Millennial changes and cooling trends in land surface warm-season temperatures during the Holocene.

The scarcity of proxies and calibration models for quantitatively reconstructing millennial timescale seasonal temperature tremendously constraints our understanding of the Holocene thermal variation and its driven mechanisms. Here, we established two global warm-season temperature models by applying deep learning neural network analysis to the branched tetraether membrane lipids originating from surface soil and lacustrine sediment bacteria. We utilized these optimal models in global well-dated lacustrine, peatland, and loess profiles covering the Holocene. All reconstructions of warm-season temperatures, consistent with climate model simulations, indicate cooling trends since the early Holocene, primarily induced by decreased solar radiation in the Northern Hemisphere due to the precession peak at the early. We further demonstrated that the membrane lipids can effectively enhance the future millennial seasonal temperature research, including winter temperatures, without being restricted by geographical location and sedimentary carrier.

Will large-scale forestation lead to a soil water deficit crisis in China's drylands?

Trading water for carbon has cautioned large-scale afforestation in global drylands. However, model simulations suggested that the consumption of soil water could be partially offset by increasing precipitation due to vegetation feedback. A systematic meta-analysis of long-term and large-scale field observations is urgently required to address the abovementioned limitations, and the implementation of large-scale afforestation since 1978 in northern China provides an ideal example. This study collected data comprising 1226 observations from 98 sites in northern China to assess the variation in soil water content (SWC) with stand age after afforestation and discuss the effects of tree species, precipitation and conversions of land use types on SWC. We found that the SWC has been decreased by coniferous forest and broadleaf forest at rates of 0.6 and 3.2 mm decade-1, respectively, since 1978. There is a significant declining trend of SWC with the stand age of plantations, and the optimum growth stage for plantation forest is 0-20 a in northern China. However, we found increases in SWC for the conversion from grassland to forest and in the low-precipitation region, both are corresponding to the increased SWC in coniferous forest. Our study implies that afforestation might lead to a soil water deficit crisis in northern China in the long term at the regional scale but depends on prior land use types, tree taxa and the mean annual precipitation regime, which sheds light on decision-making regarding ecological restoration policies and water resource management in drylands.

Background-Free and Reversible Upconversion Hydrogel Sensing Platform for Visual Monitoring of Sulfite.

Excessive sulfite usage in food and pharmaceutical production causes respiratory and neurological diseases, underscoring the need for a sensitive and rapid quantification strategy. The portable sensing platform based on a luminescent hydrogel sensor is a powerful tool for the on-site, real-time detection of sulfite ions. However, the lack of recyclability in almost all reaction-based hydrogel sensors increases the application cost. This study constructed a reversible and upconversion nanoprobe combining upconversion nanoparticles (UCNPs) and pararosaniline (PAR) for sulfite detection. The upconversion nanoprobe was further encapsulated in a three-dimensional polyacrylamide hydrogel matrix to create a background-free, reversible hydrogel sensor. The near-infrared excitation of UCNPs avoids the autofluorescence in the hydrogel and real samples. Meanwhile, PAR serves as a specific recognition unit for sulfite ions. After the addition of sulfites, a specific reaction occurs between PAR and sulfites, leading to the recovery of characteristic emission at 540 nm, achieving sensitive detection of sulfite ions. Importantly, this specific reaction is reversible under thermal treatment, allowing the hydrogel sensor to return to its initial state and thus enabling reversible detection of sulfite ions. Furthermore, a portable sensing platform is developed to realize point-of-care, real-time quantitative detection of sulfite ions. The proposed upconversion reversible hydrogel sensor provides a new sensing strategy for the detection of hazardous substances in food and offers new insights into the preparation of reversible, highly sensitive hydrogel sensors.

Nitrogen addition enhances tree radial growth but weakens its recovery from drought impact in a temperate forest in northern China.

Forest growth in the majority of northern China is currently limited by drought and low nitrogen (N) availability. Drought events with increasing intensity have threatened multiple ecosystem services provided by forests. Whether N addition will have a detrimental or beneficial moderation effect on forest resistance and recovery to drought events was unclear. Here, our study focuses on Pinus tabulaeformis, which is the main plantation forest species in northern China. We investigated the role of climate change and N addition in driving multi-year tree growth with an 8-year soil nitrogen fertilization experiment and analyzing 184 tree ring series. A moderate drought event occurred during the experiment, providing an opportunity for us to explore the effects of drought and N addition on tree resistance and recovery. We found that N addition was beneficial for increasing the resistance of middle-aged trees, but had no effect on mature trees. The recovery of trees weakened significantly with increasing N addition, and the reduction in fine root biomass caused by multiyear N addition was a key influencing factor limiting recovery after moderate drought. Our study implies that the combined effect of increasing drought and N deposition might increase the risk of pine forest mortality in northern China.

Decline in stability of forest productivity in the tropics as determined by canopy water content.

The impacts of low soil moisture (SM) and high vapour pressure deficit (VPD) on tree's photosynthesis and productivity are ultimately realized by changing water content in the canopy leaves. In this study, variations in canopy water content (CWC) that can be detected from microwave remotely sensed vegetation optical depth (VOD) have been proposed as a promising measure of vegetation water status, and we first reported that the regulation of CWC on productivity stability is universally applicable for global forests. Results of structural equation model (SEM) also confirmed the significant negative effect of CWC on coefficient of variation (CV) of productivity, indicating that the decrease in CWC could inevitably induce the instability of forest productivity under climate change. The most significant decrease (p < 0.01) of CWC is observed primarily in evergreen broadleaf forest in the tropics, implying an increasing instability of the most important carbon sink in terrestrial ecosystem.

A process-based model reveals the restoration gap of degraded grasslands in Inner Mongolian steppe.

Due to the influence of climate change and extensive grazing, a large proportion of steppe grassland has been degraded worldwide. The Chinese government initiated a series of grassland restoration programs to reverse the degradation. However, the limiting factors and the restoration potential remain unknown. Here we present a process-based model to assess the restoration gap (RG) defined as maximum biomass differences between non-degraded and degraded grasslands with different degrees of soil and vegetation degradation. The process-based model Agricultural Production Systems Simulator (APSIM) was evaluated utilizing observation data from both typical and meadow steppes under natural conditions in terms of phenology, dynamics of above-ground biomass and soil water content. Scenario analysis and sensitivity analysis were subsequently performed to address the RG and controlling factors during 1969-2018. The results showed that the calibrated model performed well with r > 0.75 and model efficiency factor EF > 0.5 for all the simulation components. According to our model results, the RG was larger in typical steppe compared to that of meadow steppe and it increased with increasing soil and/or vegetation degradation, to ~60% under extremely degraded scenarios. Both soil and vegetation degradation led to reduced water use efficiency, with an elevated proportion of soil evaporation to evapotranspiration (Es/ET), however, the limiting factor for RG varied. The degradation of soil water holding capacity contributed more to RG regardless of climate conditions for typical steppe in all years and for meadow steppe in dry years. In wet years the importance of vegetation coverage reduction increased for RG in meadow steppe, where the relative importance of vegetation coverage (valued at 62.8%) was 25.6% higher than that of soil degradation. Our results demonstrated the importance of considering climate variations when developing protection and restoration programs for grassland ecosystems.

Planted forest is catching up with natural forest in China in terms of carbon density and carbon storage.

Over the last several decades, China has taken multiple measures for afforestation and natural forest protection, including setting the goal of carbon neutrality by the middle of 21th century. In order to support the practice of relevant policies from the scientific perspective, it is essential to precisely estimate the carbon storage of arbor forest, as it plays an important role in the carbon cycle of ecosystems. In this study, we first used the latest four phases of national forest inventory data to investigate the variation of carbon storage for both natural and planted arbor forest in China during the covered period (1999-2018). Then we used machine leaning methods to simulate the carbon density based on various kinds of environmental factors and analyzed the contribution of each influencing factor. Our results demonstrate that the total carbon storage for arbor forest in China kept increasing over the last two decades, but this increment was mainly brought about by the continuous expansion of forest land. The gap of carbon sequestration between natural forest and planted forest showed a significant trend of reduction. Additionally, tree age was identified as the dominant factor for influencing the spatiotemporal variation of carbon density among all the independent variables while the impact of climatic factor was limited. Therefore, the future improvement of carbon sequestration of arbor forest in should mainly rely on additional projects of afforestation, reforestation, green space conservation and reduction of emissions in China. Conclusions of this study have important implications for policy makers and other stakeholders to evaluate the previous achievement of environmental projects and can also help to set future plans and finally realize the goals of carbon neutrality.

High forest stand density exacerbates growth decline of conifers driven by warming but not broad-leaved trees in temperate mixed forest in northeast Asia.

Increasing temperature over recent decades is expected to positively impact tree growth in humid regions. However, high stand density could increase the negative effects of warming-induced drought through inter-tree competition. How neighborhood competition impacts tree growth responding to climate change remains unclear. Here, we utilized the Changbai Mountain region in northeastern Asia as our study area. We quantified individual tree growth using tree-ring samples collected from three dominant tree species growing in three forest stand density levels. We estimated the effects of climate warming and forest stand density on growth processes and tested for a species-specific response to climate. Our results demonstrated that overall 25% of Korean pine, but only ~3% of Mongolian oak and ~ 4% of Manchurian ash experienced growth reduction. Increased forest density can also exacerbate growth reduction. We identified a climate turning point in 1984, where warming rapidly increased, and defined two groups, "enhance group" (EG) and "decline group" (DG), according to the individual tree growth trend after 1984. For the EG, climate warming increased temperature sensitivity, but the temperature sensitivity declined with increasing stand density for the whole study period. For the DG, tree growth sensitivity shifted from temperature to precipitation after 1984, driven by increased competition pressure under climate warming. Our study concludes that growth decline from warming-induced drought might be amplified by high forest stand density, was especially pronounced in conifer trees.

Modeling vegetation greenness and its climate sensitivity with deep-learning technology.

Climate sensitivity of vegetation has long been explored using statistical or process-based models. However, great uncertainties still remain due to the methodologies' deficiency in capturing the complex interactions between climate and vegetation. Here, we developed global gridded climate-vegetation models based on long short-term memory (LSTM) network, which is a powerful deep-learning algorithm for long-time series modeling, to achieve accurate vegetation monitoring and investigate the complex relationship between climate and vegetation. We selected the normalized difference vegetation index (NDVI) that represents vegetation greenness as model outputs. The climate data (monthly temperature and precipitation) were used as inputs. We trained the networks with data from 1982 to 2003, and the data from 2004 to 2015 were used to validate the models. Error analysis and sensitivity analysis were performed to assess the model errors and investigate the sensitivity of global vegetation to climate change. Results show that models based on deep learning are very effective in simulating and predicting the vegetation greenness dynamics. For models training, the root mean square error (RMSE) is <0.01. Model validation also assure the accuracy of our models. Furthermore, sensitivity analysis of models revealed a spatial pattern of global vegetation to climate, which provides us a new way to investigate the climate sensitivity of vegetation. Our study suggests that it is a good way to integrate deep-learning method to monitor the vegetation change under global change. In the future, we can explore more complex climatic and ecological systems with deep learning and coupling with certain physical process to better understand the nature.

Examining land surface phenology in the tropical moist forest eco-zone of South America.

Using leaf area index (LAI) data from 1981 to 2014 in the tropical moist forest eco-zone of South America, we extracted start (SOS) and end (EOS) dates of the active growing season in forest and savanna at each pixel. Then, we detected spatiotemporal characteristics of SOS and EOS in the two vegetation types. Moreover, we analyzed relationships between interannual variations of SOS/EOS and climatic factors, and simulated SOS/EOS time series based on preceding mean air temperature and accumulated rainfall. Results show that mean SOS and EOS ranged from 260 to 330 day of year (DOY) and from 150 to 260 DOY across the study region, respectively. From 1981 to 2014, SOS advancement is more extensive than SOS delay, while EOS advancement and delay are similarly extensive. For most pixels of forest and savanna in tropical moist forest eco-zone, preceding rainfall correlates predominantly negatively with SOS but positively with EOS, while the relationship between preceding temperature and phenophases is location-specific. In addition, preceding rainfall is more extensive than preceding temperature in simulating SOS, while both preceding rainfall and temperature play an important role for simulating EOS. This study highlights the reliability of using LAI data for long-term phenological analysis in the tropical moist forest eco-zone.

Temporal coherence of phenological and climatic rhythmicity in Beijing.

Using woody plant phenological data in the Beijing Botanical Garden from 1979 to 2013, we revealed three levels of phenology rhythms and examined their coherence with temperature rhythms. First, the sequential and correlative rhythm shows that occurrence dates of various phenological events obey a certain time sequence within a year and synchronously advance or postpone among years. The positive correlation between spring phenophase dates is much stronger than that between autumn phenophase dates and attenuates as the time interval between two spring phenophases increases. This phenological rhythm can be explained by positive correlation between above 0 °C mean temperatures corresponding to different phenophase dates. Second, the circannual rhythm indicates that recurrence interval of a phenophase in the same species in two adjacent years is about 365 days, which can be explained by the 365-day recurrence interval in the first and last dates of threshold temperatures. Moreover, an earlier phenophase date in the current year may lead to a later phenophase date in the next year through extending recurrence interval. Thus, the plant phenology sequential and correlative rhythm and circannual rhythm are interacted, which mirrors the interaction between seasonal variation and annual periodicity of temperature. Finally, the multi-year rhythm implies that phenophase dates display quasi-periodicity more than 1 year. The same 12-year periodicity in phenophase and threshold temperature dates confirmed temperature controls of the phenology multi-year rhythm. Our findings provide new perspectives for examining phenological response to climate change and developing comprehensive phenology models considering temporal coherence of phenological and climatic rhythmicity.