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 DOI: 10.1177/0959683611400202

2011 21: 997 originally published online 19 April 2011The HoloceneYuecong Li, M. Jane Bunting, Qinghai Xu, Suxue Jiang, Wei Ding and Lingyun Hun

Chinaclimate relationships in some desert and desert-steppe communities in northern−vegetation−Pollen

  

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Research paper

Introduction

Quantitative, spatially explicit reconstruction of past vegetation from pollen records has been one of the ultimate goals of pollen analysts from the start of the discipline (von Post, 1916). One approach to reconstruction is to model the pollen–vegetation relationship by conceptualizing the relationship, expressing it algebraically and estimating any parameters necessary for a quantitative reconstruction. Recent developments in this type of reconstruction approach have used the Prentice-Sugita model, which assumes that aerial transport is the dominant mode of dis-persal of pollen grains and characterizes the pollen–vegetation relationship as a taxon-specific linear relationship between pollen influx and distance-weighted vegetation abundance (e.g. Prentice, 1985, 1988; Sugita, 1993). In this model, each taxon is characterized by a measure of relative pollen production (the amount of pollen produced per unit area relative to a specified reference taxon, referred to as Relative Pollen Productivity (RPP)) and a measure of the dispersability of the pollen type in air (the sedimentation velocity, often approximated as the fall speed of the grain). Once these characters are defined for each taxon, it is possible to use an inverse form of the relationship to reconstruct past vegetation cover from pollen assemblages using approaches such as the Landscape Reconstruction Algorithm (LRA; Sugita, 2007a, b) or the Multiple Scenario Approach (MSA, Bunting and Middleton, 2009).

Fall speed can be estimated from measurements of grain geometry and assumptions about grain density (e.g. Gregory,

1973; Sugita et al., 1999) or measured directly (e.g. Di-Giovanni et al., 1995; McCubbin, 1918). RPP can be estimated from vege-tation data and percentage pollen data from surface samples using an iterative method, the extended R-value approach (Parsons and Prentice, 1981; Prentice and Parsons, 1983). RPP and fall speed have been estimated for major plant taxa in various locations in temperate northern Europe and North America (e.g. Broström et al., 2004, 2008; Bunting et al., 2005; Calcote, 1995; Sugita et al., 1999), although differences in vegetation data collection methodology for RPP estimates mean that there is still consider-able uncertainty about the actual value and geographical consis-tency (or otherwise) of these values (e.g. Broström et al., 2008; Bunting and Hjelle, 2010).

In China, pollen–vegetation relationships have been widely studied, but most studies focus on the characters of pollen assem-blages. Only a few papers (Herzschuh et al., 2003, 2004; Li et al.,

400202 HOL21610.1177/0959683611400202Li et al.The Holocene

1Hebei Normal University, China2University of Hull, UK

Received 20 April 2010; revised manuscript accepted 20 November 2010

Corresponding author:Qinghai Xu, College of Resources and Environment, and Hebei Key Laboratory of Environmental Change and Ecological Construction, Hebei Normal University, Shijiazhuang, 050016, ChinaEmail: xuqinghai@mail.hebtu.edu.cn

Pollen–vegetation–climate relationships in some desert and desert-steppe communities in northern China

Yuecong Li,1 M. Jane Bunting,2 Qinghai Xu,1 Suxue Jiang,1 Wei Ding1 and Lingyun Hun1

AbstractIn this paper, we consider the relationship between pollen assemblages, vegetation and climate in some desert and desert-steppe areas in northern China using both surface soil samples and pollen trap samples. Discriminant analysis shows that samples originating from different climatic or geographical regions can be separated reliably on the basis of pollen assemblage regardless of sample type. DCCA analysis indicates that surface soil pollen assemblages show significant correlations with climate parameters. DCCA Axis 1 is negatively correlated with the mean temperature in the warmest month (MTwa; r = −0.58), whilst axis 2 is positively correlated with mean annual precipitation (Pann; r = −0.73). Artemisia-to-Chenopodiaceae ratios are generally lower in desert areas than in desert-steppe areas. Pollen productivity relative to Chenopodiaceae (RChenopodiaceae) was estimated using least-squares linear regression of pollen influx data against vegetation data and ERV model analysis of percentage pollen data against vegetation data. Rank order of RChenopodiaceae is consistent regardless of data set or analysis method. Artemisia has RChenopodiaceae values greater than 3, whilst RChenopodiaceae Nitraria is around 0.1 and RChenopodiaceae Poaceae is below 0.1. Our results provide useful information for quantitative reconstructions of paleovegation and paleoclimate in arid or semi-arid Asia.

KeywordsArtemisia-to-Chenopodiaceae (A/C) ratios, detrended canonical correspondence analysis (DCCA), discriminant analysis, extended R-value (ERV) analysis, modern pollen assemblages, Relative Pollen Productivity (RPP)

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2000, 2005; Wong et al., 1993; Xu et al., 2007) attempt to cali-brate the pollen–vegetation relationship for individual taxa. No studies so far have estimated RPP using the extended R-value approach or used pollen influx data to parameterize the pollen–vegetation relationship.

In this paper, we compare pollen and vegetation data from des-ert, semi-desert and desert-steppe areas in northern China. Multi-variate statistical methods are used to investigate spatial patterns in the pollen assemblage data. Estimates of fall speed are made for the main pollen taxa. RPP is estimated for the major pollen types from pollen influx data from pollen traps and from applying the extended R-value approach to percentage data from surface samples.

Study area

Samples were collected from Northern China in the west Ordos and East Alashan Plateau area (see Figure 1), a broad plateau with elevation typically 1000–1200 m a.s.l. The zonal vegetation is desert, semi-desert and desert-steppe. Mean annual temperature (Tann) ranges from 6°C to 9°C, and mean annual precipitation (Pann) from 80 mm to 300 mm. The study area is dissected by the Helan Mountains (38°10′–39°30′N and 105°45′–106°45′E) which form a climatic and vegetational divide. Most of the area east of the Helan Mountains is desert-steppe, dominated by plant species such as Stipa klemenzii, Cleistogenes keng, Artemisia frigida and Caragana tibetica. Plant coverage is normally above 35%, mean

Figure 1. Sketch map showing the location of the study area and of surface soil samples collected in 2002 (solid symbols) and 2004 (open symbols). Samples are divided into biogeographic groups as follows: Group 1 (desert: Pann (annual precipitation) < 150 mm), triangles; Group 2 (semi-desert: 50 mm < Pann < 250 mm), circles; and Group 3 (desert-steppe: Pann > 250 mm), squares. Vegetation types are explained here and local communities defined in Table 1: 1, Sympegma regelii rock-desert; 2, Reaumuria soongorica gravel desert; 3, Anabasis brevifolia gravel desert; 4, Kalidium foliatum salt desert; 5, Ceratoides lateens sand and gravel desert; 6, Potaninia mongolica-Ammopiptanthus mongolicus-Tetraena mongolica sand and gravel desert; 7, Artemisia ordosica-A. sphaerocephala sand desert; 8, Stipa bungeana-S. breviflora desert-steppe; 9, S gobica desert-steppe; 10, Haloxylon ammodendron-Reaumuria soongorica desert; 11, Poaceae salt-meadow; 12, Caragana intermedia-Salix flavida-S. psammonphila- Artemisia shrub; 13, alpine Stipa steppe; 14, Picea crassifolia forest; 15, farmland

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annual precipitation in the range 250–300 mm. In contrast, most of the area to the west of the Helan Mountains has a desert climate and vegetation, with vegetation coverage usually less than 30% and the dominant plant taxa include Artemisia, Nitraria, Salola, Suaeda, Reaumuria and Zygophyllum. Mean annual precipitation is usually less than 150 mm (Wu, 1980; Zhang, 2006). Close to the Helan Mountains the vegetation is typically semi-desert, where plant coverage is normally in the range 25–40% and Nitraria, Artemisia desterorum, Reaumaria soongorica and Stipa klemenzi the dominant plant taxa. Mean annual precipitation usu-ally ranges from 150 to 250 mm.

Materials and methodsField methodsFigure 1 shows a generalized vegetation map of the study area and the locations of sample points. Sample points were located every 30–40 km along a series of transects through the region, and each point was placed within an area of apparently uniform vegetation which was at least 1000 m in extent. Samples were collected in 2002 (27 samples) and in 2004 (37 samples) (see Figure 1). Vegetation cover (total ground cover and frequency of main species present) was recorded within a 10 m radius of the sampling point. Table 1 summarises the vegetation communities sampled.

Two types of pollen data were collected. At all points, surface soil samples were collected by scraping the upper 1–2 cm of soil

from a randomly chosen location. Pollen assemblages from soil samples are assumed to represent accumulation over several years (Adam and Mehringer, 1975), but in these dry climates soils do not offer optimum conditions for pollen preservation. At each location in 2004, Tauber traps were also deployed for one calen-dar year to record pollen influx. Traps consisted of a bucket 10 cm in diameter and 30 cm in height with a specially made lid having a 5.2 cm diameter aperture surrounded by a sloped collar. The trap was sunk into the ground at each site such that the opening was 4–5 cm above the ground surface to minimize the risk of sur-rounding surface soil getting into the trap whilst still allowing comparison with the ground level assemblages from the surface soil samples. When these locations were revisited in 2005, only 24 of the original 37 traps deployed were recovered.

Climate data

Climate data for each sample point were estimated using an interpolation method. Daily records from 243 meteorological stations in northern China were taken from the Chinese Meteoro-logical Statistical Annals (supplied by the Chinese National Weather Service) and averaged for the 30 year period from 1961 to 1990. Pann, Tann, MTwa (mean temperature of the warmest month) and MTco (mean temperature of the coldest month) were estimated using the minimum distance method in Polation1.0 (Nakagawa, unpublished, 2006) and corrections for altitudinal variations in temperature were made using the universal temperature lapse

Table 1. Vegetation communities sampled in the desert and desert steppe regions in the north of China

Code Dominant plant taxon/each community Number of sample points Groupa

Desert 1 Artemisia ordosica or Artemisia sphaerocephala 4 Group12 Nitraria sibirica, Alhagi sparsifolia, Reaumaria soongorica 2 Group13 Nitraria sphaerocarpa, N. sibirica 2 Group14 Reaumaria soongorica, Nitraria sibirica, N. sphaerocarpa 5 Group15 Salsola arbuscula 2 Group16 Salsola passerine, Nitraria sibirica, N. sphaerocarpa 3 Group17 Salsola passerine, Reaumaria soongorica, Nitraria sibirica, N. sphaerocarpa 2 Group18 Tamarix chinensis, Salsola passerine, Nitraria sibirica, N. sphaerocarpa 2 Group19 Tetraena mongolica 5 Group1

Semi-desert 10 Artemisia desterorum 1 Group211 Nitraria sibirica, Reaumaria soongorica, Oxytropis aciphylla 4 Group212 Nitraria sibirica 1 Group213 Reaumaria soongorica 4 Group214 Reaumaria soongorica, Stipa klemenzii 2 Group215 Reaumaria soongorica, Cleistogenes 2 Group216 Stipa klemenzii, Suaeda glauca, Salsola passerina 3 Group217 Stipa klemenzii, Artemisia frigida 2 Group218 Cleistogenes, Stipa klemenzii, Reaumaria soongorica 1 Group2

Desert-steppe 19 Artemisia ordosica, Cleistogenes songorica 2 Group320 Artemisia ordosica 3 Group321 Leymus chinensis 1 Group322 Artemisia ordosica, Stipa klemenzii 2 Group323 Artemisia ordosica,Caragana intermedia 3 Group324 Caragana intermedia, C. korshinskii, C. tibetica 3 Group325 Chenopodium aristatum 1 Group326 Stipa klemenzii, Cleistogenes squarrosa 1 Group3

aGroup 1 (desert) = Pann (annual precipitation) < 150 mm. Group 2 (desert steppe) = 150 mm < Pann < 250 mm. Group 3 (desert-steppe) = Pann > 250 mm.

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rate of 0.60°C/100 m. The accuracy of the interpolation method was tested by the leave-one-out method. Correlation coefficients between estimated and observed values averaged 0.95 for Pann and ranged from 0.96 to 0.99 for the temperature parameters (Li et al., 2007).

Laboratory methods

Subsamples of 10 g soil were prepared for pollen analysis following standard methods (Faegri and Iversen, 1989). One Lycopodium tablet was added as an exotic tracer (Stockmarr, 1971). Soil or pollen trap samples were sieved through a 200 µm mesh to remove larger plant fragments, and then treated with standard methods (Faegri and Iversen, 1989). The pollen-rich fraction was further concentrated by sieving through 10 µm mesh screens, fol-lowed by heavy liquid separation (using a solution of HI and KI at specific-gravity 1.9 to 2.2 g/cm3) and acetolysis treatment if nec-essary. Residues were then mounted in glycerine for counting.

Both surface soil pollen and trap pollen were identified and counted with a BX-51 Olympus light microscope at 400× magni-fication. More than 400 pollen grains were counted for most sam-ples. Percentages were calculated based on sum of all the pollen and spore taxa. For pollen trap samples, annual pollen influx was calculated in units of grains/cm2 of the opening. Assemblages are summarised in Figure 2.

Data analysis methods

Ordination analysis. Relationships between pollen assemblages and climate were explored using two approaches. First, Discrimi-nant Analysis was used to identify whether there was a significant difference between pollen assemblages from different areas (e.g. Horrocks and Ogden, 1994; Liu and Lam, 1985; Reese and Liu, 2005). Pann values were used to assign samples to one of three biogeographic groups, desert (group 1: generally Pann <150 mm), semi-desert (group 2: generally 150 mm < Pann < 250 mm) and desert-steppe (group 3: generally 250 mm < Pann < 300 mm) and discriminant analysis was carried out using SPSS11.5.

Detrended canonical correspondence analysis (DCCA) was carried out to explore the relationship between individual pollen assemblages and climate parameters. Pollen data were trans-formed using a square-root transformation to stabilise the vari-ance and maximise the ‘signal to noise’ ratio in the data. DCCA was carried out using Canoco4.0 (ter Braak and Smilauer, 1998). To eliminate co-linearity, variance inflation factors (VIFs) were calculated for each climatic parameter. If the VIF value of one climatic parameter is higher than 20, this indicates that the param-eter is co-linear with at least one other parameter, and the param-eter is removed from the data set. Analysis is then repeated on the reduced parameter set until all VIF values are lower than 20 (Lv et al., 2006; ter Braak and Prentice, 1988).

Relative pollen productivity estimates. In this study, we aim to estimate RPP for some major taxa, as a first step towards quantita-tive reconstruction of past vegetation patterns and processes. Since pollen influx and vegetation cover values are independent variables for each taxon, it is possible to directly apply a linear model (in this case, least-squares linear regression) and estimate pollen productivity from the slope of the best-fit line. The linear model can be written in the form:

yik i ik ia | ~= + (1)

where: yik is pollen influx of taxon i at site k; αi is pollen pro-ductivity of taxon i relative to a specified reference taxon; χik is distance-weighted plant abundance of taxon i around site k within the pollen source area; ωi is regional pollen signal component of type i, sourced from beyond the pollen source area, and constant between sampling locations.

It is possible to estimate αi directly from pollen influx data for one year, and therefore RPP in that year could be estimated by linear regression of distance-weighted plant abundance against pollen influx.

The time period represented by the soil sample pollen assem-blages is not as clearly defined, so assemblages are expressed as pollen percentages. Since percentage data are interdependent, the relationship between distance weighted plant abundance and pollen percentage will not be linear. RPP was therefore esti-mated using the extended R-value (ERV) approach, which uses a maximum likelihood approach to estimate parameters for the pollen–vegetation relationship (Parsons and Prentice, 1981; Prentice and Parsons, 1983). ERV models are based on this equation:

P f zik i ik k ia o= + (2)

Where: Pik is pollen percentage of taxon i at site k; vik is distance-weighted vegetation data for taxon i out to a specified distance z around site k; fk is a ‘site correction factor’ for site k: a function of the α and z values of all taxa occurring at site k (formula for cor-rection factor varies with ERV model chosen).

ERV model 1 assumes that the background pollen compo-nent for each taxon is constant relative to the total pollen influx at the site, whilst model 2 assumes that the component is con-stant relative to the total vegetation abundance around the site. In discontinuous vegetation, as found at many of the sample locations in this study, the model 1 assumption is probably more robust. ERV analysis was carried out using PolERV software (Middleton, unpublished data, 2004; Sugita, personal communi-cation). RPP was estimated for the four main plant taxa, Arte-misia, Chenopodiaceae, Nitraria and Poaceae, since they were present in most samples and had the widest range of vegetation abundance values. Chenopodiaceae was chosen as the reference taxa because of moderate productivities compared with other three taxa in the study area and because it occurred at a wide range of vegetation abundances and pollen percentages in the data set.

Vegetation data was only recorded at a single distance around each sample point during field work but sample points were deliberately located in areas of uniform vegetation. This uniform vegetation was divided into concentric rings centred on the sam-ple point, and distance-weighted plant abundance calculated for the main taxa using the Sutton form of vegetation distance weighting (Prentice, 1985; Sutton, 1953), which weights each taxon differently according to its aerodynamic behaviour as esti-mated from the fall speed. Fall speeds were estimated using mea-surements of 30 grains of each type taken from pollen slides from the study area, using the geometric method described by Gregory (1973). Estimated fall speeds of Artemisia, Chenopodiaceae, Nitraria and Poaceae are 0.0096, 0.0090, 0.0162 and 0.0231 m/s, respectively.

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ResultsPollen assemblages

Figure 2a shows the main components of the soil sample pollen assemblages. A total of 82 pollen taxa were identified from the 64 surface soil samples, including 16 arboreal pollen taxa, 23 shrubby pollen taxa and 43 herb pollen taxa. Most assemblages are dominated by Artemisia, Chenopodiaceae, Poaceae and Nitraria. Tauber trap assemblages are summarised in Figure 2b. 56 pollen taxa (10 arboreal types, 18 shrubs and 38 herbaceous taxa) and 2 spore taxa were identified, all of which were also found in the surface soil samples. Artemisia, Chenopodiaceae, Poaceae and Nitraria are still dominant, making up over 50% of most samples, however Asteraceae, Fabaceae and Rosaceae are also common (10–70%) (see Figure 2a). Annual influx is gener-ally 1–2 × 104 grains/cm2 per yr (see Figure 2c).

The Artemisia-to-Chenopodiaceae (A/C) ratios (see Figure 3) for most samples (about 70%) in desert communities were lower

than 0.5 and 30% of the samples had values in the range of 0.5–3, which is consistent with the results of other studies in similar areas (Wong et al., 1993; Yan, 1991; Zhao et al., 2008, 2009). In semi-desert or desert-steppe communities the A/C ratios were between 0.5 and 1.2 for only three samples, whilst most other values (27 samples; about 84%) ranged from 1.2 to 23, which is different from previous studies in similar areas (Herzschuh, 2007; Herzschuh et al., 2004; Wong et al., 1993; Yan, 1991; Zhao et al., 2008, 2009).

Ordination analysis results

The 24 trap samples included 15 from biogeographic group 1, 3 from group 2 and 6 from group 3. Discriminant analysis using all taxa showed that 100% of samples could be correctly classi-fied into their original groups (see Figure 4a). The 64 surface soil samples included 18 from group 1 (all from the desert area to the west of the Helan mountain), 32 from group 2 (all located

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Figure 3. Pollen percentage ratio of Artemisia to Chenopodiaceae for 64 soil surface samples. Samples are divided into biogeographic groups as follows: Group 1 (desert) = Pann (annual precipitation) < 150 mm. Group 2 (semi-desert) = 150 mm < Pann < 250 mm. Group 3 (desert-steppe) = Pann > 250mm, then grouped by year of sample collection. The figure shows that A/C ratios are the lowest in desert both in 2002 and 2004, and highest in desert-steppe in 2004, while A/C ratios are similar for group2 and 3 in 2002

Figure 4. Results of discriminant analysis of pollen assemblages. Samples are divided into biogeographic groups as follows: Group 1 (desert) = Pann (annual precipitation) < 150 mm; Group 2 (semi-desert) = 150 mm < Pann < 250 mm. Group 3 (desert-steppe) = Pann > 250 mm. (a) Pollen influx data from 24 Tauber traps; (b) pollen percentages from 64 soil surface samples. The results indicate the biogeographic groups can be seperated both for soil samples and for pollen traps

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Li et al. 1005

south of the Helan mountain) and 14 from group 3 (typically from the west Ordos). Discriminant analysis using all pollen taxa indicated that 93.8% of desert-steppe samples (94.7% of group 2 and 94.4% of group 3) and 96.3% of desert samples could be correctly classified. Cross-validation suggested less success in classifying group 2 (44.7% correct) and group 3 (66.7%), but overall these results show strong geographical pat-terning, implying that there is a strong climate signal within the pollen data (see Figure 4b).

The pollen assemblage–climate relationship was explored in more detail using DCCA. Checking for co-linearity among environmental variables gave a VIF value of 170 for Tann, so this parameter was removed from further analysis. Repeat DCCA of the remaining variables producing VIF values below 20 for all. The results (Table 2) show that the correlation coef-ficients between axis 1, axis 2 and climate parameters were 0.76 and 0.77, respectively. Axis 1 is negatively correlated with MTwa (r = −0.58) whilst axis 2 is positively correlated with Pann (r = −0.73) (Table 2). Pollen assemblages are therefore strongly reflecting climate variation. Desert (group 1) and desert-steppe (groups 2 and 3) samples can be distinguished on the basis of sample scores on the DCCA axes (Figure 5), and within the semi-desert or desert-steppe group samples have only limited overlap.

Relative pollen productivity values

Correlation between distance-weighted plant abundance and pollen influx was calculated for each biome (desert, group 1 and desert-steppe, groups 2 and 3) separately and for all locations combined (Table 3). The results indicated that Chenopodiaceae shows a statis-tically significant correlation (p < 0.05) between pollen and vegeta-tion abundance in all three data sets and also has the largest range of values for both pollen and vegetation data, therefore it is selected as the reference taxon for estimation of RPP. Nitraria and Artemisia also show strong significant correlation (p < 0.005) using the whole data set, but correlation is weak for Artemisia when only desert-steppe samples are considered and for Nitraria when only desert samples are considered. Poaceae only shows a significant correla-tion (p < 0.05) when desert samples are considered. RPP estimates from pollen influx are summarised in Table 4a and Figure 6a.

ERV analysis was then carried out on pollen percentages from soil samples, compared with distance-weighted plant abundance. Models 1 and 2 produce similar likelihood function scores for all subsets, suggesting that they are generating parameterised models giving similar goodness of fit. The rank order of RChenopodiaceae (relative pollen productivity estimated relative to Chenopodia-ceae) is always the same as the direct estimates from the influx plots, but the actual values using ERV model 2 are generally higher than ERV model 1 or direct estimate from influx. The model 1 assumption is considered to be more appropriate for the data (see above) than model 2, and model 1 results only are pre-sented from other ERV analyses.

Table 4b and c and Figure 6b summarise the results of ERV analysis from the soil surface sample data set. All samples were analysed together, but since the multivariate analyses showed a strong climate effect, we also analysed subsets of data based on biogeographic group and on year of sample collection. When bio-geographic group only is considered (Figure 6b), differences are apparent. All pollen taxa RChenopodiaceae are higher in desert samples than in desert-steppe samples. This might reflect different mixes of plant species contributing to each palynological taxon or reflect changes in reproductive strategy in response to environmental stress by one or more palynological types.

Interannual differences are also apparent (Figure 6c). In gen-eral, Artemisia RChenopodiaceae is higher in 2004 than in 2002, but biogeographic group also affects the value. Artemisia RChenopodiaceae is higher in desert-steppe areas in 2004 than in 2002, but higher in 2002 than 2004 in desert areas. In desert areas, Nitraria and Poaceae RChenopodiaceae are noticeably higher in 2004 than in 2002, but the opposite is seen in the desert-steppe areas.

Axi

s 2

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

DesertSemi-desertDesert-steppe

Axis 1

Figure 5. DCCA analysis of pollen percentage data from 64 surface soil samples; plot shows axis 1 against axis 2. Samples are divided into biogeographic groups as follows: Group 1 (desert) = Pann (annual precipitation) < 150 mm; Group 2 (semi-desert steppe) = 150 mm < Pann < 250 mm; Group 3 (desert-steppe) = Pann > 250 mm The results indicate the biogeographic groups can be seperated very well

Table 2. Summary statistics for the first four axes of DCCA carried out on all surface soil pollen assemblages using three environmental variables

Axis 1 Axis 2 Axis 3 Axis 4

Lengths of gradient: 1.381 1.151 1.144 1.105 Species–environment correlation 0.759 0.769 0.408 0 Total inertia = 0.97Cumulative % variance of species data 5.9 8.6 9.5 15.7 Cumulative % variance of species–environment relationship 46.8 73

Interset correlation of significant environmental variables with axesa

Mean annual temperature (mm) 0.307 0.731 −0.034 −0.106 Mean temperature of warmest month (°C) −0.585 −0.337 0.214 0.029 Mean temperature of coldest month (°C) 0.198 0.242 0.375 0.160

aValues in bold are statistically significant (p < 0.01).

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DiscussionPollen–climate relationship

As previous studies have shown, pollen assemblages from des-ert and desert-steppe regions have a strong climate signal, with a clear gradient in assemblages across the studied area. The pollen assemblages from different biogeographic regions can

be separated with discriminant analysis or DCCA analysis, which also indicated that pollen assemblages contain a clear signal of two climatic factors, mean temperature of the warm-est month and mean annual precipitation, implying that desert and desert-steppe pollen assemblages can be used to recon-struct past climate (e.g. Chen et al., 2006; Feng et al., 2006; Ma et al., 2003).

(b) For 64 surface soil surface samples in different biographic groups with ERV model 1, see text for details

Biogeographic groupsa Group 1 Group 2 + Group 3 All samples

Number of samples 27 37 64Artemisia 4.04 0.87 3.17Poaceae 0.33 0.02 0.014Nitraria 0.64 0.15 0.28Likelihood function score 2566 2823 5480

(c) For 64 soil surface samples in different biographic groups and different year with ERV model 1, see text for details

Biogeographic groupa Group1 Group 2 + Group 3 All groups

Year of sample collection 2002 2004 2002 2004 2002 2004Number of samples 7 20 20 8 28 36Artemisia 4.31 3.86 0.13 6.18 0.46 4.16Poaceae 0.00 0.83 0.005 0.00 0.01 0.03Nitraria – 0.84 0.19 0.09 0.17 0.16Likelihood function score 519 2003 1604 1084 2161 3229

aGroup 1 (desert) = Pann (annual precipitation) < 150 mm. Group 2 (semi-desert) = 150 mm < Pann < 250 mm. Group 3 (desert-steppe) = Pann > 250mm.

Table 3. Least-squares linear regression analysis and estimation of relative pollen productivity of vegetation abundance and pollen influx data set from Tauber trap locations for the four dominant taxa. RChenopodiaceae (pollen productivity relative to Chenopodiaceae) is estimated from the ratio of the slopes of the fitted line

Artemisia Chenopodiaceae Poaceae Nitraria

Group 1 (desert)r 0.93 0.56 0.68 0.39n 14 14 14 13p (one-tailed) <0.005 <0.005 <0.005 0.10>p>0.05RChenopodiaceae 17.55 1 0.42 0.16

Groups 2 and 3 (semi-desert + desert-steppe)r 0.45 0.82 −0.76 −0.14n 10 10 10 10p 0.25>p>0.10 <0.005 n.s.a n.s.RChenopodiaceae 3.86 1 −0.39 −0.11

All samplesr 0.56 0.56 −0.22 0.54n 24 24 24 23p <0.005 <0.005 n.s. <0.005RChenopodiaceae 3.99 1.00 −0.10 0.20

aCorrelation is not significant.

Table 4. Estimates of RChenopodiaceae (pollen productivity relative to Chenopodiaceae)

(a) With different ERV- models for 24 pollen influx data, see text for details

Biogeographic groupsa Group 1 Group 2 + Group 3 All samples

Number of samples 14 10 24

ERV model 1 2 1 2 1 2Artemisia 3.50 9.23 9.83 0.0001 4.29 8.93Poaceae 0.07 0.14 0.00 0.00 0.08 0.03Nitraria 0.11 0.23 – – 0.12 0.16Likelihood function score 967 965 149 148 1218 1201

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Li et al. 1007

A/C ratios and their vegetation and climatic implicationsA/C ratios are normally considered to be sensitive to humidity change, and treated as a reliable index for distinguishing vegeta-tion types and reconstructing paleoclimate in arid regions (El-Moslimany, 1990; Herzschuh, 2007; Herzschuh et al., 2004; Wong et al., 1993; Yan, 1991; Zhao et al., 2008, 2009) using guide-lines of A/C ratio < 0.5 in desert areas, > 1 in typical steppe area, and between 0.5 and c. 1.2 in desert-steppe areas. In this study very few samples (about 10%) from the desert-steppe communities had A/C ratios within this range; most values (27 samples; about 84%) are much higher (ranged from 1.2 to 23). This may suggest that A/C ratios are not only determined by climate, but also reflect local community composition. In this field area, Artemisia-dominated communities can occur in desert areas and Chenopo-diaceae can be a major vegetation component in desert-steppe climate areas. Our results show that pollen percentages for Artemisia and Chenopodiacea have significant correlations with correspond-ing plant cover. Thus if we want to reconstruct a broad picture of past climate, the A/C ratios are useful, but if we want to recon-struct past vegetation composition and distribution, A/C ratio alone is insufficient, and a more complex approach is needed, such as quantitative reconstruction of vegetation composition.

Pollen–vegetation relationships

In this data set (see Figure 1 and Table 1), Artemisia and Chenopo-diaceae pollen percentages are usually higher than 80% suggesting

that they represent the vegetation well. However, even when plants of these taxa are rare or absent in communities, they still dominate pollen assemblages, indicating strong over-representation in the pollen signal. Even where Poaceae or Nitraria dominate the local community, their pollen percentages usually are lower than 20%, indicating under-representation in the pollen signal.

Taxonomic resolution and pollen preservation are two impor-tant factors influencing the pollen record in arid or semi-arid areas of China. The same pollen morphotype is produced by multiple related plant species, and they may have different RPP, since that depends on reproductive biology, partitioning of resources between different functions and climate responses. Although tax-onomic resolution and phenotypic variation within palynological equivalents presents a challenge to pollen analysts, most interpre-tations of palaeo-pollen records assume reasonably consistent relationships between pollen morpho-types and vegetation ele-ments; clearly this is an area which requires further investigation. Since different morphotypes are known to decay at different rates (e.g. Havinga, 1967, 1971, 1984), potential for differential post-depositional losses and subsequent alteration of the assemblage even in subannual timeframes is clearly present in soil. For exam-ple, Artemisia is regarded as being considerably more resistant to decay than Poaceae (e.g. Bunting and Tipping, 2000), which might partly explain why Artemisia is always present at relatively high pollen percentages in soil samples, even when it is rare or absent from the vegetation.

Pollen traps allow measurement of annual influx and protect samples from some soil processes, therefore may represent

Artemisia

Chenopodiaceae

Poaceae

Nitraria

RChenopodiaceae

0 1 2 3 4 5 6 7 8 9

InfluxPercent ERV model 1

Percent ERV model 2

(a)

RChenopodiaceae

0 1 2 3 4

Nitraria

Poaceae

Chenopodiceae

Artemisia

DesertSemi-desert & desert-steppe

All samples

(b)

Chenopodiaceae

Artemisia

Poaceae

Nitraria

RChenopodiaceae

2002

0 1 2 3 4

Desert

Semi-desert & desert-steppeAll samples

(c) Chenopodiaceae

Artemisia

Poaceae

Nitraria

RChenopodiaceae

2004

0 1 2 3 4 5 6 7

DesertSemi-desert & desert-steppe

All samples

(d)

Figure 6. Comparisons of estimated pollen productivity relative to Chenopodiaceae (RChenopodiaceae) for the four main pollen taxa. (a) Comparison of results obtained from Tauber trap data set (24 samples) using three different methods, extended R-value models 1 and 2, or linear regression of influx against distance-weighted plant abundance; (b) comparison of results from soil surface samples (64 samples) from desert and desert-steppe biogeographic groups with results for pooled data; and (c) comparison of results from soil surface samples divided by year of sample collection as well as biogeographic group. No matter which method or which year, the RPPE orders of four taxa are the same

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1008 The Holocene 21(6)

vegetation better than soil samples. In this study, when Artemisia or Chenopodiaceae are rare in the vegetation around the trap sites, the pollen percentages recorded in the traps are lower than in equivalent soil surface samples. Long series of trapping records from the Pollen Monitoring Project (e.g. Hicks et al., 2001) show high interannual variability, and much of this variability cannot be readily detected when screening the data from a single year. Though there are differences in pollen assemblages between years, RPP estimates (Figure 6a, b) are comparable between soil samples and pollen traps, suggesting that both can be used to parameterise paleovegetation reconstruction models.

Reliability of relative pollen productivity

We propose approximate RChenopodiaceae of c. 3 for Artemisia and c. 0.1 for Poaceae and Nitraria for desert and desert-steppe con-texts. These values suggest some important implications for the interpretation of fossil pollen assemblages from desert or steppe environments, including those in the Lateglacial, since Artemisia is apparently strongly over-represented relative to grasses. There-fore interpretation of Artemisia-rich pollen assemblages as Artemisia steppe rather than grass steppe may be flawed; in some cases, pollen percentages of Poaceae around 10% and of Artemisia around 70% can form when Poaceae is more abundant in the vegetation than Artemisia (Li et al., 2005; Liu et al., 1999). That in turn raises interesting questions related to the role of vegetation in the ecosystem, such as carbon balance, climate feedbacks and estimations of biomass production as the basis of a food web supporting large herbivores and in the most recent Lateglacial period hominid populations (e.g. Meng et al., 2009; Wang and Sun, 1994). The data set used here is far from ideal for estimation of relative pollen productivities (e.g. Broström et al., 2008), but these results show some consistent results which allow initial estimates to be made for a region and for taxa which have not previously been investigated.

Problems with the data set are manifold. First, the data were collected from a fairly large geographic area (5° longitude, 3° lati-tude) including three biomes, and interrupted by a mountain range. RPP estimates from northwest Europe have typically been based on samples collected over a smaller geographic region, but their use is then extrapolated across a similarly wide geographic region, therefore we do not consider this a fundamental problem with the approach. A more important problem relates to variations in plant community across this range, since different species mix-tures are represented by the same palynological equivalent taxon. This problem is already recognised in European studies, particu-larly in the context of choice of reference taxon (Broström et al., 2008). Choice of a family-level palynological equivalent for the reference taxon (Chenopodiaceae) is intuitively problematic; however most European studies so far have used a similar mor-photype, Poaceae, therefore the problems are at least consistent across studies. Third, different grazing regimes, whether natural or managed by humans, will potentially affect pollen productivity of grazed taxa, since some plant species show facultative switch-ing between sexual and vegetative reproduction (e.g. Barbara et al., 2003; Teng et al., 2006) in response to herbivory. The data presented in Figure 6b and c show consistently higher RChenopodiaceae values for Artemisia from desert-steppe samples (biogeographical group 3), which may be due to factors such as those discussed here. Vegetation survey was only carried out in the immediate

vicinity of the surface sample points, assuming that the vegetation was uniform in the area. This assumption clearly needs to be tested, since discontinuous vegetation around spot samples (rather than basin samples) is unlikely to be uniform at the spatial scale sensed by the pollen assemblage (Bunting et al., 2004).

Interannual variation in pollen production also occurs in the data set. Since different transects were used in 2002 and 2004, it is impossible to directly compare the results. If they are consid-ered to be equivalent data sets, this finding suggests that soil sam-ple pollen assemblages mainly reflect pollen deposited over the last year or so. This suggests rapid loss of pollen from the assem-blages, meaning that alteration of the assemblage composition by post-depositional preservation biasing is likely to be a problem. These questions can only be addressed by repeating sampling at the same locations over a multiyear period. Similar findings are reported in studies in temperate mixed forest (Jackson and Kearsley, 1998) and at the northern treeline (Hicks, 1985).

Addressing all these weaknesses will increase the time and resource costs of fieldwork, but they are necessary to produce more reliable RPP estimates for these and other taxa in desert-steppe and desert environments. In our future work, we plan to collect clusters of samples within areas of 10–20 km radius and do more detailed vegetation survey at several distances, including taxonomic recording within palynological equivalent types around each sample point to allow exploration of the relative source area of pollen for this type of sample and enable better estimates of distance-weighted plant abundance, improving and increasing the number of taxa estimates of RChenopodiaceae.

ConclusionsPollen assemblages from surface soil samples and pollen traps reflect both climatically determined vegetation patterns and local vegetation composition. Individual surface soil pollen assem-blages show significant correlations with annual mean tempera-ture and annual mean precipitation. This will increase our confidence in the interpretation of vegetation and climate with pollen data.

Artemisia-to-Chenopodiaceae (A/C) ratios are often used to distinguish between desert and steppe conditions when interpret-ing sedimentary pollen records, but in this region they are not reliable indicators. Although most desert samples have A/C ratios < 0.5, in desert-steppe A/C ratios varied widely from 0.1 to 23. A/C ratios also varied in different years within the same geo-graphic area. These findings suggest that, in desert-steppe areas, A/C ratios may not be as reliable indicators of paleovegetation and paleoclimate as was previously reported (Wong et al., 1993; Yan, 1991; Zhao et al., 2008, 2009).

Initial RPP estimates were obtained for four taxa from desert and desert-steppe communities, using both surface soil samples and pollen traps. Artemisia has notably higher pollen productivity than Chenopodiaceae, whilst Nitraria and Poaceae have lower pollen productivity than Chenopodiaceae. Values of RPP may vary with climatic region and between years, but the data set is small and problematic, therefore no further conclusions can be drawn.

Acknowledgements

This research is supported by National Science Foundation of China (Grant No. 40672107, 40730103), Science and Technology

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Li et al. 1009

Ministry of China (Grant No. 2003CCA01800) and Hebei Natural Science Foundation (Grant No. D2008000186, D2009000300). Thanks go to Dr Yu Zicheng, editor, and two reviewers for their helpful comments and suggestions.

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