the Giant Panda National Park (GPNP). We constructed a SDM based on the Random Forest algorithm and made use of accessible remote sensing and big data to predict the distribution of giant panda habitat (GPH) in 2020. Interpretable machine learning methods, namely Partial dependence plots (PDPs) and SHapley Additive exPlanations (SHAP), were utilized to uncover the underlying mechanisms of environmental and anthropogenic variables influencing the GPH distribution. Through GIS overlay analysis, areas where conflicts between human settlements, transportation infrastructure, and GPH exist were identified. Our findings indicated a potential 28.44 % decrease in GPH from 2014 to 2020. Environmental factors such as temperature, topography, and vegetation type, as well as anthropogenic factors including distance to built-up areas and transportation infrastructure, notably distance to national roads, provincial roads and city arterial roads, influenced the GPH distribution with threshold effects significantly. The overlay analysis revealed escalated conflicts between human settlements, transportation infrastructure, and GPH in 2020 compared to 2014. Currently, the Sichuan region of the GPNP implements two zones: a core protection zone and a general control zone, covering 63.71 % of the GPH, while 36.29 % remains outside the management scope. Drawing from the analysis above, this study provided suggestions for the adjustment of zoning designations and management measures in the GPNP.

An integrated approach to assess spatial and temporal changes in the contribution of the ecosystem to sustainable development goals over 20 years in China

Ecosystems are an important basis for promoting sustainable development goals (SDGs) through the provision of stable ecosystem services (ESs). In the past 20 years, China has implemented a series of forestry ecological development projects, resulting in the improvement of the ecological environment. In this context, changes in ESs in China may affect the contribution of ecosystems to the SDGs, but there is a lack of research in this area. Studies have shown that ESs can support multiple SDGs, and quantifying the contribution of ecosystems to SDGs is currently a research focus. However, few studies have quantified the extent of the contribution of different ESs to the SDGs, although these differences are generally assumed. To narrow this knowledge gap, we construct an assessment approach that integrates the extent of the contribution of different ESs to the SDGs and assesses the temporal and spatial dynamics of the contribution of ESs to the SDGs in China from 2000 to 2020. Our analysis results indicate that during the study period, fractional vegetation cover improved in China. In general, water provision, soil conservation, and food provision services improved, while carbon storage and biodiversity maintenance services declined. The contribution capacity of provincial ecosystems to the SDGs increased, except in Tibet, between 2000 and 2020. Overall, the contributions to the SDGs had obvious spatial differences. The research results can support policy formulation and research on ES management and SDGs.

Spatio-Temporal Change of Land Use/Land Cover and Vegetation Using Multi-MODIS Satellite Data, Western Ethiopia

Land use and land cover (LULC) change and variability are some of the challenges to present-day water resource management. The purpose of this study was to determine LULC and Normalized Difference Vegetation Index (NDVI) fluctuations in western Ethiopia during the last 20 years. The first part of the study used MODIS LULC data for the change analysis, change detection, and spatial and temporal coverage in the study region. In the second part, the study analyzes the NDVI change and its spatial and temporal coverage. In this study, The Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data were applied to determine LULC and NDVI changes over four different periods. Evergreen broadleaf forests, deciduous broadleaf forests, mixed forests, woody savannas, savannas, grasslands, permanent wetlands, croplands, urban and built-up lands, and water bodies are the LULC in the period of analysis. The overall classification accuracy for the classified image from 2001 to 2020 was 85.4% and the overall kappa statistic was 81.2%. The results indicate a substantial increase in woody savannas, deciduous broadleaf, grasslands, permanent wetlands, and mixed forest areas by 119.6%, 57.7% 45.2%, 37%, and 21.3%, respectively, followed by reductions in croplands, water bodies, savannas, and evergreen broadleaf forest by 90.1%, 19.8%, 13.2%, and 4.8%, respectively, for the catchment between 2001 and 2020. The result also showed that the area's vegetation cover increased by 64% from 2001 to 2022. This study could provide valuable information for water resource and environmental management as well as policy and decision-making.

Modeling the Wintering Habitat Distribution of the Black Stork in Shaanxi, China: A Hierarchical Integration of Climate and Land Use/Land Cover Data

Species distribution models (SDMs) are effective tools for wildlife conservation and management, as they employ the quantification of habitat suitability and environmental niches to evaluate the patterns of species distribution. The utilization of SDMs at various scales in a hierarchical approach can provide additional and complementary information, significantly improving decision-making in local wildlife conservation initiatives. In this study, we considered the appropriate spatial scale and data resolution to execute species distribution modeling, as these factors greatly influence the modeling procedures. We developed SDMs for wintering black storks at both the regional and local scales. At the regional scale, we used climatic and climate-driven land use/land cover (LULC) variables, along with wintering occurrence points, to develop models for mainland China. At the local scale, we used local environmental variables and locally gathered wintering site data to develop models for Shaanxi province. The predictions from both the regional and local models were then combined at the provincial level by overlapping suitable areas based on climatic and local conditions. We compared and evaluated the resulting predictions using seven statistical metrics. The national models provide information on the appropriate climatic conditions for the black stork during the wintering period throughout China, while the provincial SDMs capture the important local ecological factors that influence the suitability of habitats at a finer scale. As anticipated, the national SDMs predict a larger extent of suitable areas compared to the provincial SDMs. The hierarchical prediction approach is considered trustworthy and, on average, yields better outcomes than non-hierarchical methods. Our findings indicate that human-driven LULC changes have a significant and immediate impact on the wintering habitat of the black stork. However, the effects of climate change seem to be reducing the severity of this impact. The majority of suitable wintering habitats lie outside the boundaries of protected areas, highlighting the need for future conservation and management efforts to prioritize addressing these conservation gaps and focusing on the protection of climate refuges.

Spatial pattern evolution and prediction scenario of habitat quality in typical fragile ecological region, China: A case study of the Yellow River floodplain area

The Yellow River basin is an important area for China to implement ecological protection policies. Studying the habitat quality of the Yellow River floodplain area is of great significance to the ecological security and sustainable development of the entire basin. This study primarily investigated the spatial pattern of habitat quality in the Yellow River floodplain area from 2000 to 2020, then, we also simulated changes of habitat quality in 2025-2035 and analyzed the influencing factors by coupling the PLUS (Patch-generating Land Use Simulation) model, InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) model and RF (Random Forest) model. The results showed that:(1) From 2000 to 2020, cultivated land and build-up land constituted an important part of the Yellow River floodplain area, and the growth rate of build-up land was fast. (2) We also found that the ecological land (forest land, grassland, waterbody) had a higher contribution value to the habitat quality, while the build-up land had a lower contribution value to the habitat quality. (3) Overall, the habitat quality of the floodplain area showed a degradation trend from 2000 to 2020. In addition, the regions with low habitat quality accounted for the major proportion. (4) Based on the calculation results of the Random Forest (RF) model, we found that topographical relief (TR) and land use intensity (LUI) were the two most important factors affecting habitat quality of the floodplain area. (5) According to the four scenarios from 2025 to 2035, it is found that the habitat quality level would be the highest under the ecological protection scenario, while under the urban development scenario its level would be the lowest. This study attempts to combine the RF model with PLUS model to improve the objectivity and accuracy of the future prediction scenario of habitat quality, which can provide scientific reference for ecological governance and policy formulation in the Yellow River floodplain area.