Dominant role of the non-forest woody vegetation in the post 2015/16 El Niño tropical carbon recovery.

The extreme dry and hot 2015/16 El Niño episode caused large losses in tropical live aboveground carbon (AGC) stocks. Followed by climatic conditions conducive to high vegetation productivity since 2016, tropical AGC are expected to recover from large losses during the El Niño episode; however, the recovery rate and its spatial distribution remain unknown. Here, we used low-frequency microwave satellite data to track AGC changes, and showed that tropical AGC stocks returned to pre-El Niño levels by the end of 2020, resulting in an AGC sink of 0.18 0.14 0.26 $$ {0.18}_{0.14}^{0.26} $$ Pg C year-1 during 2014-2020. This sink was dominated by strong AGC increases ( 0.61 0.49 0.84 $$ {0.61}_{0.49}^{0.84} $$ Pg C year-1) in non-forest woody vegetation during 2016-2020, compensating the forest AGC losses attributed to the El Niño event, forest loss, and degradation. Our findings highlight that non-forest woody vegetation is an increasingly important contributor to interannual to decadal variability in the global carbon cycle.

Site-specific apparent optimum air temperature for vegetation photosynthesis across the globe.

The apparent optimum air temperature for vegetation photosynthesis (Topt) is a key temperature parameter in terrestrial ecosystem models estimating daily photosynthesis or gross primary production (GPP, g C/m2/day). To date, most models use biome-specific Topt (Topt-biome) parameter values. Given vegetation acclimation and adaptation to local climate, site-specific Topt (Topt-site) is needed to reduce uncertainties in estimating daily GPP across the scales from site to region and the globe. Previous studies have demonstrated using the Enhanced Vegetation Index (EVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) images and daytime air temperature data to estimate the Topt-site at the eddy covariance tower sites. This study used MODIS-derived EVI and ERA5 climate data to estimate and generate global Topt-site data products from 2000 to 2019. The Topt-site of individual pixels within a biome has large variation, which clearly cannot be represented accurately by the widely used Topt-biome. Therefore, using this global dataset of Topt-site estimates might significantly affect GPP simulation in current ecosystem models.

How urban heat island magnifies hot day exposure: Global unevenness derived from differences in built landscape.

Urban heat-islands reportedly expose densely populated areas to higher temperatures. However, the magnitude of the impact of extra hot-day exposure (EHDE) and its association with the effects of urbanization on a global scale remain unclear. As local climate zones (LCZs) refine the impact of differences in urban built-type on heat-island effects, this study aimed to quantify the global EHDE caused by the urban heat-island effect based on LCZs and explored the joint impacts of low gross-domestic product and an increasing vulnerable-age population on EHDE. The results showed that EHDE accounted for 48.01 % of overall hot-day exposure. Additionally, despite a significant geographic differentiation among LCZ types with the highest EHDE intensity, they are almost typically building-intensive LCZs. Furthermore, our study revealed regional differences in the structure of the EHDE share in LCZs, which support the adoption of targeted EHDE mitigation strategies.

Correlative ecological niche model applications to predicting landscape-scale woody plant encroachment in Kansas tallgrass prairie systems.

Woody plant encroachment (WPE) in grassland ecosystems has been a pervasive process across the Great Plains, yet a predictive understanding of where it will occur has been elusive. As an exploration of tools of potential utility in this challenge, we mapped WPE processes over the years 2015-2021 in a set of 9 counties in central Kansas. We developed and tested two correlative models based on landscape features: one that assessed distribution of evergreen trees in 2015, and another that assessed areas of WPE in succeeding years. Both models were successful, being able to predict 2015 forest distributions and being able to predict WPE during 2015-2021, as functions of characteristics of landscapes. These simple, correlative models will certainly not be able to predict WPE processes globally, or even regionally, but provide first proof-of-concept explorations for the central Great Plains region.

Shared insights for heat health risk adaptation in metropolitan areas of developing countries.

Global warming has led to a surge in heat health risks (HHRs), the impacts of which are particularly pronounced in metropolitan areas of developing countries. In the current study, six metropolitan areas - Beijing, China; Cairo, Egypt; Jakarta, Indonesia; Mumbai, India; Rio de Janeiro, Brazil; and Tehran, Iran - were selected as the study area to further differentiate the built-up landscapes by utilizing the concept of local climate zones. Moreover, we assessed the similarities and differences in HHR associated with the landscape. Results revealed a 30.67% higher HHR in compact built-up landscapes than in the open built-up type. Urban green spaces played an effective but differentiated role in mitigating HHR. That is, low vegetation in urbanized areas and trees in suburban areas significantly mitigated HHR. Collectively, our findings emphasize the role of effective planning and management in addressing HHR and provide empirical support for implementing HHR mitigation and adaptation strategies.

A 10-m annual grazing intensity dataset in 2015-2021 for the largest temperate meadow steppe in China.

Mapping grazing intensity (GI) using satellites is crucial for developing adaptive utilization strategies according to grassland conditions. Here we developed a monitoring framework based on a paired sampling strategy and the classification probability of random forest algorithm to produce annual grazing probability (GP) and GI maps at 10-m spatial resolution from 2015 to 2021 for the largest temperate meadow in China (Hulun Buir grasslands), by harmonized Landsat 7/8 and Sentinel-2 images. The GP maps used values of 0-1 to present detailed grazing gradient information. To match widely used grazing gradients, annual GI maps with ungrazed, moderately grazed, and heavily grazed levels were generated from the GP dataset with a decision tree. The GI maps for 2015-2021 had an overall accuracy of more than 0.97 having significant correlations with the statistical data at city (r = 0.51) and county (r = 0.75) scales. They also effectively captured the GI gradients at site scale (r = 0.94). Our study proposed a monitoring approach and presented annual 10-m grazing information maps for sustainable grassland management.

Critical thresholds for nonlinear responses of ecosystem water use efficiency to drought.

Climate change is expected to lead to greater variability in precipitation and drought in different regions. However, the responses of ecosystem carbon and water cycles (i.e., water use efficiency, WUE) to different levels of drought stress are not fully understood. Here, we examined the relationship between WUE and precipitation anomalies and identified the critical drought threshold (DrCW) above which WUE showed substantial decrease. The results revealed that 85.56 % of the study area had nonlinear WUE responses to drought stress; that is, the WUE decreased sustainably and steeply when the precipitation deficit exceeded the DrCW. DrCW indicates inflection points for changing ecosystem responses from relatively resistant to vulnerable to drought stress, thus providing an instructive early warning for intensifying suppressive impacts on vegetation growth. Additionally, DrCW varies across aridity gradients and among vegetation types. Based on the DrCW at the pixel level, the future eco-drought is projected to increase in >67 % of the study area under both the SSP2&RCP4.5 and SSP5&RCP8.5 scenarios by the end of the 21st century. Our study elucidates the response of the ecosystem function to drought and supports the development of accurate ecosystem adaptation policies for future drought stress.

Comparison of venetoclax and ivosidenib/enasidenib for unfit newly diagnosed patients with acute myeloid leukemia and IDH1/2 mutation: a network meta-analysis.

Because of lacking of head-to-head comparison between venetoclax and IDH1/IDH2 inhibitors (ivosidenib/enasidenib) for newly diagnosed unfit patients with acute myeloid leukemia (AML), the optimal option for these patients still remains undefined. We searched relevant published reports. Three RCTs with 180 IDH1 mutant and 165 IDH2 mutant patients were identified. Indirect comparison of OS using fixed effects network meta-analysis (NMA) models indicated venetoclax plus azacitidine (Ven-Aza) significantly improved survival than enasidenib plus azacitidine (Ena-Aza) (HR:0.30, p = 0.005) for those newly diagnosed patients with AML and IDH2 Mutation. And, for those IDH2 mutation patients, Ven-Aza also had the highest probability of 98.3% (OS analysis) and 84.0% (CR/CRi analysis) to be the best intervention among these first-line treatment regimens (Ven-Aza, Ena-Aza and Aza). And, there was a favorable trend towards Ven-Aza in survival analysis (HR:0.69, p = 0.42), when compared to ivosidenib plus azacitidine (Ivo-Aza) for those newly diagnosed patients with AML and IDH1 Mutation. For those IDH1 Mutation, venetoclax plus azacitidine (Ven-Aza) had the highest probability of 65.8% (OS analysis) and 73.0% (CR/CRi analysis) to be the best intervention among these first-line treatment regimens (Ven-Aza, ivosidenib plus azacitidine (Ivo-Aza) and azacitidine (Aza)). In conclusion, venetoclax plus azacitidine could be a good option for unfit newly diagnosed patients with acute myeloid leukemia and IDH1/2 mutation. Considering our limits (only trial data-based network meta-analysis et al.), future trials directly comparing these regimens are warranted.

Land use conversion increases network complexity and stability of soil microbial communities in a temperate grassland.

Soils harbor highly diverse microbial communities that are critical to soil health, but agriculture has caused extensive land use conversion resulting in negative effects on critical ecosystem processes. However, the responses and adaptations of microbial communities to land use conversion have not yet been understood. Here, we examined the effects of land conversion for long-term crop use on the network complexity and stability of soil microbial communities over 19 months. Despite reduced microbial biodiversity in comparison with native tallgrass prairie, conventionally tilled (CT) cropland significantly increased network complexity such as connectivity, connectance, average clustering coefficient, relative modularity, and the number of species acting at network hubs and connectors as well as resulted in greater temporal variation of complexity indices. Molecular ecological networks under CT cropland became significantly more robust and less vulnerable, overall increasing network stability. The relationship between network complexity and stability was also substantially strengthened due to land use conversion. Lastly, CT cropland decreased the number of relationships between network structure and environmental properties instead being strongly correlated to management disturbances. These results indicate that agricultural disturbance generally increases the complexity and stability of species "interactions", possibly as a trade-off for biodiversity loss to support ecosystem function when faced with frequent agricultural disturbance.

Urbanization expands the fluctuating difference in gross primary productivity between urban and rural areas from 2000 to 2018 in China.

Urban and rural vegetation are affected by both climate change and human activities, but the role of urbanization in vegetation productivity is unclear given the dual impacts. Here, we delineated urban area (UA) and rural area (RA), quantified the relative impacts of climate change and human activities on gross primary production (GPP) in 34 major cities (MCs) in China from 2000 to 2018, and analyzed the intrinsic impacts of urbanization on GPP. First, we found that the total urban impervious surface coverage (ISC) of the 34 MCs increased by 13.25 % and the mean annual GPP increased by 211 gC m-2 during the study period. GPP increased significantly in urban core areas, but decreased significantly in urban expansion areas, which was mainly due to a large amount of vegetation loss due to land use conversion. Second, the variability of GPP in UA was generally lower than in RA. Both climate change and human activities had a positive impact on GPP in UA and RA in the 34 MCs, of which the contribution was 49 % and 51 % in UA, and 76 % and 24 % in RA, respectively. Third, under climate change and human activities, the increase in GPP offset 4.96 % and 12.35 % of the impact of land use conversion on GPP in 2000 and 2018, respectively, which indicated that the offset strengthened over time. These findings emphasize the role of human activities in promoting carbon sequestration in urban vegetation, which is crucial for better understanding the processes and mechanisms of urban carbon cycles. Decision-makers can manage urban vegetation based on vegetation carbon sequestration potential as regions urbanize, aiding comprehensive decision-making.

Ground-based remote sensing provides alternative to satellites for monitoring cyanobacteria in small lakes.

Cyanobacteria are the most prevalent bloom-forming harmful algae in freshwater systems around the world. Adequate sampling of affected systems is limited spatially, temporally, and fiscally. Remote sensing using space- or ground-based systems in large water bodies at spatial and temporal scales that are cost-prohibitive to standard water quality monitoring has proven to be useful in detecting and quantifying cyanobacterial harmful algal blooms. This study aimed to identify a regional 'universal' multispectral reflectance model that could be used for rapid, remote detection and quantification of cyanoHABs in small- to medium-sized productive reservoirs, such as those typical of Oklahoma, USA. We aimed to include these small waterbodies in our study as they are typically overlooked in larger, continental wide studies, yet are widely distributed and used for recreation and drinking water supply. We used Landsat satellite reflectance and in-situ pigment data spanning 16 years from 38 reservoirs in Oklahoma to construct empirical linear models for predicting concentrations of chlorophyll-a and phycocyanin, two key algal pigments commonly used for assessing total and cyanobacterial algal abundances, respectively. We also used ground-based hyperspectral reflectance and in-situ pigment data from seven reservoirs across five years in Oklahoma to build multispectral models predicting algal pigments from newly defined reflectance bands. Our Oklahoma-derived Landsat- and ground-based models outperformed established reflectance-pigment models on Oklahoma reservoirs. Importantly, our results demonstrate that ground-based multispectral models were far superior to Landsat-based models and the Cyanobacteria Index (CI) for detecting cyanoHABs in highly productive, small- to mid-sized reservoirs in Oklahoma, providing a valuable tool for water management and public health. While satellite-based remote sensing approaches have proven effective for relatively large systems, our novel results indicate that ground-based remote sensing may offer better cyanoHAB monitoring for small or highly dendritic turbid lakes, such as those throughout the southern Great Plains, and thus prove beneficial to efforts aimed at minimizing public health risks associated with cyanoHABs in supply and recreational waters.

Mapping global distribution of mangrove forests at 10-m resolution.

Mangrove forests deliver incredible ecosystem goods and services and are enormously relevant to sustainable living. An accurate assessment of the global status of mangrove forests warrants the necessity of datasets with sufficient information on spatial distributions and patch patterns. However, existing datasets were mostly derived from âˆ¼30 m resolution satellite imagery and used pixel-based image classification methods, which lacked spatial details and reasonable geo-information. Here, based on Sentinel-2 imagery, we created a global mangrove forest dataset at 10-m resolution, namely, High-resolution Global Mangrove Forests (HGMF_2020), using object-based image analysis and random forest classification. We then analyzed the status of global mangrove forests from the perspectives of conservation, threats, and resistance to ocean disasters. We concluded the following: (1) globally, there were 145,068 km2 mangrove forests in 2020, among which Asia contained the largest coverage (39.2%); at the country level, Indonesia had the largest amount of mangrove forests, followed by Brazil and Australia. (2) Mangrove forests in South Asia were estimated to be in the better status due to the higher proportion of conservation and larger individual patch size; in contrast, mangrove forests in East and Southeast Asia were facing intensive threats. (3) Nearly, 99% of mangrove forest areas had a patch width greater than 100 m, suggesting that nearly all mangrove forests were efficient in reducing coastal wave energy and impacts. This study reports an innovative and up-to-date dataset and comprehensive information on mangrove forests status to contribute to related research and policy implementation, especially for supporting sustainable development.

Diversified land conversion deepens understanding of impacts of rapid rubber plantation expansion on plant diversity in the tropics.

Understanding the status and changes of plant diversity in rubber (Hevea brasiliensis) plantations is essential for sustainable plantation management in the context of rapid rubber expansion in the tropics, but remains very limited at the continental scale. In this study, we investigated plant diversity from 10-meter quadrats in 240 different rubber plantations in the six countries of the Great Mekong Subregion (GMS)-where nearly half of the world's rubber plantations are located-and analyzed the influence of original land cover types and stand age on plant diversity using Landsat and Sentinel-2 satellite imagery since the late 1980s. The results indicate that the average plant species richness of rubber plantations is 28.69 ± 7.35 (1061 species in total, of which 11.22 % are invasive), approximating half the species richness of tropical forests but roughly double that of the intensively managed croplands. Time-series satellite imagery analysis revealed that rubber plantations were primarily established in place of cropland (RPC, 37.72 %), old rubber plantations (RPORP, 27.63 %), and tropical forests (RPTF, 24.12 %). Plant species richness in RPTF (34.02 ± 7.62) was significantly (p < 0.001) higher than that in RPORP (26.41 ± 7.02) and RPC (26.34 ± 5.37). More importantly, species richness can be maintained for the duration of the 30-year economic cycle, and the number of invasive species decreases as the stand ages. Given diverse land conversions and changes in stand age, the total loss of species richness due to rapid rubber expansion in the GMS was 7.29 %, which is far below the traditional estimates that only consider tropical forest conversion. In general, maintaining higher species richness at the earliest stages of cultivation has significant implications for biodiversity conservation in rubber plantations.

Anthropogenic activities dominated tropical forest carbon balance in two contrary ways over the Greater Mekong Subregion in the 21st century.

The tropical forest carbon (C) balance threatened by extensive socio-economic development in the Greater Mekong Subregion (GMS) in Asia is a notable data gap and remains contentious. Here we generated a long-term spatially quantified assessment of changes in forests and C stocks from 1999 to 2019 at a spatial resolution of 30 m, based on multiple streams of state-of-the-art high-resolution satellite imagery and in situ observations. Our results show that (i) about 0.54 million square kilometers (21.0% of the region) experienced forest cover transitions with a net increase in forest cover by 4.3% (0.11 million square kilometers, equivalent to 0.31 petagram of C [Pg C] stocks); (ii) forest losses mainly in Cambodia, Thailand, and in the south of Vietnam, were also counteracted by forest gains in China due mainly to afforestation; and (iii) at the national level during the study period an increase in both C stocks and C sequestration (net C gain of 0.087 Pg C) in China from new plantation, offset anthropogenetic emissions (net C loss of 0.074 Pg C) mainly in Cambodia and Thailand from deforestation. Political, social, and economic factors significantly influenced forest cover change and C sequestration in the GMS, positively in China while negatively in other countries, especially in Cambodia and Thailand. These findings have implications on national strategies for climate change mitigation and adaptation in other hotspots of tropical forests.

Variations of Urban Thermal Risk with Local Climate Zones.

Due to the differences in land cover and natural surroundings within cities, residents in various regions face different thermal risks. Therefore, this study combined multi-source data to analyze the relationship between urban heat risk and local climate zones (LCZ). We found that in downtown Shenyang, the building-type LCZ was mainly found in urban centers, while the natural- type LCZ was mainly found in suburbs. Heat risk was highest in urban centers, gradually decreasing along the suburban direction. The thermal risk indices of the building-type LCZs were significantly higher than those of the natural types. Among the building types of LCZs, LCZ 8 (open middle high-rise) had the highest average thermal risk index (0.48), followed by LCZ 3 (0.46). Among the natural types of LCZs, LCZ E (bare rock and paved) and LCZ F (bare soil and sand) had the highest thermal risk indices, reaching 0.31 and 0.29, respectively. This study evaluated the thermal risk of the Shenyang central urban area from the perspective of LCZs and combined it with high-resolution remote sensing data to provide a reference for thermal risk mitigation in future urban planning.

Regional thermal environment changes: Integration of satellite data and land use/land cover.

Land surface temperature (LST) is subject to location and environmental influences, which makes quantification difficult in terms of timeliness. Based on 10-d geostationary satellite LST TCI products, we quantitatively evaluated the thermal environment differentiation of various ground objects in North, South, and Northwest China from 2017 to 2021. We found that the thermal condition index (TCI) in Northwest China decreased, whereas it increased in North and South China. In contrast, Moran's I index increased in Northwest and South China, with strong spatial agglomeration. The TCI for artificial surfaces decreased from North (0.633) to Northwest (0.554) and South China (0.384). The bare land TCI was always the lowest among the land use/land cover (LULC) types in each region. Our results reflect the LULC thermal environment of China against the background of new urbanization and provide theoretical support for scientific planning to improve the ecological environment.

Spatiotemporal variability and key factors of evergreen forest encroachment in the southern Great Plains.

Woody plant encroachment has been long observed in the southern Great Plains (SGP) of the United States. However, our understanding of its spatiotemporal variability, which is the basis for informed and targeted management strategy, is still poor. This study investigates the encroachment of evergreen forest, which is the most important encroachment component in the SGP. A validated evergreen forest map of the SGP (30 m resolution, for the time period 2015 to 2017) from our previous study was utilized (referred to as evergreen_base). Sample plots of evergreen forest (as of 2017) were collected across the study area, based on which a threshold of winter season (January and February) mean normalized difference vegetation index (NDVIwinter) was derived for each of the 5 sub-regions, using Landsat 7 surface reflectance data from 2015 to 2017. Then a NDVIwinter layer was created for each year within the four time periods of 1985-1989, 1995-1999, 2005-2009, and 2015-2017, with winter season surface reflectance data from Landsat 4, 5, and 7. By applying the sub-region specific NDVIwinter thresholds to the annual NDVIwinter layers and the evergreen_base, a SGP evergreen forest map was generated for each of those years. The annual evergreen forest maps within each time period were composited into one. According to the resulting four composite evergreen forest maps, mean annual encroachment rate (km2/year) was calculated at sub-region and ecoregion scales, over each of the three temporal stages 1990-1999, 2000-2009, and 2010-2017, respectively. To understand the spatiotemporal variability of the encroachment, the encroachment rate at each temporal stage was related to the corresponding initial evergreen forest area, mean annual precipitation (MAP), and mean annual burned area (MABA) through linear regression and pairwise comparison. Results suggest that most of the ecoregions have seen a slowing trend of evergreen forest encroachment since 1990. The temporal trend of encroachment rate tends to be consistent with that of MAP, but opposite to that of MABA. The spatial variability of the encroachment rate among ecoregions can be largely (>68%) explained by initial evergreen forest area but shows no significant relationship with MAP or MABA. These findings provide pertinent guidance for the combat of woody plant encroachment in the SGP under the context of climate change.

Rapid and large changes in coastal wetland structure in China's four major river deltas.

Coastal wetlands provide essential ecosystem goods and services but are extremely vulnerable to sea-level rise, extreme climate, and human activities, especially the coastal wetlands in large river deltas, which are regarded as "natural recorders" of changes in estuarine environments. In addition to the area (loss or gain) and quality (degradation or improvement) of coastal wetlands, the information on coastal wetland structure (e.g., patch size and number) are also major metrics for coastal restoration and biodiversity protection, but remain very limited in China's four major river deltas. In this study, we quantified the spatial-temporal dynamics of total area (TA) and patch number (PN) of coastal wetlands with different sizes in the four deltas and the protected areas (PAs) and assessed the effects of major driving factors during 1984-2020. We also investigated the effectiveness of PAs through the comparison of TA and PN of coastal wetlands before and after the years in which PAs were listed as Ramsar Sites. We found both TA and PN experienced substantial losses in the Liaohe River Delta and Yellow River Delta but recent recoveries in the Yangtze River Delta. The coastal wetlands had a relatively stable and variable trend in TA but had a continually increasing trend in PN in the Pearl River Delta. Furthermore, reduced coastal reclamation, ecological restoration projects, and rapid expansion of invasive plants had great impacts on the coastal wetland structure in various ways. We also found that PAs were effective in halting the decreasing trends in coastal wetland areas and slowing the expansion of reclamation, but the success of PAs is being counteracted by soaring exotic plant invasions. Our findings provide vital information for the government and the public to address increasing challenges of coastal restoration, management, and sustainability in large river deltas.