<figure><img src="image_68.png" /><figcaption>**Figure 4.** Examples of ERV-corrected pollen-vegetation relationships for six taxa from the ERV analysis using the full pollen-vegetation dataset (30 sites), 12 taxa, ERV submodel 2 and the Prentice's Gaussian Plume Model for distance weighting of vegetation (see Methods and SOM-12.1 for details). The coefficient of determination (R²) is provided as a measure of linearity, the higher the value the more linear the relationship.</figcaption></figure>

estimates from pollen assemblages in large bogs will be biased by pollen coming from the surface of the bog. LOVE estimates 'background pollen' using REVEALS estimates of regional vegetation, and subtracts 'background pollen' to derive local vegetation cover within the RSAP (see ERV model above and SOM-12.1). The RSAP represents the smallest spatial scale of the LOVE reconstruction. Both REVEALS and LOVE provide estimates of plant abundance/cover with their SEs. Note that the plant abundance estimated by LOVE is distance weighted.

The application of the LRA requires a minimum of two pollen records from the same vegetation/landscape type, one from a large lake for a REVEALS-based reconstruction of the regional plant cover, and one from a small lake for a LOVE reconstruction of the local plant cover. However, calculation of the RSAP of small sites requires pollen records from several sites (see SOM-12.2.3 for further explanations). Due to the small number of pollen records appropriate for a LRA in the study region, we made the following assumptions:

(1) Pollen of Cyperaceae are primarily of local origin, growing on the swamp (Mbi) or at lake shores (Bambili, Tilla, Mbalang), and Cyperaceae are the dominant plants in these biotopes,

(2) The RSAP of the small sites is 2 km (SOM-12.2.3),

(3) The pollen record (Cyperaceae excluded) from Mbi and Tilla represent the mean regional vegetation cover around Bambili and Mbalang, respectively.

Possible departures from these assumptions are described in SOM-12.2.3. A local origin of most pollen from Cyperaceae is supported by the analysis of > 1000 modern pollen samples from Africa, Madagascar and Arabia (Gajewski et al. 2002) suggesting that Cyperaceae pollen is related primarily to humid regions and habitats in Africa.
<table><caption>**Table 1.** Habitus, pollination, Fall Speed of Pollen (FSP) and Relative Pollen Productivities (RRP) with their standard errors (SE) for 12 major taxa in: (A) Cameroon (this study), (B) from other studies in South Africa (Hill *et al.*, this volume); Namibia (Tabares *et al.* 2021); Bolivia (Whitney *et al.* 2018); Brazil (Piraquive Bermúdez *et al.* 2019); and (C) from other studies in Subtropical China: left (Fang *et al.* 2020), centre (Jiang *et al.* 2020), right (Chen *et al.* 2019); Tropical China (Wan *et al.* 2020). ^ FSP from Jiang *et al.* (2020). Reference taxon (Poaceae=1): * indicates that Poaceae is the reference taxon used in the analysis. ** indicates that other taxa were used as reference and RPP values were converted to Poaceae=1. Abbreviations: H herb, S shrub, T tree; Pollinat Pollination; A anemophilous, E entomophilous; (A) and (E) indicate that the one type of pollination is less common than the other.</caption><caption>(A) Cameroon (this study)</caption><thead><tr><th>Taxa</th><th>Habitus</th><th>Pollinat.</th><th>FSP (m/s)</th><th>RPP</th><th>SE</th></tr></thead><tbody><tr><td>Alchornea</td><td>S/T</td><td>E/A</td><td>0.012</td><td>4.00</td><td>0.188</td></tr><tr><td>Celtis</td><td>T</td><td>E/A</td><td>0.010</td><td>7.00</td><td>0.270</td></tr><tr><td>Combretaceae/Melastomataceae</td><td>T/H</td><td>E</td><td>0.012</td><td>0.50</td><td>0.039</td></tr><tr><td>Cyperaceae</td><td>H</td><td>A/(E)</td><td>0.022</td><td>0.23</td><td>0.045</td></tr><tr><td>Macaranga</td><td>T</td><td>E/(A)</td><td>0.013</td><td>1.70</td><td>0.054</td></tr><tr><td>Moraceae</td><td>T</td><td>A/E</td><td>0.006</td><td>0.60</td><td>0.070</td></tr><tr><td>Nuxia type</td><td>T</td><td>E</td><td>0.003</td><td>1.40</td><td>0.397</td></tr><tr><td>Poaceae</td><td>H</td><td>A</td><td>0.035</td><td>1.00*</td><td>0.000</td></tr><tr><td>Podocarpus</td><td>T</td><td>A</td><td>0.017</td><td>0.06</td><td>0.018</td></tr><tr><td>Prunus africana/Rubus pinnatus<br>(Rosaceae)</td><td>T</td><td>E</td><td>0.013<br>(0.021)^</td><td>0.40</td><td>0.037</td></tr><tr><td>Schefflera</td><td>T</td><td>E</td><td>0.009</td><td>1.80</td><td>0.164</td></tr><tr><td>Syzygium</td><td>T</td><td>E</td><td>0.003</td><td>2.20</td><td>0.107</td></tr></tbody></table>

<table><caption>(B) Other studies:</caption><thead><tr><th>Taxa</th><th>S Africa</th><th colspan="2">Namibia</th><th colspan="2">Bolivia</th><th colspan="4">Brazil</th></tr><tr><th></th><th>RPP</th><th>RPP</th><th>SE</th><th>RPP</th><th>RPP</th><th>SE</th><th>RPP</th><th>RPP</th><th>SE</th></tr></thead><tbody><tr><td>Alchornea</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Celtis</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Comb./Mel.</td><td></td><td>0.217</td><td>0.014</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Cyperaceae</td><td></td><td></td><td></td><td></td><td>0.48</td><td>0.11</td><td>0.63</td><td>0.33</td><td></td></tr><tr><td>Macaranga</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Moraceae</td><td></td><td></td><td></td><td>0,25</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Nuxia type</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Poaceae</td><td><strong>1.00*</strong></td><td><strong>1.00*</strong></td><td><strong>0.00</strong></td><td>1.00**</td><td>1.00**</td><td>0.00</td><td>1.00**</td><td>0.00</td><td></td></tr><tr><td>Podocarpus</td><td>6.50</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>P. africana/R. pinnatus<br>(Rosaceae)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Schefflera</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Syzygium</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

(Continued)
<table><caption>**Table 1. Continued**</caption><thead><tr><th rowspan="2">Taxa</th><th colspan="8">Subtropical China</th><th colspan="2">Tropical China</th></tr><tr><th>RPP</th><th>SE</th><th>RPP</th><th>SE</th><th>RPP</th><th>SE</th><th>RPP</th><th>SE</th><th>RPP</th><th>SE</th></tr></thead><tbody><tr><td><em>Alchornea Celtis</em><br>Comb./Mel.<br>Cyperaceae</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><em>Macaranga</em></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>2.21</td><td>0.08</td></tr><tr><td>Moraceae</td><td></td><td></td><td></td><td></td><td>0.50</td><td>0.35</td><td></td><td></td><td>6.52</td><td>0.08</td></tr><tr><td><em>Nuxia</em> type</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Poaceae</td><td><strong>1.00*</strong></td><td><strong>0.00</strong></td><td>1.00**</td><td>0.00</td><td>1.00**</td><td>0.00</td><td><strong>1.00*</strong></td><td><strong>0.00</strong></td><td></td><td></td></tr><tr><td><em>Podocarpus P africana/R. pinnatus</em></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(Rosaceae)</td><td>0.41</td><td>0.28</td><td>0.84</td><td>0.045</td><td>1.00</td><td>0.45</td><td></td><td></td><td></td><td></td></tr><tr><td><em>Schefflera Syzygium</em></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Relying on the three assumptions above, we used the pollen records from Mbi and Tilla (large lake sites) for REVEALS reconstructions of the regional plant abundance, and from Bambili and Mbalang (small lake sites) for the LOVE reconstructions of the local plant abundance around each lake. We excluded Cyperaceae from the pollen records and used the 11 taxa for which we have RPP estimates (Table 1A). In addition, we tested alternative RPP values for *Podocarpus*, i.e. the mean RPPs of *Pinus* (6.38) and *Picea* (2.62) in Europe, to evaluate their effect on the reconstructions and discuss them in the light of the existing knowledge on the dispersal and deposition of *Podocarpus* pollen. Only one additional RPP value is available for *Podocarpus* to date, i.e. 6.54 (related to Poaceae) from a study in South Africa (Duthie 2015; Hill *et al.*, this volume) (Table 1B). *Pinus*, *Picea* and *Podocarpus* all belong to coniferous plants for which large pollen productivity is common. Note also that Hill *et al.'s* value is very close to *Pinus* mean RPP in Europe. The REVEALS and LOVE reconstructions were performed for continuous 500-year time windows over the last 12 ka years, except for Mbalang (last 6 ka years). $Z_{max}$ was set to 200 km and wind speed to 3 m/s (see 12.1.4, above, and SOM-12.1.4). We used the computer programme LRA.REVEALS.v6.2.4.exe and LRA.LOVE.v6.2.3.exe (Sugita, unpublished) to calculate the REVEALS estimates of regional plant cover at Mbi and Tilla and LOVE estimates of local plant cover within 2 km-radius areas around Bambili and Mbalang. See SOM-12.2.3 for details on access to the computer programmes.

## 12.3 RESULTS

### 12.3.1 ERV analysis and RPP estimates

Vegetation dispersion and mean plant cover over distance from the pollen sample points (Figure 3A) based on the collected vegetation dataset (SOM-12.1.3) indicate that the ERV model's assumption of stationary vegetation is violated in the study region. Both vegetation dispersion and mean plant cover increase with distance until maximum values at c. 50–100 m and then decrease until c. 600 m instead of reaching an asymptote. The values of mean plant cover for *Alchornea*, *Macaranga*, *Celtis*, *Moraceae* and *Poaceae* indicate that these taxa have a more stable
composition already from *c.* 200 m. The log likelihood curve shows a similar pattern as vegetation dispersion, although with a much less prominent 'overshoot' of the curve at 50 m. From 600 m, the values of mean plant cover remain relatively constant for all plant taxa. This distance also corresponds to the RSAP (585 m) as calculated from the log L curve (Figure 3A) with the moving-window regression method. An attempt to analyse forest and savannah sites separately to decrease vegetation dispersion did not improve the results; instead, the log L curves diverged from the theoretically correct curve even more (results not shown).

Most curves of RPPs plotted against distance from the pollen sample point deviate from the expected behaviour (increase to reach an asymptote at a certain distance), but the RPP values are relatively constant from *c.* 1500 m for all taxa except *Celtis*. The RPPs of *Podocarpus* exhibit more or less constant values over 3 km. The RPPs of *Combretaceae/Melastomataceae* and *Schefflera* reach maximum values at very short distances, decrease abruptly, and maintain relatively constant values from a few metres. The RPPs of *Alchornea* and *Celtis* increase to maximum values at *c.* 200 m and then decrease gradually until 1500 m and 3000 m, respectively. The RPPs of Moraceae and *Syzygium* increase until 200 m where they reach an asymptote, although the values of Moraceae are very unstable. Because RPPs still decrease after the calculated RSAP distance (585 m), we selected the final RPP values at the distance for which they stabilize for all taxa, i.e. 1500 m (Table1A).

Figure 4 presents examples of ERV model-corrected pollen-vegetation relationships for six of the 12 taxa. *Schefflera*, Poaceae and *Syzygium* exhibit the most linear relationships with coefficients of determination (R²) of 0.63, 0.54 and 0.58, respectively. Moraceae and *Combretaceae/Melastomataceae* have weaker linear relationships with R² values of 0.42 and 0.26, respectively. A R² value of 0.14 indicates a very weak linearity for *Podocarpus*, *Combretaceae/Melastomataceae*, *Schefflera*, and *Syzygium* are strongly overrepresented, Poaceae is slightly overrepresented, and *Podocarpus* strongly underrepresented by pollen. The pollen-vegetation relationship of Moraceae clearly indicates either overrepresentation or underrepresentation by pollen depending on the site.

## 12.3.2 Landscape Reconstruction Algorithm (LRA): Regional and local plant cover

The REVEALS reconstructions represent regional plant abundance in an area of *c.* 100 km × 100 km around Mbi and Tilla, and the LOVE reconstructions the local plant abundance in an area of minimum 2 km radius around Bambili and Mbalang (see Methods and SOM-12.2.4). The REVEALS and LOVE estimates with their SEs for the 11 taxa are provided for Mbi and Bambili in Figures S2–S5) and highlight the effect of the RPPs’ SEs on the reconstructions. *Podocarpus*, *Nuxia*-type, Moraceae, and *Prunus africana/Rubus pinnatus* have the largest SEs in relation to their RPP value (Table 1A). These taxa also exhibit the largest SEs of LRA reconstructions. When RPP 0.06 is used for *Podocarpus*, SEs are very large for *Podocarpus* and relatively large for *Nuxia*-type (for both REVEALS and LOVE plant cover) (Figure S2). SEs are also quite large for Moraceae and *Prunus africana/Rubus pinnatus* (for LOVE plant cover) in the Early Holocene (Figures S2 and S3). All other taxa exhibit small SEs over most of the Holocene. There are generally few values not different from zero (i.e. SD ≥ REVEALS/LOVE estimate) and they occur mainly for very low values of plant cover, except for *Podocarpus* (RPP 0.06) that has also very large SEs for high LOVE plant cover over most of the Holocene (Figure S2).

### 12.3.2.1 Mountain woodlands: Mbi and Bambili (Figure 5; Figures S2–S5)

The pollen sum of the 11 taxa used in the LRA reconstructions represent 61–81% (mean 69.4%) (Mbi) and 59–76% (mean 65.5%) (Bambili) of the total pollen sum of terrestrial plant taxa (SOM-12.2.1, Table S3A). The REVEALS and LOVE reconstructions are strongly influenced by the RPP used for *Podocarpus*. The low RPP (0.06) results in a very strong dominance of *Podocarpus* 9.5–0.5 ka BP in the regional vegetation (Mbi) and 10.5–2 ka BP locally around
<figure><img src="image_69.png" /><figcaption>**Figure 5.** REVEALS-based regional and LOVE-based local plant cover using the pollen records from Mbi and Bambili, respectively, and three alternative RPPs for *Podocarpus* (0.06, 2.62, and 6.38). Plant cover in percentage of an area of 100 km × 100 km for Mbi, and of minimum 2 km radius for Bambili. Upper panel: pollen percentages for Mbi and Bambili, REVEALS (Mbi)- and LOVE (Bambili)-based plant cover using 0.06 for *Podocarpus*; Lower panel: REVEALS (Mbi)- and LOVE (Bambili)-based plant cover using RPP 2.62 (left) and 6.38 (right) for *Podocarpus*. The colours relate to those in Figures 1, 2 and 3; the five shades of red correspond to carmine red and three shades of green to the two shades of green together in Figures 1 and 2.</figcaption></figure>

Bambili. The other nine tree taxa have a reduced abundance compared to their pollen %, except Moraceae. Regional woodland cover (Mbi) is larger than total tree pollen %, and local woodland cover at Bambili is 100% 12–3 ka BP while percentages of total tree pollen increase from c. 50% (12 ka BP) to 97–98% (8–4.5 ka BP) to decrease back to c. 55% at 3 ka BP.

When higher RPPs (2.62 and 6.38) are used for *Podocarpus*, regional openland (Poaceae) around Mbi represents > 50% of the total vegetation cover over the entire Holocene, while pollen % exceed 50% only 12–11 ka BP, 5–4.5 ka BP, and 1 ka BP to present. In contrast, local vegetation cover at Bambili is 100% wooded 11–3.5 ka BP, and the decrease of woodland from 3 ka BP is much larger and more abrupt than the decrease in total tree pollen %. Woodland cover drops from 100% to 29% (RPP 2.62) or 22% (RPP 6.38) between 3.5 and 3 ka BP, whereas pollen % decrease from 98% to 55% between 4.5 and 3 ka BP. While *Podocarpus* maintains pollen % > 10% at Bambili 7–4.5 ka BP, its reconstructed local cover around the site is < 10% and not different from zero 6–5.5 ka BP (Figure S2). The major effect of using RPP 2.62 rather than 6.38 on the reconstruction of regional vegetation is a larger cover (c. double) of *Podocarpus* throughout the Holocene at the expense of Poaceae, while the cover of other trees does not change significantly. The largest differences in composition of tree taxa between pollen %
and regional plant cover (Mbi) is mainly a larger cover of Combretaceae/Melastomataceae in relation to *Alchornea*, *Macaranga*, and *Celtis*. In terms of the local tree composition around Bambili, the major difference between pollen percentage and plant cover is a larger cover of *Schefflera* at the expense of *Podocarpus* 10.5–3.5 ka BP, and a larger cover of Moraceae and *Macaranga* at the expense of *Alchornea* and *Celtis* (except for the last 50 years). In recent time, the cover of Combretaceae/Melastomataceae is larger than earlier and *Schefflera* dominates the tree cover.

#### 12.3.2.2 The Sudanian and Guineo-Congolian wooded grasslands: Tilla and Mbalang

The pollen sum of the 11 taxa used in the LRA reconstructions represent 68.1–95.8% (mean 82.7%) (Tilla) and 48.7–94% (mean 72.2%) (Mbalang) of the total pollen sum of terrestrial plant taxa (SOM-12.2.1, Table S1A) (Figure 6). The REVEALS reconstructions of regional plant cover around Tilla are characterized by a very large cover of openland (Poaceae) throughout the Holocene whatever RPP is used. Moreover, the cover of Poaceae is larger than their pollen %.

<figure><img src="image_70.png" /></figure>

**Figure 6.** REVEALS-based regional and LOVE-based local plant cover using the pollen records from Tilla and Mbalang, respectively, and three alternative RPPs for *Podocarpus* (0.06, 2.62, and 6.38). Plant cover in percentage of an area of 100 km × 100 km for Tilla, and an area of minimum 2 km radius for Mbalang. Upper panel: pollen percentages for Tilla and Mbalang, REVEALS (Tilla)- and LOVE (Mbalang)-based plant cover using 0.06 for *Podocarpus*; Lower panel: REVEALS (Tilla)- and LOVE (Mbalang)-based plant cover using RPP 2.62 (left) and 6.38 (right) for *Podocarpus*. The colours relate to those in Figures 1 and 2; the five shades of red correspond to carmine red and three shades of green to the two shades of green together in Figures 1 and 2.
There is no significant difference between the three reconstructions except a slightly larger cover of *Podocarpus* when RPP 0.06 is used. The total cover of trees is smaller than tree pollen %, and Combretaceae/Melastomataceae are dominant over the other tree taxa, which is not the case in pollen %.

The LOVE reconstructions for Mbalang exhibit a total cover of tree of 100% with RPP 0.06 for *Podocarpus* over the period 6–3 ka BP, and of 40–60% with RPPs 2.62 and 6.38. With RPP 0.06, *Podocarpus* is dominant, while all other trees have a cover smaller or much smaller than their pollen percentage, except *Prunus africana/Rubus pinnatus* and Moraceae that have a higher, respectively slightly higher cover than their pollen %. With RPPs 2.62 and 6.38, Poaceae has a cover slightly smaller than its pollen %, especially 6–3 ka BP. *Prunus africana/Rubus pinnatus* has a larger cover than its pollen % over the entire period 6–0 ka BP, as well as Moraceae and *Macaranga* 6–3 ka BP, and Combretaceae/ Melastomataceae 4.5–3 ka BP. *Podocarpus*, Combretaceae/ Melastomataceae, *Celtis* and *Alchornea* have a smaller cover than their pollen %. LOVE plant cover and pollen of *Syzygium* and *Schefflera* are similar in proportions. *Podocarpus* has a larger cover in the reconstruction with RPP 2.62 than in the one with RPP 6.38. The decrease in total trees at 3 ka BP is sharp in both cover and pollen %, but the decrease in cover (48 to 9%) is larger than in pollen % (30 to 7%).

## 12.4 DISCUSSION

### 12.4.1 Relative Pollen Productivity (RPP)

#### 12.4.1.1 Effects of violation of model assumptions and other factors

The effects on RPPs of deviations from the ERV model assumptions and other factors have been discussed earlier in syntheses of RPP values in Europe (Broström et al. 2008; Mazier et al. 2012), temperate China (Li et al. 2017) and the Northern hemisphere (Wieczorek and Herzschuh 2020). The number of factors affecting RPPs and/or causing between-study variability in RPP values for a taxon are numerous. They span from between-study region differences in climate, land use, vegetation type/structure and plant species (including their pollination strategies), between-study variation in methods used to collect the pollen-vegetation dataset (including methods in field and laboratory, and data handling), to differences between models and/or model implementation (including dissimilarities in parameter setting, parameter values, and calculation procedures). Because of the relatively low number of RPP values (response variables) compared to the number of factors (explanatory variables), statistical quantification of the effect of these factors on RPP estimates has never been attempted so far. In this pilot study, the major factors that need to be considered with care are the violation of the ERV model’s assumptions in terms of stationarity of the vegetation in the study area, random distribution of sites, mode of dispersal and deposition of pollen, and taphonomy and preservation of pollen in soil samples.

Vegetation dispersion and mean plant composition over distance (Figure 3A) indicate that vegetation is not stationary in the study region, and site distribution did not follow a true random scheme, but a stratified random one. Both factors may explain the shape of the log L curves obtained from the ERV runs (example in Figure 3A) (e.g. Broström et al. 2005; Li et al. 2017). Moreover, the difference in vegetation data collection within and beyond 100 m (also seen in the difference in mean plant composition within and beyond 100 m, Figure 3A) may also cause the ‘overshoot’ of log L values at short distances (< 100 m) before the curve decreases and/or plans out. The shape of the log L curve implies that the definition of the RSAP is uncertain. Based on the empirical and simulation study of Broström et al. (2005) on the effect of non-random site distribution on likelihood function scores and the identification of the RSAP, we assume that the large RSAP, i.e. the distance at which all curves of RPP with distance stabilize (1500 m), is most realistic.
Eight of the twelve plant taxa involved in the analysis are entomophilous, of which three have been described as partly anemophilous (Table 1A). The assumption of the ERV model that all pollen is transported by wind is therefore violated. However, these taxa are common in lake sediments and peat deposits of tropical Africa, which suggests that part of the pollen produced is transported by wind. Therefore, an estimate of the relationship between vegetation and pollen transported to palaeoecological archives such as lakes and bogs remains useful. Nevertheless, such estimate will not be comparable with true pollen productivity in the plant stamina, but can be used as a measure of the proportion of pollen transported and deposited in soils, moss pollsters, lakes and bogs. The use of soil samples instead of moss pollsters in regions where mosses are uncommon and/or poor in pollen (e.g. Wan *et al.* 2020) may also imply biases of the pollen assemblages compared to mosses/lake sediment/peat. Gajewski *et al.* (2002) analysed a database of all surface pollen data available in Africa and concluded that percentages of the three most abundant pollen taxa did not show any bias among sampling methods. However many studies have indicated biases of pollen assemblages in soil samples compared to, e.g. pollen traps due to poor preservation conditions (e.g. Zhang *et al.* 2015). We therefore assume that the most common pollen types in soils are those that are well preserved due to their species/taxon-specific wall structure, and that their amount corresponds to the pollen transported and deposited in the soil. To our knowledge, little is known on the number of years of pollen deposition in soil samples (e.g. Havinga 1984). Thus, we assume that they represent some years of pollen deposition. One of the basic assumptions of pollen analysis as a tool to reconstruct past vegetation change is that pollen productivity is a constant, although pollen-trap studies have shown that inter-annual variation occurs in the absence of changes in plant cover (e.g. Abraham *et al.* 2021; Gosling *et al.* 2005; Haselhorst *et al.* 2013; Julier *et al.* 2019). These known variations are considered as ‘noise’ in terms of the pollen productivity measure needed to interpret sedimentary pollen records (e.g. Fang *et al.* 2020). Individual pollen assemblages from lake sediments or peat used? to be from multiple years-samples, and each quantitative reconstruction of plant cover using the LRA (REVEALS and LOVE) seldom represents less than 100 years, and generally covers 100 to 500 years (e.g. Cui *et al.* 2014; Trondman *et al.* 2015).

Fall Speed of Pollen (FSP) is an important parameter in pollen-vegetation modelling. Between-study differences in FSP for a given taxon will influence the RPP results (e.g. Bunting *et al.* 2004). However, there is little difference in the estimated FSPs of pollen taxa between this study and other studies in the tropics and subtropics, except for *Podocarpus* (0.0175 m/s in this study; 0.074 in Hill *et al.*, this volume). Fall speed for Poaceae varies between studies from 0.0133 m/s in tropical China (Wan *et al.* 2020) to 0.050 m/s in South Africa (Hill *et al.*, this volume). However, the FSP estimate from the study in Namibia (0.031 m/s, Tabares *et al.* 2021) is close to the FSP we are using (0.035 m/s; Broström *et al.* 2004).

### 12.4.1.2 Comparison with RPPs from other studies in the tropics

RPP values are available from other studies in the tropics for six taxa included in our study (number of values in brackets, our study excluded): Combretaceae/Melastomataceae (1), Cyperaceae (2), *Macaranga* (1), *Moraceae* (3), *Podocarpus* (1) and *Rosaceae* (3; for comparison with the RPP of *Prunus africana/Rubus pinnatus* (Table 1B). RPPs for each of these taxa are surprisingly similar given that studies are from geographically very different regions and often use different methods. The comparison confirms RPPs smaller than that of Poaceae for Combretaceae/Melastomataceae, Cyperaceae, and *Rosaceae*, and RPPs larger than that of Poaceae for *Macaranga*. However, *Moraceae* exhibit contradicting results with RPPs that are either larger (one value in tropical China) or smaller (two values) than the RPP of Poaceae. Similarly, *Podocarpus* has a much smaller RPP than Poaceae in our study, but much larger RPP than Poaceae in South Africa (see Hill *et al.*, this volume). Earlier studies have also reported that both *Moraceae* and *Podocarpus* can exhibit high and low pollen quantities in surface samples within only a few
meters (e.g. Gosling *et al.* 2005; Maley *et al.* 1990; Vincens 1984; Wan *et al.* 2020). Moreover, results indicated that *Podocarpus* pollen % in surface soils were often high within the forest (up to 40% in Vincens (1984) or 60% in Maley *et al.* (1990)), and decreased abruptly over the forest edge (to c. 10%) and beyond, to < 5% already 10 m from the edge (Maley *et al.* 1990) or to c. 5% and 1% 10 km and 50 km from the edge, respectively (Vincens 1984). Lézine (unpublished) also reported that *Podocarpus* pollen percentage in soil samples within the *Podocarpus* forests of Cameroon can vary between very low and high values within a few meters. The very low RPP of *Podocarpus* obtained in our study is probably due in part to the low proportions of the taxon in the studied forests (c. 8–23%, max. 29%, in distance-weighted plant abundance within 100 m of the studied sites) with corresponding pollen percentages of 0.2–2.5%, maximum 8% for 29% plant cover. Such ‘fuzzy’ pollen-vegetation relationships need to be further investigated. New, carefully designed RPP studies are necessary before we can conclude on this issue and the relevance of quantitative reconstruction for this type of taxa. Moreover, the pollen-vegetation relationship of *Podocarpus* can be biased by long-distance pollen transport, as reported in earlier studies (e.g. Maley *et al.* 1990; Vincens 1984; Wan *et al.* 2020).

Earlier studies of modern and fossil pollen spectra and related vegetation provide some
hints on whether the RPP we obtained for the remaining six taxa roughly agree with those results
in terms of representation of vegetation by pollen. Pollen of several entomophilous tropical taxa
have been found in large numbers, e.g. *Alchornea* (Elenga *et al.* 2000; Reynaud-Farrera 1995;
Vincens *et al.* 1997, 2000; Watrin *et al.* 2007), and *Celtis* and *Syzygium* (e.g. Verlhac *et al.* 2018;
Watrin *et al.* 2007). Lézine *et al.* (2013) suggested that *Schefflera* might be overrepresented in
the pollen record from Bambili due to high pollen productivity and long-distance transport. Our
results suggest that these four taxa all have RPPs larger (*Schefflera*, *Syzygium*) or much larger
(*Alchornea*, *Celtis*) than Poaceae (Table 1A).

12.4.2 LRA reconstructions of Holocene plant cover at regional and local spatial scales

Although the 11 pollen taxa used in the LRA reconstructions generally represent > 60–70% of the total pollen sum of terrestrial pollen types in the four pollen records (Table S3A), it is essential to keep in mind that we are missing in many cases a third of the pollen record. Major taxa for which we would need RPP estimates to achieve more complete reconstructions of the plant cover are: *Aerva*-type, *Antidesma*-type, *A. venosum*, *Ficus*, *Gnidia*, *Justicia*, *Maesa*, and *Rapanea* (mountain woodlands), and *Cussonia*, *Hymenocardia*, *Ixora*, *Olea*, *Tetrorchidium*, and *Uapaca* (wooded grasslands). One RPP value (19.19 relative to Poaceae) is available for *Justicia* (Navya 2020). Based on a large database of surface pollen samples in Africa, Gajewski *et al.* (2002) concluded that *Olea* pollen (*O. capensis*-type) represent the presence and abundance of the plant correctly. *Uapaca* was found to be either over-represented or under-represented by pollen (Julier *et al.* 2019). The information we have so far on the pollen-vegetation relationships of the taxa mentioned above is still too poor to anticipate the effect they would have on the LRA reconstructions. They could influence (in terms of plant cover) the timing, rate and magnitude of change in the woodland/openland relationship, and the taxa composition in woodlands over time.

We consider that the LRA reconstructions using the lowest RPP (0.06) for *Podocarpus* are
unrealistic. The low RPP results in an extreme dominance of *Podocarpus* over all other tree taxa,
and over Poaceae for most of the Holocene (see also discussion on RPPs above). We therefore
focus on the reconstructions performed with the alternative RPPs 2.62 and 6.38, i.e. pollen
productivities higher than that of Poaceae. These RPP values are also in a comparable range of
values as common entomophilous tree taxa in African pollen records such as *Alchornea*, *Celtis*,
*Schefflera*, and *Syzygium*.
### 12.4.2.1 Mountain woodlands: Mbi and Bambili

The assumed minimum size of the landscape represented by REVEALS estimates of plant cover is 100 km × 100 km (see Methods), which corresponds to an area around the site Mbi covering today montane and submontane vegetation (c. 55%), Guineo-Congolian rain and semi-deciduous forests (c. 20%), and secondary forests and grassland (25%), i.e. vegetation types from altitudes between c. 1000 and 3000 m asl. (Figures 1 and 2).

The REVEALS reconstructions suggest a clear dominance of grasses in the regional vegetation over the entire Holocene. The composition of trees is significantly different in plant cover compared to the pollen percentages, with a generally smaller cover than pollen percentage of *Schefflera*, *Podocarpus* (high altitude taxa) and *Alchornea* (low altitude taxon), and larger cover than pollen percentage of Moraceae and Combretaceae/Melastomataceae (mid-altitude taxa) and Poaceae (mainly mid- and low altitude) (see Figure 2 for the altitudinal distribution of taxa). Thus, the REVEALS reconstruction suggests that the regional landscape was much more open than suggested by the pollen percentages, and the high altitude and low altitude trees less abundant in relation to mid altitude trees. However, the REVEALS reconstruction does not alter the overall history and timing of changes in woodland abundance documented by the pollen percentages.

The LOVE reconstructions represent plant cover over a 2 km-radius area (assumed RSAP, Methods and SOM-12.1.4) around the lake of Bambili, which includes essentially afro-alpine grasslands and montane forests. The reconstructions suggest that the vegetation at Bambili was 100% forested 11–3.5 ka BP with a stronger dominance of the high altitude taxa (including Moraceae; *Ficus* in Figure 2) over the low altitude taxa than pollen percentages indicate. The sudden decreases in cover of *Podocarpus* at 7 ka and 3 ka BP are more pronounced than the declines in percentages, and the general collapse of the mountain forest more rapid. The landscape openness (cover of Poaceae) 3–0.5 ka BP is significantly larger than suggested by the pollen percentages.

### 12.4.2.2 The Sudanian and Guineo-Congolian wooded grasslands: Tilla and Mbalang

The REVEALS reconstructions of Tilla represent the regional plant cover in the Sudanian dry forests with grasslands and shrublands at low altitudes (Figures 1 and 2) and confirm that tree cover was very small all through the Holocene in contrast to Sahel's more tree-rich vegetation over early and Mid-Holocene (Salzmann *et al.* 2002). However, Combretaceae/Melastomataceae might have been more abundant in relation to *Alchornea*, *Celtis*, and *Macaranga* than pollen percentages suggest.

The assumed RSAP of Lake Mbalang (2 km) lies within the Sudano-Guinean wooded grasslands. The LOVE reconstruction suggests a 10–15% higher cover of tree taxa in the vegetation compared to the pollen percentages 6–3 ka BP and confirms a local presence of *Podocarpus* and *Prunus africana/Rubus pinnatus*. However, the larger abundance of trees compared to pollen percentages might be a bias due to the pollen record from Tilla representing only the very open regional vegetation North of Mbalang and not the areas West and South of the lake that might have been more wooded. Therefore, the LRA might have subtracted too large amounts of Poaceae (background pollen inferred from the REVEALS-based regional vegetation of Tilla). If this is the case, pollen percentage of Poaceae might be closer to the actual vegetation openness at Mbalang than its LOVE-estimated cover indicates. A pollen record from a large lake closer to Mbalang would be more appropriate for a LOVE reconstruction.

## 12.5 CONCLUSIONS

The results from this first attempt at estimating RPP for major taxa of the Holocene vegetation of Cameroon confirm the insights gained earlier from traditional studies of the relationship between
pollen and vegetation. *Alchornea*, *Celtis*, *Macaranga*, *Schefflera*, and *Syzygium* have been found to be overrepresented by pollen (Elenga *et al.* 2000; Lézine *et al.* 2013; Reynaud-Farrera 1995; Verlhac *et al.* 2018; Vincens *et al.* 1997, 2000; Watrin *et al.* 2007) and are also the taxa for which the estimated RPP are larger than that of Poaceae. Of the taxa for which RPP values are available from other studies, Combretaceae, Cyperaceae, *Macaranga* and *Prunus africana/Rubus pinnatus* exhibit relatively small between-study differences in RPPs. Existing RPP values for both Moraceae and *Podocarpus* are either lower or higher than that of Poaceae, which corroborates earlier observations on the representation of these taxa in modern pollen samples compared to the plant abundance in the vegetation (e.g. Gosling *et al.* 2005; Maley *et al.* 1990; Vincens 1984; Wan *et al.* 2020).

The major uncertainties of our RPP study are due to the strong effect of non-stationary vegetation in the study region on the calculation of RPPs using the maximum likelihood approach. It implies that the RSAP of the pollen samples (here surface soil) is difficult to identify, and therefore the best RPP values are uncertain. Nevertheless, whatever RSAP we would choose (either a few hundred meters or 3 kilometres), ranking of the RPP values would remain the same and, therefore, the correction of the pollen percentages identical. However, the relative cover/abundance of taxa would probably be influenced. In regions where it might be difficult to find large enough areas of stationary vegetation to estimate RPP of forest taxa in particular, one might test the robustness of the obtained RPPs using other models and calculation approaches that might be less sensitive to non-stationary vegetation (Fang *et al.* 2020; Tabares *et al.* 2021). In future work, the use of combined satellite and drone data might help to decrease the amount of field work and increase the consistency of vegetation datasets over large areas around each pollen sample site. The latter would probably improve the performance of models for estimation of RPPs.

The LRA reconstructions in this study provide a first tentative quantification of changes in the Afromontane woodlands and wooded grasslands of Cameroon over the Holocene. The results suggest that the regional landscape in the mountain region was more open than pollen percentages suggest, and that the collapse of the high altitude forests around Bambili at 3 ka BP was faster and of larger magnitude than indicated by the pollen record. The latter will need to be confirmed by further studies involving a larger number of major taxa characteristic of the Holocene vegetation history of Cameroon. The reconstructions also suggest that the woodlands' taxa composition, at both regional and local scales, is different once the pollen percentages are corrected using the RPP dataset and RPPs 2.62 or 6.38 for *Podocarpus*. The LRA reconstruction for Mbalang confirms the local occurrence of mountain woodland around the lake in Mid-Holocene. The correction of the pollen percentages indicate a larger cover of *Prunus africana/Rubus pinnatus* and Moraceae than their pollen percentages. *Podocarpus* and Moraceae are the most problematic taxa to model with the RPPs currently available. The reconstructions using high RPPs for *Podocarpus* are the most probable so far. It is not possible, based on this study, to assess the reliability of the available RPPs for Moraceae.

## ACKNOWLEDGEMENTS

This research was supported by the National Research Agency (ANR), France, through the C3A (ANR-09-PEXT-001) and VULPES (ANR-15-MASC-0003) projects and by the Labex L-IPSL. We gratefully acknowledge funds from the CNRS (A.-M. Lézine), and Linnaeus University (Faculty of Health and Life Sciences, Kalmar, Sweden) and the Swedish Strategic Research Area 'ModElling the Regional and Global Ecosystem' (MERGE; http://www.merge.lu.se/) (M.-J. Gaillard, E. Githumbi, F. Li). This study is also a contribution to the Past Global Change (PAGES) project and its working group LandCover 6k (http://pastglobalchanges.org/landcover6k), which in turn received support from the Swiss National Science Foundation, the Swiss Academy of Sciences, the US National Science Foundation, and the Chinese Academy of Sciences. We thank
U. Salzmann (University of Northumbria, Newcastle) and A. Vincens (CEREGE, France) for sharing their pollen data (Tilla and Mbalang, respectively). All pollen records are archived in the African Pollen Database (APD).

REFERENCES

Abraham, V., Hicks, S., Svobodová-Svitavská, H., Bozilova, E., Panajiotidis, S., Filipova-Marinova, M., Eldegard Jensen, C., Tonkov, S., Pidek, I. A., Swieta-Musznicka, J., Zimny, M., Kvavadze, E., Filbrandt-Czaja, A., Hättestrand, M., Karlíoglu Klíč, N., Kosenko, J., Nosova, M., Severova, E., Volkova, O., Hallsdóttir, M., Kalnina, L., Noryskiewicz, A. M., Noryskiewicz, B., Pardoe, H., Christodoulou, A., Koff, T., Fontana, S. L., Alenius, T., Isaksson, E., Seppä, H., Veski, S., Pedziszewska, A., Weiser, M. and Giesecke, T., 2021, Patterns in recent and Holocene pollen influxes across Europe – the Pollen Monitoring Programme Database as a tool for vegetation reconstruction. *Biogeosciences*, **18**(15), 4511–4534. 10.5194/bg-18-4511-2021.

Archer, C.L. and Jacobson, M.Z., 2005, Evaluation of global wind power. *Journal of Geophysical Research*, **110**, article: D12110, 10.1029/2004JD005462.

Barnosky, A., Matzke, N., Tomiya, S., Wogan, G. O. U., Swartz, B., Quental, T. B., Marshall, C., McGuire, J. L., Lindsey, E. L., Maguire, K. C., Mersey, B. and Ferrer, E. A., 2011, Has the Earth’s sixth mass extinction already arrived? *Nature*, **471**, pp. 51–57, 10.1038/nature09678.

Bouimetarhan, I., Dupont, L., Reddad, H., Baqloul, A., Lézine, A.-M. and APD contributors this volume, Vegetation history and climate change of Africa: A synthesis of deep-sea pollen records. Vegetation response to millennial- and orbital-scale climate changes: A view from the Ocean. *Palaeoecology of Africa*, **35**, chapter: 22, 10.1201/9781003162766-22.

Broström, A., Sugita, S. and Gaillard M.-J., 2004, Pollen productivity estimates for the reconstruction of past vegetation cover in the cultural landscape of southern Sweden. *The Holocene*, **14**, pp. 368–381, 10.1191/0959683604h1713rp.

Broström, A., Sugita, S., Gaillard, M.-J. and Pilesjö, P., 2005, Estimating the spatial scale of pollen dispersal in the cultural landscape of southern Sweden. *The Holocene*, **15**, pp. 252–262, 10.1191/0959683605h1790rp.

Broström, A., Nielsen, A.B., Gaillard, M.-J., Hjelle, K.L., Mazier, F., Binney, H., Bunting, M.J., Fyfe, R., Duffin, K., Meltsov, V., Poska, A., Räsänen, S., Soepboer, W., von Stedingk, H., Suutari, H. and Sugita, S., 2008, Pollen productivity estimates of key European plant taxa for quantitative reconstruction of past vegetation — a review. *Vegetation History and Archaeobotany*, **17**, 461–478, 10.1007/s00334-008-0148-8.

Bunting MJ, Gaillard MJ, Sugita S, Middleton, R. and Broström, A., 2004, Vegetation structure and pollen source area. *The Holocene*, **14**, pp. 651–660, 10.1191/0959683604h1744rp.

Bunting, M.J., Armitage, R., Binney, H.A. and Waller, M., 2005, Estimates of ‘relative pollen productivity’ and ‘relevant source area of pollen’ for major tree taxa in two Norfolk (UK) woodlands. *The Holocene*, **15**, pp. 459–465, 10.1191/0959683605h1821rr.

Bunting, M. J., Farrell, M., Broström, A., Hjelle, K., Mazier, F., Middleton, R., Nielsen, A. B., Rushton, E., Shaw, H. and Twiddle, C. L., 2013, Palynological perspectives on vegetation survey: A critical step for model-based reconstruction of Quaternary land cover. *Quaternary Science Reviews*, **82**, pp. 41–55, 10.1016/j.quascirev.2013.10.006.

Chen, H., Xu, Q., Zhang, S., Sun, Y., Wang, M. and Zhou, Z., 2019, Relative pollen productivity estimates of subtropical evergreen and deciduous braodleaved mixed forest in Ta-pieh Mountains. *Quaternary Sciences*, **39**, pp. 469–482, 10.11928/j.issn.1001-7410.2019.02.19.

Cui, Q.Y., Gaillard, M.-J., Lemdahl, G., Stenberg, L., Sugita, S. and Zernova, G., 2014, Historical land-use and landscape change in southern Sweden and implications for present and future biodiversity. *Ecology and Evolution*, **4**, pp. 555–3, 10.1002/ece3.1198,570.
Duffin, K.I. and Bunting, M.J., 2008, Relative pollen productivity and fall speed estimates for southern African savanna taxa. *Vegetation History and Archaeobotany*, **17**, pp. 507–525, 10.1007/s00334-007-0101-2.

Duthie, T. J., 2015, Relative pollen productivity estimates (PPE) and relevant source area of pollen (RSAP) for key taxa from vegetation communities in Cathedral Peak, KwaZulu-Natal Drakensburg. *MSc Thesis, Univ. of KwaZulu-Natal*.

Elenga, H., Namur, C.D, Vincens, A., Roux, M. and Schwartz, D., 2000, Use of plots to define pollen-vegetation relationships in densely forested ecosystems of Tropical Africa. *Review of Palaeobotany and Palynology*, **112**, pp. 79–96, 10.1016/S0034-6667(00)00036-1.

Fang, Y., Ma, C. and Bunting, M J., 2019, Novel methods of estimating relative pollen produc-
tivity: A key parameter for reconstruction of past land cover from pollen records. *Progress in
Physical Geography*, **46**, pp. 731–753, 10.1177/0309133319861808.

Gaillard, M.-J., Sugita, S., Bunting, M.J., Middleton, R., Broström, A., Caseldine, C., Giesecke, T., Hellman, S. E. V., Hicks, S., Hjelle, K., Langdon, C., Nielsen, A. B., Poska, A., von Stedingk, H., Veski, S. and POLLANDCAL members, 2008, The use of modelling and simulation approach in reconstructing past landscapes from fossil pollen data: A review and results from the POLLANDCAL network. *Vegetation History and Archaeobotany*, **17**, pp. 419–443, 10.1007/s00334-008-0169-3.

Gaillard, M.-J., Sugita, S., Mazier, F., Trondman, A.-K., Broström, A., Hickler, T., Kjellström, E., Punes, P., Lemmen, C., Olofsson, J., Smith, B., Strandberg, G., Kokfelt, Ulla., Miller, P., Poska, A., Rundgren, M. and Barnekow, L., 2010, Holocene land-cover reconstructions for studies on landcover-climate feedbacks. *Climate of the Past*, **6**, pp. 483–499, 10.5194/cp-6-483-2010.

Gajewski, K., Lézine, A.-M., Vincens, A., Delestan, A., Sawada, M. and APD members, 2002,
Modern climate-vegetation-pollen relations in Africa and adjacent areas. *Quaternary Science
Reviews*, **21**, pp. 1611–1631, 10.1016/S0277-3791(01)00152-4.

Gosling, W.D., Mayle, F.E., Tate, N.J. and Killeen, T., 2005, Modern pollen-rain characteristics of tall terra firme moist evergreen forest, southern Amazonia. *Quaternary Research*, **64**, pp. 284–297, 10.1016/j.yqres.2005.08.008.

Grassi, S., Veronesi, F., Schenkel, R., Peier, C., Neukom, J., Volkwein, S., Raubal, M. and Hurni, N., 2015, Mapping of the global wind energy potential using open source GIS data. 2nd International Conference on Energy and Environment: bringing together Engineering and Economics. Guimarães, Portugal 18–19 June, 2015.

Haselhorst, D.S., Moreno, J.E. and Punyasena, S.W., 2013, Variability within the 10-year pollen rain of a seasonal neotropical forest and its implications for paleoenvironmental and phenological research. *PLoS One*, **8**, article: e53485, 10.1371/journal.pone.0053485.

Havinga, A.J., 1984, A 20-year experimental investigation into the differential corrosion susceptibility of pollen and spores in various soil types. *Pollen et Spores*, **26**, pp. 541–558.

Hellman, S., Gaillard, M.-J., Broström, A. and 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. *Journal of Quaternary Science*, **23**, pp. 21–42, 10.1002/jqs.1126.

Hellman, S., Bunting, M. J. and Gaillard, M. J., 2009a, Relevant Source Area of Pollen in patchy cultural landscapes and signals of anthropogenic landscape disturbance in the pollen record: a simulation approach. *Review of Palaeobotany and Palynology*, **153**, pp. 245–258, 10.1016/j.revpalbo.2008.08.006.

Hellman, S., Gaillard, M.-J., Bunting, J. M. and Mazier, F., 2009b, Estimating the rele-
vant source area of pollen in the past cultural landscapes of southern Sweden - a for-
ward modelling approach. *Review of Palaeobotany and Palynology*, **153**, pp. 259–271,
10.1016/j.revpalbo.2008.08.008.

Hély, C., Lézine, A.-M. and APD contributors, 2014, Holocene changes in African vegeta-
tion: tradeoff between climate and water availability. *Climate of the Past*, **10**, pp. 681–686,
10.5194/cp-10-681-2014.
Hill, T. R., Duthie, T.J., and Bunting, M.J., this volume, Relevant source area of pollen and pollen productivity estimates from KwaZulu-Natal. *Palaeoecology of Africa, 35*, chapter: 16, 10.1201/9781003162766-16.

Jiang, F., Xu, Q., Zhang, S., Li, F., Zhang, K., Wang, M., Shen, W., Sun, Y. and Zhou, Z., 2020, Relative pollen productivities of the major plant taxa of subtropical evergreen–deciduous mixed woodland in China. *Journal of Quaternary Science, 35*, pp. 526–538, 10.1002/jqs.3197.

Julier, A.C.M., Jardine, P. E., Adu-Bredu, S., Coe, A. L., Fraser, W. T., Lomax, B. H., Malhi, Y., Moore, S. and Gosling, W. D., 2019, Variability in modern pollen rain from moist and wet tropical forest plots in Ghana, West Africa. *Grana, 58*, pp. 45–62, 10.1080/00173134.2018.1510027.

Kaplan, J.O., Krumhardt, K.M., Gaillard, M.-J., Sugita, S., Trondman, A.-K., Fyfe, R., Marquer, L., Mazier, F. and Nielsen, A.B., 2017, Constraining the deforestation history of Europe: evaluation of historical land use scenarios with pollen-based land cover reconstructions. *Land, 6*, article: 91, 10.3390/land6040091.

Kuparinen, A., 2006, Mechanistic models for wind dispersal. *Trends in Plant Science, 11*, pp. 296–301, 10.1016/j.tplants.2006.04.006.

Kuparinen, A., Markkanen, T., Riikonen, H. and Vesala, T., 2007, Modeling air-mediated dispersal of spores, pollen and seeds in forested areas. *Ecological Modelling, 208*, pp. 177–188, 10.1016/j.ecolmodel.2007.05.023.

Letouzey, R., 1985, Notice de la carte phytogeographique du Cameroun au 1: 500,000, (Toulouse: Institut de la carte internationale de la végétation).

Lézine, A.-M., Assi-Kaudjhis, C., Roche, E., Vincens, A. and Achoundong, G., 2013, Towards an understanding of West African montane forest response to climate change. *Journal of Biogeography, 40*, pp. 183–196, 10.1111/j.1365-2699.2012.02770.x.

Lézine, A.-M., Lemonnier, K., Waller, M.P., Bouimetarhan, I. and Dupont, L., this volume, Changes in the West African landscape at the end of the African Humid Period *Palaeoecology of Africa, 35*, chapter: 6, 10.1201/9781003162766-6.

Lézine, A.-M., Izumi, K. and Achoundong, G., 2021, Mbi Crater (Cameroon) illustrates the relations between mountain and lowland forests over the past 15,000 years in western equatorial Africa. *Quaternary International*. DOI: 10.1016/j.quaint.2020.12.014.

Li, F.-R., Gaillard, M.-J., Sugita, S., Mazier, F., Xu, Q.-H., Zhou, Z.-Z., Zhang, Y., Li, Y. and Laffly, D., 2017, Relative pollen productivity estimates for major plant taxa of cultural landscapes in central eastern China. *Vegetation History and Archaeobotany, 26*, pp. 587–605, 10.1007/s00334-017-0636-9.

Li, F., Gaillard, M.-J., Cao, X., Herzschuh, U., Sugita, S., Tarasov, P.-E., Wagner, M., Xu, Q., Ni, J., Wang, W., Zhao, Y., An, C., Beusen, A.H.W., Chen, F., Feng, Z., Klein Goldewijk, C.G.M., Huang, X., Li, Y., Li, Y., Liu, H., Sun, A., Yao, Y., Zheng, Z. and Jia, X., 2020, Towards quantification of Holocene anthropogenic land-cover change in temperate China: A review in the light of pollen-based REVEALS reconstructions of regional plant cover. *Earth-Science Reviews, 203*, article 103119, 10.1016/j.earscirev.2020.103119.

Lu, Z., Miller, P.A., Zhang, Q., Zhang, Q., Warlind, D., Nieradzik, L., Sjolte, J. and Smith, B., 2018, Dynamic vegetation simulations of the mid-Holocene green Sahara. *Geophysical Research Letters 45*, pp. 8294–8303, 10.1029/2018GL079195.

Maley, J. Caballe, G. and Sita, P., 1990, Etude d'un peuplement résiduel à basse altitude de *Podocarpus latifolius* sur le flanc congolais du massif du Chaillu. Implications paléoclimatique et biogéographiques. Etude de la pluie pollinique actuelle. In : *Paysages Quaternaires de l'Afrique Centrale Atlantique*, edited by Lanfranchi R. and Schwartz D., *Paysages quaternaires de l'Afrique centrale atlantique*, pp. 336-352 (Paris : ORSTOM).

Masson-Delmotte, V., Schulz, M., Abe-Ouchi, A., Beer, J., Ganopolski, A., Gonzaalez Rouco, J.F., Jansen, E., Lambeck, K., Luterbacher, J., Naish, T., Osborn, T., Otto-Bliesner, B., Quinn,
T., Ramesh, R., Rojas, M., Shao, X. and Timmermann, A., 2013, Information from paleoclimate archives. In *Climate Change 2013: the Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change*, edited by Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V. and Midgley, P.M. pp. 383–464 (Cambridge University Press).

Mazier, F., Gaillard, M. J., Kuneš, P., Sugita, S., Trondman, A. K. and Broström, A., 2012, Testing the effect of site selection and parameter setting on REVEALS-model estimates of plant abundance using the Czech Quaternary Palynological Database. *Review of Palaeobotany and Palynology*, **187**, pp. 38–49, 10.1016/j.revpalbo.2012.07.017.

Navya R., 2020, *Pollen based estimates of Holocene vegetation in southern India: An LRA (Land-cover Reconstruction Algorithm) approach*”, PhD Thesis, Pondicherry University, Puducherry, India, pp. 1–268.

Parsons, R.W. and Prentice, I.C., 1981, Statistical approaches to R-values and the pollen-vegetation relationship. *Review of Palaeobotany and Palynology*, **32**, pp. 127–152, 10.1016/0034-6667(81)90001-4.

Piraquive Bermúdez, D., Theuerkauf, M. and Giesecke, T., 2019, Towards quantifying Holocene changes in forest cover in the Araucaria forest grassland mosaic of southern Brazil. Abstract. 20th Congress of the International Union for Quaternary Research (INQUA), Dublin 25–31 July 2019, https://app.oxfordabstracts.com/events/574/program-app/submission/92213.

Prentice, I.C., 1985, Pollen representation, source area, and basin size – toward a unified theory of pollen analysis. *Quaternary Research*, **23**, pp. 76–86, 10.1016/0033-5894(85)90073-0.

Prentice, I.C. and Parsons, R.W., 1983, Maximum likelihood linear calibration of pollen spectra in terms of forest composition, *Biometrics*, **39**, pp. 1051–1057, 10.2307/2531338.

Prentice, I.C. and Webb III, T., 1986, Pollen percentages, tree abundances and the Fagerlind effect. *Journal of Quaternary Science*, **1**, pp. 35–43, 10.1002/jqs.3390010105.

Reynaud-Farrera, I., 1995, *Histoire des paléoenvironnements forestiers du sud Cameroun à partir d'analyses palynologiques et statistiques de dépôts holocènes et actuels*. PhD Thesis, University of Montpellier, France.

Salzmann, U., Hoelzmann, P. and Morczinek, I., 2002, Late Quaternary climate and vegetation of the Sudanian zone of northeast Nigeria. *Quaternary Research*, **58**, pp. 73–83, 10.1006/qres.2002.2356.

Sugita, S., 1994, Pollen representation of vegetation in Quaternary sediment: theory and method in patchy vegetation. *Ecology*, **82**, pp. 881–897, 10.2307/2261452.

Sugita, S., 2007a, Theory of quantitative reconstruction of vegetation I: pollen from large sites REVEALS regional vegetation composition. *The Holocene*, **17**, pp. 229–241, 10.1177/0959683607075837.

Sugita, S., 2007b, Theory of quantitative reconstruction of vegetation II: all you need is LOVE. *The Holocene*, **17**, pp. 243–257, 10.1177/0959683607075838.

Tabares, X., Ratzmann, G., Kruse, S., Theuerkauf, M., Mapani, B. and Herzschuh, U., 2021, Relative pollen productivity estimates of savanna taxa from southern Africa and their application to reconstruct shrub encroachment during the last century. *The Holocene*.

Theuerkauf, M., Kuparinen, A. and Joosten, H., 2013, Pollen productivity estimates strongly depend on assumed pollen dispersal. *The Holocene*, **23**, pp. 14–24, 10.1177/0959683612450194.

Trondman, A. K., Gaillard, M.-J., Mazier, F., Sugita, S., Fyfe, R., Nielsen, A. B., Twiddle, C., Barratt, P., Birks, H. J. B., Bjune, A. E., Bjorkman, L., Broström, Anna. Caseldine, C., David, R., Dodson, J., Doerfler, W., Fischer, E., van Geel, B., Giesecke, T., Hultberg, T., Kalnina, L., Kangur, M., van der Knaap, P., Koff, T., Kunes, P., Lageras, P., Latalowa, M., Lechterbeck, J., Leroyer, C., Leydet, M., Lindbladh, M., Marquer, L., Mitchell, F. J. G., Odgaard, B. V., Peglar, S. M., Persson, T., Poska, A., Roesch, M., Seppa, H., Veski, S. and Wick, L., 2015, Pollen-based quantitative reconstructions of Holocene regional vegetation cover (plant-functional
types and land-cover types) in Europe suitable for climate modelling. *Global Change Biology*, **21**, pp. 676–697.

Verlhac, L., Izumi K., Lézine, A.-M., Lemonnier, K., Buchet, G., Achoundong, G. and Tchiengué, B., 2018, Altitudinal distribution of pollen, plants and biomes in the Cameroon highlands. *Review of Palaeobotany and Palynology*, **259**, pp. 21–28, 10.1016/j.revpalbo.2018.09.011.

Vincens, A., 1984, Environnement végétal et sédimentation pollinique lacustre actuelle dans le Bassin du Lac Turkana (Kenya). *Revue de Paléobiologie*, volume spécial, **1**, pp. 235–242.

Vincens, A., Ssemmanda, I., Roux, M. and Jolly, D., 1997, Study of the modern pollen rain in western Uganda with a numerical approach. *Review of Palaeobotany and Palynology*, **96**, pp. 145–168, 10.1016/S0034-6667(96)00022-X.

Vincens, A., Schwartz, D., Elenga, H., Reynaud-Farrera, I., Alexandre, A., Bertaux, J., Mariotti, A., M., Meunier, J.D., Nguetsop, F., Servant, M., Servant-Vildary, S. and Wirrmann, D., 1999, Forest response to climate changes in Atlantic Equatorial Africa during the last 4000 years BP and inheritance on the modern landscapes. *Journal of Biogeography*, **26**, pp. 879–885, 10.1046/j.1365-2699.1999.00333.x.

Vincens, A., Dubois, M.A., Guillet, B., Achoundong, G., Buchet, G., Kamgang Kabeyene Beyala, V., de Namur, C. and Riera, B., 2000, Pollen-rain-vegetation relationships along a forest-savanna transect in southeastern Cameroon. *Review of Palaeobotany and Palynology*, **110**, pp. 191–208, 10.1016/S0034-6667(00)00009-9.

Vincens, A., Lézine, A.M., Buchet, G., Lewden, D. and Le Thomas, A., 2007, African pollen database inventory of tree and shrub pollen types. *Review of Palaeobotany and Palynology*, **145**, pp. 135–141, 10.1016/j.revpalbo.2006.09.004.

Vincens, A., Buchet, G., Servant, M. and ECOFIT Mbalang collaborators, 2010, Vegetation response to the "African Humid Period" termination in Central Cameroon (7°N) - new pollen insight from Lake Mbalang. *Climate of the Past*, **6**, pp. 281–294, 10.5194/cp-6-281-2010.

Wan, Q., Zhang, Y., Huang, K., Sun, Q., Zhang, X., Gaillard, M.-J., Xu, Q., Li, F. and Zheng, Z., 2020, Evaluating quantitative pollen representation of vegetation in the tropics: A case study on the Hainan Island, tropical China. *Ecological Indicators*, **114**, article: 106297, 10.1016/j.ecolind.2020.106297.

Watrin, J., Lézine, A.-M., Gajewski, K. and Vincens, A., 2007, Pollen plant-climate relationships in sub-Saharan Africa. *Journal of Biogeography*, **34**, pp. 489–499, 10.1111/j.1365-2699.2006.01626.x.

White, F., 1983, *The vegetation of Africa*. (Paris: UNESCO).

Whitney, B.S., Smallman, T.L., Mitchard, E.T., Carson, J.F., Mayle, F.E., and Bunting, M.J., 2018, Constraining pollen-based estimates of forest cover in the Amazon: a simulation approach. *The Holocene*, **29**, pp. 262–269, 10.1177/0959683618810394.

Wieczorek, X. and Herzschuh, U., 2020, Compilation of relative pollen productivity (RPP) estimates and taxonomically harmonised RPP datasets for single continents and Northern Hemisphere extratropics. *Earth System Science. Data*, **12**, 3515–3528, 10.5194/essd-12-3515-2020.

Zhang, S.-R., Xu, Q.-H., Gaillard, M.-J., Cao, X.-Y., Li, J., Zhang, L., Li, Y., Tian, F., Zhou, L., Lin, F., and Yang, X., 2016, Characteristic pollen source area and vertical pollen dispersal and deposition in a mixed coniferous and deciduous broad-leaved woodland in the Changbai mountains, northeast China. *Vegetetation History and Archaeobotany*, **25**, pp. 29–43, 10.1007/s00334-015-0532-0.
<figure><img src="image_71.png" /></figure>

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CHAPTER 13

# A Holocene pollen record from Mboandong, a crater lake in lowland Cameroon

Keith Richards<fnref n="1" />

*KrA Stratigraphic, Deganwy, Conwy, North Wales, United Kingdom*

**ABSTRACT:** Mboandong is a small crater lake situated approximately 30 km to the north of Mount Cameroon. A core 13 m in length was collected from the lake centre in December 1981 with the objective of studying Holocene vegetation change in the lowlands of West Cameroon. Radiocarbon dating indicates that the base on the core is close to 7000 years old. Volcanic eruptions of Mount Cameroon between c. 7000 and 6000 years ago are indicated by ash deposits which are overlain by diatomite. Otherwise the lake sediments consist of fine-grained organic mud, with no obvious sedimentary breaks or any sign of the lake having dried out. A fairly rapid sedimentation rate of at least 1 m per 500 years is indicated. Predominantly forested conditions existed throughout much of the Holocene, possibly of the Congo type (everwet) rainforest prior to c. 2700 cal yr BP. At this time man was probably encouraging fruits of the 'atili' tree *Canarium schweinfurthii* as an oil-rich food. A major increase in Poaceae pollen after c. 2500 cal yr BP is most probably linked to a drying climate, as has now been inferred at numerous sites in tropical West Africa. An increase in pollen from regrowth and semi-deciduous trees possibly indicates a change to an Atlantic type (more seasonal) rainforest after this time. This climate change probably led to subsequent forest clearance, perhaps linked to the southwards migration of the Bantu people and spread of iron technology. Increased numbers of *Elaeis guineensis* pollen recorded after 1700 cal yr BP are indicative of cultivation. The oil palm appears to have replaced the *atili* tree as an oil-rich food source.

## 13.1 INTRODUCTION

The fact that major changes in climate and vegetation occurred in Africa during the Quaternary is now well established, with evidence from a large number of sites from around the continent (e.g. Dupont et al. 2000; Rossignol-Strick and Duzer 1979; Lézine et al. 2013b, 2019; Maley and Brenac 1998; Miller and Gosling 2014; Vincens et al. 1999; Sowunmi 1981; Talbot et al. 1984). Hamilton (1976), in a review of botanical and zoological evidence, however, had proposed that the forested areas of present-day Cameroon were part of a refugium where forest had remained relatively unaffected by periods of climatic drying during the Quaternary. A primary aim of this study was to collect a core from this suggested refugium region of Cameroon in order to determine the extent, if any, of vegetation change by means of pollen analysis (Figure 1). Mboandong was chosen for a number of reasons. A variety of lakes in Cameroon have been described, for example by Gèze (1943), Green (1972) and Corbet *et al.* (1973). Of these lakes, Barombi Mbo was too

<footnote n="1">¹Other affiliation: Department of Geography and Planning, University of Liverpool, Liverpool, United Kingdom</footnote>
<figure><img src="image_72.png" /><figcaption>**Figure 1.** Simplified map of West African vegetation (based on Keay 1959; White 1983) showing the Mboandong study site location.</figcaption></figure>
deep for coring at the time and Barombi Kotto was excluded as its shores and island are inhabited and it was a known centre for schistosomiasis (Awo et al. 2018, 2020; Duke and Moore 1971; Zahra 1953). Mboandong is only 5 m deep (Green 1972), relatively undisturbed by man and accessible by road. The base of the core has been dated within the range of 6480 to 7020 cal yr BP and therefore provides a record of more or less the last 7000 years. 'Mbo' is the local name for 'lake' hence there is no need to add the prefix of 'lake' to Mboandong (Corbet et al. 1973).

## 13.2 REGIONAL SETTING

Mboandong (4.4500°N, 9.2689°E) is situated in a shallow crater at about 120 m altitude on the low-lying plain between Mount Cameroon, the Roumpi Hills and Mount Koupé. The area consists mainly of basaltic rocks and evidence for widespread volcanic activity is provided by the hundred or so historic eruption points (Figure 2), several of which now contain crater lakes. Geological information from Mboandong is lacking but the lake may be of the same approximate age as nearby Barombi Kotto, a larger crater lake, created by explosive action during a geologically recent, presumably Quaternary, eruptive phase (Gèze 1943). Mboandong lies in the rain shadow of Mount Cameroon. Annual rainfall is the region of 2000 to 2600 mm per year (Hawkins and Brunt 1965); this compares with rainfall typically of 8500 to 10,300 mm per year to the west of the mountain, in the Atlantic coastal region. The wettest months tend to be July, August and September and the driest December, January and February. Even so, Richards (1963) noted that this region of Cameroon, which falls within the 'sous climat bas Camerounian' of Aubréville (1949), is characterised by the absence of a well-defined and prolonged dry season.

A detailed study of the vegetation of Cameroon was carried out by Letouzey (1968) and the main vegetation types are shown in Figure 1. The western boundary of the Southern Bakundu Forest Reserve is only 2 km from Mboandong. In a survey of vegetation carried out in 1948, Richards (1963) described the Bakundu forest as 'essentially similar' to other African Mixed Rain Forests, though not uniform in structure or floristic composition. Patches of high forest with emergent trees occurred in a mosaic, together with gaps with less tall trees and climbing plants. The exceptionally high floristic diversity of this area prompted Richards (1963) to consider the region a likely refugium. Since the 1948 study of Bakundu, timber extraction has increased substantially and this has also been the case for the area around Mboandong. Figure 3 shows the vegetation in the immediate vicinity of the lake before 1976, but large tracts of forest have been entirely removed and plantations extended since that date. In December 1981, the lake was surrounded by a narrow band of rainforest including both primary and secondary forest elements. Most distinctive were *Musanga cecropioides*, *Raphia hookeri* and *Ceiba pentandra* (Baker et al. 1986). A few patches of relatively undisturbed rainforest remained on the eastern side of the lake. A small area had been cleared on the north western shore for cocoa plantations. Away from the lake, large areas of land had been cleared for cultivation including oil palm, banana, plantain, cocoa, rubber and cassava.

## 13.3 METHODS

A single core 13 m in length was collected from the approximate centre of the lake, using a modified Livingstone mud sampler (Livingstone 1955; Walker 1964) operated from a raft. Aluminium casing was used to prevent the coring rods from bending and to ensure that each section of the core was collected from precisely the same location. A continuous sediment core was obtained except for the interval 12.50-12.00 m which was lost. The core was extruded and described in the field, before being sealed in polythene sheet to prevent contamination and
<figure><img src="image_73.png" /><figcaption>**Figure 2.** Geological map of South-west Cameroon (modified after Gèze 1943) showing key geographical features and study sites. Note: the many additional volcanic cones formed in recent years in the Mount Cameroon vicinity (e.g. Wantim *et al.* 2013) are not shown.</figcaption></figure>

oxidation during transportation. Twenty-seven samples were prepared for pollen analysis using KOH (10%), cold HCl (20%) and warm HF (40%) following the methods described by Moore and Webb (1978). One Stockmarr *Lycopodium* tablet (batch 201890) was added to 30 g dry weight of sample. Samples were sieved using ultrasound to remove particles less than 5 µm in diameter and subject to brief acetolysis (using a 9:1 mix of acetic anhydride and sulphuric acid) and oxidation (using sodium chlorate and concentrated HCl). Residues were mounted in silicone oil. A minimum of 400 pollen and spores were counted for each sample, with the exception of 10.89 m where recovery of pollen was particularly low. All laboratory preparations were carried out at Hull University. Microscopy was carried out in 1983 and 1984 at Hull University and Duke University. Selected grains were photographed from ancient and modern reference material to ensure consistency in identification (Supporting Online Material [SOM] Plates I-V). Three bulk mud samples were radiocarbon dated and results recalibrated by Beta Analytic in October 2020 using the High Probability Density Range method (Bronk Ramsey 2009). The results of the pollen analysis were constrained by CONISS cluster analysis (Grimm 1987). The core was kept in cold storage at Hull University but unfortunately deteriorated and was disposed of around the year 2010 (Jane Bunting, personal communication, 2020).
<figure><img src="image_74.png" /><figcaption>**Figure 3.** Map showing vegetation and land-use around Mboandong prior to 1976. Adapted from 1:50,000 Map of Cameroon (Buea-Douala NB-32-IV sheets 1c and 1d), Centre Géographique National, Yaoundé.</figcaption></figure>

<table><caption>**Table 1.** Radiocarbon ages from Mboandong bulk mud samples. Ages were calibrated by Beta Analytic Inc. in October 2020 using the INTCAL13 database and High Probability Density Range Method (HPD). No adjustments for Reservoir Effect were made.</caption><thead><tr><th>Depth (m)</th><th>Reference</th><th><sup>14</sup>C age</th><th>δ<sup>13</sup>C (‰)</th><th>cal yrs BP (95%)</th></tr></thead><tbody><tr><td>4.37 – 4.48</td><td>Beta-11198</td><td>2380 ± 70</td><td>-26.01</td><td>2310 to 2720 cal BP</td></tr><tr><td>8.37 – 8.48</td><td>Beta-11197</td><td>4520 ± 90</td><td>-33.29</td><td>4870 to 5330 cal BP</td></tr><tr><td>12.93 – 12.98</td><td>Harwell-5028</td><td>5930 ± 110</td><td>-32.30</td><td>6480 to 7020 cal BP</td></tr></tbody></table>

## 13.4 SEDIMENT DESCRIPTION AND RADIOCARBON DATING

Between 13 m and 10 m the core exhibits several distinct lithological changes. At least two bands of volcanic ash occur (12.71 to 12.50 m, 12.00 to 11.80 m and 11.64 to 11.00 m). Samples from these intervals were prepared for pollen analysis but recovery was too low to give a useable count. The upper ash layer is directly overlain by a single bed of diatomite (11.00 m to 10.05 m). Above 10.05 m the core consists of uniform fine-grained organic mud. Results of radiocarbon dating are shown in Table 1. The base of the core produced a conventional <sup>14</sup>C age of 5930±110 BP which is within the range (95% confidence) of 6480 to 7020 cal yr BP. The radiocarbon dates appear to be fairly consistent and give no indication of any serious errors. The δ<sup>13</sup>C values of -26.01 to -32.30‰ suggest no significant old carbon contamination. Calcareous deposits of Cretaceous age do occur in the south-west province of Cameroon but the water catchment of Mboandong is more or less restricted to the overlying volcanic strata. In washing of old carbon from calcareous rock is therefore considered unlikely, especially as there are no rivers inflowing to the lake. No traces of modern rooted vegetation or other sources of possible modern carbon contamination were observed.
# 13.5 PALYNOLOGY

## 13.5.1 General observations

The results of the pollen analysis are given in Figure 4. Assigned names have been amended to follow the format of the African Pollen Database (Vincens et al. 2007). The pollen taxa have been grouped in ‘ecological groups’. However, as pollen determination has often only been possible to generic or even family level, this is problematic when a recorded pollen type could have originated from more than one life form or vegetation type. The groupings in Figure 4, therefore, represent the life forms and vegetation types from which the recorded pollen types are most probably derived, although this is an over-simplification. In making this zonation, reference has been made, in particular, to Hutchinson and Dalziel (1954–1968), Hall and Swaine (1981), Letouzey (1968, 1979), Nielson (1965) and Sowunmi (1981). Quite high percentages of the pollen sum are taken up by *Alchornea* and Urticaceae undiff. but no real ecological significance can be attributed to these widely fluctuating occurrences. *Alchornea* pollen is very probably derived mostly from *Alchornea cordifolia*, a common understory rainforest tree, which occurs also in swamp forest, montane forest and savanna. In Africa, Urticaceae are mainly forest herbs, some of which are very high pollen producers (Hamilton 1982). The pollen of this group is very small, often less than 10 µm in diameter, and may therefore have been subject to losses during decantation and ultrasonic sieving, although all samples were processed using identical methods. A complete list of pollen and spore taxa recorded is provided in SOM (Appendix I).

## 13.5.2 Pollen in relation to local vegetation

Due to time constraints, it was not possible to obtain information on modern pollen rain from the Mboandong locality. However, the near-surface sample from the lake core (0.14 m) probably gives a reasonable indication of modern pollen deposition at the site, and can be compared with floral data from the nearby Bakundu Forest Reserve. The floral data from Bakundu (Richards 1963) included all tree taxa over 30 cm girth, listed as number of boles in 15 size classes ranging from 30 cm to over 4.0 m. These data were re-calculated and compared to the pollen data from Mboandong (Richards 1987), grouped by family rather than genera or species, except where a finer taxonomic division could be detected in the pollen record. Percentage girth data from Bakundu were plotted against the percentage of total tree pollen at 0.14 m in the lake core. Considerable over representation in the pollen sample was shown by *Elaeis guineensis*, *Alchornea* and Urticaceae undiff. These taxa are part of the ‘local’ pollen element and were not recorded in the ‘regional’ tree data from Bakundu. The pollen to vegetation relationship can be summarised as follows: 1) Taxa which feature strongly in the pollen record but have limited or no representation at Bakundu. These include a) Trees associated with semi-deciduous forest, secondary forest or understory conditions such as *Ceiba pentandra*, *Macaranga*, *Lophira*, *Bosqueia*, *Musanga*, *Antidesma* and *Celtis*; b) Trees associated with swampy conditions such as *Uapaca*, *Raphia* and Palmae. These taxa are most probably derived predominantly from the forest edge, swamp forest or disturbed forest and therefore represent the local pollen element, mainly from around the study site. Rhizophoraceae pollen is most probably sourced from *Anopyxis klaineana*, a rainforest tree (a mangrove pollen source is unlikely). 2) Taxa which are represented in both the pollen record and the vegetation survey, and are therefore likely to be good indicators of the regional pollen component. These include: Anacardiaceae, Combretaceae, Euphorbiaceae e.g. *Phyllanthus*, *Rubiaceae*, *Pycnanthus*, *Coulea*, *Vitex* and Sapotaceae. 3) Tree taxa recorded in
<figure><img src="image_75.png" /></figure>

(Continued)
<figure><img src="image_76.png" /><figcaption>**Figures 4A and 4B.** Diagrams showing the pollen and spores recorded in the Mboandong study. Tree and shrub pollen are shown as a percentage of Arboreal Pollen (AP). Non Arboreal Pollen (NAP) from herbs and lianes are calculated as a percentage of AP+NAP. Spores are calculated as a percentage of AP+Spores. *Alchornea*, *Urticaceae undiff.* and undetermined pollen are excluded from all sums.</figcaption></figure>
the Bakundu forest that show little or no representation in the pollen record, such as Olacaceae, *Diospyros*, Irvingiaceae, *Cola*, Annonaceae, Flacourtiaceae, Sterculiaceae, Clusiaceae, Lecythidaceae, *Alstonia*, Violaceae and Lauraceae. These taxa may be entomophilous, have low pollen production and/or poor dispersal, have poor preservational properties or may have pollen that is not readily distinguishable. Most of the trees and shrubs represented in the pollen study were also identified in an ecological survey of the riparian vegetation around Lake Barombi Kotto, situated around 2 km to the north of Mboandong (Awo 2018).

### 13.5.3 Pollen diagram zonation

In this section values for trees and shrubs are percentages of Arboreal Pollen (AP) whereas values for non-arboreal pollen (NAP) are percentages of AP+NAP. Values for spores are percentages of AP+Spores. Pollen of *Alchornea*, Urticaceae undiff. and undetermined taxa are excluded from all sums. Values given for the 'ecological groups' are calculated in the same way. Zones are assigned on the basis of CONISS clusters (Figure 4). Age ranges shown are estimates based on extrapolation of the radiocarbon ages (Table 1).

#### 13.5.3.1 Zone MB I: 12.98 -9.88 m (6800–5600 BP)

This interval is characterised by the presence of common *Alchornea* (11-36%) and Urticaceae undiff. (up to 26%). Swamp forest elements are represented more or less consistently at between 28% and 36%, with a slightly lower abundance of 19% at 10.35 m. Important components in this interval are Rhizophoraceae undiff. (3-14%), *Macaranga*-type (3-8%), *Gilbertiodendron*-type (up to 4%) and Sapotaceae undiff. (up to 7%). Lowland forest elements are co-dominant in this interval with abundances of between 43% and 61%, with taxa including *Canarium*-type (up to 11%), *Lophira* (up to 15%), Irvingiaceae undiff. (up to 9%) and Olacaceae undiff. (less than 1%). Minor peaks occur in Meliaceae undiff. and *Coulea edulis*. Semi-deciduous forest elements are consistently represented at between 14% and 25%. Montane elements are consistent but rare with *Podocarpus milanjianus*-type occurring at 2% or less. Other taxa of note are *Uncaria*-type (up to 19%) which accounts for a significant proportion of NAP (21% at 10.89 m), and Celastraceae undiff. (up to 14%). Fern spores account for between 7% and 14% of the sum. Of the disturbance indicators, Poaceae pollen is rare (less than 1%) as are *Elaeis guineensis* (2-7%), *Trema*-type *orientalis* (up to 12%) and *Musanga*-type *cecropioides* (up to 3%).

#### 13.5.3.2 Zone MB II: 9.35–7.88 m (5400–4700 BP)

This interval is characterised by a predominance of pollen most probably from lowland forest and swamp arboreal sources. Frequent *Canarium*-type pollen (up to 18%) occurs together with common *Gilbertiodendron*-type (up to 12%) and Sapotaceae undiff. (up to 9%). Pollen from likely swamp elements is common, occurring fairly consistently at 28% to 37%. Important components of this group are *Uapaca* which reaches 15% at 7.88 m and Rhizophoraceae undiff. (up to 7%). The lowland forest elements are dominant in this interval, ranging between 42 and 56%, and include *Maytenus* (up to 2%), *Antidesma*-type (up to 2%) and Olacaceae undiff. (up to 2%) and many other rare but consistent taxa. Other important pollen types present include two types thought likely to originate from the Anacardiaceae family: *Sclerocarya*-type is consistently common (5-11%), and *Trichoscypha*-type occurs rarely (less than 1%) but is more or less restricted to this interval of the core, as is *Icacinaceae* undiff. (3% at 9.35m). Pollen of *Cissus* (up to 7%), derived from a climber in the Vitaceae family, and fern spores (up to 20%) are consistent in this interval. Semi-deciduous forest elements are less common but occur consistently at between 15% and 22% and include *Pycnanthus angolensis*-type (up to 10%). Poaceae, *Elaeis guineensis* and *Musanga*-type *cecropioides* pollen are present in very low numbers.
#### 13.5.3.3 Zone MB III: 7.35–6.88 m (4400–4100 BP)

*Elaeis guineensis* pollen ranges from 1% or less to 6% at 7.35 m. A slight increase in Poaceae pollen (7%) occurs in the same sample, which is also marked by the first major increase in *Musanga* comp. (16%).

#### 13.5.3.4 Zone MB IV: 6.35–4.88 m (3700–2800 BP)

This interval is characterised by a common presence of pollen of *Canarium*-type (up to 18%), *Gilbertiodendron*-type (up to 13%) as well as a strong input from the Urticaceae, Rubiaceae and Euphorbiaceae families. A slight increase in *Podocarpus milanjianus*-type occurs. Poaceae and *Elaeis guineensis* pollen occur but are not frequent (maximum of 3% and 7% respectively). Monolete fern spores are consistently present (6-11%).

#### 13.5.3.5 Zone MB V: 4.35–3.35 m (2500–1900 BP)

This interval is characterised by the overwhelming abundance of grass pollen (family Poaceae) which shows a gradual increase towards the top of the interval where it accounts for 50 to 53% of the pollen sum. Consistent *Elaeis guineensis* (up to 14%) is also present together with other disturbed forest types such as *Musanga*-type *cecropioides* (up to 17%). This interval is also marked by an increase in fern spores (up to 32%). Lowland rainforest elements are also reduced (24-40%) and are characterised by a large number of rare taxa such as *Lophira* (up to 13%) and *Antidesma*-type (up to 6%). Also of interest is the presence of pollen types associated with disturbed or semi-deciduous forest (up to 62%), notably *Trilepisium*-type *madagascariense* [=*Bosqueia angolensis*] (up to 36%), which occurs with *Milicia*-type [=*Chlorophora*] (up to 30%), *Trema*-type *orientalis* (up to 4%) and *Celtis* (up to 9%).

#### 13.5.3.6 Zone MB VI: 2.88–0.14m (1700–100 BP)

This interval is characterised by the presence of abundant *Elaeis guineensis* (up to 44%), and by common Poaceae (up to 15%) but with lower proportions of grass pollen than in Zone MB V. Pollen of understory and/or secondary forest trees, such as *Alchornea* (up to 26%), is well represented. Lowland rainforest elements occur fairly consistently (18-40%) as do swamp forest elements (13-27%). Also notably common is tree pollen from disturbed or semi-deciduous vegetation (up to 69%), including *Trilepisium*-type *madagascariense* (up to 11%), *Pycnanthus angolensis*-type (up to 11%) and *Celtis* (up to 9%). Pollen of *Ceiba pentandra*, another semi-deciduous tree, occurs commonly (up to 11%) towards the top of this interval.

## 13.6 INTERPRETATION

### 13.6.1 The pollen and sedimentary record

Zone MB I (12.98-9.88 m; 6800-5600 BP) contains a mixture of sediment types including at least two volcanic ash deposits and thin layers of organic mud, which are overlain by a 1 m thick deposit of diatomite (11.00 m to 10.05 m). In all likelihood, the diatomite has formed in response to an increased availability of silica, provided by volcanic ash fall, as described by Wallace *et al.* (2006). It is likely that the ash originates from nearby Mount Cameroon which has been active during the Late Quaternary: at least 19 eruptive events of the mountain have been documented during the last 2500 years (Global Volcanism Program 2013). No confirmed eruption dates are known from the earlier part of the Holocene. On the basis of the radiocarbon age obtained at the base of the core, the volcanic ash in the core would have been deposited between approximately 7000 and 6000 years ago. This is several thousand years after the ash layers recorded in core at Barombi Mbo (Maley *et al.* 1991). Interpretation of the vegetation is problematic due to the very high numbers of *Alchornea* and Urticaceae pollen. The vegetation
close to the site probably consisted of mixed primary and secondary rainforest with some swamp
elements. The very slight increase of *Trema*-type *orientalis* and *Uncaria*-type pollen at 10.89 m,
immediately above the uppermost of the volcanic ash bands, may be due to localised volcanic
disturbance. Very low levels of *Musanga*-type *cecropioides* and Poaceae pollen argue against
widespread forest disturbance at this time.

Zone MB II (9.35-7.88 m; 5400-4700 BP) is characterised primarily by humid forest and swamp tree taxa, whereas Zone MB III (7.35-6.88 m; 4400-4100 BP) probably marks the first significant forest disturbance around the study site, after 4400 BP, on the basis of frequent *Musanga*-type *cecropioides* pollen. A mechanism for disturbance cannot be established with certainty, but man cannot be ruled out. The overall pollen assemblages are consistent with predominantly lowland humid rainforest with swamp and regrowth components. Zone MB IV (6.35-4.88 m; 3700-2800 BP) contains mostly tree pollen indicating closed forest. The co-occurrence of Caesalpiniaceae (*Gilbertiodendron*-type), *Canarium*-type and *Podocarpus milanjianus*-type in this interval at Mboandong is explained by Giresse *et al.* (2020) as ‘cloud forest effect’ due to increased condensation associated with cooling temperatures. This may also account for the relatively high numbers of fern spores recorded.

The main feature of Zone MB V (4.35-3.35 m; 2500-1900 BP) is the major increase in pollen from Poaceae and semi-deciduous tree taxa such as *Trilepisium*-type *madagascariense* and *Milicia*-type. Swamp elements and lowland rainforest elements are reduced, whereas fern spores are frequent. The overall assemblages from this interval conclusively show a change from humid rainforest to more open vegetation, characterised mainly by Poaceae and semi-deciduous trees. The uppermost interval Zone MB VI (2.88-0.14 m; 1700-100 BP) shows a marked decrease in Poaceae pollen offset by significantly increased *Elaeis guineensis*. Most tree pollen taxa are represented although it is not possible to determine if these arboreal taxa (e.g. Rhizophoraceae undiff., *Macaranga*-type and *Uapaca*) are derived from the immediate vicinity of the lake or from further afield. Reduced percentages of lowland and swamp forest taxa are balanced by an increase in semi-deciduous elements. The overall assemblage points to forest disturbance with likely cultivation of oil palm, *Elaeis guineensis*.

13.6.2 Vegetation representation

13.6.2.1 Swamp pollen

A fairly consistent input of pollen from swamp sources suggests that a fringing element of swamp
forest has persisted around the lake for much of the last 7000 years. Scattered *Raphia* palms
overhang the lake at the present time although, from the low levels of *Raphia* pollen recorded,
it is unlikely that extensive ‘*raphiales*’ swamp communities existed by the lake. Rhizophoraceae
pollen is probably derived predominantly from trees such as *Anopyxis klaineana* which are known
to occur in freshwater conditions, rather than as a result of long-distance transport from mangrove.

13.6.2.2 Montane pollen

Podocarpus pollen is an important indicator of Afromontane vegetation (Lézine et al. 2013a). *Podocarpus milanjianus* [=P. latifolius, Migliore et al. 2020] grows on several West African mountains but it is apparently absent from Mount Cameroon (Letouzey 1968). Low levels of this easily dispersed pollen type occur in the Mboandong core, but had *Podocarpus* occurred on Mount Cameroon at any time during the last 7000 years, the amount of *Podocarpus* pollen recorded in the lake is likely to have been greater. The small amount of *Podocarpus* pollen present in the lake core may be derived from the Roumpi Hills, some 15 km to the north or from peaks such as Mount Koupé which supports well-developed montane vegetation (Baker 1986).
#### 13.6.2.3 Dry forest pollen

Trees such as *Triplochiton scleroxylon* and *Celtis*, which are characteristic of semi-deciduous forest, may also occur in disturbed areas within humid forest. *Trilepisium*-type *madagascariense* pollen, common in the upper regions of the core in Zones MB-V and MB-VI (after 2500 cal yr BP), is most probably derived from *Bosqueia angolensis*, a tree characteristic of semi-deciduous forest patches within the humid rainforest zone. Pollen of *Milicia*-type is probably derived from the semi-deciduous tree *Chlorophora excelsa* (Moraceae), although the pollen is also morphologically similar to *Antiaris toxicaria*. *Milicia*-type pollen is notably rare in the core with the exception being a brief peak at around 4 m (c. 2500-2300 BP). This may be related to a short-lived abundance of these trees at around the time when the first signs of major forest disturbance are visible, or a response to a drying climate. The slight increase in *Ceiba pentandra* pollen in the uppermost few samples in the core may be due to species protection because of its useful 'kapok'. The consistent presence of *Ceiba pentandra* pollen appears to confirm the view of Baker (1965) who considered the species native to West Africa; Hutchinson and Dalziel (1954–1968) and others believed it to be an ancient introduction from tropical America.

## 13.7 DISCUSSION

### 13.7.1 Floristic affinities of forest communities

The pollen assemblages indicate that predominantly humid rainforest has been present in the Mboandong region for the last 7000 years, although it is unclear whether the rainforest represented is of the Atlantic or Congo type, as defined by Letouzey (1968). Two important tree pollen taxa well represented in the lower intervals of the core are *Gilbertiodendron*-type and Sapotaceae undiff. The pollen attributed to *Gilbertiodendron*-type is fairly large and coarsely striate but several other genera within the Caesalpiniaceae produce similar pollen. These include *Berlinia*, *Didelotia*, *Microberlinia* and others which are all genera characteristic of the Atlantic type forest, although none of these trees were found during the floristic survey of Bakundu by Richards (1963). *Gilbertiodendron*-type pollen occurs only very rarely in the upper reaches of the core. If, therefore, forest of the Atlantic type has persisted in the Mboandong region for the last 6000 or more years, the marked decline in *Gilbertiodendron*-type pollen after c. 3000 BP must be linked either to the direct felling of these leguminous trees or the overwhelming dominance of pollen such as Poaceae and *Elaeis guineensis*.

Another possibility is that the abundance of *Gilbertiodendron*-type pollen in the lower regions of the core is from *Gilbertiodendron dewevrei*. This species is characteristic of Congo type forests and is now mainly restricted to the Dja region of south-east Cameroon, (Letouzey 1968). It is possible, therefore, that the former distribution of this forest type may have extended further westwards than at present. The abundance of pollen from the Sapotaceae family shows an almost identical pattern of distribution in the core to *Gilbertiodendron* comp. Both are more common in the basal parts of the core, with peaks at 8.35 m and 5.90 m. It is possible that the Sapotaceae pollen in the basal parts of the core could be derived from *Baillonella toxisperma*, a large forest emergent which shows similar relict distribution patterns to *Gilbertiodendron dewevrei* in Cameroon (Letouzey 1968). *Baillonella toxisperma* was the only Sapotaceae species of note recorded at Bakundu (Richards 1963), occurring as a single, very large individual tree. It is certainly possible that forest of Congo floristic affinities could have occurred in the vicinity of Mboandong, particularly between c. 5400 and 2500 BP. The 'relict' characteristics evident both at Bakundu (Richards 1963) and in the present distributions shown by *Gilbertiodendron dewevrei* and *Baillonella toxisperma* (Letouzey 1968) are certainly consistent with a more extensive distribution of Congo type forest in the past.
Letouzey (1968) also lists *Alchornea floribunda* and *Trichoscypha congoensis* [= *T. acuminata*] as characteristic species of the Congo type forest, although they also occur in Atlantic type forest. *Alchornea* pollen is common throughout the Mboandong core and is therefore of little interpretative value. Pollen attributed to *Trichoscypha*-type is present in the core but only in low quantities in Zones MB I, II and III where *Gilbertiodendron* comp. and Sapotaceae are common. Its presence, therefore, is consistent with, but not indicative of, a Congo type forest. Similarly, pollen of *Cissus* and Celastraceae undiff. are also common in Zones MB I, II, III and IV. These are also taxa considered characteristic of Congo forest by Letouzey (1968) but they are not exclusive to it. In view of the large degree of species overlap between the Congo and Atlantic forest types, the distinction between the two forest types could at least partly be due to the higher degree of forest disturbance in the western regions of the country, as noted by Letouzey (1968). The boundary between Zones MB-IV and MB-V which sees a sharp decline in the abundance of *Gilbertiodendron*-type and Sapotaceae pollen also coincides with increased evidence of forest disturbance. Additionally, the Atlantic forest occurs in the region with a more seasonal (i.e. periodically dry) climate whereas the Congo forest occurs in the more equatorial (i.e. everwet) regions. Hence a climatic control may partly account for the present distributions and species composition of Atlantic and Congo type forests.

## 13.7.2 Climate change or man-made change?

The cause of the vegetation change evident at Mboandong after 2500 cal yr BP is the subject of ongoing debate. Increasing aridity is invoked by Maley (2002) and Lebamba et al. (2012) as the primary cause of the ‘late Holocene rainforest crisis’ between 2500 and 2000 years BP. Bostoen et al. (2013, 2015) provided supporting evidence based on linguistics. Garcin et al. (2018), in a study from Lake Barombi, however, considered that the ‘late Holocene rainforest crisis’ after 2600 cal yr BP was a consequence of human activity, and resulted from the Bantu expansion, although this view was countered by Clist et al. (2018). The balance of pollen-based evidence from multiple sites presented by Giresse et al. (2020), however, does support a drying climate as the initial cause of the change from forest-dominated to grass-dominated vegetation around 2500 years ago. This is a change of emphasis from that previously reported by Richards (1986, 1987) but is made on the basis of the wealth of new information from the region that has become available since that time.

The significant increase of *Elaeis guineensis* pollen (up to 23%) after c. 1700 BP suggests that cultivation of oil palm was taking place in the Mboandong region. Low levels of this pollen type in the lower reaches of the core may represent the natural presence in forest clearings. The relationship between the oil palm (*Elaeis guineensis*) and the ‘atili tree’ (*Canarium schweinfurthii*) is also relevant (Lézine et al. 2013b; Maley and Chepstow-Lusty 2001; Sowunmi 1999). At Mboandong, based on pollen evidence, *Canarium* appears to have been more important in the past than at the present time, as *Canarium*-type pollen accounts for 13% of the pollen sum at 8.35 m, around 5000 years ago, but is absent from the uppermost levels of the core, where *Elaeis* is an important element. Based on comparisons with reference pollen, it is almost certain that the pollen recorded in the core is from *Canarium schweinfurthii*. It is therefore highly likely that *Canarium* was a significant oil-food source in Cameroon, very probably an example of ‘vegeculture’ which has subsequently declined with *Elaeis* cultivation within the last 2000 years.

## 13.8 CONCLUSIONS

Early Holocene volcanic activity in the region, presumably of Mount Cameroon, is indicated by the deposits of volcanic ash in the Mboandong core deposited between c. 7000 and 6000 years
ago. Silica from the ash enabled a bloom of diatoms, resulting in a 1 m thick diatomite deposited after c. 6000 cal yr BP. Since this time, the Mboandong lake sediments consist of fine-grained organic mud, but with no evidence of any hiatus or other significant stratigraphic change. No signs of the lake having dried out within the last 7000 years were observed. From extrapolation of the radiocarbon ages, a fairly rapid sedimentation rate of at least 1 m per 500 years is indicated, equivalent to c. 2 mm per year.

The pollen record indicates that predominantly forested conditions have prevailed in the vicinity of the study site throughout much of the Holocene. Between c. 7000 and 2500 cal yr BP, the dominant forest elements were derived from humid rainforest and swamp forest, with important components derived from the Caesalpiniaceae and Sapotaceae families. There is a possible affinity with Congo type (everwet) rainforest although this is speculative. During this time, man was probably encouraging the tree *Canarium schweinfurthii* as an oil-rich food source. After c. 2500 cal yr BP increased proportions of regrowth and semi-deciduous trees are represented in the pollen record, possibly indicating a change to an Atlantic type (more seasonal) rainforest, as occurs in undisturbed vegetation in West Cameroon at the present time. Pollen from the oil palm *Elaeis guineensis* occurs throughout the Mboandong core, initially in low numbers, suggesting that oil palm at first grew naturally as secondary forest element. Significantly increased numbers of *Elaeis* pollen recorded after 1700 cal yr BP are indicative of cultivation. The oil palm *Elaeis guineensis* appears to have replaced the *atili* tree *Canarium schweinfurthii* as a source of oil-rich food.

A major increase in Poaceae pollen at c. 2500 cal yr BP is most probably linked to a drying climate, as has now been inferred at numerous sites in tropical West Africa. This climate change probably led to subsequent forest clearance, shown by an increase in pollen from semi-deciduous trees such as *Bosqueia angolensis*, which probably exploited openings within the forest canopy. This time-line is broadly consistent with dates for iron working in West Africa, which suggests that iron technology and forest clearance were related events, but which followed on from a climatic shift. It is quite conceivable that a significant drying within northern and western Africa within the last few thousand years prompted movement of people southwards to more humid latitudes. It is probable that iron technology was also disseminated at this time, perhaps linked to the migration of the Bantu people. This regional picture from West Africa is supported by the pollen data from Mboandong and suggests that a changing climate first affected the regional vegetation around 2500 years ago, followed by forest clearance and crop cultivation within the last 2000 years.

## ACKNOWLEDGEMENTS

This work was carried out as an MSc project in the early 1980's at the Department of Geography, University of Hull under the guidance of the late John R. Flenley and at Duke University, assisted by the late Daniel A. Livingstone. This paper is dedicated to the fond memory of both JRF and DAL whose support and encouragement made this study possible. Partial funding was received from The Nuffield Foundation. Margaret Adebisi Sowunmi (University of Ibadan) provided assistance with some pollen identification and Miranda Awo (University of Buea) provided up-to-date information on the vegetation, plant-pollen relationships and limnology of the study region. Ron Hatfield of Beta Analytic Inc. assisted with calibration of radiocarbon ages. Figures 2 and 3 were re-drawn by Alison Davies of The Mapping Company, UK, from originals drawn at Hull University, Department of Geography. The core was collected by members of the 1981-82 Hull University Cameroon Expedition: Richard Baker, Stephen Compton, Sean Edwards, David Newsome, Keith Richards, Carrie Rimes, Howard Smith and Ollie Smith.
REFERENCES

Aubréville, A., 1949, *Climats. Fôrets et desertification de l'Afrique tropicale*. Paris : Société d'Editions Géographiques, Maritimes et Coloniales, Paris.

Awo M.E., 2018, *Water quality, riparian vegetation and algal ecological assessment of Lake Barombi Kotto, Cameroon*. PhD Thesis, University of Buea, Cameroon.

Awo, M.E., Tabot, P.T., Goodenough, N. and Ambo, F.B., 2018, Spatiotemporal variation of phytoplankton community structure in the crater lake Barombi Kotto, Cameroon. *International Journal of Current Research in Biosciences and Plant Biology*, **5**(2), pp. 36–55, 10.20546/ijcrbp.2018.502.005.

Awo, M.E., Fonge, B.A., Tabot, P.T. and Akoachere, J.T.K., 2020, Water quality of the volcanic crater lake, Lake Barombi Kotto, in Cameroon. *African Journal of Aquatic Sciences*, **45**(4), pp. 401–411, 10.2989/16085914.2020.1737799

Baker, H.G., 1965, The evolution of the cultivated Kapok tree; a probable west African product. In *Ecology and Economic Development in Tropical Africa* edited by Brokensha, D., pp.185–216. Institute of International Studies, University of California.

Baker, R.G.E, 1986, Introduction: The Biogeography of Cameroon. University of Hull Department of Geography Miscellaneous Series, **30**, pp. 1–13.

Baker, R.G.E., Richards, K., and Rimes, C.A., 1986, The Hull University Cameroun Expedition: 1981-82 Final Report. University of Hull Department of Geography Miscellaneous Series, **30**, pp. 1–109.

Bostoen, K., Clist, B., Doumenge, C., Grollemund, R., Hombert J.-M., Muluwa, J.K. and Maley, J. 2015, Middle to Late Holocene paleoclimate change and the early Bantu expansion in the rain forests of western central Africa. *Current Anthropology*, **56**(3), pp. 354–384, 10.1086/681436.

Bostoen, K., Grollemund, R., Hombert J.-M. and Muluwa, J.K., 2013, Climate-induced vegetation dynamics and the Bantu Expansion: Evidence from Bantu names for pioneer trees (*Elaeis guineensis*, *Canarium schweinfurthii*, and *Musanga cecropioides*). *Comptes Rendus Geoscience*, **345**, pp. 336–349, 10.1016/j.crte.2013.03.005.

Bronk Ramsey, C., 2009, Bayesian analysis of radiocarbon dates. *Radiocarbon* **51**(1), pp. 337–360, 10.1017/S0033822200033865.

Clist, B., Bostoen, K., de Maret, P., Eggert, M.K., Höhn, A., Mindzié, C.M., Neumann, K. and Seidensticker, D., 2018, Did human activity really trigger the late Holocene rainforest crisis in Central Africa? *Proceedings of the National Academy of Sciences*, **115**(21), pp. E4733–E4734, 10.1073/pnas.1805247115.

Corbet, S.A., Green, J., Griffith, J. and Betney, E., 1973, Ecological studies on crater lakes in west Cameroon: Lakes Kotto and Mboandong. *Journal of Zoology*, **170**, pp. 309–324, 10.1111/j.1469-7998.1973.tb01380.x.

Duke, B.C.L. and Moore, P.J., 1971, The control of *Schistosoma haematobium* in west Cameroon. *Transactions of the Royal Society of Tropical Medicine and Hygiene*, **65**, pp. 841–843, 10.1016/0035-9203(71)90106-4.

Dupont, L.M., Jahns, S., Marret, F. and Ning, S. 2000, Vegetation change in equatorial West Africa: time-slices for the last 150ka. *Palaeogeography, Palaeoclimatology, Palaeoecology*, **155**, pp. 95–122, 10.1016/S0031-0182(99)00095-4.

Garcin, Y., Deschamps, P., Ménot, G., de Saulieu, G., Schefuß, E., Sebag, D., Dupont, L.M., Oslisly, R., Brademann, B., Mbusnum, K.G., Onana, J.-M., Akon, A.A., Epp, L.S., Tjallingii, R., Strecker, M.R., Brauer, A. and Sachse, D., 2018, Early anthropogenic impact on Western Central African rainforests 2,600 y ago. *Proceedings of the National Academy of Sciences*, 115(13), pp. 3261–3266, 10.1073/pnas.1715336115.

Gèze, B., 1943, Géographie physique et géologie du Cameroun occidental. *Mémoires du Muséum Nationale d'Histoire Naturelle Paris*, **17**, pp. 1–272.
Giresse, P., Maley, J. and Chepstow-Lusty, A., 2020, Understanding the 2500 yr BP rainforest crisis in West and Central Africa in the framework of the Late Holocene: pluridisciplinary analysis and multi-archive reconstruction. *Global and Planetary Change*, **192**, article: 103257, 10.1016/j.gloplacha.2020.103257.

Global Volcanism Program, 2013, Cameroon (224010) in Volcanoes of the World, v. 4.9.1 (17 Sep 2020) Smithsonian Institution. Downloaded 09 Oct 2020. https://volcano.si.edu/volcano.cfm?vn=224010

Green, J., 1972, Ecological studies on crater lakes in west Cameroun: zooplankton of Barombi Mbo, Mboandong, Lake Kotto and Lake Soden. *Journal of Zoology*, **166**, pp. 283–301, 10.1111/j.1469-7998.1972.tb03099.x.

Grimm, E.C., 1987, CONISS: a FORTRAN 77 program for stratigraphically constrained cluster analysis by the method of incremental sum of squares. *Computers and Geosciences*, **13**(1), pp. 13–35, 10.1016/0098-3004(87)90022-7.

Hall, J.B. and Swaine, M.D., 1981, *Distribution and Ecology of Vascular Plants in a Tropical Rain Forest: Forest Vegetation in Ghana*. Geobotany Series 1 (The Hague and London: Junk).

Hamilton, A.C., 1976, The significance of patterns of distribution shown by forest plants and animals in tropical Africa for the reconstruction of Upper Pleistocene palaeoenvironments: a review. *Palaeoecology of Africa*, **9**, pp. 63–97.

Hamilton, A.C., 1982, *Environmental History of East Africa. A Study of the Quaternary*, (London: Academic Press).

Hawkins, P. and Brunt, M., 1965, *The Soils and Ecology of West Cameroon*, Volumes 1-2, Food and Agriculture Organization of the United Nations, report 2083, Rome, pp. 1–516.

Hutchinson, J. and Dalziel, J.M., 1954–1968, *Flora of West Tropical Africa*, Edition 1, (London: Crown Agents for Overseas Governments).

Keay, R.W.J., 1959, *An Outline of Nigerian Vegetation*, (Lagos: Federal Government Printer) pp. 1–34.

Lebamba, J., Vincens, A. and Maley, J., 2012, Pollen, vegetation change and climate at Lake Barombi Mbo (Cameroon) during the last ca. 33,000 cal. yr BP: a numerical approach. *Climate of the Past*, **8**, pp. 59–78, 10.5194/cp-8-59-2012.

Letouzey, R., 1968, *Etude Phytogéographique du Cameroun*, Encyclopédie Biologique, volume LXIX, Paris.

Letouzey, R., 1979, Végétation. In *Atlas de la République Unie du Cameroun*, edited by Laclavère, G. (Paris: Editions Jeune Afrique).

Lézine, A.-M., Assi-Kaudjhis, C., Roche, E., Vincens, A. and Achoundong, G., 2013a, Towards an understanding of West African montane forest response to climate change. *Journal of Biogeography*, **40**(1), pp. 183–196, 10.1111/j.1365-2699.2012.02770.x.

Lézine, A.-M., Holl, A.F.C., Lebamba, J., Vincens, A., Assi-Khaudjis, C., Février, L. and Sultan, E., 2013b, Temporal relationship between Holocene human occupation and vegetation change along the northwestern margin of the Central African rainforest. *Comptes Rendus Geoscience*, 345(7–8), pp. 327–335, 10.1016/j.crte.2013.03.001.

Lézine, A.-M., Izumi, K., Kageyama, M. and Achoundong, G., 2019, A 90,000-year record of Afromontane forest responses to climate change. *Science* **363**, pp. 177–181, 10.1126/science.aav6821.

Livingstone, D.A., 1955, A light-weight piston sampler for lake deposits. *Ecology*, **36**, pp. 137–139, 10.2307/1931439.

Maley, J., 2002, A catastrophic destruction of African forests about 2,500 years ago still exerts a major influence on present vegetation. *Institute of Development Studies Bulletin*, **33**(1), pp. 13–30, 10.1111/j.1759-5436.2002.tb00003.x.

Maley, J. and Brenac, P., 1998, Vegetation dynamics, palaeoenvironments and climatic changes in the forests of West Cameroon during the last 28,000 years. *Review of Palaeobotany and Palynology*, **99**, pp. 157–88, 10.1016/S0034-6667(97)00047-X.
Maley, J., Livingstone, D.A., Giresse, P., Brenac, P., Kling, G., Stager, C., Thouveny, N., Kelts, K., Haag, M., Fournier, M., Bandet, Y., Williamson, D. and Zogning, A., 1991, West Cameroon Quaternary lacustrine deposits: preliminary results. *Journal of African Earth Sciences*, **12** (1-2), pp. 147-157, 10.1016/0899-5362(91)90065-7.

Maley, J. and Chepstow-Lusty, A., 2001, *Elaeis guineensis* Jacq. (oil palm) fluctuations in central Africa during the late Holocene: climate or human driving forces for this pioneering species? *Vegetation History and Archaeobotany*, **10**, pp. 117-20, 10.1007/PL00006920.

Migliore, J., Lézine, A.-M. and Hardy, O.J., 2020, The recent colonization history of the most widespread *Podocarpus* tree species in Afromontane forests. *Annals of Botany*, **126**, pp. 73-83, 10.1093/aob/mca049.

Miller, C.S. and Gosling, W.D., 2014, Quaternary forest associations in lowland tropical West Africa. *Quaternary Science Reviews*, **84**, pp. 7-25, 10.1016/j.quascirev.2013.10.027.

Moore, P.D. and Webb, J.A., 1978, *An Illustrated Guide to Pollen Analysis*, (London: Hodder and Stoughton), pp. 1-133.

Nielson, M.S., 1965, *Introduction to the Flowering Plants of West Africa*, (London: University of London Press), pp. 1-246.

Richards, K., 1986, Preliminary results of pollen analysis of a 6,000 year core from Mboandong, a crater lake in Cameroun. *University of Hull Department of Geography Miscellaneous Series*, **30**, pp. 14-28.

Richards, K., 1987, *A palynological study of the Late Quaternary vegetational history of Mboandong, a lowland lake in Cameroun*. MS. Thesis, University of Hull.

Richards, P.W., 1963, Ecological notes on West African vegetation, II. Lowland forest of the Southern Bakundu Forest Reserve. *Journal of Ecology*, **51**, pp. 123-149, 10.2307/2257510.

Rossignol-Strick, M. and Duzer, D., 1979, West African vegetation and climatic change since 22,500 BP from deep-sea cores palynology. *Pollen et Spores 21*, pp. 105-134.

Sowunmi, M.A., 1981, Aspects of the late Quaternary vegetational changes in West Africa. *Journal of Biogeography*, **8**, pp. 457-474, 10.2307/2844565.

Sowunmi, M.A., 1999, The significance of the oil palm (*Elaeis guineensis* Jacq.) in the late Holocene environments of west and west central Africa: a further consideration. *Vegetation History and Archaeobotany*, **8**, pp. 199-210, 10.1007/BF02342720.

Talbot, M.R., Livingstone, D.A., Palmer, P.G., Maley, J., Melack, J.M., Delibrias, G. and Guilliksen, S., 1984, Preliminary results from sediment cores from Lake Bosumtwi, Ghana. *Palaeoecology of Africa*, **16**, pp. 173-192.

Vincens, A., Lézine, A.M., Buchet, G., Lewden, D. and Le Thomas, A., 2007, African pollen database inventory of tree and shrub pollen types. *Review of Palaeobotany and Palynology*, **145**(1-2), pp. 135-141, 10.1016/j.revpalbo.2006.09.004.

Vincens, A., Schwartz, D., Elenga, H., Reynaud-Farrera, I., Servant, M. and Wirrmann, D., 1999, Forest response to climate changes in Atlantic Equatorial Africa during the last 4000 years BP and inheritance on the modern landscapes. *Journal of Biogeography*, **26**, pp. 879-85, 10.1046/j.1365-2699.1999.00333.x.

Walker, D., 1964, A modified Vallentyne mud sampler. *Ecology*, **45**, pp. 642-644, 10.2307/1936118.

Wallace, A. R., Frank, D. G. and Founie, A., 2006, Freshwater diatomite deposits in the western United States. *US Department of the Interior, US Geological Survey Fact Sheet* 2006-3044.

Wantim, W.N., Kervyn, M., Ernst, G.G.J., Del Marmol, M.-A., Suh, C.E. and Jacobs, P., 2013, Morpho-Structure of the 1982 Lava Flow Field at Mount Cameroon Volcano, West-Central Africa. *International Journal of Geosciences*, **4**, pp. 564-583, 10.4236/ijg.2013.43052.

White, F., 1983, *The vegetation of Africa*, (Paris: UNESCO).

Zahra, A., 1953, Some notes on the incidence of *Schistosomiasis* in the southern Cameroons. *West African Medical Journal*, **2**(1), pp. 26-29.
<figure><img src="image_77.png" /></figure>

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CHAPTER 14

# Future directions of palaeoecological research in the hyper-diverse Cape Floristic Region: The role of palynological studies

Lynne J. Quick

*African Centre for Coastal Palaeoscience, Nelson Mandela University, Port Elizabeth, South Africa*

**ABSTRACT:** The Cape Floristic Region (CFR) is a key focus area within southern Africa due to its botanical importance in terms of high levels of biodiversity as well as its rich cultural and archaeological heritage. The area is sensitive to cycles of regional and global environmental change, and palynological records obtained from the region can potentially provide valuable information regarding past vegetation dynamics and climate variability. Prior to the last decade, few high resolution palaeoenvironmental records were recovered from the CFR, and therefore its Late Quaternary environmental history was previously poorly understood. Significant progress was made over recent years and a considerable body of new palynological (as well as palaeoclimatological) evidence emerged. These new records provide greater insight into the nature and timing of past vegetation shifts and improve our understanding of how different subregions of the CFR have responded to past climate changes. They also highlight that there is a much higher degree of complexity, in terms of both vegetation and climate change, than previously thought. This paper provides a perspective on the progress made towards elucidating the palaeoecological history of the CFR, it highlights the importance of continuing and expanding upon the existing body of work and outlines current and future directions for palynological research in this hyper-diverse southwestern corner of southern Africa.

## 14.1 INTRODUCTION

The southwestern Cape of South Africa has long been recognized as a unique region particularly in terms of the distinctiveness of its flora. Due to the extraordinarily high levels of biodiversity and plant endemism found within the region it has previously been recognized as one of the six global floral kingdoms (Marloth 1908; Takhtajan *et al.* 1986) and is currently world-renowned as a key biodiversity hotspot (Mittermeier *et al.* 2004; Myers *et al.* 2000). The Fynbos Biome (Figure 1) and Cape Floristic Region (CFR) are considered by many to be synonymous, however the 'biome' refers only to the two dominant vegetation groups (Fynbos and Renosterveld), whereas the 'region' refers to the general geographical area and includes other vegetation types in the Afrotemperate Forest, Nama Karoo, Succulent Karoo and Albany Thicket Biomes. The CFR has been the subject of decades of intensive botanical research due to its unique floristic, evolutionary and ecological characteristics as well as its conservation appeal (e.g. Allsopp *et al.* 2014; Bond and Goldblatt 1984; Campbell 1985; Cowling 1992; Cowling *et al.* 1995; Goldblatt and Manning 2000; Rebelo *et al.* 2006).
<figure><img src="image_78.png" /></figure>

**Figure 1.** The distribution of fossil pollen records for the Cape Floristic Region (as demarcated by the Fynbos Biome) (A) and in relation to seasonality of rainfall (defined in terms of percentage of winter rainfall) (B). Records are grouped according to subregion and the type of archive is demarcated in brackets after the site name (w = wetland deposit; h = hyrax midden and c = cave deposit, hc = hyena coprolites). 1 – Groenkloof (w) (MacPherson *et al.* 2018), 2 – Pakhuis Pass (h) (Scott and Woodborne 2007b, a), 3 – De Rif (h) (Quick *et al.* 2011; Valsecchi *et al.* 2013), 4 – Driehoek and Sneeuberg vleis (w) (Meadows and Sugden 1991), 5 – Truitjes Kraal 4 (h) (Chase *et al.* 2015b; Meadows *et al.* 2010), 6 – Katbakkies 1 (h) (Chase *et al.* 2015b; Meadows *et al.* 2010), 7 – Vankraal Spring (w) (Forbes *et al.* 2018), 8 – Elands Bay Cave (c) (Meadows and Baxter 1999), 9 – Grootdrift (w) (Meadows *et al.* 1996), 10 – Klaarfontein Springs (w) (Meadows and Baxter 2001), 11 – Rietvlei (w) (Schalke 1973), 12 – Cecilia Cave (c) (Meadows and Baxter 1999), 13 – Princessvlei (w) (Cordova *et al.* 2019; Neumann *et al.* 2011), 14 – Cape Flats (w) (Schalke 1973), 15 – Cape Hangklip (w) (Schalke 1973), 16 – Voelvlei (w) (Carr *et al.* 2006), 17 – Soetendalsvlei (w) (Carr *et al.* 2006), 18 – Rietvlei Still Bay (w) (Quick *et al.* 2015), 19 – Boomplaas (c) (Deacon *et al.* 1984), 20 – Norga (w) (Scholtz 1986), 21 – Eilandvlei (w) (Quick *et al.* 2018), 22 – Bo Langvlei (w) (du Plessis *et al.* 2020), 23 – Groenvlei (w) (Martin 1968), 24 – Vankervelsvlei (w) (Irving and Meadows 1997; Quick *et al.* 2016), 25 – Platbos 1 (w) (MacPherson *et al.* 2019), 26 – Oyster Bay (hc) (Carrion *et al.* 2000) [Data: South African National Biodiversity Institute (2006-). The Vegetation Map of South Africa, Lesotho and Swaziland, Mucina, L., Rutherford, M.C. and Powrie, L.W. (Editors), Online, http://bgis.sanbi.org/SpatialDataset/Detail/18, Version 2018].
The southwestern Cape is also important from a climatic perspective as it is a particu-
larly dynamic region in terms of long-term climate change. Unlike most of South Africa which
receives summer rainfall, the CFR mainly falls within the winter rainfall zone (WRZ) (sensu
Chase and Meadows 2006), presently receiving most of its rainfall during the austral winter
when the southern westerly storm track migrates northward (Taljaard 1996; Tyson 1986; Tyson
and Preston-Whyte 2000). Whereas during the austral summer, the westerlies and the South
Atlantic Anticyclone shift southward limiting the influence of both frontal systems and tropical
moisture sources (Reason et al. 2006; Tyson and Preston-Whyte 2000), resulting in warm, dry
summer conditions. This Mediterranean-type climatic setting has played an important part in
the development of the CFR and is thought to have helped foster the region's botanical diver-
sity (Bradshaw and Cowling 2014; Cowling and Lombard 2002; Cowling et al. 2015; Goldblatt
1978; Linder 2005; Linder et al. 1992). While long-term climatic stability during the Pliocene
and Pleistocene is hypothesized to be one of the key factors for the diversification of the Cape
flora (Verboom et al. 2014; Cowling and Lombard 2002; Cowling et al. 2015; Cowling et al.
1997; Linder 2005), little direct evidence is available. Furthermore, species diversity within the
CFR is not homogeneous and at finer spatial and temporal scales the concept of climatic stabil-
ity driving diversity and species richness becomes more complex (Cowling 1992; Cowling and
Lombard 2002; Cowling et al. 1997).

Palaeoecological studies within the CFR have the potential to contribute important insight
into biodiversity patterns and processes, providing long-term perspectives of climatic and ecosys-
tem changes. The CFR has a rich archaeological history as it is the locality for speciation events
that culminated in the evolution of anatomically and behaviourally modern humans (Brown
et al. 2009; Brown et al. 2012; Henshilwood et al. 2001; Henshilwood et al. 2002; Marean
2010; Marean et al. 2007). Determining the environmental context for these events represents
an essential tool to understand human evolution, behaviour, and dispersal patterns. Not only can
palaeoecology provide important insights into long-term human evolutionary trends but palaeoe-
cological studies within the CFR can also help contextualize more recent archaeological trends
such as migration patterns of indigenous pastoralists (e.g. Deacon 1992; Sadr 1998, 2008)) and
their possible impact on late Holocene environments and ecosystems (e.g. Meadows and Baxter
2001; Neumann et al. 2011).

In terms of palaeoecological data, palynology represents the principal method available to
determine the responses of vegetation to environmental change over various spatial and temporal
scales (Bennett and Willis 2001; Birks et al. 2016; Faegri and Iversen 1989). Despite being a
key focus area within southern Africa (as outlined above) no systematic study of the palynology
of the Cape Flora has been conducted, considerable efforts have however been made towards the
generation of individual fossil pollen records.

This paper presents a brief synopsis of the current state of the palaeoecological record of the
CFR, with a focus on the pollen records that have been produced over the last decade. The role
that palynology can play in advancing palaeoecological research is further explored by outlining
current initiatives and future directions for palynological research within the CFR.

14.2 THE LATE QUATERNARY PALAEOECOLOGICAL HISTORY OF THE CAPE
FLORISTIC REGION

As this is a short perspective piece, this section is not an exhaustive review of all the palynological
records that fall within the CFR (although all pollen records are presented in Figure 1), but rather
focuses on several of the most recently established records.

In general, the significant advances that were made in palaeoenvironmental/palaeoecologi-
cal research could not have been achieved without the generation and evaluation of multiple lines
of proxy evidence as well as the use of unique environmental archives. This is particularly the
case for the CFR as organic sediments (which are traditionally the main source of fossil pollen) are relatively rare, given the semi-arid nature of the region. Records from rock hyrax (*Procavia capensis*) middens represent the single most significant contribution to a more nuanced and detailed understanding of the palaeoclimates of the CFR as they are remarkably sensitive to environmental changes and contain a great diversity of proxies (e.g. fossil pollen, stable isotopes, plant biomarkers) (Chase *et al.* 2012; Scott 1990). Hyrax middens are however largely preserved only within the more arid inland mountainous regions of the CFR (i.e. within mountain ranges associated with the Cape Fold Belt) while more conventional palaeoecological archives, such as coastal lakes and wetlands, are found along the west and southern Cape coasts. Records derived from these sites helped to provide a more balanced coverage across the CFR (Figure 1). While inherent differences exist between pollen assemblages derived from hyrax middens and those from wetland deposits (i.e. pollen spectra from wetland deposits often include high proportions of local hydrophilic taxa whereas pollen found in hyrax middens is likely a more direct representation of the terrestrial vegetation), each record is carefully considered in the context of its unique environmental, ecological, hydrological and geomorphological setting – all of which can influence pollen production, dispersal and representation. These factors are also taken into account when comparing records across the CFR.

That the existing spatial heterogeneity within the CFR is a key determining factor for the nature of any identified palaeoenvironmental changes is not a new observation (Chase and Meadows 2007; Cowling 1992; Cowling and Holmes 1992; Meadows and Baxter 1999), however, with more records generated, and the increasing availability of high-resolution data, we are now able to explore this theory to a fuller extent. To best illustrate how distinct subregions of the CFR have responded differently to palaeoenvironmental changes and to highlight the contribution of the most recent published pollen records, two key subregions – the southern Cape coast and the Cederberg mountains – are briefly considered.

### 14.2.1 Palaeoecology of the southern Cape: The significance of coastal environments

The southern Cape coastal plain forms the eastern lowland subregion of the CFR (Figure 1) and is influenced by atmospheric dynamics associated with both the winter and summer rainfall zones (the aseasonal or year-round rainfall zone; sensu Chase and Meadows 2007).

While several sediment cores have been extracted from the Vankervelsvlei wetland site (Irving 1998; Irving and Meadows 1997), limited reliable palaeoenvironmental evidence has emanated from these studies due to poor chronological control and inconsistent pollen preservation. However, with the addition of optically stimulated luminescence (OSL) as a dating technique, Quick et al. (2016) established a chronology for a sediment sequence spanning the last 140,000 years. The variability within the pollen assemblage from this record indicates a complex response to the transition from interglacial to glacial conditions. In general, the dominance of afrotemperate forest pollen from Marine Isotope Stage (MIS) 5d (c. 108–96 ka) alludes to enhanced summer rainfall under warmer conditions allowing for the development of more extensive forests in the area (Quick et al. 2016). In contrast, the pollen from the last glacial period was dominated by fynbos types indicating cooler temperatures and a more seasonal winter-dominated rainfall regime (Quick et al. 2016).

Due to the virtual absence of palaeoenvironmental data from the southern Cape covering MIS 5 to MIS 3, the Vankervelsvlei pollen data represents an important contribution, however as no pollen was preserved from c. 27–8.7 ka, the record is incomplete. This highlights a particularly frustrating trend of the Last Glacial Maximum (LGM; 26.5–19 cal ky BP (Clark et al. 2009)) period being absent from pollen sequences from the southern Cape coast. Either the pollen records recovered from the subregion do not extend as far back as the LGM (e.g. du Plessis et al. 2020; Martin 1968; Scholtz 1986) or as is the case of Vankervelsvlei and Rietvlei-Still Bay (Quick et al. 2015), there is limited to no pollen preserved. Supplementary proxy data from Vankervelsvlei
and Rietvlei-Still Bay suggest that conditions were likely quite arid during the LGM (Quick et al. 2015; Quick et al. 2016), which could explain the lack of pollen preservation, and is supported by evidence of decreased moisture availability from other studies from the subregion (Chase and Meadows 2007, and references therein). Other factors that could potentially have contributed to decreased wetland productivity and a lack of pollen preservation within these coastal or near-coastal wetlands during the LGM are lower sea-levels (affecting groundwater recharge and regional hydrology) and enhanced aeolian activity. As pollen is found within the LGM sections of records from the western interior (e.g. the Pakhuis Pass and De Rif hyrax midden records (Quick et al. 2011; Scott and Woodborne 2007a, b; Valsecchi et al. 2013)) and the southwestern Cape coast within the modern WRZ (Pearly Beach record (Quick et al., 2021), the lack of preservation seems to be confined to the year-round rainfall portion of the southern Cape (Southern Cape subregion, Figure 1) and therefore likely to be tied to subregion-scale changes in moisture availability. This theory can be tested with the acquisition of new sites from both the coast and the interior.

With an average sample resolution of 57 years, the pollen record from the coastal lake of Eilandvlei (Quick et al. 2018; Figure 1) represents the most continuous and highest resolution record of Holocene vegetation change for the southern Cape subregion. Similar to the Vankervelsvlei record, the Eilandvlei pollen assemblage primarily reflects shifts in the abundance of afrotemperate forest and fynbos taxa. For example, fynbos dominates the assemblage during the early Holocene, indicating generally cool, seasonal conditions, whereas increasing percentages of afrotemperate forest taxa from 3.5 cal ky BP to present day suggest that the late Holocene was characterized by a long-term trend of steadily increasing moisture availability (Quick et al. 2018). Comparisons of this high-resolution record with regional and extra-regional palaeoclimatic data highlight the complex nature of southern Cape climate dynamics (Quick et al. 2018). The similarities between the Eilandvlei record and records from more tropical regions of South Africa led to the proposal that summer rainfall is of great importance in terms of maintaining high moisture availability and that the Agulhas Current could be responsible for transmitting this signal of tropical variability along the coast (Chase and Quick 2018; Quick et al. 2018). These insights emphasize the importance of considering proxy evidence, including fossil pollen, from sites on the coast separately from those from the interior and caution against extrapolating results beyond the subregion scale.

### 14.2.2 Cederberg palaeoenvironments: Environmental stability vs. extreme palaeoclimatic changes

The Cederberg mountains represent the core of the western interior CFR subregion (Figure 1) and while free from the direct, localized influence of coastal dynamics, the palaeoenvironmental history of the Cederberg is no less complex than that of the southern Cape coast.

Several fossil pollen records from this subregion infer that extremely muted changes in vegetation composition took place during the last glacial and Holocene (Meadows et al. 2010; Meadows and Sugden 1991; Meadows and Sugden 1993; Quick et al. 2011). It has been suggested that mountain fynbos (the dominant vegetation type in the area) is highly resilient to changes in moisture availability, which could account for the low-amplitude variability within these palaeoecological records (Quick et al. 2011). However, this is contrasted by a growing body of data that indicates more dynamic patterns in vegetation and, especially, climate change (Chase et al. 2019; Chase et al. 2015a; Chase et al. 2011; Scott and Woodborne 2007a, b; Valsecchi et al. 2013). This evidence mainly consists of stable isotope records derived from hyrax middens situated within the central (Chase et al. 2011; Chase et al. 2015), northern (Chase et al. 2019) and eastern (Chase et al. 2015) sections of the Cederberg. These records indicate that substantial, rapid changes in hydroclimatic conditions occurred since the LGM, and that while the drivers
of climate dynamics appear to be shared across the subregion, there are distinct local responses, resulting in a great degree of variability across short spatial gradients (Chase *et al.* 2019).

The discrepancies between the older pollen records and the more recent stable isotope records could be related to differences in the nature of the information obtained from these two proxies. Stable carbon and nitrogen isotope data from hyrax middens are excellent recorders of changes in moisture availability (Chase *et al.* 2012) whereas palynological data is more nuanced and it can be difficult to disentangle ecological and climatic drivers of vegetation change. A further possibility is that while species turnover may be high, because it is generally only possible to identify pollen to genus or family level, this variability is not captured within the pollen assemblages.

It is clear from the emerging complexity of the palaeoenvironmental dynamics within these two subregions that substantial variability can occur across relatively short distances and interpolations beyond site level cannot always be reliably applied to regional syntheses.

## 14.3 CURRENT AND FUTURE DIRECTIONS FOR PALAEOECOLOGICAL RESEARCH IN THE CAPE FLORISTIC REGION

We now have a more nuanced understanding of palaeoenvironmental change within the CFR, however new high-resolution data are still required to fully explore subregional variability and to provide a more comprehensive understanding of the climate dynamics, drivers and the impact of climate change on CFR environments. There is great potential for fossil pollen data to contribute to this research agenda and to provide long-term perspectives of ecological dynamics within the CFR. The following sections outline current and potential future research directions that align to these goals.

### 14.3.1 The influence of the Agulhas Current on coastal palaeoenvironments

To expand on the theory outlined in Chase and Quick (2018) that a strong dipole in hydroclimatic conditions exist between the coast and interior during the Holocene, and that the Agulhas Current acts as a vector for the propagation of tropical southeast African climate signals to the southern Cape, more data is required. For a clearer understanding of the spatiotemporal dynamics of this hypothesized relationship additional high-resolution records extending into the last glacial period are needed from both the interior and coastal subregions of the southern Cape as well as from offshore (i.e. sea-surface temperature records). To begin to address this gap, a new project (by the author and collaborators) is currently underway (2020–2023) that focuses on the generation of palaeoenvironmental data, particularly fossil pollen, from wetland sites situated on a west-east transect along the southern Cape coastline.

### 14.3.2 Exploring steep palaeoenvironmental gradients in the Cederberg mountains

As previously outlined, the findings from the high-resolution stable isotope data from the Cederberg suggest that considerable palaeoclimatic change occurred across the subregion during the last glacial and Holocene (Chase *et al.* 2019). To fully evaluate and contextualize these findings, more sites from transects across the subregion are required. A range of proxies (such as stable isotopes, pollen, plant biomarkers, phytoliths and microcharcoal) will be analysed. New pollen data would be especially useful as it is still unclear as to whether vegetation change was in fact relatively muted or if the pollen records currently in existence are not of high enough spatial or temporal resolution to adequately capture the variability evident in the stable isotope records.
### 14.3.3 Palaeoenvironments of the eastern Cape: The final frontier?

Encompassing the transition from fynbos to Albany/subtropical thicket as well as from fynbos to Succulent Karoo, the eastern Cape represents an important, understudied eastern boundary of the CFR. Palaeoecological data for the eastern Cape is extremely limited and no palynological research has been conducted within the Albany Thicket biome. Only one pollen record exists for the region and this record, derived from hyena coprolite samples from a Middle Stone Age archaeological site, has no chronology and the pollen content was not sufficient for a detailed palaeoenvironmental reconstruction to be performed (Carrion et al. 2000; Figure 1). There are two additional publications that include pollen data from sites situated within the Eastern Cape Province, however both of these sites fall outside the CFR: the Dunedin and Salisbury pollen records from the Winterberg escarpment (Meadows and Meadows 1988) and the more recently published preliminary pollen results from ongoing archaeological research at Waterfall Bluff, eastern Pondoland (Esteban et al. 2020; Fisher et al. 2020).

There is great scope for the generation of new pollen records from both the montane (e.g. the Baviaanskloof mountains) and the coastal regions (e.g. between Tsitsikamma and Port Elizabeth). Stable isotope data from a hyrax midden site within the Baviaanskloof provide new evidence for a high degree of spatiotemporal climatic variability during the Holocene (Chase et al. 2020). The generation of pollen data from this midden is currently underway, this data will be able to shed light on how vegetation within this ecotonal subregion of the CFR may have responded to the documented changes in hydroclimatic variability.

### 14.3.4 Bridging the gap between palaeoecology and ecology

Despite the similarities in their names, ecology and palaeoecology have historically been almost entirely distinct fields of research. Both fields have contributed to this disassociation: While ecologists generally recognize the need for long-term perspectives of vegetation change, they have been sceptical of the robustness of palaeoecological research methods and the inferences that are drawn from proxy records. Consequently, they have had reservations about the usefulness and reliability of palaeoecological data for addressing ecological questions. While this may once have been a legitimate concern, there have been vast methodological improvements to the generation of palaeoecological data which have resulted in much higher resolution, more accurately dated, proxy sequences (Birks et al. 2016). Conversely, due to the different spatial and temporal scales, palaeoecologists have often assumed that contemporary ecology is too far removed methodologically and theoretically from their field to be truly applicable. They have tended to focus on directly answering climate-related questions and in doing so have often overlooked the ecological associations of the data which has significant implications for palaeoclimatic reconstructions.

While several studies integrating ecological theory and palynological research have been conducted elsewhere in South Africa (e.g. Gillson and Duffin 2007; Gillson and Ekblom 2020; Gillson and Marchant 2014) until recently, this approach had not been attempted for the CFR. Drawing from the relatively muted changes within two late Holocene pollen records derived from sediment cores from biome boundaries (MacPherson et al. 2018; MacPherson et al. 2019), Gillson et al. (2020) present new hypotheses exploring possible ecological mechanisms to explain the apparent resilience of fynbos to environmental change. An assessment of how the ecosystem resilience and resistance hypotheses outlined in Gillson et al. (2020) align to the greater shifts in fynbos-forest dynamics documented in other palaeoecological records from ecotonal regions of the CFR (e.g. du Plessis et al. 2020; Quick et al. 2018; 2016) remains to be undertaken. However these studies as well as Forbes et al. (2018) highlight the potential for applying a more analytical ecological lens to CFR palynological records.

Fire is a major driving force in most Mediterranean-type ecosystems including fynbos (Cowling 1992). Without fire, fynbos communities become senescent and other plant types can
invade into the biome (Cowling *et al.* 1992; Mucina and Rutherford 2006). Information on past fire regimes is therefore particularly important in order to understand changes at the plant community level as well as shifts in biome boundaries, especially boundaries shared with fire-intolerant communities such as forest and thicket (Mucina and Rutherford 2006). Microcharcoal abundances (which are routinely counted simultaneously with pollen) can be used to reconstruct local and regional fire regimes (Cowling *et al.* 1997). Determining fire intensities from charcoal found in sedimentary sequences is now possible as a result of the development of a new method (Gosling *et al.* 2019), this method should be explored within the context of the CFR.

By providing a better understanding of ecological responses to natural and anthropogenic impacts, palaeoecological data can directly contribute to conservation management and restoration practices, however this will rely on the establishment of effective multidisciplinary working groups (Gillson, this volume; Manzano *et al.* 2020).

### 14.3.5 New methods and resources

The CREST (Climate REconstruction SofTware) method developed by Chevalier *et al.* (2014) has been very successful in producing quantitative climate reconstructions for southern Africa using contemporary climate data, modern plant distributions and fossil pollen records (Chase *et al.* 2015a; Chase *et al.* 2015b; Chevalier and Chase 2015; Chase *et al.* 2017; Chevalier *et al.* 2021; Cordova *et al.* 2017; Lim *et al.* 2016). A major advantage of this method is that a large modern pollen database (which does not currently exist for the CFR) is not required. The steep environmental gradients and coastal settings of many of the CFR pollen records have proven to introduce a great degree of complexity to the application of CREST within this region. However, there certainly still is scope for its application particularly with the addition of a new resource:

A 'by-product' of the CREST method has given rise to the 'Atlas of southern African pollen taxa: Distributions and Climatic Affinities' (Chevalier *et al.* this volume) which will greatly improve, refine and standardize qualitative interpretations of fossil pollen data as well as quantitative reconstructions (such as CREST). This reference atlas includes a series of diagnostic tools and results based on statistical analyses of modern distributions of common southern African pollen taxa in relation to key climate variables. This new resource reduces the subjectivity often inherent in more qualitative interpretations and presents more accurate determination of absolute and relative climate sensitivities of southern African pollen types, including a large proportion of CFR taxa.

The recent digitization of CFR pollen reference material (originally from the Palaeoecological Laboratory in the Department of Environmental and Geographical Science, University of Cape Town) and the establishment of a new open access, searchable electronic database (http://pollen.mandela.ac.za) represents a new primary palynological resource. While the resumption of the African Pollen Database (APD, Vincens *et al.* 2007) and its integration into the Neotoma Palaeoecology Database (Goring *et al.* 2015), will ensure that previously published and new pollen datasets are archived within an open access online platform (Ivory *et al.* 2020). If the majority of fossil pollen records for the CFR were included in the new APD - Neotoma database, new forms of spatiotemporal analyses could be conducted which would provide greater insight into ecosystem-climate linkages across the region.

### 14.4 CONCLUSIONS

While our understanding of paleoenvironmental change within the CFR has been greatly improved by the establishment of recent high-resolution palaeoclimatic and palaeoecological records, fundamental questions still remain relating to the full extent of the subregional variability of both climate and vegetation change, and how these subregions are associated/disassociated with each
other. Further insight into the long-term environmental dynamics within the CRF will be possible if the spatial and temporal coverage of sites are expanded and by employing the new methods and resources now available.

In the face of global climate change and ever-increasing ecological uncertainty as a result of unprecedented climatic and societal changes, there has never been a more urgent need for scientific collaboration and the establishment of multidisciplinary studies. The wide sphere of applicability of pollen analysis to a multitude of fields (e.g. vegetation modelling, restoration ecology, conservation biology, aerobiology and archaeology) highlights the incredible potential, both current and future, of palaeoecology's role within multidisciplinary studies. Hopefully in the coming years palaeoecological research within the CFR will continue to advance and contribute to a greater understanding of the region's unique biodiversity and climatic history.

## ACKNOWLEDGEMENTS

While this paper is a personal perspective, the conceptual frameworks, ideas and insights are the culmination of collaboration particularly with Michael Meadows and Brian Chase, who originally set me on this research path. Two anonymous reviewers are thanked for their insightful comments and suggestions on an earlier version of this paper.

## REFERENCES

Allsopp, N., Colville, J.F. and Verboom, G.A., 2014, Fynbos: ecology, evolution, and conservation of a megadiverse region. Oxford University Press, USA.

Bennett, K.D. and Willis, K.J., 2001, Pollen. In *Tracking Environmental Change Using Lake Sediments. Volume 3: Terrestrial, Algal, and Siliceous Indicators*, edited by Smol, J.P., Birks, H.J.B., Last, W.M., (Dordrecht: Kluwer Academic Publishers), pp. 5–26.

Birks, H.J.B. and Birks, H.H. and Ammann, B., 2016, The fourth dimension of vegetation. *Science* **354**, pp. 412–413, 10.1126/science.aai8737.

Bond, P. and Goldblatt, P., 1984, Plants of the Cape flora: A descriptive catalogue, In *Journal of South African Botany*, Supplementary Volume, edited by Eloff, J.N., pp. 1–455.

Bradshaw, P.L. and Cowling, R.M., 2014. Landscapes, rock types, and climate of the Greater Cape Floristic Region. In *Fynbos: Ecology, Evolution, and Conservation of a Megadiverse Region*, edited by Allsopp, N., Colville, J.F., Verboom, G.A. (Oxford: Oxford University Press) pp. 26–46.

Brown, K.S., Marean, C.W., Herries, A.I.R., Jacobs, Z., Tribolo, C., Braun, D., Roberts, D.L., Meyer, M.C. and Bernatchez, J., 2009, Fire as an engineering tool of early modern humans. *Science*, **325**, pp. 859–862, 10.1126/science.1175028.

Brown, K.S., Marean, C.W., Jacobs, Z., Schoville, B.J., Oestmo, S., Fisher, E.C., Bernatchez, J., Karkanas, P. and Matthews, T., 2012, An early and enduring advanced technology originating 71,000 years ago in South Africa. *Nature* **491**, pp. 590–593, 10.1038/nature11660.

Campbell, B.M., 1985, *A Classification of the Mountain Vegetation of the Fynbos Biome*. (Pretoria: Botanical Research Institute, Department of Agriculture and Water Supply).

Carr, A.S., Thomas, D.S.G., Bateman, M.D., Meadows, M.E. and Chase, B., 2006, Late Quaternary palaeoenvironments of the winter-rainfall zone of southern Africa: Palynological and sedimentological evidence from the Agulhas Plain. *Palaeogeography, Palaeoclimatology, Palaeoecology*, **239**, pp. 147–165, 10.1016/j.palaeo.2006.01.014.

Carrion, J.S., Brink, J.S., Scott, L. and Binneman, J.N.F., 2000, Palynology and palaeoenvironment of Pleistocene hyaena coprolites from an open-air site at Oyster Bay, Eastern Cape coast, South Africa. *South African Journal of Science*, **96**, pp. 449–453.
Chase, B., Boom, A., Carr, A., Chevalier, M., Quick, L., Verboom, A. and Reimer, P., 2019, Extreme hydroclimate gradients within the western Cape Floristic region of South Africa since the Last Glacial Maximum. *Quaternary Science Reviews*, **219**, pp. 297–307, 10.1016/j.quascirev.2019.07.006.

Chase, B.M. and Meadows, M.E., 2007, Late Quaternary dynamics of southern Africa’s winter rainfall zone. *Earth-Science Reviews*, **84**, pp. 103–138, 10.1016/j.earscirev.2007.06.002.

Chase, B.M., Quick, L.J., Meadows, M.E., Scott, L., Thomas, D.S.G. and Reimer, P.J., 2011, Late-glacial interhemispheric climate dynamics revealed in South African hyrax middens. *Geology*, **39**, pp. 19–22, 10.1130/G31129.1.

Chase, B.M., Scott, L., Meadows, M.E., Gil-Romera, G., Boom, A., Carr, A.S., Reimer, P.J., Truc, L., Valsecchi, V. and Quick, L.J., 2012, Rock hyrax middens: A palaeoenvironmental archive for southern African drylands. *Quaternary Science Reviews*, **56**, pp. 107–125, 10.1016/j.quascirev.2012.08.018.

Chase, B.M., Boom, A., Carr, A.S., Carré, M., Chevalier, M., Meadows, M.E., Pedro, J.B., Stager, J.C. and Reimer, P.J., 2015a, Evolving southwest African response to abrupt deglacial North Atlantic climate change events. *Quaternary Science Reviews*, **121**, pp. 132–136, 10.1016/j.quascirev.2015.05.023.

Chase, B.M., Lim, S., Chevalier, M., Boom, A., Carr, A.S., Meadows, M.E. and Reimer, P.J., 2015b, Influence of tropical easterlies in southern Africa’s winter rainfall zone during the Holocene. *Quaternary Science Reviews*, **107**, pp. 138–148, 10.1016/j.quascirev.2014.10.011.

Chase, B.M., Chevalier, M., Boom, A. and Carr, A.S., 2017, The dynamic relationship between temperate and tropical circulation systems across South Africa since the last glacial maximum. *Quaternary Science Reviews*, **174**, pp. 54–62, 10.1016/j.quascirev.2017.08.011.

Chase, B.M. and Quick, L.J., 2018, Influence of Agulhas forcing of Holocene climate change in South Africa’s southern Cape. *Quaternary Research*, **90**, pp. 303–309, 10.1017/qua.2018.57.

Chase, B.M., Boom, A., Carr, A.S., Quick, L.J. and Reimer, P.J., 2020, High-resolution record of Holocene climate change dynamics from southern Africa’s temperate-tropical boundary, Bavi-aanskloof, South Africa. *Palaeogeography, Palaeoclimatology, Palaeoecology*, **539**, article: 109518, 10.1016/j.palaeo.2019.109518.

Chevalier, M., Cheddadi, R. and Chase, B.M., 2014, CREST (Climate REconstruction Soft-ware): a probability density function (PDF)-based quantitative climate reconstruction method. *Climate of the Past*, **10**, pp. 2081–2098, 10.5194/cp-10-2081-2014.

Chevalier, M. and Chase, B.M., 2015. Southeast African records reveal a coherent shift from high- to low-latitude forcing mechanisms along the east African margin across last glacial-interglacial transition. *Quaternary Science Reviews* **125**, pp. 117–130, 10.1016/j.quascirev.2015.07.009.

Chevalier, M., Chase, B.M., Quick, L.J., Dupont, L.M., and Johnson, T.C., 2021, Temperature change in subtropical southeastern Africa during the past 790,000 yr. *Geology*, **49**(1), pp. 71–75, 10.1130/G47841.1.

Chevalier, M., Chase, B.M., Quick, L.J. and Scott, L., this volume, An atlas of southern African pollen types and their climatic affinities. *Palaeoecology of Africa*, **35**, chapter 15, 10.1201/9781003162766-15.

Clark, P.U., Dyke, A.S., Shakun, J.D., Carlson, A.E., Clark, J., Wohlfarth, B., Mitrovica, J.X., Hostetler, S.W. and McCabe, A.M., 2009, The last glacial maximum. *Science*, **325**, pp. 710–714, 10.1126/science.1172873.

Cordova, C.E., Scott, L., Chase, B.M. and Chevalier, M., 2017, Late Pleistocene-Holocene vegetation and climate change in the Middle Kalahari, Lake Ngami, Botswana. *Quaternary Science Reviews*, **171**, pp. 199–215, 10.1016/j.quascirev.2017.06.036.

Cordova, C.E., Kirsten, K.L., Scott, L., Meadows, M. and Lücke, A., 2019, Multi-proxy evidence of late-Holocene paleoenvironmental change at Princessvlei, South Africa: The
effects of fire, herbivores, and humans. *Quaternary Science Reviews*, **221**, article: 105896, 10.1016/j.quascirev.2019.105896.

Cowling, R.M., 1992, *The Ecology of Fynbos: Nutrients, Fire, and Diversity*. (Cape Town: Oxford University Press).

Cowling, R.M. and Holmes, P.M., 1992, Endemism and speciation in a lowland flora from the Cape Floristic Region. *Biological Journal of the Linnean Society*, **47**, pp. 367–383, 10.1111/j.1095-8312.1992.tb00675.x.

Cowling, R.M., Richardson, D.M., Paterson-Jones, C., 1995, *Fynbos: South Africa's Unique Floral Kingdom*. (Vlaeberg: Fernwood Press).

Cowling, R.M., Richardson, D.M., Schulze, R.E., Hoffman, M.T., Midgley, J.J. and Hilton-Taylor, C., 1997. Species diversity at the regional scale. In *Vegetation of Southern Africa*, edited by Cowling, R.M., Richardson, D.M., Pierce, S.M, (Cambridge: Cambridge University Press), pp. 447–273.

Cowling, R.M. and Lombard, A.T., 2002, Heterogeneity, speciation/extinction history and climate: explaining regional plant diversity patterns in the Cape Floristic Region. *Diversity and Distributions* **8**, pp. 163–179, 10.1046/j.1472-4642.2002.00143.x.

Cowling, R.M., Potts, A.J., Bradshaw, P.L., Colville, J., Arianoutsou, M., Ferrier, S., Forest, F., Fyllas, N.M., Hopper, S.D. and Ojeda, F., 2015, Variation in plant diversity in mediterranean-climate ecosystems: The role of climatic and topographical stability. *Journal of Biogeography*, **42**, pp. 552–564, 10.1111/jbi.12429.

Deacon, H.J., Deacon, J., Scholtz, A., Thackeray, J.F. and Brink, J.S., 1984, Correlation of palaeoenvironmental data from the Late Pleistocene and Holocene deposits at Boomplaas Cave, southern Cape. In *Late Cainozoic Palaeoclimates of the Southern Hemisphere*, edited by Vogel, J.C., (Rotterdam: Balkema), pp. 339–360.

Deacon, H.J., 1992, Human settlement. In *The Ecology of Fynbos: Nutrients, Fire and Diversity*, edited by Cowling, R.M. (Cape Town: Oxford University Press), pp. 260–270.

du Plessis, N., Chase, B.M., Quick, L.J., Haberzettl, T., Kasper, T. and Meadows, M.E., 2020, Vegetation and climate change during the Medieval Climate Anomaly and the Little Ice Age on the southern Cape coast of South Africa: Pollen evidence from Bo Langvlei. *The Holocene*, **30**(12), pp. 1716–1727, 10.1177/0959683620950444.

Esteban, I., Bamford, M.K., House, A., Miller, C.S., Neumann, F.H., Schefuß, E., Pargeter, J., Cawthra, H.C. and Fisher, E.C., 2020. Coastal palaeoenvironments and hunter-gatherer plant-use at Waterfall Bluff rock shelter in Mpondoland (South Africa) from MIS 3 to the Early Holocene. *Quaternary Science Reviews*, **250**, article: 106664, 10.1016/j.quascirev.2020.106664.

Faegri, K. and Iversen, J., 1989, *Textbook of Pollen Analysis*, 4th Edition, (Chichester: John Wiley & Sons).

Fisher, E.C., Cawthra, H.C., Esteban, I., Jerardino, A., Neumann, F.H., Oertle, A., Pargeter, J., Saktura, R.B., Szabó, K. and Winkler, S., 2020, Coastal occupation and foraging during the last glacial maximum and early Holocene at Waterfall Bluff, eastern Pondoland, South Africa. *Quaternary Research*, **97**, pp. 1–41, 10.1017/qua.2020.26.

Forbes, C.J., Gillson, L. and Hoffman, M.T., 2018, Shifting baselines in a changing world: Identifying management targets in endangered heathlands of the Cape Floristic Region, South Africa. *Anthropocene* **22**, pp. 81–93, 10.1016/j.ancene.2018.05.001.

Gillson, L. and Duffin, K., 2007, Thresholds of potential concern as benchmarks in the management of African savannahs. *Philosophical Transactions of the Royal Society B: Biological Sciences*, **362**, pp. 309–319, 10.1098/rstb.2006.1988.

Gillson, L. and Marchant, R., 2014, From myopia to clarity: sharpening the focus of ecosystem management through the lens of palaeoecology. *Trends in Ecology & Evolution*, **29**, pp. 317–325, 10.1016/j.tree.2014.03.010.
Gillson, L. and Ekblom, A., 2020, Using palaeoecology to explore the resilience of southern African savannas. *Koedoe, **62***, pp. 1–12, 10.4102/koedoe.v62i1.1576.

Gillson, L., MacPherson, A.J. and Hoffman, M.T., 2020, Contrasting mechanisms of resilience at mesic and semi-arid boundaries of fynbos, a mega-diverse heathland of South Africa. *Ecological Complexity, **42***, article: 100827, 10.1016/j.ecocom.2020.100827.

Gillson, L., this volume, The role of palaeoecology in conserving African ecosystems. *Palaeoecology of Africa, **35***, chapter: 24, 10.1201/9781003162766-24.

Goldblatt, P., 1978, An analysis of the flora of southern Africa: its characteristics, relationships, and origins. *Annals of the Missouri Botanical Garden **65***, pp. 369–436, 10.2307/2398858.

Goldblatt, P. and Manning, J., 2000, *Cape Plants. A Conspectus of the Cape Flora of South Africa*. (Pretoria and St. Louis: National Botanical Institute and Missouri Botanical Gardens).

Goring, S., Dawson, A., Simpson, G., Ram, K., Graham, R., Grimm, E. and Williams, J., 2015, Neotoma: A programmatic interface to the Neotoma Paleoecological Database. *Open Quaternary* 1(1), article 2, 10.5334/oq.ab.

Gosling, W.D., Cornelissen, H. and McMichael, C.N.H., 2019, Reconstructing past fire temperatures from ancient charcoal material. *Palaeogeography, Palaeoclimatology, Palaeoecology, **520***, pp. 128–137, 10.1016/j.palaeo.2019.01.029.

Henshilwood, C.S., d'Errico, F., Marean, C.W., Milo, R.G. and Yates, R., 2001, An early bone tool industry from the Middle Stone Age at Blombos Cave, South Africa: implications for the origins of modern human behaviour, symbolism and language. *Journal of Human Evolution, **41***, pp. 631–678, 10.1006/jhev.2001.0515.

Henshilwood, C.S., d'Errico, F., Yates, R., Jacobs, Z., Tribolo, C., Duller, G.A.T., Mercier, N., Sealy, J.C., Valladas, H., Watts, I. and Wintle, A.G., 2002, Emergence of modern human behavior: Middle stone age engravings from South Africa. *Science, **295***, pp. 1278–1280, 10.1126/science.1067575.

Irving, S.J.E. and Meadows, M.E., 1997, Radiocarbon chronology and organic matter accumulation at Vankervelsvlei, near Knysna, South Africa. *South African Geographical Journal, **79***, pp. 101–105, 10.1080/03736245.1997.9713630.

Irving, S.J.E., 1998. Late Quaternary palaeoenvironments at Vankervelsvlei, near Knysna, South Africa, Department of Environmental and Geographical Sciences. University of Cape Town, Cape Town.

Ivory, S., Lézine, A.-M., Grimm, E. and Williams, J.W., 2020, Relaunching the African Pollen Database: Abrupt change in climate and ecosystems. *Past Global Changes Magazine, **28*(1), pp. 26.

Lim, S., Chase, B.M., Chevalier, M. and Reimer, P.J., 2016, 50,000 years of vegetation and climate change in the southern Namib Desert, Pella, South Africa. *Palaeogeography, Palaeoclimatology, Palaeoecology, **451***, pp. 197–209, 10.1016/j.palaeo.2016.03.001.

Linder, H.P., 2005. Evolution of diversity: The Cape flora. *Trends in Plant Science, **10***, pp. 536–541, 10.1016/j.tplants.2005.09.006.

Linder, H.P., Meadows, M. and Cowling, R.M., 1992, History of the Cape Flora. In *The Ecology of Fynbos: nutrients, fire and diversity*, edited by Cowling, R.M. (Cape Town: Oxford University Press), pp. 113–134.

MacPherson, A., Gillson, L. and Hoffman, M., 2018, Climatic buffering and anthropogenic degradation of a Mediterranean-type shrubland refugium at its semi-arid boundary, South Africa. *The Holocene, **28***, pp. 651–666, 10.1177/0959683617735582.

MacPherson, A.J., Gillson, L. and Hoffman, M.T., 2019, Between-and within-biome resistance and resilience at the fynbos-forest ecotone, South Africa. *The Holocene, **29***, 1801–1816, 10.1177/0959683619862046.

Manzano, S., Julier, A.C.M., Dirk, C.J., Razafimanantsoa, A.H.I., Samuels, I., Petersen, H., Gell, P., Hoffman, M.T. and Gillson, L., 2020, Using the past to manage the future: the role
of palaeoecological and long-term data in ecological restoration. *Restoration Ecology*, **28**, pp. 1335-1342, 10.1111/rec.13285.

Marean, C.W., Bar-Matthews, M., Bernatchez, J., Fisher, E., Goldberg, P., Herries, A.I.R., Jacobs, Z., Jerardino, A., Karkanas, P., Minichillo, T., Nilssen, P.J., Thompson, E., Watts, I., and Williams, H.M., 2007, Early human use of marine resources and pigment in South Africa during the Middle Pleistocene. *Nature*, **449**, pp. 905-908, 10.1038/nature06204.

Marean, C.W., 2010, Pinnacle Point Cave 13B (Western Cape Province, South Africa) in context: The Cape Floral kingdom, shellfish, and modern human origins. *Journal of Human Evolution*, **59**, pp. 425-443, 10.1016/j.jhevol.2010.07.011.

Marloth, R., 1908. Das Kapland: Insonderheit das Reich der Kapflora, das Waldgebiet und die Karroo, pflanzengeographisch dargestellt. (Jena: G. Fischer).

Martin, A.R.H., 1968. Pollen analysis of Groenvlei lake sediments, Knysna (South Africa). *Review of Palaeobotany and Palynology* **7**, pp. 107-144, 10.1016/0034-6667(68)90029-8.

Meadows, M.E. and Baxter, A.J., 1999, Late Quaternary palaeoenvironments of the southwestern Cape, South Africa: A regional synthesis. *Quaternary International*, **57-8**, pp. 193-206.

Meadows, M.E. and Baxter, A.J., 2001, Holocene vegetation history and palaeoenvironments at Klaarfontein Springs, Western Cape, South Africa. *The Holocene* **11**, pp. 699-706, 10.1191/09596830195726.

Meadows, M.E., and Meadows, K.F., 1988, Late Quaternary vegetation history of the Winterberg Mountains, Eastern Cape, South Africa. *South African Journal of Science*, **84**, pp. 253-259.

Meadows, M.E. and Sugden, J.M., 1991, A vegetation history of the last 14,000 years on the Cederberg, southwestern Cape Province. *South African Journal of Science*, **87**, 34-43.

Meadows, M.E. and Sugden, J.M., 1993, The late Quaternary palaeoecology of a floristic kingdom: the southwestern Cape, South Africa. *Palaeogeography, Palaeoclimatology, Palaeoecology*, **101**, pp. 271-281, 10.1016/0031-0182(93)90019-F.

Meadows, M.E., Baxter, A.J. and Parkington, J., 1996, Late Holocene environments at Verlorenvlei, Western Cape Province, South Africa. *Quaternary International*, **33**, pp. 81-95, 10.1016/1040-6182(95)00092-5.

Meadows, M.E., Seliane, M. and Chase, B.M., 2010, Holocene palaeoenvironments of the Cederberg and Swartruggens mountains, Western Cape, South Africa: pollen and stable isotope evidence from hyrax dung middens. *Journal of Arid Environments*, **74**, pp. 786-793.

Mittermeier, R.A., Robles Gil, P., Hoffman, M., Pilgrim, J., Brooks, T., Goettsch Mittermeier, C., Lamoreux, J. and da Fonseca, G.A.B., 2004, *Hotspots Revisited*. (Mexico City: CEMEX, Agrupación Sierra Madre, S.C.).

Myers, N., Mittermeier, R.A., Mittermeier, C.G., da Fonseca, G.A.B. and Kent, J., 2000, Biodiversity hotspots for conservation priorities. *Nature*, **403**, pp. 853-858, 10.1038/35002501.

Neumann, F.H., Scott, L., Bamford, M.K., 2011, Climate change and human disturbance of fynbos vegetation during the late Holocene at Princess Vlei, Western Cape, South Africa. *The Holocene*, **21**, pp. 1137-1149, 10.1177/0959683611400461.

Quick, L.J., Chase, B.M., Meadows, M.E., Scott, L. and Reimer, P.J., 2011, A 19.5 kyr vegetation history from the central Cederberg Mountains, South Africa: Palynological evidence from rock hyrax middens. *Palaeogeography, Palaeoclimatology, Palaeoecology* **309**, pp. 253-270, 10.1016/j.palaeo.2011.06.008.

Quick, L.J., Carr, A.S., Meadows, M.E., Boom, A., Bateman, M.D., Roberts, D.L., Reimer, P.J. and Chase, B.M., 2015, A Late Pleistocene-Holocene multi-proxy record of palaeoenvironmental change from Still Bay, southern Cape Coast, South Africa. *Journal of Quaternary Science*, **30**, pp. 870-885, 10.1002/jqs.2825.

Quick, L.J., Meadows, M.E., Bateman, M.D., Kirsten, K.L., Mäusbacher, R., Haberzettl, T. and Chase, B.M., 2016, Vegetation and climate dynamics during the last glacial period in the fynbos-afrotemperate forest ecotone, southern Cape, South Africa. *Quaternary International*, **404**, pp. 136-149, 10.1016/j.quaint.2015.08.027.
Quick, L.J., Chase, B.M., Wündsch, M., Kirsten, K.L., Chevalier, M., Mäusbacher, R., Meadows, M.E. and Haberzettl, T., 2018, A high-resolution record of Holocene climate and vegetation dynamics from the southern Cape coast of South Africa: pollen and microcharcoal evidence from Eilandvlei. *Journal of Quaternary Science*, **33**, pp. 487–500, 10.1002/jqs.3028.

Quick, L.J., Chase, B.M., Carr, A.S., Chevalier, M., Grober, B.A. and Meadows, M.E., 2021, A 25,000-year record of climate and vegetation change from the southwestern Cape coast, South Africa. *Quaternary Research*, **10, 1017/qua.2021.31**.

Reason, C.J.C., Landman, W. and Tennant, W., 2006, Seasonal to decadal prediction of southern African climate and its links with variability of the Atlantic ocean. *Bulletin of the American Meteorological Society*, **87**, pp. 941–955, 10.1175/BAMS-87-7-941.

Rebelo, A.G., Boucher, C., Helme, N., Mucina, L. and Rutherford, M.C., 2006, Fynbos Biome. In *The vegetation of South Africa, Lesotho and Swaziland*, edited by Mucina, L., Rutherford, M.C. (Preroria: South African National Biodiversity Institute), pp. 221–299.

Sadr, K., 1998, The first herders at the Cape of Good Hope. *African Archaeological Review* **15**, pp. 101–132, 10.1023/A:1022158701778.

Sadr, K., 2008, Invisible herders? The archaeology of Khoekhoe pastoralists. *Southern African Humanities*, **20**, pp. 179–203.

Schalke, H.J.W.G., 1973, The Upper Quaternary of the Cape Flats area. *Scripta Geologica*, **15**, pp. 1–57.

Scholtz, A., 1986, Palynological and Palaeobotanical Studies in the Southern Cape. University of Stellenbosch, Stellenbosch, South Africa.

Scott, L., 1990. Hyrax (*Procaviidae*) and dassie rat (*Petromuridae*) middens in palaeoenvironmental studies in Africa. In *Packrat Middens: The Last 40,000 Years of Biotic Change*, edited by Betancourt, J.L., van Devender, T.R., Martin, P.S. (Tucson: University of Arizona Press), pp. 408–427.

Scott, L. and Woodborne, S., 2007a, Vegetation history inferred from pollen in Late Quaternary faecal deposits (hyraceum) in the Cape winter-rain region and its bearing on past climates in South Africa. *Quaternary Science Reviews*, **26**, pp. 941–953, 10.1016/j.quascirev.2006.12.012.

Scott, L. and Woodborne, S., 2007b, Pollen analysis and dating of late Quaternary faecal deposits (hyraceum) in the Cederberg, Western Cape, South Africa. *Review of Palaeobotany and Palynology*, **144**, pp. 123–134, 10.1016/j.revpalbo.2006.07.004.

Takhtajan, A., Crovello, T.J. and Cronquist, A., 1986, Floristic Regions of the World. (Berkeley: University of California Press).

Taljaard, J., 1996, *Atmospheric Circulation Systems, Synoptic Climatology and Weather Phenomena of South Africa*. South African Weather Service Technical Paper 32. (Pretoria: South African Weather Service).

Tyson, P.D., 1986, *Climatic Change and Variability in Southern Africa*. (Cape Town: Oxford University Press).

Tyson, P.D. and Preston-Whyte, R.A., 2000, The Weather and Climate of Southern Africa. Oxford University Press, Cape Town.

Valsecchi, V., Chase, B.M., Slingsby, J.A., Carr, A.S., Quick, L.J., Meadows, M.E., Cheddadi, R. and Reimer, P.J., 2013, A high resolution 15,600-year pollen and microcharcoal record from the Cederberg Mountains, South Africa. *Palaeogeography, Palaeoclimatology, Palaeoecology*, **387**, pp. 6–16, 10.1016/j.palaeo.2013.07.009.

Vincens, A., Lézine, A.-M., Buchet, G., Lewden, D. and Le Thomas, A., 2007, African pollen database inventory of tree and shrub pollen types. *Review of Palaeobotany and Palynology*, **145**, pp. 135–141, 10.1016/j.revpalbo.2006.09.004.

Verboom, G.A., Linder, H.P., Forest, F., Hoffmann, V., Bergh, N.G. and Cowling, R.M., 2014, Cenozoic assembly of the Greater Cape flora. In *Fynbos: Ecology, Evolution, and Conservation of a Megadiverse Region*, edited by Allsopp, N., Colville, J.F., and Verboom, G.A., (Oxford: Oxford University Press), pp. 93–118.
# An atlas of southern African pollen types and their climatic affinities

Manuel Chevalier<fnref n="1" />

*Institute of Earth Surface Dynamics, Geopolis, University of Lausanne, Switzerland*

Brian M. Chase<fnref n="2" />

*Institut des Sciences de l'Evolution-Montpellier (ISEM), University of Montpellier, Centre National de la Recherche Scientifique (CNRS), EPHE, IRD, Montpellier, France.*

Lynne J. Quick

*African Centre for Coastal Palaeoscience, Nelson Mandela University, Port Elizabeth, South Africa*

Louis Scott

*Department of Plant Sciences, University of the Free State, Bloemfontein, South Africa*

**ABSTRACT:** Interpretations of fossil pollen data are often limited to broad, qualitative assessments of past climatic and environmental conditions (e.g. colder vs. warmer, wetter vs. drier, open vs. closed landscape). These assessments can be particularly imprecise in regions such as southern Africa, where botanical biodiversity is high, and there exists an associated uncertainty regarding the climatic/environmental sensitivities of the plants contributing to a given pollen type. This atlas addresses this limitation by characterising the climate sensitivities of the 140 pollen morphotypes most often recorded in Late Quaternary palaeoecology studies in southern Africa, relying on their parent plant distributions as one of the basic factors that determine their presence. The atlas is designed as a suite of graphical diagnostic tools and photographs together with analyses of the modern geographical distribution of more than 22,000 plant species to identify their primary climatic sensitivities across southern Africa. Together, the elements included span the complete workflow from pollen identification through interpretation and climate reconstruction. The atlas can be accessed from https://doi.org/10.5281/zenodo.4013452.

<footnote n="1">¹Other affiliation: *Institute of Geosciences, Sect. Meteorology, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany*</footnote>

<footnote n="2">²Other affiliation: *Department of Environmental and Geographical Science, University of Cape Town, Rondebosch, South Africa*</footnote>
## 15.1 INTRODUCTION

Fossil pollen records have been a key source of palaeoenvironmental information in southern Africa for decades (Coetzee 1967; Finch and Hill 2008; Lim et al. 2016; Neumann et al. 2010, 2011, 2014; Quick et al. 2016, 2018; Scott and Nyakale 2002; Scott and Woodborne 2007; Scott et al. 2012; Scott 1982, 1989, 1999; Valsecchi et al. 2013; van Zinderen Bakker and Coetzee 1988; van Zinderen Bakker 1957, 1982; e.g. Bousman et al. 1988). While it is generally understood that a range of edaphic and ecological factors may influence plant distributions and vegetation composition, it is common for interpretations of pollen records to focus on plant climatic affinities and the reconstruction of past climate change. However, plant-climate relationships are often complex, and interpretive frameworks based on individuals' assessments of climate conditions where a given taxon occurs often lack the comprehensive consideration required to establish the relative influence of specific climatic parameters. Further, as pollen morphotypes (hereafter referred to as pollen types) commonly comprise more than one single species, defining the climatic affinity of a pollen type quickly becomes exceedingly complicated. These issues are amplified in regions with high botanical diversity, such as southern Africa, where tens or even hundreds of species, sometimes with distinct climate requirements, are subsumed within undifferentiated pollen types that can cover wide ranges of ecological and climatic conditions. In practice, robust statistical estimates of the specific climate affinities (both in terms of their tolerances and associated optima) are missing for the vast majority of the commonly observed pollen types, which often limits interpretations of fossil pollen data to relatively coarse-scale qualitative – and often subjective – climatological and/or environmental considerations.

The development of the CREST (Climate REconstruction SofTware) method (Chevalier et al. 2014) has enabled the production of the first quantitative climate reconstructions using fossil pollen records in southern Africa. CREST is based on the definition of independent, taxon-specific probability density functions (pdfs) to model a probabilistic relationship between pollen and climate from an ensemble of modern plant geolocalised occurrence data (hereafter 'plant distributions'). While more commonly used reconstruction techniques – such as modern analogues (Overpeck 1985; e.g. Imbrie and Kipp 1971), or regression techniques (ter Braak and Looman 1986; e.g. ter Braak and Juggins 1993) – rely on large collections of modern pollen samples to estimate pollen-climate relationships, such samples cannot be obtained for every environment or vegetation type of southern Africa. This limitation has strongly restricted their use in the region (Chevalier et al. 2014). In contrast, CREST uses widely-available plant distributions as calibration data (Chevalier 2019), and has proven efficient at reconstructing the different climates and vegetation types of the region (Chase et al. 2015a, 2015b, 2017; Chevalier and Chase 2016; Chevalier et al. 2021a; Cordova et al. 2017; Lim et al. 2016). As with all reconstruction techniques, the most reliable results are usually obtained when the method is run using a subset of taxa that are strongly influenced by the climatic parameter being reconstructed (Juggins et al. 2015; Kühl et al. 2002; Truc et al. 2013). The selection of the subset of taxa for climate reconstructions can be, however, subjective, as these selections are often based on the field experience of individuals, which could lead to inaccuracies because either the full distribution of the taxon is not considered, or the taxon is misattributed to certain conditions.

In this paper and accompanying supplementary atlas (Chevalier et al. 2021b), we present a series of diagnostic tools and results based on statistical analyses of the modern distributions of the parent plants of 140 southern African pollen types most commonly found in fossil records in relation to five key climate variables that capture the main climate gradients of the regions. This paper discusses how these diagnostics tools can be used to improve both palaeoecological interpretations and palaeoclimatological analyses derived from fossil pollen records. This atlas allows for a more accurate determination of the absolute and relative climate sensitivities of plants that produced the pollen types considered, which will facilitate both improved qualitative interpretations and the use of quantitative reconstruction methods. The user should be cognisant
of the fact that this atlas does not account for the effect of *non-climatic* parameters, such as soil
characteristics or fire, which may also be important determinants of some plant distributions.
As such, the plant-climate relationships described in this atlas should always be balanced with
considerations of the ecology of the studied taxa.

15.2 MATERIAL AND METHODS

15.2.1 Botanical Data

To estimate the climate responses of the different taxa, we used botanical data from the Global Bio-
diversity Information Facility (GBIF) database (GBIF.org 2020a, 2020b, 2020c, 2020d, 2020e,
2020f, 2020g, 2020h, 2020f; ‘GBIF: Global Biodiversity Information Facility,’ 2018) using the
curated dataset of Chevalier (2019, 2018). These data are available as ‘presence-only’ obser-
vations (i.e. the absences and abundances are not documented) and their spatial resolution are
homogenised at 0.25° × 0.25° grid square (c. 900 km² at the latitudes of southern Africa). To
build the atlas, we have used a total of 475,712 occurrence records, which correspond to 22,496
species grouped in 140 pollen types. The number of observed species in each grid cell varies
between 1 and 2815, with a median of 17 species and a mean of 81 species.

The distribution of these data is spatially heterogenous (Figure 1A), with >75% of the
records being concentrated in South Africa and its biodiversity hotspot (Rutherford *et al.* 2012;
the Greater Cape Floristic Region; Myers *et al.* 2000). This sampling bias is also visible along

<figure><img src="image_79.png" /><figcaption>**Figure 1.** (A) Distribution of presence records (plant unique observations) across the study area (note the log scale for the colour gradient). This figure highlights the two biodiversity hotspots of the region, as well as a different degree of sampling across the study area. (B) TWetQ across the study area.</figcaption></figure>
the South African border. The other countries composing our study area are less well represented, especially eastern Angola and western Zambia. However, the modern climate and vegetation gradients are less steep in those regions than they are in the southern part of the study area, where orography increases spatial variability. As such, it may be considered that the lower sampling density is sufficient to capture the full range of climate response of the studied taxa in these regions. It should also be remembered that some of the botanical data has been obtained from environments that have been influenced by human activity. While this could be considered as a factor that might undermine the natural plant-climate relationships that form the basis for this work, studies have shown that the plant-climate relationship remains robust as long as the full range of conditions experienced by the plant are sampled (Chevalier *et al.* 2014), and any anthropogenic bias is minimal.

Finally, it is important to note that a strict classification of plant species into pollen types is often not possible and our classification may contain related errors and/or regional biases. As plant and pollen taxonomies are being regularly improved, our classification is a compromise between the most recent changes and decades of palynological expertise. In general, the classification scheme adopted here maintains older names to remain consistent with published pollen records. For example, we use ‘*Acacia*-type’, even as African *Acacia* are now *Vachellia* and *Senegalia*. The full list of species composing each pollen type in southern Africa is available at the end of the atlas (Chevalier *et al.* 2021b).

### 15.2.2 Climate Data

We used a subset of the Worldclim 2 climatology (Fick and Hijmans 2017) to generate the atlas. These climate data represent the mean climate between the years 1970 and 2000 and are available at several spatial resolutions. Here, we used the 5 arcmin resolution climatologies (c. 0.083°) and upscaled them to the resolution of the botanical data (Chevalier 2019). Among the wide range of annual and seasonal bioclimatic variables that are generally considered important elements in studying the eco-physiological tolerance of plants species (Guisan and Thuiller 2005; e.g. Elith *et al.* 2006), we identified five key variables based on the following criteria: (i) they have a direct impact on plants life cycle (i.e. variables also called ‘direct gradients’ by Guisan and Zimmermann 2000), which is critical to estimate reliable plant-climate relationships, (ii) they are important descriptors of southern African climates and also (iii) they are largely uncorrelated across the study area (Table 1). Together, the selected five variables reflect many important aspects of the regional climatic drivers and are expected to be major determinants of plant distributions. By extension, it is expected that these variables can be reconstructed from regional fossil pollen records (Chevalier *et al.* 2020). The five variables we selected are:

(1) the mean temperature of the wettest quarter (TWetQ [°C], Figure 1B), which we use here as a proxy for the temperature of the growing season,

(2) the minimum temperature of the coldest month (TColdM [°C], Figure 2A) used here as a proxy for cold/frost tolerance,

(3) the accumulated precipitation of the warmest Quarters (PWarmQ [mm], Figure 2B),

(4) the accumulated precipitation of the coldest Quarters (PColdQ [mm], Figure 2C), and

(5) an Aridity Index (Aridity [unitless], Figure 2D) also derived from Worldclim 2 as a ratio between potential evapotranspiration (PET) and mean annual precipitation (Trabucco and Zomer 2019; MAP; Trabucco *et al.* 2008). With this definition, environmental changes along the Aridity gradient are not linear and environmental changes are larger across specific sections of the gradient. We, therefore, calculated the square-root of the Aridity values to ‘stretch’ the spatially-abundant arid to sub-humid section of the southern African Aridity
<table><caption>**Table 1.** Spearman's correlation indices between the five selected climate descriptors. None of these correlations are significant at the 5% level (without even correcting for multiple testing), highlighting the statistical independence of our climate descriptors across the entire study area.</caption><thead><tr><th></th><th>TWetQ</th><th>TColdM</th><th>PWarmQ</th><th>PColdQ</th><th>Aridity</th></tr></thead><tbody><tr><th>TWetQ</th><td>1</td><td></td><td></td><td></td><td></td></tr><tr><th>TColdM</th><td>-0.017</td><td>1</td><td></td><td></td><td></td></tr><tr><th>PWarmQ</th><td>0.004</td><td>0.014</td><td>1</td><td></td><td></td></tr><tr><th>PColdQ</th><td>-0.006</td><td>0.019</td><td>-0.006</td><td>1</td><td></td></tr><tr><th>Aridity</th><td>-0.002</td><td>0.011</td><td>0.003</td><td>-0.014</td><td>1</td></tr></tbody></table>

gradient and 'shrink' the most humid sections to increase the sensitivity of our modelling approach (e.g. Turner *et al.* 2020).

### 15.2.3 Pollen Photographs

Most of the pollen images are taken from the reference collection of African pollen types that was created by E.M. van Zinderen Bakker and co-workers at the University of the Free State, for a series entitled South African Pollen Grains and Spores, between 1953 and 1970 after extensive collection from several herbaria in Africa and Europe (Coetzee 1955; van Zinderen Bakker and Coetzee 1959; van Zinderen Bakker *et al.* 1970; van Zinderen Bakker 1953, 1956). Other photos were taken from the University of Cape Town reference collections (created by Jean Sugden and Sue De Villiers) and the African Pollen Database.

## 15.3 THE CREST METHOD

CREST uses probability density functions (pdfs) to estimate univariate statistical relationships between pollen and climate variables (Chevalier *et al.* 2014; Chevalier 2019). The process is based on two steps (Figure 3). First, each plant species is considered independently to calculate its pdfs, which can be interpreted as the realised niche of the species (Kearney 2006). Species pdfs are fully defined by two parameters, a mean and a standard deviation, which can be interpreted as the plant's climate preference (the optimum position on the climate gradient) and tolerance (the shape/width of the niche), respectively. To ensure a robust estimation of these two parameters, only species with a minimum of 20 grid cells in their distributions are considered in this study. Climate values are obtained by associating their occurrence data with climate gradients of interest, each being weighted according to the inverse of their abundance in the climate space (i.e. the histograms on Figures 1 and 2; Bray *et al.* 2006; Kühl *et al.* 2002). Following the recommendations of Chevalier *et al.* (2014), we imposed a symmetrical normal distribution for the species pdfs of TWetQ and TColdM and a right-skewed log-normal distribution (negative values have a probability of 0) for the species pdfs of PWarmQ, PColdQ and Aridity.

Finally, all the species pdfs of the species composing a pollen type are given a weight that corresponds to the square-root of the number of grid cells composing their distribution (i.e. species with larger distributions get a higher weight). A weighted average of their pdfs is then performed to integrate the full climatic spectrum occupied by the different species into one single response curve and meet the taxonomic resolution of the pollen type. No additional constraints are imposed to the shape of the resulting curve, which can be asymmetric (Figure 3) or multimodal
<figure><img src="image_80.png" /><figcaption>**Figure 2.** Distribution of (A) TColdM, (B) PColdQ, (C) PWarmQ and (D) Aridity Index values (low to high values for arid to humid conditions) across the study area.</figcaption></figure>
<figure><img src="image_81.png" /><figcaption>**Figure 3.** Conceptual representation of the fitting of probability density functions (pdfs) using randomly generated data. (A) Modern distribution of the environmental variable of interest. (B) Modern distribution of four species producing undifferentiated pollen grains in the environment. (C) Four curves representing four pdfs of the species represented in B. Inset histogram represents distribution of modern environment (white) that is occupied by at least one of the four species of interest (black), highlighting the preference for lower values. (D) Representation of statistics (optimum, mean, and width/uncertainty) that can be measured from each *pdf* to infer climate preferences and tolerances.</figcaption></figure>

(i.e. exhibiting two or more different climate preferences) if necessary. In this study, the pdfs are only used to characterise the climatic sensitivities of various pollen types. For more details on how to use them to reconstruct climate, we refer to the original publications (Chevalier *et al.* 2014; Chevalier 2019).
**15.4 IDENTIFICATION OF STRONGEST CLIMATIC DETERMINANTS**

This section presents how the different diagrams and pdfs curves generated for each taxon in the atlas (a combination of Figures 3 to 6) can be used to evidence specific or relative climate sensitivities. The first two examples (Poaceae – Figure 4 – and Menispermaceae – Figure 5) provide simple diagnostic criteria to identify the taxa that cannot be used as clear indicators of climate in (semi-)quantitative frameworks, while the last one (Artemisia – Figure 6) uses the graphical diagnostics tools to identify the most influential climatic parameters of the plant distribution. However, it is important to clearly differentiate ‘non-informative taxa’ from ‘taxa from which quantitative information cannot be extracted’ and to stress that excluding pollen types, such as Poaceae, from quantitative interpretations does not mean they do not convey any valuable information. Integrated in an ecological/qualitative, rather than in a statistical/quantitative approach, they can provide critical indications of palaeoenvironmental changes, as shown by the sensitive, but biome-dependent, Asteraceae:Poaceae pollen ratio at Pella, South Africa (Lim et al. 2016) and as previously suggested in central South Africa (Coetzee 1967; Cooremans 1989). Non-informative taxa can also be used as a semi-independent, but complementary source of information to support climate reconstructions.

<figure><img src="image_82.png" /><figcaption>**Figure 4.** Distribution of Poaceae (black dots) compared to Aridity Index values across the study area; refer to Figure 2D to see the masked parts (top panel). Histogram that represents the proportion of the aridity space (grey scale) that is occupied (hatched) by at least one Poaceae species (bottom panel).</figcaption></figure>
A. Distribution of Menispermaceae in southern Africa

<figure><img src="image_83.png" /></figure>

B. Distribution of Menispermaceae in the climate space of southern Africa

<figure><img src="image_84.png" /></figure>

<figure><img src="image_85.png" /></figure>

<figure><img src="image_86.png" /></figure>

C. pdfs of Menispermaceae in the climate space of southern Africa

<figure><img src="image_87.png" /></figure>

<figure><img src="image_88.png" /></figure>

<figure><img src="image_89.png" /><figcaption>**Figure 5.** (A) Distribution of Menispermaceae across the study area (661 occurrences, 494 unique occurrences). (B) Histogram that represents the proportion of the climate space (grey scalee histogram) that is occupied (hatched histogram) by at least one Menispermaceae species. 5-10% of each class are occupied by at least one species. (C) pdfs fitted on each species (grey) highlighting the diversity of the individual responses and their broad combined pdf (black).</figcaption></figure>
<figure><img src="image_90.png" /><figcaption>**Figure 6.** Distribution of *Artemisia* (white dots; 172 unique occurrences) plotted against four different climate gradients and summarised as histograms and respective pdf. The combination of TWetQ and Aridity (top row) allows inferring that *Artemisia* grows in cool and mesic conditions in southern Africa.</figcaption></figure>
Finally, it should also be noted that any assessments of sensitivity from this study relate to the southern African context and the geographical and climatic spaces here defined (Figures 1 and 2), with a clear focus on the southern half of the study area due to distribution of plant occurrence data (Figure 1A). These results are also mainly valid at the spatial scale of the atlas and they could be superseded by local constraints (e.g. edaphic conditions). As such, extrapolating these results beyond this geographical range and spatial resolution should only be done carefully.

### 15.4.1 Identification of climatically ‘uninformative’ taxa

Some pollen types are not useful for quantitative climatic reconstructions because of the diversity of species producing pollen of indistinguishable morphologies. Best illustrated by Poaceae (1266 species grouped into 242 genera in our southern African botanical database; Figure 4), the diversity issue highlights one limitation of many statistical tools as there is no simple, unique link between the presence records and the associated climate values. Presumably present (almost) everywhere in southern Africa (Poaceae species have been observed in about 60% of all Aridity classes, see Figure 4), only their spatially-varying relative abundance in the modern environment (unavailable data) may contain reliable climatic information. In fact, when the number of species becomes too large, and the niches too diverse, the pdfs of such taxa are not flat - as with the pdfs of uninformative taxa - but rather exhibit a peak that reflects the distribution of the whole climate space rather than specific sensitivities (see Poaceae on pp. 234–235 of the atlas (Chevalier et al. 2021b), but also other taxa, such as Scrophulariaceae-type [pp. 272–273], Asteraceae [pp. 60–61] or Fabaceae [pp. 124–125]).

The degree of scattering of the different species pdfs along the climate gradient is another characteristic that may mask any potential climatic signal from being conveyed. As an example, Menispermaceae - composed of 30 species in southern Africa, but with only 10 having at least 20 grid cells in their distribution - can be found in a large range of distinct environments, as each species occupies a unique geographical and climatic niche (Figure 5). This means that Menispermaceae pollen cannot easily be interpreted in terms of climate (at least not for the five climate variables selected here) and, as such, cannot be directly used to reconstruct past climates without additional assumptions to deconvolve the different species signals. Such situations can be identified by observing the geographical distribution of the taxon (Figure 5A), but also by looking at the distribution of the taxon in the climate space (Figure 5B) and the shape of the pdfs, which are generally multimodal and/or associated with a high variance despite a limited number of composing species (Figure 5C).

### 15.4.2 Identification of climate sensitivities

Geographical distributions, species diversity, and the shapes of the pdfs can be reliable criteria for identifying taxa from which climatic information cannot be extracted from their distributions. However, identifying the most important climate sensitivities of the ‘informative’ taxa (i.e. the taxa from which specific climate sensitivities can be inferred from their distribution) is less direct, especially since plants are often sensitive to a range of climatic parameters. In practice, the distribution, availability and accessibility of different climates within any definite climate space generally reduce that multifactorial sensitivity aspect to one or two specific parameters that most strongly limit the plants’ distributions.

Using the monospecific *Artemisia* pollen type (*Artemisia afra*) as a case study to illustrate the process of identification of climate sensitivities, the criteria aforementioned - especially the shapes of the pdfs - are unreliable indicators of climate sensitivities (Figure 6). By construction,
the shape of monospecific pollen types is constrained and will appear smooth. In such situations, looking at the distribution of *Artemisia* in the southern African climate space, and more specifically where it grows and where it does not grow, provides additional valuable information to discriminate between constraining and non-constraining variables. Real absence data are not available here, but it has been suggested that when the modern presence record dataset is sufficiently extensive, pseudo-absence records can be inferred (Elith and Leathwick 2009; Elith et al. 2006; e.g. Birks et al. 2010). Superimposing plant distributions on climate layers thus sheds light on whether a variable is important or not. Maps in Figure 6 show that most records of *Artemisia* (c. 70% of the distribution) occur between TWetQ values of 17.5 to 25°C. *Artemisia* is rarely found in warmer or colder areas, and, therefore, TWetQ seems to apply a relatively strong constraint on the plant's distribution. Similarly, the distribution of Aridity Index values seems to also constrain the distribution of *Artemisia* which is mainly found at values between 0.25-0.75. In contrast, *Artemisia* is observed across the entire PWarmQ (23 to 616 mm) and PColdQ (1 to 340 mm) precipitation gradients. The false impression that *Artemisia* needs elevated summer rainfall is caused by the orographic effect of the higher altitude regions of eastern South Africa that locally correlates cool temperatures with high summer precipitation. Combined these maps suggest that *Artemisia* is more directly limited by a combination of average-to-cool temperatures of the growing season and mesic conditions rather than by precipitation amount specifically, at least in the southern African context.

The same approach of superposing the distributions with climate gradients can be used to highlight some specific sensitivities. For example, the distribution of *Podocarpus* is strongly correlated with high humidity values (Chevalier *et al.* 2021b, pp. 236–237), while *Zygophyllum's* distribution is concentrated on the other side of the aridity gradient in the most arid grid cells [pp. 312–313]. *Brachystegia* [pp. 66–67] lives in the hottest and most humid sections of our climate space, while *Burkea* [pp. 68–69] has a similar thermal niche but a preference for more mesic climates. These assessments are consistent with previous interpretations, but the data presented here clarifies and validates these interpretations, and perhaps more importantly, contributes to a common understanding of a broader range of plant-climate relationships and environmental sensitivities in southern Africa.

### 15.4.3 Relative comparisons of the climate preferences

The pdfs allow for an individual assessment of each pollen type for each variable. However, it can also be useful to assess relative sensitivities (Does taxon X prefer colder temperatures than taxon Y?) to better understand vegetation dynamics from a pollen diagram. To do so, the atlas also contains comparative figures of the variable-specific pdfs and also an analysis of their climate preferences by Principal Component Analysis (PCA) run on the five climate optima (Chevalier *et al.* 2021b, pp. 315–338). Here, we present the same analysis performed on the 40 most common pollen types in southern African records (Figures 7 and 8).

The first two axes represent >80% of the total variance and separate plant taxa according to different groups. The bottom left corner is occupied with taxa with preferences for low temperatures during the growing season, the top left quadrant shows a gradient of taxa that preferentially live in arid to semi-arid conditions, while the right half consists of taxa that prefer summer-rainfall conditions. The position of *Artemisia* near the centre of the PCA biplot supports our interpretation that this taxon is preferentially associated with average climate conditions in southern Africa. However, it should be noted that this PCA approach is only one element to consider and is not an absolute descriptor of relative tolerances. This PCA analysis is only based on climate optima and not the tolerances and of secondary optima of the pdfs. As shown in Figure 8, Geraniaceae has an arid climate optimum (c. 0.25), but its tolerance is large (c. 0.15 – 0.8), as distinct Geraniaceae species can grow across the entire aridity gradient of the region (Chevalier
<figure><img src="image_91.png" /><figcaption>**Figure 7.** Scatterplot of the two first principal components of the PCA run with the five climate optima of the 40 most frequent pollen types in southern African records.</figcaption></figure>

et al. 2021b, pp. 136–137). Scrophulariaceae-type (Chevalier *et al.* 2021b, pp. 272–273) as a similarly broad tolerance to the aridity gradient but with an optimum towards sub-humid values.

**15.5 DISCUSSION AND CONCLUSIONS**

The diagnostic tools presented in this paper are derived from the statistical analyses of modern plant distributions and provide a robust framework from which consistent interpretations of pollen data can be obtained. The three case studies were chosen to represent specific issues associated with the identification of climate sensitivities (high taxonomic diversity with Poaceae, scattering in the climate space with Menispermaceae and locally-correlated factors with *Artemisia*). While the first two are easily identified, confounding factors may be more difficult, and can lead to erroneous interpretations, especially when transposed back in time (Juggins 2013; Telford and Birks 2011). Confounding factors can only be detected when all the climatic and environmental aspects that matter to the plant life cycle are being considered. However, it should be kept in mind that since we only considered five climate variables, the full range of possibilities may not be always realised, and climatic factors important to certain taxa may not have been included in the atlas.
<figure><img src="image_92.png" /><figcaption>**Figure 8.** Aridity Index pdfs of the 40 most frequent taxa in southern African pollen records. Taxa are sorted from bottom (drier) to top (more humid) according to their climate optimum, not their tolerance.</figcaption></figure>
Further, plant taxon distributions do not necessarily equal their pollen distributions precisely, as indicated by Hamilton (1972) for East Africa. Where available, knowledge of pollen distribution and preservation mechanisms could be used to adjust pollen-interpreted reconstructions. It is also important to remember that non-climatic factors such as fire or edaphic conditions are also important - even decisive - drivers of some plant distributions. Therefore, even if these diagnostic tools represent a valuable source of information regarding plant-climate relationships, they should always be considered in combination with ecological/botanical knowledge to ensure that essential aspects have not been ignored.

In addition, it is important to note that a climate sensitivity does not give any indication of the actual sign of the signal conveyed by the pollen (e.g. wetter or drier, warmer or colder). In fact, the sign of a signal is a relative concept that depends exclusively on the assemblage in which the taxon was observed. For instance, the relatively xeric *Pentzia*-type (Chevalier *et al.* 2021b, pp. 224–225) was on average an indicator of more humid environments in the Desert Biome at Pella, South Africa during the Holocene (Lim *et al.* 2016), while it was generally indicative of more arid environments in records from the more humid climates of eastern South Africa (Chevalier and Chase 2016). This relativity includes temporally-varying signatures relating to long-term changes in climate (e.g. *Tarchonanthus* [pp. 292–293] as a high and low precipitation indicator at Wonderkrater, South Africa (Chevalier and Chase 2015)).

Finally, while this paper is dedicated to the analysis of southern African pollen types, all the diagnostic tools presented are transferable and applicable to other regions of the World and/or different ecosystems as they are based on the *crestr* R package (https://github.com/mchevalier2/crestr) and can be generated using R scripts available at https://github.com/mchevalier2/Papers/tree/master/2021_Chevalier_etal_Pollen_Atlas_SA. This technique will help to formalise and standardise pollen-climate interpretations and facilitate and refine interpretations of regional fossil pollen data.

## 15.6 DATA AVAILABILITY

The 'Atlas of southern African pollen types and their climatic affinities' is available from https://doi.org/10.5281/zenodo.4013452.

## ACKNOWLEDGEMENTS

This work was supported by the European Research Council (ERC) under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC Starting Grant 'HYRAX', grant agreement no. 258657. The authors also acknowledge the support of the South African National Botanical Institute (SANBI) in sharing their botanical data. Lloyd Rossouw, Alma Fuller and Frederick Scott for scanning and arranging negatives of the pollen reference collection of the Department of Plant Sciences University of the Free State in a database. LS was supported by the National Research foundation (South Africa) (NRF Grant no. 85903). Any opinion, finding, and conclusion or recommendation expressed in this material is that of the authors, and the NRF does not accept any liability in this regard.

## REFERENCES

Birks, H.J.B., Heiri, O., Seppä, H. and Bjune, A.E., 2010, Strengths and weaknesses of quantitative climate reconstructions based on Late-Quaternary biological proxies. *The Open Ecology Journal*, **3**, pp. 68–110, 10.2174/1874213001003020068.