Browsing by Author "Qian, Jianqiang"

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    The feasibility of using soil seed bank for natural regeneration of degraded sandy grasslands
    (Elsevier, 2022-09) Wang, Yongcui; Chu, Lei; Liu, Zhimin; Ala, MuSa; Lin, Jixiang; Qian, Jianqiang; Zhou, Quanlai; Wang, Lixin; Earth Sciences, School of Science
    Desertification in degraded grasslands is manifested through the development of bare sandy patches, which eventually lead to habitat fragmentation. The ability of these bare sandy patches to regenerate naturally through in-situ soil seed banks is not well understood. To fill this knowledge gap, we randomly selected 24 bare sandy patches with areas ranging from 19 to 898 m2 in a desertified grassland of the Horqin sandy land, Northern China to determine whether soil seed bank can be used for natural regeneration of bare sandy patches. Species composition and density of soil seed bank as well as aboveground vegetation composition, abundance and coverage were investigated. We then determined their relationships with in-situ habitat characteristics. Our observations showed that the studied area had low soil seed bank density and species richness, as well as depauperate soil seed bank communities. Consequently, local soil seed bank was not able to provide sufficient seed source for natural regeneration. This was indicated by the relationships between aboveground vegetation, soil seed bank and the in-situ habitat characteristics. For bare patches with an area between 300 m2 and 900 m2, increase the soil seed bank density and species richness should be the main restoration measures. For bare patches with a small area of less than 50 m2, restoration of vegetation density should be the main measure. Our data highlighted that different extents of desertification, indicated by different bare patches, are requiring distinct restoration measures.
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    Relationship between seed morphological traits and wind dispersal trajectory
    (CSIRO, 2019) Zhou, Quanlai; Liu, Zhimin; Xin, Zhiming; Daryanto, Stefani; Wang, Lixin; Qian, Jianqiang; Wang, Yongcui; Liang, Wei; Qin, Xuanping; Zhao, Yingming; Li, Xinle; Cui, Xue; Liu, Minghu; Earth Sciences, School of Science
    The structure and dynamics of plant populations and communities are largely influenced by seed dispersal. How the wind dispersal trajectory of seeds shifts with differences in seed morphology remains unknown. We used a wind tunnel and video camera to track the dispersal trajectory of seven species of Calligonum whose seeds have different kinds of appendages and other morphological traits, using variable wind speeds and release heights to determine the relationship between seed morphological traits and wind dispersal trajectory. Concave-, straight-line-, horizontal-projectile- and projectile-shaped trajectories were found. Dispersal trajectories such as the horizontal projectile (HP) and projectile (P) tended to have a long dispersal distance. Straight line (SL) and concave curve (CC) trajectories tended to have a short dispersal distance. Seeds with bristles and large mass tended to have SL and CC trajectories, those with wings or balloon and small mass tended to have HP and P trajectories. Wind speed tended to have a stronger influence on the dispersal trajectory of light and low-wing-loading seeds, and release height tended to have a stronger influence on the dispersal trajectory of heavy and high-wing-loading seeds. Thus, seed wind dispersal trajectory is not only determined by seed morphological characteristics but also by environmental factors such as wind speed and release height.
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    Scale effect of climate factors on soil organic carbon stock in natural grasslands of northern China
    (Elsevier, 2023-02) Liu, Zhimin; Zhou, Quanlai; Ma, Qun; Kuang, Wennong; Daryanto, Stefani; Wang, Lixin; Wu, Jing; Liu, Bo; Zhu, Jinlei; Cao, Chengyou; Li, Xuehua; Kou, Zhenwu; Shou, Wenkai; Qian, Jianqiang; Liu, Minghu; Xin, Zhiming; Cui, Xue; Liang, Wei; Earth and Environmental Sciences, School of Science
    Changes in grassland soil organic carbon stock (SOCS) may significantly affect the regional climate and carbon cycle of terrestrial ecosystems. However, how the impact of climate factors on SOCS and the dominant climate factors are regulated by the area scale of grasslands remains unclear. To understand the scale effects of climate on SOCS and how to accurately estimate SOCS at different scales, three area scales were defined by extending grassland types on the basis of meadow, typical and desert grasslands (Scale I (average area 37.22 × 104 km2) included each of these three types of grasslands, Scale II (average area 74.45 × 104 km2) was achieved by a pairwise combination of these three types of grasslands. Scale III (area 111.67 × 104 km2) was an aggregate of these three types of grasslands), the relationship between climate factors (i.e., mean annual precipitation, mean annual temperature, annual maximum temperature, annual minimum temperature, mean annual ground temperature, mean annual humidity, annual sunshine duration, annual maximum depth of accumulated snow, and the number of snow-covered days) and SOCS at the three scales were explored in the grasslands of northern China. Our results indicated that the total SOCS in grasslands at the three scales was ordered as desert grassland < meadow grassland < typical grassland. Of the nine climate factors, mean annual precipitation, positively correlated with SOCS, was the most significant climatic factor for all three scales. The dominant climatic factors of the SOCS differed across grassland area scales (i.e., MAP and MAH for meadow grassland, AMAT, MAP, NSD, and MAH for typical grassland, MAP, NSD, MAH, AMAT, and ASD for meadow-typical grassland scale, MAP, MAT, and MAGT for typical-desert grassland scale, MAP and MAT for meadow-typical-desert grassland scale). The impact of climate factors on the SOCS decreased as the scale increased. It is essential to screen appropriate climate predictors according to a given area scale when assessing regional SOCS. Multiple climate factors are better predictors for accessing SOCS at a small scale. At a large scale, however, dominant climatic factors are predictors that are more efficient.