Habitat loss and fragmentation are widely acknowledged as the main driver of the decline of giant panda populations. The Chinese government has made great efforts to protect this charming species and has made remarkable achievements, such as population growth and habitat expansion. However, habitat fragmentation has not been reversed. Protecting giant pandas in a large spatial extent needs to identify core habitat patches and corridors connecting them. This study used an equal‐sampling multiscale random forest habitat model to predict a habitat suitability map for the giant panda. Then, we applied the resistant kernel method and factorial least‐cost path analysis to identify core habitats connected by panda dispersal and corridors among panda occurrences, respectively. Finally, we evaluated the effectiveness of current protected areas in representing core habitats and corridors. Our results showed high scale dependence of giant panda habitat selection. Giant pandas strongly respond to bamboo percentage and elevation at a relatively fine scale (1 km), whereas they respond to anthropogenic factors at a coarse scale (≥2 km). Dispersal ability has significant effects on core habitats extent and population fragmentation evaluation. Under medium and high dispersal ability scenarios (12,000 and 20,000 cost units), most giant panda habitats in the Qionglai mountain are predicted to be well connected by dispersal. The proportion of core habitats covered by protected areas varied between 38% and 43% under different dispersal ability scenarios, highlighting significant gaps in the protected area network. Similarly, only 43% of corridors that connect giant panda occurrences were protected. Our results can provide crucial information for conservation managers to develop wise strategies to safeguard the long‐term viability of the giant panda population.

A new species, named Primula xinjingensis , has been described from north-eastern Guizhou, China. It belongs to the sect. Monocarpicae. The species resembles P. pellucida in having efarinose plants, the shape of the leaf blade, and recurved calyx lobes in fruit. However, it differs from P. pellucida by possessing a campanulate calyx, lanceolate calyx lobes, narrowly cuneate-obovate corolla limb lobes, and a style reaching the throat in the pin flowers. It also shares similarities with P. divaricata and P. epilithica in terms of efarinose plants and the shape of the leaf blade. Nevertheless, it is distinct from these two species by the absence of rhizomes, having recurved calyx lobes in fruit, lanceolate calyx lobes, and narrowly cuneate-obovate corolla limb lobes. The new taxon has been assessed as “Data Deficient” (DD) according to the IUCN standards.

Global climate change and land use change have led to substantial range contractions and shifts. Even more dramatic changes are projected for the future. The Lady Amherst's pheasant is a typical ground‐dwelling bird primarily distributed in China, with very limited flight capabilities, making it more vulnerable to rapid environmental changes. We used a multi‐scale ensemble species distribution modeling approach to model the habitat suitability of the Lady Amherst's pheasant and projected the model to several combinations of SSP‐RCP scenarios and time. We found that the characteristic scale for most variables is relatively large. The ensemble model had an AUC value of 0.98, outperforming individual models. The area of suitable habitat for the Lady Amherst's pheasant is projected to decrease at varying degrees (−2.1% ~ −62.1%) under different scenarios. Dispersal ability only has a little influence on the assessment of suitable habitat loss (−1.5% ~ −60.9%). Loss of suitable habitat mainly occurred in low‐altitude areas, while gain of habitat occurred in medium‐to‐high‐altitude areas. Our results may provide a scientific basis for future conservation strategies of this beautiful pheasant.

The Silver pheasant, a species in the Phasianidae family, is a widely distributed ground-dwelling bird in southern China and a national Class II protected animal. Global climate change and land use and land cover change are recognized as major drivers of wildlife habitat loss and shift, especially for species with low dispersal ability, like the Silver pheasant. However, no studies have assessed the influence of climate change and land-cover change on suitable habitat at the regional scale. In this study, we aim to assess the combined effects of climate change and land cover change on the habitat of the Silver pheasant. We used an ensemble species distribution model to predict habitat suitability for the Silver pheasant under the current scenario and three shared socioeconomic pathways (SSPs) across two time periods (2041–2070, 2071–2100). The results demonstrate that the ensemble model outperforms individual models in predicting suitable habitat for the Silver pheasant. The ensemble model achieved an area under the receiver’s operating curve (AUC) of 0.90, a true skill statistic (TSS) of 0.61, and a Boyce index of 0.96, indicating excellent predictive performance. Currently, suitable habitat for the Silver pheasant in China, covering 4.33 × 10⁵ km², primarily exists in eight provinces (Sichuan, Yunan, Jiangxi, Fujian, Guangdong, Guangxi, Zhejiang, and Hunan). Land cover emerged as the most influential variable (43%), followed by annual precipitation amount (36%), elevation (9%), and slope (6%). Silver pheasant is projected to experience large habitat expansion (24.24% to 63.08%) depending on different scenarios and time. The centroid of suitable habitat is expected to shift northeastward, but with short distances (<39 km). Our results provide accurate predictions of the distribution patterns of the Silver pheasant habitat in China under current and future environmental change scenarios, which is the fundamental basis for scientific-based management and conservation decisions at the national level.

Geographic heterogeneity, encompassing both species‐environment interactions and interspecific relationships, significantly influences the ecological attributes of wildlife habitat selection and population distribution. However, the impact of geographic heterogeneity on the distribution of target species within predator–prey systems, particularly in human‐dominated landscapes, remains unclear. By conducting line transect surveys, utilizing a monitoring network, and applying logistic geographically weighted regression (GWR) in conjunction with generalized linear models (GLM), we examined the spatial heterogeneity of habitat selection by the Amur tiger, Amur leopard, and their main ungulate prey, wild boar and roe deer, in Northeast China. Our results suggest that the factors affecting the spatial distribution of predators are more complex than those for prey. More significantly, the selection coefficients of roe deer and wild boar for certain habitat factors serve as crucial explanatory variables in the Amur tiger and leopard models. Our findings emphasize the importance of spatial non‐stationarity in predator–prey habitat selections, and the heterogeneous selection by prey may drive dispersals of large felids across complex road landscapes. This study offers new insights into how to help apex predators cross road barriers by effectively managing prey habitat selection in a landscape dominated by roads, providing valuable guidance for future habitat conservation policies.

Handeliodendron bodinieri is a rare tree species endemic to the karst mountains of southwestern China and is listed as a national Class II key protected wild plant. Due to its highly specialized habitat requirements and limited reproductive dispersal ability, the species is exceptionally vulnerable to environmental changes. To predict the impacts of future climate change on its habitat suitability while minimizing predictive uncertainties, this study compiled multi-source occurrence data alongside bioclimatic, topographic, and edaphic variables. We deployed an ensemble species distribution modeling framework to project the current and future (2100) distribution patterns and dynamic shifts in suitable habitat for H. bodinieri under three Shared Socioeconomic Pathways (SSP126, SSP370, and SSP585). Three independent evaluation metrics (AUCprg, TSS, and the Boyce index) consistently demonstrated that the ensemble model significantly outperformed individual algorithmic models (GLM, MARS, MAXENT, and RFd). Under current conditions, the weighted-average ensemble model estimated the suitable habitat area for H. bodinieri to be 70,582 km². By 2100, the suitable habitat area is projected to contract under all three SSP scenarios, with reduction rates ranging from 27.38% to 80.43%. Concurrently, the centroid of the suitable habitat is expected to shift southeastward or southward by 13–40 km, accompanied by varying degrees of shifts in mean elevation. Furthermore, we identified climate-insensitive regions across the scenarios, which are primarily clustered along the border between Guangxi and Guizhou provinces. Our study provides a robust scientific foundation for formulating effective conservation and management strategies for H. bodinieri amidst ongoing climate change, ultimately maintaining the sustainable survival of the species.