Evaluation of tourism competitiveness is useful for measuring the level of regional tourism development. It is of great importance to understand the advantages and disadvantages of tourism development correctly and formulate corresponding development strategies. To investigate tourism competitiveness, this paper established an evaluation index system, including tourism development competitiveness, tourism resource competitiveness, and tourism-support competitiveness, for 14 prefectures and cities in Xinjiang in China. The characteristics and laws of spatial differentiation were analyzed. Factor analysis was applied to examine the spatial differentiation of regional tourism competitiveness. The results showed an obvious spatial differentiation in tourism competitiveness among the 14 prefectures and cities. In terms of development competitiveness, Yili and Urumqi constituted the spatial center, followed by Changji, Altay, and Ba Prefecture. As the provincial capital, Urumqi has political, economic, cultural, transportation, and geographic advantages, but its competitiveness is not prominent in terms of monopoly and efficiency. In terms of resource competitiveness, Yili is the core attraction, while Urumqi, Kashgar, Altay, and Ba Prefecture are dominant attractions. With respect to supporting competitiveness, Bo Prefecture has high value, followed by Urumqi City and Aksu. Hetian and Ke Prefecture have the lowest values. The comprehensive competitiveness of tourism is centered on Yili. Urumqi and Bo Prefecture are subcenters, and Changji, Altay, Ba Prefecture, Aksu, and Kashgar are characterized as multi-polar competition areas. Using the KMO and Bartlett’s sphericity tests, the cumulative contribution variance of the eigenvalues of the eight factors extracted by the maximum variance rotation method was found to be 92.714%. Socio-economic conditions, tourism resources, infrastructure construction, regional cultural influence, ecological environment carrying capacity, tertiary industry development, tourism service level, and living security system are the main driving factors affecting the spatial differentiation of tourism competitiveness in Xinjiang. Analyzing the spatial evolution characteristics and the driving factors of the regional tourism competitiveness in Xinjiang, this paper seeks to promote the optimal allocation of tourism production factors in the macro regional system, and provide theoretical guidance and an empirical basis for the comprehensive and harmonic development of regional tourism.

Background: A wetland is a special ecosystem formed by the interaction of land and water. The moisture content variation will greatly affect the function and structure of the wetland internal system. Method: In this paper, three kinds of wetlands with different flooding levels (Phragmites australis wetland (long-term flooding), Calamagrostis epigeios wetland(seasonal flooding) and Ditch millet wetland (rarely flooded)) in Ili Valley of Xinjiang China were selected as research areas. The changes of microbial biomass carbon, soil physical and chemical properties in wetlands were compared, and redundancy analysis was used to analyze the correlation between soil physical and chemical properties, microbial biomass carbon and enzyme activities (soil sucrase, catalase, amylase and urease). The differences of soil enzyme activities and its influencing factors under different flooding conditions in Ili Valley were studied and discussed. Result: The results of this study were the following: (1) The activities of sucrase and amylase in rarely flooded wetlands and seasonally flooded wetlands were significantly higher than those in long-term flooded wetlands; the difference of catalase activity in seasonal flooded wetland was significant and the highest. (2) Redundancy analysis showed that soil organic carbon, dissolved organic carbon, total phosphorus and soil microbial biomass carbon had significant effects on soil enzyme activity (p < 0.05). (3) The correlation between soil organic carbon and the sucrase activity, total phosphorus and the catalase activity was the strongest; while soil organic carbon has a significant positive correlation with invertase, urease and amylase activity, with a slight influence on catalase activity. The results of this study showed that the content of organic carbon, total phosphorus and other soil fertility factors in the soil would be increased and the enzyme activity would be enhanced if the flooding degree was changed properly.

Soil salinity is one of the major factors causing land degradation and desertification on earth, especially its important damage to farming activities and land-use management in arid and semiarid regions. The salt-affected land is predominant in the Keriya River area of Northwestern China. Then, there is an urgent need for rapid, accurate, and economical monitoring in the salt-affected land. In this study, we used the electrical conductivity (EC) of 353 ground-truth measurements and predictive capability parameters of WorldView-2 (WV-2), such as satellite band reflectance and newly optimum spectral indices (OSI) based on two dimensional and three-dimensional data. The features of spectral bands were extracted and tested, and different new OSI and soil salinity indices using reflectance of wavebands were built, in which spectral data was pre-processed (based on First Derivative (R-FD), Second Derivative (R-SD), Square data (R-SQ), Reciprocal inverse (1/R), and Reciprocal First Derivative (1/R-FD)), utilizing the partial least-squares regression (PLSR) method to construct estimation models and mapping the regional soil-affected land. The results of this study are the following: (a) the new OSI had a higher relevance to EC than one-dimensional data, and (b) the cross-validation of established PLSR models indicated that the β-PLSR model based on the optimal three-band index with different process algorithm performed the best result with R²_V = 0.79, Root Mean Square Errors (RMSE_V) = 1.51 dS·m⁻¹, and Relative Percent Deviation (RPD) = 2.01 and was used to map the soil salinity over the study site. The results of the study will be helpful for the study of salt-affected land monitoring and evaluation in similar environmental conditions.

Macroinvertebrates are sensitive to environmental disturbances, however, the effects of human activities on macroinvertebrate community structures and functional traits in glacier-fed streams are concerning. To elucidate the effects of horse, cattle and sheep grazing, hot spring scenic development, and historic iron ore mine development on macroinvertebrate communities, we conducted a study in three glacier-fed streams of the Tianshan Mountains in northwest China in April 2021. Our results showed that the species richness and density significantly decreased due to grazing (p < 0.05). There were more taxa with resilience traits such as “small size at maturity” in the grazing stream. The EPT richness and density affected by hot spring scenic development significantly decreased compared to the undisturbed point (p < 0.05). There was a significant increase in taxa with resistance traits such as “bi-or-multivoltine” in the hot spring stream. The stream affected by historic mine development is currently in the self-recovery stage following the closure of the mine ten years ago. Additionally, the species richness, EPT richness, and density at the mining site were significantly higher than the source site (p < 0.05), reflecting that the habitat fragmentation caused by previous mining activities prevented the upward dispersal of macroinvertebrates. The taxa in the mining stream were also characterized by higher resistance traits such as “abundant occurrence in drift”. These results were attributed to the impacts of human disturbance on habitat stability, habitat heterogeneity, water quality, and material cycling of stream ecosystems, indicating human disturbance on the efficiency of resource utilization and functional diversification. In addition, our results showed that functional indicators of macroinvertebrate communities are helpful for monitoring and evaluating habitat conditions.

Background: Sophora alopecuroides L. (S. alopecuroides L.) is a perennial herb widely distributed throughout Xinjiang, China. It is characterized by its rapid diffusion ability. Methods: To reveal the ecological mechanism of the rapid spread of S. alopecuroides, and to elucidate the ecological stoichiometric characteristics of C, N, and P (and the influencing factors) in the leaves of S. alopecuroides, leaves were sampled from four habitats—forest, roadside, farmland, and desert—across the Yili River Valley. The variation rules of the ecological stoichiometric characteristics of C, N, and P in the leaves of S. alopecuroides were analyzed. Correlations between the ecological stoichiometric characteristics of leaves and environmental factors were examined using redundancy analysis (RDA). Results: (1) The C, N, and P contents of S. alopecuroides leaves were 391.30–533.10 g/kg, 8.90–43.14 g/kg, and 0.71–2.04 g/kg, respectively, and the C/N, C/P, and N/P ratios were 10.34–4.94, 209.05–698.73, and 10.78–31.43 respectively. (2) The C content and C/P ratio of S. alopecuroides leaves were the highest in the desert habitat, leaf N content and N/P ratio were the highest in the forest habitat, leaf P content was the highest in the farmland habitat, and the leaf C/N ratio was the highest in the roadside habitat. (3) RDA showed that available potassium (AK) and pH were the main factors affecting the ecological stoichiometric characteristics of S. alopecuroides leaves in Yili Valley (p ≤ 0.05), and these factors were positively correlated with C, N, P, and N/P, and negatively correlated with C/P and C/N. AK was the dominant factor that affected the P content of S. alopecuroides leaves, and appropriate reduction of K fertilizer would be conducive to restraining the spread of S. alopecuroides. Soil C, N, P, and K content, soil organic matter (OM), nitrate nitrogen (NO₃⁻-N), ammonium nitrogen (NH₄⁺-N), and AK had no significant effect on the ecological stoichiometric characteristics of leaves (p > 0.05).

Chlorophyll is an essential pigment for photosynthesis in tea plants, and fluctuations in its content directly impact the growth and developmental processes of tea trees, thereby influencing the final quality of the tea. Therefore, achieving rapid and non-destructive real-time monitoring of leaf chlorophyll content (LCC) is beneficial for precise management in tea plantations. In this study, derivative transformations were first applied to preprocess the tea hyperspectral data, followed by the use of the Stable Competitive Adaptive Reweighted Sampling (SCARS) algorithm for feature variable selection. Finally, multiple individual machine learning models and stacking models were constructed to estimate tea LCC based on hyperspectral data, with a particular emphasis on analyzing how the selection of base models and meta-models affects the predictive performance of the stacking models. The results indicate that derivative processing enhances the sensitivity of hyperspectral data to tea LCC; furthermore, compared with individual machine learning models, the stacking models demonstrate superior predictive accuracy and generalization ability. Among the 17 constructed stacking configurations, when the meta-model is fixed, the predictive performance of the stacking model improves continuously with an increase in the number and accuracy of the base models and with a decrease in the structural similarity among the selected base models. Therefore, when constructing stacking models, the base model combination should comprise various models with minimal structural similarity while ensuring robust predictive performance, and the meta-model should be chosen as a simple linear or nonlinear model.