Literature DB >> 30233604

A Review of Relative Pollen Productivity Estimates From Temperate China for Pollen-Based Quantitative Reconstruction of Past Plant Cover.

Furong Li1, Marie-José Gaillard1, Qinghai Xu2, Mairi J Bunting3, Yuecong Li2, Jie Li2, Huishuang Mu2, Jingyao Lu2, Panpan Zhang2, Shengrui Zhang2, Qiaoyu Cui4, Yahong Zhang5, Wei Shen5.   

Abstract

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.

Entities:  

Keywords:  Extended R-Value (ERV) model; fall speed of pollen (FSP); modern pollen sampling; relevant source area of pollen (RSAP); vegetation-data collection

Year:  2018        PMID: 30233604      PMCID: PMC6134201          DOI: 10.3389/fpls.2018.01214

Source DB:  PubMed          Journal:  Front Plant Sci        ISSN: 1664-462X            Impact factor:   5.753


  3 in total

1.  From forest to farmland: pollen-inferred land cover change across Europe using the pseudobiomization approach.

Authors:  Ralph M Fyfe; Jessie Woodbridge; Neil Roberts
Journal:  Glob Chang Biol       Date:  2014-12-03       Impact factor: 10.863

2.  Pollen-based quantitative reconstructions of Holocene regional vegetation cover (plant-functional types and land-cover types) in Europe suitable for climate modelling.

Authors:  A-K Trondman; M-J Gaillard; F Mazier; S Sugita; R Fyfe; A B Nielsen; C Twiddle; P Barratt; H J B Birks; A E Bjune; L Björkman; A Broström; C Caseldine; R David; J Dodson; W Dörfler; E Fischer; B van Geel; T Giesecke; T Hultberg; L Kalnina; M Kangur; P van der Knaap; T Koff; P Kuneš; P Lagerås; M Latałowa; J Lechterbeck; C Leroyer; M Leydet; M Lindbladh; L Marquer; F J G Mitchell; B V Odgaard; S M Peglar; T Persson; A Poska; M Rösch; H Seppä; S Veski; L Wick
Journal:  Glob Chang Biol       Date:  2014-10-23       Impact factor: 10.863

3.  Historical land-use and landscape change in southern Sweden and implications for present and future biodiversity.

Authors:  Qiao-Yu Cui; Marie-José Gaillard; Geoffrey Lemdahl; Li Stenberg; Shinya Sugita; Ganna Zernova
Journal:  Ecol Evol       Date:  2014-09-01       Impact factor: 2.912

  3 in total

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