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Species diversity at different altitudes exhibits different variation patterns. In alpine ecosystems, species migration from low to high altitudes has changed the intensity of interspecific competition and association, inevitably adjusting or changing the existing prevention and control objects and strategies. In this context, selecting a typical area to probe the variations in species diversity and interspecific competition and association is imperative. Thus, this study investigated the diversity of locusts and grasshoppers at 400 m intervals in the grasslands at different altitudes in the Ili River basin of China. The responses of locusts (or grasshoppers) diversity, ecological niche, and interspecific association to altitudinal variations were comprehensively analyzed using univariate linear regression and redundancy analysis. The results showed that the diversity of locusts and grasshoppers declined with rising altitude. Average vegetation height and wind velocity significantly affected the diversity of locusts and grasshoppers at altitudes of 1,000–1,400 and 1,400–1,800 m, respectively. The vegetation dominance, aspect, and relative humidity significantly affected the diversity of locusts and grasshoppers at 1,800–2,200 m. Nonetheless, no ecological factor significantly affected the diversity of locusts and grasshoppers at ≤ 1,000 m. Before 1,400 m, the rise in altitude intensified the interspecific competition and tightened interspecific relationships. From 1,400 m, the interspecific competition was weakened, and interspecific relationships were loosened. Calliptamus italicus, Oedaleus decorus, and Calliptamus barbarus were the dominant locust taxa in the Ili River basin, with C. italicus and O. decorus being distributed at multiple altitudes, especially within ≤ 1,800 m. The high population density of C. italicus and O. decorus within this range required intensive monitoring and control. By contrast, Egnatius apicalis and Conophyma sp. were sparsely distributed within the > 1,400 m range, posing challenges in collection of locusts and grasshoppers. Therefore, protective measures should be taken to increase insect diversity in grasslands.

Background: The composition of the gut microbiome has been recorted to be strongly associated with gestational diabetes mellitus (GDM), but mutational characterization of the microbiome in patients with GDM has been overlooked. Here, we revealed the genetic variation landscape of the gut microbiome and assessed its clinical significance in a cohort of patients with GDM. Methods: We employed a macrogenomic dataset made up of a discovery cohort of 54 cases and a validation cohort of 220 cases to screen for high-abundance microbial flora and identified single nucleotide variants (SNVs) and insertions/deletions (indels). Subsequently, we analyzed the mutation spectra of genomes of the intestinal flora by using the previously identified SNVs and identified mutation signatures. Additionally, we utilized the Random Forest algorithm to identify key differential SNVs and elucidated their biological functions and associations with the clinicopathological parameters of GDM. Results: We screened 15 key microbial flora and found that the GDM group had more SNVs and indels in the intestinal flora than the control group, with a significant increase in C > T and T > C base mutations and were more susceptible to sequence mutations. Compared to the control group, the GDM group underwent a more significant evolution, as evidenced by the presence of a unique mutational spectrum and mutational characteristics. Random Forest algorithm analysis showed that the combined characterization of five gut microbial species and 21 SNV-related markers was effective in distinguishing between GDM and control subjects in both discovery (area under the curve (AUC) = 0.86) and validation (AUC = 0.73) sets. These markers also revealed that GDM is strongly associated with sphingolipids, galactose, and proteins containing the DUF structural domain. Conclusions: The GDM intestinal flora has unique mutational features that correlate significantly with clinicopathological involvement and may be involved in the development of the disease.

Vegetation Optical Depth (VOD), a vegetation parameter that quantifies the extinction effect of microwaves penetrating the canopy, plays a crucial role in global-scale biomass monitoring and climate change research. However, the spatial gridding of existing long-term VOD products is relatively coarse (approximately 25 km), with restrictions on their application at a regional scale. High-resolution active-microwave proxies and optical vegetation indices can potentially be used to disaggregate coarse-resolution VOD, but it is unclear which proxy is optimal. In this paper, the Normalized Difference Vegetation Index (NDVI) and active-microwave proxies (VH, VV, and cross-polarization ratio CR) from Sentinel-1 were quantitatively assessed with VOD products at various frequencies (L-/C-/X-VOD) across the contiguous United States (U.S.). The results showed that VH (R = 0.80) and NDVI (R = 0.77) exhibit a high spatial correlation with L-VOD products. For temporal correlation, NDVI had the highest overall performances with all VOD products, but good correlations were also achieved with CR and, to a lesser extent, VH. Further comparisons of the performance between Brightness Temperature (TB) and VOD revealed that while TB displayed a strong temporal correlation with active-microwave proxies, its spatial correlations with such proxies were low. In contrast, VOD had good correlations both temporally and spatially with active-microwave proxies (e.g., VH). These evidences suggested that the downscaling of VOD using the combination of VH and other proxies could be an alternative promising method to estimate high-resolution VOD.

North American boreal forests are known to be an important carbon pool in boreal ecosystems, but have experienced extensive tree mortality and carbon loss due to multiple agents of stand-replacing disturbances in recent decades. However, the impacts of these stand-replacing disturbances on forest dynamics are still unknown. We used a recently developed remote-sensing based stand-replacing disturbance product, coupled with aboveground biomass (AGB), gross primary productivity (GPP) and leaf area index (LAI) datasets to estimate the impacts of stand-replacing disturbances (e.g., fires, logging and insect outbreaks) on the carbon balance of western North American boreal forests during 2000–2012. Our results showed that fire, logging and insect outbreaks resulted in AGB losses of 23.4, 16.6, and 4.7 Tg/yr, respectively. In the post-disturbance periods, AGB did not recover to its pre-disturbed levels in the 10th year, which is longer than the recovery time of GPP and LAI. Furthermore, the losses of AGB, GPP and LAI in fire events were the dominant factors for forest recovery after stand-replacing fire. Vapor Pressure Deficit (VPD), soil clay content, temperature and precipitation were the important factors for forest recovery after stand-replacing insect outbreaks and stand-replacing logging. When removing the impact of environmental factors, our results showed a smaller magnitude of AGB, GPP and LAI loss relative to the results including these factors, although similar recovery trajectories were observed among the two results. The results have important implications for understanding the effects of stand-replacing disturbances on the carbon dynamics of boreal forests, which is required to adopt effective forest management strategies after disturbance.

Vegetation optical depth (VOD) retrieved from microwave remote sensing techniques has been used as an important proxy for monitoring the vegetation dynamics at large scales. In a first study we developed a new X-VOD indice from inversion of the X-MEB model from the Microwave Scanning Radiometer 2 (AMSR2, 2012-present) observations. The Advanced Microwave Scanning Radiometer for EOS (AMSR-E, 2002–2011) and AMSR2 share many characteristics, such as local crossing time, incidence angle, frequency at X-band, etc, which makes it possible to produce a consistent long-term X-VOD product over a long period spanning from 2002 to present. The objective of this study was to merge the AMSR-E and AMSR2 observations in order, in a second step, to produce a consistent long-term X-VOD product spanning over the combined periods of AMSR-E and AMSR2.The main challenges in retrieving a consistent X-VOD data set from AMSR-E and AMSR2 are that (i) there is a bias between observations from the two sensors; and (ii) the lack of overlapping observations between the two sensors makes it impossible to achieve a direct inter-calibration. Here, to overcome this problem, we used AMSR-E slow rotation data (AMSR-E L1S), which has similar characteristics as AMSR-E and provided observations concurrently with AMSR2, as a bridge to calibrate the AMSR-E brightness temperature (TB) observations with AMSR2 TB.As our main objective in this TB calibration study was to produce a consistent long-term AMSR-E/AMSR2 X-VOD product, we evaluated the retrieved X-VOD product based on different inter-calibration, either global-based or pixel-based, methods. Both AMSR-E and AMSR2 X-VOD were evaluated against the Aboveground Biomass (AGB), Leaf Area Index (LAI) and the Normalized Difference Vegetation Index (NDVI). The results suggest that global-based inter-calibration methods using homogeneous and temporally stable land covers (Evergreen Broadleaf Forests and Snow and Ice) as reference calibration data sets provided the best results. For instance, the spatial relationships between X-VOD and AGB/LAI/NDVI are highly consistent over the AMSR-E and AMSR2 periods after the calibration work. This study laid a solid foundation for monitoring the dynamics of X-VOD, as a proxy of AGB, over the combined periods of AMSR-E and AMSR2 sensors (almost 20 years).