Forest biomass, as a carrier of carbon, is an important indicator for judging forest productivity, stability and sustainable development capacity. Using the survey data of sample plots in eight forest areas in central Hainan, the biomass distribution of tropical mountain rainforests in National Park of Hainan Tropical Rainforest in different community sizes, diameter classes, altitudes and spaces was measured to explore the relationship between forest biomass and environmental factors. The results show that (1) the total area of tropical montane rainforests in National Park of Hainan Tropical Rainforest was about 983.70 km², distributed within an altitude range of 700–1300 m; the total aboveground biomass was about 25.208 million tons, which decreased first and then increased with increasing altitude, with an average aboveground biomass per unit area of 236.00 t·hm⁻²; (2) the primary forest accounted for 83.23% of the total aboveground biomass of the tropical mountain rainforest with only 29.84% of the total area, and the aboveground biomass per unit area was generally higher than that of the secondary forest; and (3) medium- and large-diameter trees were the main carriers of aboveground biomass in tropical mountain rain forests. More than 83.73% of the aboveground biomass was concentrated in large-diameter trees. The results of this study provide a reference for others aiming to perform measurement and evaluation of the carbon sink and the capacity for carbon neutrality in tropical rainforest ecosystems or to maintain regional biodiversity.

As the net gain of carbon by plants after accounting for respiration, vegetation net primary productivity (NPP) plays a central role in the terrestrial carbon cycle. However, a systematic and quantitative analysis of the spatiotemporal evolution and driving mechanisms of vegetation NPP on Hainan Island, a tropical region, is still lacking. Focusing on Hainan Island, this study employs an integrated approach—including the coefficient of variation, Mann–Kendall test, Hurst exponent, geographical detector, and PLS-SEM—to investigate the spatiotemporal dynamics of vegetation NPP and its underlying drivers from 2001 to 2022. The main conclusions as follows: (1) Vegetation NPP on Hainan Island showed a fluctuating upward trend from 2001 to 2022, with a mean annual increase of 3.6 g C·m⁻²·yr⁻¹, and displayed a spatial pattern of decrease from the central-southern mountainous areas toward the coastal regions. (2) NPP changes were generally stable; historically, areas showing an increasing trend exceeded those with a decreasing trend by 30.55%. In the future, the predominant projected trends are “persistent decrease” and “increase to decrease,” which together account for over 80% of the total area. (3) Topography and climate were the dominant drivers of NPP spatial heterogeneity. Elevation had the strongest explanatory power, followed by evapotranspiration and temperature. A significant, nonlinear enhancement effect was observed in the interaction between any two factors. (4) Topographic, climatic, anthropogenic, and vegetation factors all exerted direct positive effects on vegetation NPP. Anthropogenic activities also indirectly promoted NPP by influencing pathways such as vegetation growth. The conclusions of this research provide support for the implementation and evaluation of land-use planning, afforestation projects, and ecological protection and restoration measures on Hainan Island.

Hainan Tropical Rainforest National Park is a prime example of a “continental island” tropical rainforest and holds significant value for biodiversity conservation. However, human activities have led to frequent changes in land use and increased habitat fragmentation within the park; a precise analysis of the underlying mechanisms is necessary for ecological restoration. Consequently, drawing upon land-use data from 2000 to 2020, this study coupled multi-dimensional fragmentation metrics (CFI, AFI, and SFI) with the XGBoost-SHAP framework to systematically unravel the spatiotemporal dynamics and underlying driving mechanisms of landscape fragmentation in Hainan Tropical Rainforest National Park. Our findings revealed that the spatial configuration of fragmentation predominantly propagated along river networks and transport corridors, accompanied by a fluctuating ‘decline–rise–decline’ temporal trajectory. Notably, the XGBoost-SHAP attribution highlighted a distinct temporal shift in the dominant drivers: fragmentation was primarily mitigated (negatively driven) by NDVI between 2000 and 2010 but was subsequently exacerbated (positively driven) by GDP growth from 2010 to 2020. The findings of this study provide data support and a scientific basis for ecosystem restoration and land use planning in Hainan Tropical Rainforest National Park.

Forest fires significantly disturb the global ecosystem, profoundly impacting carbon cycling, biodiversity, and socio-economic sustainability. Understanding the seasonal patterns of forest fire occurrence is crucial for developing effective prevention and control strategies that support sustainable forest management and disaster risk reduction. However, research on the seasonal dynamics of forest fires in the tropical forests of China’s Hainan Island remains scarce, and the underlying driving mechanisms, particularly in relation to the distinct dry and rainy seasons, are still insufficiently understood. In this study, we analyzed the seasonal patterns and driving factors of forest fires on Hainan Island, a tropical region, from 2000 to 2022, and compared the applicability of emerging machine learning algorithms and traditional Logistic Regression algorithms in constructing tropical forest fire prediction models for the dry and rainy seasons. The results show that tropical forest fires in Hainan Island are primarily concentrated from January to May, peaking in April, with a lagged effect relative to the dry-season months (November to April of the following year). Average temperature and average rainfall were identified as common important predictors of fire occurrence probability in both seasons, with the Normalized Difference Vegetation Index additionally included in only the dry season. The predictive accuracy of the Random Forest model consistently surpassed that of the traditional Logistic Regression model in both seasons. Additionally, our models enable the generation of accurate forest fire probability maps, highlighting risk areas in Hainan Island during both seasons and providing new insights for wildfire prevention strategies and sustainable forest management in tropical regions.

The construction of the Hainan Free Trade Port (FTP) is guided by the core philosophy of “ecological priority and green development.” To meet the practical requirements of building a “Green and Beautiful FTP,” this study constructed a kernel Normalized Difference Vegetation Index (kNDVI) dataset using Landsat series satellite imagery. By integrating methods including the Mann–Kendall (MK) trend test, Hurst exponent, and coefficient of variation (CV), an in-depth analysis was conducted on the spatiotemporal evolution characteristics and trends of growing-season vegetation in Hainan Island from 1994 to 2023. Additionally, the Extreme Gradient Boosting (XGBoost) model and the SHapley Additive exPlanations (SHAP) interpretation method were employed to quantitatively unravel the driving mechanisms of climatic factors and human activities on kNDVI variations. The results indicate that: (1) Over the past three decades, the overall kNDVI of Hainan Island has exhibited a significant upward trend, characterized spatially by an evolution pattern of “stable recovery in the central region and localized degradation along the coast.” (2) The vegetation evolution demonstrates strong persistence and is highly consistent with community stability. The high stability in the central mountainous areas stems from a superior natural background and strict ecological protection; the transition zone is jointly influenced by vegetation types and anthropogenic management; meanwhile the coastal areas exhibit significant degradation characteristics driven by high-intensity human disturbances. (3) The analysis of driving mechanisms reveals a complex control logic of “topographical foundation—human reshaping—extreme climate triggering.” Static topographical factors, such as elevation and slope, occupy an absolute dominant position; human activities, represented by rubber plantation expansion and urbanization, exert a bidirectional reshaping effect characterized by “inland greening and coastal suppression”; furthermore, extreme drought and high temperatures in the later stages of the study demonstrated a significant pulse-like impact, exacerbating the risks of short-term climatic stress. This study clarifies the core patterns of vegetation evolution in Hainan Island, validates the effectiveness of ecological policies, and provides a quantitative scientific basis for ecological conservation and sustainable development in tropical island regions.

Soil microorganisms play essential roles in maintaining the function and stability of forest ecosystems. In this study, conducted in the Jianfengling Nature Reserve in Hainan, we performed Illumina high-throughput sequencing, in combination with principal coordinate analysis (PCoA), redundancy analysis, and other statistical methods, to compare the composition, community structure, and diversity of the soil microbiota in four types of forest vegetation, namely, monsoon forest, lowland rainforest, montane rainforest, and artificial forest (AF), and to identify the environmental variables associated with differences among the forest types. Different vegetation types differed significantly with respect to pH, total phosphorus content (TP), elevation, and concentrations of manganese (Mn) and iron (Fe). In contrast, no significant differences were observed in soil water content, bulk density, total nitrogen (TN), and total organic carbon (TOC) content. Compared to AF, natural forests were characterized by higher concentrations of TN, TOC, TP, Mn, and Fe, although the differences in TN and TOC were not statistically significant. For all four of the assessed vegetation types, Pseudomonadota, Actinomycetota, Acidobacteriota, and Verrucomicrobiota were identified as the dominant bacterial phyla, whereas Ascomycota, Basidiomycota, and Mucoromycota were predominant among the fungal phyla, indicating that these phyla are capable of surviving in different soil ecosystems. Although no significant differences in bacterial diversity were detected among vegetation types, PCoA revealed pronounced differentiation in bacterial community composition (PERMANOVA, p = 0.0001). Notably, within-group multivariate dispersion also differed significantly (PERMDISP, p = 0.0129), indicating that the observed compositional differences may be associated with the combined effects of centroid location effect and within-group multivariate dispersion. In contrast, significant differences were observed in fungal diversity, whereas differences in fungal community composition were not significant in the PCoA (PERMANOVA, p = 0.1777). These results suggest that vegetation type primarily influenced fungal diversity rather than fungal community structure. Mn concentration exhibited the highest marginal explanatory power for the variation in the bacterial community, with a significant effect; it was positively correlated with the ACE, Chao1, and Simpson indices, and negatively correlated with the Shannon index. TOC exhibited the highest marginal explanatory power within the fungal community, showing a negative correlation with the ACE, Chao1, and Shannon indices and a positive correlation with the Simpson index. Collectively, these findings provide insights into microbial differentiation within forest soils associated with different vegetation types and highlight distinct patterns in bacterial and fungal diversity, which will potentially serve as a basis for the development of conservation strategies.