Sirex nitobei, an Asian native wood wasp species, is a major pest in coniferous commercial forestry, infesting and weakening conifers through its obligate mutualism with a wood-rotting fungus species. The combination of wood wasp larvae and obligate mutualistic fungus causes the breakdown of plant vascular tissue, leading to the weakening of the plant and eventually to death, as well as a high economic cost in commercial forestry. Since it was first recorded in China in the early 1980s, S. nitobei has widely spread and become successfully established. Despite its extensive distribution range, little is known about the factors influencing current and future distribution patterns for potential pest control and monitoring. We used a maximum entropy model in conjunction with climate variables and shared socio-economic pathways to predict the current and future distribution of S. nitobei in China. We used the jackknife method and correlation analysis to select the bioclimatic and environmental variables that influence the geographic distribution of S. nitobei, which resulted in the inclusion of the monthly total precipitation in July (prec7), the monthly average maximum temperature in February (tmax2), the monthly average minimum temperature in July (tmin7), the monthly total precipitation in December (prec12), and isothermality (bio3). We found that precipitation and temperature influenced the potentially suitable areas, as predicted by the maximum entropy model. Moreover, the association of the fungus, the wood wasp, and the host plant impacts are related to availability of moisture and temperature, where moisture affects the growth of the fungus, and temperature influences the emergence, development and growth of larvae. Under the current climate conditions, the total potential suitable areas increased by 18.74%, while highly suitable and moderately suitable areas increased by 28.35 and 44.05%, respectively, under the 2081–2100 ssp245, 370 scenarios. Favorable conditions under climate change, low rainfall, and high temperature will favor the speedy larval development, the growth of its obligate nutritional fungal mutualist and the ability of S. nitobei to rapidly spread in previously unsuitable areas.

Glossina morsitans is a vector for Human African Trypanosomiasis (HAT), which is mainly distributed in sub-Saharan Africa at present. Our objective was to project the historical and future potentially suitable areas globally and explore the influence of climatic factors. The maximum entropy model (MaxEnt) was utilized to evaluate the contribution rates of bio-climatic factors and to project suitable habitats for G. morsitans. We found that Isothermality and Precipitation of Wettest Quarter contributed most to the distribution of G. morsitans. The predicted potentially suitable areas for G. morsitans under historical climate conditions would be 14.5 million km², including a large area of Africa which is near and below the equator, small equatorial regions of southern Asia, America, and Oceania. Under future climate conditions, the potentially suitable areas are expected to decline by about −5.38 ± 1.00% overall, under all shared socioeconomic pathways, compared with 1970–2000. The potentially suitable habitats of G. morsitans may not be limited to Africa. Necessary surveillance and preventive measures should be taken in high-risk regions.

Wood wasp species in the genus Sirex are known pests of forestry. They cause significant economic losses due to their impacts on plant health and wood quality. S. juvencus (Hymenoptera: Siricidae), widely distributed in Asia, Europe, and North America, is known to negatively impact forestry, infesting Picea, Pinus, Larix, Abies, Cupressus, and Pseudotsuga species. This pest destroys plants by depositing eggs, mucus, and its obligate mutualistic fungus, Amylostereum areolatum. Its obligate mutualistic fungus is to provide nutrition for S. juvencus larva. Despite its extensive distribution range, little is known about which environmental variables significantly impact current and future distribution patterns of S. juvencus for pest control and monitoring. Here we used the maximum entropy model in conjunction with occurrence points of S. juvencus and environmental variables to predict the current and future global potential distribution of S. juvencus. We used the jackknife method and Pearson’s correlation analysis to select the environmental variables that influence the geographic distribution of S. juvencus, which resulted in the inclusion of the monthly average maximum temperature in February, the max temperature of warmest month, monthly average minimum temperature in July, monthly total precipitation in June, precipitation of the driest month, monthly total precipitation in September, and the temperature annual range. Temperature and precipitation are mainly likely to drive the distribution enabled by its obligate mutualistic fungus and the potential to co-infect with other Sirex species. The high temperature and low humidity influence S. juvencus eggs and larvae directly and indirectly via fungus-growth, which enables the larvae to survive. Furthermore, S. juvencus may increase its distribution to moderately suitable areas due to competition or dependency on other Sirex species during the infestation. Under the future climatic conditions, the highly suitable area increased by 32.79%, while the moderately suitable area, low suitable area, and unsuitable area increased by 28.14%, 3.30%, and 2.15%. Under climate changes, S. juvencus may spread in previously unsuitable areas rapidly.

In this study, we predicted the global climatic suitability of Tuta absoluta, using maximum entropy (MaxEnt) modeling. We used species’ natural occurrence records in 1981–2024 and environmental variables in 1981–2010, reflecting near-current climate conditions, for modeling. The occurrence records used for modeling excluded the data from greenhouses and summer-only presence. The optimized MaxEnt models demonstrated an excellent predictive performance; Jaccard’s and Sørensen’s indices were greater than 0.8. Temperature, particularly the mean daily air temperature in February (tas2), was identified as the primary influencing factor. Projections based on five global climate models (GCMs) and four shared socioeconomic pathways (SSPs) indicated an increasing risk of T. absoluta expansion. Under SSP126, the lowest-risk period (2011–2040) exhibited a 7.08% increase in suitable areas, while SSP370 during the highest-risk period (2071–2100) projected an 18.13% increase relative to near-current conditions. Model outputs underestimated the pest’s actual distribution, underscoring its invasive potential. We recommend stringent quarantine measures, particularly for artificial facilities that support overwintering, to mitigate future invasions. These findings provide critical insights for policymakers and agricultural stakeholders to safeguard global tomato production against this invasive threat.

Obolodiplosis robiniae was discovered in Eurasia at the beginning of the 21st century. In this study, we explore the present and future (in the years 2050 and 2070) trends in the potential distribution of O. robiniae in Eurasia under diverse climate change scenarios based on a maximum entropy model. Our findings indicated that the current potential distribution area of O. robiniae is within the range of 21°34′ and 65°39′ N in the Eurasian continent. The primary factor controlling the distribution of O. robiniae is temperature. The highly and moderately suitable areas are mainly distributed in the semi-humid and semi-arid regions, which also happen to be the locations where the host black locust (Robinia pseudoacacia L.) grows at its fastest rate. The forecast of the potential distribution area of O. robiniae revealed that the species would benefit from global warming. The region suitable for the habitat of O. robiniae is characterized by a large-scale northward expansion trend and an increase in temperature. This information would help the forestry quarantine departments of Asian and European countries provide early warnings on the probable distribution areas of O. robiniae and provide a scientific basis for the prevention and control of O. robiniae spread and outbreaks.

Intensive ornamental planting is increasingly prevalent in urban green spaces, yet its effects on soil microbial community assembly and interaction networks remain poorly understood. Here, we examined shifts in soil properties, microbial diversity, community composition, and interaction networks across successive planting cycles. Bacterial alpha-diversity remained relatively stable, whereas fungal communities showed pronounced sensitivity to early planting stages. Beta-diversity analyses revealed that bacterial community composition was jointly influenced by planting stage and site type, while fungal communities were primarily structured by site characteristics. Co-occurrence network analysis revealed contrasting reassembly trajectories between microbial groups. Bacterial networks exhibited increasing complexity and modularity, indicating enhanced interaction intensity and competitive structuring under intensive management. In contrast, fungal networks displayed reduced connectivity but maintained or recovered modular organization, suggesting structural buffering. Notably, keystone taxa remained taxonomically conserved, indicating that network reorganization was driven by interaction rewiring rather than species turnover. We propose a dual-speed reassembly framework in which bacteria function as fast-responding components with dynamic interaction networks, whereas fungi act as slow-buffering, structurally persistent elements. This decoupling of short-term functional responsiveness and long-term stability provides new insights into how intensive management reshapes soil microbiomes in botanical garden ecosystems.