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<figure><img src="image_93.png" /></figure>

Taylor & Francis

Taylor & Francis Group

http://taylorandfrancis.com
# Pollen productivity estimates from KwaZulu-Natal Drakensberg, South Africa

Trevor R. Hill & Tristan J. Duthie<fnref n="1" />

*Discipline of Geography, School of Agriculture, Earth and Environmental Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa*

Jane Bunting

*Department of Geography, Geology and Environment, University of Hull, Hull, United Kingdom*

**ABSTRACT:** Pollen assemblages from sedimentary sequences reflect the landscapes surround-
ing the basin at the time of deposition, however the translation of pollen records into estimates
or maps of past landcover is complex. Models of the pollen-vegetation relationship can help, but
need to be calibrated first against modern data on pollen and vegetation for the pollen types of
interest. This study presents initial estimates of Relative Pollen Productivity in the KwaZulu-Natal
Drakensberg, South Africa, a palaeoecologically significant region of southern Africa, using the
Extended R-Value approach. Modern pollen spectra from soil surface samples were extracted
from three dominant vegetation communities, and vegetation data were collected around each
sample point using a 3-tiered ring surveying approach. Fall speeds of key taxa were calculated
using Stokes Law for spherical pollen grains and Falck's assumption for ellipsoidal grains. Pollen
and vegetation data were subjected to Extended R-value analysis to calculate Relevant Source
Area of Pollen (RSAP) and Relative Pollen Productivity estimates (RPP). RSAPs were found to
be approximately 100-150 m in all three communities. Asteraceae and Proteaceae have low RPPs
relative to Poaceae, the RPP of Ericaceae is comparable to Poaceae, and Pteridophyte spores and
Rosaceae and Podocarpaceae pollen have significantly higher RPP values than Poaceae. These
results imply that the cover of some important shrub taxa may well be underestimated in palaeo-
cological records from the area relative to forested communities. The number of modern pollen
samples collected and analysed was small, and therefore these results can only be considered
initial estimates, however they demonstrate that it is possible to use soil surface samples to obtain
RSAP and PPE estimates in this important ecological and cultural region of southern Africa.

**16.1 INTRODUCTION**

A key aim of Quaternary palynology investigations is the quantitative reconstruction of past veg-
etation (Broström 2002; Sugita 2007a). A quantified understanding of the relationship between
pollen assemblages retrieved at a site and the surrounding vegetation provides a sound basis for
quantitative reconstruction of past environments (Jackson 1994). Plants have a range of growth

<footnote n="1">¹Other affiliation: University of Suwon, South Korea</footnote>
forms and reproductive strategies, and these lead to differences in the amount of pollen pro-
duced by equivalent amounts of plant and to the dispersal potential of those pollen grains. This
means pollen diagram interpretation is complicated (Jackson 1994; Prentice 1988); despite recent
developments, much of the interpretation of pollen diagrams remains subjective and based on
intuition. The challenge thus faced by contemporary palynologists has been to develop and supply
the study of pollen analysis with improved tools for quantifying past vegetation and reconstruction
techniques of palaeoenvironments (Räsänen et al. 2007).

There are three main approaches to the reconstruction of past land cover from pollen data: biomisation, modern analogue approaches and modelling the pollen-vegetation relationship. Biomisation approaches convert pollen data into plant functional types, then use ratios of these types to identify the biomes present in past landscapes (Jolly et al. 1998; Vincens et al. 2006), combining sites to map past land cover at biome level across regions and continents. Modern analogue approaches take spatially extensive datasets of modern pollen assemblages and associated environmental parameters (e.g. climate factors such as precipitation or mean annual temperature, or indices of landcover), develop transfer functions or other mathematical models such as the CREST model (e.g. Chevalier et al. 2014), then apply these models to pollen records to reconstruct the proxies of interest for the duration of the record (e.g. Chevalier et al. 2021). Both approaches assume that modern biomes/ecosystems, and therefore the pollen assemblages derived from them, are comparable with those present in past landscapes. As pollen is widely dispersed in the landscape from the original plants, pollen assemblages reflect both the local environment and the wider region, often referred to as a source of ‘background’ pollen. This ‘background’ component can make up 40–60% of the pollen influx recorded in a small lake or wetland (Sugita 1994), and whilst nature reserves and areas with lower human occupation may protect ecosystems which strongly resemble past conditions, the wider landscape has been heavily modified. The pollen-vegetation relationship modelling approach aims to address this concern by understanding how the pollen signal is formed, developing models of the relationship which are parameterised against modern data. The most widely used models of the pollen-vegetation relationship are linear, building on the R-value approach suggested by Davis (1963) (Andersen 1970; Davis 1963; Prentice 1985; Sugita 1993; reviewed by Jackson and Lyford 1999). This approach assumes that the pollen-vegetation relationship is constant in time, but is explicitly designed to reconstruct landcover options which have no modern analogues (Sugita 2007a 2007b; Bunting and Middleton 2009). This paper calibrates one such model for the main plant taxa found today in the KwaZulu-Natal Drakenberg, South Africa.

In the pollen-vegetation modelling approach, plants and their signal in a pollen assemblage
are connected through models of pollen dispersal and deposition (Abraham and Kozáková 2012;
Bunting 2008; Bunting and Hjelle 2010; Davis 2000). Using the modelling approach allows
reconstruction of both abundance and spatial distribution of past vegetation (e.g. Broström 2002;
Gaillard et al. 2008; Prentice 1985; Sugita 2007a, 2007b). The development of modern models
of pollen dispersal and deposition has been reviewed by many authors and is not considered
further here (e.g. Davis 2000; Li et al. 2018). The linear models used depend on estimates of
Relative Pollen Productivity (RPP) and pollen fallspeed for the taxa of interest, and over the last
20 years, sets of these parameters have been developed for many regions of Europe and China
(e.g. Abraham and Kozáková 2012; Broström et al. 2004; Bunting et al. 2005; Hjelle 1998; Li
et al. 2018; Mazier et al. 2008; Mazier et al. 2012; Nielsen 2003; Räsänen et al. 2007; Soepboer
et al. 2007; Sugita et al. 1999; von Stedingk et al. 2008). To date, there has been one such study
published from Africa, in which Duffin and Bunting (2008) calculated RSAP and RPP estimates
for southern Africa savanna taxa in the Savanna Biome of the Kruger National Park; a second
such paper is included in this volume (Githumbi et al.).

The aim of this study was to obtain estimates of pollen productivity in the KwaZulu-Natal
Drakensberg, to extend the range of taxa available for model use in South Africa and as a first
step towards land cover reconstruction in the wider region, in order to strengthen the quality and
<figure><img src="image_94.png" /></figure>

**Figure 1.** Location of Cathedral Peak study site in relation to: (a) or provinces and geographic features of South Africa, (b) the Drakensberg mountains in KwaZulu-Natal, and (c) kocal geographic features. Note: the wetlands highlighted in part c are those referred to in the study mentioned in the text by Lodder *et al.* (2018).

reliability of interpretations of long term palaeoecological records and improve the usefulness of
palaeoecological data for allied disciplines such as conservation and archaeology.

16.2 MATERIALS AND METHODS

16.2.1 Study site

The study was carried out in the Cathedral Peak region of the KwaZulu-Natal Drakensberg (28°56'26" S, 29°14'06" E) (Figure 1). The Drakensberg mountain range is an ecologically important area of South Africa with a number of archaeological and palaeo-ecological records, and is therefore a significant area of study (Mitchell and Lane 2013). The vegetation shows a strong altitudinal progression from Drakensberg Foothill Moist Grassland to Northern Drakensberg Highland Grassland, then to uKhahlamba Basalt Grassland and finally, Drakensberg Afro-alpine Heathland at the highest altitudes (Mucina and Rutherford 2011).

Three vegetation communities were focused on for this study; *Themeda* dominated Highland Sourveld (Acocks, 1988) that will be referred to as *Themeda* grassland, *Protea* savanna and *Leucosidea sericea* scrubland. These three communities are the dominant vegetation communities represented in the region, contain between them the plants producing the major pollen types seen in palaeoecological-records, and occupy different altitudinal climate zones, therefore changes in the distribution of these communities are expected in response to environmental change (Hill 1996). The range of altitudes sampled varies between 1300–2000 m asl mean annual rainfall in this altitudinal range fluctuates between 700–1500 mm, and predominately falls in the summer months between October and March.
## 16.2.2 Methods

A suite of standard methods were applied to collect and analyse the data required (Figure 2).

<figure><img src="image_95.png" /><figcaption>**Figure 2.** Schematic breakdown of methods used in this research: Field, Laboratory and Analysis.</figcaption></figure>

### 16.2.2.1 Field methods

A total of five samples in each of the three vegetation communities were randomly located using ArcGIS 10.2, giving a total of 15 sample sites. Random positioning of sample locations is important wherever possible to obtain reliable RSAP and RPP estimates (Broström et al. 2005; Mazier et al. 2008). The central sampling point was located within a geo-rectified aerial photograph of the site, and locations in the field were found using a hand-held Garmin eTrex 10 GPS. At each sample site, the upper 2 cm of surface soil was collected following the methods described by Adam and Mehringer (1975) and Bunting et al. (2013) for collection of modern pollen surface samples. Vegetation surveys were carried out using a 3-tiered surveying approach, known as the Crackles vegetation protocol (Bunting et al. 2013), which was developed to standardise vegetation data collection for model calibration and thereby facilitate comparison of results between modelling studies. The vegetation surveying method is partitioned into three zones (A, B, C), with a comprehensive vegetation survey undertaken in zone A (0–10 m from the sample point), an intermediate vegetation survey in zone B (10–100 m), and GIS and remote sensing techniques used to extract broad-scale vegetation data from maps in zone C (100-1000+m).

### 16.2.2.2 Laboratory methods

Soil surface samples were processed following standard chemical extraction methods as described by Moore et al. (1991) and Duffin and Bunting (2008) at the University of KwaZulu-Natal (UKZN) Palaeoecology laboratory. Count size was determined by initially processing one soil surface sample from each of the vegetation communities, counting the pollen and recording the results with increasing sum size, and carrying out a 2-way Analysis of Variance (ANOVA) to assess if there was any significant difference in assemblages between sample counts of 250, 500 and 1 000 pollen grains in each community (Sokal and Rohlf 1969). In all three communities
it was found that no statistically significant difference existed between the population means of pollen counts of 250, 500 and 1 000 at the level of significance (α = 0.05), therefore remaining samples were counted to a sum of 250 grains (Hill 1996). All counting was conducted at x630 magnification using a Leica DM750 light microscope, with reference to the African Pollen database and UKZN Pollen repository specific to the region.

### 16.2.3 Data analysis

#### 16.2.3.1 Estimating pollen fall speed

To use the taxon-specific Prentice-Sutton distance-weighting model to the vegetation data, estimates of the sedimentation velocity of the pollen types are required. An estimation method based on grain shape and size was used (e.g. Duffin and Bunting 2008). Pollen grains were categorised into two shape classes – spherical and ellipsoidal. Pollen grain geometries were measured using a Leica DM750 light microscope at ×400 magnification. Spherical grains were measured along one axis only, recording a diameter measurement, whereas ellipsoidal grains were measured along major and minor axes. For each taxon, a total of 50 random pollen grains were measured from pollen reference slides collected in the region, and an average diameter (spherical) and major and minor axis (ellipsoidal) measurement calculated. Fall speed was estimated using Stokes Law for spherical grains (Equation 1) and Falck's Assumption for ellipsoidal grains (Equation 2) (Gregory 1973).

$$v_s = \frac{2r^2 \cdot g(\rho_0 - \rho)}{9\mu} \quad \text{Equation 1. Stokes Law}$$

where:

$v_s$ = spherical settling velocity (cm s⁻¹)

$r$ = pollen grain radius (cm)

$g$ = acceleration due to gravity constant taken as 981 cm s⁻²

$\rho_0$ = grain density (cm⁻³) taken as 1 cm⁻³

$\rho$ = air density (cm⁻³) taken as 1.27 × 10⁻³ cm⁻³

$\mu$ = dynamic viscosity (cm⁻¹ s⁻¹) taken as 1.8 × 10⁻⁴ cm⁻¹ s⁻¹

$$v_e = v_s \sqrt[3]{\frac{a}{b}} \quad \text{Equation 2. Falck's Assumption}$$

where:

$v_e$ = ellipsoidal settling velocity (cm s⁻¹)

$v_s$ = spherical settling velocity (cm s⁻¹)

$a$ = major axis (cm)

$b$ = minor axis (cm)

#### 16.2.3.2 Distance weighting vegetation data

Distance weighting of vegetation data is necessary before comparing vegetation with pollen assemblages formed at a specific location, so as to account for the empirical observation that plants situated closer to a sampling location contribute more pollen than those situated further away. This study used the Prentice-Sugita distance weighting term (Prentice 1985, 1988; Sugita 1993), which assumes aerial transport of pollen above the vegetation canopy is the mode of connection between plant and pollen assemblage. Vegetation data were collated into rings and distance weighted plant abundance (dwpa) calculated using the ring-based approach described in the Crackles vegetation protocol (Bunting et al. 2013).
The Prentice-Sugita-Sutton weighting term gᵢ for taxon i at distance z is calculated as:

$$g_i(z) = b_i \gamma z^{\gamma-1} e^{b_i z^{\gamma}} \quad \text{Equation 3}$$

where

$$b_i = \frac{4v_g}{nu\sqrt{\pi}C_z} \quad \text{Equation 3a}$$

and,

z = distance

γ = a coefficient of 0.125 (Prentice 1985)

v_g = approximated by v_s (fall speed, m sec⁻¹)

C_z = the vertical diffusion coefficient (m¹/⁸)

n = a dimensionless turbulence parameter equal to 2

u = windspeed (m sec⁻¹), set equal to 3.

### 16.2.3.3 Extended R-value analysis (ERV)

Extended R-value analysis is an iterative computational approach to estimating values of relative pollen productivity from a bivariate dataset of modern pollen counts and distance-weighted vegetation abundance (Parsons and Prentice 1981; Prentice and Parsons 1983). By applying this analysis to vegetation data surveyed out to different distances from the sample point, it is possible to assess the spatial sensitivity of the pollen signal in the samples (by identifying the Relevant Source Area of Pollen-Sugita 1994), which provide the best estimates of relative pollen productivity (RPP - amount of pollen produced per unit vegetation, expressed as a ratio relative to a reference taxon) for the studied landscapes and can inform the spatial interpretation of past land cover around pollen sites with similar characteristics.

On examination of the pollen counts and vegetation data, it was clear that the species lists of the three communities were too different to allow all 15 samples to be analysed together, therefore data analysis was carried out separately for each community. It is generally recommended that the number of samples included in an analysis should be at least twice the number of pollen taxa considered (Bunting and Hjelle 2010), however this was not possible in this study. All pollen taxa recorded as present in both the pollen and vegetation data from all five samples in each community were included in the analysis, and ERV analyses were run using the HUMPOL (Hull Method of Pollen dispersal and deposition model) software suite on each of the vegetation communities separately to estimate RSAP (Relevant Source Area of Pollen, the distance beyond which adding additional vegetation survey data does not improve the fit between data and model) and to calculate RPP values for dominant taxa from each community. ERV analysis was run using sub-models 1 and 2 only, as these models use pollen and vegetation percentage data (as was collected in this research), whereas sub-model 3 was not used as it requires absolute vegetation data (Gaillard et al. 2008).

ERV model 1 assumes a taxon-specific background pollen component for each taxon i that is constant relative to the total influx of pollen at a site (Parsons and Prentice, 1981), whereas ERV model 2 assumes a taxon-specific background pollen component for each taxon i that is a constant relative to the total plant abundance around a site (Prentice and Parsons, 1983). ERV model 3 was proposed by Sugita (1994) and can be utilised if pollen proportion and absolute plant abundance data are available, which it is not for this study. Gaillard et al. (2008) suggest that similar results should be obtained from the different ERV models if the background pollen loading is low relative to the total pollen loading at a sampling site. Broström (2002) points out that there is no apparent motive for choosing ERV model 1 over 2 (or vice versa) a priori, and that both should be used simultaneously to see if they provide comparable model outputs (i.e. relative pollen productivity αᵢ and background pollen component zᵢ to deduce their robustness).
### 16.2.4 Application of RPP estimates to palaeoecology

One published pollen record is available from the study area, an unnamed small wetland within the management catchment called ‘catchment six’ (c. 0.1 ha – see Figure 1) (Lodder et al. 2018). The small size of the site and lack of other nearby sites means that full reconstruction of past landcover using the Landscape Reconstruction Algorithm (Sugita 2007a; b) is not appropriate, since this method relies on the availability of multiple pollen sites. The Multiple Scenario Approach (e.g. Bunting et al. 2018) could be applied to a single small site, however it is time-consuming and will be considered for future research. As a simple demonstration of how RPP values can be used to inform interpretation of pollen diagrams, therefore, we have undertaken a simple correction, taking no account of background pollen influx. Using count data for the taxa listed in Table 1, we summed data into a single sample for each pollen zone then calculated unweighted and weighted pollen percentages for comparison. Weightings were calculated as follows:

$$p_{iw} = \frac{n_i/R_i}{\sum_{m}^{1} n_j/R_j} \qquad \text{Equation 4. Simple weighting}$$

where

* $p_{iw}$ is the weighted proportion of taxon i

* $n_i$ is the count of grains of taxon i

* $R_i$ is the RPP$_{Poaceae}$ value for taxon i

* m is the number of taxa for which RPP$_{Poaceae}$ values are available.

<table><caption>**Table 1.** Palynological equivalent taxa identified from Cathedral Peak. Shown are the ten most prevalent taxa for all sample sites.</caption><thead><tr><th rowspan="2">Vegetation Community</th><th rowspan="2">Sub-sample number</th><th colspan="10">Percentage of pollen sum (%)</th></tr><tr><th>Anacardiaceae</th><th>Asteraceae</th><th>Cyperaceae</th><th>Ericaceae</th><th><i>Pinus</i></th><th>Poaceae</th><th><i>Podocarpus</i></th><th>Proteaceae</th><th>Pteridiophyte spores</th><th><i>Leucosidea sericea</i></th></tr></thead><tbody><tr><td rowspan="5"><i>Themeda</i> grassland</td><td>1</td><td>2.68</td><td>9.96</td><td>2.30</td><td>0.00</td><td>1.15</td><td>55.17</td><td>0.00</td><td>0.00</td><td>5.75</td><td>0.00</td></tr><tr><td>2</td><td>1.18</td><td>10.20</td><td>1.96</td><td>0.39</td><td>3.53</td><td>60.78</td><td>0.00</td><td>0.00</td><td>14.12</td><td>0.00</td></tr><tr><td>3</td><td>0.00</td><td>8.95</td><td>2.72</td><td>0.00</td><td>1.95</td><td>62.26</td><td>1.17</td><td>0.00</td><td>14.79</td><td>0.00</td></tr><tr><td>4</td><td>0.79</td><td>7.91</td><td>2.77</td><td>0.00</td><td>1.98</td><td>67.98</td><td>0.79</td><td>0.40</td><td>9.88</td><td>0.00</td></tr><tr><td>5</td><td>0.78</td><td>5.08</td><td>4.30</td><td>0.39</td><td>3.52</td><td>73.05</td><td>0.78</td><td>0.00</td><td>5.47</td><td>0.00</td></tr><tr><td rowspan="5"><i>Protea</i> savanna</td><td>1</td><td>0.00</td><td>6.25</td><td>0.00</td><td>0.39</td><td>5.08</td><td>57.81</td><td>2.34</td><td>9.38</td><td>11.33</td><td>0.00</td></tr><tr><td>2</td><td>0.79</td><td>3.56</td><td>9.49</td><td>0.40</td><td>1.19</td><td>54.94</td><td>1.19</td><td>6.32</td><td>8.30</td><td>0.00</td></tr><tr><td>3</td><td>0.39</td><td>4.28</td><td>3.50</td><td>0.00</td><td>2.33</td><td>56.81</td><td>0.39</td><td>8.17</td><td>14.40</td><td>0.00</td></tr><tr><td>4</td><td>1.20</td><td>3.19</td><td>2.79</td><td>0.40</td><td>1.20</td><td>41.83</td><td>0.80</td><td>5.58</td><td>36.25</td><td>0.00</td></tr><tr><td>5</td><td>0.39</td><td>5.43</td><td>3.88</td><td>1.94</td><td>3.88</td><td>46.12</td><td>1.94</td><td>9.69</td><td>15.89</td><td>0.00</td></tr><tr><td rowspan="5"><i>Leucosidea sericea</i> scrubland</td><td>1</td><td>0.79</td><td>15.75</td><td>3.54</td><td>0.79</td><td>2.36</td><td>47.24</td><td>0.00</td><td>0.00</td><td>11.42</td><td>10.63</td></tr><tr><td>2</td><td>0.00</td><td>18.33</td><td>3.19</td><td>1.99</td><td>1.59</td><td>54.98</td><td>0.00</td><td>0.00</td><td>9.56</td><td>2.79</td></tr><tr><td>3</td><td>0.39</td><td>24.02</td><td>2.36</td><td>1.97</td><td>1.57</td><td>41.73</td><td>0.00</td><td>0.00</td><td>14.17</td><td>1.57</td></tr><tr><td>4</td><td>0.40</td><td>14.68</td><td>3.97</td><td>1.59</td><td>0.79</td><td>44.84</td><td>0.00</td><td>0.00</td><td>25.79</td><td>2.38</td></tr><tr><td>5</td><td>1.20</td><td>14.34</td><td>3.59</td><td>1.20</td><td>0.00</td><td>44.62</td><td>0.00</td><td>0.00</td><td>20.32</td><td>1.99</td></tr></tbody></table>
# 16.3 RESULTS

## 16.3.1 Vegetation data

Poaceae is the dominant vegetation cover type in all three studied vegetation communities, interspersed with shrubs and herbs (Asteraceae, Ericaceae, Euphorbiaceae, Fabaceae, Rubiaceae and Thymelaeaceae species). Bracken fern (*Pteridium aquilinum* (L.) Kuhn) is omnipresent throughout all vegetation communities. The *Themeda* grassland primarily consisted of Poaceae and small herbs and shrubs of Acanthaceae (*Barleria monticola*), Asteraceae (*Helichrysum aureonitens*, *Vernonia natalensis*, *Senecio coronatus*), Euphorbiaceae (*Acalypha punctata*), and Fabaceae (*Eriosema simulans*). Predominant woody vegetation cover was in the form of patchy indigenous *Podocarpus* type forest in all communities. Higher altitudes and valley areas along river courses are dominated by scrub vegetation which was particularly evident in the *Leucosidea* and *Protea* communities. Proteaceae (*Protea caffra*, *P. roupelliae*) was scattered throughout the *Protea* community, which is otherwise dominated by grasses and shrubs. Rosaceae (*Leucosidea sericea*) was pervasive throughout the *Leucosidea* community; vegetation in this landscape was dominated by grasses, ferns and *Leucosidea sericea*, a woody species that will invade un-burnt grassland.

## 16.3.2 Pollen data

Forty palynological equivalent taxa were identified from a total of 15 samples (Table 1 and 2). All samples had high proportions of Poaceae pollen (40–70%). The *Themeda* grassland community samples consistently contained Pteridophyte spores (5–15%), and herbaceous Asteraceae pollen (5–10%). Anacardiaceae, Cyperaceae and Pinaceae pollen grains were recorded in significant quantities. Anacardiaceae can be explained by the omnipresence of *Searsia dentata* shrubs throughout the *Themeda* grassland landscape and Cyperaceae reflects the presence of aquatic-type environments, explained by a nearby wetland and stream system close to all *Themeda* samples. The presence of exotic Pine is interesting considering the closest known Pinaceae plantation is approximately 7 km away, however isolated pine trees were observed invading nearby surrounding grasslands. Samples from *Protea* savanna communities contained Pteridophyte spores (10–40%), Proteaceae (5–10%) and Asteraceae (6%) pollen. The *Leucosidea* scrubland samples have substantial proportions of Asteraceae (10–25%) pollen and Pteridophyte spores (5–25%), and lower of Rosaceae (10%), Ericaceae (<2%) and Cyperaceae (5%) pollen values.

## 16.3.3 Data analysis

Taxa chosen for ERV analysis of samples from the *Themeda* grassland were Poaceae, Pteridophyte and Asteraceae, ERV analysis from the *Protea* savannah included Poaceae, Pteridophyte, Asteraceae, Proteaceae and Podocarpaceae, and ERV analysis from the *Leucosidea* shrubland included Poaceae, Pteridophyte, Ericaceae, and Rosaceae. Outcomes of ERV analysis (using both ERV model 1 and 2) show that all communities have a RSAP range of 100–150 m, suggesting that the local vegetation cover has a strong influence in the pollen spectra (Figure 3). Plots of likelihood function score reach an asymptote approximately 100–150 m from sample points (Figure 3). While RSAP values from sub-model 1 and 2 were similar, Relative Pollen Productivity values were markedly different, and the model which produced the most strongly clustered RPP values was chosen as the most appropriate one for each habitat (Broström 2002); this was ERV sub-model 1 in all three cases (Table 3). RPP<sub>Poaceae</sub> (pollen productivity relative to Poaceae) values were calculated by averaging all ERV analysis estimates from distances beyond 150 m.
<table><caption>**Table 2.** Shape, diameter and fall speeds of dominant taxa, Cathedral Peak.</caption><thead><tr><th>Cathedral Peak taxa</th><th></th><th>Approx. shape</th><th>Avg. diameter (µm)</th><th>Fall speed (m·s<sup>-1</sup>)</th><th>Avg. fall speed (m·s<sup>-1</sup>)</th></tr></thead><tbody><tr><td rowspan="5">Poaceae</td><td>Themeda triandra</td><td rowspan="5">Spherical</td><td>50.98</td><td>0.079</td><td rowspan="5">0.050</td></tr><tr><td>Alloteopsis semialata</td><td>33.91</td><td>0.035</td></tr><tr><td>Digitaria flaccida</td><td>37.44</td><td>0.042</td></tr><tr><td>Panicum natalensis</td><td>30.78</td><td>0.029</td></tr><tr><td>Tristachya leucothrix</td><td>46.67</td><td>0.066</td></tr><tr><td rowspan="3">Asteraceae</td><td>Aster bakeranus</td><td>Spherical</td><td>33.21</td><td>0.033</td><td rowspan="3">0.026</td></tr><tr><td>Helichrysum aureonitens</td><td>Spherical</td><td>20.16</td><td>0.012</td></tr><tr><td>Vernonia type</td><td>Spherical</td><td>32.62</td><td>0.032</td></tr><tr><td>Cornaceae</td><td>Curtisia dentata</td><td>Spherical</td><td>16.89</td><td>0.009</td><td>0.009</td></tr><tr><td>Ebenaceae</td><td>Diospyros austro-africana</td><td>Spherical</td><td>48.32</td><td>0.071</td><td>0.071</td></tr><tr><td rowspan="3">Ericaceae</td><td>Erica drakensbergensis</td><td rowspan="3">Spherical</td><td>33.24</td><td>0.033</td><td rowspan="3">0.077</td></tr><tr><td>Erica cerinthoides</td><td>65.97</td><td>0.132</td></tr><tr><td>Erica straussianna</td><td>47.05</td><td>0.067</td></tr><tr><td>Euphorbiaceae</td><td>Acalypha punctata</td><td>Spherical</td><td>15.48</td><td>0.007</td><td>0.007</td></tr><tr><td>Fabaceae</td><td>Eriosema simulans</td><td>Spherical</td><td>36.81</td><td>0.041</td><td>0.041</td></tr><tr><td>Geraniaceae</td><td>Pelargonium type</td><td>Spherical</td><td>91.77</td><td>0.255</td><td>0.255</td></tr><tr><td>Rubiaceae</td><td>Pentanisia prunelloides</td><td>Spherical</td><td>42.12</td><td>0.054</td><td>0.054</td></tr><tr><td>Thymelaeaceae</td><td>Gnidia kraussiana</td><td>Spherical</td><td>25.79</td><td>0.020</td><td>0.020</td></tr><tr><td>Pteridophyte spores</td><td>Pteridium aquilinum</td><td>Spherical</td><td>38.18</td><td>0.044</td><td>0.044</td></tr><tr><td rowspan="2">Proteaceae</td><td>Protea caffra</td><td rowspan="2">Spherical</td><td>23.38</td><td>0.017</td><td rowspan="2">0.018</td></tr><tr><td>Protea roupillae</td><td>24.45</td><td>0.018</td></tr><tr><td rowspan="2">Anacardiaceae</td><td>Searsia dentata</td><td rowspan="2">Ellipsoid</td><td></td><td></td><td></td></tr><tr><td>x-axis</td><td>26.95</td><td>0.018</td><td>0.018</td></tr><tr><td></td><td>y-axis</td><td>22.13</td><td></td><td></td><td></td></tr><tr><td rowspan="2">Commelinaceae</td><td>Commelina type</td><td rowspan="2">Ellipsoid</td><td></td><td></td><td></td></tr><tr><td>x-axis</td><td>43.33</td><td>0.037</td><td>0.037</td></tr><tr><td></td><td>y-axis</td><td>28.61</td><td></td><td></td><td></td></tr><tr><td rowspan="2">Podocarpaceae</td><td>Podocarpus latifolius</td><td rowspan="2">Ellipsoid</td><td></td><td></td><td></td></tr><tr><td>x-axis</td><td>68.52</td><td>0.074</td><td>0.074</td></tr><tr><td></td><td>y-axis</td><td>38.19</td><td></td><td></td><td></td></tr><tr><td rowspan="2">Pinaceae</td><td>Pinus patula</td><td rowspan="2">Ellipsoid</td><td></td><td></td><td></td></tr><tr><td>x-axis</td><td>64.94</td><td>0.073</td><td>0.073</td></tr><tr><td></td><td>y-axis</td><td>37.20</td><td></td><td></td><td></td></tr><tr><td rowspan="2">Rosaceae</td><td>Leucosidea sericea</td><td rowspan="2">Ellipsoid</td><td></td><td></td><td></td></tr><tr><td>x-axis</td><td>29.51</td><td>0.016</td><td>0.016</td></tr><tr><td></td><td>y-axis</td><td>17.98</td><td></td><td></td><td></td></tr><tr><td rowspan="3">Cyperaceae</td><td>Carex austro-africana</td><td rowspan="3">Ellipsoid</td><td></td><td></td><td></td></tr><tr><td>x-axis</td><td>38.49</td><td>0.035</td><td>0.036</td></tr><tr><td>y-axis</td><td>30.26</td><td></td><td></td></tr><tr><td rowspan="2">Fionia type</td><td></td><td rowspan="2">Ellipsoid</td><td></td><td></td><td></td></tr><tr><td>x-axis</td><td>37.17</td><td>0.037</td><td></td></tr><tr><td></td><td>y-axis</td><td>25.39</td><td></td><td></td><td></td></tr></tbody></table>
<figure><img src="image_96.png" /><figcaption>**Figure 3.** ERV analysis output for RSAP estimations. Illustrated are likelihood function score graphs for pollen-vegetation datasets run with Sutton's taxon specific distance-weighting using ERV model 1. RSAP is visually estimated from the graphs where the curve tends to an asymptote. While the maximum vegetation survey distance was 5000 m, a logarithmic scale was used to 1000 m from sample points as an asymptote had been reached by this distance.</figcaption></figure>

<caption>**Table 3.** Calculated RPP estimates for taxa selected from sampled sites. All values indicated are unit-less ratios relative to a reference taxon, where RPP = 1. TT = *Themeda* grassland, PS = *Protea* savanna, LS = *Leucosidea* scrubland.</caption>

*Taxon family names used in ERV analysis: Poa = Poaceae, Aster = Asteraceae, Pteri = Pteridophyte, Podo = Podocarpaceae, Prot = Proteaceae, Rosa = Rosaceae, Erica = Ericaceae.

<table><thead><tr><th rowspan="2">Vegetation type</th><th rowspan="2">ERV model</th><th colspan="7">PPE</th></tr><tr><th>Poa</th><th>Aster</th><th>Pterio</th><th>Podo</th><th>Prot</th><th>Rosa</th><th>Erica</th></tr></thead><tbody><tr><td><i>Themeda</i></td><td>1</td><td>1</td><td>0.00015</td><td>4.60171</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td><i>triandra</i></td><td>2</td><td>1</td><td>0.00026</td><td>3.14094</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td rowspan="2"><i>Protea savanna</i></td><td>1</td><td>1</td><td>0.00038</td><td>8.49329</td><td>6.54313</td><td>0.22356</td><td>—</td><td>—</td></tr><tr><td>2</td><td>1</td><td>0.00057</td><td>9.51827</td><td>5.13538</td><td>0.00050</td><td>—</td><td>—</td></tr><tr><td><i>Leucosidea</i></td><td>1</td><td>1</td><td>—</td><td>25.71204</td><td>—</td><td>—</td><td>3.33815</td><td>0.80950</td></tr><tr><td><i>sericea</i></td><td>2</td><td>1</td><td>—</td><td>12988</td><td>—</td><td>—</td><td>7291</td><td>743</td></tr></tbody></table>

In the *Themeda* grassland, Asteraceae pollen (RPP<sub>Poaceae</sub> = 0.00026) producing plant species are substantially less productive in terms of pollen production than grasses, whereas Pteridophyte (RPP<sub>Poaceae</sub> = 3.14) are significantly higher producers. In the *Protea* savanna, Asteraceae pollen (RPP<sub>Poaceae</sub> = 0.00038) and Proteaceae pollen (RPP<sub>Poaceae</sub> = 0.22) producing plant species are less productive in terms of pollen production than grasses, whereas Pteridophyte (RPP<sub>Poaceae</sub> = 8.49) and Podocarpaceae pollen (RPP<sub>Poaceae</sub> = 6.54) producing species are significantly higher producers. RPP estimates obtained for the *Leucosidea* scrubland show Ericaceae pollen (RPP<sub>Poaceae</sub> = 0.81) producing plant species are less productive, whereas Pteridophyte (RPP<sub>Poaceae</sub> = 25.71) and Rosaceae (RPP<sub>Poaceae</sub> = 3.34) are higher palynomorph producers (Table 2).
<figure><img src="image_97.png" /><figcaption>**Figure 4.** Original and RPP-corrected pollen data for the four pollen zones from the Cathedral Peak Wetland pollen diagram (Lodder *et al.* 2018). (A) comparison of uncorrected and corrected values for 7 taxa (Table 1 other than Asteraceae – see text for details) – note variable x-axis scale. (B) Poaceae:Podocarpaceae ratio (an index of landscape openness).</figcaption></figure>

### 16.3.4 Application of RPP values to palaeoecological data

Counts of the taxa listed in Table 1 made up 60–74% of the total pollen count – Cyperaceae (a local wetland taxon which dominates the pollen diagram; Lodder *et al.* 2018) of the four pollen zones from the Cathedral Peak Wetland. Figure 4 (a and b) shows the results of correction with Asteraceae excluded, since some Asteraceae may be local wetland vegetation; correction increases the apparent abundance of Poaceae, Ericaceae, Anacardaceae and Proteaceae, and reduces the apparent abundance of Podocarpaceae, Proteaceae and Rosaceae. An example of the importance of considering RPP when interpreting fossil pollen data is given by the ratio between Poaceae and Podocarpaceae pollen (Figure 4b). Podocarpaceae are forest taxa with well dispersed pollen, whilst Poaceae dominate grasslands and rarely flower under forest canopies, therefore this ratio provides an indication of the balance between open grassland and forested areas in the wider landscape. Without weighting, the ratio ranges from 11 to 49 between zones, whilst with the weighting included, values range from 44–196, giving a different impression of the role of forest in the wider landscape.

## 16.4 DISCUSSION

### 16.4.1 Vegetation data

The vegetation survey method and distance-weighting methods used allocates surveyor effort according to the relative importance of plants as potential pollen sources for the sample location, since plants closer to a sample site contribute more directly to the pollen signal than those further away. The dominant species recorded were as expected for the plant communities mapped in the area.
### 16.4.2 Pollen data

Pollen assemblages from all surface samples were dominated by Poaceae, reflecting the general character of the research area. Bracken fern was pervasive throughout all environments, and this was recorded by high spore counts.

### 16.4.3 Data analysis – estimates of RSAP and PPE

RSAP values are similar in all three communities, with estimates ranging between 100–150 m, suggesting that the variation in pollen signal between sample locations in each community is controlled by vegetation composition and distribution within a 100–150 m radius of the sample point (Sugita 1994).

Where a taxon appears in more than one community, the absolute RPP values obtained differ, however the rank of taxon RPP relative to Poaceae is consistent, suggesting that despite the small number of samples included, real features of the pollen vegetation relationship are being reliably detected by the analysis which can be used as the basis for future interpretation of pollen records.

Asteraceae is under-represented compared to Poaceae in both the *Themeda* grassland samples and the *Protea* savannah, Proteaceae are under-represented in *Themeda* grassland and Ericaceae are under-represented in *Leucosidea* scrubland. Pteridophyta are over-represented in all three habitats, whilst Podocarpaceae are over-represented in *Protea* savannah and Rosaceae are over-represented in *Leucosidea* scrubland.

Duffin and Bunting (2008) adopted the same approach to analyse modern pollen spectra from 34 surface sediment samples in the Kruger National Park and present RPP values. The RSAP for all sites was estimated at 700 m, which is considerably greater than the 150 m found in this study, however that is expected as Duffin and Bunting (2008) sampled small ponds rather than soil samples (e.g. Sugita 1994). RSAP differences could be partially attributed to the different sampling strategies in zones A and B of the vegetation surveys done between the two research projects or due to the differences in the Kruger National Park landscape and the topographical complexity of Cathedral Peak (Gaillard *et al.* 2008).

The taxa studied by Duffin and Bunting (2008), other than Poaceae, were different from those presented here. Combining the results (Table 4) and rounding RPP values, which is appropriate given the uncertainties associated with both studies, forms a useful basis for simulation studies and land cover reconstruction in advance of more detailed modern pollen studies in the region.

Results of analysis were considered more robust when using ERV model 1 rather than ERV model 2. In theory both models should be equivalent, however model 1 assumes that the taxon-specific background pollen (pollen travelling from beyond the RSAP) is constant relative to the total influx of pollen at the sample location, whereas model 2 assumes that the component is constant relative to the total vegetation abundance around the site. In landscapes with discontinuous vegetation cover, it can be argued that the model 1 assumption is more justifiable, which would accord with the results presented here.

### 16.4.4 Implications of findings for palaeoecological research in the region

The set of RPP values presented in Table 3 are an essential first step for the application of pollen-vegetation model-based reconstruction approaches in the region. For detailed landcover reconstruction, the next step is to compile all available pollen records and, where gaps exist, to seek out suitable sedimentary archives. One small wetland record in the KwaZulu-Natal Drakenberg area has produced a pollen record (Lodder *et al.* 2018) and a simple demonstration of how interpretation of the record can be altered by using RPP values to adjust the pollen counts taking into account differential pollen production presented in Figure 4.
<table><caption>**Table 4.** Pollen productivity and fall speed estimates for taxa in South Africa; RPP estimates rounded to powers of 2.</caption><thead><tr><th>Taxon</th><th>RPP<sub>Poaceae</sub><br>(categorised)</th><th>Fallspeed<br>estimate (ms<sup>-1</sup>)</th><th>Source</th></tr></thead><tbody><tr><td><em>Acacia</em>-type</td><td>0.5</td><td>0.096</td><td>Duffin and Bunting (2008)</td></tr><tr><td>Anacardiaceae</td><td>0.5</td><td>0.019</td><td>Duffin and Bunting (2008)</td></tr><tr><td>Asteraceae</td><td>0.01</td><td>0.026</td><td>This study</td></tr><tr><td><em>Colophospermum mopane</em></td><td>0.25</td><td>0.042</td><td>Duffin and Bunting (2008)</td></tr><tr><td>Combretaceae</td><td>0.5</td><td>0.014</td><td>Duffin and Bunting (2008)</td></tr><tr><td>Ericaceae</td><td>1.0</td><td>0.077</td><td>This study</td></tr><tr><td>Podocarpaceae</td><td>4.0</td><td>0.074</td><td>This study</td></tr><tr><td><em>Protea</em></td><td>0.25</td><td>0.017</td><td>This study</td></tr><tr><td>Pteridophyte spores</td><td>8.0</td><td>0.044</td><td>This study</td></tr><tr><td>Rosaceae</td><td>4.0</td><td>0.016</td><td>This study</td></tr></tbody></table>

### 16.4.5 Limitations and future direction

Soil pollen assemblages are considered to be relatively poor analogues for the pollen trapping properties of the sedimentary sequences palaeoecologists use for reconstruction compared to moss or lake sediment samples (e.g. Adam and Mehringer 1975; Li *et al.* 2018), but are often the only natural option in savannah and grassland environments. Future studies using a larger number of samples, or including pollen trap studies and surface sediment studies, would enable more robust conclusions to be drawn. However, the consistency of the results presented here, between the different communities, suggests that despite small numbers of samples the results do reflect meaningful properties of the studied ecosystem, allowing us to present initial values to support simulation studies and landcover reconstruction in the region. Balancing effort/cost with outcomes is a known and challenging issue in modern pollen studies, but the implications of being able to better translate pollen assemblages into past land cover demonstrate the incentive to continue to work.

### 16.5 CONCLUSIONS

Modern pollen and vegetation data from three dominant vegetation communities in the KwaZulu-Natal Drakensberg were collected and analysed, producing estimates of RSAP (100–150 m) and RPP for the dominant palynomorph-equivalent taxa in three communities, *Themeda* grassland, *Protea* savanna and *Leucosidea* scrubland. Interpretation of ERV results has shown that, while the model outputs are not ‘perfect’ as would be the case in an open, homogeneous and flat landscape, they are perfectly coherent and within the realm of expectations of results from a study with a relatively small amount of samples in a varied landscape. The findings of this study are therefore that developments made in pollen modelling have a very relevant place in South African pollen research and can significantly impact future work by strengthening the foundation from which we base our understanding – the interpretation of results.

The results, combined with the findings of Duffin and Bunting (2008), form a solid basis for the application of simulation and landcover reconstruction approaches in the KwaZulu-Natal Drakensberg area, which is an area possessing rich archaeological and palaeo-ecological archives. Further validation of these models in South African pollen research can only further serve to improve our confidence in pollen data, and it is the hope that this research has in the very least
informed and sparked interest for future researchers in this new and significant aspect in pollen
science.

ACKNOWLEDGEMENTS

To the University of KwaZulu-Natal Geography Department for the use of field equipment and laboratory space. Ezemvelo KZN Wildlife for agreeing to allow the research be conducted in Cathedral Peak Research Station. This research was supported by financial assistance from the National Research Foundation.

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10.1177/0959683607086769.
CHAPTER 17

Modern pollen-vegetation relationships in the
Drakensberg Mountains, South Africa

Trevor R. Hill & Jemma M. Finch

*Discipline of Geography, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa*

ABSTRACT: Contemporary pollen rain studies are conducted to determine pollen-vegetation relationships and develop modern analogues to facilitate palaeoreconstruction from fossil pollen assemblages. In this study, a two year modern pollen sampling campaign was conducted for eleven vegetation communities across a range of altitudes in the Drakensberg Mountains, South Africa. The aim was to compare pollen assemblage composition, diversity, and influx between vegetation communities, and investigate representivity of taxa. Despite a high degree of within and between community homogeneity, characteristic pollen taxa are suggested as indicative of particular communities. We highlight over- and under-representation of certain taxa within the vegetation communities and list 'palynologically silent' taxa - those that appear in the vegetation but are absent from the modern pollen spectra. From the resultant modern pollen assemblages, it is not possible to distinguish the grassland types, although the possibility does exist for the forest, shrubland and wetland communities. Arboreal *Celtis* and *Podocarpus* pollen types are diagnostic for the Afrotemperate forest community, and *Leucosidea sericea* for the shrubland community which it dominates. We suggest that modern pollen analogues are feasible for the non-grassland Drakensberg communities and echo the sentiments of others that modern pollen rain-vegetation dynamics are a prerequisite to accurately constrain interpretation of fossil pollen spectra.

**17.1 INTRODUCTION**

The modern pollen-vegetation relationship underpins vegetation reconstructions from fossil pollen data, and yet foundational studies exploring this relationship are often lacking, particularly in the tropics and subtropics. Outside of temperate zones, traditional assumptions surrounding pollen representivity of anemophilous and zoophilous taxa have been challenged, and require further investigation to understand complex bias introduced by variable pollen production and dispersal (Watrin et al. 2007). Detailed studies of local pollen and parent vegetation can be used to determine pollen taphonomy, and explore representivity within the pollen rain. Few such small-scale studies exist for the African tropics and subtropics (e.g. Elenga et al. 2000; Henga-Botsikabobe et al. 2020; Julier et al. 2018; Marchant and Taylor 2000; Schüler et al. 2014), thereby limiting palaeoecological inferences and attempts to model vegetation from pollen data.

Southern Africa has an established tradition of palynological research, dating back to the pioneering efforts of van Zinderen Bakker (1950, 1951). Early palynologists often incorporated a few surface pollen spectra to support fossil pollen records (e.g. Cooremans 1989; Scott 1982a, b, 1989; Scott and Cooremans 1992; Scott et al. 1992), however, relatively few dedicated studies, particularly those with a temporal component, exist (Hill 1995). A recent modelling synthesis of modern pollen data from southern Africa pulled together a dataset of mostly surface soil samples
from 211 sites across the subregion (Sobol et al. 2019), showing strong bias towards savanna (n = 67) and grassland (n = 53) relative to the other seven biomes. More detailed local studies, which employ passive pollen traps to calibrate modern-pollen vegetation relationships, include those from the Winterberg (Meadows and Meadows 1988), Nuweveldberg (Sugden and Meadows 1989), and Cederberg (Meadows and Sugden 1990; 1991a;b), all in the winter rainfall zone. In the absence of such detailed contemporary pollen studies, Meadows (1989, p. 160) commented that the reconstruction of palaeovegetation communities is ‘largely guesswork’. Recent advances in the field include modelled estimates of relevant source area of pollen and relative pollen productivity undertaken in the Kruger National Park (Duffin and Bunting 2008) and Drakensberg Mountains (Duthie et al., this volume). Despite these efforts, modern pollen-vegetation relationships in the subregion ‘remain poorly resolved relative to other parts of the world’ (Meadows 2015, p. 4).

In this paper, we present the results of a highly detailed modern pollen rain survey from the KwaZulu-Natal Drakensberg, comprising data from eleven vegetation communities and corresponding surface soil samples, in addition to seasonal pollen trap data spanning a two year period. The aim is to characterise the modern pollen rain produced by each vegetation community by (i) comparing pollen spectra, diversity and influx across the different vegetation communities; and (ii) determining which taxa are under- and overrepresented in the pollen assemblage. The research was carried out in the Cathedral Peak area of the northern KwaZulu-Natal Drakensberg Mountains (29°00′S, 29°15′E; Figure 1), in South Africa’s summer rainfall zone. This mountainous environment presents the opportunity to study a range of vegetation communities within a confined region due to the altitudinal gradient, ranging from open grassland to ericaceous scrub, closed woodland and forest. The broader Maloti-Drakensberg region is recognised as having the preservation potential for late Quaternary palynological studies due to the availability of high altitude polliniferous deposits (e.g. Fitchett et al. 2016; 2017; Lodder et al. 2018; Neumann et al. 2014; Norström et al. 2009; van Zinderen Bakker 1955).

## 17.2 MATERIALS AND METHODS

Sampling design was based on eleven vegetation communities from three altitudinal zones (Table 1; Figure 2), viz. the montane (1280–1829 m asl), subalpine (1830–2865 m asl) and the alpine belts (2866–3353 m asl) (Killick 1963). Selected communities represented either (i) the dominant community within a vegetation belt; (ii) communities which follow a particular successional pathway; or (iii) communities that characterise a distinct topographic or geomorphological feature within the altitudinal vegetation belt. Killick (1963) and Hill (1996) describe the species composition and abundance within each of these vegetation communities in detail.

Vegetation surveys were undertaken within each of the communities and species present and percentage aerial cover were recorded (Table 1). The percentage aerial cover was deemed more appropriate than the more conventional basal cover (Bradshaw 1981) as aerial cover is an estimate of the dominance of a particular species within the community. As the pollen is produced from the upper aerial part of the plant it was presumed that this measure would provide a better representation of the amount of pollen produced. Percentage aerial cover was measured as the proportion of the ground occupied by perpendicular projections onto it of the aerial parts of the individuals of a species under consideration (Kershaw and Looney 1985).

Meadows (1989) advocates for the collection of surface samples, and corresponding trap data, from as many plant communities as possible. Here, modern pollen rain was sampled passively using a modified version of the Oldfield pollen trap (Flenley 1973), which is easy to construct, transport, install and recollect in an inaccessible mountainous environment. Pollen traps were randomly installed within each community at a height of 1 m above the ground. The number of pollen traps placed in each vegetation community varied between 5-18 based on the
<figure><img src="image_98.png" /><figcaption>**Figure 1.** (a) Location map and (b) aerial photograph of the Cathedral Peak area of the KwaZulu-Natal Drakensberg, indicating the position of vegetation communities investigated relative to important topographic features.</figcaption></figure>
<table><caption>**Table 1.** Distribution of pollen and vegetation samples among the eleven vegetation communities. Pollen traps were sampled on a seasonal basis across a two-year period, hence total pollen samples exceeds individual samples per community. Alpine communities were sampled using soils, while subalpine and montane communities used pollen traps.</caption><thead><tr><th>Vegetation community (code)</th><th>Geographical position (GPS)</th><th>Elevation (m asl)</th><th>No. pollen samples</th><th>Total pollen samples (all seasons)</th><th>No. of vegetation quadrats</th><th>Quadrat size</th></tr></thead><tbody><tr><td><strong>Alpine</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Alpine grassland (AG)</td><td>-28.98; 29.13</td><td>3000</td><td>20</td><td>20</td><td>20</td><td>1 × 1 m</td></tr><tr><td>Alpine wetland (AW)</td><td>-29.01; 29.18</td><td>2965</td><td>20</td><td>20</td><td>20</td><td>1 × 1 m</td></tr><tr><td><em>Erica Helichrysum</em> shrubland (EH)</td><td>-29.00; 29.18</td><td>3010</td><td>20</td><td>20</td><td>20</td><td>1 × 1 m</td></tr><tr><td><strong>Subalpine</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><em>Leucosidea sericea</em> shrubland (LS)</td><td>-28.99; 29.26</td><td>1895</td><td>10</td><td>63</td><td>20</td><td>10 × 10 m</td></tr><tr><td><em>Microchloa altera</em> grassland (MA)</td><td>-28.95; 29.22</td><td>1810</td><td>5</td><td>32</td><td>20</td><td>1 × 1 m</td></tr><tr><td>Subalpine fynbos (SF)</td><td>-29.01; 29.20</td><td>2690</td><td>10</td><td>37</td><td>20</td><td>10 × 10 m</td></tr><tr><td>Subalpine grassland (SG)</td><td>-28.99; 29.21</td><td>1975</td><td>25</td><td>175</td><td>60</td><td>1 × 1 m</td></tr><tr><td>Subalpine wetland (SW)</td><td>-28.99; 29.25</td><td>1890</td><td>6</td><td>31</td><td>25</td><td>1 × 1 m</td></tr><tr><td><strong>Montane</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Afrotemperate forest (AF)</td><td>-28.96; 29.22</td><td>1550</td><td>18</td><td>125</td><td>20</td><td>10 × 10 m</td></tr><tr><td>Highland sourveld (HS)</td><td>-28.93; 29.25</td><td>1350</td><td>5</td><td>32</td><td>25</td><td>1 × 1 m</td></tr><tr><td><em>Protea</em> savanna (PS)</td><td>-28.95; 29.20</td><td>1545</td><td>18</td><td>115</td><td>20</td><td>10 × 10 m</td></tr><tr><td><strong>Total</strong></td><td></td><td></td><td><strong>159</strong></td><td><strong>670</strong></td><td><strong>270</strong></td><td></td></tr></tbody></table>

size and complexity of the community (Table 1). The traps have proved successful collectors of pollen rain and, in some instances, provide a better reflection of the vegetation community prevailing in the catchment area than surface soil samples (Crowder and Starling 1980; Meadows and Sugden 1991b; Sugden 1990). Twenty surface soil samples were taken randomly from each vegetation community. A sample of approximately 100 g constituting the top 3–5 cm of the soil was collected, with overlying organic matter having been first removed. Samples were placed into paper bags, sealed and refrigerated to reduce bacterial and fungal growth, until the necessary laboratory techniques could be carried out (Hicks and Hyvärinen 1986).

Fieldwork was undertaken from June 1988 to June 1990, thereby gaining a full two years of modern pollen rain data. The traps were replaced on a tri-monthly basis until November-December 1989. A seventh sample of six months duration, December 1989 to June 1990, was collected to compare a six month sample with the usual tri-monthly samples. Two full years of data were not achieved for the subalpine fynbos and subalpine wetland communities (Table 1).
<figure><img src="image_99.png" /><figcaption>**Figure 2.** (a) 1 × 1 m quadrat used for aerial vegetation survey; (b) passive pollen trap; (c) subalpine grassland; (d) alpine grassland; (e) *Erica Helichrysum* shrubland; (f) alpine wetland; (g) subalpine fynbos; (h) subalpine wetland; (i) *Leucosidea sericea* shrubland; (j) Afrotemperate forest; (k) *Protea* savanna; (l) highland sourveld.</figcaption></figure>

Pollen was extracted from the traps and soil samples using acetolysis (Faegri and Iversen 1989). Identification was achieved using a predominantly local pollen reference collection of c. 700 species (Hill 1992). Absolute counts of c. 500 on trap samples and c. 1000 on soil samples were performed on pollen grains and spores (hereafter collectively termed ‘pollen’ for brevity). Pollen influx values were calculated based on sample volume as grains/cm²/30 days (Berglund and Ralska-Jasiewiczowa 1986). R-rel values were calculated (R-rel = % mean abundance in pollen rain/% aerial cover of vegetation) (Davis 1963; Gosling *et al.* 2005) for each taxon in each community to understand the representivity of pollen taxa across the landscape. The Shannon-Wiener Index was calculated for each pollen sample and each vegetation community to determine how well the pollen assemblage captured vegetation diversity. The index ranges from 0–5, with most values falling between 1.5 and 3.5, and is a comparative index with higher values indicating higher diversity (Barnes *et al.* 1998). It should be acknowledged that many pollen taxa cannot be
distinguished below the family level and thus the diversity index was calculated at the taxonomic
level of the identified palynomorphs. The multivariate structure of the pollen assemblage data
was represented using non-metric multidimensional scaling (nMDS) using the software PRIMER
v7 (Clarke and Gorley 2015). The data were 4th root transformed to down weight the dominant
species and Bray-Curtis similarity matrix calculated. Vectors of important species were based
on Pearsons rank correlation. A pollen diagram showing characteristic taxa for each community
was plotted using C2 (Juggins 2007).

**17.3 RESULTS**

From a total of *c*. 700 pollen traps deployed over the two year period, 16 were lost or damaged by animals or fire. All 36 traps deployed in the alpine belt over two attempts were stolen, thus surface soil samples were relied upon as a substitute within this vegetation belt. Pollen assemblage count size varied with an average of 1120 grains counted per soil sample in the alpine communities (ranging from 965-1272 grains per sample), and an average of 560 grains counted per trap in the subalpine and montane communities (ranging from 207-3392 grains per sample). A complete list of all pollen taxa recorded together with potential parent vegetation based on vegetation survey data is provided in supplementary information and used to infer likely parent taxa, which are indicated in parentheses (Hill 1992; Table S1).

**17.3.1 Characterisation of vegetation communities by pollen assemblage**

A total of 25 characteristic taxa were designated on the basis of being recorded from at least 80% of the samples from a vegetation community, and at >3% abundance in one sample or more (Table S1). Asteraceae, Cyperaceae, Poaceae and Pteridophyta are ubiquitous and characteristic across all altitudinal zones and vegetation communities, and are therefore deemed to be of limited value as diagnostic taxa.

For pollen trap data, relative abundance data are averaged across the seven time intervals
due to a focus on spatial rather than temporal assemblage variability. Spatial variability in the
pollen assemblage, both between and within vegetation communities, is depicted according to
relative abundance of characteristic taxa, pollen influx, and assemblage diversity (Figure 3a-c).
Influx values are indicated for all subalpine and montane belt communities together with
relative abundance data for each trap (Figure 3b-c). It is worth noting that while some vegetation
communities were represented by a high number of samples (up to 25 traps or 20 soil samples), the
*M. altera* grassland and highland sourveld contained only five traps each, which may contribute
towards their apparent homogeneity.

17.3.1.1 *Alpine belt*

The alpine vegetation communities display a relatively high degree of homogeneity within each
community, and all three are distinctive for the presence of bryophyte spores which are absent from
other altitudinal zones (Figure 3a). Within the alpine grassland the diversity index ranged from
1.2–1.5 with an average of 1.4 (Figure 4), and the most abundant taxa are Poaceae and Cyperaceae.
Similar to the alpine grassland, the alpine wetland is dominated by Poaceae and Cyperaceae. The
consistent presence of Umbelliferae (Apiaceae) in the pollen assemblage distinguishes the alpine
wetland from other communities across the altitudinal range. The alpine wetland diversity ranged
from 1.6–2.2 with an average of 1.9 (Figure 4). Distinctive taxa within the *Erica Helichrysum*
shrubland include Ericaceae and Thymelaeaceae. Diversity ranged from 1.1 to 1.9, with an
average of 1.6 (Figure 4).
### 17.3.1.2 Subalpine belt

Aside from the subalpine fynbos, the other subalpine communities are relatively homogenous within individual communities.

*Leucosidea sericea shrubland* – *L. sericea* shrubland is distinctive for dominant *L. sericea* pollen (30-60%), and the presence of Ericaceae. Diversity ranged from 1.2-2, with an average of 1.7. Influx rates varied from 65 to 275 grains/cm²/month, with an average of 107 grains/cm²/month (Figure 3b).

*Microchloa altera grassland* – The *M. altera* pollen assemblage is fairly uniform, with the main distinction being the low presence of extra-local forest taxa *Celtis* (<5%) and *Podocarpus* (<5%), due to the close proximity of this community to the edge of an Afrotemperate forest patch (<300 m). In the *M. altera* grassland, diversity varied from 2-2.1 with an average of 2.1 (Figure 4). Influx rates varied from 104 to 239 grains/cm²/month, with an average of 145 grains/cm²/month.

*Subalpine fynbos* – In the subalpine zone, the subalpine fynbos community displays a high degree of heterogeneity across the community, largely centred around variations in three taxa, viz. Ericaceae, Proteaceae and Scrophulariaceae (Figure 3b). In the subalpine fynbos, diversity ranged from 1.8–2.5 (average 2.3) (Figure 4). Influx rates varied from 101 to 269 grains/cm²/month, with an average of 172 grains/cm²/month.

*Subalpine grassland* – The subalpine grassland and *M. altera* grassland communities are similar, with the exception of forest taxa presence in the latter. In the subalpine grassland, pollen assemblage diversity ranged from 1.6 to 1.8, with an average of 1.7 (Figure 4). Influx rates varied from 118 to 409 grains/cm²/month, with an average of 199 grains/cm²/month (Figure 3b).

*Subalpine wetland* – As with subalpine grassland communities, the subalpine wetland pollen assemblage is Poaceae-Asteraceae dominated, and includes relatively high Cyperaceae (10–15%) and Pteridophyta (10–20%) frequencies. Diversity ranged from 1.8–2.1 (average 1.9) (Figure 4). Influx rates varied from 140 to 274 grains/cm²/month, with an average of 198 grains/cm²/month.

### 17.3.1.3 Montane belt

*Afrotemperate forest* – In the montane belt, there is a high degree of heterogeneity across the Afrotemperate forest community, particularly in the relative dominance of *Podocarpus* (5–40%) and *Celtis* (5–40%) (Figure 3c). This community displays a relatively strong arboreal pollen assemblage, muting the ubiquitous Asteraceae/Poaceae signal evident in other communities. The Afrotemperate forest diversity varied from 1.8–2.5 (average 2.1) (Figure 4). Pollen influx was highly variable, ranging from 111 to 493 grains/cm²/month, with an average of 264 grains/cm²/month (Figure 3c).

*Protea savanna* – The *Protea* savanna is distinctive for its high level of Proteaceae pollen (15–50%). Pollen assemblage diversity varied from 1.7 to 2 with an average of 1.9 (Figure 4). Influx rates varied from 68 to 275 grains/cm²/month, with an average of 129 grains/cm²/month.

*Highland sourveld* – The highland sourveld is similar to the subalpine grassland community, with no distinctive characteristic taxa evident from the modern pollen data. In the highland sourveld, diversity varied between 1.5–1.6, with an average of 1.5 (Figure 4). Pollen influx varied from 94 to 185 grains/cm²/month (average 127 grains/cm²/month) (Figure 3c).

## 17.3.2 Representivity of the pollen assemblage

Where characteristic pollen taxa are recorded together with the corresponding parent vegetation type in the surrounding community, the relative frequencies of pollen and vegetation can be directly compared to provide an indication of the degree of correspondence between pollen rain and parent vegetation composition (Figure S1). The R-rel value can be used as an indicator of representivity, with values <1 indicating underrepresentation and values >1 indicating overrepresentation (Figure 5). Furthermore, we can plot pollen abundance against vegetation
<figure><img src="image_100.png" /><figcaption>(b) Subalpine vegetation belt</figcaption></figure>

<figure><img src="image_101.png" /><figcaption>(c) Montane vegetation belt</figcaption></figure>

<figure><img src="image_102.png" /></figure>

**Figure 3.** Pollen diagram showing pollen assemblages with samples grouped by vegetation community in the (a) alpine belt; (b) subalpine belt; and (c) montane belt, along with total influx values for each trap. For the three alpine vegetation communities, pollen data are derived from surface soil samples rather than traps.
<figure><img src="image_103.png" /><figcaption>**Figure 4.** Boxplot showing diversity (Shannon-Wiener Index) of the pollen assemblages across each vegetation community. Communities are ranked according to species level diversity of the plots (triangle symbols), in order of decreasing diversity. For alpine grassland, alpine wetland, and *Erica Helichrysum* shrubland, pollen data are derived from surface soil samples rather than traps.</figcaption></figure>

abundance using a R-rel reference line of 1 as a guide to over- and underrepresentation (Figure S2). Results show that for many taxa the representivity of the pollen type varies between vegetation communities.

In both the alpine and subalpine wetland communities, Cyperaceae is notably underrepresented, with a R-rel of 0.2–0.4 (Figure 5), whilst Poaceae is overrepresented. Some dominant taxa, such as Poaceae, are highly variable in their degree of representivity between communities (Figure S2). In general, Asteraceae appear to be overrepresented with the exception of the *Erica Helichrysum* shrubland where they are notably underrepresented (R-rel = 0.2) (Figure 5, S2). Based on high R-rel values exceeding 2 (Figure 5), Scrophulariaceae pollen appears to be highly overrepresented in all vegetation communities where it is recorded, with the exception of the *L. sericea* shrubland and subalpine wetland (Figure 5). Similarly, Crassulaceae are overrepresented in the alpine wetland and *Erica Helichrysum* communities (Figure 5).

Ericaceae pollen appears to be fairly representative of the vegetation cover in the subalpine fynbos and *L. sericea* shrubland, with a R-rel of 1.1–1.2 (Figure 5). However, in the *Erica Helichrysum* shrubland Ericaceae pollen is overrepresented with a R-rel of 1.7 (Figures 5, S2). *L. sericea* pollen abundance mirrors that of the parent vegetation in the *L. sericea* shrubland community, with a R-rel of c. 1.1 (Figures 5, S2).

When comparing aerial vegetation cover of *Celtis* with the pollen assemblage in the Afrotem-
perate forest, *Celtis* pollen is found to be overrepresented with a R-rel value of 1.7 (Figures 5,
S2). *Podocarpus* pollen is underrepresented relative to the vegetation cover in the Afrotem-
perate forest, with a R-rel of 0.4 (Figures 5, S2). In the *Protea* savanna, Proteaceae pollen is
underrepresented relative to the surrounding vegetation, with a R-rel of 0.6 (Figures 5, S2).

In the alpine grassland, Brassicaceae (*Heliophila*) pollen was absent, despite the par-
ent taxon was recorded in the surrounding vegetation. These ‘palynologically silent’ taxa are
<figure><img src="image_104.png" /></figure>

**Figure 5.** Bar chart showing R-rel (representivity) values of taxa represented in both pollen and vegetation. Asterisks indicate that the R-rel values were too high to show on the figure; For the alpine wetland *Crassulaceae* = 4.05, *Poaceae* = 3.35; for *Erica Helichrysum* *Crassulaceae* = 3.35; For Highland sourveld *Asteraceae* = 3.32; For *M. altera* grassland *Hypoxidaceae* = 5.06, *Asteraceae* = 4.43, *Fabaceae* = 3.52, for *Protea savanna* *Scrophulariaceae* = 3.60; for subalpine grassland *Scrophulariaceae* = 5.78, *Asteraceae* = 3.07; for subalpine wetland *Asteraceae* = 8.09, *Poaceae* = 3.99. For alpine grassland, alpine wetland, and *Erica Helichrysum* belt, pollen data are derived from surface soil samples rather than traps.

unrepresented in the pollen assemblage. For the *Erica Helichrysum* shrubland, this included Santalaceae (*Thesium*). In the alpine wetland community, all recorded vegetation types were represented in the pollen spectra, at least at family level. Afrotemperate forest appears to have the largest number of ‘silent taxa’, including *Ilex mitis*, *Gesneriaceae* (*Streptocarpus*), *Impatiens*, *Apiaceae* (*Conium*, *Sanicula*) and *Vitaceae* (*Rhoicissus*). In the *L. sericea* shrubland, such taxa included *Sterculiaceae* (*Hermannia*), *Apiaceae* (*Allipedia*), *Malvaceae* (*Hibiscus*), and *Melianthaceae* (*Melianthus*). *Orchidaceae* (*Disa*) pollen was absent from the *M. altera* grassland despite being recorded in the vegetation plots. In the subalpine fynbos, *Gunnera perpensa*, *Melianthaceae* (*Melianthus*), *Olea* and *Primulaceae* (*Lysimachia*) were absent from the pollen spectra, however, were recorded in the surrounding vegetation. *Apiaceae* (*Allepidea*) pollen was similarly absent from the subalpine grassland. *Illecibracaceae* (*Silene*) pollen was palynologically silent in both the subalpine grassland and wetland communities. Finally, *Valerianaceae* (*Valeriana*) pollen was recorded from the highland sourveld community but absent from the pollen spectra. In the *Protea savanna*, all plant taxa present were recorded in the pollen traps.

### 17.3.3 Multivariate analysis

The nMDS ordination plot depicts the distribution of the samples relative to vegetation community groupings (Figure 6). The ordination shows discrete clustering of samples within vegetation
<figure><img src="image_105.png" /><figcaption>**Figure 6.** Non-metric multidimensional scaling (nMDS) plot of pollen assemblage data, grouped by vegetation community.</figcaption></figure>

communities, with Afrotemperate forest and *Erica Helichrysum* shrubland being the most distinctive. Overall, the alpine communities are clearly distinct from the other vegetation belts, illustrating the importance of altitude. The alpine grassland and alpine wetland communities cluster fairly closely together, albeit with no direct overlap. The *L. sericea* shrubland and subalpine fynbos show some degree of overlap. Subalpine grassland, subalpine wetland, subalpine fynbos, highland sourveld and *Protea* savanna cluster fairly closely together, but with very limited direct overlap of ordination space.

## 17.4 DISCUSSION

### 17.4.1 Modern pollen-vegetation relationships

The results provide an indication of the relative uniformity of each vegetation community. Furthermore, they allow us to assess whether these vegetation communities can be qualitatively distinguished by their modern pollen assemblages. Here we evaluate whether there is a distinctive pollen signal or are distinctive 'indicator' taxa that can be used to differentiate each community.

Overall, we find that the grassland vegetation communities are relatively difficult to be separated by their pollen assemblage, which can be attributed to the limited taxonomic resolution achievable in identifying grass pollen types (i.e. family level). This is supported by the diversity data, which show that the grassland pollen assemblages consistently underrepresent the diversity of their parent vegetation, when compared with other communities. One exception to this is the alpine grassland community, which the nMDS picks out as a separate cluster from other grassland types.
The alpine vegetation communities were easily distinguished from communities in other vegetation belts using nMDS. A combination of bryophytes and Crassulaceae (likely from the common *Crassula setulosa* or possibly the less common *Crassula natans*) in the pollen assemblage appears to be relatively characteristic of the alpine communities, in particular within the alpine wetland. The alpine wetland community was rather similar to the alpine grassland, with the only differences being a higher frequencies of bryophytes and Juncaceae (*Juncus* or *Lezula*), and the presence of Umbelliferae in the alpine wetland. Umbelliferae pollen appears to be restricted in its presence to the alpine wetland community, and therefore serves as a useful indicator taxon. van Zinderen Bakker (1955) recorded Umbelliferae frequencies of 2–8% in four wetland surface samples in Lesotho.

Scott (1982b) associates the Umbelliferae with damp conditions, however its source at Cathedral Peak is unknown as no members of the Umbelliferae family were recorded in the vegetation. Similar to the situation in the alpine belt, the subalpine wetland and subalpine grassland communities showed a high degree of overlap, with differences being higher Cyperaceae and Pteridophyta in the former, and higher Scrophulariaceae (*Selago*, *Zaluzianskya*, *Diascia*, *Manulea*, *Nemesia*, *Hebenstretia*, *Sopubia*, *Sutera*) and Ericaceae (*Erica* spp.) in the latter.

The *Erica Helichrysum* shrubland is distinctive in the pollen diagram and the nMDS for consistent, and sometimes relatively high, frequencies of Thymelaeaceae (*Passerina montana* or *Gnidia polystachya*), and dominated by high representation of Ericaceae, occasionally exceeding 80% abundance. In contrast, subalpine fynbos is dominated by a mix of Proteaceae (*Protea subvestita*), Ericaceae (*Erica* spp.) and Poaceae, with Rosaceae (*Cliffortia* or *Alchemilla*) and Scrophulariaceae (*Bowkeria* or *Halleria*) consistently recorded.

The *L. sericea* shrubland has a distinctive pollen signature, with pollen of the shrub *L. sericea* dominating the assemblage (40–60%), and relatively low Poaceae and Asteraceae frequencies compared with other communities. In the nMDS, however, this community showed a slight overlap with the subalpine fynbos community. Ericaceae is prominent in *L. sericea* shrubland, as are the Pteridophyta.

The Afrotemperate forest has a highly unique pollen signature, clearly observed in the nMDS, with relatively high arboreal pollen frequencies including *Celtis* (10–45%) and *Podocarpus* (10–40%). Sapindaceae pollen, probably from the tree *Allophylus melanocarpus*, is a good indicator for this community. The *Protea* savanna community can be distinguished by high Proteaceae pollen frequencies (20–50%). Although subalpine fynbos records high Proteaceae, the *Protea* savanna assemblage can be separated by a lack of Ericaceae pollen, and the two are clearly separated in the nMDS.

The above comparison highlights the challenges of pollen analytical research in a grass dominated environment, and the need for additional supporting proxies such as grass phytoliths to detect palaeovegetation change (e.g. Alexandre *et al.* 1997). With the potential for C₃/C₄ grass species migration with climate change along a clear altitudinal gradient (e.g. Roberts *et al.* 2013) it could be of some value to attempt to quantitatively distinguish C₃ from C₄ vegetation. Despite these challenges, the nMDS was able to clearly separate almost all vegetation communities sampled, with limited overlap between samples from different communities.

Those communities with a significant tree component i.e. Afrotemperate forest, *Protea* savanna and *L. sericea* shrubland, are more easily distinguished by their pollen assemblage. The *Erica Helichrysum* shrubland and subalpine fynbos, could be distinguished by their dominant fynbos elements, although the latter overlapped with *L. sericea* in the nMDS. The two wetland communities displayed a strong degree of overlap with the surrounding grassland vegetation, but with some distinctive taxa. The presence of less abundant but nevertheless useful aquatic and ‘damp habitat’ indicators (*sensu* Scott 1982b) such as Dipsacaceae (*Scabiosa drakensbergensis*) and Gunneraceae (*Gunnera perpensa*) may assist in distinguishing wetland pollen communities. In the subalpine wetland community, Cyperaceae and Pteridophyta were the only
pollen taxa to record higher abundances than in the surrounding subalpine grassland community. The reality is that fossil pollen preserving archives in these summer rainfall mountain environments tend to be restricted to wetland sediments (e.g. Fitchett et al. 2016 2017, Lodder et al. 2018, Neumann et al. 2014, Norström et al. 2009), thus the fossil pollen signal is usually overrepresented by local wetland taxa, usually excluded from the pollen sum. In the Cathedral Peak area, wetlands representing potential fossil pollen archives are present across a range of altitudes (e.g. Finch et al. 2021; Lodder et al. 2018), from the montane to subalpine to alpine (e.g., this study), although the depth and organic content of these sites is variable.

### 17.4.2 Representivity of taxa

The modern pollen-vegetation comparison allows for some general recommendations regarding interpretation of key taxa based on their relative representivity in the pollen assemblage. These are compared with existing information in the available literature.

*Podocarpus – Podocarpus* pollen is an important arboreal pollen type in southern African pollen records, and also constitutes a key component of Afromontane vegetation in across Subsaharan Africa (Gajewski et al. 2002; Verlhac et al. 2018; Vincens et al. 2006). Previous studies designate *Podocarpus* as a very well dispersed anemophilous pollen type (Hamilton 1972), that tends to be overrepresented within the pollen signal (Marchant and Taylor 2000; Schüler et al. 2014). In terms of dispersal, a West African biome-scale study (Verlhac et al. 2018) found *Podocarpus* pollen distribution to be similar to that of the parent plant, and identified this taxon as a good indicator of modern vegetation. Coetzee (1967) developed a guideline for interpreting *Podocarpus* pollen frequencies with 10–20% indicating close proximity of the parent vegetation, and frequencies exceeding 20% indicative of local presence of *Podocarpus* forest. Schüler et al. (2014) commented that such overrepresented taxa are ‘probably significantly less important components of the surrounding vegetation than indicated by the modern pollen-rain’ (Schüler et al. 2014, p. 710). In the Cathedral Peak area, *Podocarpus* pollen is likely derived predominantly from *Podocarpus latifolius*, and to a lesser degree from *Afrocarpus* (formerly *Podocarpus*) *falcatus* and *Podocarpus henkelii* (Killick 1963). Interestingly, our data suggest that *Podocarpus* pollen is underrepresented relative to the vegetation cover in the Afrotemperate forest community (R-rel = 0.4), and indeed, with the exception of the *M. altera* grassland, *Podocarpus* pollen does not appear to be represented outside of the forest community. This is surprising given the prevalence of small *Podocarpus* forest patches which form a mosaic within the grassland community (Adie et al. 2017; Figure 1b)). The underrepresentation of *Podocarpus* within the forest community could be explained by (i) high pollen production by other taxa e.g. *Celtis*; and/or (ii) the size and complexity of the Afromontane forest community, which appears to be characterised by a heterogeneous pollen signal, with *Celtis* dominant in some areas and *Podocarpus* in others. Averaging *Podocarpus* abundance for both the pollen traps and the parent vegetation across such a heterogeneous community may explain these anomalous results.

*Celtis* – as with *Podocarpus*, *Celtis* pollen is restricted to the Afrotemperate forest community, with the exception of the nearby *M. altera* community (<300 m upward of the forest boundary). Within the forest community, *Celtis* pollen is overrepresented (R-rel = 1.7), aligning with previous studies (e.g. Marchant and Taylor 2000), with *Celtis* being a very well dispersed taxon (Hamilton 1972; Verlhac et al. 2018). Previous studies found *Celtis* to be abundant pollen producers, present outside of the parent vegetation distribution (Watrin et al. 2007). Interestingly, studies from east and west Africa (e.g., Livingstone 1967; Verlhac et al. 2018) have shown *Celtis* to be characteristic of lower altitudes than *Podocarpus*, which is not the case in southern Africa.

*Ilex mitis* – it was found to be ‘palynologically silent’ in the Afrotemperate forest community. As a highly distinctive pollen type, this is unlikely to have been missed during analysis. Hamilton (1972) commented that *I. mitis* has low to poor pollen dispersal capacity, while Marchant and
Taylor (2000) concluded that *Ilex mitis* pollen was representative of the density of the parent vegetation.

**Cyperaceae** – A surprising outcome of this study is that Cyperaceae pollen was found to be underrepresented in both wetland communities, with frequencies consistently below 20%. Cyperaceae pollen often dominates fossil pollen spectra due to the local nature of the taxon. Scott (1982b) and van Zinderen Bakker (1955) found Cyperaceae to be a dominant pollen taxon in modern surface samples, with high frequencies recorded. Lodder *et al.* (2018) investigated a 5000-yr sediment core retrieved from the subalpine wetland community at Cathedral Peak (Catchment VI), and recorded Cyperaceae abundance of 40–90% in the fossil assemblages, yet in the current study the modern pollen abundance for the same site remained <20% for the two year sampling period. The apparent underrepresentation of Cyperaceae pollen in the current study might be attributed to pollen trap height (1 m from the ground), above the parent vegetation, whereas the other modern sampling campaigns used surface samples. This inference is supported by relatively higher representation of Cyperaceae pollen in surface soil samples as compared with traps in the subalpine wetland (17% vs. 9%) (Hill 1992). In a comparison of surface and trap data from the Winterberg, Meadows and Meadows (1988) also found that sedges were more well represented in the surface samples than in the traps, which may be attributed to trap height. Irrespective of differences in apparent representivity between studies, Cyperaceae are ubiquitous across a range of habitat types and climate zones (e.g. Gajewski *et al.* 2002), and have limited applicability as a palaeoenvironmental indicator.

**Ericaceae** – The representivity of Ericaceae pollen varied from representative in the subalpine fynbos (R-rel = 1.1) and *L. sericea* (R-rel = 1.2) communities to overrepresented in the *Erica Helichrysum* shrubland (R-rel = 1.7). Marchant and Taylor (2000) and Schüler *et al.* (2014) designated Ericaceae pollen as overrepresented, although the dispersal characteristics of the pollen taxon are debated, ranging from poorly dispersed (due to large grain size; Verlhac *et al.* 2018), to moderately dispersed (Hamilton 1972), and well dispersed (Schüler *et al.* 2014). Dispersal is one facet of pollen representivity, but so too is pollen production, and to a lesser degree in modern samples, preservation.

**Poaceae** – The results indicate a wide degree of variability in the representivity of Poaceae pollen between different communities, with few obvious patterns apparent. The two wetland communities showed a high degree of overrepresentation of Poaceae pollen relative to vegetation cover. In the closed vegetation communities, Poaceae pollen varied from representative in the Afrotemperate forest (R-rel = 1.0) to underrepresented in the *L. sericea* shrubland (R-rel = 0.7) and *Protea* savanna (R-rel = 0.6). Within open ecosystems and grassland communities, the degree of Poaceae representivity was highly variable. Previous studies have designated Poaceae pollen as a generally well dispersed taxon linked to it’s anemophilous pollination syndrome (Hamilton 1972), consequently often overrepresented within the pollen signal.

## 17.5 CONCLUSIONS

The original palynologists' dream was to develop a 'signature' modern pollen assemblage for each distinct vegetation community within the three recorded vegetation belts, *viz.* montane, subalpine and alpine, in the KwaZulu-Natal Drakensberg. With this achieved, the aim was to correlate, or cross-reference, dated fossil pollen spectra against these modern analogues to infer past vegetation communities, which could then be extended to interpret palaeoenvironments. As with any palaeoreconstruction proxy, the outcome is less straightforward and open to interpretation.

We identified eleven vegetation communities across the three altitudinal belts, undertook a vegetation survey, deployed pollen traps and/or collected surface soil samples. Modern pollen sampling was conducted over two years and influx values were calculated. Overall, a high degree of homogeneity exists both within and between communities with regards to modern pollen,
although characteristic taxa could be identified for certain communities. Dominant pollen taxa were often over- or underrepresented in comparison with the surrounding vegetation composition and abundance, taking cognisance of the taxonomic resolution. We note and justify these characteristic taxa and suggest reasons why they could prove useful as signature taxa during fossil pollen interpretation. Perhaps unsurprisingly, there appears to be little variation across the various grassland communities, greater opportunity exists to recognise the forest and shrubland types, and to distinguish the communities from different altitudinal vegetation belts, e.g. alpine communities.

The Drakensberg is a region with a high palynological potential, however it is imperative that any palaeoreconstruction be based on a sound understanding of the modern pollen processes. Therefore, contemporary pollen rain-vegetation relationships and modern analogues should be considered and refined. Thus, in conjunction with other palaeoecological proxies and dating methods, one is able to build a clearer picture of past environments in a mountainous region which has a high biodiversity and displays clear altitudinal and latitudinal gradients, imperative to improving our understanding of the past environments in southern Africa.

## ACKNOWLEDGEMENTS

TRH was funded by the Foundation for Research and Development and Rhodes University. Brice Gijsbertsen professionally drafted Figure 1. The staff of Cathedral Peak Forestry and Research Station are thanked for their hospitality, and for allowing the opportunity to work in such tranquil surroundings. Thank you to Colin and Terry Everson, and Martin Hill for assisting with fieldwork. Andrew Steele assisted with data entry. Elodie Heyns-Veale kindly assisted with the multivariate analysis. We thank Henry Lamb and one anonymous reviewer for constructive feedback on the manuscript.

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CHAPTER 18

# A Late Holocene pollen and microcharcoal record from Eilandvlei, southern Cape coast, South Africa

Nadia du Plessis

*Department of Environmental and Geographical Science, University of Cape Town, Rondebosch, South Africa*

Brian M. Chase<fnref n="1" />

*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*

Michael E. Meadows<fnref n="2" />

*Department of Environmental and Geographical Science, University of Cape Town, Rondebosch, South Africa*

## 18.1 SITE DETAILS

Eilandvlei forms part of the Wilderness Lakes system located along the southern Cape coast of South Africa (Figure 1A, B). These lakes are located behind Pleistocene dune ridges that run parallel along the coastline. Situated within South Africa's aseasonal rainfall zone, climate is influenced by both temperate and tropical circulation systems.

The regional vegetation (Figure 1B, C) is most noteworthy for the extensive development of Southern Afrotemperate Forest (generally *Afrocarpus falcatus*, *Podocarpus latifolius*, *Ocotea bullata* (Lauraceae) and *Olea capensis* spp. *marcocarpa* (Oleaceae)), which is found in valleys and on the south-facing slopes of the adjacent river catchments. A variety of fynbos types occupy

<footnote n="1">¹Other affiliation: *Department of Environmental and Geographical Science, University of Cape Town, Rondebosch, South Africa*</footnote>

<footnote n="2">²Other affiliation: *School of Geographic Sciences, East China Normal University, Shanghai, China; and College of Geography and Environmental Sciences, Zhejiang Normal University, China*</footnote>
<figure><img src="image_106.png" /><figcaption>**Figure 1.** (A) Map of Africa showing the location of the southern Cape coast. (B) A section of the southern Cape coast between the towns of Wilderness and Knysna indicating the location of Eilandvlei and the current extent of Afrotemperate forest in the region. (C) The location of sediment core EV11 and the contemporary distribution of the dominant vegetation types (Mucina and Rutherford 2006).</figcaption></figure>

this region including Garden Route Shale Fynbos (distinguished by ericaceous and tall dense proteoid fynbos), Knysna Sand Fynbos (primarily *Erica curvifolia*, *Metalasia densa* (Asteraceae) and *Passerina rigida*) and Southern Cape Dune Fynbos (predominantly *Olea exasperata*, *Phylica litoralis* and a variety of *Searsia* species).

## 18.2 SEDIMENT DESCRIPTION AND METHODS

The EV11 core (33°59′23.10″S, 22°38′17.60″E, 0 m asl) was retrieved using a portable vibracorer mounted on a floating platform. The final core length measured ~1.54 m. The mean grain size varies from 5.89 to 7.27 φ with the standard deviation ranging from 1.85 to 2.33 indicating very poorly sorted, fine to coarse silts to be the principal sedimentological components of EV11.

Forty-seven subsamples, with a minimum weight of 2 g, were analysed using standard paly-
nological methods as per Faegri and Iversen (1989) and Moore *et al.* (1991) with adaptations for
dense media separation (Nakagawa *et al.* 1998). Pollen grains were examined and counted using
a Zeiss Axiostar Plus microscope. Identifications were achieved through comparison with refer-
ence material from the Environmental and Geographical Science department at the University of
Cape Town and published material (Van Zinderen Bakker 1953, Van Zinderen Bakker and Coet-
zee 1959, Welman and Kuhn 1970, Scott 1982). Charcoal particles were counted together with
the pollen grains using the particle count method (Tinner and Hu 2003). Fragments were classified according to size: 10–50 µm and 50–100 µm. The exotic marker *Lycopodium* was added during preparation to enable pollen and charcoal concentrations to be calculated (one tablet per sample; Lund University, Batch # 483216, 18583 ± 1708 spores per tablet). Two samples were omitted due to insufficient pollen concentrations; depths 80 and 138 cm. A total pollen sum of 500 grains, or three slides, was achieved for each sample. On considering the results, it was deemed appropriate to remove the local aquatic components and Amaranthaceae from the pollen sum. Pollen assemblage zones were determined by the use of cluster analysis by the application of CONISS (Constrained Incremental Sum of Squares) (Grimm 1987) – all identified taxa were included in this analysis.

## 18.3 DATING

Seven organic sediment samples were selected for AMS-¹⁴C dating at Beta Analytic Inc (USA) (Table 2). The two top samples returned pre-bomb ages and were calibrated using CALIBomb

<table><caption>**Table 1.** Sediment description for core EV11.</caption><thead><tr><th>Depth from surface (cm)</th><th>Sediment colour</th><th>Munsell notation</th><th>Sedimentology</th></tr></thead><tbody><tr><td>0–16</td><td>very dark brown</td><td>10YR 4/2</td><td>sandy clay</td></tr><tr><td>16–36</td><td>dark greyish brown</td><td>10YR 3/2</td><td>fine sand and silt</td></tr><tr><td>36–82</td><td>very dark greyish brown</td><td>2.5Y 4/1</td><td>fine sand and silt</td></tr><tr><td>82–115</td><td>very dark grey</td><td>2.5Y 3/1</td><td>fine silt, sand and clay</td></tr><tr><td>115–154</td><td>very dark grey</td><td>2.5Y 3/1</td><td>fine silt, sand and clay</td></tr></tbody></table>

<table><caption>**Table 2.** Radiocarbon and calibration details for EV11. Samples were data at Beta Analytic Inc. as indicated by the laboratory identification.</caption><thead><tr><th>Lab ID (Beta-)</th><th>Depth (cm)</th><th>¹⁴C age (yr BP)</th><th>1 σ error</th><th>Calib. data set</th><th>ΔR</th><th>2σ cal age range (cal BP)</th><th>Median probability (cal BP)</th></tr></thead><tbody><tr><td>EV11-1</td><td>0</td><td>—</td><td>—</td><td>CALIBomb</td><td>—</td><td>—</td><td>—58.7</td></tr><tr><td>EV11-2</td><td>20</td><td>—</td><td>—</td><td>CALIBomb</td><td>—</td><td>—</td><td>—4.8</td></tr><tr><td>EV11-3</td><td>40</td><td>470</td><td>30</td><td>Marine13</td><td>252 ± 64</td><td>340–352<br>451–526</td><td>497</td></tr><tr><td>EV11-7</td><td>78</td><td>1290</td><td>30</td><td>Marine13</td><td>252 ± 64</td><td>1072–1192<br>1206–1268</td><td>1171</td></tr><tr><td>EV11-4</td><td>102</td><td>2140</td><td>30</td><td>Marine13</td><td>252 ± 64</td><td>2002–2154<br>2276–2287</td><td>2074</td></tr><tr><td>EV11-6</td><td>134</td><td>3300</td><td>30</td><td>Marine13</td><td>252 ± 64</td><td>3397–3568</td><td>3485</td></tr><tr><td>EV11-5</td><td>151</td><td>3620</td><td>30</td><td>Marine13</td><td>252 ± 64</td><td>3726–3751<br>3791–3794<br>3820–3980</td><td>3880</td></tr></tbody></table>
<figure><img src="image_107.png" /><figcaption>**Figure 2.** The EV11 age-depth model. The 2σ probability distribution of calibrated $^{14}$C ages is presented in blue and the 95% confidence intervals are represented by the grey dotted line. The red line represents the best model according to the weighted mean age at each depth.</figcaption></figure>

(Reimer *et al.* 2004). The remainder of the samples were calibrated with the Marine13 data set (Reimer *et al.* 2013) with a marine reservoir correction of $\Delta R = 252 \pm 64$ (Wünsch *et al.* 2016). The age-depth model (Figure 2) was developed with the R software package Bacon (v2.2) (Blaauw and Christen 2011).

## 18.4 INTERPRETATION

In total 65 taxa were identified. Pollen concentrations range from $6.26 \times 10^3$ grains·g$^{-1}$ near the base, to a maximum of $7.86 \times 10^4$ grains·g$^{-1}$ around the top of the assemblage, with an average of $2.72 \times 10^4$ grains·g$^{-1}$. The EV11 record extends from c. 3100 cal yr BP to present.
The sequence is divided into five pollen zones labelled EV11-A to E as set out below.

### 18.4.1 EV11-A (3100 – 2560 cal yr BP; 148–130 cm; 8 samples): Drought resistant – Afrotemperate forest taxa

Elevated levels of drought resistant taxa (i.e. Aizoaceae, *Pentzia*-type and *Euphorbia*) are seen at the onset of record, with *Euphorbia* present throughout the zone. *Stoebe*-type percentages are similarly increased, though decline towards the top of zone. Ericaceae pollen percentages are somewhat higher, at an average of 5.1%, in comparison to the zone above. Afrotemperate forest pollen (primarily *Podocarpus/Afrocarpus*) increase after 3000 cal yr BP, from 7.9 to 12.7% at the top of the zone, with coastal thicket taxa (mostly *Olea*, *Searsia* and *Euclea*) well represented from the start of the record with values ranging from 12 to 19.8%. The maximum number of large charcoal fragments (50–100 µm), interpreted to be indicative of a large more local fire event, is noted at 2600 cal yr BP. These trends suggest that moisture availability was fairly limited, but rainfall was likely gradually increasing over this period.

### 18.4.2 EV11-B (2560 – 1560 cal yr BP; 128–106 cm; 11 samples): Amaranthaceae

Zone EV11-B is strongly dominated by the halophytic taxon Amaranthaceae with a distinct increase after 2600 cal yr BP from 13.7 to 43.7%. This could be representative of expanding salt marsh vegetation in response to lower sea levels. Fluctuating forest pollen percentages are noted from around 2400 until 1600 cal yr BP with maximum charcoal concentrations concomitant with the increased presence of *Podocarpus/Afrocarpus* at 2260 cal yr BP. Restionaceae is more prominent in this zone than elsewhere in the record.

### 18.4.3 EV11-C (1560 – 670 cal yr BP; 104–76 cm; 9 samples): Afrotemperate Forest

*Podocarpus/Afrocarpus* pollen is more prominent in comparison to the zones below, likely representing a phase of increasing forest expansion. At the same time, fynbos (largely Ericaceae) show a moderate decline towards the top of the zone, from 32.9 to 24.5%, indicating a shift towards a more mesic environment with reduced rainfall seasonality.

### 18.4.4 EV11-D (670 – 70 cal yr BP/AD 1880; 70–30.5 cm; 13 samples): Afrotemperate forest – drought resistant taxa; *Stoebe*-type

Forest development continues until 360 cal yr BP, with a sudden break in this trend around 620 cal yr BP. After 360 cal yr BP a rather abrupt decline in Afrotemperate forest pollen is noted – from 17.5 to 10.7% in a period of c. 50 years. Drought resistant taxa (mostly *Euphorbia*) become more prevalent from this point with a rapid increase in *Stoebe*-type pollen (13.6%) around 130 cal yr BP. These vegetation responses are likely a result of the cooler and drier conditions experienced in the region during the latter part of the so called Little Ice Age (650 – 100 cal yr BP) (Jones *et al.* 2001; Matthews and Briffa 2005).

### 18.4.5 EV11-E (70 to –30 cal yr BP/AD 1880 to 1980; 26–12 cm; 4 samples): *Pinus*

The most recent part of the record is characterised by the appearance of *Pinus* pollen c. AD 1910 marking the onset of the anthropogenic influence in the region.

These results are broadly consistent with the pollen record from EV13. For a fuller discussion on the palaeoclimatic inferences please see (Quick *et al.* 2018).
<figure><img src="image_108.png" /><figcaption>**Figure 3.** Relative pollen percentage diagram for EV11 organised according to ecological affinity, with charcoal concentrations and charcoal fragment counts. Pollen taxa occurring at less than 5% are not shown. Exaggeration curves are 2x for the taxa presented here. Charcoal and pollen concentrations were calculated in the same manner using *Lycopodium* counts.</figcaption></figure>
DATA AVALIABILITY

The pollen and microcharcoal data presented here is available at:
http://apps.neotomadb.org/Explorer/?datasetid=48877

ACKNOWLEDGEMENTS

This study was funded in part by the German Federal Ministry of Education and Research (BMBF). The investigations were conducted as part of a pilot study for the collaborative project 'Regional Archives for Integrated Investigations' (RAiN), which is embedded in the international research programme SPACES (Science Partnership for the Assessment of Complex Earth System Processes). We also thank the anonymous reviewer and William Gosling for their constructive comments.

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# A c. 650 year pollen and microcharcoal record from Vankervelsvlei, South Africa

Nadia du Plessis

*Department of Environmental and Geographical Science, University of Cape Town, Rondebosch, South Africa*

Brian M. Chase<fnref n="1" />

*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*

Paul Strobel

*Physical Geography, Institute of Geography, Friedrich Schiller University Jena, Germany*

Torsten Haberzettl

*Physical Geography, Institute of Geography and Geology, University of Greifswald, Germany*

Michael E. Meadows<fnref n="2" />

*Department of Environmental and Geographical Science, University of Cape Town, Rondebosch, South Africa*

<footnote n="1">¹Other affiliation: *Department of Environmental and Geographical Science, University of Cape Town, Rondebosch, South Africa*</footnote>

<footnote n="2">²Other affiliation: *School of Geographic Sciences, East China Normal University, Shanghai, China; and College of Geography and Environmental Sciences, Zhejiang Normal University, China*</footnote>
## 19.1 SITE DETAILS

The Vankervelsvlei wetland is situated along the southern Cape coast of South Africa, about c. 5 km inland at an elevation of 152 m asl, surrounded by a lithified aeolian dune of Middle to Late Pleistocene age (Illenberger 1996) (Figure 1A, B). The site falls within the year round rainfall zone, with moisture being delivered from both temperate and tropical climate systems.

Vankervelsvlei is an enclosed and endorheic wetland that is today covered with a floating vegetation mat primarily comprising several species of Cyperaceae, as well as some Bryophytes and Pteridophytes (Irving and Meadows 1997; Quick *et al.* 2016). The surrounding dune(s) are covered by pine plantations (*Pinus*) with scrub forest elements (e.g. *Cassine*, *Euclea*, *Kigge-laria*) occupying the area between the plantations and the wetland. Along the wetland edges, vegetation predominantly consists of fynbos pioneer communities (*Erica*, *Restionaceae*, *Leuca-dendron* (Proteaceae), *Passerina*) (Quick *et al.* 2016). Northward of Vankervelsvlei, Southern Afrotemperate forest is present in patches, largely represented by *Ocotea bullata*, *Olea capen-sis*, *Afrocarpus falcatus* and *Podocarpus latifolius* (in the pollen record we cannot differentiate between these species, as such these are all labelled *Podocarpus* for the purpose of this paper) (Midgley *et al.* 2004) (Figure 1C).

<figure><img src="image_109.png" /><figcaption>**Figure 1.** (A) Map of Africa showing the location of the southern Cape coast; (B) A section of the southern Cape coast between the towns of Wilderness and Knysna indicating the location of Vankervelsvlei and the current extent of Afrotemperate forest in the region; (C) The location of the sediment core VVV16 and the contemporary distribution of the dominant vegetation types (Mucina and Rutherford 2006).</figcaption></figure>
<table><caption>**Table 1.** Sediment description for core sections VVV16-4 and VVV16-1-1-1/2</caption><thead><tr><th>Core section (VVV)</th><th>Depth from surface (cm)</th><th>Sediment colour</th><th>Other observations</th></tr></thead><tbody><tr><td rowspan="2">16-4</td><td>0–41</td><td>brownish</td><td>mainly roots, macro plant remains</td></tr><tr><td>41–55</td><td>brownish</td><td>more sediment than above</td></tr><tr><td rowspan="3">16-1-1-1</td><td>80–90</td><td>dark brown to black</td><td>vertical plant roots wetter than above</td></tr><tr><td>90–137</td><td>darker than above</td><td>vertical plant roots drier than above</td></tr><tr><td>137–147</td><td>dark brown to redish</td><td>finer roots than above</td></tr><tr><td rowspan="2">16-1-1-2</td><td>148–183</td><td>dark brown to black</td><td>fine roots high water content</td></tr><tr><td>183–205</td><td>dark brown to redish</td><td>fine roots</td></tr></tbody></table>

## 19.2 SEDIMENT DESCRIPTION AND METHODS

The sediment cores VVV16-4 (55 cm), VVV16-1-1-1 (67 cm) and VVV16-1-1-2 (57 cm) (34°0'46.8"S, 22°54'14.4"E) were both recovered using a UWITEC piston corer. Combined, the core sections are used here to create a 205 cm long record. Due to the nature of this water-body, VVV16-4 was a push core from the surface while VVV16-1 started at 80 cm depth below the surface, resulting in the c. 25 cm gap between the two sections.

A total of 24 samples, with a minimum weight of 2 g, were processed using standard palynological methods as per Faegri and Iversen (1989) and Moore *et al.* (1991) with adaptations for dense media separation (Nakagawa *et al.* 1998). LacCore's polystaene microsphere pollen spike (0.5 ml per sample; $5.0 \times 10^4$ sph/ml ± 7%) was added to each sample to determine pollen concentrations. Pollen grains were examined and counted using a Zeiss Axiostar Plus microscope. Identification of pollen were based on comparison with reference material from the Environmental and Geographical Science department at the University of Cape Town, and published images (Van Zinderen Bakker 1953, Van Zinderen Bakker and Coetzee 1959, Welman and Kuhn 1970 and Scott 1982). Charcoal particles were counted together with pollen grains using the particle count method (Tinner and Hu 2003), and were classified according to size: 10–100 µm and >100 µm. Counts of 300 terrestrial pollen grains, or three slides, were performed for each sample. Three samples were excluded due to insufficient pollen concentrations; depths 50, 54 and 182 cm.

## 19.3 DATING

The age-depth model for this section of the VVV16 composite record is based on three AMS $^{14}$C ages from organic macro-particles in VVV16-1-1-1 and VVV16-1-1-2 (Table 2) and complements an age-depth model previously published by Strobel *et al.* (2019) (Figure 2). The samples were dated at the Poznan Radiocarbon Laboratory (Poland). The resultant ages were calibrated using the SHCal13 curve (Hogg *et al.* 2013). The age-depth model was developed with the R software package Bacon (v2.3) (Blaauw and Christen 2011).
<table><caption>**Table 2.** Radiocarbon and calibration details for VVV16. The designation Poz indicates that the samples were dated at the Poznan Radiocarbon Laboratory. All samples were calibrated using the SHCal12 curve (Hogg *et al.* 2013).</caption><thead><tr><th>Lab ID<br>(Poz-)</th><th>Depth<br>(cm)</th><th>Core<br>section<br>(VVV-)</th><th><sup>14</sup>C age<br>(BP)</th><th>1σ<br>error</th><th>2σ cal age<br>range<br>(cal BP)</th><th>Median<br>cal age<br>(cal BP)</th><th>c. AD</th></tr></thead><tbody><tr><td>92269</td><td>83</td><td>16-1-1-1</td><td>10</td><td>30</td><td>21–240</td><td>57</td><td>1893</td></tr><tr><td>92270</td><td>143</td><td>16-1-1-1</td><td>545</td><td>30</td><td>504–551</td><td>527</td><td>1423</td></tr><tr><td>102442</td><td>182</td><td>16-1-1-2</td><td>60</td><td>30</td><td>505–555</td><td>530</td><td>1420</td></tr></tbody></table>

<figure><img src="image_110.png" /><figcaption>**Figure 2.** The age-depth model for composite core VVV16. The grey box indicates the part of the sequence presented in this paper (enlarged in the inset); the lower part of the record was previously published by Strobel *et al.* (2019) (10–4 m composite depth). The 2σ probability distribution of calibrated <sup>14</sup>C ages is presented in blue and the 95% confidence intervals are represented by the grey dotted line. The dotted red line represents the best model according to the weighted mean age at each depth.</figcaption></figure>

## 19.4 INTERPRETATION

Fifty-nine different taxa were identified from this section of the VVV record, spanning the period c. AD 1300 to present. Due to the core composition, the assemblage was divided into two zones – the first comprising cores VVV16-1-1-1 and 16-1-1-2 and the second being VVV16-4. Pollen concentrations in VVV16-1-1-1/2 vary from 2.34 × 10<sup>3</sup> to 1.15 × 10<sup>4</sup> grains·g<sup>-1</sup>, and in VVV16-4 they range between 2.56 × 10<sup>3</sup> and 6.20 × 10<sup>4</sup> grains·g<sup>-1</sup>.
<figure><img src="image_111.png" /><figcaption>**Figure 3.** Relative pollen percentage diagram for VVV16-4 and VVV16-1-1-1/2 organized according to ecological affinity, with charcoal concentrations and charcoal fragment counts. Pollen taxa occurring at less than 5% are not shown. Exaggeration curves 2× for the taxa presented here. Charcoal and pollen concentrations were calculated in the same manner using microsphere counts.</figcaption></figure>
### 19.4.1 VVV16-1-1-1/2 (AD 1300–1720; 196–82 cm; 15 samples): Coastal thicket – Afrotemperate Forest

Coastal thicket is prominent from the start of the record remaining elevated until c. AD 1420. This group is mainly represented by *Morella* – likely the dune, scrub and heath species *M. quercifolia* and *M. cordifolia*. Afrotemperate forest pollen percentages are low, increasing after AD 1400 to near maximum values (5.2%) at c. AD 1420. Haloragaceae is similarly elevated here. Fynbos pollen percentages increase towards c. AD 1460, Restionaceae displays a similar trend. Ericaceae generally follows this pattern with *Stoebe*-type also present. The thermophilous taxon *Pentzia*-type, is present at maximum percentages near the start of the sequence at c. AD 1310, followed by a decline in both this taxon and the drought/resistant group, to near minimum values at c. AD 1460. *Euphorbia* is present in relatively substantial proportions throughout the record (with peaks at c. AD 1430, 1580 and 1690). The sustained presence of *Euphorbia* could be related to enhanced dune movement in the region as opposed to a climatic response. These trends might indicate generally warmer and drier conditions around c. AD 1300 with moisture availability increasing towards c. AD 1420, and a decline in temperature moving into the Little Ice Age (LIA) – a cooling period identified in the Northern Hemisphere at this time (c. AD 1300 – 1850) (Jones *et al.* 2001; Matthews and Friffa 2005).

After c. AD 1420, coastal thicket percentages are lower until c. AD 1580. Afrotemperate forest taxa are more prominent in the assemblage during this period which could reflect a stage of vegetation succession and/or climatic conditions more conducive to enhanced forest spread. Both Cyperaceae and *Podocarpus* are present at maximum percentages at c. AD 1500 which could suggest a preceding period of enhanced moisture availability and/or reduced rainfall seasonality. Maximum charcoal concentrations at c. AD 1460 are followed by a notable increase in drier asteraceous fynbos (Asteraceae HS; high-spined) until c. AD 1580. This could indicate a progressively drier environment or alternatively a local vegetation response to a large fire event, as inferred from the peak in charcoal concentrations. *Stoebe*-type values increase after c. AD 1500, remaining elevated towards the top of this section of the record, c. AD 1690, probably reflecting a colder LIA environment.

Throughout this part of the record Cyperaceae and Restionaceae display similar trends, while Haloragaceae (likely *Myriophyllum spicatum*), the most prominent taxon in the record, displays an opposite pattern. These trends are possibly related to changing water levels in Vankervelsvlei.

### 19.4.2 VVV16-4 (AD 1870–2010/Present; 42–1 cm; 6 samples): *Pinus* and *Myrtaceae*

*Pinus* and *Myrtaceae* (most likely *Eucalyptus*) are first seen in this section, reflecting the influences of the forestry industry that became established in the late AD 1700's and still remains active in the region today.

*Kiggelaria*, largely absent from the pollen assemblage, becomes more prevalent in this section. As *Kiggelaria* percentages increase, other scrub forest elements, i.e. *Euclea* and *Morella*, decline. This vegetation succession was probably triggered by the establishment of the pine plantations in the area. A significant decline in Haloragaceae is further noted in this section of the record.

## ACKNOWLEDGEMENTS

This study was funded by the German Federal Ministry of Education and Research (BMBF). The investigations were conducted as part of a pilot study for the collaborative project 'Regional
Archives for Integrated Investigations' (RAiN), embedded in the international research pro-
gramme SPACES (Science Partnership for the Assessment of Complex Earth System Processes).
We also thank the anonymous reviewer and William Gosling for their helpful comments.

DATA AVALIABILITY

The pollen and microcharcoal data presented here is available at:
http://apps.neotomadb.org/Explorer/?datasetid=48875

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# Pollen records of the 14th and 20th centuries AD from Lake Tsizavatsy in southwest Madagascar

Estelle Razanatsoa & Lindsey Gillson

Plant Conservation Unit, Department of Biological Sciences, University of
Cape Town, South Africa

Malika Virah-Sawmy

Humboldt-Universität, Geography, Berlin, Germany

Stephan Woodborne

iThemba LABS, Johannesburg, South Africa

## 20.1 SITE DETAILS

Lake Tsizavatsy is located at the north of the Mangoky River, in the southwest of Madagascar (21.780°S, 43.897°E, at 45 m asl, Figure 1). The region has an extensive formation of sandstone eroded from the Precambrian basement and also less eroded tertiary limestones of marls and chalks from marine facies formed during the Eocene (Du Puy and Moat 1996; Moat and Smith 2007) that occur near the coast between 0 and 300 m asl. The region's climate is characterised by semi-arid conditions with pronounced seasonality (Donque 1972) and annual precipitation ranging from 400–600 mm per year near the coast (Stiles 1998). The site's vegetation is classified as tropical dry forests with patches of savanna and woodland savanna and some agricultural areas (Figure 1). These savannas include a small number of tree species belonging to the Arecaceae family that are highly adapted to fire, such as cf. *Medemia nobilis* (Grubb 2003). In addition, there is a patchy sclerophyllous forest which contains taxa such as *Leptolaena* spp. and a high number of dry adapted species such as those belonging to the Euphorbiaceae andDidiereaceae families (Moat and Smith 2007). Lake Tsizavatsy is a shallow lake of approximately 500 m in diameter. The local community reports that the lake recedes to half its area during the dry season. The lake did not exceed a depth of 0.5 m during our field work in September 2015. The studied sediment core provides a record of at least 10 km² surrounding the lake representing local to landscape scale of vegetation change. The lake is surrounded by Cyperaceae at the margins and is encompassed by wooded savanna with the presence of degraded and intact dry forest in the wider landscape. Within and around the lake, taxa such as *Phragmites mauritianum* (Poaceae), *Cryptostegia madagascariensis* (Apocynaceae), *Acacia morondavensis* (Fabaceae), *Hyphaene shatan* (Arecaceae), and *Euphorbia* spp. (Euphorbiaceae) are found. The communities surrounding this area comprise foragers and maize horticulturalists (*Mikea*), agropastoralists (*Masikoro*), and fishers (*Vezo*).
<figure><img src="image_112.png" /><figcaption>**Figure 1.** Location of the study site in southwest Madagascar. The red box (black cross) indicates Lake Tsizavatsy. Source: Moat and Smith (2007).</figcaption></figure>

## 20.2 SEDIMENT DESCRIPTION AND METHODS

A 48 cm sediment core was obtained from the edge of Lake Tsizavatsy using a Russian corer in August 2015. Lithostratigraphic units were described using a modified version of the Troels-Smith classification (Kershaw 1997; Table 1). Twelve sub-samples of 1 cm³ were taken from the core for pollen extraction following Bennett and Willis (2001). Pollen was counted using a Leica microscope DM750 and identification was based on published pollen references (Gosling et al. 2013; Vincens et al. 2007). A pollen sum of at least 250 terrestrial taxa was achieved for each sample and relative abundances were calculated and plotted using C2 Version 1.7.7. (Juggins 2003). Cluster analysis was performed using the CONISS method (Analogue package) in R (Grimm 1987; R core Team 2018) to identify zones related to temporal change of the vegetation. Diversity was measured through rarefaction analysis and beta diversity (Birks and Line 1992).

## 20.3 DATING

Four AMS radiocarbon dates were obtained from sub-samples (4–10 g) of bulk sediment extracted from the Lake Tsizavatsy core (Table 2). Measurements were made at the iThemba LABS facility in Johannesburg (South Africa), Beta analytic Inc, Laboratory in Florida (USA), and 14-CHRONO labs in Belfast (UK) (Table 2). Dates were calibrated using the southern hemisphere calibration curve (Hogg et al. 2020) and the age-depth model plotted based on the linear interpolations of age point estimates for depths on weighted means of the dated levels.
<table><caption>**Table 1.** Lithostratigraphic units of the sediment core from Lake Tsizavatsy.</caption><thead><tr><th>Depth (cm)</th><th>Unit</th><th>Troels-Smith</th><th>Description</th><th>Munsel colour code</th><th>Colour</th></tr></thead><tbody><tr><td>16-0</td><td>TSZ-strat 3</td><td>As3 Ld1</td><td>Clayey and not very humified deposits</td><td>2.5Y 3//1 or 5Y 3//1</td><td>Very dark grey</td></tr><tr><td>28-17</td><td>TSZ-strat 2</td><td>As2 Ga1 Ld1</td><td>Clayey and fine sand deposits, few organic materials</td><td>10YR 4//4</td><td>dark yellowish brown</td></tr><tr><td>48-29</td><td>TSZ-strat 1</td><td>As2 Ge1 Ld1</td><td>Clayey and coarse sand deposits, low humidity deposits</td><td>5Y 3//2</td><td>Dark olive grey</td></tr></tbody></table>

<table><caption>**Table 2.** Radiocarbon age of the Lake Tsizavatsy (TSZ) sediment core. Measurements were conducted at 14-CHRONO Centre Queens University (code AUB), iThemba Labs (IT) and Beta Analytic (Beta) and raw radiocarbon dates were calibrated using the southern hemisphere calibration curve (Hogg *et al.* 2020). Uncal. = uncalibrated. Cal. = calibrated. Prob. = probability. BP = before present (AD 1950).</caption><thead><tr><th>Depth (cm)</th><th>Code</th><th>Lab ID</th><th>Uncal. <sup>14</sup>C dates</th><th>δ<sup>13</sup>C ‰</th><th>Calibrated ages</th><th>Prob. (%)</th></tr></thead><tbody><tr><td>24-25</td><td>TSZ 24</td><td>UBA-35667</td><td>106 ± 28 yr BP</td><td>−18.55</td><td>AD 1804–1935</td><td>67.4</td></tr><tr><td>29-30</td><td>TSZ 29</td><td>IT-C-1739</td><td>640 ± 62 yr BP</td><td>−19.8</td><td>AD 1378–1406 (572–544 cal. yr BP)</td><td>68.3</td></tr><tr><td>35-36</td><td>TSZ 35</td><td>IT-C-1608</td><td>680 ± 40 yr BP</td><td>−20.0</td><td>AD 1289–1396 (661–554 cal. yr BP)</td><td>94.5</td></tr><tr><td>43-44</td><td>TSZ 43</td><td>Beta-435564</td><td>640 ± 30 yr BP</td><td>−18.2</td><td>AD 1301–1365 (649–585 cal. yr BP)</td><td>94.4</td></tr></tbody></table>

## 20.4 INTERPRETATION

We identified 65 pollen taxa from 33 families, excluding broken and unidentified grains. The pollen analysis revealed two pollen zones, TSZ_1, and TSZ_2. Radiocarbon dates revealed a hiatus at about 24 cm dividing the core into two parts covering the 14th and the 20th century (Figure 2). The hiatus is probably caused by sediment scouring from the floodwaters of the Mangoky River during wet periods similar to those recorded for example at the Lake Ihotry in the south of the river (Vallet-Coulomb *et al.* 2006). The two pollen zone assemblages cover the periods from AD 1300–1420 (48–25 cm, TSZ_1) and AD 1910–2010 (24–0 cm, TSZ_2). We use these data to compare the 14th century vegetation with that of the 20th century (Figure 3).

### 20.4.1 Tsizavatsy Zone 1 (TSZ_1): 5 samples, 48–28 cm depth, AD 1300 to 1420 – *Palm-Pandanus* wooded savanna

The TSZ_1 zone is characterised as *Palm-Pandanus* wooded savanna, as indicated by a high abundance of Poaceae, Arcaceae and tree taxa, including *Acacia* and *Pandanus*. The abundance
<figure><img src="image_113.png" /><figcaption>**Figure 2.** Age depth model of the core from Tsizavatsy adapted from Razanatsoa *et al.* (in press). Horizontal lines indicate the 1-sigma calibration intervals for the radiocarbon dates done at the depth indicated in the y-axis. Dotted lines indicate the 500-year hiatus period from AD 1420 to 1910.</figcaption></figure>

of Poaceae between AD 1300 and 1420 ranged between 45% and 71% (mean c. 60%), with Arecaceae (c. 5%) and *Acacia* (c. 3%) the most abundant trees. Other notable tree taxa are *Pandanus*, which reached a maximum of c. 5% around AD 1330 (40 cm) but decreased in relative abundance until around AD 1420 (28 cm), while *Celtis*, *Gnidia*, and *Trema* have respective mean values of 3%, 2%, and 4%. All other herbaceous (Asteraceae, Urticaceae and Amaranthaceae type 1) and arboreal (Phyllanthaceae, Myrtaceae, Malvaceae and Moraceae) taxa were present at low abundances in Zone TSZ_1; while aquatic taxa, mostly Cyperaceae, are equivalent to c. 10% of the pollen sum.

### 20.4.2 Tsizavatsy Zone 2 (TSZ_2): 7 samples, 24–0 cm depth, AD 1910 to 2010 – Palm-xerophytic degraded wooded savanna

The TSZ_2 zone is characterised as Palm-xerophytic degraded wooded savanna, marked by the gradual increase in the abundance of xerophytic, herbaceous, and tree taxa, despite the high abundance of Poaceae. Poaceae pollen is highly variable with a minimum value of 45% at AD 1940 (16 cm), and a maximum value of 71% around AD 2000 (2 cm, mean 58%). The most abundant herbaceous taxon is Asteraceae (mean 4%) which reaches its highest value around AD 2010 (c. 10%). All other herbaceous taxa have relatively low abundances. The minimum value of Arecaceae was recorded around AD 2000 (2%) while the maximum value was recorded at AD 1940 (16 cm, c. 12%). Some taxa, such as *Celtis* and *Gnidia* also increased in abundance during this period with a mean value respectively of c. 3% and c. 2%. Dry adapted taxa such as *Securinega* (mean c. 2%), peak in this zone with a maximum value recorded at AD 1990 (4 cm, 7%). The aquatic taxa had a mean abundance of c. 10%; with *Myriophyl-lum* (mean c. 1%), *Colocasia* (mean c. 2%) and Cyperaceae (mean c. 6%) being important components.
<figure><img src="image_114.png" /></figure>

**Figure 3.** Lake Tsizavatsy summary pollen diagram, showing only taxa with relative abundance >2% of the terrestrial pollen sum. Colours indicate taxonomic groupings: green = arboreal taxa, yellow = xerophytic taxa, orange = herbaceous taxa, and blue = aquatic taxa.
## 20.5 COMPARISON OF THE COMPOSITIONAL TURNOVER OF THE VEGETATION DURING THE 14TH AND 20TH CENTURY

During the two periods, compositional change was recorded around AD 1360 (36cm, 2.65 SD), AD 1920 (22 cm, 2.6 SD). During the 14th century, taxa such as *Pandanus* and *Trema* were abundant at the beginning but decrease throughout the period which explains the massive turnover around AD 1360 (36 cm). The turnover recorded around AD 1920 (22 cm) is linked to the increasing dominance of pioneer and xerophytic taxa between AD 1910 to 1940 (24–16 cm). These include Asteraceae, *Gnidia*, and *Securinega* in the 20th-century record compared to the 14th century. Overall, the data show a high rate of grass and palm in the surrounding of the lake during both periods. The main difference in the 14th and 20th century pollen records is a slight increase and more variable abundance of arboreal taxa during the latter. An increase in the abundance of xerophytics taxa, possibly linked to decreasing rainfall, was also recorded during the 20th century. Similarly, although tree abundance was higher in the 20th century compared with the 14th century, it began to decrease from the middle of the 20th century. This reduction in trees was associated with a rise in pioneer taxa such as Asteraceae. The pollen data suggest a combined effect of ecosystem degradation and increasing climate impact on the vegetation which requires further investigation.

## ACKNOWLEDGEMENT

We would like to thank the editor and the reviewers for providing helpful feedback to improve the manuscript. We would also like to thank the team in Madagascar that helped with the field work and Tsilavo Razafimanantsoa for helping with the map. This project has been funded as part of the Faculty PhD fellowship (University of Cape Town, R.E.) 2015-2018 and the Applied Centre for Climate and Earth Systems Science (ACCESS NRF UID 98018, R.E.) project, the UCT University Research Committee accredited (URC) from the University of Cape Town [URC, 2019-2020] and SASSCAL (Southern African Science Service Centre for Climate Change and Adaptive Land Management) [466418 c/c BIO1056, 2020].

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<figure><img src="image_115.png" /></figure>

Taylor & Francis

Taylor & Francis Group

http://taylorandfrancis.com
CHAPTER 21

Modern pollen studies from tropical Africa and
their use in palaeoecology

Adele C.M. Julier, Saúl Manzano, Estelle Razanatsoa & Andriantsilavo H.I.
Razafimanantsoa

*Plant Conservation Unit, University of Cape Town, Cape Town, South Africa*

Esther Githumbi<fnref n="1" />

*Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden*

Donna Hawthorne

*School of Geography and Sustainable Development, Irvine Building, University of St Andrews, United Kingdom*

Glory Oden

*Department of Plant and Ecological Studies, University of Calabar, Calabar Nigeria*

Lisa Schüler

*Department of Palynology and Climate Dynamics, Albrecht-von-Haller Institute for Plant Sciences, Göttingen University, Göttingen, Germany*

Monique Tossou

*Department of Plant Biology, University of Abomey-Calavi, Benin*

Jane Bunting

*Department of Geography, Geology and Environment, Faculty of Science and Engineering, University of Hull, United Kingdom*

ABSTRACT: Modern pollen studies are valuable for the calibration of pollen records and contribute to the understanding of past vegetation dynamics. Here, we present a qualitative review of available published and (where possible) unpublished modern pollen studies conducted in tropical Africa since pollen analysis emerged as a discipline in the early 20th century. At present,

<footnote n="1">¹Other affiliation: Department of Biology and Environmental Science, Linnaeus University, Kalmar, Sweden</footnote>
modern pollen rain studies are geographically unevenly distributed across the continent. We found that most countries across tropical Africa have some modern pollen records, with East African countries being particularly well represented in both older and more recent literature. Many countries, arid regions and transitional phytochoria, however, require further study. This review is intended to guide palaeoecologists and palynologists embarking on new studies by bringing together the history of modern pollen studies conducted to date. Targeting new studies to areas where data are currently lacking will help to build a better understanding of modern pollen deposition on the continent. Moreover, we provide recommendations for designing studies so that their results can be used in quantitative modelling techniques for climate or vegetation reconstructions.

## 21.1 INTRODUCTION

Palaeoecological studies on sedimentary deposits use proxies such as pollen, charcoal, fungal spores and diatoms to reconstruct changes in past environments. These proxies can reveal crucial information about ecosystem responses to climate change, fire occurrence, herbivore activity, and impacts of humans on landscapes on centennial to millennial time scales (Gillson 2015). As such, palaeoecological data are hugely important in understanding the past dynamics of ecosystems and are increasingly being used to inform management decisions and restoration targets (Manzano et al. 2020). In order to interpret and validate records from the past, however, these proxies need to be calibrated.

Calibration most commonly takes the form of studies of modern systems; for instance, pollen from surface lake mud samples can be compared to the vegetational composition of the area around those lakes (Sugita 2007a, 2007b). These comparisons allow palaeoecologists to determine which plants produce lots of pollen grains relative to their vegetational abundance and which produce very little. Modern pollen datasets are increasingly being used to calibrate models of pollen dispersal and deposition, allowing for more quantitatively rigorous reconstructions of past vegetation to be developed (Fang et al. 2019).

In temperate regions, there has been an abundance of studies conducted on modern pollen rain (Davis et al. 2013), and these results have been used to calibrate models of pollen dispersal and deposition (Bunting and Middleton 2005; Jackson and Lyford 1999; Theuerkauf et al. 2016). These models have allowed increasing accuracy and precision of vegetation reconstructions, but often rely on extensive datasets, which are relatively rare in the tropics (Bonnefille et al. 2004; Gajewski et al. 2002; Vincens et al. 2006). Gajewski et al. is a particularly important work as it used 1170 modern pollen samples available at the time of publication to reconstruct climate across all major phytochoria and climatic zones (albeit with patchy coverage) in Africa. This demonstrates the importance and potential utility of modern pollen work in Africa, and highlights the need to target under-studied geographical regions and vegetation types.

Here, we present qualitative accounts of modern pollen work (published and unpublished, where available) conducted to date in tropical Africa with the aim of summarising key details from the studies, and highlighting those geographical regions and vegetation types that require further targeted work. This review will provide a useful starting point for researchers looking to ground their palaeoecological work in modern data, and will also highlight potential ways forward to improve our interpretations of African palaeoecological data.

## 21.2 METHODS AND TERMINOLOGY

The geographical extent of this study is the tropics of Africa, the region between 23.5° N and 23.5° S (Figure 1). This is due to the global importance of the tropical regions in terms of
<figure><img src="image_116.png" /><figcaption>**Figure 1.** Map showing locations of modern pollen studies and phytochoria (White 1983). Numbers refer to studies in Table 2.</figcaption></figure>

biodiversity and conservation status. Pollen studies were located by searching for country specific papers in, for example, Google Scholar, Microsoft Academic, ResearchGate and Web of Science, using terms such as '[country name] modern pollen' '[country] aeropalynology' and equivalent terms in French. References to unpublished theses were found in existing literature; Table 2 shows whether they are publicly available. Existing modern pollen rain studies from each country are summarised using vegetation type descriptions as they are reported in publications so that the studies retain their local relevance. We then use phytochoria as established by White (1983) (eg. Guineo-Congolian Regional Centre of Endemism) to group studies. We chose this approach to retain the diversity of vegetation classifications used in the studies, whilst also capturing continent-scale patterns.

For each of the reviewed papers, we identified (where possible) the methods used to collect the modern pollen data. Melissopalynological studies are excluded from this review as they are of limited relevance to palaeoecological studies. Sampling techniques vary in how closely they resemble the sediment records, the length of time over which they collect pollen, and which
types of pollen they are biased towards collecting. There are a variety of methods used to collect modern pollen data in the tropics (see Jantz et al. 2013). For details of the specific methods, refer to the individual studies listed in Table 2. General terms used in this paper are defined as follows:

(i) Surface samples: A general term referring to samples from the uppermost sediment layer, typically soil, peat, lake or marine sediment.

(ii) Soil samples: Samples taken from an area from the top layer of soil (including leaf litter and humus), not from within a water body.

(iii) Core top samples: samples from the top of a sediment core that are assumed to be modern.

(iv) Pollen Traps: Artificial pollen traps come in a variety of formats (Bush, 1992; Gosling et al., 2003; Tauber, 1967) and capture pollen over set periods of time.

(v) Moss/Lichen polsters: Samples of moss and/or lichen are harvested from the ground or trees.

(vi) Glycerol/Silicon oil slides: These are used to sample pollen over a very short period of time by catching grains directly onto a slide

(vii) Filters: Air filters can be used to trap pollen grains; mainly used in offshore sampling.

## 21.3 WEST AFRICA

West Africa has been the site of a large number of studies (Figure 1). Multiple studies have been carried out in Senegal, Côte d'Ivoire and Togo, (Edorh 1986; Lézine 1988; Ybert 1980), with studies covering the main vegetation types of that region. Nigeria has a large number of pollen rain studies, with many conducted for aeropalynological purposes. Several countries do not appear to have any published (or unpublished but cited elsewhere) modern pollen records, namely; Guinea, Guinea-Bissau, Sierra Leone, Liberia, Gambia, Mali, and Burkina Faso.

An extensive summary of modern pollen data for west Africa was compiled by Lézine et al. (2009), who compared modern pollen spectra to climate and vegetation. This work found that biomes are generally well represented by their pollen spectra, which capture the main components of the vegetation and can be used to reconstruct climatic parameters. The exceptions to this were tropical forest biomes, likely due to their high levels of entomophily, and human-impacted dry forest at the Sudanian-Sahelian ecotone.

Off-shore pollen studies have been conducted to complement work on marine cores in the Gulf of Guinea (Calleja et al. 1993; Caratini and Cour 1980; Dupont and Agwu 1991; Hooghiemstra et al. 1988; Melia 1984; Romero et al. 2003). Marine studies off West Africa are synthesised in Hooghiemstra et al. (2006) and in this review we focus on continental records.

### 21.3.1 Mauritania

Only five modern pollen samples have been recorded from Mauritania, (Lézine and Hooghiemstra 1990). These samples are soil samples and record high levels of Amaranthaceae and Brassicaceae pollen, reflecting the Saharan environment. Due to the dry environment, these samples likely represent a maximum of one year of pollen deposition, despite being collected from near to water sources.

### 21.3.2 Senegal

Modern pollen from soil and mud samples from Senegal was first studied by Lézine (1987) with the majority of samples published in later manuscripts (Lézine 1988; Lézine and Edorh 1991; Lézine and Hooghiemstra 1990). Samples were collected from Sudanian forests and demonstrated that pollen and vegetation from this zone are relatively well correlated (see also Togo). A strong
Sahelian influence was observed in the northernmost samples, typified by taxa such as *Acacia* and *Balanites*. In southern samples, Combretaceae represents the Sudanian vegetation type. Lake surface samples from Lake Guiers (Lézine 1988), demonstrated that the pollen record from the lake, which is located in the Sahelian zone but has a drainage basin extending across vegetation types, is dominated by relatively local taxa such as Poaceae, with a strong Sahelian element (7%) and less of a Sudanian element (2%).

### 21.3.3 Côte d'Ivoire

Work has been conducted terrestrially and in mangrove swamps in Côte d'Ivoire. Ybert (1975) covered the savannah-forest boundary using monthly sampled pollen traps across transects of forest to savannah. These revealed high levels of temporal variability in pollen rain, but showed that Poaceae dominated in the savannah sections (at percentages of >40%), along with Ulmaceae-Moraceae type and *Lippia multiflora* Moldenke. *Alchornea* was reported as being abundant across the transects. Fredoux (1978) presented modern samples from Mangrove swamps which showed low levels of *Rhizophora* and other species typical of Mangrove swamps, instead being dominated by dry land pollen types (Lézine 1997).

### 21.3.4 Ghana

Three modern pollen studies are currently known from Ghana. Maley (1983) included some modern surface samples in a preliminary study of Lake Bosumtwi (a site which went on to yield a c. 500,000 year record (Miller and Gosling 2014)). These surface samples were taken from small ponds and lakes in the semi-deciduous forest region of the country and showed high levels of *Musanga*, *Celtis* and *Triplochiton scleroxylon* pollen and low levels of Poaceae. These findings correlated with those of Julier *et al.* (2019) which showed consistently high levels of *Celtis* and *Triplochiton* in pollen trap samples from transects across 100 m by 100 m square vegetation monitoring plots, with traps placed every 20 m along the transect, near to Kumasi, also in the semi-deciduous vegetation type. Traps from a wet evergreen forest plot showed that variability in modern pollen rain was high. Unlike traps from the semi-deciduous plot, however, the wet evergreen traps demonstrated high spatial variability, with traps 20 m apart producing drastically different pollen spectra.

Trap data from the savannah-forest boundary in Ghana (Julier *et al.* 2017) showed that Poaceae abundances of >40% could be considered indicative of a savannah landscape. These two studies (Julier 2017; Julier 2019) were intended to inform palaeoecological interpretations, specifically of Lake Bosumtwi. The data gathered cover a range of vegetation types but lack temporal resolution, with the maximum number of sampled years being three for the forested ecosystems and one for the savannah transitional mosaic.

### 21.3.5 Togo

The first, and most extensive, study of modern pollen conducted in Togo was by Edorh (1986) who collected 125 soil and river surface samples from a range of vegetation types across Togo, encompassing forest and savannah. It was found that pollen broadly reflected vegetation, but at a local scale. Some of these samples were later published in Lézine and Edorh (1991), who demonstrate that some Sudanian vegetation types such as Combretaceae woodlands and *Isoberlina* woodlands are distinguishable on the basis of their pollen spectra. Anemophilous non-arboreal taxa frequently dominated the signal, and that samples may be biased towards local representation, meaning that a high spatial density of samples is necessary to reflect regional vegetation composition. The pollen spectra were also correlated successfully with rainfall, highlighting the difference between the Guinea-Congolian and Sudanian vegetation zones (Chalié *et al.* 1990).
A study of modern pollen from Mangroves in Togo (Edorh and Afidegnon 2008) demonstrated that the modern pollen spectra, on the whole, represented their parent vegetation types well and were able to distinguish between *Avicennia* and *Rhizophora* mangrove environments.

### 21.3.6 Bénin

The first known pollen rain study in Bénin is Tossou *et al.* (2012) which focuses on southern Bénin. Surface sediments were taken from the main plant formations: semi-deciduous dense humid forest, floodplain forest, mangrove, swampy forest, swampy meadow and plantation with *Acacia auriculiformis*. Pollen analysis of 38 soil samples identified a total of 126 pollen taxa from 65 families. The most abundant pollen taxa broadly reflect the different plant formations recorded, therefore suggesting that they could be used to reconstruct changes in landcover from palaeo-ecological data in Southern Bénin. A further study (Zanou *et al.* (2020)) focused on the modern pollen rain in the Lama classified forest (south Bénin) from surface sediments. This study allowed a comparison between the pollen content of the sediments and the botanical diversity of the formation. Pollen analysis of 50 sediment samples identified a total of 62 pollen taxa belonging to 31 families and 44 genera. The dominant taxa are Poaceae, *Cassia*, and Combretaceae. The index of similarity between the species inventoried and those contained in the sediments is greater than 50%. Aeropalynological work using traps has also been conducted in Bénin (Tchabi *et al.* 2017; Tossou *et al.* 2016), finding higher pollen content in the air with higher winds and temperature, but lower pollen content with high rainfall, and the most abundant pollen counts in the dry season (November to March).

### 21.3.7 Niger

The only known study from Niger is an aeropalynological study from Caratini and Frédoux (1988), which presents 27 pollen trap samples with pollen data presented as monthly values from a savannah area in Niamey, south Niger. Around 60% of the annual pollen rain was Poaceae, which peaked in August and September, the rainy season. Cyperaceae pollen was found to peak in October to December and Chenopodiaceae pollen peaked at 5% in December through January, the dry season.

### 21.3.8 Nigeria

In Nigeria, a large number of aeropalynological studies have been conducted (Abdulrahaman *et al.* 2015; Adekanmbi and Ogundipe 2010; Adeniyi *et al.* 2018; Adeonipekun *et al.* 2016; Agwu 2001 1986; Agwu and Osibe 1992; Alebiousu *et al.* 2018 2018; Ezikanyi *et al.* 2016; Ibigbami and Adeonipekun 2020; Njokuocha 2006). These studies all use some form of pollen trap (modified Tauber traps or 'Gbenga 2' aerofloral samplers) and are primarily for the study of allergenic particles in the atmosphere. They typically record results monthly, with time periods recorded varying from a few months (Abdulrahaman *et al.* 2015) to more than a year (Adeniyi *et al.* 2018). The studies generally found high pollen abundances in the wet season (aside from (Adeniyi *et al.* 2018) who found lower abundances of pollen in the wet season) and high abundances of Poaceae, regardless of their location and year of sampling. In some instances, forest elements such as *Celtis*, *Milicia*, and *Berlinia* are found, indicating long distance transport from forested areas (Ezikanyi *et al.* 2016; Njokuocha 2006). The studies are almost exclusively located in urban areas and are therefore of limited use for palaeoecological reconstructions. Modern pollen work on soil and trap samples from the north of Nigeria showed extremely high levels of Poaceae and Cyperaceae (>95%), but also that modern pollen assemblages primarily reflect their local vegetation, not long-distance transport (Salzmann 2000; Salzmann and Waller 1998).
### 21.3.9 Chad (including Lake Chad)

The first modern pollen study conducted in Chad was by Maley (1972) who took surface samples from Lake Chad and nearby areas. These results were reproduced as part of Maley's account of the palaeoecology of Lake Chad (Maley 1981). These samples demonstrated the importance of rivers in the long distance transport of pollen, but that the surface samples did capture vegetation changes from the area around the lake. Schulz (1976) also reported modern samples from Chad and compared these with 8000–9000 yr BP samples. The study showed relatively high levels of Poaceae pollen in both modern and fossil samples, but higher levels of montane pollen in the fossil samples. Ybert (1980) used pollen filters to capture pollen rain from November 1971 through December 1972. It was shown that Poaceae is the most abundant taxon, and peaks in September. Amaral et al. (2013) used modern sample data from Maley (1972) and other, previously unpublished work to perform biomisation, thereby assigning estimates of mean annual precipitation to time periods covered by a core from Lake Chad covering the period from 6700–500 yr BP.

## 21.4 CENTRAL AFRICA

In Central Africa, Cameroon, Gabon and the Republic of Congo contain the largest number of studies focusing on modern pollen-vegetation relationships (Elenga 1992; Elenga et al. 2000; Henga-Botsikabobe et al. 2020; Jolly et al. 1996; Lebamba et al. 2009; Reynaud-Farrera 1995; Tovar et al. 2019; Vincens et al. 2000). Other countries in the region lack records; the Central African Republic yields just one study (Aleman et al. 2012), the Democratic Republic of Congo has a single study spanning the Uganda-Congo border (Beuning and Russell 2004), and Equatorial Guinea has one study of marine surface pollen records, offshore in the Gulf of Guinea (Dupont and Agwu 1991).

### 21.4.1 Cameroon

Modern pollen studies in Cameroon are currently concentrated in the south and south-east of the country, and focus on pollen-vegetation relationships. Vincens et al. (2000) examine the pollen in soil and litter samples from 26 contiguous plots along a forest-savannah transect. They conclude that the two main ecosystems, forest and savannah, are clearly represented in the surface samples at the local scale, with few pollen grains found outside their producing communities. The density of the forest canopy largely inhibits regionally wind-dispersed pollen from entering the forest and in the savannah these values are typically <1% (Vincens et al. 2000).

A similar study by Lebamba et al. (2009) examines 24 surface samples from the southern Cameroon lowlands, in forest and savannah environments. They demonstrate that major vegetation types can be differentiated using modern pollen percentages, and a distinction made between secondary and mature forests and well drained and hygrophilous forest types, therefore the pollen analysis is a robust way of discriminating between the maturity, structure and composition of Central African forests. This is reinforced by a study of 50 surface samples from the forests and savannahs of southern Cameroon which defined distinct plant communities (Reynaud-Farrera (1995)). Verlhac et al. (2018) studied modern soil samples from across an elevational gradient in the Cameroon highlands and found that modern pollen assemblages reconstruct the upper and lower limits of the Afromontane forests well.

Vincens et al. (2000) note that there are individual taxa which can be misrepresented in the pollen spectra, including markers of the main community types (e.g. *Albizia* and *Rinorea*). The differences between the observed vegetation and the pollen analysed in surface samples is often a function of the production and dispersal of individual taxa. However, botanical survey
methods, such as only recording trees of more than 10 cm in diameter at breast height (DBH)
will also influence the record.

A number of modern pollen studies from Cameroon examine the transportation and dispersal of pollen throughout the Sanaga River Basin (Bengo 1996, 1992; Bengo et al. 2020) and off-shore in marine surface sediments on the continental shelf (Van Campo and Bengo 2004). Thirty-eight surface samples from the banks of the Sanaga River and twelve of its main tributaries were collected to characterize the origin of the pollen and its mode of transport to the continental shelf. Bengo et al. (2020) demonstrate that pollen analysed in the riverbank sediment clearly reflects the surrounding vegetation cover, and transport to the coast of the Gulf of Guinea is essentially through the drainage of large basins. Examining seventy-one modern samples from the continental shelf off the coast of Cameroon (Van Campo and Bengo 2004), they found a dominance of the mangrove pollen type *Rhizophora*, which is located along the shoreline, a high pollen producer and effectively transported by water. They also note the dominance of Poaceae and *Alchornea*, which likely arrive on the shelf by riverine transport from the grasslands of northern Cameroon and riverbanks and forest edges throughout the Sanaga Basin, respectively. Analysing modern surface samples along river basins therefore greatly supports the interpretation of source area of pollen captured in marine and off-shore sediments.

## 21.4.2 Central African Republic

There is a paucity of modern pollen studies in the Central African Republic. A single study by Aleman et al. (2012) investigates modern pollen (identified as arboreal and non-arboreal), phytoliths and stable carbon isotopes in 17 soil samples from a forest-savannah environment. Combining these results with field Leaf Area Index (LAI) measurements, they calibrate a multi-proxy model to reconstruct LAI in palaeo-sequences, providing a long-term estimate of tree cover. This approach provides a useful method for assessing past changes in vegetation cover, and transitions from forest to savannah and *vice versa*.

## 21.4.3 Equatorial Guinea

There is a lack of records from terrestrial Equatorial Guinea, however, one study exists which examines the distribution of pollen grains within the Gulf of Guinea and the influence of wind and water on pollen transport (Dupont and Agwu 1991), this study is reviewed in Hooghiemstra et al. (2006)

## 21.4.4 Gabon

Gabon has a number of studies incorporating modern pollen samples. An early study by Jolly et al. (1996) examined 16 surface soil samples from the rainforest of north-eastern Gabon to test pollen-vegetation relationships along a number of transects. They establish pollen markers for a number of forest types, and explore the pollen production of individual taxa (e.g. *Alchornea* and *Macaranga*) which showed the largest pollen abundance. Ngomanda (2005) carried out research in the forests of Gabon, focusing on the past 5000 years, as part of a doctoral dissertation. Later publishing, with others, palynological data from two lake cores, the tops of which represent the modern pollen signal (Ngomanda et al. 2007).

Lebamba et al. (2009) analysed 57 surface soil and litter samples from the forests of north-eastern and south western Gabon. They demonstrated that major vegetation types can be differentiated by their modern pollen percentages, and distinctions made between different forest types (e.g. secondary and mature forests).

More recently in Gabon, Henga-Botsikabobe (2015) completed a doctoral dissertation examining surface samples and palaeo-cores from Lope National Park, to reconstruct the past