the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 18 Feb 2025
Modern Pollen Dataset of the Tibetan Plateau
Abstract. Modern pollen datasets can provide invaluable data for interpreting temporal variations in climate, vegetation, land cover, and plant diversity from fossil pollen. Here we present 555 pollen count data, identified from topsoil collected within plant plots across a vast area of the Tibetan Plateau (TP) and along the southern margin of Xinjiang that borders the TP. This dataset fills a geographical gap in the published datasets that offer pollen count data for this area. Ordination analysis and multiple regression reveal that precipitation is the primary factor influencing the spatial distribution of pollen assemblages across the entire study area. Furthermore, ordination analysis indicates that pollen assemblages can be used to distinguish vegetation types in the southeastern TP, such as coniferous forest, alpine shrubland, and alpine meadow, from vegetation types found in other regions of TP. Additionally, it is possible to distinguish vegetation types that have low precipitation or moisture requirements based on pollen assemblages. Generalized additive models demonstrate that six commonly used pollen ratios, involving taxa such as Artemisia, Amaranthaceae, Cyperaceae, and Poaceae, are not sufficiently reliable for reflecting changes in annual precipitation. Nevertheless, they can provide some indication of changes in vegetation or landscape. This dataset holds various potential applications in paleoecological and paleoclimatic researches. It not only offers a scientific foundation for reconstructing changes in climate and vegetation over time, but also enables the assessment of the reliability of pollen assemblages in representing the dynamics of vegetation cover, functional traits, and plant diversity, by integrating the simultaneously measured plot-level plant communities and functional traits. Data from this study, including pollen count data for each sample and site, alongside with the geographical coordinates, altitude, local vegetation type of each sampling site, dry weight of each sample used for pollen extraction, Lycopodium (marker) grains per tablet, and the identified number of Lycopodium spores, are available at https://doi.org/10.11888/Paleoenv.tpdc.302015 (Liao et al., 2025).
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RC1: 'Comment on essd-2025-71', Anonymous Referee #1, 15 Mar 2025
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手稿 （essd-2025-71） 被拒绝或编辑决定。
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AC1: 'Reply on RC1', Mengna Liao, 20 Mar 2025
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Overall, this paper only utilized simple ordination analysis and multiple regression analysis to briefly examine the indicative significance of surface pollen on vegetation types and climate in the Tibetan Plateau. It did not carefully analyze the relationship between pollen assemblages of different vegetation types and the actual vegetation. In the western and northern Tibetan Plateau, the vegetation is sparse, dominated by steppe-desert, but the surface pollen contains an uncertain amount of arboreal pollen, which is evidently transported from distant sources. The proportion and types of long-distance transported pollen may vary across different locations, and the sources could also be different - these are issues that need to be cautiously considered when reconstructing past vegetation and climate based on surface pollen (quantitative) data. Therefore, this paper only provides some surface pollen data, without clear implications for other disciplines.
Response: As you may know that this paper is a data paper, the main purpose of this paper is to provide original pollen count data. We believe that these data will be beneficial for future researchers in conducting diverse studies related to vegetation/paleovegetation, climate change, and other related fields. Revolving around this purpose, we have provided comprehensive details on sample collection and pretreatment, methods for pollen identification, frequency and spatial distributions of pollen count for both samples and sites, the spatial distributions of the main pollen types, and the frequency distribution of the number of identified pollen types from different vegetation types. Through the application of statistical analyses, we have proposed potential uses for the dataset and cautionary notes for its utilization. Based on the results obtained, we aim to show the readers that whether or not the pollen data from this dataset can be used to distinguish vegetation types or can be reliably used to distinguish which vegetation types, and which climatic parameters are most promising to reconstruct based on the pollen data from this dataset. As for your comment that “It did not carefully analyze the relationship between pollen assemblages of different vegetation types and the actual vegetation.”, we understand that you think we have not processed or selected the pollen data to ensure that it accurately reflects the vegetation types. However, regarding the question of how to effectively process the data to enhance the reliability of the pollen data in representing vegetation types or climatic parameters—such as excluding arboreal taxa from data derived from non-forested area, or using representation value or relative pollen production to adjust the percentages of specific pollen taxa—we consider this a potential research area of interest for future researchers who will utilize this dataset, potentially leading to the publication of further research papers.
For the second question, we completely agree with you that pollen data collected from open landscape contains an uncertain amount of long-distance transported pollen types, with sources potentially located as far as over 50 kilometers away. Undoubtedly, this issue must be meticulously considered when reconstructing past vegetation and climate using surface pollen data. We will emphasize this concern in the section “5 Potential usage of the dataset”. Besides, we will add another result from the ordination analysis conducted on a processed pollen dataset that excludes samples from forested areas and removes arboreal pollen types. By doing so, we aim to show readers whether excluding arboreal pollen types from data collected in open landscape can enhance the indicative significance of the pollen data to vegetation types. You can check the result from the updated Figure 5 attached.
Q1 (Line 10): This dataset fills a geographic gap in the published pollen count data for this region. To allow the reader to clearly see which locations are covered by this new dataset, the geographic distribution of the previous data should be indicated in Figure 1.
Response: Thanks very much for this suggestion. We have revised Figure 1 to include the geographical locations of the original pollen count data from our study (indicated by circles) and from other publicly available sources (indicated by triangles, including Cao et al., 2021 and Ma et al., 2024). Please check the updated Figure 1 attached.
Q2 (Lines 23-24): Italic
Response: Thanks for pointing out this problem. We will make correction.
Q3 (Line 73): The References for the Tibetan Plateau boundary.
Response: thanks for this suggestion: The reference related to the boundary of the Tibetan Plateau is: Zhang, Y., Li, B., Liu, L., and Zheng, D. (2021) Redetermine the region and boundaries of Tibetan Plateau, Geographical Research, 40, 1543-1553, https://doi.org/10.11821/dlyj020210138, 2021.
Q4 (Line 79): In the transition zone around 3,000 meters of elevation, how can the delineation of distinct vegetation types be ensured
Response: We did not attempt to distinguish different vegetation types based on altitude. We just want to state a fact that, the samples we collected from temperate vegetation generally distributed between 800-3000 m.a.s.l. and those from alpine vegetation above 3000 m a.s.l, but not to classify the vegetation below 800-3000 m a.s.l as temperate vegetation types and those above 3000 m a.s.l. as alpine vegetation types. In fact, as we mentioned on Line 80, we determined the vegetation types for the sampling sites based on the local vegetation types.
Q5 (Line 83): Whether the pollen assemblage results obtained are representative of the different sampling densities in each vegetation area, which vary widely. If there are only three samples in a coniferous forest area, are they representative of the entire coniferous forest area?
Response: Thanks very much for this question. We acknowledge that three samples from only three sites can hardly show the entire picture of pollen assemblages from a vegetation covering a large geographic space. We will point out this problem in the section “5 Potential usage of the dataset” and emphasize the uncertainty may arise when using these data.
Q6 (Line 88): What is the weight of each sample?
Response: The dry weights of the samples in our dataset range from 5.25 to 32.7 g. Approximately 77% (427 samples) of the total samples have dry weights exceeding 20 g, 14.8% (82 samples) weigh between 10 and 20 g, and the remaining 8.2% (46 samples) weigh between 5 and 10 g. The dry weights of the samples used for pollen extraction were determined by the soil properties, particularly the grain size and the local vegetation surrounding the sampling sites. As mentioned in the manuscript, the weight of each sample has been included alongside the pollen data in the dataset.
Q7 (Line 110): Italic
Response: Thanks for pointing out this problem. We will make correction.
Q8 (Line 119): Does such a large gap in pollen grain counts all have an impact on analyses such as NMDS?
Response: Intuitively, we believe that a large gap in pollen counts would have some impact on the analyses, but the question on how significant the impact needs to be further studied. As you surely know, theoretically, 300 grains is the low limitation for pollen identification. Through communications with other researchers engaged in pollen studies on the Tibetan Plateau, we learned that for areas with sparse vegetation and few plant species, 200 grains may be enough to capture the compositional characteristics of the pollen assemblages. There is a detail that we did not emphasize in the manuscript but may be important information for readers that, the pollen data we used for analyses are the combined pollen data from multiple samples at each site. In the combined dataset, there are 9 pollen data have pollen counts less than 200 grains. Therefore, we re-analyzed the pollen dataset excluding these 9 data points and updated Figures 5 and 6 accordingly (please see updated Figures 5 & 6 attached). We will add these details on the data and data selection in the section “3.3 Numerical analyses”. In the updated Figure 5, the upper panel shows the results including samples from all vegetation types, while the lower panel shows the results including samples from non-forested area.
Q9 (Lines 122-125): Figure 2 shows that 31% of the samples contain between 200 and 300 pollen grains, and 44% contain between 400 and 600 grains. In other words, 75% of the samples have more than 200 grains. However, the key question is what percentage of samples contain fewer than 100 grains. While Figure 2 indicates the sample points with under 100 grains, it does not specify the overall percentage. If more than 20% of the samples have under 100 grains, the dataset should be reorganized to remove those low-grain samples.
Response: In our dataset, there are 310 samples have pollen count more than 300 grains, which occupy 55.86% of the total samples. 1etween 168 samples (30.27%) have pollen count between 200- 300 grains, 43 samples (7.75%) between 100-200 grains, and 34 samples (6.12%) have pollen count less than 100 grains. When considering pollen count for each site (as you may know from the manuscript that, we generally collected multiple samples from each site), there are only 3 sites (TP2019071102, TP2019071103, TP2019071202) have pollen count fewer than 100 grains. We kept these data in the dataset because we believe it might be useful to reflect some of the characteristics of vegetation. Since we not only provide pollen count data, but also the dry weight of each sample used for pollen extraction, Lycopodium grains per tablet, and the identified number of Lycopodium spores for each sample, pollen concentration of each sample can be calculated, and maybe some researchers might be interested in testing the reliability of using pollen concentration to reflect the sparseness of vegetation or can use pollen concentration to explore other scientific questions.
We realized from this question you raised that it is necessary to provide more details on the pollen counts for samples and sites, and we will add the information above into the manuscript.
Q10 (Line 161): Is it possible to consider the effect of altitude on pollen assemblages?
Response: If we have understood you question correctly, you want to know whether the pollen assemblage from our dataset can serve as indicators of changes along the altitude gradient. The answer to that is yes, as evidenced by the pollen diagram attached. However, it is important to note that the relationship between pollen assemblages and altitude is not "linear"; in other words, pollen assemblages vary across different altitude ranges. Within a specific altitude range, such as between 4065-4520 meters, it becomes challenging to discern changes in altitude based solely on the pollen assemblages.
Q11 (Line 173): Why does the A/Am also show high values in the alpine desert. The pollen assemblage should be dominated by Amaranthaceae.
Response: As you can observe in the figure 4, Amaranthaceae comprises approximately 30~50% of the pollen taxa in alpine deserts, whereas Artemisia is even more abundant, reaching up to 50~70% or even higher. This explains why we obtain higher values of the A/Am ratio for this vegetation type. According to the plant survey data collected from the same sample sites, Amaranthaceae indeed dominates in temperate desert, but this is not the case in alpine deserts. For more detail, you may refer to the published plant community dataset in the Chinese Journal of Plant Ecology (https://www.plant-ecology.com/CN/10.17521/cjpe.2022.0174). Besides, as you surely know that the representativeness of Amaranthaceae and Artemisia to their parent plants vary significantly across different vegetation types, which is another reason why the A/Am ratio is high in alpine deserts on the plateau.
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AC1: 'Reply on RC1', Mengna Liao, 20 Mar 2025
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