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Frontiers of Earth Science

ISSN 2095-0195

ISSN 2095-0209(Online)

CN 11-5982/P

邮发代号 80-963

2019 Impact Factor: 1.62

Frontiers of Earth Science  2023, Vol. 17 Issue (4): 1037-1048   https://doi.org/10.1007/s11707-022-1075-1
  本期目录
Complementarity of lacustrine pollen and sedimentary DNA in representing vegetation on the central-eastern Tibetan Plateau
Fang TIAN1(), Meijiao CHEN1, Weihan JIA2,3, Ulrike HERZSCHUH2,3,4, Xianyong CAO5
1. College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China
2. Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Polar Terrestrial Environmental Systems Research Group, Potsdam 14473, Germany
3. University of Potsdam, Institute of Environmental Science and Geography, Potsdam 14476, Germany
4. University of Potsdam, Institute of Biochemistry and Biology, Potsdam 14476, Germany
5. Group of Alpine Paleoecology and Human Adaptation (ALPHA), State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
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Abstract

Plant environmental DNA extracted from lacustrine sediments (sedimentary DNA, sedDNA) has been increasingly used to investigate past vegetation changes and human impacts at a high taxonomic resolution. However, the representation of vegetation communities surrounding the lake is still unclear. In this study, we compared plant sedDNA metabarcoding and pollen assemblages from 27 lake surface-sediment samples collected from alpine meadow on the central-eastern Tibetan Plateau to investigate the representation of sedDNA data. In general, the identified components of sedDNA are consistent with the counted pollen taxa and local plant communities. Relative to pollen identification, sedDNA data have higher taxonomic resolution, thus providing a potential approach for reconstructing past plant diversity. The sedDNA signal is strongly influenced by local plants while rarely affected by exogenous plants. Because of the overrepresentation of local plants and PCR bias, the abundance of sedDNA sequence types is very variable among sites, and should be treated with caution when investigating past vegetation cover and climate based on sedDNA data. Our finding suggests that sedDNA analysis can be a complementary approach for investigating the presence/absence of past plants and history of human land-use with higher taxonomic resolution.

Key wordsTibetan Plateau    plant DNA metabarcoding    pollen    lake sediment    plant diversity    vegetation history
收稿日期: 2022-12-21      出版日期: 2024-02-06
Corresponding Author(s): Fang TIAN   
 引用本文:   
. [J]. Frontiers of Earth Science, 2023, 17(4): 1037-1048.
Fang TIAN, Meijiao CHEN, Weihan JIA, Ulrike HERZSCHUH, Xianyong CAO. Complementarity of lacustrine pollen and sedimentary DNA in representing vegetation on the central-eastern Tibetan Plateau. Front. Earth Sci., 2023, 17(4): 1037-1048.
 链接本文:  
https://academic.hep.com.cn/fesci/CN/10.1007/s11707-022-1075-1
https://academic.hep.com.cn/fesci/CN/Y2023/V17/I4/1037
Fig.1  
Site No. Longitude/ °E Latitude/ °N Elevation/ (m a.s.l.) Vegetation
s101 99.6 33.0 4295 Subalpine broadleaf deciduous scrub
s105 99.8 33.6 3998 Kobresia spp., forb high-cold meadow
s123 98.9 33.7 4305 Kobresia spp., forb high-cold meadow
s133 98.0 32.7 4692 Kobresia spp., forb high-cold meadow
s143 98.9 32.8 3847 Kobresia spp., forb high-cold meadow
s16 97.8 34.5 4443 Kobresia spp., forb high-cold meadow
s169 98.4 33.3 4417 Kobresia spp., forb high-cold meadow
s200 97.6 33.4 4175 Kobresia spp., forb high-cold meadow
s213 96.7 34.2 4439 Kobresia spp., forb high-cold meadow
s229 96.2 34.9 4503 Kobresia spp., forb high-cold meadow
s237 95.3 33.9 4436 Kobresia spp., forb high-cold meadow
s244 95.1 34.4 4463 Kobresia spp., forb high-cold meadow
s259 96.5 33.2 4212 Kobresia spp., forb high-cold meadow
s263 97.0 32.9 3969 Kobresia spp., forb high-cold meadow
s310 97.8 31.7 4050 Kobresia spp., forb high-cold meadow
s344 95.2 31.6 3907 Subalpine needleleaf evergreen scrub
s350 94.5 31.7 4450 Kobresia spp., forb high-cold meadow
s358 94.5 32.4 4788 Kobresia spp., forb high-cold meadow
s374 93.2 32.4 4410 Kobresia spp., forb high-cold meadow
s379 92.3 32.1 4633 Kobresia spp., forb high-cold meadow
s414 91.9 32.9 5168 Kobresia spp., forb high-cold meadow
s417 92.2 33.8 4639 Kobresia spp., forb high-cold meadow
s420 92.7 34.3 4436 Kobresia spp., forb high-cold meadow
s434 92.9 34.9 4561 Grass, Carex spp. high-cold steppe
s439 93.8 35.5 4573 Grass, Carex spp. high-cold steppe
s440 94.3 35.0 4388 Kobresia spp., forb high-cold meadow
s62 99.0 34.4 4302 Kobresia spp., forb high-cold meadow
Tab.1  
Fig.2  
Fig.3  
Pollen sedDNA
Abies Atripliceae
Cedrus Berberis
Pinus Berberis japonica
Alnus Boraginaceae_st4
Betula Boraginaceae_st5
Carpinus/Ostrya Cynoglossoideae
Castanea Lepidium densiflorum
Corylus Braya
Juglans Draba
Oleaceae Pegaeophyton scapiflorum
Quercus Caprifoliaceae
Ulmus Lonicera_st2
Rhamnaceae Circaeaster agrestis
Ilex Rhodiola rosea
Nitraria Geranium
Echinops Geranium sylvaticum
Brassicaceae Juncus
Caryophyllaceae Larix
Balsaminaceae Populus
Liliaceae Deinbollia kilimandscharica
Polemoniaceae Solanoideae
Rumex Stellera chamaejasme
Koenigia fertilis Viola brevistipulata
Thalictrum
Tab.2  
Pollen sedDNA
Picea Picea
Salix Salix
Chenopodiaceae Amaranthaceae
Ephedra Ephedra_st1
Ericaceae Rhododendron lapponicum
Euphorbiaceae Euphorbia_st2
Fabaceae Astragalus variabilis, Astragalus_st1, Astragalus_st2, Caragana jubata, Hedysarum, Hedysarum tibeticum, Oxytropis, Oxytropis deflexa, Pisum
Hippophae Hippophae tibetana_st1, Hippophae tibetana_st2, Hippophae rhamnoides
Rosaceae Alchemilla, Aruncus dioicus, Colurieae, Comarum palustre, Fragariinae_st2, Maleae, Potentilla anserina, Potentilla_st2, Potentilla_st3, Spiraea
Tamaricaceae Myricaria, Myricaria germanica
Apiaceae Apiaceae_st2, Apioideae_st3, Apioideae_st4, Carum carvi, Selineae
Artemisia
Asterceae Anthemideae_st1, Asteraceae_st7, Asteraceae_st8, Asteroideae_st11, Carduinae, Gnaphalieae
Cyperaceae Blysmus compressus, Carex atrofusca, Carex maritima, Carex microglochin, Carex parva, Carex supina, Carex_st1, Carex_st2, Kobresia_st1, Kobresia_st2, Trichophorum pumilum
Urticaceae Urtica_st2
Gentianaceae Gentiana_st1, Gentiana_st2, Gentianeae, Gentianella arenaria, Halenia elliptica
Lamiaceae Dracocephalum ruyschiana, Elsholtzia, Elsholtzia densa, Lamiaceae_st3, Salvia, Thymus
Plantaginaceae Lagotis glauca, Plantago, Veronica, Veronica chamaedrys
Onagraceae Epilobium
Papaveraceae Corydalis, Meconopsis
Poaceae Agrostidinae, Festuca, Hordeum, Stipa breviflora, Poaceae, Poeae_st1, Poeae_st2, Pooideae_st1, Pooideae_st2, Puccinellia, Triticeae
Polygonum Aconogonon, Bistorta vivipara, Knorringia sibirica, Koenigia islandica, Rheum_st1, Rheum_st2
Primulaceae Androsace, Androsace chamaejasme, Primula, Primula nutans
Ranunculaceae Aconitum lycoctonum, Adonis, Anemone_st1, Caltha scaposa, Caltha sinogracilis, Caltha_st4, Caltha_st5, Delphinieae, Delphinium, Oxygraphis glacialis, Ranunculaceae_st2, Ranunculeae_st2, Ranunculus abortivus, Ranunculus arcticus, Ranunculus auricomus, Thalictrum_st2, Trollius
Saxifragaceae Ribes glandulosum, Saxifraga hirculus, Saxifraga_st2
Orobanchaceae Castilleja pallida var. hyparctica, Castilleja_st4, Castilleja_st5, Castilleja_st6, Euphrasia, Pedicularis elwesii, Pedicularis kansuensis, Pedicularis longiflora, Pedicularis oederi_st1, Pedicularis oederi_st2, Pedicularis tristis, Pedicularis verticillata_st1, Pedicularis verticillata_st2, Pedicularis verticillata_st3, Pedicularis_st4, Lancea tibetica
Tab.3  
Fig.4  
Climatic variables VIF (without Tann) VIF (add Tann) λ1/λ2 Climatic variables as sole predictor Marginal contribution based on climatic variables
Explained variance/% Explained variance/% p-value
Pann 2.0 4.6 0.49 15.3 13.3 0.007
Mtwa 4.7 153.8 0.09 3.3 2.6 0.569
Mtco 6.5 303.0 0.14 5.4 3.3 0.421
Tann 848.7
Tab.4  
Fig.5  
Climatic variables VIF (without Tann) VIF (add Tann) λ1/λ2 Climatic variables as sole predictor Marginal contribution based on climatic variables
Explained variance/% Explained variance/% p-value
Pann 2.0 4.6 0.32 7.9 5.1 0.188
Mtwa 4.7 153.8 0.18 4.9 5.9 0.101
Mtco 6.5 303.0 0.26 6.8 5.9 0.110
Tann 848.7
Tab.5  
Climatic variables VIF (without Tann) VIF (add Tann) λ1/λ2 Climatic variables as sole predictor Marginal contribution based on climatic variables
Explained variance/% Explained variance/% p-value
Pann 2.0 4.6 0.51 7.3 4.4 0.215
Mtwa 4.7 153.8 0.30 5.0 5.4 0.071
Mtco 6.5 303.0 0.35 5.7 4.7 0.157
Tann 848.7
Tab.6  
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