scholarly journals Genetic Diversity Analysis and Potential Distribution Prediction of Sophora moorcroftiana Endemic to Qinghai–Tibet Plateau, China

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1106
Author(s):  
Lan Yang ◽  
Huie Li ◽  
Qian Li ◽  
Qiqiang Guo ◽  
Jiangrong Li
Keyword(s):  
Climate Change ◽  
Genetic Diversity ◽  
Potential Distribution ◽  
Germplasm Conservation ◽  
Woody Species ◽  
Cold Weather ◽  
Tibet Plateau ◽  
Conservation Strategies ◽  
Qinghai Tibet Plateau ◽  
Sophora Moorcroftiana

Sophora moorcroftiana (Benth.) Baker is an endemic woody species distributed in the Qinghai–Tibet Plateau (QTP), a part of the world characterized by high altitude and cold weather. In this study, the genetic diversity of S. moorcroftiana was evaluated based on 300 representative samples of 15 populations using 20 polymorphic SSR markers, and its potential distribution was predicted according to 19 bioclimatic factors using MaxEnt modeling. Results showed the population genetic diversity of S. moorcroftiana was generally not high (around 0.5), and the range of variation was small (0.437–0.539). Altitude, rather than other environmental factors, was the key factor affecting the present genetic diversity. Moreover, due to climate change in the QTP, the suitable area is increasing and will continue to increase by 48.35%, 84.44%, 101.98%, and 107.30% in the four future periods of 2030s, 2050s, 2070s, and 2090s, respectively, compared to the present, which is beneficial for S. moorcroftiana. These results will provide a theoretical basis for the development of germplasm conservation strategies for S. moorcroftiana and enrich information on the impacts of climate change on plants in the QTP.

scholarly journals Potential distribution and habitat suitability of Picea crassifolia with climate change scenarios

2021 ◽  
Author(s):  
Yuan Gao ◽  
Zhibin He ◽  
Xi Zhu ◽  
Longfei Chen ◽  
Jun Du ◽  
...  
Keyword(s):  
Climate Change ◽  
Habitat Suitability ◽  
Environmental Variables ◽  
Potential Distribution ◽  
Yellow River ◽  
Tibet Plateau ◽  
Suitable Habitat ◽  
Climate Change Scenarios ◽  
Helan Mountains ◽  
Qinghai Tibet Plateau

The Qinghai-Tibet Plateau in China is a region strongly impacted by climate change, yet its effects are unknown on the keystone endemic forest species, P. crassifolia. Understanding changes in potential distribution and habitat suitability of P. crassifolia forest with the climate change will contribute to water conservation, forest management, and ecological protection in the upper reaches of the Yellow River. A total of 129 records of species distribution data and 19 environmental variables were chosen for modeling. The MaxEnt model was used to analyze the main environmental factors affecting the potential distribution of P. crassifolia in two periods (2050s and 2070s) and four representative emission pathways (RCP2.6, RCP4.5, RCP6.0 and RCP 8.5). The main results are follows: (1) the most important environmental variables affecting distribution of P. crassifolia and percentage variance explained were: altitude (41.85%), precipitation of driest month (19.76%), slope (12.35%), annual precipitation (6.56%), precipitation of wettest month (5.73%), and precipitation of warmest quarter (5.12%), (2) habitat suitability of P. crassifolia shifted to the northwest and into high-altitude areas under climate change scenarios, but its core distribution areas were concentrated in northeastern Qinghai-Tibet Plateau, Qilian Mountains, southern Ningxia, and Helan Mountains, (3) total area of potential suitable habitat of P. crassifolia will change significantly in the future, and change of habitat area of not suitable, low, moderate, and high suitability exceed 60%.

scholarly journals Assessing the Impact of Climate Change on Potential Distribution of Meconopsis punicea and Its Influence on Ecosystem Services Supply in the Southeastern Margin of Qinghai-Tibet Plateau

2022 ◽  
Vol 12 ◽  
Author(s):  
Ning Shi ◽  
Niyati Naudiyal ◽  
Jinniu Wang ◽  
Narayan Prasad Gaire ◽  
Yan Wu ◽  
...  
Keyword(s):  
Climate Change ◽  
Ecosystem Services ◽  
Potential Distribution ◽  
Future Climate ◽  
Tibet Plateau ◽  
Suitable Habitat ◽  
Climate Change Scenarios ◽  
Model Output ◽  
Southeastern Margin ◽  
Qinghai Tibet Plateau

Meconopsis punicea is an iconic ornamental and medicinal plant whose natural habitat has degraded under global climate change, posing a serious threat to the future survival of the species. Therefore, it is critical to analyze the influence of climate change on possible distribution of M. punicea for conservation and sustainable utilization of this species. In this study, we used MaxEnt ecological niche modeling to predict the potential distribution of M. punicea under current and future climate scenarios in the southeastern margin region of Qinghai-Tibet Plateau. Model projections under current climate show that 16.8% of the study area is suitable habitat for Meconopsis. However, future projections indicate a sharp decline in potential habitat for 2050 and 2070 climate change scenarios. Soil type was the most important environmental variable in determining the habitat suitability of M. punicea, with 27.75% contribution to model output. Temperature seasonality (16.41%), precipitation of warmest quarter (14.01%), and precipitation of wettest month (13.02%), precipitation seasonality (9.41%) and annual temperature range (9.24%) also made significant contributions to model output. The mean elevation of suitable habitat for distribution of M. punicea is also likely to shift upward in most future climate change scenarios. This study provides vital information for the protection and sustainable use of medicinal species like M. punicea in the context of global environmental change. Our findings can aid in developing rational, broad-scale adaptation strategies for conservation and management for ecosystem services, in light of future climate changes.

scholarly journals Spatial and Temporal Differences in Alpine Meadow, Alpine Steppe and All Vegetation of the Qinghai-Tibetan Plateau and Their Responses to Climate Change

2021 ◽  
Vol 13 (4) ◽  
pp. 669
Author(s):  
Hanchen Duan ◽  
Xian Xue ◽  
Tao Wang ◽  
Wenping Kang ◽  
Jie Liao ◽  
...  
Keyword(s):  
Climate Change ◽  
Alpine Meadow ◽  
Global Climate ◽  
Growing Season ◽  
Tibet Plateau ◽  
Vegetation Types ◽  
Analysis Method ◽  
Alpine Steppe ◽  
Qinghai Tibet Plateau ◽  
Variation Trends

Alpine meadow and alpine steppe are the two most widely distributed nonzonal vegetation types in the Qinghai-Tibet Plateau. In the context of global climate change, the differences in spatial-temporal variation trends and their responses to climate change are discussed. It is of great significance to reveal the response of the Qinghai-Tibet Plateau to global climate change and the construction of ecological security barriers. This study takes alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau as the research objects. The normalized difference vegetation index (NDVI) data and meteorological data were used as the data sources between 2000 and 2018. By using the mean value method, threshold method, trend analysis method and correlation analysis method, the spatial and temporal variation trends in the alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau were compared and analyzed, and their differences in the responses to climate change were discussed. The results showed the following: (1) The growing season length of alpine meadow was 145~289 d, while that of alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau was 161~273 d, and their growing season lengths were significantly shorter than that of alpine meadow. (2) The annual variation trends of the growing season NDVI for the alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau increased obviously, but their fluctuation range and change rate were significantly different. (3) The overall vegetation improvement in the Qinghai-Tibet Plateau was primarily dominated by alpine steppe and alpine meadow, while the degradation was primarily dominated by alpine meadow. (4) The responses between the growing season NDVI and climatic factors in the alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau had great spatial heterogeneity in the Qinghai-Tibet Plateau. These findings provide evidence towards understanding the characteristics of the different vegetation types in the Qinghai-Tibet Plateau and their spatial differences in response to climate change.

Spatial variability of topsoil δ13C across Qinghai-Tibet Plateau

2021 ◽  
Author(s):  
Yunsen Lai ◽  
Shaoda Li ◽  
Xiaolu Tang ◽  
Xinrui Luo ◽  
Liang Liu ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Spatial Variability ◽  
Soil Carbon ◽  
Tibet Plateau ◽  
Learning Approach ◽  
Carbon Turnover ◽  
Machine Learning Approach ◽  
Qinghai Tibet Plateau ◽  
Soil Carbon Turnover

<p>Soil carbon isotopes (δ<sup>13</sup>C) provide reliable insights at the long-term scale for the study of soil carbon turnover and topsoil δ<sup>13</sup>C could well reflect organic matter input from the current vegetation. Qinghai-Tibet Plateau (QTP) is called “the third pole of the earth” because of its high elevation, and it is one of the most sensitive and critical regions to global climate change worldwide. Previous studies focused on variability of soil δ<sup>13</sup>C at in-site scale. However, a knowledge gap still exists in the spatial pattern of topsoil δ<sup>13</sup>C in QTP. In this study, we first established a database of topsoil δ<sup>13</sup>C with 396 observations from published literature and applied a Random Forest (RF) algorithm (a machine learning approach) to predict the spatial pattern of topsoil δ<sup>13</sup>C using environmental variables. Results showed that topsoil δ<sup>13</sup>C significantly varied across different ecosystem types (p < 0.05).  Topsoil δ<sup>13</sup>C was -26.3 ± 1.60 ‰ for forest, 24.3 ± 2.00 ‰ for shrubland, -23.9 ± 1.84 ‰ for grassland, -18.9 ± 2.37 ‰ for desert, respectively. RF could well predict the spatial variability of topsoil δ<sup>13</sup>C with a model efficiency (pseudo R<sup>2</sup>) of 0.65 and root mean square error of 1.42. The gridded product of topsoil δ<sup>13</sup>C and topsoil β (indicating the decomposition rate of soil organic carbon, calculated by δ<sup>13</sup>C divided by logarithmically converted SOC) with a spatial resolution of 1000 m were developed. Strong spatial variability of topsoil δ<sup>13</sup>C was observed, which increased gradually from the southeast to the northwest in QTP. Furthermore, a large variation was found in β, ranging from -7.87 to -81.8, with a decreasing trend from southeast to northwest, indicating that carbon turnover rate was faster in northwest QTP compared to that of southeast. This study was the first attempt to develop a fine resolution product of topsoil δ<sup>13</sup>C for QTP using a machine learning approach, which could provide an independent benchmark for biogeochemical models to study soil carbon turnover and terrestrial carbon-climate feedbacks under ongoing climate change.</p>

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