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APPENDIX S2
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3
The LANDCLIM protocol
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The LANDCLIM protocol was established by M.-J. Gaillard (coordinator), F. Mazier, A.-K.
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Trondman, and S. Sugita.
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Reconciling the spatial resolutions and levels of detail between the land-cover reconstructions
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and the climate (RCA3) and dynamic vegetation (LPJ-GUESS) models
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The spatial resolution of the “Regional Estimates of VEgetation Abundance from Large Sites”
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(REVEALS) model-based reconstructions (ca. 100 km x 100 km) is appropriate for the study
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of land-cover–climate relationships within the LANDCLIM project because the regional
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climate model RCA3 and the dynamic vegetation model LPJ-GUESS models used in the
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project perform well at a spatial scale of 0.5° to 1º. Therefore, we use a common grid-cell size
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of 1ºx1º, which corresponds to an area of ca 100 km x 100 km or less depending on the
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latitudinal location, for the REVEALS-based reconstructions of regional vegetation/land-
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cover presented in this paper (i.e. grid-based REVEALS estimates abbreviated REVEALS).
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RCA3 includes a description of the atmosphere and its interactions with the land surface
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(Kjellström et al., 2005; Samuelsson et al., 2011). For this purpose, it uses a simple land-
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surface description comprising three land-cover types (LCTs), (1) evergreen trees (ET), (2)
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summer-green trees (ST), and (3) open land (OL, i.e. non-forested areas) (Table 1). The LPJ-
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GUESS model simulates the climate-induced composition of plant-functional types (PFTs),
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i.e. groups of species (often single genera) that have similar responses to environmental
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conditions and affect ecosystem processes in comparable ways (e.g. Diaz & Cabido, 1997)
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and, therefore, can be regarded as functionally similar (e.g. Wolf et al., 2008). REVEALS
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reconstructs percentage cover of individual taxa at a taxonomical level that depends on the
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possible degree of precision of the pollen identification (species, species groups, genera,
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genus groups, family). Hence, to make comparison of the REVEALS estimates with the LPJ-
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GUESS outputs possible, and to use them as alternative descriptions of land cover in RCA3,
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the REVEALS estimates of individual taxa were grouped into ten PFTs and three LCTs
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(Table 1). The PFTs follow Wolf et al. (2008) with some modifications needed for the north-
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west European context and human-induced vegetation. Corylus (hazel) was added to IBS
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(shade-intolerant summer-green trees) and Acer (maple) and Carpinus (hornbeam) to TBS
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(shade-tolerant summer-green trees), Fraxinus (ash) was moved from TBS to IBS (shade-
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intolerant summer-green trees), all herbs were grouped into GL (grassland), and a new PFT
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AL (agricultural land) was created.
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REVEALS test-runs
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Before deciding on the criteria for selection of sites and parameters for the LANDCLIM
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project, Mazier et al. (2012) used the Czech Quaternary Pollen Database (PALYCZ; Kuneš et
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al., 2009) to evaluate the extent to which the selection of different input data and parameters
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would affect the REVEALS reconstructions. The tested parameters were the number of
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dates per pollen record used to establish the chronology (minimum of 3 or 5 dates), the basin
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type (lake or bog) and size (site radius), the number of taxa (25, 28, or 35 taxa), the PPEs
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(three different sets of estimates), and the value of Zmax (50, 100, or 200 km). Spearman’s
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rank-order correlation coefficient rs (Siegel & Castellan, 1988) was used to compare the
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REVEALS PFTs between the different model outputs, and the significance of the correlation
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was tested. Spearman’s rank-order correlation is a non-parametric statistic test that measures
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the degree of association between two sets of data, in this case REVEALS PFTs.
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14
C
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Table 1. Land-cover types (LCTs) and plant-functional types (PFTs) according to Wolf et al.
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(2008) with modifications (see text), and their corresponding pollen morphological types. Fall
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speed of pollen (FSP) and the mean pollen productivity estimates (PPEs) for standard 2 (st2)
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with their standard errors (in brackets) are also listed. PPE st2: for each taxon, mean of all
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existing PPEs for that taxon (Mazier et al., 2012); t = type. See text for more explanations)
Land-cover
types (LCTs)
Evergreen trees
(ET)
PFT
PFT definition
FSP (m/s)
PPE.st2
Shade-tolerant evergreen trees
Plant taxa/Pollenmorphological types
(25 taxa)
Picea
TBE1
TBE2
IBE
TSE
0.056
2.62 (0.12)
Shade-tolerant evergreen trees
Shade-intolerant evergreen trees
Tall shrub, evergreen
Abies
Pinus
Juniperus
0.120
0.031
0.016
6.88 (1.44)
6.38 (0.45)
2.07 (0.04)
Alnus
Betula
Corylus
Fraxinus
Quercus
Carpinus
Fagus
Tilia
Ulmus
Salix
Calluna vulgaris
Artemisia
Cyperaceae
Filipendula
Poaceae
Plantago lanceolata
Plantago media
Plantago montana
Rumex acetosa-t
Cerealia-t
Secale cereale
0.021
0.024
0.025
0.022
0.035
0.042
0.057
0.032
0.032
0.022
0.038
0.025
0.035
0.006
0.035
0.029
0.024
0.030
0.018
0.060
0.060
9.07 (0.10)
3.09 (0.27)
1.99 (0.20)
1.03 (0.11)
5.83 (0.15)
3.55 (0.43)
2.35 (0.11)
0.80 (0.03)
1.27 (0.05)
1.22 (0.11)
0.82 (0.02)
3.48 (0.20)
0.87 (0.06)
2.81 (0.43)
1.00 (0.00)
1.04 (0.09)
1.27 (0.18)
0.74 (0.13)
2.14 (0.28)
1.85 (0.38)
3.02 (0.05)
IBS
Shade-intolerant summer-green trees
TBS
Shade-tolerant summer-green trees
TSD
LSE
Tall shrub, summer-green
Low shrub, evergreen
Summer-green
trees
(ST)
Open land
(OL)
GL
AL
Grassland - all herbs
Agricultural land
- cereals
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The results showed that the REVEALS PFTs were generally insensitive to the differences in
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parameter settings, except when entomophilous taxa were included (Mazier et al., 2012). The
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REVEALS estimates differed depending on what set of PPEs, site type (lake or bog), and site
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size (large or several small) was used, but the ranking of the estimates did not change.
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Therefore, we chose the parameter setting we found most relevant for the LANDCLIM study
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(see also sections below for more explanations):
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1. Because the REVEALS model assumes that pollen grains are wind transported, we
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chose to exclude entomophilous taxa from the PFT reconstructions, although taxa with
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mixed wind and insect transport, such as Compositae, Filipendula, Rumex, Plantago,
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and Ranunculus species were included.
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2. We selected pollen records from both lakes and bogs, small and large basins, with
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chronologies based on ≥3 dates in order to maximise the number of sites to be used.
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3. We applied the PPE set “standard 2” (for each taxon, mean of all existing PPEs for
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that taxon, Mazier et al., 2012; Table 1).
4. Zmax was set to 50 km (corresponding approximately to the size of the project grid
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cells (1° x 1°).
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The PPE set “standard 2” was preferred because the causes behind differences in PPEs
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between studies and regions are still not fully understood. Possible explanations are the
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various types of pollen and vegetation data used for the calculation of PPEs (pollen data from
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lakes, bogs, or moss polsters; pollen and vegetation data from modern or historical time) or
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the different methods used in the vegetation surveys. Between-region differences might be
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due to contrasting climate, species composition, or land-use management (Broström et al.,
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2008).
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Selection of sites, chronologies, and size of time-windows
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Five time-windows of the Holocene were selected for the study. They represent contrasting
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land-cover in terms of vegetation composition and degree of vegetation openness (forested
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land versus open/non-forested land) (Gaillard et al., 2010). Moreover, because the error
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estimates of the REVEALS reconstructions will decrease (i.e. their precision increase) with
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the increase of the size of the pollen counts (Sugita, 2007a), we chose to work with 500 year-
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long time-windows (except for the two most recent ones) to maximise the number of counted
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levels within a time-window. The five time-windows studied in the LANDCLIM project are
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as follows (in calibrated years before present (BP = before 1950)):
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1. x–0.1k BP (ca. 0.05k BP, “Present”/Recent past): industrial time (x = date of the core
surface, e.g. AD 2005-100 BP if x= AD 2005),
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2. 0.1k–0.35k BP (ca. 0.2k BP, end of the Little Ice Age): pre-industrial time,
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3. 0.35k–0.7k BP (ca. 0.5k BP, Middle Ages): decreased human impact in parts of
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Europe,
4. 2.7k–3.2k BP (ca. 3k BP, Early/Late Bronze Age transition): relatively strong human
impact in several parts of the study region,
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5. 5.7k–6.2k BP (ca. 6k BP, Neolithic period/ Mesolithic-Early Neolithic boundary in
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southern Scandinavia, northern Germany. and northern Netherlands): low human
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activity.
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The chronological control of the pollen records is essential as the synchronicity of pollen data
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extracted from all records should be as good as possible to ensure that the reconstruction at
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the regional scale represents the same time period. Therefore, the pollen records with the best
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chronological control were selected. However, the number of sites strongly decreases with the
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higher number of dates required for a very good chronological control. Also, the availability
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of pollen records from large sites in the study area is limited, which means that the multiple
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small sites approach (Sugita, 2007a) had to be applied. For these reasons, it was necessary to
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seek a reasonable compromise between the best chronological control and a large number of
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sites in order to get the best land-cover reconstructions possible at a large regional scale. For
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the objectives of the LANDCLIM project, we considered that a reasonable strategy was to
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maximise the number of sites to widen the geographical cover in the study area, but to check
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carefully all chronologies to make sure that age-depth models based on only a few dates were
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Table 2. Type of dating used – other than 14C dates – to constrain chronologies with few 14C
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dates and their error estimates.
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diagrams within the same region,
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well-dated lithostratigraphical boundaries that are synchronous in a specific region,
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dated and consistent changes in e.g. biostratigraphy of any kind, chemistry, palaeomagnetic
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properties, * if original error estimates are lacking ± 500 years is used.
1
for the last 150–200 years,
3
2
dated by correlation to dated
well-constrained and well-dated, e.g. the elm decline,
Type of dating/control point
Uncertainty
Annual laminations (varve counting)
Radiocarbon
210 Pb and 137 Cs1
Tephra
Dendrochronological dates
Pollen-stratigraphical events2
Historical events
Biostratigraphical markers3
Biostratigraphical markers
(less well constrained)
Lithostratigraphical events4
Other stratigraphical events 5
Original
Original
Original
Original
Original
Original*
± 50 years
± 250 years
± 500 years
5
4
well-
± 250 years
± 250 years
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nevertheless reasonable in terms of the well-established ages of particular vegetation changes
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in NW Europe (e.g. the age of the elm decline). Therefore, based on the results from the
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REVEALS test-runs (Mazier et al., 2012), we used pollen data from sites with chronologies
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based on ≥3 dates. The number of dates for the selected pollen records varies between 3 and
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278, with ca. 210 records with <6 dates. The selected pollen records cover at least one of the
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five Holocene time-windows above. When the chronology in calibrated years was established
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by the data contributor or the database manager we adopted it. But when the chronology was
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lacking or given in uncalibrated years, we used the available dates (from the literature or
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directly from the data contributor) and established an age-depth model using the software
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clam (Blaauw, 2010) that uses the IntCal09 calibration curve of Reimer et al. (2009). These
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chronologies are specified as “LANDCLIM chronologies” in Table S2 in Supplemantary
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Information.
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Harmonisation of pollen types and their ascription to pollen productivity estimates (PPEs)
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To maximise the number of useful pollen-morphological types and the size of pollen counts to
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be used in the REVEALS runs we ascribed the available PPEs and related fall speed of pollen
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(FSP) to as many pollen taxa as possible. The decisions were based on knowledge of the
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pollen-morphological types and their corresponding plant taxa, and the morphology and
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biology of the plant taxa represented by a pollen-morphological type, i.e. the type of flower,
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number of flowers per individual, number of stamens per flower, and type(s) of pollen
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dispersal (entomophilous or/and anemophilous). Examples of harmonisation are provided in
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Table 3.
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REVEALS runs and calculation of PFTs and LCTs
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This study uses the REVEALS program (version REVEALS.v4.2.2.Tallinn.wks.exe; Sugita,
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unpublished) assuming neutral atmospheric conditions and wind speed of 3 m s−1 as in
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Prentice (1985) and Sugita (1993, 1994, 2007a,b). Zmax is set to 50 km (see Appendix S1 for
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more information on those parameters). The number of pollen records available for the
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reconstruction varies between grid-cells and time-windows. For the REVEALS runs, pollen
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counts from all samples in a given time-window are aggregated. The original pollen data files
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(Excel files with chronology and the selected 25 taxa as columns, and levels with raw pollen
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counts as rows) were reformatted into REVEALS input files using a conversion program
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(File.format.conversion.for.LRA.12Oct09.exe; Sugita, unpubl.) before running the REVEALS
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program. The conversion program sums up the pollen counts for each time-window defined in
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the LANDCLIM project, and incorporates information about site radius.
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Table 3. Examples of pollen-type harmonisation and assignment to pollen productivity
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estimates (PPEs) and plant functional types (PFTs) (see text for further explanation).
PFT
PPEs
IBS
Betula
AL
Cerealia-type
GL
Rumex acetosa-type
pollen-morphological types
Betula
Betula “alba”
Betula pubescens
Betula undiff.
Betula/Corylus/Myrica
Avena
Avena/Triticum-type
Avena/Triticum
Avena-type
Cerealia
Cerealia (-Secale)
Cerealia-type
cf. Avena
cf. Hordeum
cf. Triticum diccocon
Hordeum
Hordeum/Secale
Hordeum/Triticum-type
Hordeum-type
Triticum
Triticum-type
Oxyria digyna
Oxyria-type
Rumex
Rumex acetosa
Rumex acetosa/R. acetosella
Rumex acetosa/R. acetosella-type
Rumex acetosa-type
Rumex acetosa-type/Oxyria
Rumex acetosella
Rumex acetosella-type
Rumex alpinus
Rumex cf. R. obtusifolius
Rumex conglomeratus-type
Rumex longifolius-type
Rumex obtusifolius
Rumex obtusifolius-type
Rumex sp
Rumex Subgen. Acetosa
Rumex Subgen. Acetosa/R. Subgen. Acetosella
Rumex undiff.
Rumex/Oxyria
Rumex-type
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Hence, the REVEALS input files include raw pollen counts for each of the 25 taxa for the five
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Holocene time-windows, and site radii for each time-window. REVEALS also requires one
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input file with the PPE and FSP values for each taxon, and one input file with the variance8
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covariance matrix of the PPEs (all the covariances of the PPEs between individual taxa are set
163
to zero). REVEALS was run separately for lakes and bogs using Sugita’s model for lakes and
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ponds (Sugita, 1993) and Prentice’s model for bogs (Prentice, 1985, 1988). The REVEALS
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program estimates the mean regional abundance in proportions (0 to 1, i.e. 0–100%) and the
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standard error (SE) for each taxon in each grid-cell with available pollen data for a given
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time-window (grid cell-based REVEALS taxon estimate (s), hereafter abbreviated REVEALS
168
taxon (taxa)). The SEs of the REVEALS taxa are calculated using the Delta method (Stuart &
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Ord, 1994). The REVEALS estimates for PFTs and LCTs are the mean of the REVEALS
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taxon of all taxa included in a given PFT or LCT (in proportions). Therefore, the REVEALS
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taxa for either bogs or lakes are converted to grid cell-based REVEALS estimates for PFTs or
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LCTs (REVEALS PFT(-s) or LC (-s) bog or lake) and their SEs using the “Plant-PFT
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conversion program” (Plants.PFT.RCA.classification.22Nov10.v9.exe; Sugita, unpubl.). To
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get a single REVEALS PFT or LCT for grid cells with pollen records from both lakes and
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bogs, the mean of the REVEALS PFT bog (or LCT bog) and REVEALS PFT lake (or LCT
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lake) and the mean SE were calculated with the program “Bog-Lake fusion program”
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(bog.lake.data.fusion.24Nov10.v5.exe; Sugita, unpubl.), abbreviated REVEALS PFT(-s) (or
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LCT(-s)) for simplicity.
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