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APPENDIX S1
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3
The REVEALS model
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Pollen percentages (or proportions) have a non-linear relationship with vegetation abundance
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(in percentage cover or proportions), which makes quantitative reconstructions of vegetation
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problematical (e.g. Sugita et al., 1998). Moreover, the pollen–vegetation relationship is
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influenced by inter-taxonomic differences in pollen productivity and dispersal properties, as
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well as by the size and type of the sedimentary basin (e.g. Sugita, 1994). The “Regional
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Estimates of VEgetation Abundance from Large Sites” (REVEALS) model developed by
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Sugita (2007a) accounts for these factors and corrects for some of the biases inherent in pollen
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data. Given that estimates of pollen productivity and fall speed of pollen are available for
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some plant taxa, the REVEALS model can calculate estimates of past, regional vegetation
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abundance in proportions or percentage cover using fossil pollen counts from large sites (>48
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ha, i.e. mean radius of the site > ca. 390 m, according to simulations; Sugita, 2007a).
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The equation of the model is as follows:
ni ,k
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Vˆi 
Z max
ˆ i
 g ( z )dz




 n j ,k

Z max



j 1
ˆ j  g j ( z )dz 

R


m
ni ,k ˆ i K i
i
R

m
 (n
j 1
j ,k
ˆ j K j )
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where Vˆi is the estimate of the regional vegetation abundance for taxon i (proportion or
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percentage), ni,k is the pollen count of taxon i at site k, ̂i is the estimate of pollen productivity
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(PPE) for taxon i, z is the distance between the centre of the sedimentary basin and the pollen
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source, gi (z) is the pollen dispersal/deposition function for taxon i expressed as a function of
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distance z, R is the radius of a sedimentary basin, Zmax is the maximum distance within which
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most pollen originates (i.e. the maximum spatial extent of the regional vegetation), m is the
1
𝑍𝑚𝑎𝑥
𝑔𝑖 (𝑧)𝑑𝑧 is the “pollen dispersal-deposition
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total number of taxa included, and 𝐾𝑖 = ∫𝑅
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coefficient” of taxon i from the border of the study site (distance from the pollen sample
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corresponding to the radius R of the lake) to Zmax; for Ki the Prentice model (Prentice 1985,
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1988) is used for pollen records from bogs, and Sugita’s model (1993) is used for pollen
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records from lakes and ponds. Prentice’s and Sugita’s models both use Sutton’s model of
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pollen dispersal from a ground-level source under neutral atmospheric conditions (Sutton,
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1953; Sugita, 2007a).
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Besides pollen counts (ni,k in the equation) the following parameters need to be set up for the
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application of the REVEALS model:
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-
PPEs, (i.e. ̂ i in the equation) and their standard errors (SEs) and fall speed of pollen
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(FSP – needed for the calculation of gi(z)) for each individual modelled taxon (see
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Appendix S2 for values of PPE and FSP),
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-
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basin type (i.e. lake or bog, influences which model to use to calculate Ki) and basin
size (i.e. site radius, R in the equation),
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-
the maximum extent of the regional vegetation (Zmax)
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-
wind speed (m/s in)
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-
atmospheric conditions (for calculation of Ki – Cz: the vertical diffusion coefficient,
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Cy: the horizontal diffusion coefficient, and n: an empirical turbulence parameter set to
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neutral conditions by default).
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The model implies a number of assumptions that are listed and explained in Sugita (2007a).
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The most important ones are:
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-
wind is the dominant agent of pollen transport and is blowing from all directions
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The atmospheric conditions ( the turbulence parameter) are neutral
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48
-
the deposition basin (lake or bog) is round
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-
no vegetation is growing on the surface of the deposition basin
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-
pollen productivity does not change over time
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Deviations from the assumptions in empirical situations have been discussed and tested in the
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context of model simulations (e.g. Gaillard et al., 2008), and the many empirical tests and
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validations of the REVEALS model, i.e. in southern Sweden (Hellman et al., 2008a,b),
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Denmark (Nielsen & Odgaard, 2010), on the Swiss Plateau (Soepboer et al., 2010), and in the
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upper Great Lakes region of USA (Sugita et al., 2010). These studies show that REVEALS
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provides reasonable estimates of regional vegetation abundance using historical (Denmark)
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and modern pollen from large sites (≥50 ha) and related vegetation data. The spatial resolution
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of REVEALS reconstructions was shown to be ca. 104 km2 (100 km x 100 km) in southern
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Sweden (Hellman et al., 2008b).
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The effect of wind speed on the relevant source area of pollen (RSAP sensu Sugita, 1994) was
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studied by Nielsen and Sugita (2005). Differences in RSAPs were not found when wind speed
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was changed between 1 and 25 m·s-1, while the RSAPs were smaller for wind speeds lower
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than 1 m·s-1. It implies that changing wind speeds between 1 and 25 m·s-1 in the past would
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not influence pollen dispersal and deposition and, thus, would probably not impact on
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REVEALS estimates of plant cover either. The modern mean wind speed for southern
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Scandinavia, 3 m·s-1, was applied in this study for the sake of consistency with former studies
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on REVEALS estimates (e.g. Hellman et al., 2008a,b; Soepboer et al., 2010). The “neutral
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atmospheric conditions” assumption was tested by simulations (Gaillard et al., 2008). The
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results showed that different atmospheric conditions did not have a significant impact on the
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RSAP, which implies that REVEALS estimates would probably not be influenced either
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Finally, Mazier et al. (2012) tested the effect on REVEALS estimates of different values of
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Zmax and found that values between 50 and 200 km did not influence the results (see Appendix
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S2).
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Although the REVEALS model was developed to reconstruct regional vegetation abundance
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using pollen data from large sites, simulations have shown that REVEALS can provide
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reliable estimates of regional plant abundances even when multiple small sites (< ca. 50 ha)
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are used; however, the error estimates will be much larger (Sugita, 2007a). The performance
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of REVEALS using small sites has been empirically evaluated in the Czech Republic (Mazier
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et al., 2012), Britain and Ireland (Fyfe et al., 2013), and southern Sweden (Trondman et al., in
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prep.). These studies show that the mean REVEALS estimates of a group of small sites (lakes
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and/or bogs) generally agree reasonably well with the REVEALS estimates from large lakes,
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although the error estimates may be very large, in particular for taxa with low pollen counts.
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The minimum number of small sites required to obtain reasonable outcomes is difficult to
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define as it will depend on the spatial patterns of vegetation within a region and on the size
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and type of sites (lakes or bogs). So far, Fyfe et al. (2013) have shown that a group of six
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small lakes in the Western Isles of Scotland was enough to produce REVEALS estimates
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comparable to those obtained from the pollen record of a large lake in the same region.
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Trondman et al. (in prep.) observe for southern Sweden that a group of 2–3 small lakes
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produced REVEALS estimates that are closer to the REVEALS estimates of a large lake than
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a group of 12–13 small bogs. Therefore, REVEALS estimates obtained using pollen data from
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a small number of small sites (bogs in particular) have to be interpreted with caution. Because
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REVEALS also assumes that no pollen-bearing plants grow in the sedimentary basin, pollen
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records from bogs might be problematic.
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The performance of REVEALS with pollen records from large and small bogs has not yet
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been validated with empirical modern and historical data. Nevertheless, the REVEALS test-
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runs by Mazier et al. (2012) show that the rank order of the grid-based REVEALS estimates
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for the defined plant functional types (PFTs) (abbreviated REVEALS PFTs) (see
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LANDCLIM protocol in Appendix S2) based on a single large bog pollen record or several
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small bog pollen records together are not significantly different, except for the PFT GL
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(grassland) (significantly higher values when data from several small bogs are used rather
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than from one large bog). The REVEALS IBEs (shade-intolerant evergreen trees), GLs, and
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ALs (agricultural land) show higher values when pollen data from lakes rather than bogs are
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used, and the REVEALS TBE2s (shade-tolerant evergreen trees), IBSs (shade-intolerant
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summer-green trees), TBSs (shade-tolerant summer-green trees), and LSE (low evergreen
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shrub) are generally higher when pollen data from bogs rather than lakes are used. However,
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the differences between the results obtained from bogs and those obtained from lakes are not
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always consistent and a much larger number of tests need to be performed before any general
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rules can be inferred from such studies.
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References
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Fyfe R, Twiddle C, Sugita S et al. (2013) The Holocene vegetation cover of Britain and
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Ireland: overcoming problems of scale and discerning patterns of openness. Quaternary
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Science Reviews, 73, 132-148.
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Gaillard M-J, Sugita S, Bunting MJ et al. (2008) The use of modelling and simulation
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approach in reconstructing past landscapes from fossil pollen data: a review and results from
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the POLLANDCAL network. Vegetation History and Archaeobotany, 17, 419-443.
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Hellman S, Gaillard M-J, Broström A, Sugita S (2008a) The REVEALS model, a new tool
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to estimate past regional plant abundance from pollen data in large lakes: validation in
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southern Sweden. Journal of Quaternary Science 23, 21-42.
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Hellman S, Gaillard M-J, Broström A, Sugita S (2008b) Effects of the sampling design and
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selection of parameter values on pollen-based quantitative reconstructions of regional
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vegetation: a case study in southern Sweden using the REVEALS model. Vegetation History
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and Archaeobotany, 17, 445-459.
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Mazier F, Gaillard M-J, Kuneš P, Sugita S, Trondman A-K, Broström A (2012) Testing the
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effect of site selection and parameter setting on REVEALS-model estimates of plant
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abundance using the Czech Quaternary Palynological Database. Review of Palaeobotany and
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Palynology, 187, 38-49.
131
Nielsen AB, Odgaard BV (2010) Quantitative landscape dynamics in Denmark through the
132
last three millennia based on the Landscape Reconstruction Algorithm approach. Vegetation
133
History and Archaeobotany, 19, 375-387.
134
135
136
137
Nielsen AB, Sugita S (2005) Estimating relevant source area of pollen for small Danish
lakes around AD 1800. The Holocene, 15, 106-120.
Prentice IC (1985) Pollen representation, source area, and basin size: Toward a unified
theory of pollen analysis. Quaternary Research, 23, 76-86.
138
Prentice IC (1988) Records of vegetation in time and space: the principles of pollen
139
analysis. In: Vegetation History. Handbook of Vegetation Science (eds Huntley B, Webb III
140
T), pp. 17-42, Kluwer Academic Publishers, Dordrecht, The Netherlands.
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Soepboer W, Sugita S, Lotter AF (2010) Regional vegetation-cover changes on the Swiss
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Plateau during the past two millennia: A pollen-based reconstruction using the REVEALS
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model. Quaternary Science Reviews, 29, 472-483.
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144
145
146
147
148
149
Sugita S (1993) A model of pollen source area for an entire lake surface. Quaternary
Research, 39, 239-244.
Sugita S (1994) Pollen representation of vegetation in Quaternary sediments: Theory and
method in patchy vegetation. Journal of Ecology, 82, 881-897.
Sugita S (2007a) Theory of quantitative reconstruction of vegetation I: pollen from large
sites REVEALS regional vegetation composition. The Holocene, 17, 229-241.
150
Sugita S, Andersen ST, Gaillard M-J, Mateus JE, Odgaard BV, Prentice IC, Vorren K-D
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(1998) Modelling and data analysis for the quantification of forest clearance signals in pollen
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records. Paläoklimaforschung, 27, 125-131.
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Sugita S, Parshall T, Calcote R, Walker K (2010) Testing the Landscape Reconstruction
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Algorithm for spatially explicit reconstruction of vegetation in northern Michigan and
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Wisconsin. Quaternary Research, 74, 289-300.
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Sutton OG (1953) Micrometeorology. McGraw-Hill.
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