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A Review of Relative Pollen Productivity Estimates From Temperate China for Pollen-Based Quantitative Reconstruction of Past Plant Cover
Model-based quantitative reconstruction of past plant cover in Europe has shown great potential for: (i) testing hypotheses related to Holocene vegetation dynamics, biodiversity, and their relationships with climate and land use; (ii) studying long term interactions between climate and land use. Sim...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6134201/ https://www.ncbi.nlm.nih.gov/pubmed/30233604 http://dx.doi.org/10.3389/fpls.2018.01214 |
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author | Li, Furong Gaillard, Marie-José Xu, Qinghai Bunting, Mairi J. Li, Yuecong Li, Jie Mu, Huishuang Lu, Jingyao Zhang, Panpan Zhang, Shengrui Cui, Qiaoyu Zhang, Yahong Shen, Wei |
author_facet | Li, Furong Gaillard, Marie-José Xu, Qinghai Bunting, Mairi J. Li, Yuecong Li, Jie Mu, Huishuang Lu, Jingyao Zhang, Panpan Zhang, Shengrui Cui, Qiaoyu Zhang, Yahong Shen, Wei |
author_sort | Li, Furong |
collection | PubMed |
description | Model-based quantitative reconstruction of past plant cover in Europe has shown great potential for: (i) testing hypotheses related to Holocene vegetation dynamics, biodiversity, and their relationships with climate and land use; (ii) studying long term interactions between climate and land use. Similar model-based quantitative reconstruction of plant cover in China has been restricted due to the lack of standardized datasets of existing estimates of relative pollen productivity (RPP). This study presents the first synthesis of all RPP values available to date for 39 major plant taxa from temperate China and proposes standardized RPP datasets that can be used for model-based quantitative reconstructions of past plant cover using fossil pollen records for the region. We review 11 RPP studies in temperate China based on modern pollen and related vegetation data around the pollen samples. The study areas include meadow, steppe and desert vegetation, various woodland types, and cultural landscapes. We evaluate the strategies of each study in terms of selection of study areas and distribution of study sites; pollen- and vegetation-data collection in field; vegetation-data collection from satellite images and vegetation maps; and data analysis. We compare all available RPP estimates, select values based on precise rules and calculate mean RPP estimates. We propose two standardized RPP datasets for 31 (Alt1) and 29 (Alt2) plant taxa. The ranking of mean RPPs (Alt-2) relative to Poaceae (= 1) for eight major taxa is: Artemisia (21) > Pinus (18.4) > Betula (12.5) > Castanea (11.5) > Elaeagnaceae (8.8) > Juglans (7.5) > Compositae (4.5) > Amaranthaceae/Chenopodiaceae (4). We conclude that although RPPs are comparable between Europe and China for some genera and families, they can differ very significantly, e.g., Artemisia, Compositae, and Amaranthaceae/Chenopodiaceae. For some taxa, we present the first RPP estimates e.g. Castanea, Elaeagnaceae, and Juglans. The proposed standardized RPP datasets are essential for model-based reconstructions of past plant cover using fossil pollen records from temperate China. |
format | Online Article Text |
id | pubmed-6134201 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-61342012018-09-19 A Review of Relative Pollen Productivity Estimates From Temperate China for Pollen-Based Quantitative Reconstruction of Past Plant Cover Li, Furong Gaillard, Marie-José Xu, Qinghai Bunting, Mairi J. Li, Yuecong Li, Jie Mu, Huishuang Lu, Jingyao Zhang, Panpan Zhang, Shengrui Cui, Qiaoyu Zhang, Yahong Shen, Wei Front Plant Sci Plant Science Model-based quantitative reconstruction of past plant cover in Europe has shown great potential for: (i) testing hypotheses related to Holocene vegetation dynamics, biodiversity, and their relationships with climate and land use; (ii) studying long term interactions between climate and land use. Similar model-based quantitative reconstruction of plant cover in China has been restricted due to the lack of standardized datasets of existing estimates of relative pollen productivity (RPP). This study presents the first synthesis of all RPP values available to date for 39 major plant taxa from temperate China and proposes standardized RPP datasets that can be used for model-based quantitative reconstructions of past plant cover using fossil pollen records for the region. We review 11 RPP studies in temperate China based on modern pollen and related vegetation data around the pollen samples. The study areas include meadow, steppe and desert vegetation, various woodland types, and cultural landscapes. We evaluate the strategies of each study in terms of selection of study areas and distribution of study sites; pollen- and vegetation-data collection in field; vegetation-data collection from satellite images and vegetation maps; and data analysis. We compare all available RPP estimates, select values based on precise rules and calculate mean RPP estimates. We propose two standardized RPP datasets for 31 (Alt1) and 29 (Alt2) plant taxa. The ranking of mean RPPs (Alt-2) relative to Poaceae (= 1) for eight major taxa is: Artemisia (21) > Pinus (18.4) > Betula (12.5) > Castanea (11.5) > Elaeagnaceae (8.8) > Juglans (7.5) > Compositae (4.5) > Amaranthaceae/Chenopodiaceae (4). We conclude that although RPPs are comparable between Europe and China for some genera and families, they can differ very significantly, e.g., Artemisia, Compositae, and Amaranthaceae/Chenopodiaceae. For some taxa, we present the first RPP estimates e.g. Castanea, Elaeagnaceae, and Juglans. The proposed standardized RPP datasets are essential for model-based reconstructions of past plant cover using fossil pollen records from temperate China. Frontiers Media S.A. 2018-09-05 /pmc/articles/PMC6134201/ /pubmed/30233604 http://dx.doi.org/10.3389/fpls.2018.01214 Text en Copyright © 2018 Li, Gaillard, Xu, Bunting, Li, Li, Mu, Lu, Zhang, Zhang, Cui, Zhang and Shen. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Li, Furong Gaillard, Marie-José Xu, Qinghai Bunting, Mairi J. Li, Yuecong Li, Jie Mu, Huishuang Lu, Jingyao Zhang, Panpan Zhang, Shengrui Cui, Qiaoyu Zhang, Yahong Shen, Wei A Review of Relative Pollen Productivity Estimates From Temperate China for Pollen-Based Quantitative Reconstruction of Past Plant Cover |
title | A Review of Relative Pollen Productivity Estimates From Temperate China for Pollen-Based Quantitative Reconstruction of Past Plant Cover |
title_full | A Review of Relative Pollen Productivity Estimates From Temperate China for Pollen-Based Quantitative Reconstruction of Past Plant Cover |
title_fullStr | A Review of Relative Pollen Productivity Estimates From Temperate China for Pollen-Based Quantitative Reconstruction of Past Plant Cover |
title_full_unstemmed | A Review of Relative Pollen Productivity Estimates From Temperate China for Pollen-Based Quantitative Reconstruction of Past Plant Cover |
title_short | A Review of Relative Pollen Productivity Estimates From Temperate China for Pollen-Based Quantitative Reconstruction of Past Plant Cover |
title_sort | review of relative pollen productivity estimates from temperate china for pollen-based quantitative reconstruction of past plant cover |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6134201/ https://www.ncbi.nlm.nih.gov/pubmed/30233604 http://dx.doi.org/10.3389/fpls.2018.01214 |
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