Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Present-Day Vegetation Helps Quantifying Past Land Cover in Selected Regions of the Czech Republic

  • Vojtěch Abraham ,

    Affiliations Department of Botany, Faculty of Science, Charles University in Prague, Prague, Czech Republic, Department of Botany, Faculty of Science, University of South Bohemia, České Budějovice, Czech Republic

  • Veronika Oušková,

    Affiliation Nature Conservation Agency of the Czech Republic, Prague, Czech Republic

  • Petr Kuneš

    Affiliations Department of Botany, Faculty of Science, Charles University in Prague, Prague, Czech Republic, Institute of Botany of the Academy of Sciences of the Czech Republic, Průhonice, Czech Republic

Present-Day Vegetation Helps Quantifying Past Land Cover in Selected Regions of the Czech Republic

  • Vojtěch Abraham, 
  • Veronika Oušková, 
  • Petr Kuneš


The REVEALS model is a tool for recalculating pollen data into vegetation abundances on a regional scale. We explored the general effect of selected parameters by performing simulations and ascertained the best model setting for the Czech Republic using the shallowest samples from 120 fossil sites and data on actual regional vegetation (60 km radius). Vegetation proportions of 17 taxa were obtained by combining the CORINE Land Cover map with forest inventories, agricultural statistics and habitat mapping data. Our simulation shows that changing the site radius for all taxa substantially affects REVEALS estimates of taxa with heavy or light pollen grains. Decreasing the site radius has a similar effect as increasing the wind speed parameter. However, adjusting the site radius to 1 m for local taxa only (even taxa with light pollen) yields lower, more correct estimates despite their high pollen signal. Increasing the background radius does not affect the estimates significantly. Our comparison of estimates with actual vegetation in seven regions shows that the most accurate relative pollen productivity estimates (PPEs) come from Central Europe and Southern Sweden. The initial simulation and pollen data yielded unrealistic estimates for Abies under the default setting of the wind speed parameter (3 m/s). We therefore propose the setting of 4 m/s, which corresponds to the spring average in most regions of the Czech Republic studied. Ad hoc adjustment of PPEs with this setting improves the match 3–4-fold. We consider these values (apart from four exceptions) to be appropriate, because they are within the ranges of standard errors, so they are related to original PPEs. Setting a 1 m radius for local taxa (Alnus, Salix, Poaceae) significantly improves the match between estimates and actual vegetation. However, further adjustments to PPEs exceed the ranges of original values, so their relevance is uncertain.


Pollen-based quantification of past land cover is important for understanding vegetation-climate interactions and human induced changes [1]. The Landscape Reconstruction Algorithm (LRA) [2], [3] is a robust method for quantitative vegetation reconstruction, and is therefore widely used for studying Holocene sequences [4][9] and interglacial deposits [10]. The LRA can be used in combination with other data, for example, to estimate the spatial extent of cereal fields [7], to examine the role of different factors on long-term vegetation changes [11] or as input for climate reconstructions [12].

The necessary parameters include taxon-specific relative pollen productivity and parameters of the pollen dispersal function (such as the size of the basin, size of the region, fall speed of pollen and wind speed). Various model parameters have been shown to vary significantly among regions [13]. To provide Holocene vegetation estimates for the Czech Republic, we therefore need to examine the model parameters in light of modern pollen assemblages at fossil sites and compare them with actual vegetation composition. In the present paper, we focus only on the first step of the LRA: REgional Vegetation Estimates from Large Sites (REVEALS) [2]. This model estimates vegetation for large regions (106 km2) based on single or multiple pollen sites and provides a baseline for the second step of the LRA: LOcal Vegetation Estimates (LOVE), which produce single-site vegetation proportions for a limited local area (few km2) [3].

Actual vegetation data for large areas can be compiled from various sources: forest inventories, crop statistics, land-cover information and remote sensing data such as aerial or satellite images [14]. Until now, Czech vegetation has not been reconstructed on a quantitative basis, although its components have been examined separately to solve certain partial problems such as land cover changes in the last two decades [15]. Here it is important to note that detailed qualitative overviews of vegetation in the Czech Republic are available (e.g. [16]).

The REVEALS algorithm was originally developed for large sites, as they better reflect regional vegetation than small ones (e.g., [17]). However, simulations and empirical data show that good mean estimates of regional vegetation can be attained even when many small sites are simultaneously included in a REVEALS model [2], [18]. Many studies have already successfully employed the REVEALS algorithm using the Prentice-Sugita dispersal-deposition model, but always in areas with large lakes [4]. One exception are pilot tests carried out by Sugita et al., [18], who used many same-sized small bogs. This model assumes that no taxa of interest grow within the sedimentation basin. Yet, most Czech palynological records come from bogs, which differ in size and in the number of taxa of interest growing within their sedimentation basins, depending on the region. The size of the sedimentation basin influences the size of the region, hence the term “characteristic radius” [19]. Previous testing suggests that the extent of a vegetation survey (region) has little effect on model validation [20].

Apart from this dispersal-deposition consideration, it is necessary to work with correct relative pollen productivities (PPEs) for all taxa. This parameter has already been calculated for many parts of Europe, and differences in methodology (lakes vs. moss polsters) or environmental setting (climate, landscape structure) cause substantial variations [13]. Since PPEs of 13 pollen taxa from moss polsters are available for Central Bohemia [21], any methodological and environmental biases associated with taxa under study should theoretically be reduced to a minimum. However, PPEs of missing target taxa (Picea, Fagus, etc.) must be filled in with data from other areas. It has been shown that averaging different values from Europe [22] yields applicable results using the dataset from the Czech Quaternary Palynological Database [23]. These averaged PPEs, in spite of their slightly different input parameters (type and size of basin, set of taxa), assure the consistency among REVEALS estimates of past vegetation. Which PPEs are the most appropriate for actual vegetation remains uncertain, however.

The main goal of our present study was to ascertain the best REVEALS settings and adjustments of parameters for producing a reliable quantitative vegetation reconstruction. We therefore i) examined actual vegetation data for the Czech Republic, ii) tested the effects of taxa growing in peat bogs and iii) identified the best set of PPEs. We particularly addressed questions related to the effects of wind speed and the characteristic radius of regional vegetation.

Theoretical Assumptions

The LRA [2], [3] is the inverse form of the ERV model [24][26]. Both methods therefore deal with space in a similar way. The whole space is divided into a sedimentation basin (R), a relevant source area of pollen (RSAP) and an area of background pollen.

No taxa producing pollen are assumed to grow in the sedimentation basin. Its radius and type are set as parameters prior to the analysis. The size of the radius can range from 0.5–1 m (e.g. in studies of moss polsters using the ERV model) to several kilometres, as in the case of some large lakes.

According to the ERV model, the area of the background pollen is defined as the source area of a certain proportion of pollen which lies beyond the RSAP and does not have an exact extent. The REVEALS model produces vegetation estimates pertaining to the area from the edge of the sedimentation basin to the maximum range of the regional vegetation (Zmax). This is an input parameter. The size of the background area is generally 105 km2; however, the REVEALS model deals with PPEs from a much smaller area. We therefore need to validate our selection of PPEs in a REVEALS model.

PPEs are not the only parameters for translating pollen data into vegetation proportions when using the REVEALS model. As already mentioned above, the maximum range of the regional vegetation (Zmax), the radius of the sedimentation basin (R) and also parameters of the deposition function – wind speed (u) and fall speed of pollen (vg) – each play a significant role. We could have adhered to widely used settings of these parameters (see Methods for default settings) but decided to explore how changing these parameters affects the final results of simulations. A similar approach to testing the effect of changing radius was taken in Norway [27].

Secondly, we tested selected settings on real data and, finally, adjusted PPEs according to the dataset. Adjustment of PPEs is based on the following general assumptions: (1) The REVEALS algorithm with selected deposition function describes realistic conditions; (2) The fall speed of pollen (vg) is universally valid; (3) The given sets of pollen assemblages represent regional pollen rain; and lastly (4) Vegetation data reflect actual vegetation. If input parameters (u, R, Zmax) are chosen correctly, we can use the dataset to recalibrate PPEs.

Characteristic Radius

The maximum range of the regional vegetation (Zmax) can be approximated as the characteristic radius, assuming homogenous vegetation [20]. The characteristic radius is a distance (z) from which part of the pollen loading (Fi(R,z)) of taxon i arrives at the sedimentation basin with radius R (Equation 1). Taxon specificity is given by parameter bi that depends on how fast pollen is lost from the atmosphere. Parameter γ was set to ∼1/8, which corresponds to typical daytime conditions. The term bi is given approximately by 75 vg/u, where vg is the fall speed of the pollen and u is the wind velocity [19].(1)


A REVEALS estimate (Vi) is the proportion of regional vegetation composition belonging to taxon i. It is defined for one site (k) as pollen counts of taxon i (ni) weighted by its pollen productivity (αi) and dispersal term (Ki), divided by the sum of weighted pollen counts for all taxa [2]. In the case of multiple sites (Equation 2), sums of weighted pollen counts of taxon i from all sites are divided by the total sum of the same sums of weighted pollen counts for all taxa (j) at all sites (k) (Sugita, pers. comm.).(2)

The dispersal term (Ki) can be substituted by any dispersal function. We used the Prentice model (Equation 3), which considers the distance of source plants (z), dispersal properties of pollen and the type of sediment where pollen is deposited without mixing (bog model). The distance is considered from the edge of the sedimentary basin (R) to the edge of the maximum range of the regional vegetation (Zmax). A sedimentary basin is defined an area of a peat bog or lake where no plants of interest grow. However, this assumption can be violated if the size of a peat bog vegetated by target species is set to the size of the sedimentary basin. We thus propose that this fact is taken into account by considering the ecology of each taxon. For all extra-local taxa, the site radius should be set according to the size of the sedimentary basin; for local taxa, this radius should be decreased. Optimally, we would obtain a matrix of different radii for all taxa at all sites per region (Ri,k); however, we do not have data about distances of local taxa from the centre of the sedimentation basin. We therefore suggest that the R of local taxa be approximated by the radius used in studies of the pollen/vegetation relationship based on pollen trapped in moss posters (i.e. 1 m).(3)


Characteristic Radius

We designated 70% of the pollen loading, following [19], [20], as a representative part of the major source area of pollen collected at a certain point in the canopy. Its radius is further referred to as the “characteristic radius 70”. Using the wind speed of 3 m/s and the corresponding fall speed of pollen from Table 1, we get parameter bi. We calculated distances corresponding to the “characteristic radius 70” for all taxa at all sites (120). There is an advantage to performing separate computations for all sites instead of just working with mean values: Instead of a single number, we obtain a distribution of “characteristic radii 70” that follows the distribution of site radii and reflects the dispersal properties of all taxa included in the study.

Table 1. Fall speed of pollen (vg), relative pollen productivity estimates (PPE) and their standard error (SE).

REVEALS Simulation Setting

(Table 2) We simulated the effects of increasing four parameters. Each simulation scenario has a pair scenario with control settings. In each simulation, we thus consider two effects: i) gradual changes of REVEALS estimates along the x axis and ii) changes against the control scenario. To ensure comparability between scenarios, all control simulations share the same setting at one reference point. This reference setting used the following parameters: original PPEs listed in Table 1, radius of the sedimentation basin R = 100 m and maximum range of the regional vegetation Zmax = 60 km; an even pollen assemblage of 100 pollen grains per taxon was used as the dataset. We used the Prentice model to devise the pollen dispersal-deposition function, using bi = 75 vg/u, where vg (terminal velocity) is listed in Table 1 and u (wind speed) equals 3 m/s.

Table 2. Input parameters for simulations of REVEALS vegetation estimates.

We asked the following questions: Simulation A – What is the effect of increasing R on REVEALS vegetation estimates of each taxon? How is it influenced by higher wind speed? Simulation B – What is the effect of increasing Zmax on REVEALS vegetation estimates of each taxon? How is it influenced by higher wind speed? Simulation C – How do REVEALS vegetation estimates change when we calculate them for two sites differing in size? Simulation D – Can approximation of R by the moss polster size of 1 m for local taxa improve REVEALS vegetation estimates when local taxa have higher pollen proportions than other taxa? We expected the results to be influenced by PPEs and fall speed of pollen. We therefore selected Picea and Alnus (see their values in Table 1) as local taxa in simulation D. This combination represents the tree layer of the wetland community Thelypterido palustris-Alnetum glutinosae [28].

Pollen Data

We used the shallowest sample from every core in the Czech Quaternary Pollen Database (PALYCZ, accessed 01.07.2013). However, these samples were not collected in the same year as the vegetation data (see Table S1), but within the last 40 years, and not all of them have exactly 0 cm depth. Still, we assume that they are the closest representation of recent pollen deposition. Centroids of regional circles were placed visually around the spatial clusters of the sites. When two regions overlapped, certain sites fell into both of them at the same time. In these cases, the region was assigned according to the environmental conditions surrounding the core (vegetation, altitude, climate, etc.) (Figure 1). The pollen sum of selected taxa was limited to 100 pollen grains per sample, but only seven sites had less than 200 grains per sample; the average pollen sum of all 120 sites is 542 grains per sample (Table S1). Names of genera, except Pinus, used to denote selected pollen types refer to all species within the given genera. The name Pinus refers only to species belonging to the subgenus Pinus (diploxylon pines). Plantago lanceolata is the only pollen taxon defined at the species level. Poaceae encompass all wild grasses, and Cerealia comprise the genera Triticum, Hordeum and Avena; if distinguished, pollen of Secale and Zea was excluded.

Figure 1. Map of the Czech Republic with pollen sites (yellow crosses) and assigned regions (red circles).

Northern Bohemia [49][53], Northeastern Bohemia [49], [54][63], Polabí [64][67], Southern Bohemia [68][73], Southern Moravia [74][79], Bohemian-Moravian Highland [80][85], Šumava Mts. [86][88]. Yellow lines and arrows show the allocation of sites to regions in cases when sites are situated in two overlapping regions.

Data Sources of Actual Vegetation

To establish the proportions of all target taxa in actual vegetation (Table 1) and the size of each regional circle (Figure 1), we extracted data from various sources. Distribution and abundance information is available for two groups of taxa. Data on the first group (crops and trees) can be relatively easily obtained because its biomass is ascertained periodically, as they directly benefit mankind. Data on the second group of taxa (Poaceae, Plantago lanceolata and Corylus) are available thanks to the habitat mapping project coordinated by the Nature Conservation Agency of the Czech Republic, which was initiated for delimiting Natura 2000 sites and is continually updated for the purpose of reporting under the Habitats Directive (92/43/EEC). We used the habitat mapping layer (Nature Conservation Agency of the Czech Republic, unpublished data) – a large dataset containing spatial and tabular data from habitat surveys (species presence or abundance) – to estimate the proportion of taxa in the second group. The first results (number of segments and area of every habitat) have already been published [29].

We used several data sources to obtain the most reliable data on regional vegetation cover. The CORINE Land Cover (CLC) map from 2006 [30] served as a spatial basis for most of the area. Areas under farm crops from 2006 [31] in the resolution of administrative regions (average area 5,637 km2) were included in CLC class 211 (Arable fields). Forest inventory data from 2006 [32] in the resolution of municipalities with extended competence (average area 383 km2) were joined with CLC classes 311, 312 and 313 (coniferous, deciduous and mixed forest). Areas of forested and arable land provided by numerical and spatial data (statistics and CLC) were compared at the corresponding resolution.

Processing of Habitat Mapping Data

We combined the CLC map with habitat mapping data (see below) to obtain average abundances of Poaceae, Corylus and Plantago lanceolata. The following reasons made us select these taxa: Poaceae are the key taxon of open landscapes, Corylus became subdominant in pollen assemblages during the Early Holocene, and Plantago lanceolata is classified as an anthropogenic indicator [33]. These taxa allow us to reconstruct landscape openness, Early Holocene vegetation and the magnitude of human impact. The habitat mapping layer consists of two datasets: (i) a map of natural habitats covering 20% of the Czech Republic obtained by a field survey carried out between 2000 and 2011; and (ii) the presence or abundance of taxa estimated in some segments (0.4% of the total area for Poaceae, Corylus and Plantago lanceolata) collected between 2008 and 2011. The whole map of habitats was intersected with the CLC map. Abundance data were averaged and extrapolated to all habitats and CLC classes.

The details of the method of habitat mapping are described elsewhere [29]; however, it is helpful to mention its most important aspects. Recorded taxa include vegetation dominants and taxa of interest for nature conservation. In segments with habitats categorized as “natural”, abundances of diagnostic or typical species were recorded using the Braun-Blanquet scale. In segments with transitional or human-influenced biotopes, only the presence of target species was recorded. The Braun-Blanquet scale was converted into percentages according to the following key: “r”–0.1%; “+”–0.5%; “1”–3%; “2”–15%; “3”–37.5%; “4”–62.5; “5”–87.5%. The total cover of segments had to be standardized due to the presence of multiple vegetation layers (more than one species recorded as “5”). If the total cover of a segment was less than half the standard value, the record was treated as a mere presence. The standard total cover was obtained from [34], [35].

The extrapolated cover of Corylus, Poaceae and Plantago lanceolata was calculated by bootstrapping from random resampling from all segments with replacement. We then calculated the average percentage for segments containing abundance data of the habitat/CLC class and multiplied it by its proportional presence in all segments. Bootstrap calculations (N = 5000) of these weighted means permitted estimation of variances for each habitat/CLC class and standard deviation in each region (see Table S2, S3, S4 in Supporting Information). Due to the focus of vegetation mapping on natural and semi-natural biotopes, the surface of some CLC classes suitable for vegetation mapping was estimated as follows: 35% – discontinuous urban fabric, industrial or commercial units (121, 112), 10% – forests (311, 312, 313) and 5% – arable land (211). Herb taxa (Poaceae and Plantago lanceolata) were considered only in non-forest and shrub vegetation (excl. T, K, X9, X8 sensu [36]).

The layer was intersected with circles representing regions. Some of the circles extended beyond the borders of the Czech Republic. We therefore extrapolated the vegetation composition in parts of the circles within the country to parts of the circles overlapping into neighbouring countries. Similarly, we assumed regional proportions of bare land and areas vegetated by plants beyond our interest to be zero. We converted absolute plant abundances into vegetation proportions to compare them with vegetation estimates.

Data Analysis

REVEALS estimates were calculated by the REVEALS.v4.2.2.Tallinn.wks.exe binary (Sugita unpublished) and by a script written in R [37] (Text S1). Unlike our script, the original programme calculates standard errors, but offers only limited parameter settings. These default settings together with our initial parameters include: Prentice’s [38] model as the dispersal function using bi = 75 vg/u, the fall speed of pollen (vg) extracted from previously published works [39][41] and wind speed (u = 3 m/s). The radius of the sedimentation basin (R) was set to the size of the peatbog, a parameter extracted from the Czech Quaternary Palynological Database (Figure 2, Table S1). The maximum range of the regional vegetation Zmax was set 60 km.

Figure 2. Histograms of sizes of sedimentation basins in different regions.

The number of sites included is indicated at top right.

We selected PPEs during preliminary REVEALS calculations. Assuming environmental similarities, we first picked PPEs for all available taxa in Central Bohemia; PPEs for missing taxa were completed from other studies with the following priority Swiss Plateau, Swiss Jura and Southern Sweden. PPEs of taxa not matching vegetation estimates were substituted by other values until we reached the best set of PPEs. A change of one PPE value shifts the estimates of all taxa; however, rare taxa have a smaller effect than abundant ones, so we began by evaluating abundant taxa. We first assessed the goodness of fit visually and then calculated two kinds of dissimilarities, hereafter referred to as “dissimilarity A” and “dissimilarity B”. The two dissimilarity indices highlight differences of REVEALS model from pollen proportions and also differences among alternative scenarios (representing expected improvements over widely used settings). Dissimilarity A was calculated by subtracting estimates (or pollen proportions) from actual vegetation values and summing the results of this subtraction for each taxon across all regions. As regards dissimilarity B, the results obtained by the previous subtraction (calculated for dissimilarity A) were squared, summed together for each taxon and divided by the sum of squared residuals from a linear regression calculated between the two variables for each taxon. To keep dissimilarity B close to 1, the linear regression line should be close to the line of best fit (one-to-one line). Another particularity of dissimilarity B is that it can be very high when both variables are linearly related, albeit far from the line of best fit.

We hypothesize that the results of our regional vegetation reconstruction in the study area can be biased by individuals growing in the sedimentation basin and by wind speed. Wind speed (u), the radius of the sedimentation basin (R) and PPEs were adjusted in a three-step process considering alternative scenarios. After every step, we assessed the goodness of fit between REVEALS estimates and actual vegetation proportions. First, we ran the REVEALS model with two different wind speeds: 3 m/s and 4 m/s (scenarios S3MS and S4MS, respectively). Average seasonal wind velocity in spring varies from 2.5 m/s in lowlands to 4.5 m/s in mountains [42]. In this first step, it was assumed that no taxa of interest grow in the sedimentation basin, whose size corresponds to the default setting.

In the second step, we added the wind speed setting which better matched actual vegetation proportions to the alternative setting representing the radius of the sedimentation basin. The presence of local taxa was estimated by comparing pollen percentages, general wetland vegetation of the regions and the ecology of species corresponding to our pollen taxa. Approximation using the size of moss polster sites (radius of the sedimentation basin R = 1 m) was applied to Alnus, Salix and Poaceae at all sites within the following regions: Southern Moravia, Southern Bohemia, the Polabí lowland and Northern Bohemia; Alnus and Poaceae were also considered local in the Bohemian-Moravian Highland.

Finally, the two scenarios with the lowest sums of both dissimilarities were used for the adjustment of PPEs. As we approached the optimal set of adjusted PPEs, dissimilarity A tended to decrease to 0, while dissimilarity B decreased towards 1. PPE values of mismatching taxa were adjusted until the best match was obtained. If the PPE of Poaceae (reference taxon = 1) needed to be adjusted, all values were adjusted accordingly to keep the value of Poaceae at 1. This was done to retain comparability with other studies.

To test the robustness of REVEALS vegetation estimates, we applied a leave-one-site-out approach. Following [22], we calculated non-parametric Spearman rank-order correlation coefficients and their statistical significance for the relationship between estimates for all sites (default scenario) and estimates with one site omitted. We tested the null hypothesis (H0) that there is no association between the two types of estimates and used a two-tailed test with the significance level of p = 0.01. Additionally, we calculated scores of Principal Component Analysis (without transformation) for all leave-one-site-out samples and their corresponding estimates for all sites. For each region, we compared the variability of all leave-one-site-out samples and also their distances from the default scenario. We expected the site radius to have a substantial influence, so we plotted each leave-one-site-out sample as a symbol whose size indicates the radius of the corresponding site.


Characteristic Radius

(Figure 3) The maximum “characteristic radius 70” refers to the distance of approximately 250 km pertaining to all taxa at all sites. This large distance is given by the size of the largest sedimentation basin (1,262 m) and by taxa with light pollen (Alnus, Fraxinus and Salix). However, most of the “characteristic radii 70” are accumulated within the first 60–80 km. Within this distance fall also the maximum characteristic radii of taxa with medium-weight to heavy pollen grains. The reason behind this result is that the distribution of site radii is skewed towards smaller radii, with quartiles ranging from 30 to 250 m (Figure 2).

Figure 3. Histogram of “characteristic radii 70” (km) for all pollen taxa (17) at all sites (120).

Note the different distribution of taxa with light pollen grains (e.g. Alnus – purple) and heavy pollen grains (e.g. Abies – light green). The wide distribution is given by the variability of the radii (see Fig. 2). The name of each taxon is placed at the maximum distance given by the maximum sedimentation basin (1,262 m).

REVEALS Simulation

(Figure 4) Pollen productivity and the dispersal term are inversely proportional to REVEALS estimates. Hence, the highest REVEALS estimates are those for Cerealia, which have the lowest PPE. However, Abies, the taxon with the highest PPE, does not have the lowest REVEALS estimates because it has a low dispersal term. The first two simulations (Figure 4A, 4B) show how parameters of the dispersal term – wind speed (u), fall speed (vg), radius of the sedimentation basin (R) and maximum range of the regional vegetation (Zmax) – influence REVELS vegetation estimates. Taxa within each simulation show a similar pattern to taxa with similar fall speed of pollen. Alnus-Capinus, Picea-Cerealia and Abies delimit three groups of taxa corresponding to three ranges of terminal velocities of pollen: slow (0.021–0.042 m/s), medium (0.056–0.06 m/s) and fast (0.12 m/s). All remaining taxa fall within one of these ranges (Table 1).

Figure 4. Simulation of REVEALS estimates (proportion in log scale).

Alnus-Capinus, Picea-Cerealia and Abies delimit three groups of terminal velocities of pollen: slow, medium and fast, which also represent the pattern of the rest of taxa not plotted here. Arrows show the change of estimates from control (full line) to scenario (dashed line); for details, see Table 2. (A) Simulation A: Increasing radius of the sedimentation basin under two wind speed velocities (B) Simulation B: Increasing radius of the area of the background pollen under two wind speed velocities. (C) Simulation C: Fixed versus increasing radius of the sedimentation basin of the second site. (D) Simulation D: Increasing pollen counts of Alnus and Picea under two radii of the sedimentation basin. The reference setting common to all control simulations is marked on the secondary horizontal axis by “REF”.

Light pollen grains show a significantly decreasing trend. Medium-weight pollen grains indicate no trend or one that is only slightly decreasing or increasing. Heavy pollen grains of Abies show a significantly increasing trend. Higher wind speed increases REVEALS estimates of light pollen grains, decreases estimates of heavy pollen grains and has no significant influence on medium-weight pollen grains. In general, decreasing the radius of the sedimentation basin has a similar effect as increasing the wind speed parameter from 3 to 4 m/s.

Within the reasonable Zmax ranges of ca. 10–500 km, the increasing maximum range of the regional vegetation has very little effect on REVEALS estimates for all taxa.

When a combination of two differently sized sites is used (Figure 4C), REVEALS estimates of Abies and light-pollen taxa are similar to REVEALS estimates calculated for the larger site only (or two sites the size of the larger one). Our comparison of standard errors from the first and third simulation shows that combining two sites differing in size increases standard errors. The standard errors grow with the size difference in taxa on which the size of the site has a notable effect (e.g. Abies and, to a lesser extent, light-pollen taxa).

Decreasing the site radius of Picea and Alnus (100 m → 1 m) decreases the REVEALS estimates of these taxa (Figure 4D). This effect is stronger for Picea than for Alnus because a pollen assemblage with a site radius of 1 m and 210 more Picea grains or more 70 Alnus grains than the rest of the taxa produces similar REVEALS vegetation estimates as an assemblage with a 100 m radius and equal pollen counts for all taxa. It is important to note the difference against the first simulation; in other words, when the site radius decreases for all taxa, REVEALS estimates of Alnus increase.

It is necessary to note the log scale of the vertical axis. Changes in the vegetation estimates of Alnus (Figure A–C) are actually very low (<0.5%). Similarly, however, although the decreases in control simulation D of Cerealia, Abies and Carpinus seem similar; Cerealia exhibit the highest absolute decline (70 → 47%).

Actual Vegetation Cover Derived from CLC and Habitat Mapping

(Figure 5, 6) Areas of arable land and forests derived from two different sources yield similar results and are thus combinable (Figure 5). There is a slight trend towards overestimating forest and arable land in CLC mapping with increasing size of the municipality or region.

Figure 5. Comparison of the area derived from the CLC map [30] and numerical data sources.

(A) Forest area within municipalities with extended competence [32]. (B) Arable land within regions [31].

The selected taxa cover 56% of the area of the Czech Republic, dominants being Cerealia, Picea, Poaceae and Pinus; the remaining taxa never reach 2%. The taxa reach similar proportions in all regions (Figure 6). The selected taxa cover only 35–57% of the surface of the regions studied (60 km radius circles). The remainder of the circles is bare land, land covered by other taxa or land outside the Czech borders. Standard deviations of regional proportion of Poaceae, Plantago lanceolata and Corylus turned out to be lower than the plotting limit (<0.1%), so they are only listed in Table S4.

Pollen-based Estimated Vegetation by REVEALS Model

(Figure 7, 8) Table 3 shows both measures of dissimilarity between REVEALS estimates and actual vegetation. Their sums (0.32–1.6 and 64–957) are several times lower than in the case of pollen proportions (7.31 and 4875). According to both dissimilarities, scenario S4MS better matches actual vegetation than the original setting (S3MS). Considering the taxa individually, Abies, Poaceae and Cerealia show the best improvement in these three scenarios; on the other hand, the match of Pinus and Picea deteriorates. The setting from scenario S4MS was used in cases of local taxa with small radii (Poaceae, Salix, Alnus).

Figure 7. Comparison of actual regional vegetation with REVEALS estimates.

Settings: original PPEs under wind speed of 3 m/s - S3MS (black squares, confidence intervals show their standard errors); adjusted PPEs under wind speed of 4 m/s - S4MS_P (red dots). Both settings deal with original sizes of the sedimentation basin. The diagonal line shows the position of the optimal fit of the model to expected values.

Figure 8. Principal Component Analysis of REVEALS estimates.

Colours cluster all leave-one-site-out runs to their corresponding regions. The size of each sample reflects the size of the site which was left out.

Table 3. Two measures of dissimilarity between actual vegetation and REVEALS estimates or pollen proportion.

Scenario S4MSR generally yields brings estimates which are closer to actual vegetation because the sum of both dissimilarities is lower than in scenario S4MS; individually, however, Poaceae and Cerealia perform worse. The lowest dissimilarities appear in scenarios S4MS and S4MSR, so both settings (i.e. wind speed of 4 m/s and wind speed of 4m/s together with the reduced radius for local taxa) are used to adjust the PPEs.

Initial PPEs and their geographical origins are summarized in Table 1, which also shows adjusted values for both alternative scenarios. Considering scenario S4MS, adjusted PPEs exceed the ranges of previously published values for Fraxinus 0.18, Ulmus 6, Quercus 0.42 and Carpinus 0.5. In the cases of Cerealia (0.046), Pinus (2), Abies (12.77), Tilia (0.5), Picea (0.47), Acer (0.22), Betula (2.62) and Plantago lanceolata (0.9), adjusted PPEs stay in the ranges of standard errors or close to them. Most of adjusted PPEs using settings from scenario S4MSR are higher than the initial PPEs and out of the range of their standard errors. This is because adjustment decreased the PPE of Poaceae. The sums of the two dissimilarities for both scenarios of adjusted PPEs (S4MS_P, S4MSR_P) are very similar (0.32 vs. 0.38 and from 64.5 vs. 70.2, respectively; Table 3).

All REVEALS estimates calculated by the leave-one-site-out approach across all regions are significantly correlated with corresponding values calculated for all sites (p<0.01). We therefore reject the null hypothesis. The PCA (Figure 8) shows that the highest variability among leave-one-site-out estimates lies within Northern Bohemia, Southern Bohemia and Southern Moravia. Moreover, the variability within Northern Bohemia is caused by large sites. In other words, REVEALS estimates deviate far from the mean when a large site is excluded.


Accuracy of Actual Vegetation Data

Agricultural statistical data and forest inventories.

Areas obtained from CLC classes (211, 311, 312 and 313) tend to be overestimated when compared with areas derived from forest and agricultural statistics. Such discrepancies are caused by small-scale owners of forests and arable land, who are not required to maintain forest inventories or to provide data for agricultural statistics. This lack of accuracy becomes apparent in larger areas because of accumulated noise. Another reason behind the overestimation of forest CLC areas is that military zones, although abandoned and overgrown by forests, are registered as non-forest areas. Even though there is a tendency to overestimate forest CLC areas, some municipalities have underestimated them. Such municipalities are located within regions with high forest regeneration, since young forests do not show up as forested areas in remote sensing. Fortunately, these kinds of errors do not on average exceed 6% in the area considered here. Forestry data on taxa deemed marginal for timber production may lack accuracy and should therefore be considered unreliable, particularly in cases of rare tree taxa with actual vegetation data below 0.1% (Carpinus in two regions, Ulmus and Salix in six regions, Abies in one region).

Habitat mapping.

This third source of vegetation data was used for extrapolation. Habitat mapping covers a high number of independent observations over a relatively large area; however, the number of records for Poaceae, Corylus and Plantago lanceolata differs (Table S2). These variations are due to different frequencies of these three taxa in the vegetation, but we must point out certain biases introduced by methodological aspects of the mapping. The taxon Poaceae consists of dominants or diagnostic species of many important habitats and plant communities (e.g. Corynephorus canescens, Molinia caerulea, Arrhenatherum elatius and Bromus erectus). These species were recorded relatively well, at least when it comes to their presence. Corylus avellana is favoured by nature protection, so it was a well recorded taxon in all habitats where it was present and not only in habitats where its recording was compulsory (K3, L3.1, L3.2, L3.3, L8.2 and S1.5 sensu [36]). On the other hand, mapping of Plantago lanceolata was the least accurate because it is utterly uninteresting from the standpoint of nature conservation and thus its recording was compulsory only in a few habitats (T1.3, T3.5A, T3.5B and T5.5) [43]. Its mean values are nevertheless based on 7,363 segments with recorded abundances and 21,319 segments with recorded presence, which we consider sufficient.


Generally, estimates of the REVEALS model from all scenarios are several times closer to vegetation data than mere pollen proportions (see Table 3 and Figure 7). However, Abies and Quercus matches were poorer worse when using the REVEALS model with standard settings (u = 3 m/s) than simple pollen proportions.

Maximum Range of the Regional Vegetation (Zmax)

The general grain of the landscape mosaic in Central Europe is sufficiently fine to fulfill the assumption of vegetation homogeneity for the area of the background pollen [19]. Landscape heterogeneity can matter if we get for a comparison with REVEALS estimates different regional vegetation at different radiuses, for example if there is an altitudinal gradient of vegetation. The centre of the Šumava region is situated in forested mountains, but the peripheral part of the circle reaches lowlands with a cultural landscape. On the other hand, Zmax (as a one of the REVEALS parameters) has little effect on vegetation estimates in accordance with Hellman et al [20]. So in practice, when comparing regional vegetation estimates and regional vegetation data, setting the Zmax parameter is more important for the vegetation survey. Heterogeneous mosaics are inevitable in real landscapes, but the theoretical size of the region can be at least approximated using characteristic radius concept. Most “characteristic radii 70” of our set of taxa and set of sites are smaller than 60 km; i.e. the length we set as the maximum range of the regional vegetation.

Wind Speed (u)

The REVEALS settings with the wind speed parameter of 4 m/s decreased the sums of dissimilarity A and dissimilarity B compared to the default settings. The lower sum of dissimilarity A can also be attributed to the decrease in the dissimilarity of Abies. Our simulation shows that lower REVEALS estimates for Abies can be attained using the same pollen counts provided that either the sedimentation basin is smaller or wind speed is faster.

To decrease the mismatch of Abies caused by the default settings, we could have also decreased the sedimentation radius, either for Abies or for all taxa. Although Abies can grow in wet subtypes of phytosociological associations [44], it is not a typical tree of wetlands. Decreasing the radius would entail neglecting the size of the sedimentation basin as an important theoretical concept of pollen analysis.

We also tried to keep default setting (S3MS) and adjusted the PPE for Abies in one of the preliminary analyses, which resulted in a PPE of Abies higher than 20. However, such a high PPE for Abies is well out of any range of PPEs published so far and does not seem very realistic.

We thus decided to correct the mismatch of Abies by setting the wind speed parameter to 4 m/s. This finding corresponds to observations of pollen trapping, which show that heavy pollen grains fall closer to their trees and that only a small part of them is able to reach air currents high enough above the canopy and thus contribute to the regional component of pollen rain [45].

Moreover, this value falls within the theoretical range of wind speeds blowing above the canopy [46]. In most of the regions considered, the average wind speed is approximately 4 m/s. Winds in the Polabí lowland and in South Bohemia are somewhat slower [42]. The wind speed of 4 m/s corresponds better to pollen and regional vegetation data in this study, especially because Abies is included in the set of taxa.

Combining Different-sized Sites for REVEALS Estimates

A correctly set radius of the sedimentation basin becomes important when Abies or light-pollen taxa are included in the dataset. Changes of the site radius do not affect REVEALS estimates of taxa with intermediate fall speed of pollen (Cerealia, Picea, Fagus). The simulation also showed the expected pattern – larger sites have a stronger influence. This effect is again the strongest for taxa with the heaviest and lightest pollen grains. As radii of our sites within each region vary substantially, we could expect high variability between estimates from each leave-one-site-out run. However, taxa with intermediate fall speed of pollen represent dominants of actual regional vegetation, so our results appear to be robust. Even when we omitted one large site from the analysis, the estimates remained significantly similar to those provided by the analysis of all sites in the dataset. The variability of leave-one-site-out out runs for Northern Bohemia showed a pattern that agreed with the simulation. Omitting a large site yielded the most aberrant results.

Alternative Radius of Sedimentation Basin (R) for Local Taxa

Setting a small radius of the sedimentation basin for local taxa decreases their REVEALS estimates, which would otherwise be too high because of their high pollen proportion. In accordance to this simulation result, the setting had the same effect on real data. Their estimates are even closer to actual vegetation than in the S4MS scenario (Table 3). This possibly confirms our initial hypothesis that the presence of local taxa in the sedimentation basin can be corrected by a smaller radius.

One could object that this technique is too subjective, because the decision as to which pollen is assumed local is made based on the high pollen proportion. We argue that it can be a way of formalizing certain a priori information that is well known among palynologists. This knowledge can be obtained from the fossil record either by establishing pollen percentage thresholds or combining pollen and macrofossil data [47].

Selection of PPEs

PPEs of half of the taxa giving the best match between REVEALS estimates and regional vegetation were determined on the Swiss Plateau [40] (Pinus, Picea, Abies and Betula) or in other regions of Central Europe [21], [41], [48] (Fagus, Cerealia, Carpinus, Quercus and Acer). PPE values of Alnus, Salix, Plantago lanceolata, Corylus, Ulmus, Fraxinus and Tilia originating from northern Europe do not differ substantially (less than two-fold) from values ascertained in Central Europe.

The close semblance between Swiss and Czech PPE data is due to the geographical proximity of the regions under study and the similarity in their climatic conditions, especially when compared with Northern Europe. Apart from the climate, similarities between the landscape mosaics in the Czech and Swiss studies may also play an important role. Both studies deal with plantations of Pinus and Picea, which create dense and shadowy stands. This explains the lower pollen productivities than those determined in Northern Europe. Moreover, the similarity with the Swiss Plateau also exists on the taxonomic level. Czech and Swiss Cerealia are dominated by autogamous Triticum whereas north-European fields host anemogamous Secale. Furthermore, we extracted the most suitable PPEs for main vegetation dominants in our study, including both the highest and lowest pollen producers from the study carried out on the Swiss Plateau. PPEs of the remaining taxa (except Carpinus and Ulmus) are comparable to this data set. This demonstrates the consistency among different PPE studies.

The unexpectedly bad suitability of PPEs from Central Bohemia might be caused by the small size of the sampling area. The PPEs were calculated for an area of 56 km2 [21], which can magnify any local anomaly. By contrast, sampling sites on the Swiss Plateau and in southern Sweden are scattered over an area which corresponds to the background pollen area (104–105 km2).

Adjustment of PPEs

Most adjusted PPEs calculating the wind speed setting of 4 m/s remain within the range of standard errors of original values. The PPE of Ulmus exceeds this range whereas adjusted PPE values of Fraxinus, Quercus and Carpinus are lower than published values. Such discrepancies might be caused by the scarcity of data (one or two) for Ulmus and Carpinus. The value for Quercus is significantly lower than northern European data, but still reasonably close to Central European data.

A problem arises, however, with adjusted PPEs calculated using the lowered setting of the radius for local taxa (S4MSR_P). The values of these PPEs fall well out of the ranges of original PPEs and seem very improbable. Their relationship to PPEs from pollen/vegetation studies and the ERV model remains unclear. A simulation of PPEs in a heterogeneous landscape with a gradient of local taxa might prove interesting.


Our simulation shows that changing the site radius influences substantially the REVEALS estimates of taxa with very heavy (Abies) or light pollen grains. Decreasing the site radius has a similar effect as increasing the wind speed parameter. We conclude that the initial mismatch between the estimates of Abies and its actual proportion was caused by an inappropriate setting of the wind speed parameter. We propose that wind speed should be set to 4 m/s, which is the average wind speed during the flowering season in most regions of the Czech Republic [42].

We found the best set of PPE values and adjusted them to make them as appropriate as possible for estimating present-day vegetation using the REVEALS algorithm. Most PPE values originate either from Central Europe (Swiss Plateau and Central Bohemia) or do not markedly differ from those values (more than two-fold). Ad hoc adjustment of PPEs with respect to present vegetation under the setting of wind speed 4 m/s improves the match 3–4-fold. We consider these values to be appropriate, because all except four of them fall within the ranges of standard errors of original PPEs and retain their relationship with original PPEs. The fact that even adjusted PPEs are cohesive with natural values confirms the theoretical assumption that PPE values from different studies are compatible.

Our initial hypothesis that the effect of local taxa can be corrected by decreasing the radius of the sedimentation basin is correct; however, satisfactory adjustments of PPEs to this setting remain to be identified.

Supporting Information

Table S1.

List of pollen sites used for REVEALS estimates of present-day vegetation.


Table S2.

Table of mean values of the cover of Poaceae, Corylus and Plantago lanceolata extrapolated into habitat classes. Codes of habitats correspond to [36]. Standard total vegetation cover was taken from [34], [35,35]. “Segments total” means all segments of habitats distinguished during the mapping with presence or abundance data. “Average abundance” was calculated from segments with abundance data and presence data.


Table S3.

Table of mean values of the cover of Poaceae, Corylus and Plantago lanceolata extrapolated into CORINE classes. For further information, see Table S2.


Table S4.

Actual vegetation (%) in each region. The brackets show standard deviation.



We thank all palynologists contributing to the Czech Quaternary Pollen Database PALYCZ, especially to Helena Svitavská-Svobodová, Libor Petr, Radka Kozáková and Petr Pokorný, who allowed us to use their unpublished pollen data in this study. We further thank Shinya Sugita for providing us with source files of his programme for calculating REVEALS estimates and offering lots of advice concerning REVEALS calculations. Tomáš Herben deserves gratitude for his advice on bootstrapping. Thanks are also due to Jane Bunting and two anonymous referees for their invaluable input. We also acknowledge the NordForsk-sponsored LANDCLIM network for inspiring workshops related to this paper. Finally, we would like to thank Frederick Rooks and Clara Rey Sánchez for making the paper more readable.

Author Contributions

Conceived and designed the experiments: VA. Performed the experiments: VA. Analyzed the data: VA. Wrote the paper: VA PK. Provided vegetation data and edited the manuscript: VO. Designed the software used in analysis: VA PK.


  1. 1. Gaillard M-J, Sugita S, Mazier F, Trondman A-K, Broström A, et al. (2010) Holocene land-cover reconstructions for studies on land cover-climate feedbacks. Clim Past 6: 483–499.
  2. 2. Sugita S (2007) Theory of quantitative reconstruction of vegetation I: pollen from large sites REVEALS regional vegetation composition. Holocene 17: 229–241.
  3. 3. Sugita S (2007) Theory of quantitative reconstruction of vegetation II: all you need is LOVE. Holocene 17: 243–257.
  4. 4. Soepboer W, Sugita S, Lotter AF (2010) Regional vegetation-cover changes on the Swiss Plateau during the past two millennia: A pollen-based reconstruction using the REVEALS model. Quat Sci Rev 29: 472–483
  5. 5. Nielsen AB, Giesecke T, Theuerkauf M, Feeser I, Behre K-E, et al. (2012) Quantitative reconstructions of changes in regional openness in north-central Europe reveal new insights into old questions. Quat Sci Rev 47: 131–149
  6. 6. Overballe-Petersen MV, Nielsen AB, Bradshaw RHW (2012) Quantitative vegetation reconstruction from pollen analysis and historical inventory data around a Danish small forest hollow. J Veg Sci 24: 755–771
  7. 7. Fredh D, Broström A, Rundgren M, Lagerås P, Mazier F, et al. (2013) The impact of land-use change on floristic diversity at regional scale in southern Sweden 600 BC–AD 2008. Biogeosciences 10: 3159–3173
  8. 8. Fyfe RM, Twiddle C, Sugita S, Gaillard M-J, Barratt P, et al. (2013) The Holocene vegetation cover of Britain and Ireland: overcoming problems of scale and discerning patterns of openness. Quat Sci Rev 73: 132–148
  9. 9. Marquer L, Gaillard M-J, Sugita S, Trondman A-K, Mazier F, et al. (2014) Holocene changes in vegetation composition in northern Europe: why quantitative pollen-based vegetation reconstructions matter. Quat Sci Rev 90: 199–216
  10. 10. Kuneš P, Odgaard BV, Gaillard M (2011) Soil phosphorus as a control of productivity and openness in temperate interglacial forest ecosystems. J Biogeogr 38: 2150–2164
  11. 11. Reitalu T, Seppä H, Sugita S, Kangur M, Koff T, et al. (2013) Long-term drivers of forest composition in a boreonemoral region: the relative importance of climate and human impact. J Biogeogr 40: 1524–1534
  12. 12. Strandberg G, Kjellström E, Poska A, Wagner S, Gaillard M-J, et al. (2013) Regional climate model simulations for Europe at 6 k and 0.2 k yr BP: sensitivity to changes in anthropogenic deforestation. Clim Past Discuss 9: 5785–5836
  13. 13. Broström A, Nielsen AB, Gaillard M-J, Hjelle K, Mazier F, et al. (2008) Pollen productivity estimates of key European plant taxa for quantitative reconstruction of past vegetation: a review. Veg Hist Archaeobot 17: 461–478
  14. 14. Hellman S, Gaillard M-J, Broström A, Sugita S (2008) The REVEALS model, a new tool to estimate past regional plant abundance from pollen data in large lakes: validation in southern Sweden. J Quat Sci 23: 21–42
  15. 15. Feranec J, Šúri M, Ot’ahel’ J, Cebecauer T, Kolář J, et al. (2000) Inventory of major landscape changes in the Czech Republic, Hungary, Romania and Slovak Republic 1970s –1990s. Int J Appl Earth Obs Geoinformation 2: 129–139
  16. 16. Chytrý M (2012) Vegetation of the Czech Republic: diversity, ecology, history and dynamics. Preslia: 427–504.
  17. 17. Jacobson GL, Bradshaw RHW (1981) The selection of sites for paleovegetational studies. Quat Res 16: 80–96
  18. 18. Sugita S, Parshall T, Calcote R, Walker K (2010) Testing the Landscape Reconstruction Algorithm for spatially explicit reconstruction of vegetation in northern Michigan and Wisconsin. Quat Res 74: 289–300
  19. 19. Prentice IC (1988) Records of vegetation in time and space: the principles of pollen analysis. In: Huntley BJ, Webb T, editors. Vegetation history, Handbook of vegetation science. Kluwer Academic Publishers. 17–42.
  20. 20. Hellman SEV, Gaillard M, Broström A, Sugita S (2008) Effects of the sampling design and selection of parameter values on pollen-based quantitative reconstructions of regional vegetation: a case study in southern Sweden using the REVEALS model. Veg Hist Archaeobot 17: 445–459
  21. 21. Abraham V, Kozáková R (2012) Relative pollen productivity estimates in the modern agricultural landscape of Central Bohemia (Czech Republic). Rev Palaeobot Palynol 179: 1–12
  22. 22. Mazier F, Gaillard M-J, Kuneš P, Sugita S, Trondman A-K, et al. (2012) Testing the effect of site selection and parameter setting on REVEALS-model estimates of plant abundance using the Czech Quaternary Palynological Database. Rev Palaeobot Palynol 187: 38–39
  23. 23. Kuneš P, Abraham V, Kovářík O, Kopecký M (2009) Czech Quaternary Palynological Database (PALYCZ): review and basic statistics of the data. Preslia 81: 209–238.
  24. 24. Parsons RW, Prentice IC (1981) Statistical approaches to R-values and the pollen-vegetation relationship. Rev Palaeobot Palynol 32: 127–152
  25. 25. Prentice IC, Parsons RW (1983) Maximum Likelihood Linear Calibration of Pollen Spectra in Terms of Forest Composition. Biometrics 39: 1051–1057.
  26. 26. Sugita S (1994) Pollen Representation of Vegetation in Quaternary Sediments: Theory and Method in Patchy Vegetation. J Ecol 82: 881–897.
  27. 27. Hjelle KL, Sugita S (2012) Estimating Pollen Productivity and Relevant Source Area of Pollen Using Lake Sediments in Norway: How Does Lake Size Variation Affect the Estimates? The Holocene 22: 313–324
  28. 28. Chytrý M, editor (2013) Vegetace České republiky 4. Lesní a křovinná vegetace. Praha: Academia. 552 p.
  29. 29. Härtel H, Lončáková J, Hošek M, editors (2008) Mapování biotopů v České republice: Východiska, výsledky, perspektivy. Praha: AOPK ČR. 135 p.
  30. 30. European Enviromental Agency (2012) Corine Land Cover 2006 seamless vector data. Available: Accessed 10 October 2012.
  31. 31. Czech Statistical Office (2007) Areas under farm crops on 31.5.2006 (ZEM5032PU_KR). Available: Accessed 10 January 2013.
  32. 32. Forest management institute (2007) Aggregated information from forst invetonry (SLHP) -2006. Available: Accessed 10 January 2013.
  33. 33. Behre K-E (1981) The interpretation of anthropogenic indicators in pollen diagrams. Pollen Spores 23: 225–245.
  34. 34. Chytrý M, editor (2007) Vegetace České republiky 1. Travinná a keříčková vegetace. Praha: Academia. 528 p.
  35. 35. Chytrý M, editor (2009) Vegetace České republiky 2. Ruderální, plevelová, skalní a suťová vegetace. Praha: Academia. 524 p.
  36. 36. Chytrý M, Kučera T, Kočí M, editors (2001) Katalog biotopů. Praha: AOPK ČR. 262 p.
  37. 37. R Development Core Team (2013) R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.
  38. 38. Prentice IC (1985) Pollen representation, source area, and basin size: Toward a unified theory of pollen analysis. Quat Res 23: 76–86
  39. 39. Broström A, Sugita S, Gaillard M-J (2004) Pollen productivity estimates for the reconstruction of past vegetation cover in the cultural landscape of southern Sweden. Holocene 14: 368–381
  40. 40. Soepboer W, Sugita S, Lotter AF, van Leeuwen JFN, van der Knaap WO (2007) Pollen productivity estimates for quantitative reconstruction of vegetation cover on the Swiss Plateau. Holocene 17: 65–77
  41. 41. Mazier F, Broström A, Gaillard M-J, Sugita S, Vittoz P, et al. (2008) Pollen productivity estimates and relevant source area of pollen for selected plant taxa in a pasture woodland landscape of the Jura Mountains (Switzerland). Veg Hist Archaeobot 17: 479–495
  42. 42. Czech Hydrometeorological Institute (2005) Climate Atlas of Czechia. Praha-Olomouc: ČHMÚ, Univerzita Palackého v Olomouci.
  43. 43. Filippov P, Grulich V, Guth J, Hájek M, Kocourková J, et al.. (2008) Příručka hodnocení biotopů. Praha: AOPK ČR. 401 p.
  44. 44. Boublík K, Zelený D (2007) Plant communities of silver fir (Abies alba) forests in southeastern Bohemia. Tuexenia: 73–90.
  45. 45. Poska A, Pidek I (2009) Pollen dispersal and deposition characteristics of Abies alba, Fagus sylvatica and Pinus sylvestris, Roztocze region (SE Poland). Veg Hist Archaeobot 19: 91–101
  46. 46. Andersen ST (1974) Wind Conditions and Pollen Deposition in a Mixed Deciduous Forest. Grana 14: 57–63
  47. 47. Petr L, Sádlo J, Žáčková P, Lisá L, Novák J, et al.. (2013) Late-Glacial and Holocene Environmental History of an Oxbow Wetland in the Polabí Lowland (River Elbe, Czech Republic); a Context-Dependent Interpretation of a Multi-Proxy Analysis. Folia Geobot. Available: Accessed 26 November 2013.
  48. 48. Matthias I, Giesecke T, Nielsen AB (2012) Evaluating the effect of flowering age and forest structure on pollen productivity estimates. Veg Hist Archaeobot 21: 471–484.
  49. 49. Pokorný P, Kuneš P (2005) Holocene acidification process recorded in three pollen profiles from Czech sandstone and river terrace environments. Ferrantia 44: 101–107.
  50. 50. Abraham V (2006) Přirozená vegetace a její změny v důsledku kolonizace a lesnického hospodaření v Českém Švýcarsku. Praha: Charles University.
  51. 51. Abraham V, Pokorný P (2008) Vegetační změny v Českém Švýcarsku jako důsledek lesnického hospodaření - pokus o kvantitativní rekonstrukci. In: Beneš J, Pokorný P, editors. Bioarchaeologie v České Republice. Praha: Archeologický ústav AV ČR. 443–470.
  52. 52. Jankovská V (1992) Vegetationsverhältnisse und Naturumwelt des Beckens Jestřebská kotlina am Ende des Spätglazials und im Holozän (Doksy-Gebiet). Folia Geobot Phytotax 27: 137–148.
  53. 53. Peša V, Kozáková R (2012) Die nacheiszeitliche Landschaftsentwicklung des Lausitzer Gebirges/Vývoj krajiny Lužických hor za posledních 15 000 let. In: Puttkammer T, editor. Auf den Spuren der Germanen/Po stopách Germánů. Kamenz. 128–143.
  54. 54. Peichlová M (1979) Historie vegetace Broumovska. Brno: Academy of Science CR.
  55. 55. Kuneš P, Jankovská V (2000) Outline of Late Glacial and Holocene Vegetation in a Landscape with Strong Geomorphological Gradients. Geolines 11: 112–114.
  56. 56. Chaloupková K (1995) Pylová analýza v Adršpašsko-teplických skalách. Praha: Charles University.
  57. 57. Svobodová H (2004) Vývoj vegetace na Úpském rašeliništi v holocénu. Opera Corcon 41: 124–130.
  58. 58. Speranza A, Hanke J, van Geel B, Fanta J (2000) Late-holocene human impact and peat development in the Cerna Hora bog, Krkonose Mountains, Czech Republic. Holocene 10: 575–585.
  59. 59. Speranza A, van der Plicht J, van Geel B (2000) Improving the time control of the Subboreal/Subatlantic transition in a Czech peat sequence by C-14 wiggle-matching. Quat Sci Rev 19: 1589–1604.
  60. 60. Engel Z, Nývlt D, Krízek M, Treml V, Jankovská V, et al. (2010) Sedimentary evidence of landscape and climate history since the end of MIS 3 in the Krkonose Mountains, Czech Republic. Quat Sci Rev 29: 913–927
  61. 61. Speranza A (2000) Solar and anthropogenic forcing of late-Holocene vegetation changes in the Czech Giant Mountains. Amsterdam: University of Amsterdam.
  62. 62. Svobodová H (2002) Preliminary results of the vegetation history in the Giant Mountains (Úpská rašelina mire and Černohorská rašelina bog). Opera Corcon 39: 5–15.
  63. 63. Skrzypek G, Baranowska-Kacka A, Keller-Sikora A, Jedrysek M-O (2009) Analogous trends in pollen percentages and carbon stable isotope composition of Holocene peat – Possible interpretation for palaeoclimate studies. Rev Palaeobot Palynol 156: 507–518
  64. 64. Petr L, Pokorný P (2008) Přirozená jezera na území České republiky. Jejich význam pro studium pravěkého osídlení a přírodního prostředí. In: Beneš J, Pokorný P, editors. Bioarcheologie v České Republice. 73–98.
  65. 65. Břízová E (1999) Late Glacial and Holocene Development of the vegetation in the Labe (Elbe) river flood-plain (central Bohemia, Czech Republic). Acta Palaeobot Suppl. 2: 549–554.
  66. 66. Dreslerová D, Břízová E, Růžičková E, Zeman A (2004) Holocene environmental processes and alluvial archaeology in the middle Labe (Elbe) valley. In: Gojda M, editor. Ancient Landscape, Settlement Dynamics and Non-destructive Archaeology. Praha: Academia. 121–171.
  67. 67. Kozáková R, Pokorný P, Mařík J, Čulíková V, Boháčová I, et al.. (2014) Early to high medieval colonization and alluvial landscape transformation of the Labe valley (Czech Republic): evaluation of archaeological, pollen and macrofossil evidence. Veg Hist Archaeobotany. Available: Accessed 20 March 2014.
  68. 68. Pokorný P, Kuneš P (2009) 5. Kožlí (S. Bohemia, Czech Republic). Grana 48: 77
  69. 69. Jankovská V (1980) Paläogeobotanische Rekonstruktion der Vegetationsentwicklung im Becken Třeboňská pánev während des Spätglazials und Holozäns. Praha: Academia.
  70. 70. Jankovská V (1987) Entwicklung des Moores Mokré Louky bei Třeboň im Postglazial. Folia Geobot Phytotax 22: 199–216.
  71. 71. Rybníčková E, Rybníček K (1985) Paleogeobotanical Evaluation of the Holocene Profile from the Rezabinec Fish-Pond. Folia Geobot Phytotax 20: 419–437.
  72. 72. Rybníčková E, Rybníček K, Jankovská V (1975) Palaeoecological Investigations of Buried Peat Profiles from the Zbudovská blata Marshes, Southern Moravia. Folia Geobot Phytotax 10: 157–178.
  73. 73. Jankovská V (1996) Past vegetation changes reconstructed on the basis of pollen analyses. In: Prach K, Jeník J, Large AR., editors. Floodplain Ecology and Management. Amsterdam: SPB Academic Publishing. 53–62.
  74. 74. Břízová E (2009) Quaternary enviromental history of the Čejčské Lake (S. Moravia, Czech Republic). Bull Geosci 84: 632–652.
  75. 75. Svobodová H (1997) Die Entwicklung der Vegetation in Südmähren (Tschechien) während des Spätglazials und Holozäns - eine palynologische Studie. Verh Zool-Bot Ges Österr 134: 317–356.
  76. 76. Svobodová H (1989) Rekonstrukce přírodního prostředí a osídlení v okolí Mistřína. Palynologická studie. Památky Archeol 80: 188–206.
  77. 77. Rybníček K (1983) The environmental evolution and infilling process of a former lake near Vracov (Czechoslovakia). Hydrobiologia 103: 247–250.
  78. 78. Rybníček K, Rybníčková E (2008) Upper Holocene dry land vegetation in the Moravian-Slovakian borderland (Czech and Slovak Republics). Veg Hist Archaeobot 17: 701–711.
  79. 79. Svobodová H (1991) Vývoj vegetace jižní Moravy v pozdním glaciálu a holocénu v závislosti na osídlení. Brno: Institute of Botany, AS ČR.
  80. 80. Rybníčková E (1974) Die Entwicklung der Vegetation und Flora im südlichen Teil der Böhmisch-Mährischen Höhe während des Spätglazials und Holozäns. Praha: Academia. 163 p.
  81. 81. Rybníčková E, Rybníček K (1988) Holocene palaeovegetation and palaeoenvironment of the Kameničská kotlina basin (Czechoslovakia). Folia Geobot Phytotax 23: 285–301.
  82. 82. Rybníčková E, Rybníček K (1975) Ergebnisse einer palaeogeobotanischer Erforschung. In: Nekuda R, editor. Pfaffenschlag. Brno: Blok. 183–198.
  83. 83. Rybníček K, Rybníčková E (1968) The history of flora and vegetation on the Blato mire in southeast Bohemia (palaeoecological study). Folia Geobot Phytotax 3: 117–142.
  84. 84. Jankovská V (1971) The Development of Vegetation on the Western Slopes of the Bohemian-Moravian Uplands during the Late Holocene Period: A study based on Pollen and Macroscopic Analyses. Folia Geobot Phytotax 6: 281–302.
  85. 85. Jankovská V (1990) The Evolution of Late-Glacial and Holocene Vegetation in the Vicinity of Světlá nad Sázavou (in the Western Forland of the Bohemian-Moravian Uplands). Folia Geobot Phytotax 25: 1–26.
  86. 86. Svobodová H, Reille M, Goeury C (2001) Past vegetation dynamics of Vltavsky luh, upper Vltava river valley in the Sumava mountains, Czech Republic. Veg Hist Archaeobot 10: 185–199.
  87. 87. Stalling H (1987) Untersunchungen zur spät-und postglazialen Vegetationsgeschichte im Bayerischen Wald. Göttingen: Georg-August-Universität.
  88. 88. Svobodová H, Soukupová L, Reille M (2002) Diversified development of mountain mires, Bohemian Forest, Central Europe, in the last 13,000 years. Quat Int 91: 123–135.
  89. 89. Sugita S, Gaillard M-J, Broström A (1999) Landscape openness and pollen records: a simulation approach. The Holocene 9: 409–421
  90. 90. Nielsen AB (2004) Modelling pollen sedimentation in Danish lakes at 1800: an attempt to validate the POLLSCAPE model. J Biogeogr 31: 1693–1709
  91. 91. Poska A, Meltsov V, Sugita S, Vassiljev J (2011) Relative pollen productivity estimates of major anemophilous taxa and relevant source area of pollen in a cultural landscape of the hemi-boreal forest zone (Estonia). Rev Palaeobot Palynol 167: 30–39