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Journal of Arid Land
Journal of Arid Land  2023, Vol. 15 Issue (3): 327-343    DOI: 10.1007/s40333-023-0096-2     CSTR: 32276.14.s40333-023-0096-2
Research article     
Modern pollen assemblages and their relationships with vegetation and climate on the northern slopes of the Tianshan Mountains, Xinjiang, China
ZHANG Wensheng1, AN Chengbang1,*(), LI Yuecong2, ZHANG Yong1, LU Chao1, LIU Luyu1, ZHANG Yanzhen1, ZHENG Liyuan1, LI Bing2, FU Yang1, DING Guoqiang1
1Key Laboratory of Western China's Environmental Systems, Ministry of Education, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
2College of Geographical Sciences, Hebei Normal University, Shijiazhuang 050024, China
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Abstract  

The reconstruction of paleovegetation and paleoclimate requires an understanding of the relationships between surface pollen assemblages and modern vegetation and climate. Here, we analyzed the characteristics of surface pollen assemblages across different vegetation zones in the Tianshan Mountains. Using surface pollen analysis and vegetation sample surveys at 75 sites on the northern slopes of the Tianshan Mountains, we determined the correlation between the percentage of dominant pollen types and the corresponding vegetation cover. Redundancy analysis was used to investigate the relationships between surface pollen assemblages and environmental factors. Our results show that the Tianshan Mountains contain several distinct ecological regions, which can be divided into five main vegetation zones from low to high altitudes: mountain desert zone (Hutubi County (HTB): 500-1300 m; Qitai County (QT): 1000-1600 m), mountain steppe zone (HTB: 1400-1600 m; QT: 1650-1800 m), mountain forest zone (HTB: 1650-2525 m; QT: 1850-2450 m), subalpine meadow zone (HTB: 2550-2600 m; QT: 2500-2600 m), and alpine mat vegetation zone (HTB: 2625-2700 m; QT: 2625-2750 m). The surface pollen assemblages of different vegetation zones can accurately reflect the characteristics of the mountainous vegetation patterns on the northern slopes of the Tianshan Mountains when excluding the widespread occurrence of Chenopodiaceae, Artemisia, and Picea pollen. Both average annual precipitation (Pann) and annual average temperature (Tann) affect the distribution of surface pollen assemblages. Moreover, Pann is the primary environmental factor affecting surface pollen assemblages in this region. A significant correlation exists between the pollen percentage and vegetation cover of Picea, Chenopodiaceae, Artemisia, and Asteraceae. Moreover, Picea, Chenopodiaceae, and Artemisia pollen are over-represented compared with their corresponding vegetation cover. The Asteraceae pollen percentage roughly reflects the distribution of a species within the local vegetation. These results have important implications for enhancing our understanding of the relationship between surface pollen assemblages and modern vegetation and climate.

Key words： surface pollen assemblages      environmental factors      vegetation cover      redundancy analysis      Tianshan Mountains     
Received: 20 August 2022      Published: 31 March 2023
Corresponding Authors: * AN Chengbang (E-mail: cban@lzu.edu.cn)
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ZHANG Wensheng
AN Chengbang
LI Yuecong
ZHANG Yong
LU Chao
LIU Luyu
ZHANG Yanzhen
ZHENG Liyuan
LI Bing
FU Yang
DING Guoqiang
Cite this article:

ZHANG Wensheng, AN Chengbang, LI Yuecong, ZHANG Yong, LU Chao, LIU Luyu, ZHANG Yanzhen, ZHENG Liyuan, LI Bing, FU Yang, DING Guoqiang. Modern pollen assemblages and their relationships with vegetation and climate on the northern slopes of the Tianshan Mountains, Xinjiang, China. Journal of Arid Land, 2023, 15(3): 327-343.

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http://jal.xjegi.com/10.1007/s40333-023-0096-2     OR     http://jal.xjegi.com/Y2023/V15/I3/327

Fig. 1 Location (a and c) and vegetation distribution (b and d) of surface pollen sampling sites in the regions of Qitai County (QT) and Hutubi County (HTB) (drawn from Hou (2001))
Lab
number		 Sample number Latitude Longitude Altitude
(m)		 Lab
number		 Sample number Latitude Longitude Altitude
(m)
1 HTB1 44°09′19′′N 86°34′16′′E 500 39 HTB39 43°43′31′′N 86°19′19′′E 2675
2 HTB2 44°07′51′′N 86°33′18′′E 600 40 HTB40 43°43′31′′N 86°19′20′′E 2700
3 HTB3 44°06′37′′N 86°32′51′′E 650 41 QT1 43°52′26′′N 89°32′40′′E 1000
4 HTB4 44°04′49′′N 86°32′14′′E 700 42 QT2 43°49′45′′N 89°32′01′′E 1100
5 HTB5 44°04′02′′N 86°32′13′′E 750 43 QT3 43°47′25′′N 89°31′33′′E 1200
6 HTB6 43°59′31′′N 86°30′55′′E 850 44 QT4 43°45′18′′N 89°31′47′′E 1300
7 HTB7 43°57′52′′N 86°30′27′′E 950 45 QT5 43°43′53′′N 89°33′44′′E 1400
8 HTB8 43°56′54′′N 86°28′24′′E 1000 46 QT6 43°40′24′′N 89°38′09′′E 1500
9 HTB9 43°55′13′′N 86°25′54′′E 1100 47 QT7 43°39′12′′N 89°37′31′′E 1600
10 HTB10 43°53′43′′N 86°24′24′′E 1200 48 QT8 43°38′34′′N 89°37′32′′E 1650
11 HTB11 43°52′07′′N 86°23′26′′E 1300 49 QT9 43°38′15′′N 89°36′57′′E 1700
12 HTB12 43°50′39′′N 86°22′36′′E 1400 50 QT10 43°37′27′′N 89°36′59′′E 1750
13 HTB13 43°50′35′′N 86°21′49′′E 1500 51 QT11 43°37′04′′N 89°36′51′′E 1800
14 HTB14 43°50′30′′N 86°21′01′′E 1600 52 QT12 43°36′58′′N 89°36′02′′E 1850
15 HTB15 43°49′41′′N 86°17′22′′E 1650 53 QT13 43°35′56′′N 89°36′50′′E 1900
16 HTB16 43°49′37′′N 86°17′27′′E 1700 54 QT14 43°35′31′′N 89°36′47′′E 1950
17 HTB17 43°49′35′′N 86°17′34′′E 1750 55 QT15 43°34′36′′N 89°36′50′′E 2000
18 HTB18 43°47′36′′N 86°19′37′′E 1800 56 QT16 43°34′05′′N 89°36′46′′E 2050
19 HTB19 43°47′20′′N 86°19′27′′E 1850 57 QT17 43°33′42′′N 89°36′43′′E 2100
20 HTB20 43°47′07′′N 86°19′16′′E 1900 58 QT18 43°33′11′′N 89°36′39′′E 2150
21 HTB21 43°46′55′′N 86°19′12′′E 1950 59 QT19 43°32′42′′N 89°36′39′′E 2200
22 HTB22 43°46′54′′N 86°19′16′′E 2000 60 QT20 43°32′21′′N 89°36′43′′E 2250
23 HTB23 43°46′15′′N 86°18′37′′E 2050 61 QT21 43°32′04′′N 89°36′40′′E 2300
24 HTB24 43°46′01′′N 86°18′36′′E 2100 62 QT22 43°31′36′′N 89°36′22′′E 2350
25 HTB25 43°47′12′′N 86°16′01′′E 2150 63 QT23 43°31′20′′N 89°36′16′′E 2400
26 HTB26 43°47′08′′N 86°15′53′′E 2200 64 QT24 43°30′59′′N 89°36′13′′E 2450
27 HTB27 43°47′07′′N 86°15′50′′E 2250 65 QT25 43°30′41′′N 89°36′10′′E 2500
28 HTB28 43°44′01′′N 86°20′27′′E 2300 66 QT26 43°30′27′′N 89°36′10′′E 2525
29 HTB29 43°43′55′′N 86°20′14′′E 2350 67 QT27 43°30′35′′N 89°36′16′′E 2550
30 HTB30 43°43′48′′N 86°19′59′′E 2400 68 QT28 43°30′34′′N 89°36′18′′E 2575
31 HTB31 43°43′43′′N 86°20′04′′E 2450 69 QT29 43°30′34′′N 89°36′20′′E 2600
32 HTB32 43°43′44′′N 86°20′06′′E 2500 70 QT30 43°30′09′′N 89°36′15′′E 2625
33 HTB33 43°43′44′′N 86°19′30′′E 2525 71 QT31 43°30′02′′N 89°36′11′′E 2650
34 HTB34 43°43′42′′N 86°19′20′′E 2550 72 QT32 43°30′09′′N 89°36′19′′E 2675
35 HTB35 43°43′37′′N 86°19′17′′E 2575 73 QT33 43°29′58′′N 89°36′13′′E 2700
36 HTB36 43°43′36′′N 86°19′17′′E 2600 74 QT34 43°29′49′′N 89°36′03′′E 2725
37 HTB37 43°43′35′′N 86°19′17′′E 2625 75 QT35 43°29′45′′N 89°36′04′′E 2750
38 HTB38 43°43′32′′N 86°19′17′′E 2650
Table 1 Longitude, latitude, and altitude of the sampling sites
Fig. 2 Pollen percentage and concentration spectra of surface samples in the region of HTB on the northern slopes of the central Tianshan Mountains. A/C, Artemisia/Chenopodiaceae ratio; AP/NAP, arboreal pollen/non-arboreal pollen ratio; Zone Ⅰ, mountain desert zone; Zone II, mountain steppe zone; Zone III, mountain forest zone; Zone IV, subalpine meadow zone; Zone V, alpine mat vegetation zone.
Fig. 3 Pollen percentage and concentration spectra of surface samples in the region of QT on the northern slopes of the eastern Tianshan Mountains
Fig. 4 Redundancy analysis of pollen samples and environmental factors in the study area. Pann, average annual precipitation; Tann, annual average temperature; TJul, July mean temperature; TJan, January mean temperature; PJul, July mean precipitation; PJan, January mean precipitation.
Fig. 5 Characteristics of vegetation cover and pollen percentage changes of dominant pollen types in the regions of HTB (a-d) and QT (e-h) under different altitude conditions
Fig. 6 Scatter plots of the relationships between the pollen percentage of dominant pollen types and their vegetation cover. (a), Picea; (b), Chenopodiaceae; (c), Asteraceae; (d), Artemisia. R-value is the representativeness of dominant pollen types, and ** indicates significant correlation at the 0.01 level.
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