Articles | Volume 14, issue 9
https://doi.org/10.5194/essd-14-3961-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-3961-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
02 Sep 2022
Data description article |
| 02 Sep 2022
| 02 Sep 2022
Artemisia pollen dataset for exploring the potential ecological indicators in deep time
Li-Li Lu
State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, 20 Nanxincun Xiangshan, Beijing 100093, China
University of Chinese Academy of Sciences, Beijing 100049, China
Bo-Han Jiao
State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, 20 Nanxincun Xiangshan, Beijing 100093, China
University of Chinese Academy of Sciences, Beijing 100049, China
Feng Qin
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Gan Xie
State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, 20 Nanxincun Xiangshan, Beijing 100093, China
Kai-Qing Lu
State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, 20 Nanxincun Xiangshan, Beijing 100093, China
University of Chinese Academy of Sciences, Beijing 100049, China
Jin-Feng Li
State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, 20 Nanxincun Xiangshan, Beijing 100093, China
Bin Sun
State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, 20 Nanxincun Xiangshan, Beijing 100093, China
Min Li
State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, 20 Nanxincun Xiangshan, Beijing 100093, China
David K. Ferguson
Department of Paleontology, University of Vienna, Althanstrasse 14, Vienna 1090, Austria
Tian-Gang Gao
CORRESPONDING AUTHOR
State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, 20 Nanxincun Xiangshan, Beijing 100093, China
University of Chinese Academy of Sciences, Beijing 100049, China
Yi-Feng Yao
CORRESPONDING AUTHOR
State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, 20 Nanxincun Xiangshan, Beijing 100093, China
University of Chinese Academy of Sciences, Beijing 100049, China
State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, 20 Nanxincun Xiangshan, Beijing 100093, China
University of Chinese Academy of Sciences, Beijing 100049, China
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We developed the first 30 m, wall-to-wall annual dataset of national grassland canopy cover from 1990 to 2023. By integrating drone and Landsat-series imagery, our model generates high predictive accuracy and robust temporal transferability compared to historical validation dataset. This dataset provides a critical foundation for advancing the understanding of grassland ecosystem dynamics and for supporting evidence-based conservation and sustainable management strategies.
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Short summary
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The objective of this study is present the first gridded and temporally continuous quantitative plant-cover reconstruction for temperate and northern subtropical China over the last 12 millennia. The reconstructions are based on 94 pollen records and include estimates for 27 plant taxa, 10 plant functional types, and 3 land-cover types. The dataset is suitable for palaeoclimate modelling and the evaluation of simulated past vegetation cover and anthropogenic land-cover change from models.
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Zizka, A., Silvestro, D., Andermann, T., Azevedo, J., Ritter, C. D., Edler, D., Farooq, H., Herdean, A., Ariza, M., Scharn, R., Svantesson, S., Wengstrom, N., Zizka, V., and Antonelli, A.: CoordinateCleaner: Standardized cleaning of occurrence records from biological collection databases, Methods Ecol. Evol., 10, 744–751, https://doi.org/10.1111/2041-210X.13152, 2019.
Short summary
Artemisia is one of the dominant plant elements in the arid and semi-arid regions. We attempt to decipher the underlying causes of the long-standing disagreement on the correlation between Artemisia pollen and aridity by using the dataset to recognize the different ecological implications of Artemisia pollen types. Our findings improve the resolution of palaeoenvironmental assessment and change the traditional concept of Artemisia being restricted to arid and semi-arid environments.
Artemisia is one of the dominant plant elements in the arid and semi-arid regions. We attempt to...
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