Statistical approaches to R-values and the pollen— vegetation relationship

Parsons, Richard; Prentice, Iain Colin

doi:10.1016/0034-6667(81)90001-4

Cited by 205 publications

(192 citation statements)

References 26 publications

Supporting

Mentioning

182

Contrasting

Unclassified

Order By: Relevance

“…The REVEALS model is similar to the R-value model (Davis 1963), with the added inclusion of the pollen dispersal coefficient of each taxon. The model aims at reconstructing vegetation abundance within an entire region, so background pollen loading coming from outside the region is ignored, unlike the Extended R-value models (Parsons and Prentice 1981;Prentice and Parsons 1983;Sugita 1994), where regional background pollen loading is included. The formula for the REVEALS model is (Sugita 2007a):…”

Section: The Landscape Reconstruction Algorithmmentioning

confidence: 99%

“…Over the last decades, methods have been developed that allow us to distance weight vegetation data according to models of pollen dispersal and deposition (Prentice 1985;Sugita 1993); to estimate the relative pollen productivity of different plant taxa from pollen/vegetation calibration datasets (Parsons and Prentice 1981;Prentice and Parsons 1983;Sugita 1994) and to assess the source area of pollen for different basin types and sizes (Sugita 1994).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quantitative landscape dynamics in Denmark through the last three millennia based on the Landscape Reconstruction Algorithm approach

Nielsen

Odgaard

2010

Veget Hist Archaeobot

View full text Add to dashboard Cite

This paper explores the spatial and temporal land-cover variability within the main cultural landscape units in Denmark during the last 3,000 years. Quantitative estimates of the cover of trees, grasses, Cerealia and Calluna around nine Danish lakes were obtained using the recently developed Landscape Reconstruction Algorithm (LRA) (Sugita 2007a, b). The performance of the approach was evaluated by comparing reconstructed vegetation based on A.D. 1800 pollen spectra to land cover from historical maps of the same period. Although the model tended to overestimate grassland cover by 10-20%, the reconstructed vegetation was much more similar to the observed than the uncorrected pollen proportions. The LRA was then applied to 3,000 year long pollen records to reconstruct the vegetation development around each of the nine sites. The results support earlier conclusions regarding the relative stability of woodland, agrarian and heathland dominated landscapes in Denmark (Odgaard and Rasmussen 2000), with the distribution of the main landscape types determined by topography and soil characteristics. The present study indicates that the transition zones between agricultural and forest dominated landscapes were the most dynamic, acting as buffer zones where most of the expansions and contractions of agricultural activities took place. The quantitative vegetation reconstructions underline the importance of farming and especially pastoral activities in shaping the Danish landscapes throughout the study period.

show abstract

Section: The Landscape Reconstruction Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative landscape dynamics in Denmark through the last three millennia based on the Landscape Reconstruction Algorithm approach

Nielsen

Odgaard

2010

Veget Hist Archaeobot

View full text Add to dashboard Cite

show abstract

“…Sugita modified this taphonomic model to incorporate mixing of pollen across a lake surface, and defined a pollen source area for the whole pollen assemblage, the Relevant Source Area of Pollen or RSAP (Sugita, 1993(Sugita, , 1994. The RSAP is defined in terms of the Extended R-value approach, an iterative means of estimating the parameters of the pollen dispersal and deposition model from empirical data (Parsons and Prentice, 1981;Prentice and Parsons, 1983). Where vegetation data are available from multiple radii around the basin, the distance at which adding further vegetation data to the analysis leads to no improvement in the likelihood function score (a measure of goodness of fit between the fitted model parameters and the empirical data) is defined as the RSAP for the whole assemblage being studied.…”

Section: Introductionmentioning

confidence: 99%

Pollen source areas of lakes with inflowing rivers: modern pollen influx data from Lake Baiyangdian, China

Tian

Bunting

et al. 2012

Quaternary Science Reviews

View full text Add to dashboard Cite

a b s t r a c tComparing pollen influx recorded in traps above the surface and below the surface of Lake Baiyangdian in northern China shows that the average pollen influx in the traps above the surface is much lower, at 1210 grains cm À2 a À1 (varying from 550 to 2770 grains cm À2 a À1 ), than in the traps below the surface which average 8990 grains cm À2 a À1 (ranging from 430 to 22310 grains cm À2 a À1 ). This suggests that about 12% of the total pollen influx is transported by air, and 88% via inflowing water. If hydrophyte pollen types are not included, the mean pollen influx in the traps above the surface decreases to 470 grains cm À2 a À1 (varying from 170 to 910 grains cm À2 a À1 ) and to 5470 grains cm À2 a À1 in the traps below the surface (ranging from 270 to 12820 grains cm À2 a À1 ), suggesting that the contribution of waterborne pollen to the non-hydrophyte pollen assemblages in Lake Baiyangdian is about 92%. When trap assemblages are compared with sedimentewater interface samples from the same location, the differences between pollen assemblages collected using different methods are more significant than differences between assemblages collected at different sample sites in the lake using the same trapping methods. We compare the ratios of terrestrial pollen and aquicolous pollen types (T/A) between traps in the water and aerial traps, and examine pollen assemblages to determine whether proportions of long-distance taxa (i.e. those known to only grow beyond the estimated aerial source radius); these data suggest that the pollen source area of this lake is composed of three parts, an aerial component mainly carried by wind, a fluvial catchment component transported by rivers and another waterborne component transported by surface wash. Where the overall vegetation composition within the 'aerial catchment' is different from that of the hydrological catchment, the ratio between aerial and waterborne pollen influx offers a method for estimating the relative importance of these two sources, and therefore a starting point for defining a pollen source area for a lake with inflowing rivers.

show abstract

“…The Extended R-Value or ERV analysis (Parsons and Prentice, 1981;Prentice and Parsons, 1983) approach offers a means of determining RPP and u i from interdependent percentage data using the maximum likelihood method (Fisher, 1912) to find the set of parameter estimates which give the best fit between corrected values of a dataset of pollen-vegetation data pairs from multiple locations. Although ERV analysis can be carried out on singledistance vegetation survey data (e.g.…”

Section: Model Calibrationmentioning

confidence: 99%

“…Estimates of relative pollen productivity can be extracted from extended R-value analysis (Parsons and Prentice, 1981) using comparisons between pollen assemblages deposited into sedimentary contexts, such as moss polsters, and measurements of the present day vegetation cover around the sampled location. Vegetation survey method has been shown to have a profound effect on estimates of model parameters (Bunting and Hjelle, 2010), therefore a standard method is an essential pre-requisite for testing some of the key assumptions of pollen-based reconstruction of past vegetation; such as the assumption that relative pollen productivity is effectively constant in space and time within a region or biome.…”

mentioning

confidence: 99%

Palynological perspectives on vegetation survey: a critical step for model-based reconstruction of Quaternary land cover

Bunting

Farrell

Broström

et al. 2013

Quaternary Science Reviews

View full text Add to dashboard Cite

Pollen dispersal and deposition models Pollen surface sample PrenticeeSugita model of pollen dispersal and deposition Remote sensing data Sutton model Vegetation data processing a b s t r a c t 1. Quantitative reconstruction of past vegetation distribution and abundance from sedimentary pollen records provides an important baseline for understanding long term ecosystem dynamics and for the calibration of earth system process models such as regional-scale climate models, widely used to predict future environmental change. Most current approaches assume that the amount of pollen produced by each vegetation type, usually expressed as a relative pollen productivity term, is constant in space and time.2. Estimates of relative pollen productivity can be extracted from extended R-value analysis (Parsons and Prentice, 1981) using comparisons between pollen assemblages deposited into sedimentary contexts, such as moss polsters, and measurements of the present day vegetation cover around the sampled location. Vegetation survey method has been shown to have a profound effect on estimates of model parameters (Bunting and Hjelle, 2010), therefore a standard method is an essential pre-requisite for testing some of the key assumptions of pollen-based reconstruction of past vegetation; such as the assumption that relative pollen productivity is effectively constant in space and time within a region or biome.3. This paper systematically reviews the assumptions and methodology underlying current models of pollen dispersal and deposition, and thereby identifies the key characteristics of an effective vegetation survey method for estimating relative pollen productivity in a range of landscape contexts.4. It then presents the methodology used in a current research project, developed during a practitioner workshop. The method selected is pragmatic, designed to be replicable by different research groups, usable in a wide range of habitats, and requiring minimum effort to collect adequate data for model calibration rather than representing some ideal or required approach. Using this common methodology will allow project members to collect multiple measurements of relative pollen productivity for major plant taxa from several northern European locations in order to test the assumption of uniformity of these values within the climatic range of the main taxa recorded in pollen records from the region.

show abstract

Statistical approaches to R-values and the pollen— vegetation relationship

Cited by 205 publications

References 26 publications

Quantitative landscape dynamics in Denmark through the last three millennia based on the Landscape Reconstruction Algorithm approach

Quantitative landscape dynamics in Denmark through the last three millennia based on the Landscape Reconstruction Algorithm approach

Pollen source areas of lakes with inflowing rivers: modern pollen influx data from Lake Baiyangdian, China

Palynological perspectives on vegetation survey: a critical step for model-based reconstruction of Quaternary land cover

Contact Info

Product

Resources

About