Surface pollen and its relationships with modern vegetation and climate in the Tianshan Mountains, northwestern China

A dataset consisting of 70 surface pollen samples from forest, alpine meadow, alpine steppe, temperate steppe, desert steppe, shrub/semi-shrub steppe and desert sites in the Tianshan Mountains, northwestern China provides an opportunity to study the relationships between surface pollen assemblages a...

Ausführliche Beschreibung

Bibliographische Detailangaben
Veröffentlicht in:Vegetation History and Archaeobotany. - Springer Science + Business Media. - 25(2016), 1, Seite 19-27
1. Verfasser: Wei, Haicheng (VerfasserIn)
Weitere Verfasser: Zhao, Yan
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2016
Zugriff auf das übergeordnete Werk:Vegetation History and Archaeobotany
Schlagworte:Biological sciences Physical sciences Environmental studies
LEADER 01000caa a22002652 4500
001 JST130344753
003 DE-627
005 20240625132738.0
007 cr uuu---uuuuu
008 210731s2016 xx |||||o 00| ||eng c
035 |a (DE-627)JST130344753 
035 |a (JST)43923299 
040 |a DE-627  |b ger  |c DE-627  |e rakwb 
041 |a eng 
100 1 |a Wei, Haicheng  |e verfasserin  |4 aut 
245 1 0 |a Surface pollen and its relationships with modern vegetation and climate in the Tianshan Mountains, northwestern China 
264 1 |c 2016 
336 |a Text  |b txt  |2 rdacontent 
337 |a Computermedien  |b c  |2 rdamedia 
338 |a Online-Ressource  |b cr  |2 rdacarrier 
520 |a A dataset consisting of 70 surface pollen samples from forest, alpine meadow, alpine steppe, temperate steppe, desert steppe, shrub/semi-shrub steppe and desert sites in the Tianshan Mountains, northwestern China provides an opportunity to study the relationships between surface pollen assemblages and modern vegetation and climate in this region. Redundancy analysis (RDA), the human influence index (HII) and pollen ratios were used to facilitate analysis of the pollen data. The modern pollen assemblages are primarily composed of Picea, Artemisia, Chenopodiaceae, Poaceae, Asteraceae, Nitraria and Ephedra. The results suggest that the surface pollen assemblages of different vegetation types largely represent the modern vegetation in terms of the primary taxa and dominant types. The RDA indicates that the mean annual precipitation (MAP) and the July temperature (TJuly) are the major climate variables that control the modern pollen assemblages. Picea, Artemisia, Poaceae, Cyperaceae, Fabaceae, Asteraceae, Polygonaceae and Apiaceae pollen assemblages are positively correlated with MAP and negatively correlated with TJuly, while the pollen ratios for certain other types, such as Chenopodiaceae, Ephedra and Nitraria, are negatively correlated with MAP and positively correlated with TJuly. The arboreal/non-arboreal ratios are notably high in the forest samples, indicating a sensitive response to forest vegetation. Moreover, the Artemisia/Chenopodiaceae pollen ratios are generally correlated with the vegetation type and annual precipitation change, suggesting that these factors could be useful indicators of moisture variability in arid regions. However, it is difficult to distinguish between steppe and steppe desert based on this ratio, due partly to human disturbance. The HII is significantly correlated with certain pollen taxa, including Poaceae, Plantago, Polygonaceae and Elaeagnaceae, particularly in the alpine meadow and steppe samples. Our results have implications for interpreting the available fossil pollen data in the study region and other arid and semi-arid regions. 
540 |a © Springer-Verlag Berlin Heidelberg 2016 
650 4 |a Biological sciences  |x Biology  |x Botany  |x Palynology  |x Pollen 
650 4 |a Biological sciences  |x Biology  |x Botany  |x Plant ecology  |x Vegetation 
650 4 |a Biological sciences  |x Ecology  |x Ecological zones  |x Ecoregions  |x Steppes 
650 4 |a Biological sciences  |x Biology  |x Biological taxonomies  |x Taxa 
650 4 |a Physical sciences  |x Earth sciences  |x Geography  |x Geomorphology  |x Landforms  |x Deserts 
650 4 |a Environmental studies  |x Atmospheric sciences  |x Climatology  |x Paleoclimatology 
650 4 |a Environmental studies  |x Atmospheric sciences  |x Climatology  |x Climatic zones  |x Arid zones 
650 4 |a Biological sciences  |x Ecology  |x Ecosystems  |x Biomes  |x Grasslands  |x Meadows 
650 4 |a Environmental studies  |x Atmospheric sciences  |x Climatology  |x Climatic zones 
650 4 |a Physical sciences  |x Earth sciences  |x Geography  |x Geographic regions  |x Temperate regions 
655 4 |a research-article 
700 1 |a Zhao, Yan  |e verfasserin  |4 aut 
773 0 8 |i Enthalten in  |t Vegetation History and Archaeobotany  |d Springer Science + Business Media  |g 25(2016), 1, Seite 19-27  |w (DE-627)300183801  |w (DE-600)1481434-1  |x 16176278  |7 nnns 
773 1 8 |g volume:25  |g year:2016  |g number:1  |g pages:19-27 
856 4 0 |u https://www.jstor.org/stable/43923299  |3 Volltext 
912 |a GBV_USEFLAG_A 
912 |a SYSFLAG_A 
912 |a GBV_JST 
912 |a GBV_ILN_11 
912 |a GBV_ILN_20 
912 |a GBV_ILN_22 
912 |a GBV_ILN_23 
912 |a GBV_ILN_24 
912 |a GBV_ILN_31 
912 |a GBV_ILN_32 
912 |a GBV_ILN_39 
912 |a GBV_ILN_40 
912 |a GBV_ILN_60 
912 |a GBV_ILN_62 
912 |a GBV_ILN_63 
912 |a GBV_ILN_69 
912 |a GBV_ILN_70 
912 |a GBV_ILN_73 
912 |a GBV_ILN_74 
912 |a GBV_ILN_90 
912 |a GBV_ILN_95 
912 |a GBV_ILN_100 
912 |a GBV_ILN_105 
912 |a GBV_ILN_110 
912 |a GBV_ILN_120 
912 |a GBV_ILN_138 
912 |a GBV_ILN_150 
912 |a GBV_ILN_151 
912 |a GBV_ILN_161 
912 |a GBV_ILN_170 
912 |a GBV_ILN_171 
912 |a GBV_ILN_187 
912 |a GBV_ILN_213 
912 |a GBV_ILN_224 
912 |a GBV_ILN_230 
912 |a GBV_ILN_250 
912 |a GBV_ILN_267 
912 |a GBV_ILN_281 
912 |a GBV_ILN_285 
912 |a GBV_ILN_293 
912 |a GBV_ILN_370 
912 |a GBV_ILN_374 
912 |a GBV_ILN_602 
912 |a GBV_ILN_636 
912 |a GBV_ILN_647 
912 |a GBV_ILN_702 
912 |a GBV_ILN_2001 
912 |a GBV_ILN_2003 
912 |a GBV_ILN_2004 
912 |a GBV_ILN_2005 
912 |a GBV_ILN_2006 
912 |a GBV_ILN_2007 
912 |a GBV_ILN_2009 
912 |a GBV_ILN_2010 
912 |a GBV_ILN_2011 
912 |a GBV_ILN_2014 
912 |a GBV_ILN_2015 
912 |a GBV_ILN_2018 
912 |a GBV_ILN_2020 
912 |a GBV_ILN_2021 
912 |a GBV_ILN_2025 
912 |a GBV_ILN_2026 
912 |a GBV_ILN_2027 
912 |a GBV_ILN_2031 
912 |a GBV_ILN_2034 
912 |a GBV_ILN_2037 
912 |a GBV_ILN_2038 
912 |a GBV_ILN_2039 
912 |a GBV_ILN_2044 
912 |a GBV_ILN_2048 
912 |a GBV_ILN_2049 
912 |a GBV_ILN_2050 
912 |a GBV_ILN_2055 
912 |a GBV_ILN_2057 
912 |a GBV_ILN_2059 
912 |a GBV_ILN_2061 
912 |a GBV_ILN_2064 
912 |a GBV_ILN_2065 
912 |a GBV_ILN_2068 
912 |a GBV_ILN_2070 
912 |a GBV_ILN_2086 
912 |a GBV_ILN_2088 
912 |a GBV_ILN_2093 
912 |a GBV_ILN_2106 
912 |a GBV_ILN_2107 
912 |a GBV_ILN_2108 
912 |a GBV_ILN_2110 
912 |a GBV_ILN_2111 
912 |a GBV_ILN_2112 
912 |a GBV_ILN_2113 
912 |a GBV_ILN_2116 
912 |a GBV_ILN_2118 
912 |a GBV_ILN_2119 
912 |a GBV_ILN_2122 
912 |a GBV_ILN_2129 
912 |a GBV_ILN_2143 
912 |a GBV_ILN_2144 
912 |a GBV_ILN_2147 
912 |a GBV_ILN_2148 
912 |a GBV_ILN_2152 
912 |a GBV_ILN_2153 
912 |a GBV_ILN_2188 
912 |a GBV_ILN_2190 
912 |a GBV_ILN_2193 
912 |a GBV_ILN_2232 
912 |a GBV_ILN_2336 
912 |a GBV_ILN_2446 
912 |a GBV_ILN_2470 
912 |a GBV_ILN_2472 
912 |a GBV_ILN_2507 
912 |a GBV_ILN_2522 
912 |a GBV_ILN_2548 
912 |a GBV_ILN_2946 
912 |a GBV_ILN_2949 
912 |a GBV_ILN_2951 
912 |a GBV_ILN_4012 
912 |a GBV_ILN_4035 
912 |a GBV_ILN_4037 
912 |a GBV_ILN_4046 
912 |a GBV_ILN_4112 
912 |a GBV_ILN_4125 
912 |a GBV_ILN_4126 
912 |a GBV_ILN_4242 
912 |a GBV_ILN_4246 
912 |a GBV_ILN_4249 
912 |a GBV_ILN_4251 
912 |a GBV_ILN_4305 
912 |a GBV_ILN_4306 
912 |a GBV_ILN_4307 
912 |a GBV_ILN_4313 
912 |a GBV_ILN_4322 
912 |a GBV_ILN_4323 
912 |a GBV_ILN_4324 
912 |a GBV_ILN_4325 
912 |a GBV_ILN_4326 
912 |a GBV_ILN_4333 
912 |a GBV_ILN_4334 
912 |a GBV_ILN_4335 
912 |a GBV_ILN_4336 
912 |a GBV_ILN_4338 
912 |a GBV_ILN_4346 
912 |a GBV_ILN_4393 
912 |a GBV_ILN_4700 
951 |a AR 
952 |d 25  |j 2016  |e 1  |h 19-27