Many forests have suffered serious economic losses and ecological consequences of pine wilt disease (PWD) outbreaks. Climate change and human activities could accelerate the distribution of PWD, causing the exponential expansion of damaged forest areas in China. However, few studies have analyzed the spatiotemporal dynamics and the factors driving the distribution of PWD-damaged forests using continuous records of long-term damage, focusing on short-term environmental factors that influence multiple PWD outbreaks. We used a maximum entropy (MaxEnt) model that incorporated annual meteorological and human activity factors, as well as temporal dependence (the PWD distribution in the previous year), to determine the contributions of environmental factors to the annual distribution of PWD-damaged forests in the period 1982–2020. Overall, the MaxEnt showed good performance in modeling the PWD-damaged forest distributions between 1982 and 2020. Our results indicate that (i) the temporal lag dependence term for the presence/absence of PWD was the best predictor of the distribution of PWD-damaged forests; and (ii) Bio14 (precipitation in the driest month) was the most important meteorological factor for affecting the PWD-damaged forests. These results are essential to understanding the factors governing the distribution of PWD-damaged forests, which is important for forest management and pest control worldwide.

Accurate prediction of forest carbon sequestration potential requires a comprehensive understanding of tree growth relationships. However, the studies for estimating carbon sequestration potential concerning tree growth relationships at fine spatial-scales have been limited. In this paper, we assessed the current carbon stock and predicted sequestration potential of Lushan City, where a region has rich vegetation types in southern China, by introducing parameters of diameter at breast height (DBH) and tree height in the method of coupling biomass expansion factor (BEF) and tree growth equation. The partial least squares regression (PLSR) was used to explore the role of combined condition factors (e.g., site, stand, climate) on carbon sequestration potential. The results showed that (1) in 2019, the total carbon stock of trees in Lushan City was 9.22 × 105 t, and the overall spatial distribution exhibited a decreasing tendency from northwest to south-central, and the carbon density increased with elevation; (2) By 2070, the carbon density of forest in Lushan City will reach a relatively stable state, and the carbon stock will continue to rise to 2.15 × 106 t, which is 2.33 times of the current level, indicating that Lushan forest will continue to serve as a carbon sink for the next fifty years; (3) Excluding the effect of tree growth, regional forest carbon sequestration potential was significantly influenced on site characteristics, which achieved the highest Variable Importance in Projection (VIP) value (2.19) for slope direction. Our study provided a better understanding of the relationships between forest growth and carbon sequestration potential at fine spatial-scales. The results regarding the condition factors and how their combination characteristics affect the potential for carbon sequestration could provide crucial insights for Chinese carbon policy and global carbon neutrality goals.

Identifying key ecological nodes/corridors and priority restoration areas (KENPRA) is the key link for optimizing land use and ecological security patterns (ESPs). However, few studies have considered future land use/cover change (LUCC) and urban sprawl in identifying KENPRA for ESP maintenance. To optimize KENPRA, we took Quanjiao County, Anhui Province, China as a case study area, a typical unit for Chinese Land Spatial Planning and a suburb of Yangtze River Delta agglomeration challenging LUCC and ecological security pattern maintenance. A comprehensive framework for optimizing KENPRA has been established by integrating ESP and land use conflict (LUC) to adapt to land use change for corresponding urbanization processes. A CA-Markov model was used to predict future land use under different KENPRA-based scenarios in 2030. The results found that the total area of 4,357.2 ha priority restoration areas and 17 key ecological nodes was KENPRA, which were approximately 50% and concentrated in intensive LUC areas. The result of the simulation model showed that KENPRA-based scenarios integrating LUC indicated less urban expansion and better effectiveness for maintaining ESPs in 2030. The findings and proposed framework provide new and important information and implications for planners and policymakers to understand and improve land planning/policy; the results also can provide better understanding of the coupled human–nature system linking LUCC, ecosystem services, and land and restoration planning.

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Context The biodiversity of ecosystems is under severe threat from landscape fragmentation resulting from rapid urbanization. To understand the future trajectory of landscape patterns, it is imperative to examine the impact of current spatial planning constraint policies on the preservation of natural and semi-natural landscapes, as well as the promotion of ecosystem services and sustainability. Methods We employed a Patch-generating Land Use Simulation (PLUS) model to simulate and predict the land use and landscape pattern alterations in Lushan City under two distinct scenarios: "Planning Constraints (PC)" and "Natural Development (ND)". Subsequently, we identified an appropriate Landscape Fragmentation Index (LFI) that effectively captures the essence of fragmentation. To determine the optimal scale, we adopted an experimental approach using both the Moving Window (MW) method and the semi-variance function. By constructing a spatiotemporal sequence of LFI and the following trend analysis, we selected the Potential Fragmentation Areas (PFA) with significant tendencies toward landscape fragmentation. Results The spatial planning constraints 1) would prevent the encroachment of construction land into 2.14 km2 of cropland, 0.21 km2 of forest, and 0.13 km2 of grassland; 2) shift the highly fragmented area from the northeastern portion of Lushan to the planned area defined by the development boundary; 3) will mitigate and decelerate the trend of landscape fragmentation in natural and semi-natural landscapes, decrease PFA by 7.74 km2 and preserve 15.61 km2 of natural landscapes. Conclusions Spatial planning constraints have effectively controlled the expansion of the construction land. This control mechanism has greatly protected natural and semi-natural landscapes and ensured the conservation of habitats. Moreover, it provides an opportunity to incorporate landscape fragmentation risk considerations into future eco-management optimization.