In recent years, invasive alien plants (IAPs) have caused serious ecological disasters and economic losses in China. This study combined three IAP species richness-related indices (species richness of IAPs, first records of IAPs, and the relative species richness of IAPs), as well as indices reflecting distribution and dispersal patterns (average similarity coefficient of IAPs) and invasiveness (average risk score of IAPs), to conduct an integrated regional-invasion risk assessment based on the principal component analysis (PCA) method. Partial least-squares (PLS) regression was conducted to explore the explanatory power of 12 environmental and anthropogenic factors on different invasion indices. The results indicated that coastal provinces and Yunnan had high IAP introduction risk, as well as high synthetic-risk scores. The dispersal of IAPs in mid-latitude provinces should be particularly prevented. For species richness of IAPs, more environmental factors with variable importance for the project (VIP) values higher than 1 were retained in the optimal model, reflecting the importance of environmental filtering on IAPs. Visitors were the most important predictor for first records of IAPs. Compared to species richness (R² = 79.5%), first records were difficult to predict (R² = 60.4%) and were influenced by anthropogenic factors. There was spatial distribution congruence of various families of IAPs. Generally, the correlations of the residuals of species richness were still significant, with 0.421 (p < 0.05) as the lowest Pearson correlation coefficient, which indicated that external factors could not fully explain the spatial distribution congruence. These findings could enrich the relevant research on IAP invasion mechanisms and provide suggestions for regional IAP detection and response.

The biodiversity in mountainous ecosystems is high but is threatened by rapid environmental change. Urbanization and other anthropogenic factors in the mountains can affect land use and spatial fragmentation. Moreover, patterns of habitat are closely related to elevation and have a major effect on montane biodiversity. The aim of this study was to analyze the effects of spatial fragmentation on the vertical distribution pattern of bird diversity by characterizing the structure of the bird community, species diversity, and landscape factors at different altitudes. From 2016 to 2019, this study made a four years of continuous monitoring of the breeding birds. The result indicated that Mount Tai harbored a high bird diversity. Bird richness, abundance, and Shannon‐Wiener index decreased with latitude in Mount Tai monotonically. Moreover, the structure of bird communities varied along altitudinal gradients, and some special species were supported in different elevational bands due to the environmental filtering. Road density, number of habitat patches, patch density, and the percentage of forest were significantly related to bird diversity. Sufficient habitat and more patches in the low‐mountain belt supported higher bird diversity. The middle‐mountain belt and high‐mountain belt showed contrasting patterns. Our results highlight the effects of on‐going urbanization and human activities on montane biodiversity and emphasize the need for artificial habitats in the mountains to be managed.

The true frogs of the genus Rana are a complex and diverse group, containing approximately 60 species with wide distribution across Eurasia and the Americas. Recently, many new species have been discovered with the help of molecular markers and morphological traits. However, the evolutionary history in Rana was not well understood and might be limited by the absence of mitogenome information. In this study, we sequenced and annotated the complete mitochondrial genome of R. longicrus and R. zhenhaiensis, containing 22 tRNAs, 13 protein-coding genes, two ribosomal RNAs, and a non-coding region, with 17,502 bp and 18,006 bp in length, respectively. In 13 protein codon genes, the COI was the most conserved, and ATP8 had a fast rate of evolution. The Ka/Ks ratio analysis among Rana indicated the protein-coding genes were suffering purify selection. There were three kinds of gene arrangement patterns found. The mitochondrial gene arrangement was not related to species diversification, and several independent shifts happened in evolutionary history. Climate fluctuation and environmental change may have played an essential role in species diversification in Rana. This study provides mitochondrial genetic information, improving our understanding of mitogenomic structure and evolution, and recognizes the phylogenetic relationship and taxonomy among Rana.

Accurate estimation of forest aboveground biomass (AGB) and understanding its ecological drivers are vital for carbon monitoring and sustainable forest management. However, AGB estimation using remote sensing is hindered by signal saturation in high-biomass areas and insufficient attention to ecological structural factors. Focusing on Guangdong Province, this study proposes a novel approach that spatially extrapolates airborne LiDAR-derived Forest structural parameters and integrates them with Sentinel-1/2 data to construct an AGB prediction model. Results show that incorporating structural parameters significantly reduces saturation effects, improving prediction accuracy and AGB maximum range in high-AGB regions (R² from 0.724 to 0.811; RMSE = 10.64 Mg/ha; max AGB > 180 Mg/ha). Using multi-scale geographically weighted regression (MGWR), we further examined the spatial influence of forest type, age structure, and species mixture. Forest age showed a strong positive correlation with AGB in over 95% of the area, particularly in mountainous and hilly regions (coefficients up to 1.23). Species mixture had positive effects in 87.7% of the region, especially in the north and parts of the south. Natural forests consistently exhibited higher AGB than plantations, with differences amplifying at later successional stages. Highly mixed natural forests showed faster biomass accumulation and higher steady-state AGB, highlighting the regulatory role of structural complexity and successional maturity. This study not only mitigates remote sensing saturation issues but also deepens understanding of spatial and ecological drivers of AGB, offering theoretical and technical support for targeted carbon stock assessment and forest management strategies.

In recent years, increased species extinction and habitat loss have significantly reduced biodiversity, posing a serious threat to both nature and human survival. Environmental factors strongly influence bird distribution and diversity. The potential distribution patterns and species richness offer a conservation modeling framework for policymakers to assess the effectiveness of natural protected areas (PAs) and optimize their existing ones. Very few such studies have been published that cover a large and complete taxonomic group with fine resolution at regional scale. Here, using birds as a study group, the maximum entropy model (MaxEnt) was used to analyze the pattern of bird species richness in Jiangsu Province. Using an unparalleled amount of occurrence data, we created species distribution models (SDMs) for 312 bird species to explore emerging diversity patterns at a resolution of 1 km². The gradient of species richness is steep, decreasing sharply away from water bodies, particularly in the northern part of Jiangsu Province. The migratory status and feeding habits of birds also significantly influence the spatial distribution of avian species richness. This study reveals that the regions with high potential bird species richness are primarily distributed in three areas: the eastern coastal region, the surrounding area of the lower reaches of the Yangtze River, and the surrounding area of Taihu Lake. Compared with species richness hotspots and existing PAs, we found that the majority of hotspots are well‐protected. However, only a small portion of the regions, such as coastal areas of Sheyang County in Yancheng City, as well as some regions along the Yangtze River in Nanjing and Zhenjiang, currently have relatively weak protection. Using stacked SDMs, our study reveals effective insights into diversity patterns, directly informing conservation policies and contributing to macroecological research advancements.