Relative pollen productivity estimates for alpine meadow vegetation, northeastern Tibetan Plateau

Abstract

A promising method of reconstructing palaeovegetation from pollen records uses 18 mathematical models of the relationship between pollen and vegetation, which can be 19 calibrated using the Extended R-Value (ERV) approach on datasets of modern pollen 20 assemblages and related vegetation survey data. This study presents the results of 21 calibrating the models for non-arboreal pollen types in alpine meadow habitats on the 22 Tibetan Plateau. 23Pollen assemblages from surface soil samples and surrounding vegetation data 24 were collected for 30 randomly located sites in the Zoige Basin, northeastern Tibetan 25 Plateau. ERV analysis found that the most reliable results were obtained when using 26 ERV sub-models 1 and 2 and distance-weighting the vegetation data by applying the 27 taxon-specific Prentice-Sutton method. 28The relevant source area of pollen (RSAP) for soil samples in alpine meadows of 29 the northeastern Tibetan Plateau is ca. 200 m. Relative pollen productivities (RPPs) of 30 15 non-arboreal taxa were estimated relative to Cyperaceae (RPPCyp). The taxa can be 31 divided into 3 groups according to their RPPs, those with high RPPCyp (> 1.68: 32

Compilation of relative pollen productivity (RPP) estimates and taxonomically harmonised RPP datasets for single continents and Northern Hemisphere extratropics

“…The RPP estimates of Li et al (2015, reference taxon Quercus) were recalculated based on the mean Quercus RPP provided by F. , Zhang et al (2017, Changbai), and . The RPP estimates of Matthias et al (2012, reference taxon Pinus) were recalculated based on the mean Pinus RPP provided by Räsänen et al (2007) and Abraham and Kozáková (2012). The study of Jiang et al (2020) used Quercus as the reference taxon but included a value for Poaceae, which was used as the basis for recalculation.…”

“…x McLauchlan et al (2011) (count data) x Bunting and Hjelle (2010) (comparison of different data collection methods) Nielsen (2004) Bunting et al (2005) Niemeyer et al (2015) Bunting et al 2013Poska et al (2011) Calcote (1995) Qin et al (2020) (from Jiang et al, 2020) Chaput and Gajewski (2018) Räsänen et al (2007) Chen et al (2019 x Sjögren et al (2006) Li et al, 2018) x Soepboer et al (2008) 2017x Sugita et al (2010) (absolute pollen values) He et al (2016) (from Li et al, 2018) Theuerkauf et al 2013x Heide and Bradshaw (1982)…”

Sensitivity of Erosion‐Rate in Permafrost Landscapes to Changing Climatic and Environmental Conditions Based on Lake Sediments From Northwestern Alaska

“…This negative association provides hints about the processes through which vegetation influences erosion in the watershed of Burial Lake. Increased pollen counts may reflect increased abundance of vegetation in the region, as well as increased pollen productivity and/or background pollen deposition (Qin et al., 2020; Sugita, 2007). Assuming that mineral flux represents the intensity of physical erosion over the lake watershed, the negative association between mineral flux and normalized pollen counts (Figures 3 and 5) likely reflects decreased erosion‐rate with increased vegetation abundance.…”

“…(2010), labeled pollen sum/exotics in Figures 2–5), computed by normalizing the total count of pollen grains by the count of artificially added exotic pollen grains (used as a counting reference) (Abbott et al., 2010; Salgado‐Labouriau & Rull, 1986). Increased pollen count is associated with increased pollen productivity and background pollen flux, and may be indicative of increased vegetation abundance (Qin et al., 2020; Sugita, 2007); (c) Percent pollen from trees and shrubs (PTS, labeled Trees & Shrubs [%] in Figures 2–5) relative to a total of trees, shrubs and herbs (source: datasets associated with Abbott et al. (2010)).…”

“…We focused on 10 sedimentary proxies (Figure 2) reflecting environmental conditions that might influence erosion-rate: (a) Mean July temperature [°C] based on Chironomid assemblage (source: Kurek et al (2009)); (b) Normalized pollen counts (source: Abbott et al (2010), labeled pollen sum/exotics in Figures 2-5), computed by normalizing the total count of pollen grains by the count of artificially added exotic pollen grains (used as a counting reference) (Abbott et al, 2010;Salgado-Labouriau & Rull, 1986). Increased pollen count is associated with increased pollen productivity and background pollen flux, and may be indicative of increased vegetation abundance (Qin et al, 2020;Sugita, 2007); (c) Percent pollen from trees and shrubs (PTS, labeled Trees & Shrubs [%] in Figures 2-5) relative to a total of trees, shrubs and herbs (source: datasets associated with Abbott et al ( 2010)). Increased PTS may reflect a higher relative abundance of trees and shrubs; (d) Charcoal-based proxy for frequency of local fires [ka −1 ] (e.g., P. E. Chipman & Hu, 2017).…”