Invasive alien plants (IAPs) present a severe threat to native ecosystems and biodiversity. Comprehending the potential distribution patterns of these plant invaders and their responses to climate change is essential. Parthenium hysterophorus, native to the Americas, has become an aggressively invasive species since its introduction to China in the 1930s. This study aims to collect and reconstruct the historical occurrence and invasion of P. hysterophorus. Using the optimal MaxEnt model, the potential geographical distributions of P. hysterophorus were predicted based on screened species occurrences and environmental variables under the current and three future scenarios in the 2030s, 2050s, and 2070s (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5), and the invasion risk of P. hysterophorus in Chinese cities, croplands, forests, and grasslands was assessed. The results show that: (1) The species initially invaded highly suitable areas and further spread to regions with non-analogous climate conditions. (2) Under the current climatic conditions, the overall potential distribution of P. hysterophorus is characterized by more in the southeast and less in the northwest. Climate variables, including mean annual temperature (bio1), precipitation in the wettest month (bio13), isothermality (bio3), and temperature seasonality (bio4), are the primary factors influencing its distribution. (3) The potential distribution of P. hysterophorus will expand further under future climate scenarios, particularly toward higher latitudes. (4) Forests and crop lands are the areas with the most serious potential invasion risk of P. hysterophorus. Therefore, we suggest that the government should strengthen the monitoring and management of P. hysterophorus to prevent its spread and protect agro-ecosystems and human habitats. Depending on the potential risk areas, measures such as quarantine, removal, and publicity should be taken to mitigate the threat of P. hysterophorus invasion and to raise awareness of P. hysterophorus invasion prevention.

The distribution of individuals of different species across different sampling units is typically non-random. This distributional non-independence can be interpreted and modelled as a correlated multivariate distribution. However, this correlation cannot be modelled using a totally independent and random distribution such as the Poisson distribution. In this study, we utilized the negative multinomial distribution to overcome the problem encountered by the commonly used Poisson distribution and used it to derive insight into the implications of field sampling for rare species’ distributions. Mathematically, we derived, from the negative multinomial distribution and sampling theory, contrasting relationships between sampling area, and the proportions of locally rare and regionally rare species in ecological assemblages presenting multi-species correlated distribution. With the suggested model, we explored the cross-scale relationships between the spatial extent, the population threshold for defining the rarity of species, and the multi-species correlated distribution pattern using data from two 50-ha tropical forest plots in Barro Colorado Island (Panama) and Heishiding Provincial Reserve (Guangdong Province, China). Notably, unseen species (species with zero abundance in the studied local sample) positively contributed to the distributional non-independence of species in a local sample. We empirically confirmed these findings using the plot data. These findings can help predict rare species–area relationships at various spatial scales, potentially informing biodiversity conservation and development of optimal field sampling strategies.

This research investigated the growth performance and understory dynamics of Chinese fir (Cunninghamia lanceolata) in two ecological plantations 10 km apart in Guangdong Province, China. One ecological plantation was a monoculture comprised solely of Chinese fir. In contrast, the other ecological plantation featured a mix of 30% Chinese fir and 22 other species, encompassing three exotic, five shade-tolerant, six shade-intolerant, and eleven neutral species. Both ecological plantations utilized 1-year-old seedlings planted at 2m × 2m spacing. After 16 years, soil analysis revealed no disparities between the two ecological plantations in total nitrogen, phosphorus, potassium, or pH. Chinese fir in the mixed ecological plantation displayed superior growth, with a 13% increase in diameter, an 18% increase in height, and a 33% increase in crown width compared to those in the monoculture. The leaf area index was higher, and canopy openness was lower in the mixed ecological plantation, indicating an improved canopy structure. Competition indices indicated reduced competition for diameter and increased competition for crown width in the mixed ecological plantation, with no significant difference in competition for height. The monoculture ecological plantation showed higher understory shrub diversity and richness, but no differences were observed in herb diversity and richness between the two ecological plantations. These findings underscore the advantages of mixed-species ecological plantation in enhancing the growth performance of Chinese fir while demonstrating differing effects on shrub and herbaceous understory layers.

Climate change is reshaping species distributions across karst landscapes, yet large-scale ecological restoration in southern China remains hampered by species and site mismatch, rooted in inadequate macro-scale assessment of climate–vegetation equilibrium. Existing species distribution model (SDM) studies in karst regions are dominated by single-species, locally calibrated analyses. Here we assembled occurrence records spanning the full Chinese range of 12 dominant tree species (1,376 records) and combined them with bioclimatic, soil, and topographic predictors. We built optimized MaxEnt models and projected suitable habitat for the northern Guangdong limestone region under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios for 2061–2080. We then integrated these projections into a bivariate spatial framework that couples current species richness with net richness change (ΔS) to delineate climate trajectory zones. Models performed well (AUC 0.907–0.984; TSS 0.749–0.933). Annual precipitation (BIO12), precipitation of the driest month (BIO14), and mean temperature of the warmest quarter (BIO10) were the most pervasive drivers across the assemblage. Total suitable area changed by less than 10% across all scenarios. Highly suitable habitat, however, often contracted even where total area held steady, identifying habitat-quality erosion rather than extent loss as the principal threat. Under SSP1-2.6 it shrank for 11 of 12 species, most in Toona sinensis (−88%) and Celtis sinensis (−85%), while Sinosideroxylon wightianum alone gained (up to +208%). Centroids of highly suitable habitat migrated 3.3–90.9 km in varying directions, most erratically under SSP1-2.6. Counterintuitively, the SSP1-2.6 scenario produced the most severe degradation. Its high-risk degradation zone covered 85.2% of the region, against 51.0% under SSP2-4.5 and 54.0% under SSP5-8.5. Critically, areas of highest current species richness coincided spatially with the largest climate-driven degradation risk. Within this high-richness core, the climatically stable zones of the limestone mountains of Yingde City and Yangshan County emerged as the most resilient afforestation targets. These findings provide a spatially explicit, climate-informed framework for tree species selection and afforestation site prioritization in the karst regions of southern China, directly supporting regional ecological restoration planning and long-term forest management under climate change.