Canopy species need to shift their adaptive strategy to acclimate to very different light environments as they grow from seedlings in the understory to adult trees in the canopy. However, research on how to quantitively detect ecological strategy shifts in plant ontogeny is scarce. In this study, we hypothesize that changes in light and tree height levels induce transitions in ecological strategies, and growth phases representing different adaptive strategies can be classified by leaf trait variation. We examined variations in leaf morphological and physiological traits across a vertical ambient light (represented by the transmittance of diffuse light, %TRANS) and tree height gradient in Parashorea chinensis, a large canopy tree species in tropical seasonal rainforest in Southwestern China. Multivariate regression trees (MRTs) were used to detect the split points in light and height gradients and classify ontogenetic phases. Linear piecewise regression and quadratic regression were used to detect the transition point in leaf trait responses to environmental variation and explain the shifts in growth phases and adaptive strategies. Five growth phases of P. chinensis were identified based on MRT results: (i) the vulnerable phase, with tree height at less than 8.3 m; (ii) the suppressed phase, with tree height between 8.3 and 14.9 m; (iii) the growth release phase, with tree height between 14.9 and 24.3 m; (iv) the canopy phase, with tree height between 24.3 and 60.9 m; and (v) the emergent phase, with tree height above 60.9 m. The suppressed phase and canopy phase represent “stress-tolerant” and “competitive” strategies, respectively. Light conditions drive the shift from the “stress-tolerant” to the “competitive” strategy. These findings help us to better understand the regeneration mechanisms of canopy species in forests.

Beta‐diversity, or the spatio‐temporal variation in community composition, can be partitioned into turnover and nestedness components in a multidimensional framework. Forest structure, including comprehensive characteristics of vertical and horizontal complexity, strongly affects species composition and its spatial variation. However, the effects of forest structure on beta‐diversity patterns in multidimensional and multiple‐scale contexts are poorly understood. Here, we assessed beta‐diversity at local (a 20‐ha forest dynamics plot) and regional (a plot network composed of 19 1‐ha plots) scales in a Chinese subtropical evergreen broad‐leaved forest. We then evaluated the relative importance of forest structure, topography, and spatial structure on beta‐diversity and its turnover and nestedness components in taxonomic, functional, and phylogenetic dimensions at local and regional scales. We derived forest structural parameters from both unmanned aerial vehicle light detection and ranging (UAV LiDAR) data and plot inventory data. Turnover component dominated total beta‐diversity for all dimensions at the two scales. With the exception of some components (taxonomic and functional turnover at the local scale; functional nestedness at the regional scale), environmental factors (i.e., topography and forest structure) contributed more than pure spatial variation. Explanations of forest structure for beta‐diversity and its component patterns at the local scale were higher than those at the regional scale. The joint effects of spatial structure and forest structure influenced component patterns in all dimensions (except for functional turnover) to some extent at the local scale, while pure forest structure influenced taxonomic and phylogenetic nestedness patterns to some extent at the regional scale. Our results highlight the importance and scale dependence of forest structure in shaping multidimensional beta‐diversity and its component patterns. Clearly, further studies need to link forest structure directly to ecological processes (e.g., asymmetric light competition and disturbance dynamics) and explore its roles in biodiversity maintenance.

The ‘Returning Farmland to Forest Program’ (RFFP) in China has become an essential factor in land cover changes and forest transition, especially in terms of the ecological processes between two adjacent ecosystems. However, accurately delineating ecotones is still a big challenge for vegetation and landscape ecologists. Acquiring high spatial resolution imagery from a small, unmanned aircraft system (UAS) provides new opportunities for studying ecotones at a small scale. This study aims to extract forest-agriculture ecotones by RGB ultrahigh-resolution images from a small UAS and quantify the small biotopes in 3D space. To achieve these objectives, a canopy height model (CHM) is constructed based on a UAS-photogrammetric-derived point cloud, which is derived from the digital surface model (DSM) minus the digital terrain model (DTM). Afterward, according to the difference of plant community height between abandoned farmland ecosystem and forest ecosystem, the ecotones are delineated. A landscape pattern identified with ecotones and other small biotopes at the fine scale. Furthermore, we assess the accuracy of the ecotones’ delineation based on the transects method with the previous situ work we carried out and quantify the landscape structure using common landscape metrics to describe its spatial and geometric characteristics. Through transect-based analysis at three transects, the overall accuracy of the width of UAS-derived delineation is greater than 70%, and the detection accuracy for the occurrence location is 100%. Finally, we conclude that ecotones extraction from UAS images would also provide the possibility to gain a comprehensive understanding of the entire ecological process of agricultural abandoned land restoration through continuous investigation and monitoring.

As one of the important factors affecting plant productivity and plant distribution, temperature also affects the physiological and ecological characteristics of plants to a large extent. We report canopy leaf temperature distribution over a 36 m tall primary tropical rain forest and samplings of 28 tree species in SW China by means of two high resolution thermal cameras (P25, Flir systems, Wilsonville, OR, USA). The leaf temperature of dominant tree Species Pometia tomentosa was the highest (31.8 °C), 10.2 °C higher than that of tree species Mezzettipsis creaghii (21.6 °C). The mean leaf to air temperature difference (T_c–T_a) of Pometia tomentosa was the highest (6.4 K), the second highest was Barringtonia pendula (6.1 K), and Mezzettipsis creaghii had the lowest (T_c–T_a) (1.9K). (T_c–T_a) of tree species with smaller leaves and larger stomatal conductance was lowly sensitive to climate factors. Leaf size and stomatal conductance together decided the effect of climate change to (T_c–T_a) of the different tree species. We have shown that the composition of tree species in tropical rain forest areas is important to the climate through our research.

Human-elephant conflict (HEC) poses a major socio-ecological challenge across elephant range states. Since 2015, the National Forest Ecosystem Research Station of China based in Xishuangbanna has developed the Elephant Early-warning System (EEWS), a novel approach that has demonstrably reduced the risk of HEC incidents—particularly those involving direct encounters between people and elephants. By dynamically maintaining safety buffers, this system safeguards endangered elephants while mitigating human safety risks during livelihood activities and ensuring uninterrupted elephant movement. Building upon the C4ISR framework (Command, Control, Communications, Computers, Intelligence, Surveillance, and Reconnaissance), EEWS integrates key technological and institutional innovations—including the widespread adoption of mobile internet, deployment of camera traps, use of drones, and cross-sectoral governance reforms. The EEWS’s conceptual framework and technical architecture have been already recognized by local government and are now being scaled up from Xishuangbanna to the entire Asian elephant range in China, establishing a replicable “China model” for achieving harmonious human-elephant coexistence. This study reviews the conceptual foundations, development, and field implementation of EEWS, and offers recommendations to guide future refinement and broader application.

Pan evaporation (Epan) is a critical measure of the atmospheric evaporation demand. Analyzing meteorological data from the Tropical Rainforest Comprehensive Meteorological Observation Field in the Xishuangbanna Tropical Botanical Garden (XTBG Meteorological Observing Station) based on physical models is helpful to improve our understanding of the state of the hydrological cycle in the Xishuangbanna tropical rainforest region. In this study, we investigated the long-term trend in Epan using the observation data from 1959 to 2021. Moreover, correlation analyses of Epan were performed, such as trend test, assessment of periodic properties and abrupt change analysis. Then, D20 Epan data and related meteorological data from 1979 to 2008 were used to drive Penman‒Monteith and PenPan models for simulating Epan. The partial derivative attribution method was used to analyze the dominant factors affecting Epan. The results showed that Epan exhibits obvious periodic changes, the 19a is the first primary period. In addition, there was a clear ‘evaporation paradox’ phenomenon in Xishuangbanna. Epan showed a decreasing trend during both 1959–2008 and 2009–2018, and the decreasing trend reached a significant level with a rate of −3.404 mm·a−2 during 1959–2008. Through comparative analysis, the PenPan model was considered more suitable for simulating Epan in Xishuangbanna. In order to identify the main meteorological factors influencing Epan, complete data from the D20 pan monitoring period, namely, 1979–2008, were selected for attribution calculations. The variations in the net radiation and saturated vapor pressure deficit are the main triggers that explain the ‘evaporation paradox’ phenomenon in Xishuangbanna.