Land use/cover change (LUCC) attributed to natural factors and human activities has led to the loss of ecosystem services, making the quantitative valuation of ecosystem services the scientific focal pointfor sustainable development. This study assessed changes in the ecosystem services values (ESVs) due to LUCC during the period 1990–2016 in the Kilombero Valley floodplain, located in southeastern Tanzania. Moderate resolution Landsat images from 1990, 2010 and 2016 were obtained and analyzed using a random forest (RF) algorithm for classification, and ArcGIS Desktop software (version 10.2, Esri, Redlands, CA, USA) for mapping to assess the LUCC. The ESVs were estimated based on the benefit transfer approach using adopted global value coefficients and modified local value coefficients. The results revealed that the aggregated ESVs of the forests, bushlands, wetlands, and water had decreased, consequently leading to a total loss of US$ 811.5 million (26.6%) in ESVs over the past 26 years when calculated with the modified local value coefficients to US$ 3000.7 million (42.3%) when calculated with global value coefficients. Moreover, the loss in the ESV was attributed to the decreased values of water regulation, climate regulation, erosion control, nutrient cyclying, habitat/refugia, and water supply, with the exception of the values of food production and biological control, which gradually increased during the study period. This study provided minimum estimates of the ecosystem service values, which willcontribute to the formulation of policy actions and strategies for sustainable management of the Kilombero Valley floodplain and inform various stakeholders on the tradeoffs involved in the use of land resources.

Soil microbial carbon use efficiency (CUE) refers to the efficiency of microorganisms in converting absorbed carbon into their own biomass carbon. Soil microbial CUE is a key parameter to understanding the soil carbon cycle. Biotic and abiotic factors are widely considered to be important factors influencing CUE. However, the related underlying mechanisms remain unclear. This review elaborates on the concept of soil microbial CUE and the various approaches used for its measurement. We reviewed the effects of various abiotic factors, such as temperature, soil moisture, pH, nutrient addition, and substrate type, and biotic factors, such as microbial community structure and diversity, on CUE. Finally, we discussed the focus areas that future studies need to further explore. We hope this review can provide a comprehensive understanding of the factors impacting soil microbial CUE, which is a fundamental step to improving soil carbon storage capacity.

Nowadays, huge volume of air quality data provides unprecedented opportunities for analyzing pollution. However, due to the high complexity, most traditional analytical methods focus on abstracting data, so these techniques discard the original structure and limit the understanding of the results. Visual analysis is a powerful technique for exploring unknown patterns since it retains the details of the original data and gives visual feedback to users. In this paper, we focus on air quality data and propose the AirInsight design, an interactive visual analytic system for recognizing, exploring, and summarizing regular patterns, as well as detecting, classifying, and interpreting abnormal cases. Based on the time-varying and multivariate features of air quality data, a dimension reduction method Composite Least Square Projection (CLSP) is proposed, which allows appreciating and interpreting the data patterns in the context of attributes. On the basis of the observed regular patterns, multiple abnormal cases are further detected, including the multivariate anomalies by the proposed Noise Hierarchical Clustering (NHC) method, abruptly changing timestamps by Time diversity (TD) indicator, and cities with unique patterns by the Geographical Surprise (GS) measure. Moreover, we combine TD and GS to group anomalies based on their underlying spatiotemporal correlations. AirInsight includes multiple coordinated views and rich interactive functions to provide contextual information from different aspects and facilitate a comprehensive understanding. In particular, a pair of glyphs are designed that provide a visual representation of the temporal variation in air quality conditions for a user-selected city. Experiments show that CLSP improves the accuracy of Least Square Projection (LSP) and that NHC has the ability to separate noises. Meanwhile, several case studies and task-based user evaluation demonstrate that our system is effective and practical for exploring and interpreting multivariate spatiotemporal patterns and anomalies in air quality data.

The management of stand structure and the protection of species diversity are crucial to forest ecosystem service functions. Changbai Mountain is one of the three major forest regions in Northeast China, and understanding the characteristics of stand structure and the allometric relationship between stand structure variables and the correlation between stand factors and species diversity is the basis for achieving effective forest management and ecological function improvement. In this study, the typical deciduous broad-leaved mixed forest and coniferous and broad-leaved mixed forest of the Longwan Nature Reserve in the Changbai Mountain were taken as the research objects, and the stand structure and diversity were investigated in detail. Allometric growth equations were established using the breast height diameter (DBH), tree height, and crown width of the main dominant species. Pearson correlation analysis was conducted on the stand structure and diversity of the forest community. The experimental results showed that young forests with small diameters and small individuals were the main part of the stands. Optimal allometric growth models of the main tree species (Acer mandshuricum, Ulmus davidiana var. japonica, Juglans mandshurica, Acer mono, and Tilia amurensis) were 0.807D^0.646, 0608D^0.381H^0.390, 0.502D^0.533H^0.295, 0.795D^0.540H^0.157, and 0.541D^0.484H^0.301, respectively, with R² values ranging between 0.6 and 0.8, indicating a good fit for the models. Furthermore, tree density, crown width, and DBH were the main factors affecting tree diversity. The research results will provide theoretical support for the efficient management of forest stand structure and diversity conservation in the Longwan Reserve.

Changes in vegetation, such as shrub encroachment in grassland and wetland ecosystems, significantly influence soil microbial communities and biogeochemical processes. However, the specific impact of shrub encroachment on peatland ecosystems remains poorly understood. This study used a “space-for-time” approach, collecting soil samples from three encroachment stages—uninvaded, shrub invasion, and shrub invasion expansion—at two depths (0–30 cm and 30–60 cm). Metagenomic sequencing was used to assess the microbial community composition and functional gene dynamics. Shrub encroachment significantly alters soil physicochemical properties, nutrient availability, and microbial communities. Alpha diversity of bacteria and fungi was influenced by shrub encroachment and depth, whereas beta diversity varied mainly with depth. Functional carbon fixation genes ( korA and pps ) increased during shrub encroachment, while methane oxidation ( hdrA2 ) and carbon degradation genes ( GH31 and GH51 ) decreased before increasing. In addition, functional genes linked to nitrogen cycling ( nifD , nifH , amoA , and amoC ) declined, indicating a reduction in nitrogen fixation and nitrification pathways. Correlation and Mantel tests revealed that the total soil carbon content was the primary driver of these functional changes. These findings highlight the dynamic microbial responses to shrub encroachment and offer insights into the soil carbon and nitrogen-cycling mechanisms in peatlands. IMPORTANCE: Shrub encroachment is transforming peatlands and altering their ecological and biogeochemical functions. This study provides critical insights into how shrub invasion affects microbial communities and functional genes responsible for carbon and nitrogen cycling in peatland soils. By revealing the underlying genetic mechanisms, this study enhances our understanding of the consequences of vegetation shifts on ecosystem processes. These findings are essential for predicting and managing peatland responses to environmental changes, helping to preserve their role as vital carbon and nutrient reservoirs.