National Forest Villages in China are village types with significant environmental resources and cultural landscapes that play a vital role in ecological conservation, tourism development, territorial planning, and other elements. Understanding tourists’ landscape perception preferences is important to the long-term development of their landscape systems. Using Xidi village from the second batch of National Forest Villages as a research case, we gathered the evaluation digital footprints of visitors to the village with Python (version 3.12.0) and then used the ROST CM6 software and the semantic network analysis method to sort out the general characteristics of visitors’ perceptions of the village’s landscape. Finally, we constructed a multidimensional landscape perception model by studying precise details of passengers’ views of Xidi village on four dimensions: natural landscape, humanistic landscape, landscape environmental atmosphere, and emotional state. The findings show that travelers’ perceptions of Xidi village’s landscape primarily focus on scenery, architecture, and culture, with the humanistic landscape having the strongest degree of perception, the landscape environment atmosphere having the middle degree of perception, and the natural landscape having a weaker degree of perception. Most travelers have positive emotions, and commercialization and homogenization are negative factors that trigger negative emotions. The optimization recommendations for Xidi village’s landscape system are then offered in light of the study’s findings.

China’s rare and endangered bamboo species belonging to the Indocalamus genus, specifically Indocalamus decorus Q. H. Dai, Indocalamus hirsutissimus Z. P. Wang & P. X. Zhang, and Indocalamus pedalis (Keng) P. C. Keng, possess notable value in biodiversity conservation and have significant potential for use in landscape design. Using an enhanced MaxEnt model, this study forecasted shifts in the species’ potential range under four separate climate scenarios (SSP1-RCP2.6, SSP2-RCP4.5, SSP3-RCP7.0, and SSP5-RCP8.5), considering both the historical period (1970–2000, referred to as “the current period”) and upcoming timeframes (2021–2040, 2041–2060, 2061–2080, and 2081–2100). The analysis disclosed that the present total potential habitat area for these species is approximately 251.79 × 10⁴ km², with high, medium, and low suitability areas occupying 0.15 × 10⁴ km², 125.39 × 10⁴ km², and 126.26 × 10⁴ km², respectively. The minimum temperature of the coldest month emerged as the critical determinant of their potential habitat distribution. Expected climate changes are likely to increase the suitable habitat for these species, although areas with low suitability might slightly diminish, with Guizhou and Chongqing showing the least impact. The distribution centers of suitable habitats for the three plant species consistently exhibit a westward shift under various climate scenarios. These results contribute valuable insights for the spatial distribution, continuous monitoring, sustainable management, and ex situ conservation in response to climate change.

Determining the minimum sampling area is crucial for accurate and cost-effective biodiversity surveys. This study identifies the main factors (e.g., species richness and evenness) influencing the inflection point method and precision method, aiming to scientifically establish the minimum sampling area for studying the shrub layer diversity of the Pinus massoniana community on Sandu Island, Ningde. Using 64 nested samples (20 m × 20 m) from eight sampling plots, we analyzed the species–area relationship and minimum sampling area. Key findings include the following: (1) The power function model (S = 4.053A^0.404) best described the species–area relationship. (2) Species richness significantly influenced the inflection point method, with rare species increasing the required sampling area. (3) Species distribution uniformity and sampling methods affected model outcomes. (4) Repeated sampling from the four corners of the plots reduced bias caused by uneven species distribution. (5) Considering cost, accuracy, and inflection points, a minimum sampling area of 142 m² achieved 70% sampling accuracy. This study provides a methodological framework for accurate minimum sampling area analysis and offers valuable insights for biodiversity studies of P. massoniana shrub layers in island ecosystems, with implications for international research on similar forest communities.