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Autumn phenology plays a key role in regulating the terrestrial carbon and water balance and their feedbacks to the climate. However, the mechanisms underlying autumn phenology are still poorly understood, especially in subtropical forests. In this study, we extracted the autumn photosynthetic transition dates (APTD) in subtropical China over the period 2003–2017 based on a global, fine‐resolution solar‐induced chlorophyll fluorescence (SIF) dataset (GOSIF) using four fitting methods, and then explored the temporal–spatial variations of APTD and its underlying mechanisms using partial correlation analysis and machine learning methods. We further predicted the APTD shifts under future climate warming conditions by applying process‐based and machine learning‐based models. We found that the APTD was significantly delayed, with an average rate of 7.7 days per decade, in subtropical China during 2003–2017. Both partial correlation analysis and machine learning methods revealed that soil moisture was the primary driver responsible for the APTD changes in southern subtropical monsoon evergreen forest (SEF) and middle subtropical evergreen forest (MEF), whereas solar radiation controlled the APTD variations in the northern evergreen‐broadleaf deciduous mixed forest (NMF). Combining the effects of temperature, soil moisture and radiation, we found a significantly delayed trend in APTD during the 2030–2100 period, but the trend amplitude (0.8 days per decade) was much weaker than that over 2003–2017. In addition, we found that machine learning methods outperformed process‐based models in projecting APTD. Our findings generate from different methods highlight that soil moisture is one of the key players in determining autumn photosynthetic phenological processes in subtropical forests. To comprehensively understand autumn phenological processes, in‐situ manipulative experiments are urgently needed to quantify the contributions of different environmental and physiological factors in regulating plants' response to ongoing climate change. [ABSTRACT FROM AUTHOR]

Coral reef ecosystems support significant biological activities and harbor huge diversity, but they are facing a severe crisis driven by anthropogenic activities and climate change. An important behavioral trait of the coral holobiont is coral motion, which may play an essential role in feeding, competition, reproduction, and thus survival and fitness. Therefore, characterizing coral behavior through motion analysis will aid our understanding of basic biological and physical coral functions. However, tissue motion in the stony scleractinian corals that contribute most to coral reef construction are subtle and may be imperceptible to both the human eye and commonly used imaging techniques. Here we propose and apply a systematic approach to quantify and visualize subtle coral motion across a series of light and dark cycles in the scleractinian coral Montipora capricornis. We use digital image correlation and optical flow techniques to quantify and characterize minute coral motions under different light conditions. In addition, as a visualization tool, motion magnification algorithm magnifies coral motions in different frequencies, which explicitly displays the distinctive dynamic modes of coral movement. Specifically, our assessment of displacement, strain, optical flow, and mode shape quantify coral motion under different light conditions, and they all show that M. capricornis exhibits more active motions at night compared to day. Our approach provides an unprecedented insight into micro-scale coral movement and behavior through macro-scale digital imaging, thus offering a useful empirical toolset for the coral research community. [ABSTRACT FROM AUTHOR]

• There was no significant correlation between soil fungal and bacterial a-diversity and elevation. • Soil temperature (ST), soil water content (SWC), and total phosphorus (TP) were the driving factors affecting the elevation distribution pattern of soil fungal communities. • Soil fungi were more sensitive than bacteria in response to elevation changes. • The network complexity of soil bacteria is higher than that of fungi. The Qinghai-Tibet Plateau is one of the most sensitive regions in response to global climate change. Because soil bacteria have a considerable influence on ecosystem function, it is essential to understand their responses to different climates. We investigated the effects of elevation change on soil microbial diversity and community structure in the Abies georgei var. smithii forest of the Sygera Mountains in southeastern Tibet. We sampled soils from 0 to 10 cm depth at 3500 m, 3900 m and 4300 m asl, and examined changes in fungal and bacterial communities using Illumina MiSeq sequencing. The results showed that 1) fungal and bacterial diversity (Shannon index) did not change significantly (P > 0.05) with increasing elevation, and 2) elevation had no effect on the community structure of bacteria but had a significant effect on the community structure of fungi. Redundancy analysis showed that elevation, soil temperature (ST), soil water content (SWC), and total phosphorus (TP) were drivers of changes in fungal community structure. 3) Elevation had no significant effect (P > 0.05) on the relative abundance of the dominant bacterial phylum in the soil but had a significant effect (P < 0.05) on the relative abundance of the dominant fungal phylum. The dominant bacterial phyla were Proteobacteria, Actinobacteria, Acidobacteria, and Choroflexi, whereas the dominant fungal phyla were Basidiomycota, Ascomycota and Mortierllomycota. The relative abundance of Basidiomycota at 3900 m was significantly higher (P < 0.05) than that of Ascomycota at 3500 m and 4300 m, while the relative abundance of Ascomycota at 4300 m was significantly lower (P < 0.05) than that of Basidiomycota at 3500 m and 4300 m. and 4) the complexity of the bacterial network was the lowest at 3900 m elevation, while the most complex at 3500 m elevation. The fungal network complexity was lower than that of the bacteria. In conclusion, soil fungal communities are more sensitive to changes in elevation than are bacterial communities. [ABSTRACT FROM AUTHOR]