Understanding the spatial distribution characteristics of protected areas is the basis to balance the conservation and regional development. With the increasing number and area of protected areas, China has also made decisive progress in the fight against poverty. However, the spatial distribution characteristics of various types of protected areas in poor counties in China are still unclear and lacking further analysis on the differences compared to non-poor counties. Here, we first integrated the spatial distribution data of 8133 protected areas in China and overlaid them with 832 poor counties. Then we explored the spatial distribution characteristics of protected areas and the relationship with socio-economic and natural environment in poor and non-poor counties. The results showed that the area coverage of nature reserves in poor counties in China was significantly higher than that in non-poor counties (p < 0.001), while the area coverage of natural parks in non-poor counties was significantly higher than that in poor counties (p < 0.05). The area coverages of protected areas in poor counties in Northeast (p < 0.05), Southwest (p < 0.001), Central (p < 0.05), and East China (p < 0.01) were significantly higher than that in non-poor counties. Furthermore, the area coverage of nature reserves in poor counties was significantly positively correlated with mean elevation (p < 0.001), and the area coverage of natural parks in non-poor counties was significantly positively correlated with road network density (p < 0.05) and negatively correlated with the proportion of farmland (p < 0.001). This study can provide a reference to help China and other similar countries in the establishment of protected area systems to balance the conservation and poverty eradication for regional sustainable development.

Ornamental plants play a crucial role in the mitigation of urban heat islands. Recent decades have seen an increased frequency of abnormal climatic events like warm springs, but how these climatic events impact plant phenology in ornamental plants in urban areas is unclear. This study examines how climate fluctuations affect the flowering patterns (1963–2018) and thermal requirements of 28 woody ornamental species in Xi’an, a principal city in Central China. Years were classified as cold (17.2 °C) based on March–May temperatures. The results show that the first flowering dates (FFDs) advanced by 10.63 days in warm years but were delayed by 6.14 days in cold years compared to normal years. Notably, thermal requirements (5 °C threshold) were 11.3% higher in warm years (343.05 vs. 308.09 °C days) and 9.4% lower in cold years (279.19 °C days), likely due to reduced winter chilling accumulation in warm conditions. While thermal time models accurately predicted FFDs in normal years (error: 0.33–1.37 days), they showed systematic biases in abnormal years—overestimating advancement by 1.56 days in warm years and delays by 3.42 days in cold years. These findings highlight that the current phenological models assuming fixed thermal thresholds may significantly mispredict flowering times under climate variability. Our results emphasize the need to incorporate dynamic thermal requirements and chilling effects when forecasting urban plant responses to climate change, particularly for extreme climate scenarios.

Grassland ecosystems provide substantial social and cultural value but are vulnerable to climate change and human activities. Large‐scale evidence on how climate and disturbance jointly affect above‐ground biomass (AGB), species richness, and their relationship remains limited. We conducted a meta‐analysis by compiling and standardizing plot‐level observations from 226 studies of Chinese grasslands. The final database contained 1192 records from 378 sites surveyed between 1982 and 2019, with matched climatic variables (mean annual temperature, annual precipitation, and radiation) and disturbance categories (grazing, mowing, and fencing). Random‐forest models identified disturbance, precipitation, and radiation as the strongest predictors of AGB, whereas temperature was the strongest predictor of species richness. AGB was higher under mowing and fencing than under grazing and declined with increasing grazing intensity; it also increased with local precipitation. Species richness was highest under mowing, followed by fencing and grazing, and was greatest under wetter conditions and moderate temperatures. Across the national dataset, AGB and species richness were linearly and positively related overall, although several plant formations showed a unimodal pattern. Heavy grazing weakened the linear relationship, whereas moderate grazing strengthened it. Our study provides a scientific basis for the management of grassland ecosystems under the influence of climate change and human activities.

The temperature sensitivity of plant phenology reflects how and to what extent plants respond to climate change and is significantly related to their ability to adapt to climate change. Previous studies on the temperature sensitivity of first leaf date (FLD) primarily focus on temperate regions, with relatively few studies conducted in subtropical areas. This study analyzed observational data on the FLD of 63 typical woody plant species from 1980 to 2019 in Guiyang, located in the subtropical zone of China. We quantified the trend of FLD and its sensitivity to temperature changes and then assessed the impact of sample size on the stability of sensitivity estimates. The results showed that (1) significant warming occurred in Guiyang during the study period, with the largest warming occurring in spring. (2) The FLD of the vast majority of plants (95.2%) showed an earlier trend during the study period (19.0% significantly at p < 0.05). The earlier trend of most species ranged from −3 to −1 days decades⁻¹. The median of trends for all 63 species investigated was 1.97 days decades⁻¹. (3) The interannual variation in FLD was significantly negatively correlated with the preseason average temperature (p < 0.05). Most of the temperature sensitivity of FLD was between −5 and −3 days °C⁻¹, with a mean of −4.53 days °C⁻¹. (4) The sample size significantly influenced the stability of the temperature sensitivity estimates. Using randomly selected 20-year data could limit the standard deviation of the sensitivity estimate to 0.3 days °C⁻¹. These results suggest that the leaf unfolding date of subtropical species could track climate warming closely like temperature species. The temperature sensitivity of FLD should be estimated based on long-term observation data.