Rapidly developing droughts, including flash droughts, have occurred frequently in recent years, causing significant damage to agroforestry ecosystems, and they are expected to increase in the future due to global warming. The artificial forest area in China is the largest in the world, and its carbon budget is crucial to the global carbon sink. As the most prominent plantation plant in the tropics, the rubber (Hevea brasiliensis (Willd. ex A. Juss.) Muell. Arg.) ecosystem not only has important economic significance, but also has the potential to be a major natural carbon sink in hot areas. Frequent drought events have a significant impact on rubber ecosystem productivity, yet there have been few reports on the vulnerability of rubber productivity to drought. The objective of this study is to evaluate the vulnerability of rubber ecosystem gross primary production (GPP) to short-term flash drought (STFD) in Hainan Island, utilizing the localized EC-LUE model (eddy covariance–light use efficiency) validated by flux tower observations as the research tool to conduct the scenario simulations which defined by standard relative humidity index (SRHI), in a total of 96 scenarios (timing × intensity). The results show that, in terms of time, the rubber ecosystem in Hainan Island has the highest vulnerability to STFD during the early rainy season and the lowest at the end of the rainy season. From the dry season to the rainy season, the impact of STFD gradually extends to the northeast. Spatially, the vulnerability of the northern island is higher than that of the southern island and that of the western part is higher than that of eastern Hainan Island. With the increase in STFD intensity, the spatial distribution center of the vulnerability of rubber ecosystem GPP in Hainan Island gradually moves southward. The spatiotemporal pattern of the vulnerability of the rubber ecosystem GPP to STFD over Hainan Island plotted by this study is expected to provide decision makers with more accurate information on the prevention and control of drought disaster risk in rubber ecosystems.

We studied the influence of altitude on the spatial heterogeneity of tree diversity and forest structure in a subtropical evergreen broadleaf forest in southern China. Significant positive correlation was found between tree species diversity, basal area and altitude, but negative correlation between slenderness of trunks and altitude. According to topography, tree species diversity, diameter at breast height, height and basal area increased from ridges to valleys, while slenderness and stem density did not differ significantly with topography. Redundancy analysis (RDA) was employed to clarify relationships between tree species diversity and environmental factors (topography and soil water holding capacity). Topography and water conditions jointly explained 57.8% of the diversity variation. Tree species diversity was significantly correlated with altitude, slope and bulk density (Monte Carlo permutation test with 999 permutations, p < 0.05). A positive relationship existed between altitude, soil non-capillary porosity and diversity, while slope, aspect and soil water content were the opposite. Our results show that soil water holding capacity has a positive effect on maintaining species diversity. When comparing with topographic factors—the main driving forces affecting the pattern of tree species diversity—the effect of soil water holding capacity was weak.

Droughts that occur in tropical forests (TF) are expected to significantly impact the gross primary production (GPP) and the capacity of carbon sinks. Therefore, it is crucial to evaluate and analyze the sensitivities of TF-GPP to the characteristics of drought events for understanding global climate change. In this study, the standardized precipitation index (SPI) was used to define the drought intensity. Then, the spatially explicit individual-based dynamic global vegetation model (SEIB-DGVM) was utilized to simulate the dynamic process of GPP corresponding to multi-gradient drought scenarios—rain and dry seasons × 12 level durations × 4 level intensities. The results showed that drought events in the dry season have a significantly greater impact on TF-GPP than drought events in the rainy season, especially short-duration drought events. Furthermore, the impact of drought events in the rainy season is mainly manifested in long-duration droughts. Due to abundant rainfall in the rainy season, only extreme drought events caused a significant reduction in GPP, while the lack of water in the dry season caused significant impacts due to light drought. Effective precipitation and soil moisture stock in the rainy season are the most important support for the tropical forest dry season to resist extreme drought events in the study area. Further water deficit may render the tropical forest ecosystem more sensitive to drought events.

The classical environmental control model assumes that species diversity is primarily determined by environmental conditions (e.g., microclimate and soil) on the local scale. This assumption has been challenged by the neutral theory that assumes that the maintenance of biodiversity mainly depends on the ecological drift and dispersal limitation. Understanding the mechanisms that maintain biodiversity depends on decomposing the variation of species diversity into the contributions from the various components that affect it. We investigated and partitioned the effects of the biotic component (productivity, forest spatial structure) and the environmental component (topography and soil fertility) on the distribution of tree species richness jointly (the combined effect of environment and biotic process) and separately (the effect of environment or biotic process alone) in 25 permanent plots of 600 m² in a subtropical evergreen broadleaf secondary forest in southern China. The analysis was also completed for trees at different growth stages based on diameter breast height (young trees: 5 cm ≤ DBH < 10 cm, mature trees: 10 cm < DBH ≤ 20 cm, old trees: DBH > 20 cm) within each plot. Our results indicated that (1) tree species richness had significant negative relationship with productivity and a unimodal relationship with its spatially structured distribution; (2) biotic and environmental factors both have significant influence on species richness and jointly explain ~60% of the variation for the overall tree assemblage, and the variation explained by the two components jointly increased across growth stages (34%, 44%, and 75%, respectively); (3) additive variation partitioning revealed that the tree species richness was dominantly controlled by environmental factors (32%), while the biotic component also independently contributed a non-negligible effect (16%); and (4) the dominant fraction changed from the biotic component to the environmental component across growth stages. Results suggest that the tree species richness may be governed from neutral process to environmental control during tree life span in subtropical evergreen broadleaf secondary forests.

Given that rubber is an important strategic material and the prevalence of rubber tree powdery mildew (RTPM) is a serious issue, the study of RTPM is becoming increasingly significant in aiding our understanding and managing rubber plantations. By enhancing our understanding, we may improve both the yield and quality of the rubber produced. Using meteorological station and reanalysis data, we employed factor expansion and three different feature-selection methods to screen for significant meteorological factors, ultimately constructing a data-driven RTPM disease index (RTPM-DI) model. This model was then used to analyze the spatiotemporal distribution of RTPM-DI in Hainan Island from 1980 to 2018, to reproduce and explore its patterns. The results show that (1) the RTPM-DI is dominantly negatively influenced by the average wind speed and positively affected by days with moderate rain; (2) the average wind speed and the days with moderate rain could explain 71% of the interannual variations in RTPM-DI, and a model established on the basis of these can simulate the changing RTPM-DI pattern very well (RMSE = 8.2511, MAE = 6.7765, MAPE = 0.2486, KGE = 0.9921, MSE = 68.081, RMSLE = 0.0953); (3) the model simulation revealed that during the period from 1980 to 2018, oscillating cold spots accounted for 72% of the whole area of Hainan Island, indicating a declining trend in RTPM-DI in the middle, western, southwestern, and northwestern regions. Conversely, new hot-spots and oscillating hot-spots accounted for 1% and 6% of the entire island, respectively, demonstrating an upward trend in the southeastern and northern regions. Additionally, no discernible pattern was observed for 21% of the island, encompassing the southern, eastern, and northeastern regions. It is evident that the whole island displayed significant spatial differences in the RTPM-DI pattern. The RTPM-DI model constructed in this study enhances our understanding of how climate change impacts RTPM, and it provides a useful tool for investigating the formation mechanism and control strategies of RTPM in greater depth.

Meteorological conditions play a crucial role in driving outbreaks of rubber tree powdery mildew (RTPM). As the climate warms and techniques improve, rubber cultivation is expanding to higher latitudes, and the changing climate increases the RTPM risk. Rubber plantations on Hainan Island, situated on the northern margin of the tropics, have been selected as a case study to explore the meteorological mechanisms behind RTPM outbreaks quantitatively using a structural equation model, and project current and future RTPM outbreak patterns under different climate change scenarios by building predictive models based on data-driven algorithms. The following results were obtained: (1) days with an average temperature above 20 °C and days with light rain were identified as key meteorological drivers of RTPM using structural equation modeling (R² = 0.63); (2) the Bayesian-optimized least-squares boosted trees ensemble model accurately predicted the interannual variability in the historical RTPM disease index (R² = 0.79); (3) currently, due to the increased area of rubber plantations in the central region of Hainan, there is a higher risk of RTPM; and (4) under future climate scenarios, RTPM shows a decreasing trend (at a moderate level), with oscillating and sporadic outbreaks primarily observed in the central and northwest regions. We attribute this to the projected warming and drying trends that are unfavorable for RTPM. Our study is expected to enhance the understanding of the impact of climate change on RTPM, provide a prediction tool, and underscore the significance of the climate-aware production and management of rubber.