Search

Traditional protected areas (PAs) with fixed boundaries are insufficient in the face of global climate change, undermining biodiversity conservation and climate impact mitigation. Fujian Province, a biodiversity hotspot in China, requires dynamic conservation strategies to maintain habitat quality (HQ) and carbon storage (CS). This research introduces a novel framework that integrates land-use simulation and ecosystem service valuation models to address conservation challenges under varying climate scenarios. We employ the System Dynamics (SD), the Patch-generating Land Use Simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to explore how future climate scenarios might alter land use, HQ, and CS, and determines the conservation priorities for Fujian Province by 2050 based on HQ, CS capacity, and a future vision for protection. Findings indicate: (1) Under SSP1-2.6, Fujian Province exhibits an increased demand for forests, grasslands, and water resources, while SSP2-4.5 and SSP5-8.5 predict an elevated demand for urban land, extending into inland areas. (2) In SSP1-2.6, HQ and CS are expected to improve, with average HQ projected to rise to 0.71 and CS to 15.63 Mg/ha by 2050. Conversely, under SSP5-8.5, HQ is anticipated to decline to 0.66, and CS to 15.14 Mg/ha. SSP2-4.5 shows moderate levels, with slight decreases in HQ to 0.69 and CS to 15.34 Mg/ha due to moderate urban expansion. Nature reserves demonstrate the highest potential, while national parks remain stable and ocean parks perform poorly. (3) Hotspots are primarily located in the western Wuyishan and central Daiyun mountain ranges. Conservation targets are recommended at 15 %, 30 %, and 43 % of Fujian Province’s area for 2030, 2040, and 2050, respectively. This research provides a framework for enhancing protected areas, empowering governments to advance biodiversity conservation and achieve carbon neutrality.

Increasing construction land demands and population growth within urban agglomerations have led to increasingly prominent landscape ecological risk (LER) issues. However, the multi-scale nature and spatial non-stationarity characteristics of the driving patterns have not been fully investigated. In this study, taking the Xiamen-Zhangzhou-Quanzhou Urban Agglomeration (XZQUA) as a case, we analyzed the spatio-temporal evolution characteristics of LER utilizing the long time series land use data from 1980 to 2020; then, the Exploratory Regression Analysis (ERA) was employed to investigate the optimal combination of key influential elements affecting LER. Finally, the Multiscale Geographically Weighted regression (MGWR) model was applied to explore the spatial non-stationarity effects of the key driving elements. The results reveal that: (1) The LER of the XZQUA has shown a declining trend from 1980 to 2020, characterized with significantly and increasingly spatial autocorrelation as well. (2) The optimal variable combination is the temperature, distance from roads, elevation, and construction land area. (3) The MGWR model results reveal that the impact of various factors on LER demonstrates significant spatial non-stationarity. The mechanisms and intensity of these influences change depending on geographical location. The negative proportion of regression coefficients for construction land area (−0.203 to −0.002) and elevation (−0.046 to −0.001) all exceeded 80%, indicating a high negative effect on LER; The positive proportion of the regression coefficient for distance from roads (−0.026 to 0.042) exceeded 60%, indicating a predominantly positive effect on LER. Our findings provide guidance for regional policymakers to adopt targeted ecological risk intervention measures, thus reducing LER in the urban agglomeration.

A comprehensive quantification of the relationships between forest vegetation changes and forest landscape fragmentation is urgently required to provide valuable insights for informed forest management decisions. Previous efforts often focused on these two aspects separately, overlooking their intricate relationships, restricts the formulation of precise forest protection and recovery measures. To address this gap, taking Southeast China as a case, this study employed the Google Earth Engine (GEE) platform to calculate the annual Normalized Difference Vegetation Index (NDVI) from 2000 to 2020, a forest fragmentation comprehensive index (FFCI) was constructed to evaluate the static and dynamic forest landscape fragmentation over the study period, then the Theil-Sen and Mann-Kendall, the two-dimensional framework and the bivariate spatial autocorrelation, and a machine learning algorithm (random forest, RF) were employed to explore the forest vegetation change and forest landscape fragmentation, and their spatial coupling relationships and driving patterns, respectively. The results showed that the temporal trend of the forest vegetation can be categorized into three distinct phases, with the pattern of “decreasing-rising-decreasing”, 78.2% of the area improved in forest vegetation, while 11.0% degraded. Approximately 6.0% of forest landscape showed a decline in fragmentation, while 9.3% experienced increased fragmentation trends, including 3.8% of forest landscape with moderate to high static fragmentation and 5.5% of forest landscape with low to very low static fragmentation. Throughout the study period, approximately 66.8% of forest landscape remained stability with the improved forest vegetation; however, there were cases (more than 7% of forests) with improved forest vegetation, but experienced severe landscape fragmentation. The RF outcome revealed that the forest vegetation change dynamics were influenced by the multiple factors, including distance to forest edge, distance to road, and elevation; while distance to forest edge emerged as the overwhelming dominant factor driving the forest landscape fragmentation. This research offers significant insights into the intricate interconnections between the forest vegetation changes and the forest landscape fragmentation dynamics.

It is essential to better integrate wilderness representations of different stakeholders into wilderness conservation. The way in which local residents and other stakeholders frame the construction of wilderness of protected areas in developing countries are poorly understood. In these areas, land use policy and decision may lead to conflicts. This study aims to explore existing public wilderness representations using a questionnaire survey (n = 514) administered amongst tourists and other stakeholders in the Wuyishan National Park, in southeast China. The spatial differences in public representations of wilderness across different stakeholder groups were compared against expert knowledge. We found that integrated wilderness representation maps of different stakeholder groups were consistent, namely ‘area where wild animals live’, ‘area with no human influence’, ‘a barren and lonely area’. However, three sub-representations of the individual stakeholders varied significantly. Moreover, expert-based wilderness mapping did not reflect public representations accurately, and an integrated wilderness quality map considering wilderness representations across both stakeholders and experts can better identify detailed wilderness areas. Our study provides new insights and technical support for future exploration of wilderness conservation and mapping in China and other countries with insufficient awareness of wilderness values and investigations in a regional scale.

The land use and land cover change (LUCC) associated with climate change and human activities poses a persistent threat to ecosystems, profoundly impacting ecological security. Simulating spatiotemporal changes in ecosystem service values (ESVs) and ecological security patterns (ESPs) under different future land use scenarios is crucial for formulating sustainable development policies. This study introduces a comprehensive framework for multi-scenario analysis, which combines the system dynamics (SD) model, patch-generated land-use simulation (PLUS) model, and minimum cumulative resistance (MCR) model to simulate ESV and ESP changes under future LUCC scenarios using the SSP-RCP scenarios provided by CMIP6. The results show that under the three scenarios (SSP245, SSP585, and SSP126), the ESV for the year 2050 is predicted to be 10.285 billion yuan, 10.248 billion yuan, and 10.327 billion yuan, respectively. Additionally, the area of ecological sources is estimated to be 78.56 km2, 65.90 km2, and 141.38 km2, while the lengths of ecological corridors are predicted to be 428.05 km, 332.45 km, and 527.10 km, and the numbers of ecological nodes are 14, 9, and 15, respectively. Notably, the SSP126 scenario demonstrates a significant improvement in ESP compared to the SSP245 and SSP585 scenarios, indicating effective protection of ecological land. This study provides valuable insights and serves as a crucial reference for future decision-making concerning ecosystem conservation and land resource management at the county and municipal scales.