Climate change, together with intensifying human activities, is reshaping global plant invasion dynamics and increasingly threatening ecosystem stability and biodiversity. Cockleburs are highly invasive weeds with strong ecological plasticity and dispersal capacity, causing widespread impacts on agricultural systems and native ecosystems. Here, we used the maximum entropy (MaxEnt) model to assess the current (2001–2020) and future (2021–2040, 2041–2060, and 2061–2080) potential distributions, key driving factors, and centroid shifts of four invasive cocklebur species—Cyclachaena xanthiifolia (=Iva xanthiifolia), Xanthium chinense, Xanthium italicum, and Xanthium spinosum—at the global scale under current climate conditions and three Shared Socioeconomic Pathway scenarios (SSP126, SSP245, and SSP585). Species occurrence records were integrated with climatic, topographic, and anthropogenic variables to project habitat suitability. Model performance was robust, with mean training and testing area under the receiver operating characteristic curve (AUC) values > 0.8 for all species and mean true skill statistic (TSS) values > 0.8 for three species (0.660 for Xanthium spinosum). Suitable habitats were jointly shaped by climatic and anthropogenic factors, although the dominant drivers differed among species. Cyclachaena xanthiifolia and Xanthium spinosum were primarily constrained by temperature and precipitation, whereas Xanthium italicum and Xanthium chinense were more strongly associated with human activity. At present, suitable habitat areas for Cyclachaena xanthiifolia, Xanthium chinense, Xanthium italicum, and Xanthium spinosum were 1196.92 × 10⁴, 358.76 × 10⁴, 888.34 × 10⁴, and 1985.14 × 10⁴ km², respectively. Future projections indicated overall contractions in suitable habitat, with pronounced interspecific variation. Xanthium chinense showed the largest mean decline (−161.23 × 10⁴ km² relative to the present), whereas Cyclachaena xanthiifolia experienced the smallest reduction (−53.15 × 10⁴ km² on average). Centroid analyses further suggested overall shifts toward higher latitudes and elevations under warming scenarios. Despite uncertainties related to climate scenario variability and assumptions inherent in species distribution modelling, these findings provide quantitative evidence to support global invasion risk assessment and climate-adaptive management of invasive cockleburs.

This study was conducted in the Xinjiang region, China, along the Sino-Kazakh border, an area recognized as high-risk for locust outbreaks and characterized by ongoing shifts in dominant pest species. This study systematically examined the structural characteristics of locust communities across different grassland types and identified the underlying environmental driving mechanisms. Field surveys were conducted to assess the diversity characteristics, density variations, and niche width of the locust communities across the different grassland types. The locust community in the mountain meadows had a significantly lower Shannon diversity index compared with the other grassland types (p < 0.05). Although the Simpson dominance index and Pielou evenness index were also the lowest in the mountain meadows, these differences were not statistically significant (p > 0.05). Permutational multivariate analysis of variance (PermANOVA) revealed highly significant differences in locust density among the grassland types (p = 0.001). Ecological niche analysis revealed stronger interspecific competition in the lowland meadow, and weaker competition in the temperate steppe-enriched deserts and temperate desert grasslands. Structural equation modeling and random forest analysis identified soil organic, plant total potassium, and soil pH as the key factors driving locust community structure across grassland types. This study clarifies the diversity patterns of locust communities in the Xinjiang region along the Sino-Kazakh border and provides empirical data to better understand locust community structure and distribution. It also offers a scientific basis for developing sustainable locust management strategies.

The Italian locust (Calliptamus italicus) is a major agricultural pest causing significant damage to crops and grasslands in arid and semi-arid regions. To investigate its response to climate change, we applied ensemble species distribution modeling combined with niche analysis to predict its current and future potential distributions, and assess climatic niche dynamics and potential transboundary spread risks. We found that key environmental drivers included altitude, temperature variables (bio10, bio9), precipitation variables (bio18, bio17), and human footprint. Suitable habitat of Calliptamus italicus currently accounts for approximately 9.65% of the study area; under various future climate scenarios, this proportion is projected to rise to between 10.12% and 11.93%. Niche dynamics analysis revealed strong climatic niche conservatism and consistently high niche overlap across scenarios, suggesting that future range expansion is more likely to occur through tracking climatically analogous habitats rather than niche shifts. These findings provide spatially explicit support for risk zoning, monitoring prioritization, and the development of early-warning and adaptive management strategies, particularly within a China–Kazakhstan transboundary monitoring and biosecurity framework.

Symbiotic bacteria contribute to insect physiology and environmental adaptation, yet their dynamics and potential functions in the major migratory pest Loxostege sticticalis remain poorly understood. We characterized bacterial community composition, diversity, and predicted functional potential across developmental stages, tissues and frass samples, and environmental conditions. In laboratory-reared insects, Enterococcus and Staphylococcus predominated from the second to fifth instars, whose predicted functions emphasized carbohydrate and nucleotide metabolism. Adults were instead dominated by Pseudoalteromonas and Asaia, with predicted profiles showing greater representation of lipid and amino acid metabolism and transport and catabolism. First-instar larvae exhibited an adult-like community structure. Among sample types, Enterococcus predominated in the midgut, whereas Staphylococcus was overrepresented in the epidermis and frass. Epidermal and frass communities emphasized cellular processes and genetic information processing, whereas midgut and other internal tissue communities showed greater predicted potential for membrane transport and metabolism. Wild-caught larvae harbored distinct communities consistently enriched in Stenotrophomonas and Ralstonia, with predicted profiles shifting toward carbohydrate, amino acid, and lipid metabolism and environmental adaptation in later instars. Overall, these findings demonstrate that the symbiotic bacterial communities of L. sticticalis vary markedly with development, tissue niche, and environment, providing a basis for further studies of host–microbe interactions.

Monthly dynamics of ground-dwelling beetles remain poorly resolved in agro-pastoral ecotones. From June to September 2025, beetles were sampled monthly with pitfall traps in 81 permanent 1 m × 1 m quadrats across nine plots representing temperate steppe, temperate desert steppe, and lowland meadow in the Tacheng Basin, Xinjiang, China. Specimens were identified to family, and analyses used family-level data. Assemblage composition varied significantly among months, whereas neither the overall habitat effect nor the habitat × month interaction was significant. Habitat × month interactions were significant for total beetle activity-density and for Tenebrionidae, Scarabaeidae, and Carabidae activity-density. Tenebrionidae dominated catches in July and August, whereas Scarabaeidae contributed the largest proportions in June and September. Significant within-month habitat contrasts in diversity metrics were detected only for Margalef richness in June. Neither distance class nor the distance class × month interaction was significantly associated with activity-density, diversity metrics, or assemblage composition. After conditioning on plot identity and month, measured vegetation and soil variables did not significantly explain residual assemblage variation. These findings highlight pronounced monthly turnover and the value of repeated sampling across months during the June–September period.

Locust outbreaks are major biological disturbances in grassland ecosystems of arid and semi-arid regions. Accurate identification of locust suitable habitats is important for regional monitoring and management. However, direct comparisons between multi-criteria analysis (MCA) and species distribution models (SDMs) under a unified framework remain limited. In this study, we compared these two approaches for dominant locust species in Xinjiang, China, including Calliptamus italicus, Gomphocerus sibiricus, and Locusta migratoria manilensis. We used the same environmental variables and occurrence records for all models. The MCA methods included the analytic hierarchy process (AHP), technique for order preference by similarity to ideal solution (TOPSIS), and ordered weighted averaging (OWA). The SDMs included the generalized linear model (GLM), maximum entropy model (MaxEnt), extreme gradient boosting (XGBoost), and an ensemble model. The results showed that SDMs had higher area under the receiver operating characteristic curve (AUC) and true skill statistic (TSS) values than MCA under the internal point-based evaluation framework, although both approaches effectively identified locust-suitable habitats. The two approaches also showed high spatial agreement in moderately and highly suitable habitats, with Jaccard indices of 0.88–0.92, and consistently identified the northern slopes of the Tianshan Mountains, the Ili River Valley, and the margins of the Junggar Basin as core suitable areas. These results indicate that the two approaches are complementary for locust monitoring and management.