scholarly journals Key parameters for landscape evolution and anthropogenisation estimation in the Kamchia River downstream region (Eastern Bulgaria)

2021 ◽  
Vol 5 (1) ◽  
pp. 86-93
Author(s):  
Stoyan Ivanov Vergiev ◽  
Mariana Filipova-Marinova ◽  
Daniela Toneva ◽  
Todorka Stankova ◽  
Diyana Dimova ◽  
...  
Keyword(s):  
Source Area ◽  
Critical Parameters ◽  
Vegetation Composition ◽  
Pollen Data ◽  
Plant Abundance ◽  
Pollen Type ◽  
Modern Pollen ◽  
Downstream Region ◽  
Pollen Productivity ◽  
Gis Environment

Pollen productivity еstimate (PPE) and relevant source area of pollen (RSAP) are critical parameters for quantitative interpretations of pollen data in palaeolandscape and palaeoecological reconstructions, and for analyses of the landscapes evolution and anthropogenisation as well. In light of this, the present paper endeavours to calculate PPE of key plant taxa and to define the RSAP in the Kamchia River Downstream Region (Eastern Bulgaria) in order to use them in landscape simulations and estimations. For the purposes of this research, a dataset of pollen counts from 10 modern pollen samples together with corresponding vegetation data, measured around each sample point in concentric rings, were collected in 2020. Three submodels of the Extended R-Value (ERV) model were used to relate pollen percentages to vegetation composition. Therewith, in order to create a calibrated model, the plant abundance of each pollen type was weighed by distance in GIS environment. The findings led to the conclusion that most of the tree taxa have PPE higher than 1 (ERV3 submodel). Cichoriceae, Fabaceae and Asteraceae have lower PPE.

Modern pollen-vegetation-climate relationships and pollen productivity estimates for common East Siberian taxa

2021 ◽  
Author(s):  
Rongwei Geng ◽  
Andrei Andreev ◽  
Stefan Kruse ◽  
Yan Zhao ◽  
Ulrike Herzschuh ◽  
...  
Keyword(s):  
Field Survey ◽  
Source Area ◽  
Soil Samples ◽  
Lake Surface ◽  
East Siberia ◽  
Climate Conditions ◽  
Modern Pollen ◽  
Pollen Productivity ◽  
Paleoclimate Reconstructions ◽  
R Value

<p>East Siberia is an ideal area for investigating the relationships between modern pollen assemblages and vegetation under the extremely cold and dry climate conditions. These relationships are the basis of paleovegetation and paleoclimate reconstructions from fossil pollen records. Pollen productivity estimates (PPE) are required for reliable pollen-based quantitative vegetation reconstructions. Here, we present a new pollen dataset of 48 moss/soil and 24 lake surface sediment samples collected from Chukotka and Yakutia. Generally, tundra and taiga vegetation sites can be well distinguished in the surface pollen assemblages from East Siberia. Moss/soil and lake samples have mostly similar pollen assemblages but contents of some pollen taxa may vary significantly in different sample types. We classified drone images based on field survey to obtain high-resolute vegetation data. Pollen counts in moss/soil samples and vegetation data can? be used in the Extended R-Value (ERV) model to estimate the relevant source area of pollen (RSAP) and the PPEs of major plant taxa. The result of PPE calculation for most common taxa (Alnus, Betula, Cyperaceae, Ericaceae, Larix, Pinus and Salix) can be used to improve vegetation reconstructions.</p>

scholarly journals Pollen productivity estimates strongly depend on assumed pollen dispersal II: Extending the ERV model

The Holocene ◽  
2021 ◽  
pp. 095968362110417
Author(s):  
Martin Theuerkauf ◽  
John Couwenberg
Keyword(s):  
Model Selection ◽  
Pollen Dispersal ◽  
Local Scale ◽  
Pollen Deposition ◽  
Data Sets ◽  
Pollen Data ◽  
Modern Pollen ◽  
Dispersal Model ◽  
Pollen Productivity ◽  
Fall Speed

Pollen productivity estimates (PPEs) are a key parameter for quantitative land-cover reconstructions from pollen data. PPEs are commonly estimated using modern pollen-vegetation data sets and the extended R-value (ERV) model. Prominent discrepancies in the existing studies question the reliability of the approach. We here propose an implementation of the ERV model in the R environment for statistical computing, which allows for simplified application and testing. Using simulated pollen-vegetation data sets, we explore sensitivity of ERV application to (1) number of sites, (2) vegetation structure, (3) basin size, (4) noise in the data, and (5) dispersal model selection. The simulations show that noise in the (pollen) data and dispersal model selection are critical factors in ERV application. Pollen count errors imply prominent PPE errors mainly for taxa with low counts, usually low pollen producers. Applied with an unsuited dispersal model, ERV tends to produce wrong PPEs for additional taxa. In a comparison of the still widely applied Prentice model and a Lagrangian stochastic model (LSM), errors are highest for taxa with high and low fall speed of pollen. The errors reflect the too high influence of fall speed in the Prentice model. ERV studies often use local scale pollen data from for example, moss polsters. Describing pollen dispersal on his local scale is particularly complex due to a range of disturbing factors, including differential release height. Considering the importance of the dispersal model in the approach, and the very large uncertainties in dispersal on short distance, we advise to carry out ERV studies with pollen data from open areas or basins that lack local pollen deposition of the taxa of interest.

Relative pollen productivity estimates and changes in Holocene vegetation cover in the deciduous forest of southeastern Quebec, Canada

Botany ◽  
2018 ◽  
Vol 96 (5) ◽  
pp. 299-317 ◽  
Author(s):  
Michelle A. Chaput ◽  
Konrad Gajewski
Keyword(s):  
Vegetation Cover ◽  
Deciduous Forest ◽  
Source Area ◽  
Species Level ◽  
Vegetation Survey ◽  
Pollen Data ◽  
Regional Estimates ◽  
Pollen Productivity ◽  
R Value ◽  
Reveals Model

The Regional Estimates of VEgetation Abundance from Large Sites (REVEALS) model was used to quantify Holocene changes in vegetation cover in the deciduous forest of southeastern Quebec, Canada. The Extended R-Value (ERV) model was used to obtain relative pollen productivity estimates (PPEs) for eight tree taxa and to determine the relevant source area of pollen (RSAP) for lakes in this ecosystem. Modern vegetation was estimated using pollen data from 16 small (<0.5 km2) lakes and a species-level vegetation survey of southern Quebec. The RSAP was estimated to be within 1600 m of the lakes. Tsuga, Fagus, and Quercus were the most productive taxa, and Populus and Acer were the lowest. Reconstructed changes in absolute vegetation cover show a high abundance of Picea followed by Populus in the early Holocene. The reconstructed values for Populus suggest that it was widely distributed across the landscape. Abies and Acer were dominant on the landscape during the late to mid-Holocene, and an increase in Picea during the Neoglacial is more significant than in percentage diagrams. The REVEALS results provide estimates of land-cover change that are more realistic and informative than the use of pollen percentages alone.

Testing the Landscape Reconstruction Algorithm for spatially explicit reconstruction of vegetation in northern Michigan and Wisconsin

2010 ◽  
Vol 74 (2) ◽  
pp. 289-300 ◽  
Author(s):  
Shinya Sugita ◽  
Tim Parshall ◽  
Randy Calcote ◽  
Karen Walker
Keyword(s):  
Permutation Test ◽  
Source Area ◽  
Reconstruction Algorithm ◽  
Training Data ◽  
Vegetation Composition ◽  
Pollen Data ◽  
Large Lakes ◽  
Quantitative Reconstruction ◽  
Landscape Reconstruction ◽  
Northern Michigan

AbstractThe Landscape Reconstruction Algorithm (LRA) overcomes some of the fundamental problems in pollen analysis for quantitative reconstruction of vegetation. LRA first uses the REVEALS model to estimate regional vegetation using pollen data from large sites and then the LOVE model to estimate vegetation composition within the relevant source area of pollen (RSAP) at small sites by subtracting the background pollen estimated from the regional vegetation composition. This study tests LRA using training data from forest hollows in northern Michigan (35 sites) and northwestern Wisconsin (43 sites). In northern Michigan, surface pollen from 152-ha and 332-ha lakes is used for REVEALS. Because of the lack of pollen data from large lakes in northwestern Wisconsin, we use pollen from 21 hollows randomly selected from the 43 sites for REVEALS. RSAP indirectly estimated by LRA is comparable to the expected value in each region. A regression analysis and permutation test validate that the LRA-based vegetation reconstruction is significantly more accurate than pollen percentages alone in both regions. Even though the site selection in northwestern Wisconsin is not ideal, the results are robust. The LRA is a significant step forward in quantitative reconstruction of vegetation.

scholarly journals Linking modern pollen accumulation rates to biomass: Quantitative vegetation reconstruction in the western Klamath Mountains, NW California, USA

The Holocene ◽  
2021 ◽  
pp. 095968362098803
Author(s):  
Clarke A Knight ◽  
Mark Baskaran ◽  
M Jane Bunting ◽  
Marie Champagne ◽  
Matthew D Potts ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Plant Biomass ◽  
Source Area ◽  
Pollen Deposition ◽  
Accumulation Rates ◽  
Klamath Mountains ◽  
Plant Abundance ◽  
Circular Area ◽  
Value Analysis ◽  
Pollen Accumulation Rates

Quantitative reconstructions of vegetation abundance from sediment-derived pollen systems provide unique insights into past ecological conditions. Recently, the use of pollen accumulation rates (PAR, grains cm−2 year−1) has shown promise as a bioproxy for plant abundance. However, successfully reconstructing region-specific vegetation dynamics using PAR requires that accurate assessments of pollen deposition processes be quantitatively linked to spatially-explicit measures of plant abundance. Our study addressed these methodological challenges. Modern PAR and vegetation data were obtained from seven lakes in the western Klamath Mountains, California. To determine how to best calibrate our PAR-biomass model, we first calculated the spatial area of vegetation where vegetation composition and patterning is recorded by changes in the pollen signal using two metrics. These metrics were an assemblage-level relevant source area of pollen (aRSAP) derived from extended R-value analysis ( sensu Sugita, 1993) and a taxon-specific relevant source area of pollen (tRSAP) derived from PAR regression ( sensu Jackson, 1990). To the best of our knowledge, aRSAP and tRSAP have not been directly compared. We found that the tRSAP estimated a smaller area for some taxa (e.g. a circular area with a 225 m radius for Pinus) than the aRSAP (a circular area with a 625 m radius). We fit linear models to relate PAR values from modern lake sediments with empirical, distance-weighted estimates of aboveground live biomass (AGLdw) for both the aRSAP and tRSAP distances. In both cases, we found that the PARs of major tree taxa – Pseudotsuga, Pinus, Notholithocarpus, and TCT (Taxodiaceae, Cupressaceae, and Taxaceae families) – were statistically significant and reasonably precise estimators of contemporary AGLdw. However, predictions weighted by the distance defined by aRSAP tended to be more precise. The relative root-mean squared error for the aRSAP biomass estimates was 9% compared to 12% for tRSAP. Our results demonstrate that calibrated PAR-biomass relationships provide a robust method to infer changes in past plant biomass.

scholarly journals Relación lluvia de polen-vetación en selvas de Quintana Roo

2017 ◽  
pp. 31 ◽  
Author(s):  
Gerald A. Islebe ◽  
Rogel Villanueva-Gutiérrez ◽  
Odilón Sánchez-Sánchez
Keyword(s):  
Zea Mays ◽  
Quintana Roo ◽  
Vegetation Types ◽  
Pollen Data ◽  
Indicator Taxa ◽  
Modern Pollen ◽  
Pollen Content ◽  
Ordination Techniques ◽  
Ordination Analysis ◽  
Pollen Rain

Modern pollen rain was studied along a 450 km long transect between Cancun-La Unión (Belizean border). Ten moss samples were collected in different vegetation types and analyzed for pollen content. The data were analyzed with classification (TWINSPAN), ordination analysis (DCA) and different association indices. Classification and ordination techniques allowed us to recognize three different pollen signals from semievergreen forest (with Maclura, Apocynaceae, Moraceae, Sapotaceae, Araceae, Cecropia, Celtis, Eugenia and Bursera), acahual (with con Coccoloba, Metopium, Anacardiaceae, Urticales, Melothria, Croton, Palmae) and disturbed vegetation (with Zea mays, Mimosa and Asteraceae ) . The degree of over-representation and underrepresentation of the pollen data with respect to the modem vegetation was established, being under-represented mostly entomophilous species. We can conclude that the actual pollen signal can be used for calibrating paleosignals, if clear groups of indicator taxa can be established.

Comparison of settlement-era vegetation reconstructions for STEPPS and REVEALS pollen–vegetation models in the northeastern United States

2020 ◽  
Vol 95 ◽  
pp. 23-42 ◽  
Author(s):  
Mathias Trachsel ◽  
Andria Dawson ◽  
Christopher J. Paciorek ◽  
John W. Williams ◽  
Jason S. McLachlan ◽  
...  
Keyword(s):  
United States ◽  
Forest Dynamics ◽  
Pollen Dispersal ◽  
Parameter Estimates ◽  
Vegetation Composition ◽  
Northeastern United States ◽  
Bayesian Hierarchical ◽  
Pollen Productivity ◽  
Vegetation Models ◽  
Anthropogenic Land Use

AbstractReconstructions of prehistoric vegetation composition help establish natural baselines, variability, and trajectories of forest dynamics before and during the emergence of intensive anthropogenic land use. Pollen–vegetation models (PVMs) enable such reconstructions from fossil pollen assemblages using process-based representations of taxon-specific pollen production and dispersal. However, several PVMs and variants now exist, and the sensitivity of vegetation inferences to PVM selection, variant, and calibration domain is poorly understood. Here, we compare the reconstructions, parameter estimates, and structure of a Bayesian hierarchical PVM, STEPPS, both to observations and to REVEALS, a widely used PVM, for the pre–Euro-American settlement-era vegetation in the northeastern United States (NEUS). We also compare NEUS-based STEPPS parameter estimates to those for the upper midwestern United States (UMW). Both PVMs predict the observed macroscale patterns of vegetation composition in the NEUS; however, reconstructions of minor taxa are less accurate and predictions for some taxa differ between PVMs. These differences can be attributed to intermodel differences in structure and parameter estimates. Estimates of pollen productivity from STEPPS broadly agree with estimates produced for use in REVEALS, while comparison between pollen dispersal parameter estimates shows no significant relationship. STEPPS parameter estimates are similar between the UMW and NEUS, suggesting that STEPPS parameter estimates are transferable between floristically similar regions and scales.

The extended downscaling approach: A new R-tool for pollen-based reconstruction of vegetation patterns

The Holocene ◽  
2016 ◽  
Vol 27 (8) ◽  
pp. 1252-1258 ◽  
Author(s):  
Martin Theuerkauf ◽  
John Couwenberg
Keyword(s):  
R Package ◽  
Landscape Patterns ◽  
Statistical Computing ◽  
Pollen Deposition ◽  
Vegetation Patterns ◽  
Vegetation Composition ◽  
Large Lakes ◽  
Pollen Productivity ◽  
Complex Settings ◽  
Landscape Units

The extended downscaling approach (EDA) is a quantitative method in palynology that aims to detect past vegetation patterns and communities in the landscape. The EDA uses iterative forward modelling to fit vegetation composition to robust landscape patterns by comparing simulated with actually observed pollen deposition. The approach employs a set of pollen records, preferably from medium sized to large lakes or peatlands, as well as maps of robust landscape patterns, such as soils and relief. So far, the EDA has been applied in simple settings with only few taxa. To be able to apply the model also in more complex situations, we have implemented the EDA in the R environment for statistical computing. We here test the performance of the EDAinR function in five synthetic scenarios of increasing complexity. In all cases, the EDA is well able to reconstruct vegetation composition, also on rare landscape units. If uncertainty is added both to the pollen data and pollen productivity estimates, the EDA still correctly reconstructs species composition on more than 90% of the total landscape in all scenarios, underlining that the EDA performs well also in complex settings. The EDAinR function will be available within the R package DISQOVER.

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