Understanding nutrient quantity and quality in forest soils is important for sustainable management of forest resources and maintaining forest ecosystem services. In this study, six soil nutrient indicators, including soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), available nitrogen (AN), available phosphorus (AP), and available potassium (AK) were measured in five different aged stands of Chinese fir forests in subtropical China. A structural equation model (SEM) was developed based on these soil nutrients indicators in order to better evaluate the soil fertility index (SFI) in these studied forests. The results show that soil nutrient contents changed with the soil depth in different age groups. The SOM decreased in a specific order: over mature > mature > near mature > middle > young stands. The TN content of the soil gradually decreased with increased soil depth throughout all age groups. The SEM indicated that the TN had the highest weight of 0.4154, while the TP had the lowest weight at 0.1991 for estimating the SFI. The weights of other indicators (AN, SOM, AP, and AK) ranged 0.2138–0.3855 in our study. The established SEM satisfied the fitness reference values and was able to accurately describe the forest soil nutrient status through the SFI. The overall SFI values were significantly higher in over mature stands than in young-aged stands and in topsoil than in deeper soil in all examined forests. Soil TN, AP, and AK were the most important nutrient indicators to the evaluation of the SFI in the study sites. The results confirmed that the SEM was suitable to estimate the weights of the SFI and better describe the soil nutrient status in forests. Our research provides an innovative approach to assess a soil nutrient status and soil fertility and provides a scientific basis for accurate implementation of soil nutrient assessment in forest ecosystems.

Close-to-nature management (CTNM) is the most promising option for plantation silviculture and has received widespread attention in recent years. Stand density is a key variable in CTNM, as it directly influences growth and yield. Research for the optimal density that maximizes the total harvest has been ongoing. In this paper, a dynamic programming model was applied to the CTNM of Phoebe bournei plantations for the first time to solve the problem of stand density and target tree density control. This paper took Phoebe bournei plantations in Jindong Forest Farm of Hunan Province as the research object. Based on the data of seven consecutive years from 2015 to 2021, Richard’s growth equation was used to fit the height growth equation and basal area growth equation of Phoebe bournei. Stand growth was divided into five development stages according to the forest growth process and characteristics. Stand density and basal area were selected as two-dimensional state variables, and the maximum total harvest in the entire stand growth process was used as the objective function to establish a dynamic programming model. The optimal stand density and target tree density at each growth stage of the stand under three different site conditions were determined. According to the results obtained, the objective forest shape was designed for the stand under three types of site conditions, which can provide a theoretical basis for the CTNM of Phoebe bournei plantations to make the stand achieve the maximum harvest.

Human activities and climate change have resulted in an increasing fragmentation of forest landscapes, and the conflict between biodiversity protection and economic development has become more pronounced. The establishment of forest ecological networks can be a vital part of biodiversity conservation and sustainable forest development. Using Jindong Forest Farm as the study area, this study combines the forest ecological suitability index, morphological spatial pattern analysis, the area method, and the landscape connectivity index (PC, IIC). This will identify ecological source areas in the study area, extract ecological corridors using the minimum cumulative resistance model and the gravity model, and construct a forest ecological network with ecological source areas as points and ecological corridors as edges. This study identified 11 forest patches in highly suitable habitat regions as ecological source regions, and 54 potential corridors were extracted. The study’s results show that a careful analysis of the forest landscape’s ecological suitability and morphological spatial pattern provides a scientific method for the rational selection of ecological source regions and serves as a reference for protecting forest species diversity and sustainable forest development.

The reduction and fragmentation of forests due to human activities are two primary factors that have led to forest biodiversity losses. The lack of forest continuation may prevent organisms from escaping areas that are no longer habitable due to altered environmental conditions and the reduction in size of habitat regions. Therefore, protecting and promoting forest connectivity has become one of the important objectives of forest management. The forests were regarded as independent elements, and the connectivity of the forest itself was directly evaluated in previous studies. However, this approach ignores the maintenance of forest connectivity, while requiring the participation of other landscape elements. In this study, we indirectly determine the forest priority by evaluating the landscape priority by integrating analysis with the habitat suitability (HS) model, MSPA and and BCkPC and dPCk index. We studied the wild boar (Sus scrofa) habitats in and around Hupingshan and Houhe National Nature Reserves to illustrate the indirect evaluation method of forest priority. The results showed that forests with high priority, medium priority, low priority, and non-priority comprised 596, 64, 58 and 105 km², respectively, accounting for the 41.2, 2.7, 3.0, and 11.9% of the total forest area. Our research revealed that evaluating the forest priority by analysing the landscape priority was an effective method for forest priority identification, and this strategy can be used to other regions or species for the goal of identifying the forest priority for biodiversity conservation.