The study area covers the whole of continental Spain (504 782 km²) including the two biogeographical regions, Eurosiberian and Mediterranean.

Comparing RPPs between studies has become a challenging task due to the large number of existing studies which all make partially different assumptions. We introduce a numerical workflow designed to address such a challenge, enabling more reliable comparisons and addressing methodological discrepancies between studies (e.g., different reference taxa).

We applied this workflow to compare RPPs from published studies across Europe with our LSM-RPPs, implementing the following steps:

1.

Debiasing RPPs for heavy pollen grain taxa: we adopted the adjusted RPP for Abies according to Theuerkauf (2025), as the original estimate was based on the Gaussian Plume model.

Previous RPP studies often include multiple sets of pollen samples, analysed using different methods. For each original study conducted in Europe, we selected one representative set of RPP values corresponding to a distinct set of pollen samples. Regarding the calculation methods (e.g., the ERV models), we followed choices outlined by Githumbi et al. (2022). However, the RPPs themselves were sourced from Wieczorek and Herzschuh (2020), who provided the original, unmodified estimates without recalculation. References of the studies used for comparison are available in Table 1.

The first RPPs relative to Poaceae in STI identify low, medium and high pollen producers (Fig. 5). Low pollen producers (lower than 0.48) are Asteraceae sf. Asteroideae, Brassicaceae, Genista-type, Juniperus, Plantago, Rosaceae and Rumex; medium producers (0.49–1.42) are Betula, Asteraceae sf. Cichorioideae, Corylus, Erica, Fagus and Ranunculaceae, while high producers (>1.43) are Abies, Artemisia, Amaranthaceae, Olea, Pinus, evergreen and deciduous Quercus.

We applied the arboreal RPPs (relative to Poaceae) to the 27 coretops to test their validity (Fig. 6). We compiled all available samples containing Abies pollen that had not been used to retrieve the RPPs, selecting only 4 samples in total. The results of the validation for Abies are shown in Fig. B3 in Appendix B.

The REVEALS model corrects the uneven pollen productivity across taxa, resulting in an abundance reduction of overrepresented taxa and an increase for the underrepresented taxa (Fig. 6). This correction is evident when comparing raw pollen percentages (Fig. 6, left column) with REVEALS-based vegetation estimates (Fig. 6, right column). Pinus and both evergreen and deciduous Quercus, which dominate Iberian tree communities, show a marked reduction in estimated vegetation cover. The case of Pinus is especially illustrative: the adjusted estimates align closely with the observed vegetation cover (Fig. 6), suggesting the model performs well in accounting for its high pollen productivity. Another important example is Olea, for which REVEALS estimates indicate a reduction in the extent of olive cultivation. Nonetheless, several coretops were found to have no crops within a 45 km radius and we found that pollen from Olea correlated better with olive crop coverage at 100 km distance (see Figs. S4, S5 and S6 in the Supplement).

On the contrary, temperate forest taxa such as Betula, Corylus and Fagus, having relatively low RPPs (Fig. 5), tend to be underrepresented in pollen assemblages in Iberia. REVEALS corrects for this bias, resulting in higher vegetation estimates for these taxa. Juniperus, moderately underrepresented in raw pollen data likely due to low pollen productivity (Fig. 5), is partially corrected by REVEALS, resulting in slightly higher estimates of vegetation cover. The standardised composite improvement score of the multimetric analysis at 45 km revealed that temperate forest taxa perform better with raw pollen counts than with the REVEALS-based reconstructions when comparing with observed regional vegetation cover (Fig. 7). Nonetheless, REVEALS estimates still provide more ecological sense in accounting for the observed vegetation than pollen percentages for Betula at 15 and 100 km (Figs. S4 and S6 in the Supplement).

Comparison of RPPs demonstrated similar values, at least in one pair, in all or more than half of the previously published values for the following taxa: Amaranthaceae, Artemisia, Asteraceae sf. Asteroideae, Brassicaceae, Asteraceae sf. Cichorioideae, Corylus, Ericaceae (Erica), Fabaceae (Genista-type), Fagus, Juniperus, Plantago, Poaceae, evergreen and deciduous Quercus, Ranunculaceae and Rumex. In contrast, the remaining taxa – Abies, Betula, Pinus and Rosaceae – show agreement with only half or fewer of the European sites (Fig. S7a in Supplement).

The eight studies with the highest number of matching taxa are from Romania, Czech Republic-Slovakia, Norway, France, Switzerland and Sweden. The remaining studies, which show lower similarity between RPPs, come from Great Britain, Germany, Denmark, Poland, Estonia, and Finland (Fig. 8).

For several taxa – Quercus (deciduous), Asteraceae sf. Asteroideae, Corylus, Ericaceae, Plantago, Poaceae, and Pinus – the STI RPPs are near the midpoint of the observed range in similar studies (Fig. 9). Notably, Poaceae and Pinus form two distinct clusters across studies; the STI values are within the higher cluster (4 for Pinus, and 1 for Poaceae).

For the remaining taxa, STI RPPs are distributed towards the edges of the observed ranges (Fig. 9), though still close to some previously reported values. These include Amaranthaceae, Artemisia, Asteraceae sf. Cichorioideae, Fabaceae, Fagus, Juniperus, Ranunculaceae, and Rosaceae. In the case of Abies, our estimate is higher than from any other European studies, while for Betula and Rumex, the values are lower.

Only two studies have derived RPPs for Brassicaceae, Fabaceae and evergreen Quercus, while only the present study provides a RPP for Olea. Within this limited data, evergreen Quercus is consistently represented as a high pollen producer, whereas Brassicaceae appears as a low pollen producer (Fig. 9).

The validation presented in this study is the first carried out in heterogeneous environments of the Western Mediterranean using vegetation survey data and disaggregated by taxa rather than by total tree cover. Validation of RPPs in modern samples remains uncommon but has been performed for the whole of Europe (Serge et al., 2023), Southern Sweden (Hellman et al., 2008), Australia (Mariani et al., 2017, 2022) and various Asian regions (Jiang et al., 2020; Wan et al., 2022; Xu et al., 2014). They generally found an excellent performance for large groups of taxa, although the fit between the modelled estimates and the vegetation data was not always as good when single taxa were considered (Hellman et al., 2008). Wan et al. (2022) presented the first evaluation of RPPs in a tropical region, suggesting that the REVEALS model performs well especially when applied at the landscape level rather than for individual species. In our dataset, we could not validate REVEALS for herb and shrub taxa because MFE only includes arboreal taxa. Nevertheless, we hypothesise that some herbaceous and shrub taxa may be overrepresented when applying the REVEALS model (as in Li et al., 2023; Marquer et al., 2020). This is because, in the Iberian context, these taxa are relatively low pollen producers, similar to certain genera such as Betula, Corylus and Fagus.

REVEALS generally performed well when reconstructing the coverage of arboreal taxa (Fig. 6), although only 5 of the 8 taxa performed better with REVEALS than with pollen abundances, according to the multimetric analysis performed at 45 km radius: Pinus, Juniperus, Olea, deciduous and evergreen Quercus (Fig. 7). Juniperus showed improved performance as the distance increased, specifically up to 45 km (see multimetric results at other distances in the Supplement Figs. S4–S6), likely because junipers occur in small copses or patchy forest stands (e.g., J. thurifera), so the greater the distance considered, the more individuals are found. In this work, and as far as we know, we calculated the first pollen productivity estimate for Olea. We found that Olea is a high pollen producer, which aligns with previous studies highlighting its high dispersal capability (Cañellas-Boltà et al., 2009; Fernández-Rodríguez et al., 2014) and its presence in modern samples where sometimes the nearest olive trees are more than 70 km away (Leunda et al., 2017). In our modern samples, most of the Olea pollen comes from olive crops in southern Spain. The MFE inventory rarely includes wild olive trees, since they do not form forests or small groves naturally. These two examples underscore the need of accounting for source area when interpreting pollen records, particularly for taxa frequently employed as indicators of environmental change. The contrasting distribution patterns and dispersal capacities of Juniperus and Olea demonstrate how spatial context and distance exert a substantial influence on pollen representation.

The standard errors of the 8 validated taxa were consistently lower than their estimates, providing a measure of precision in terms of reliability (Githumbi et al., 2022; Li et al., 2023). Nevertheless, the quality of REVEALS outputs is ultimately constrained by the quality of input datasets (EMPD2 and MFE). According to the theory, deviation of REVEALS estimates from observed vegetation may suggest unreliable RPP values. We however believe that such deviation could be explained by: (i) a poor performance of REVEALS for Betula, Corylus and Fagus, since they are not present in all the STI due to environmental heterogeneity; and (ii) inaccuracies in the vegetation dataset, especially for taxa that do not form large, continuous forests such as Juniperus, Betula or Corylus (Wan et al., 2022).

Regarding the validation of Abies (Fig. B3), only 4 coretops were available to test the performance of REVEALS, both with GPM and LSM. The GPM-REVEALS yielded estimates up to three times higher than the LSM-REVEALS. These findings are consistent with previous studies (Abraham et al., 2014; Theuerkauf et al., 2013, 2016), which showed that large basins strengthen the overestimation of this taxon. The weighting factor decreases their distance-weighted plant abundance, and therefore the pollen signal is counterbalanced with high pollen productivity, ca. 2 times higher than the LSM-RPP (Fig. A1). As larger basins strengthen the overestimation of Abies (Abraham et al., 2014), RPP estimates of Abies may vary considerably among studies with different basin sizes (Fig. B3). Moreover, according to the multimetric analyses (Figs. B2 and B4), these indicate that LSM-REVEALS produced more accurate and consistent estimates of Abies cover than GPM-REVEALS at all distances. A similar pattern was also observed for Juniperus, for which LSM-REVEALS also outperformed the GPM approach. This is particularly important because Juniperus is a key taxon, containing 5 woody species in Iberia and thoroughly present in several fossil records across the Iberian Peninsula. This is the case with the longest continuous records in the western Mediterranean from the Villarquemado paleolake (Aranbarri et al., 2014; González-Sampériz et al., 2020; Moreno et al., 2012), which shows substantial proportions of juniper during the previous interglacial (the Eemian). An accurate reconstruction of the history of Juniperus is therefore essential for assessing ecosystem resilience under extreme climate fluctuations.

In light of our results, we believe that implementing our RPPs to quantitatively reconstruct vegetation cover from fossil pollen records could be promising for both arboreal and non-arboreal taxa in Iberia. The relatively low standard errors for trees in our validated coretops (especially for Pinus, Olea and both evergreen and deciduous Quercus) indicate that they can be confidently used for regional reconstructions of past vegetation. Even for some with lower performance, such as temperate forest species, these RPP values can still provide a useful reference until region-specific estimates are available. Both GPM- and LSM-RPPs proved that they can be used for reconstructions of vegetation dynamics. However, due to the aforementioned reasons, the LSM proved to provide more robust results, as the GPM may underestimate the dispersal of heavy pollen taxa, potentially leading to unrealistic vegetation reconstructions.

As for shrub and herb taxa, while direct validation was not possible, the generally robust SEs we retrieved suggest that these values could be cautiously implemented in reconstructions of open vegetation dynamics within Mediterranean landscapes. The overrepresentation of trees in pollen records aligns with findings from other European studies and with long-lasting intuitive expectations in the palaeoecological community. Notably significant are the values among dominant tree species: Pinus (4.00), evergreen (2.31), and deciduous (1.72) Quercus, suggesting a lower prevalence of pine communities in the past than has been interpreted based on previous reconstructions that indicated a higher abundance of pines, using both pollen data and/or wood charcoal and macrofossil analyses (Aranbarri et al., 2014, 2020; Carrión et al., 2004; Ezquerra et al., 2019; Múgica et al., 2001; Rubiales et al., 2010). Conversely, some deciduous trees in Iberia, such as Betula (0.66) and Fagus (0.52), showed lower productivity, potentially leading to them being underrepresented in fossil pollen records. This underestimation could affect our understanding of Pleistocene and Holocene temperate forest dynamics unless RPPs are applied (Broström et al., 2005; Githumbi et al., 2022; Trondman et al., 2015). For instance Corylus, with a RPP close to 1, provides crucial information for linking it to, e.g., early postglacial vegetation expansions (Aranbarri et al., 2014; González-Sampériz et al., 2006; Pearce et al., 2025b; Theuerkauf et al., 2014), and post-disturbance responses (Gil-Romera et al., 2014; Leunda et al., 2020).

For herb and shrub taxa with particularly low RPPs, such as Rumex (0.23), Brassicaceae (0.28), Juniperus (0.45) or Plantago (0.48), their roles in vegetation dynamics are expected to be underestimated if only raw pollen counts in fossil records are considered. This is especially critical for taxa associated with anthropogenic activities, such as Plantago or Rumex, whose historical presence in the Mediterranean landscape may be stronger than traditionally inferred without REVEALS estimates (Grindean et al., 2019; Kuneš et al., 2019; Mazier et al., 2008; Soepboer et al., 2007).

Our RPPs demonstrate that percentage pollen assemblages may bias reconstructions of Pleistocene and Holocene vegetation by overemphasising high pollen producers such as conifers and some oaks while downplaying the ecological importance of low-productivity taxa, including several deciduous broadleaved trees and herbs. Future quantitative reconstructions of vegetation dynamics in the STI hold the potential to substantially advance debates on the contentious idea of past continuous forest canopies in Iberia (Gomes et al., 2020; Pérez-Obiol et al., 2011). Incorporating revised pollen productivity estimates may reveal an even more fragmented forest landscape in the Southwestern Mediterranean, characterised by a mix of broadleaf woodlands, coniferous patches, temperate forests and open areas, as suggested in various studies (i.e., see compilations and references in Carrión et al., 2010a and González-Sampériz et al., 2010).

Future implementations of our RPPs in Iberia expand the possibilities for reconstructing past disturbances. By integrating RPPs with fossil pollen records, we can quantify vegetation composition over time and estimate biomass dynamics affected by various disturbances. With reliable RPPs, it would become feasible to produce spatially explicit models showcasing the long-term effects of environmental drivers across time and space, including anthropogenic or natural factors as well as the long-term interactions between disturbances and ecosystem recovery (Githumbi et al., 2022; Knight et al., 2022; Theuerkauf and Couwenberg, 2017).

Dominant vegetation types of the STI – pines, evergreen and deciduous oaks – exhibited RPP values successfully validated with independent top core samples. These taxa are identified as high pollen producers, consistent with findings from previous studies, particularly after adjusting original RPP values by the average scaling factor (see Sect. 2.3 “Comparison of RPPs across European studies”). Specifically, Pinus showed a RPP of 4.0 (Fig. 9), which aligns reasonably well with values reported from Central Czech Republic (4.5) and Southern Norway (4.8) (numbers 4 and 3 in Fig. 9, respectively). Evergreen Quercus exhibited a RPP of 2.31, comparable to 3.8 reported in Southern France (number 5, Fig. 9), and deciduous Quercus had a RPP of 1.72, which closely matches the value of 2.0 from the White Carpathians (number 2), in Czech Republic and Slovakia.

We also observed that our RPPs for most taxa fall near the midpoint of the range observed in other European studies, which is of interest since the STI has different environmental conditions that could have influenced pollen productivity. High pollen producers include wind-pollinated shrubs and herbs such as Amaranthaceae (3.31) and Artemisia (1.53), which yielded similar values to those reported in the Central Czech Republic (3.1 and 2.0, respectively, number 4 in Fig. 9). Medium pollen producers comprise shrubs and herbaceous taxa, including Erica (1.18), comparable to the value from Southern France (1.5) (number 5), Asteraceae sf. Cichorioideae (1.06) and Ranunculaceae (1.45), the latter two aligning with values of 1.0 and 1.3, respectively, reported from the White Carpathians (number 2 in Fig. 9). Low pollen producers – primarily entomophilous herbs or shrubs – such as Rosaceae (0.31) and Genista-type/Fabaceae (0.42) correspond well with previously reported values of 0.3 and 0.5 from Southeastern Romania (number 1).

In contrast, other taxa did not clearly match values reported in earlier studies, either due to a limited number of existing estimates or a wide variability in published data. For instance, RPP values for Asteraceae sf. Asteroideae (0.31) and Brassicaceae (0.28) are among the first reported or there are only a few previous estimates, making direct comparison difficult. For Corylus (0.94) our result lies within the mid-range of previously published values, which vary considerably (Fig. 9). Nonetheless, these values appear consistent with biological expectations – entomophilous herbs typically show lower pollen productivity than anemophilous trees – suggesting that the estimates are reasonable despite limited comparative data. For some taxa, Iberian RPPs follow a different trend than elsewhere in Europe, especially for Abies or Betula. When values are rescaled to remove the effect of the reference taxon, Abies emerged as a high pollen producer in STI (2.93), and Betula (0.66) presents lower productivity than in other European studies (Fig. 9). As discussed above, this underrepresentation of the temperate forest taxa may point to the need for computation of specific RPPs for the Eurosiberian biogeographical region, which is where these forest taxa are mainly found in STI.

RPPs for Olea (2.60) in this study are the first estimates in Europe. Both Olea and evergreen Quercus are only present in Southern Europe, and therefore new RPPs in all other Mediterranean peninsulas are needed in future studies to unravel the palaeoecological history of these species during the Holocene. It is of critical importance to better understand the history of key pollen taxa, especially when they vary their functional role in the ecosystem and become closely linked to human presence, as with Olea, offering new insights into anthropogenic triggers in the past. Indeed, Plantago and Rumex, also key taxa related to human activities, were found to present lower productivities in STI than in the eight most comparable studies from elsewhere in Europe. We argue that two main factors explain lower productivity of these taxa in Spain: first, Mediterranean climatic conditions may constrain the growth of these herbaceous taxa, thereby reducing their reproductive performance (López-Orozco et al., 2023); and second, traditional Iberian land-use systems (e.g., extensive grazing, dehesa-like agro-silvo pastoral mosaics) tend to maintain semi-open or wooded pastures rather than the continuously open, nutrient-rich grasslands common in Northern Europe (Connor et al., 2019).

Reconstructing past vegetation in Iberia with REVEALS poses unique methodological constraints owing to the region's pronounced topographic and environmental heterogeneity. This reason directly challenges one of the core assumptions of REVEALS: the need for spatially consistent pollen-vegetation relationships. Iberia's spatial heterogeneity often leads to an uneven vegetation distribution. This is particularly evident for mixed temperate forest taxa (e.g., Betula, Corylus, Fagus) which are confined to the northern fringe and are therefore not present across the entire STI. In addition, the lack of large lake basins, otherwise common in northern Europe, often forces reliance on smaller sedimentary basins, increasing uncertainty (Sugita, 2007a). Averaging RPPs across the STI may therefore underestimate productivity in optimal habitats.

Furthermore, the surface pollen collection may have introduced spatial biases in two different ways: (1) overrepresentation of certain vegetation communities and (2) overrepresentation of certain taxa. Both are related to the use of moss polsters as pollen traps, which are typically located in humid, mountainous environments (Fig. 1), especially in the Mediterranean region, limiting the representation of open vegetation landscapes from Iberia. In addition, moss polsters are often located under the tree canopy, especially in the Mediterranean region, perhaps resulting in overrepresentation of arboreal taxa. For instance, this could have been the case with Abies (see pollen percentage per grid in Fig. S9 and REVEALS estimates per grid in Fig. S10 in the Supplement), where both species (A. pinsapo and A. alba) coexist with the moss polsters in humid, cooler regions, potentially leading to higher estimates of pollen productivity.

Vegetation survey data also introduce uncertainty. The MFE maps well the dominant tree species but underrepresents taxa forming scattered stands (J. oxycedrus, J. communis), beyond the scope of forestry interest. The SIVIM database contains almost 150 000 phytosociological plant inventories. The frequency of the taxa is estimated on a scale of 1 to 5, which we transformed into percentages of abundances (equivalence table can be found in Table S4 in the Supplement). Despite the non-continuous nature of the SIVIM data, RPPs for shrub and herb taxa yielded robust and ecologically meaningful results, except for Genista-type, whose productivity estimate was lower than the standard deviation. Regarding the main source of pollen data, the EMPD2, some of the samples were incomplete or misclassified (e.g., some salt lakes were not specified as such; some samples had inaccurate coordinates or lacked information about the sample type or the sample context).

Moreover, the choice of a loss function could influence RPP estimates. In our work, we chose a weighted sum of squared errors (WSSE) method that calculates the optimal set of taxon-specific RPP values that minimises the discrepancy between observed regional vegetation composition and vegetation proportions reconstructed by REVEALS. This loss function addresses two main challenges in pollen-vegetation modelling: heteroskedasticity and taxon-specific biases. Pollen-vegetation relationships are inherently heteroskedastic, which means that the variance of the residuals scales with vegetation abundance (Sugita et al., 2010). Dominant taxa typically exhibit high pollen production, leading to smaller relative errors in their vegetation estimates, while rare or underrepresented taxa often show disproportionate noise due to low pollen counts and localised distributions (Broström et al., 2008). In order to partially account for these uncertainties, we weight errors inversely, as the WSSE function ensures that deviations for rare taxa contribute meaningfully to the optimisation, preventing their signals from being overshadowed by dominant taxa. By doing this, we acknowledge that residual uncertainty remains higher for the less frequent taxa.

Finally, a limitation of the inverted REVEALS approach is that the optimisation procedure seeks RPPs that maximise the agreement between REVEALS-derived vegetation estimates and observed vegetation cover. Consequently, part of the observed correspondence is an expected outcome of the optimisation process rather than a fully independent validation. We acknowledge that this limitation exists, and therefore a cross-validation approach was initially implemented to reduce this potential circularity, by explicitly excluding a subset of sites during RPP estimation and subsequently using them for validation, as described in Sect. 2.2.2 “Validation of RPPs”.