Bosumtwi is at 97 m asl, has a diameter of 8.5 km and a surface area of 52 km². Situated in the 'Tropical and Subtropical Moist Broadleaf Forest' biome, Lake Bosumtwi occupies the transition between shrubby/woody savannah ecosystem to the north and moist forest ecosystem to the south (Olson et al. 2001). This setting makes the pollen record ideal for western-African vegetation reconstructions. Western Africa is notably underrepresented in the archaeological record, the oldest *H. sapiens* fossil being dated to only 12 ka in southwestern Nigeria (Harvati et al. 2011).

An age-depth model is lacking in the initial publication presenting the pollen record of BOS04-5B (Miller and Gosling 2014). We follow the age model of Shanahan et al. (2013) and used by Miller et al. (2016) of the same core. The period MIS 14.2-12.33 (538-461 ka) is characterized by fluctuating Poaceae percentages (reaching values >60% in the beginning and <10% later in this interval) and the presence of moist broadleaf forest taxa like *Celtis* (c. 8%) and Moraceae (c. 15%). At MIS 12.3-11.3 (461-405 ka) Poaceae increase up to c. 80%, interrupted by a fall during MIS 12.31 (443 ka) to 40%. In this period the moist broadleaf taxa show very low percentages. Cyperaceae reach c. 10% and montane tree *Olea* shows a single peak of 40% during MIS 12.31. Interval MIS 11.3-11.2 (405-375 ka) shows low (c. 20%) Poaceae percentages, and an increase in the arboreal broadleaf taxa *Celtis* and Moraceae, as is the case for MIS 13.13-12.33 (502-461 ka), but this time there is also a notable presence of *Macaranga* (c. 10%). Pollen spectra during MIS 11.2-10.2 (375-341 ka) resemble those found during the period of MIS 12.33-11.3, followed by a period of large fluctuations (from <10% to >80%) in Poaceae percentages that lasts until MIS 7.3 (216 ka). During this period Moraceae continues to have a marked presence in the pollen record, whereas it seems that *Celtis* is replaced by montane *Olea* at MIS 8.5 (287 ka). Poaceae dominates the pollen signal from MIS 7.3 to MIS 6, with consistent percentages of 90% and virtually no other pollen types found in the sediment except for low presences of Cyperaceae and *Olea* (c. 10%). In the upper part of the sediment column, during MIS 6-5.5 (183-122 ka) and MIS 2.2-0 (19-0 ka), Poaceae pollen is almost absent whereas taxa of moist broadleaf and the montane forest dominate.

## 23.5 DISCUSSION

We characterized vegetation as represented by the palynological records on a scale from open to closed (Figure 4). Our classification was done visually, using the pollen diagram provided in the original publication and based on the original interpretations by the authors. These characterisations are based on several factors: (i) the ratio Poaceae/arboreal pollen, (ii) the abundance of pollen reflecting shrubby taxa, (iii) (when provided) charcoal data, and (iv) relevant notes in the original publication. We relate the vegetation changes to the timing of the archaic *H. sapiens* transition to AMH and the δ¹⁸O record (Lisiecki and Raymo 2005); including the MISs notation following Imbrie *et al.* (1984) indicating either glacial or interglacial periods.

Here we interpret the vegetation structure based on the palynological record and try to environmentally contextualize the three steps in archaic *H. sapiens* migration as related to our proposed Inside-of-Africa environmental framework: (i) An initial dispersal event of archaic *H. sapiens* after our lineage split off *H. neanderthalensis* (c. 700-400 ka, MIS 18.2-11.3), (ii) a period of isolation imposed by regional-scale environmental conditions, limiting the exchange of both genes and cultural knowledge, thereby promoting intraspecific variation within the *H. sapiens* clade (c. 400-250 ka, MIS 11.2-8.2), and (iii) a period of increased interconnectivity and growing population sizes that lead to homogenization and, ultimately, the full suite of AMH characteristics (c. 250-100 ka, MIS 7-5.3). To complete the picture of vegetation change in Africa we briefly review the last 100 ka and discuss the relevance of vegetation openness in this period for understanding the dispersal of hominins out of Africa (Blome *et al.* 2012; Carto *et al.* 2009; Tierney *et al.* 2017).
<figure><img src="image_127.png" /></figure>

**Figure 4.** Hominin evolution and vegetation change in Africa over the last 500 thousand years. (A) Key archaeological sites (Grun *et al.* 1996; Hublin *et al.* 2017; McDougall *et al.* 2005; White *et al.* 2003), 'Inside-of-Africa' framework (Figure 1), and global climate change as indicated by global $\delta^{18}$O record (Lisiecki and Raymo 2005). Numbers on the $\delta^{18}$O panel indicate Marine Isotope Stage numbers as defined by Imbrie *et al.* (1984). (B) Degree of vegetation openness inferred from the ancient pollen records including respective open/close vegetation curve*. (C) Blown up view on the last 150 thousand years of vegetation change to show additional details. Indicated changes in the openness of the landscape are relative only to the pollen record itself. * The following data are plotted for each site: PB: *Pinus*/Poaceae% (Torres *et al.* 2020), VVV10.1: Ericaceae% (Quick *et al.* 2016), MAG14, MAL05-1B/1C, B12: *Podocarpus*/Poaceae% (Beuning *et al.* 2011; Ivory *et al.* 2018; Lézine *et al.* 2019; Owen *et al.* 2018), BOS04-5B: Poaceae% (Miller and Gosling 2014).
### 23.5.1 Vegetational trends throughout Africa 500 to 400 ka (MIS 13.1-11.3)

Currently, only four palynological records meeting our criteria are long enough to examine the vegetation change coeval with the initial radiation of archaic *H. sapiens* after their lineage split with *H. neanderthalensis*. These four records are the Padul Basin (PB; Torres *et al.* 2020) representing a change in the northern African region, Lake Magadi (MAG14; Owen *et al.* 2018), Lake Malawi (MAL05-1B; Ivory *et al.* 2018) in eastern Africa, and Lake Bosumtwi (BOS04-5B; Miller and Gosling 2014) in western Africa (Figure 4). We expected Africa in this period to host a relatively larger proportion of open vegetation that provided hospitable environments so the earliest members of the *H. sapiens* clade could disperse throughout Africa.

In northern Africa the relatively low resolution and multiple gaps in the PB pollen record make it difficult to discern variation in the vegetation cover during this period (Torres et al. 2020). However, the overall trend is apparent with the main arboreal taxon (*Pinus*) decreasing towards 400 ka. The loss of arboreal taxa could suggest that the vegetation structure became gradually more open; however, Poaceae does not replace the trees (also diminishing and absent 425-400 ka (MIS 12.1-11.3)). The loss of *Pinus* and Poaceae are also concomitant with a decline in total pollen influx rates, suggesting either reduced pollen productivity, a faster sediment accumulation, or poor preservation, all potentially indicative of more arid conditions. The high-resolution dust flux record from Atlantic core ODP 659 located at 18°N (Tiedeman et al. 1994) shows relevant information about climatic conditions in Saharan and sub-Saharan Africa. The 400-250 ka interval shows a maximum in dust outbreaks, indicative of dry conditions in Saharan Africa. It implicates that in this period the rain bringing ITCZ (where the northeast Trades meet the moisture loaden southwest Monsoon) hardly shifted into the northern hemisphere. Therefore, rains concentrated in equatorial and adjacent low southern hemisphere latitudes, allowing over large areas dense arboreal vegetation to expand. For the 400-250 ka interval the pollen record of site ODP 658 at 21°N (Dupont 1989; Dupont and Hooghiemstra 1989) is in support of substantial vegetation cover at equatorial latitudes. On the contrary, during the intervals 500-400 ka and 250-100 ka the dustflux shows significantly lower values (Tiedeman et al. 1994) and the monsoon front had more southern positions, implicating the monsoon front reached higher latitudes: Saharan Africa had wetter conditions and Africa at equatorial latitudes received less precipitation, allowing for a more open mosaic like vegetation pattern.

In the east African Rift Valley, between 500-400 ka, Lake Magadi and Lake Malawi both record open landscapes, savannahs in the north and wooded grasslands in the south, that transition into dense Afromontane forests over the course of the 100 kyr. Owen *et al* (2018) interpret this section of the Lake Magadi record as indicative of progressively more open vegetation, however, an examination of the pollen data indicates a wide variation in the abundance of arboreal taxa, e.g. *Podocarpus* fluctuates from near absence up to c. 80%. We therefore also note that there were important woody components, and high dynamism, in the east African Rift Valley vegetation for much of the pre-400 ka part of the record. The exception is between 525-425 ka (MIS 13 and 12) where an abundance of Poaceae (80%) suggests that savannah-like conditions dominated. This trend is almost exactly mirrored at Lake Malawi, where high Poaceae percentages (also 80%) suggest grasslands to be present around the lake before 470 kyr, followed by an abrupt expansion of dense Afromontane forests in the latter part of this time interval (Ivory *et al.* 2018). This grassland period in the east African Rift Valley would likely have offered the most hospitable conditions to early hominins and, with the Congo Basin rainforest to the west of the east African Rift Valley, a passageway between southern Africa and the rest of the continent.

In western Africa no archaeological evidence for archaic *H. sapiens* during the pre-400 ka period has been found in the vicinity of Lake Bosumtwi, but environmental conditions do not necessarily exclude them from inhabiting the area. Miller *et al.* (2016) find significant grassland expansions in favour of forested areas as indicated by increasing percentages of Poaceae pollen. Grassland expansion in this region seems to be driven by increased dryness prompted by orbital
cycles, as Poaceae peaks (sometimes even reaching 99%) in record BOS04-5B are coeval with
maxima in precessional amplitudes.

We find that during the period 500-400 ka (MIS 13.1-11.3) all of the available records show a degree of openness in the vegetation structure. The potential barriers to hominins are in western Africa in the early part of this period when the high representation of arboreal taxa shows a high forest cover (although this is not likely a dense rainforest). In the later part of this period, when poor pollen preservation prevails increased aridity is suggested in northern Africa.

23.5.2 Vegetational trends throughout Africa between 400 and 250 ka (MIS 11.2-8.2)

To date, only four pollen records have been published that are long enough to examine the period of proposed less hospitable condition that limited dispersal, and thereby gene flow, in archaic *H. sapiens*; Padul Basin (PB; Torres *et al.* 2020) representing northern Africa, Lake Magadi (MAG14; Owen *et al.* 2018) and Lake Malawi (MAL05-1B/1C; Ivory *et al.* 2018) eastern Africa, and Lake Bosumtwi (BOS04-5B; Miller and Gosling 2014) western Africa (Figure 4).

At PB, following a gap in the record, the main arboreal taxa (*Pinus*) fluctuates widely from c. 80% to c. 5% between c. 380 and 250 ka suggesting, apart from changes in aeolian pollen dispersal (Hooghiemstra *et al.* 1992) also major changes in the woody component. Within this period a change from Mediterranean (365-340 ka, MIS 11), to steppe vegetation (340-310 ka, MIS 10), and back to Mediterranean (310-295 ka, MIS 9.1-8.6) has been identified (Torres *et al.* 2001). Suggesting very dynamic vegetation with no discernable directional change in openness through this period.

The sedimentary record from Lake Magadi between 375 and 300 kyr BP was analysed for pollen (17 samples) but no pollen was recovered (Owen et al. 2018). Just before and after the hiatus in the record the percentage of aquatic taxa (e.g. Cyperaceae) and Afromontane taxa (e.g. *Podocarpus*) increase. The higher representation of aquatics could be interpreted as a lowering of the lake level providing more shallow water habitat for taxa such as Cyperaceae. The hiatus in the pollen record most likely represents a period of aridity when the basin (at least periodically) dried out and consequently little or no pollen is preserved; suggesting that through MIS 9 warmer conditions resulted in evaporation exceeding precipitation and so reduced the lake level. The more arid conditions potentially formed a barrier that disconnected northern Africa from southern Africa, thereby limiting any hominin dispersal between these two regions. In strong contrast to the arid conditions at Lake Magadi, we see that Lake Malawi hosts a vegetational community that is highly dominated by arboreal pollen from c. 470 to 270 ka. It is therefore suggested in Ivory et al. (2018) that the rigid ecosystem changeovers elucidated by MAL05-1B are a product of the response of local vegetation to the regional precipitation regime, comprising of a feedback between moisture recycling in Lake Malawi and eccentricity mediated summer insolation (Ivory et al. 2016). Still, since we characterise Lake Malawi as being surrounded by dense (Afromontane) forests, it is in line with our hypothesis of inhospitable vegetational cover during the period 400-250 ka, albeit for a different reason than at Lake Magadi.

The Lake Bosumtwi record shows wide fluctuations between c. 90% and c. 10% in the abundance of Poaceae between 400 and 250 ka which was initially interpreted as abrupt high-magnitude transitions between grassland and forest (Miller et al. 2016). Later investigations of how modern forest and grasslands are represented in the pollen rain (Julier et al. 2019) suggest that this magnitude of change could, for a large part, be explained by variation in the woody component of grassland ecosystems, rather than switching to a closed evergreen forest. Therefore, during the period from which the earliest archaic *H. sapiens* fossils have been found at Jebel Irhoud the landscape around Lake Bosumtwi was most likely covered with a wooded savannah.

We find that during the period 400-250 ka (MIS 11.2-8.2) the sites representative of northern and western Africa show a dynamic vegetation cover, but likely were not inhospitable to hominins.
The eastern African record was likely arid for much of this period which could have created a
geographic barrier to hominins.

### 23.5.3 Vegetational trends throughout Africa between 250-100 ka (MIS 8.2-5.3)

Between 250 and 100 ka past vegetation data is available from the Padul Basin (PB; Torres *et al.* 2020) in northern Africa, Lake Magadi (MAG14; Owen *et al.* 2018) and Lake Malawi (MAL05-1B/1C; Ivory *et al.* 2018) in eastern Africa, and Lake Bosumtwi (BOS04-5B; Miller and Gosling 2014) in western Africa records plus, for the younger final few thousand years, parts of the Vankervelsvlei (VVV10.1; Quick *et al.* 2016) southern Africa, and Lake Malawi (1C; Beuning *et al.* 2011) eastern Africa records. In this time window, we expect a dominance of an open vegetation structure that promoted the dispersal, and consequently gene flow, between morphologically dissimilar archaic *H. sapiens* populations. This increased connectiveness would have ultimately lead to the full range of AMH associated phenotypical characteristics throughout the entire *H. sapiens* clade.

There is evidence for forest development at PB during the 250-100 ka interval (MIS 8.0-6.5) as indicated by the high abundance of evergreen taxa (*Pinus* >50% through most of this period) and deciduous taxa in parts (e.g. *Quercus*, c. 10%, 270-250 ka), indicating relatively stable vegetation structure with a strong woody component. The forest vegetation could have been less favourable to *H. sapiens* encouraging populations to move to find more open environment to which they were more familiar.

The high diversity of pollen taxa present in record MAG14 between 250-100 ka suggests that many different ecosystems were present along the banks of Lake Magadi. Within this time window Afromontane taxa diminishing in favour of the relatively open Zambezian Miombo woodland and humid evergreen woodlands, offering a diverse landscape vegetation mosaic with a wide variety in resources and thereby generating hospitable environments for archaic *H. sapiens*. The other lake core from eaststern Africa considered here, Lake Malawi, shows high variation in landscape openness. Five wooded grassland periods with a combined duration of c. 90 ka have been identified at Lake Malawi, giving ample oppurtunities for archaic *H. sapiens* to settle and disperse through the vicinity of Lake Malawi. Thus, both eastern African records indicate vegetation that would have been hospitable to archaic *H. sapiens*, and could have provided a north-south corridor allowing connectivity across the continent.

The pollen record from Lake Bosumtwi suggests an expanded savannah in western Africa, unprecedented in both stability (lasting almost 100 kyr) and cover (reaching >95% Poaceae). The consequences of this heavily grass-dominated landscape for *H. sapiens* could be interpreted in two ways: (i) as an ideal open landscape which they could occupy and traverse comfortably, or (ii) as a barrier due to high aridity and a lack of resources (absence of trees and water). The absence of any archaeological evidence of *H. sapiens* in western Africa until c. 12 ka (Harvati *et al.* 2011) tentatively provides support for the latter, but further evidence is clearly required.

The Vankervelsvlei sedimentary record begins c. 110 ka and contains pollen indicative of predominantly open woodland vegetation. This relatively open woodland could have provided a favourable to *H. sapiens* in a similar way to the vegetation of eastern Africa.

During the period 250-100 ka we find that there are potential barriers to *H. sapiens* in the north (covered by forest) and western (arid ecosystems) regions of Africa. While in the eastern and southern regions there is evidence for more hospitable vegetation configurations.

### 23.5.4 Vegetational trends throughout Africa after 100 ka (MIS 5.3)

The last 100 ka is an important period in hominin evolution and dispersal, particularly related to the movement of hominins out-of-Africa between 70 and 40 ka (MIS 5.0-3.2; (Blome *et al.* 2012; Carto *et al.* 2009; Tierney *et al.* 2017)). During this period an increasing amount of
information on past vegetation change in Africa is available, however, a detailed exploration
of all this literature is beyond the scope of this review. Instead, we limit this section to briefly
contextualizing change around the period of movement of hominins out-of-Africa from just the
six records used to provide information on longer-term vegetation change (Figure 4C).

The northern African vegetational makeup, as inferred from the PB pollen record, implies steppic environmental conditions with high *Artemisia* and Poaceae presence in the Mediterranean regions of northwest Africa for MIS 4 and MIS 3, indicating cold and arid climate for both stages (Torres *et al.* 2020). While in southern Africa the VVV10.1 pollen record suggests vegetation remain fairly stable in their characteristics over the last 100 ka, notwithstanding an expansion of fynbos vegetation between 70-40 ka (Quick *et al.* 2016). Similar vegetation stability is also seen in eastern Africa from the MAG14 pollen record (Owen *et al.* 2018), although the MAL 1C pollen record shows a trajectory of increasing woodland vegetation (Beuning *et al.* 2011). This increase in woodland vegetation has been interpreted as increased landscape diversity that could have aided hominin development and dispersal by providing access to new and more varied resources (Beuning *et al.* 2011). In central (record B12) and western Africa (record BOS04-5B) the degree of vegetation openness is shown to fluctuate; with the greatest depression of the treeline (hence most open vegetation being available at high elevation) occurring at Lake Bambili around 35 ka (Lézine *et al.* 2019).

Through the period of hominin dispersal ‘Out-of-Africa’, some portion of each of the six records indicates open vegetation that would have helped to facilitate dispersal of hominins. It is also the period in which the greatest variation in the vegetation record is seen, which could also have promoted dispersal as hominins sought to track resources. However, we should be cautious in our interpretations because the evidence is likely biased by the increased number of locations from which evidence is available, and the temporal detail of the information.

23.6 CONCLUSIONS

Growing evidence from genetic and archaeological sources paints a picture of complex pan-African hominin evolution, development and dispersal (Hublin et al. 2017; Lipson et al. 2020; Scerri et al. 2018; Stringer 2016; Wang et al. 2020). Intuitively it seems reasonable that the habitat that hominins existed within played an important role in shaping these processes and, based on archaeological evidence, it has been suggested that more open vegetation formations were the favoured habitat of early hominins (Bertola et al. 2016; Blome et al. 2012; Cerling et al. 2011; Larrasoana et al. 2013; Scerri et al. 2018). However, unfortunately, evidence for vegetation, and vegetation change, over the more than half-million years over which evolution, development and dispersal have occurred remains scant at the continental scale. The terrestrial records of past vegetation change, based on the analysis of ancient pollen, obtained from six sedimentary basins that are relevant to Africa on these timescales indicate that the degree of openness varied regionally. The balance between a predominance of open vs. closed vegetation across Africa tends to follow a pattern of indicating more hospitable (open vegetation) during the period 500-400 ka when archaic *H. sapiens* dispersed around the continent, more inhospitable (closed vegetation/desert) between 400-250 ka when many new phenotypically distinct clades of hominins arose, and a return to more hospitable (open vegetation) conditions 250-100 ka coincident with the rise of anatomically modern humans. This pattern of vegetation change fits with our proposed ‘Inside-of-Africa’ environmental framework as a backdrop to hominin evolution. While these data provide some evidence for vegetation change in Africa over the last 500 ka there are clearly huge spatial and temporal gaps in the evidence available. Consequently, any inferences drawn from this must be done so with caution, however, we hope that our proposed framework will provide food for thought among researchers and be subsequently tested against new and more detailed records of past vegetation change.
ACKNOWLEDGEMENTS

We would like to thank all the authors of the original publications which we review here for publishing their studies and making their data available either through open access databases or directly following corresponance. We thank Alayne Street-Perrott (Swansea University, UK) and Anne-Marie Lézine (CNRS, France) for their thorough evaluation and feedback on our manuscript, and the PoA editorial team for the opportunity to contribute to this volume.

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CHAPTER 24

# The role of palaeoecology in conserving African ecosystems

Lindsey Gillson

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

ABSTRACT: Africa holds critical and unique biodiversity, but conservation is challenging because of the complexity of landscapes and their history, and the multitude of drivers and stakeholder perspectives. Palaeoecology can help in developing a nuanced understanding of landscape history that can inform pressing conservation issues, including climate change integrated conservation strategies, fire management, management of herbivores and rewilding, ecosystem restoration, sustainable use of natural resources, and management of cultural landscapes. To be useful, however, the data from palaeoecological proxies needs to be converted into metrics that are useful and accessible to conservationists and managers, requiring rigorous calibration against modern and instrumental data sets, alongside sound chronologies. Palaeoecologists can help in enhancing the applied value of their work by interpreting it in ways that are underpinned by sound ecological theory in ways that are relevant to pressing conservation questions. The design of projects and the interpretation and application of data can be used as starting points for interdisciplinary, collaborative approaches with stakeholders. Finally, the modelling of palaeoecological data can help stakeholders to explore landscape changes and the effect of management interventions under different scenarios of climate and land-use change in the future.

24.1 INTRODUCTION

Africa's landscapes are home to important biodiversity hotspots as well as the world's only intact megafaunal assemblage (Archer et al. 2018). They are also characterised by a large rural population, whose livelihoods directly depend on ecosystem services, alongside rapidly changing socio-economic contexts that include urbanisation and development. Conservation in Africa takes place both inside and outside of protected areas and both approaches present challenges and opportunities. Developing conservation strategies that benefit both biodiversity and people depends on developing and understanding of landscape history both in terms of ecological variability and cultural context.

In the early days of conservation, the idea of maintaining, preserving or restoring ‘balance’ was prevalent, and ideas of equilibrium were also reflected in early ecological theory. Maintaining populations at fixed carrying capacity was an approach common in southern Africa, while in East Africa strict preservation of ‘wilderness’ – areas perceived, often wrongly, to have been little modified by people – was a dominant conservation ideal (Gillson 2015). Added to this, many colonial governments perceived fire to be destructive and implemented policies of fire suppression, disrupting traditional burning practices, such as seasonal mosaic burning (Gillson et al. 2019). However, more recent approaches to conservation accept that variability, flux and disturbance are normal to most populations and landscapes, and this raises a need for a long-term
perspective that allows managers to explore resilience and variability over centennial-millennial timescales.

Many African National Parks were founded in the early decades of the twentieth century, when ecosystems were likely to have been in an atypical state. Though most African landscapes have been managed by people for millennia, the combined impacts of fire suppression, removal or disruption of traditional management, and the decimation of megafaunal populations from the 19th century are likely to have led to atypically higher tree density in savannas. This high tree density is likely to be further exacerbated by the effects of CO₂ fertilisation, which benefits C₃ plants such as savanna trees and shrubs, more than C₄ plants, including savanna grasses, and is likely to play a role in bush encroachment (Bond and Midgley 2012; Midgley and Bond 2015; Wigley et al. 2009). On the other hand, some savannas were cleared of trees because of charcoal production or to make way for agropastoralism, and woody vegetation subsequently recovered following land abandonment (Blair et al. 2018; O'Connor et al. 2014). Therefore, interplay between environmental change and the role of anthropogenic influence, both positive and negative, are important aspects of landscape history that needs to be considered in conservation planning and ecosystem management.

Amid all of this complexity, science is only part of the basis for conservation decisions, while stakeholder values, cultural context and local perspectives must also be considered (Rogers et al. 2013). A central challenge that palaeoecologists can contribute to is developing nuanced, interdisciplinary and inclusive understanding of landscape history (Marchant and Lane 2014; Marchant et al. 2018; Mustaphi et al. 2019), that can then be applied in defining and refining conservation and management goals (Gillson 2015; Githumbi et al. 2020; Wolfe et al. 2007). Here I discuss the role of palaeoecology in understanding and managing the drivers of landscape changes, then identify some ways forward in applying this knowledge to conservation practice.

## 24.2 UNDERSTANDING, PREDICTING AND MANAGING THE DRIVERS OF LANDSCAPE CHANGE

### 24.2.1 Climate

Past warm periods such as the African Humid Period and the Mediaeval Warm Period can provide valuable insights into the response of ecosystems to climate change, as well as the feedbacks between fire, climate and vegetation structure (Lüning et al. 2017). They have a particularly important role in informing and refining the development of models that simulate changes in vegetation cover based on climatic and other variables. This has been demonstrated in work modelling the greening of the Sahara during the mid-Holocene. Palaeoecological data indicated that models based on orbital forcing alone were under-estimating the magnitude and extent of the greening event (Lézine et al. 2011; Pausata et al. 2020; Tierney et al. 2017). This helped in shaping more complex models that include for example feedbacks between vegetation and climate (Chen et al. 2020; Pausata et al. 2020; Rachmayani et al. 2015).

Most predictions of future climate change agree that Africa's climate will get warmer, but there is a high degree of uncertainty regarding future changes in rainfall. While it is commonly expected that warmer climates bring higher rainfall due to increased ocean evaporation, this is not always the case, and some areas are expected to experience aridification and/or increased extreme events (Engelbrecht et al. 2013; Pinto et al. 2018). This is of critical importance to areas where agriculture is rain fed and already marginal (Pohl et al. 2017). Reconstructing past climates and particularly the interaction between temperature and rainfall can help in understanding the interactions between regional climate systems and assist in more accurate downscaling of global models (Lüning et al. 2018). Furthermore, palaeoecological data can help in understanding vegetation-climate-fire feedbacks that in turn shape the distribution of fire-adapted ecosystems
(Bond and Midgley 2012). It can also inform the manipulation of drivers such as fire that interact with climate. Information such as this is critical in designing conservation strategies that consider climate change, for example, potential shifts in the distribution of ecosystems and species as well as identifying populations that are likely to be most vulnerable or most resilience to climate change (Hannah et al. 2002). In a world of finite space and limited conservation resources, a sound basis for conservation prioritisation in a changing climate is critical (Foden et al. 2019).

### 24.2.2 Fire

Fire is a critical component of many African ecosystems, and is essential in maintaining grasslands, savannas and heathlands in areas of sub-Saharan Africa that have rainfall that is high enough to support forest (Bond et al. 2005). Nevertheless, fire management remains a contested issue in many areas, due to risk to property and life, as well as due to lack of understanding of its important ecological role (Bowman et al. 2013; Gillson et al. 2019). Many colonial governments believed fire to be detrimental and implemented policies of fire suppression in ecosystems that were naturally fire adapted (Moura et al. 2019). Later, when attempts to suppress fire proved futile, these policies were replaced by prescribed burns at regular intervals (van Wilgen et al. 2014). Policies of fire suppression and prescribed burning disrupted traditional fire management and homogenised the fuel load, leading to artificially intense and widespread fires in subsequent decades (Humphrey et al. 2020).

People have managed fire for millennia, for example by patch mosaic burning, which involves burning small patches of vegetation starting in the early dry season (Laris 2011). As different patches are burned at different times, this creates patches of vegetation with different post-fire ages (time since burned). Species assemblages often change throughout post-fire succession, and therefore a heterogeneous mosaic of different post-fire ages provides a wider range of habitat, biodiversity and ecosystem services than would be present had only a single, large fire occurred (Parr and Brockett 1999). In addition, such skilful management of fire helps to prevent late season wildfires that homogenise the landscapes (Humphrey et al. 2020), and can be used to manipulate the balance of forest and savanna elements at the savanna-forest ecotone (Fairhead and Leach 1996).

Palaeoecology is essential in understanding the impacts of anthropogenic fire management and the effects of fire suppression and management. Such records can in addition contribute to debates of anthropogenic ‘savannisation’ and the extent of human influence on forest ecosystems (Veldman 2016; Willis et al. 2004). Studying fire history through the charcoal record can help in determining whether current fire policies are within the historical range of variability. They may also assist in reinstating the skillful management of fire by communities who have incorporated fire management into their livelihoods for millennia (Humphrey et al. 2020).

### 24.2.3 Herbivory

Herbivores have both direct and indirect effects on vegetation structure and composition through physical impact on plant biomass and their influence on ecosystem processes, such as nutrient cycling and seed dispersal (Cromsigt et al. 2018). Changes in herbivore density thus have major implications for ecological function and biodiversity conservation (Hempson et al. 2015). Hunting, disease and manipulation of carnivore populations have all altered wild and domestic herbivore numbers, leading to declines in population size and distribution of many wild species in the late 19th and early 20th centuries (Venter et al. 2017). In southern Africa, reports of vast herds of wildebeest and springbok were reported in the early 20th century, while such herds have vanished from today’s landscapes (Home 1874; Skead et al. 2007; Venter et al. 2017). In the 20th century, there have also been wide fluctuations in populations of both wild and domestic herbivores in response to, for example, changes in domestic and local markets for meat and wool,
intensification of agriculture, a transition to game farming, as well as illegal hunting of rhino and elephant (Beinart 2003). These changes would have directly affected vegetation structure and composition and influenced nutrient cycling, physical disturbance and fire regimes through changes in biomass accumulation.

The use of coprophilous fungal spores is commonly used to pinpoint the timing and effects of megafaunal extinctions (Burney et al. 2003; Feranec et al. 2011), but there are also possibilities for addressing ecological questions about changes in herbivore abundance and how they affected vegetation on more recent timescales of decades to centuries (Ekblom and Gillson 2010; Goethals and Verschuren 2019). This information is invaluable in informing the management and restoration of functional herbivore-driven landscapes (Fuhlendorf et al. 2009). It could also play a role, alongside pollen data, in addressing questions about the impacts of elephants on their habitat, specifically whether elephant impact on trees is unprecedented due to their confinement in small areas, or whether the loss of tree cover is actually a return to the less wooded landscapes of the 17th and 18th century (Gillson 2015; Veldman 2016).

With urbanisation continuing apace in Africa, and the effects of government grants on rural livelihoods, game farming and re-wilding are likely to become increasingly attractive options for conservation (Hoogendoorn et al. 2019) (though the effects of the current COVID-19 pandemic on these options remain to be seen (Lindsey et al. 2020)). Palaeoecologists can help managers decide what should the landscapes look like, and how many herbivores there should be (Hempson et al. 2015; Venter et al. 2017).

### 24.2.4 Anthropogenic Influence

Many African National Parks were founded in the early decades of the twentieth century, a time when many landscapes had been affected by intensive human impact including changes in fire and fauna as well as changing climate and rising CO₂, and as well as changes in land use including sedentarisation (i.e. loss of transhumant lifestyles) and land abandonment. There is therefore a role for palaeoecology, specifically fossil pollen and phytolith analysis, in exploring variability in ecosystem structure and composition, particularly in terms of woody vegetation density. These data allow ecosystem managers to distinguish, for example, whether increasing tree cover is a recovery from past tree clearance or an unprecedented loss of open, grassy savanna (Gillson 2015; Gillson and Marchant 2014; Parr et al. 2014). As mentioned above, palaeoecological data can also help in understanding the role of past management of fire and herbivores, and the effects of disruption of traditional fire management (Humphrey et al. 2020).

## 24.3 CHALLENGES IN APPLICATION OF PALAEOECOLOGY TO BIODIVERSITY CONSERVATION IN AFRICA

Despite the great potential for synergy between palaeoecology and conservation, practical collaboration between the two fields remains rare. Here I will identify some challenges for palaeoecology and suggest some ways forward for increasing the application of our work.

### 24.3.1 Calibration

Palaeoecological data as it is presented in Quaternary journals is not easy for conservation managers to use (Davies et al. 2014). They need to be translated into metrics that are more closely aligned with ecological parameters and ecosystem services that are usually the target of management goals (Davies and Bunting 2010; Gillson and Marchant 2014). For example, a measure of changes in tree density is much more useful in conservation than a measure of arboreal pollen abundance (Gillson and Duffin 2007). Therefore, good calibration is needed
between palaeo-proxies and ecological parameters, but calibration remains a major and barely addressed issue in palaeoecological studies of most African ecosystems (Julier et al. 2018). Accurate calibration that includes a measure of confidence limits will allow translation of pollen data to estimates of changing vegetation composition that are much more useful to managers. Much more work is needed in this regard, especially in Africa, where quantitative, multi-proxy data could be invaluable in understanding the feedbacks occur between climate, vegetation, fire and herbivory (Julier et al., 2021, this volume).

The same can be said of charcoal and spore data. Charcoal can be invaluable for reconstructing fire history, but it is also notoriously difficult to interpret, requiring comprehensive calibration efforts (Adolf et al. 2018). This is partly because different fire intensities affect the completeness of combustion and different fuel sources (e.g. grass versus woody vegetation) and thus their representation in the charcoal record (Leys et al. 2017). Amalgamation and meta-analyses of multiple charcoal data sets via the Global Charcoal data base can help in quantifying the relationship between biomass burning and charcoal representation (Hawthorne et al. 2018; Marlon et al. 2016). Emerging techniques such as Fourier Transform Infrared Spectroscopy can help in unravelling the complexity between fire intensity and charcoal representivity (Gosling et al. 2019). Similarly, studies suggest that *Sporomiella* alone do not seem to accurately represent herbivore density in African ecosystems (Baker et al. 2013). A suite of spore proxies is needed, and more quantitative work is required to relate these to herbivore biomass and if possible to different herbivore guilds (Feranec et al. 2011; Goethals and Verschuren 2019).

As well as the use of modern data sets, there is also potential for calibrating palaeoecological data against instrumental records of climate change (Jacques et al. 2015; Vallè et al. 2018), satellite imagery of fire, e.g. the MODIS data set (Adolf et al. 2018) and historical records of herbivory numbers fire (Baker et al. 2013, van Asperen et al. 2020).

### 24.3.2 Chronologies and influx rates

Calibration against instrumental records requires detailed and accurate chronologies, in order to compare measured changes in historical and palaeoecological records. Where such chronologies are available, pollen influx rates can be used. Influx rates have advantages over both percentage data, which are influenced by the Fagerlind effect (Fagerlind 1952), and concentration data, which are influenced by changes in sediment accumulation rate. In the case of pollen percentages, the change in the abundance of any particular plant will affect the abundance of all other taxa present, because the total pollen sum always has to be 100%, regardless of how much biomass is present in the landscape. Similarly, a faster (slower) rate of sediment accumulation may lead to reductions (increases) in pollen concentration, even if the abundance of a plant in the landscape has not changed. However, such detailed chronologies are rarely found in palaeoecological data from semi-arid areas, where there are often hiatuses in sediment accumulation due to extended dry periods, and bioturbation by large mammals. In such cases, influx rates are not reliable, but careful comparison of percentage and concentration data can assist interpretation. There can be more confidence that trends observed in both percentage and concentration data reflect real changes in plant abundance (Baker et al. 2013; Forbes et al. 2018).

### 24.3.3 Taxonomic resolution

A further challenge in the application of fossil pollen data in biodiversity conservation is taxonomic resolution. Important ecological changes with implications for biodiversity conservation often occur at the species and genus level. For example, loss of species is a major concern for conservationists and changes in habitat quality, such as a shift from palatable to unpalatable grasses can have conservation management implications. Complementary proxies e.g. phytoliths can help to reserve poorly resolved but ecologically important families such as the Poaceae (Breman
et al. 2019). Further, detailed work is needed on the pollen morphology and associated ecological affinity of taxa within diverse families, such as the Asteraceae, containing both generalist and specialist species (Blackmore et al. 2010). Environmental DNA from sediments (sedaDNA) is increasingly used to provide accurate taxonomic resolution (Bálint et al. 2018; Clarke et al. 2019), though further work is needed to improve coverage of African species in genomic databases (Dommain et al. 2020).

### 24.3.4 Engagement with ecological theory

Increasing the use of palaeoecology in conservation requires that palaeoecologists engage with relevant theoretical and management frameworks. The shift from 'balance of nature' to 'flux of nature' as the underlying paradigm in ecology, has had profound implications for conservation (Gillson 2015; Pickett et al. 1997). Specifically, managing ecosystems and populations that are not at equilibrium requires a different approach to old approaches to conservation that aimed to prevent change. Ecosystem management emerged in response to develop conservation approaches that accepted the heterogeneous, dynamic nature of ecosystems (Grumbine 1997). Working with variable ecosystems in which disturbance and change are normal, raises the need for information on variability over ecologically relevant timescales, in order to define conservation goals that are based on processes rather than states (Gillson 2015). Many ecosystems are dynamic and fluctuate in response to disturbance and environmental change, however long-term data are needed to help managers to identify when ecosystems are approaching 'tipping points' or novel states. There is a natural synergy between palaeoecology and resilience theory (Davies et al. 2018), in that hypotheses generated regarding resilience and thresholds in ecosystems can potentially be tested using palaeoecological data (Gillson et al. 2020; Seddon et al. 2011).

### 24.3.5 Theory to application: relevance to current conservation questions

Once palaeoecological data is calibrated into forms that are recognisable to ecologists and managers, there still remains the challenge of how to apply these data to current conservation questions. As well as climate-changed-integrated conservation strategies, mentioned above, an important interface between conservation practice and palaeoecological data is establishing acceptable limits of variation in key parameters such as woody vegetation cover, herbivore density and fire regimes. Recent trends in such parameters can be compared against data spanning decades and centuries, helping to quantifying the historical range of variability and the extent of intensifying human management, including the impact of changes in fire and fauna as well as land use implemented by colonial governments (Gell et al. 2012; Hoffman et al. 2019). Measures of the long-term variability can help to identify unprecedented change from normal background variability (Keane et al. 2009). Thus, long-term data from palaeoecology can help to understand the ecological character of an ecosystem. For example, palaeoecological data could potentially be incorporated into definitions of ecological characters, limits of acceptable change and Thresholds of Potential Concern (TPCs) (Gell 2010; Gillson and Duffin 2007).

Knowledge of long-term change is central to restoration ecology and rewilding. In restoration ecology, conservationists need to have a reference state (Falk 2017; Higgs et al. 2014; Nogué et al. 2017). While this is often a nearby undisturbed or little disturbed site, such locations are not always available. Indeed, even those that are perceived to be undisturbed may have a more complex history than can be gleaned from present day conditions (Forbes et al. 2018; Gell 2010). Thus, restoration targets including the rewilding of ecosystems requires data from before the intensive human impact of the 18th and 19th century. On the theme of restoration, there is a tendency to view grassy and other open ecosystems as less valuable than forests or woody systems, therefore their afforestation is prioritised (Bond et al. 2019; Parr et al. 2014). This tendency
has become even more pronounced given recent drives to encourage tree planting as a means of enhancing carbon storage (Bastin et al. 2019). In some cases, where forest cover has become denuded or degraded, reforestation with native species can enhance biodiversity conservation and carbon storage at the same time (Lewis et al. 2019). In others, however, ancient grasslands and heathlands have been mistakenly identified as degraded or anthropogenically derived (Bond et al. 2019; Parr et al. 2014). This has led to inappropriate afforestation schemes that threaten rare open landscapes that are home to unique biodiversity (Bond et al. 2019; Veldman 2016; Veldman et al. 2019). Palaeoecology should therefore support the protection of open ecosystems and in the design of appropriate forest restoration programmes while at the same time raising the profile of ancient grasslands and heathlands as valid conservation targets.

A further step in the translation of palaeoecological data for conservation purposes is to convert them to indices for ecosystem services, such as biodiversity, sediment regulation, soil stability, sediment quality, and water quality (Dearing et al. 2012). This approach can help engage stakeholders by alerting them to changes in key services, information which could potentially inform sustainable management of natural resources, but to date has rarely been applied in Africa.

### 24.3.6 Integrating with other disciplines and knowledge streams

A multiproxy approach that helps to elucidate the complexities of landscape history and people's role in shaping the environment can help to build a nuanced and inclusive understanding of landscape history that can provide cultural and local context for interpreting change and informing future sustainable management (Gillson 2015; Githumbi et al. 2020; Marchant and Lane 2014; Mustaphi et al. 2019; Richer and Gearey 2018). Though much palaeoecological research occurs in the lab and behind the microscope, a key component of our work can be in collaborating with stakeholders and listening to stakeholder perspective on ecosystem change (Wolfe et al. 2007). Projects can be co-designed with stakeholders with their management needs and goals in mind (Rogers et al. 2013; Wolfe et al. 2007). Palaeoecologists can attend conservation conferences and workshops where managers and ecologists articulate questions that can be usefully addressed using palaeoecological techniques, or arranging consultations with community groups (Davies et al. 2014; Wolfe et al. 2007). Where palaeo-projects are already underway, it is useful to meet with communities, government, parastatals and NGOs while in the field, explain the potential utility of the palaeoecological data and to explore questions of mutual interest.

After a project's completion, it is useful to provide results in more than one format. Scientific publications are not always the most accessible or useful way in communicating results to stakeholders, formats such as reports, poster, or stakeholder feedback workshop might be more convenient. Such workshops are especially valuable when stakeholders can help in shaping elements of the project e.g. in adding insights to possible feedbacks between drivers and/or in utilising project outputs such as model-based tools that allow them to explore the effects of different scenarios or fire, herbivory and climate (Capitani et al. 2019; Hossain et al. 2020). Stakeholders often have different perspectives on how ecosystem change affects their livelihoods and there are often important cultural components of landscape change that can only be captured through consultation with stakeholders.

### 24.3.7 The importance of modelling and future scenarios

Reconstructing the past does not always provide analogues for the future (Williams and Jackson 2007). We face unprecedented rates of climate change and human impact, as well as social and economic structures that differ vastly from the past, as well as potentially novel combinations of climate parameters and anthropogenic stressors that may produce ecological surprises (Lindsey
*et al.* 2020; Williams and Jackson 2007). Nevertheless, understanding past ecological dynamics can help to elucidate the interactions and processes that drive ecosystem change. Building models based on palaeoecological data that simulate past interactions between key drivers and responders e.g. vegetation, climate, fire and herbivory provides a basis for exploring future scenarios, especially where palaeoecological proxies have been turned into Ecosystem Service Indicators (see above) (Dearing *et al.* 2012; Perry *et al.* 2016).

Models based on palaeoecological data can simulate past interactions between human and environmental factors, for example, climate, fire, herbivory and vegetation (Perry *et al.* 2016). Such models can then be parameterised using current and future scenarios. Furthermore, some modelling approaches, such as system dynamics models offer opportunities for combining insights from long-term data with local knowledge of ecosystem change, complexity and likely future scenarios (Hossain *et al.* 2020). This has the twofold advantage of improving the relevance and applicability of palaeoecology whilst also improving access to a diverse range of voices in scenario planning and conservation management. Though highly uncertain, such models can help stakeholders to explore different possible future scenarios of land cover and ecosystem services (Caves *et al.* 2013).

## 24.4 CONCLUSIONS

Africa's landscapes are dynamic and complex, and managing them requires flexible and adaptive conservation approaches that safeguard biodiversity as well as providing ecosystem services and respecting indigenous knowledge. This means embracing a past-present-future perspective that includes a knowledge of ecosystem variability, ecological function as well as cultural heritage and the role of traditional management. There is thus enormous potential for better integration of palaeoecological and other long-term data into biodiversity conservation and natural resource management.

Carrying out palaeoecological research in Africa has some challenges, however, including the lack of sites and poor pollen preservation in semi-arid areas and the sheer diversity of the flora in forested areas. Despite this, the discipline has enormous potential value in conserving African biodiversity, especially as landscape history is often not well understood. Fine resolution studies covering the late Holocene are especially valuable to conservationists in understanding the impacts of climate change, the role of fire and herbivory, the effects of human management and the resilience of ecosystems. To realise this value, however, we in the palaeo-community must do our best to make our data accessible, and to present it in forms that are relevant to conservation. Aligning multiple proxies can help in distinguishing climatically driven from anthropogenically driven changes and provide the basis for defining restoration targets and thresholds of potential concern. Ensure that our research process is inclusive can contribute to building a nuanced and interdisciplinary understanding of landscape history that informs biodiversity conservation and the sustainable and equitable management of natural resources.

## ACKNOWLEDGEMENTS

Many thanks to Timm Hoffman and two anonymous reviewers for helpful comments on the manuscript. The NRF Competitive Programme for Rated Researcher (Grant Number 118538), African Origins Platform (Grant number 117666), and Global Change Grand Challenge/SASSCAL (Grant number 118589) provided funding for some of the research underpinning this perspective.
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## Regional Index

Aberdare Mountains 328
Adamawa 74, 185
Agulhas Western Boundary Current 352
Al-Hawa 162, 166, 168
Amboseli 108–111, 117–121, 329
Angola 242, 330, 353, 376, 377
Ankazominady 330
Arabia 38, 157–174
Arabian Peninsula 157–159, 165, 170, 172, 173
Aramis 139, 140
Asir mountains 161
Atlantic (Ocean) 52, 351, 381
Awafi 163, 166, 167, 169, 170
Awash (Valley) 327
Bakundu 209, 212, 215, 218
Bale Mountains 140
Bambili (Lake Bambili) 376, 378, 380, 384, 390
Barombi Kotto 207, 209, 215
Bateke Plateau 325
Baviaanskloof mountains 231
Benguela Coastal Current (BCC) 353
Bénin 68, 69, 322, 333
Bloemfontein 1, 3
Blombosgrot cave 382
Botswana 330
Burkina Faso 320
Burundi 327, 329, 336, 337
Cameroon 68, 69, 73, 74, 76, 79, 183–200, 207–220, 323–324, 332, 335, 376, 377, 379, 384
Cameroon Highlands 69, 73, 76, 79, 323
Canary current 352
Cape 227
Cape coast 228, 229, 293–299, 302

Cape Floristic Region (CFR) 225–233
Cape Fold Mountains 376
Cathedral Peak 261, 265, 270, 276, 277, 286–288
Cederberg 228–230, 276
Central Africa 8, 9, 78, 323, 377, 378
Central African Republic 323, 324, 335
Central Sahara 27–43, 51, 78
Chad 27–43, 68, 323, 333, 334
Chyulu Hills 109, 111, 120
Congo (Basin) 66, 67, 325, 353, 360, 387
Côte d'Ivoire 320, 321, 333
Dahna 159
Dambo 330
De Rif 226, 229
Dead Sea 164, 169
Democratic Republic of Congo 323, 325, 336, 377
Dhamar highlands 167
Dhofar (Mountains) 159, 161
Dikika 138, 140
Diogo 60, 61, 62, 67, 75, 76
Djebel Marra 40
Djibouti 326, 336
Drakensberg 261, 275–289
Dunedin 231
East (eastern) Africa 38, 118, 119, 121, 131, 135, 350, 358, 360, 361, 364, 378, 387, 388, 389
East Africa 8, 28, 86, 87, 99, 109, 128, 136, 137, 147, 149–151, 159, 161, 164, 169, 253, 326–330, 336, 377, 397
East African Rift 142, 376, 382, 383, 387
Eastern Cape 231
Egypt 376
Eilandvlei 226, 229, 293–299
Ein Gedi 166, 169

Emi Koussi 27–43

Enkongu 111, 120

Era Kohor 29, 31–34, 38, 40, 42

Eritrea 326

Esambu 108, 111, 119, 120

Ethiopia 127, 128, 135–138, 140–144, 148, 150, 151, 164, 327, 336, 337, 374, 375, 382

Ethiopian Rift 128, 136, 140, 141, 143, 144, 148

Fertile Crescent 171

Filim 162, 166, 168

Florisbad 1, 374, 375

Gabon 323–325, 335, 336, 362

Gambia 320

Ghana 15, 321, 333, 376, 380, 384–385

Granada 378

Great Nafud 159

Green Sahara 7, 9, 51, 164

Guinea 66, 76, 79, 320, 323, 324, 351, 361, 364, 377

Guinea-Bissau 320

Gulf of Aden 164, 352, 358, 360, 363

Gulf of Guinea 66, 76, 79, 320, 323, 324, 336, 351, 364

Gulf of Oman 159, 163

Hadar (Kada Hadar wadi) 146

Hadhramaut Plateau 161

Herto 374, 375, 382

Hoggar 28, 29, 31, 42

Horn of Africa 158

Hoti Cave 165, 166, 169

Indian Ocean 28, 162, 163, 169, 351, 352, 361, 362

Iran 41, 168

Isinet 329

Israel 166, 167, 169

Jebel Irhoud 374, 375, 378, 381, 388

Jikaryia 52, 61, 67, 68, 75

Jordan 169, 170, 172

Kajemarum 67, 68, 75

Kajiado 107–121

Kalahari 376

Kandara 70, 73

Kenya 111, 113, 121, 150, 326–329, 336, 337, 376, 379, 380, 382

Kigezi 327

Kilimanjaro (Mount Kilimanjaro) 110–111

Kimana 108, 109, 110, 111, 115, 116, 118, 119, 329

Kissi 68, 79

Kitandra 327

Knysna 294, 302

Kruger National Park 260, 270, 276

Kwar al Jaramah 162, 166, 168

KwaZulu-Natal 259–272, 276, 277, 288

Laetoli (Laetolil) 136, 137, 142–144, 148, 150

Lake Bambili 376, 378, 380, 384, 390

Lake Bosumtwi 15, 16, 18–21, 321, 338, 376, 378, 380, 384–385, 387–389

Lake Bujuku 327

Lake Chad 79, 323

Lake Duluti 118

Lake Edward Basin 325, 326

Lake Guiers 321

Lake Magadi 376, 378, 380, 382, 387–389

Lake Malawi 330, 376, 378, 380, 383–384, 387–389

Lake Mirabad 168

Lake Tanganyika 330

Lake Tsizavatsy 309–314

Lake Victoria 332

Lama classified forest 322

Liberia 320

Libya 68

Lielerai-Kimana 107–121

Limpopo 352, 366

Lope National Park 324, 325

Madagascar 190, 330–331, 337

Mahoma 327

Malawi 330, 337, 366, 376, 380, 383–384

Mali 320

Maloti 276

Manga 61, 75, 78, 79

Mangoky River 309, 311

Maramagambo forest reserve 326
Mau forest 329

Mauritania 69, 320, 333, 363

Mayombe 325

Mbalang 67, 68, 74, 76, 185, 190, 192, 193, 195, 196, 199–201

Mbi 69, 185, 190, 192–195, 199

Mboandong 207–220

Mboro-Baobab 52, 53, 57–62, 67, 75

Mediterranean 27–31, 38, 41–43, 158–161, 165, 167, 169, 227, 351, 353, 360–362, 376, 388, 390

Mediterranean (Sea) 159, 376

Meshellu 138, 140–141, 148

Morocco 361, 374–376, 378

Mount Cameroon 209, 210, 216, 217, 219

Mount Elgon 327, 328

Mount Kenya 327, 328

Mount Koupé 217

Mount Meru 118

Mount Shengena 119

Mouskorbé 42

Mozambique 330

Mubwindi Swamp 328

Nafud 159, 163, 169, 172

Namelok 108, 111, 119, 120

Namibia 185, 191, 197, 331, 337, 352, 353, 360, 363

National Park 159, 164, 169, 170, 172, 173

Near East 159, 164, 169, 170, 172, 173

Niaye(s) 60, 62

Niger 51, 69, 79, 322, 333, 351

Niger (delta) 360

Nigeria 51, 52, 68, 69, 185, 320, 322, 333, 334

North Africa 38, 164

North Atlantic Current 352

Nyabuiyabuyi 329

Ocean 6, 7, 51, 162, 349–366, 378, 398

Olduvai 86, 99, 137, 143

Olkesiteti 90

Olorgesailie (Mt. Olorgesailie) 87, 99

Olorgesailie Basin 85–100

Oltepesi 90

Oltulelei 94

Oman 159, 161–164, 166–168, 172, 173

Omo Kibish 374, 375, 382

Omo–Turkana basin 136

Ormakau 108, 111, 329

Ounianga 42

Oursi 68, 79

Padul Basin (PB) 376, 378–381, 387–389

Pakhuis Pass 226, 229

Persian Gulf 159, 164

Pondoland 231

Qunf Cave 165, 166, 169

Ramlat as-Sabatayn 167, 168

Republic of Congo 323, 325, 336, 377

Rietvlei-Still Bay 228, 229

Rift 87, 99, 139, 149

Roumpi Hills 209, 217

Rub al’Khai 157, 159, 167

Ruwenzori Mountains 327

Rwanda 326

Sahara 2, 8, 29, 59, 66, 69, 75, 351, 364, 376, 398

Sahel 51–62, 66, 69, 75, 76, 78, 79, 184, 332, 338, 351, 353, 360, 364, 376

Sahelian 28, 30, 31, 52, 53, 66, 70, 79, 186, 321

Salisbury vlei 231

Sanaga (River) 324

Saudi Arabia 162–164, 169, 172

Senegal 51, 52, 59, 69, 320–321, 333, 351, 353, 360

Senegal (River) 67, 75, 78, 360

Shum Laka 384

Sierra Leone 320

Somalia 326

Soreq cave 167

South Africa 1, 7, 9, 185, 191, 192, 197, 227, 229, 231, 241, 246, 253, 259–272, 275–289, 293–299, 301–306, 310, 330, 360, 363, 374, 375–377, 380–382

Southern Africa 1, 8, 227, 232, 240–242, 248–250, 260, 275, 287, 289, 330, 351, 353, 378, 382, 383, 387–390, 397, 399

Southern Ocean 2

Spain 127, 376, 378–381
Sudan 326, 336

Sudanian 30, 52, 53, 59, 66, 70, 76, 79, 160,
185, 186, 195, 199, 320, 321, 332, 338

Suwayh 162, 167, 168

Tanzania 107, 137, 140, 142–144, 148,
150, 151, 326, 337

Tarso Yega 42

Tayma 162, 169, 173

Teleki Valley 328

Tibesti 28–31, 33, 35, 38–40, 42, 43

Tilla 68, 184, 185, 187, 190, 192, 193,
195–196, 199

Tjeri 68, 79

Togo 320–322

Trou au Natron 30, 38, 42

Tsavo 119

Turkana 144

Uganda 2, 326–329, 332, 336, 337

United Arab Emirates (UAE) 159,
163, 166

Vankervelsvlei 226, 228, 229, 301–306,
376, 378, 380–382, 389

Wadi Faynan 170

Wadi Sana 167, 170, 172

Waterfall Bluff 231

West Africa 220, 287, 320

Western Africa 15, 184, 220, 350, 364,
377, 378, 385, 387–390

Wilderness 381, 397

Wilderness Lakes system 293

Winterberg 231, 288

Woranso-Mille 141–143

Yemen 159, 161, 164, 166, 167, 170,
172, 173

Yemeni Highlands 161

Yoa 38, 68, 75

Zagros Mountains 159

Zambezi (River, catchment) 352, 361

Zambia 326, 377

Zimbabwe 330
Subject Index

¹⁴C 29, 32–36, 42, 56, 69, 107, 114, 118, 185, 211, 296, 304, 353, 379

Abrupt change 8, 17, 18, 19, 51

Accelerator Mass Spectrometry (AMS) 56, 111, 113, 114

Aeropalynology 319, 333–335, 337

African Humid Period 7, 9, 28, 29, 31, 33, 38, 42, 43, 51–62, 65–79, 119, 131, 398

African margin 354–356, 361, 366

African Pollen Database (APD) 5–9, 33, 53, 67, 158, 160–162, 173, 212, 232, 243, 263, 353

Afroalpine 67, 74, 127, 129, 131, 132, 144, 145, 148, 384

Afromontane 67, 76, 120, 128, 140, 143, 146, 148, 159, 161, 168, 184, 185, 200, 217, 287, 323, 332, 361, 362, 377, 382–384, 387–389

Afrotemperate 225, 228, 229, 278, 279, 281, 283–288, 293, 294, 297, 302, 306, 381

Agulhas Current 229, 230, 353

Anthropogenic influence 75, 158, 297, 398, 400

Archaeology 2, 233, 261, 336, 339

Biodiversity 5, 28, 51, 66, 108, 135, 158, 225, 227, 233, 241, 289, 319, 397, 399–404

Biogeography 2

Biomization 6

*Botryococcus* 53, 58, 59, 61

Bush encroachment 398

Cenozoic flora 161

Charcoal 14, 17–19, 21, 38, 107–121, 127–129, 170, 232, 294, 297, 298, 303, 305, 306, 318, 398, 399, 401

Chronology 29, 69, 113–114, 116–119, 145, 228, 231

Climate 15, 159, 164, 230, 241, 243, 247, 249–251, 351

Climate change 9, 62, 86, 158, 159, 219, 220, 229, 230, 240, 286, 314, 318, 338, 339, 365, 398, 399, 401, 403, 404

Climate reconstructions 7, 9, 42, 184, 232, 240, 246

Coastal lakes 228, 229

Conference proceedings 2–3

Conservation 108, 109, 225, 232, 233, 261, 319, 397–404

Crater lake 207–220, 326, 384

CREST (Climate REconstruction SofTware) 232, 240, 243–245, 260

Dating 8, 16–18, 33, 57, 85, 86, 112, 118, 129, 171, 228, 275, 289, 295–296, 303–304, 310–311

Desert 28, 29, 31, 51, 70, 75, 168, 174, 351, 362, 390

Dry forest 129, 131, 132, 218, 309, 320, 331, 359

Environmental change, ix 5–9, 51, 58, 62, 66, 76, 78–79, 170, 227, 228, 231, 242, 261, 360, 365, 374, 377, 398, 402

Ericaceous belt 128, 129, 131, 327

Evolution 9, 61, 67, 135, 227, 327, 374, 375, 382

Fall speed 197, 263, 264, 267, 271

Fire 14, 17, 18, 20, 119, 121, 131, 170, 173, 231, 241, 253, 280, 309, 318, 397, 399–404

Forest 51, 57, 61, 62, 70, 74, 76, 144, 145, 150, 151, 198, 212, 220, 287, 289, 323–325, 329, 330, 366, 389

Fossilized termite mounds 142
Fossil-rich deposits 142

Fynbos 225, 229, 231, 293, 297, 306, 360, 362, 382, 390

Geomorphology 29–30

Global Biodiversity Information Facility (GBIF) 241

Grassland 285, 287, 324

Green Sahara 7, 9, 51, 164

Heinrich Stadials 354, 360

Herbivory 14, 17, 20, 21, 377, 399–401, 403, 404

Holocene 6, 8, 35, 40, 42–43, 58, 67, 71, 74–75, 76, 119, 164–170, 172, 174, 184, 185, 193, 194, 198–199, 200, 216, 219, 220, 358, 360–361, 363, 365, 404

Holocene Humid Period 164, 166–170, 171

Hominin remains 90, 145

Hominins 87, 99, 100, 136, 143, 146, 148, 150, 151, 327, 374, 375, 377, 385, 387–390

Human impacts 5, 360, 400, 402, 403

Humidity 28, 29, 39–43, 60, 144, 145, 150, 169, 250, 353

Hydrological regime 58–60

Hyrax middens 38, 161, 167, 170, 173, 174, 226, 228–231

Intergovernmental Panel on Climate Change (IPCC) 184

Intertropical Convergence Zone (ITCZ) 15, 66, 87, 109, 159, 165, 166, 169, 351, 363, 387

Lake deposits 90, 165

Land cover 8, 29, 108, 119–121, 158, 184, 260, 264, 270, 271, 338, 404

Landscape 20, 21, 66, 67, 70, 76, 78, 79, 86, 96, 99, 119, 120, 129, 131, 139, 145, 170–174, 185, 186, 199, 260, 265, 279, 384, 386, 388, 399–401

Landscape change 69, 78, 132, 363–365, 398–400, 403

Landscape Reconstruction Algorithm (LRA) 185–187, 189–200, 265

Last Glacial Maximum (LGM) 6–8, 127, 228, 229, 354, 360, 363, 364

Late Holocene 52, 58, 76, 108, 119, 120, 219, 227, 229, 231, 293–299, 360, 363, 404

Late Pleistocene 13–21, 29, 85–100, 158, 161, 170, 171, 373–390

Lead ($^{210}$Pb) 112

Local Vegetation Estimates (LOVE) 9, 185, 189, 190, 192–197, 199

Micro-charcoal 17, 18, 20

Mid-Pleistocene 353, 356, 357, 361–362

Mid-Pliocene 135–151

Miocene 356, 362, 364, 366

Modern pollen 1, 6, 73, 137, 139, 140, 143, 149, 168, 169, 184, 200, 212, 240, 260, 262, 264, 271, 275–289, 317–339, 363

Monsoon 15, 28, 29, 42, 66, 109, 139, 151, 164, 167–170, 173, 351, 352, 363, 387

Neotoma 8, 129, 232

Non-pollen palynomorphs (NPP) 53, 56, 57, 59, 119

Orbital forcing 21, 398

Palaeoecology 1–3, 158, 174, 227–229, 231, 265, 317–339, 397–404

Palaeolake 29, 31, 38, 39, 42, 167, 168, 170, 172, 174

Palaeosciences 1

Palaeoecology of Africa (PoA) 1–3

*Palaeohydrology* 165–167

Palustrine sediments 41, 107–121

Palynology 1–3, 76, 111, 212–216, 227, 335, 365

Pangaea 8, 353

*Pediastrum* 40, 53, 57, 59

Phytoliths 14, 17, 18, 20, 21, 86, 90, 93, 95, 96, 99, 140, 143, 167, 230, 286, 324, 401

Pleistocene 29, 38, 100, 111, 227, 293

Pliocene 86, 137–141, 147–149, 151, 227, 356, 362, 364

Pollen Database 232

Pollen preservation 8, 32, 142, 228, 229, 381, 388, 404

Pollen trap 197, 271, 276, 279, 280, 288, 321, 322, 330, 337

Pollen-vegetation relationships 188, 193, 198, 260, 338
Pollination syndromes 288

Probability density functions (pdfs) 240, 243, 245

Radiocarbon 16, 33–35, 42, 57, 108, 111, 118, 119, 210, 211, 215, 216, 220, 295, 304, 311, 312, 353

RSAP (Relevant Source Area of Pollen) 188, 190, 193, 196, 199, 200, 260, 262, 264, 266, 268, 270, 271, 276

RStudio 112

R-value 260

Saharan Humid Period (SHP) 30

Satellite image 30, 52, 128, 401

Savanna 15, 161, 164, 167, 212, 309, 398, 399

Sediment 15, 16, 18, 19, 53, 69, 107, 111–113, 116, 118, 119, 138, 140, 145, 146, 148, 173, 216, 228, 231, 270, 271, 288, 294, 295, 302, 303, 311, 319, 320, 328, 330, 331, 350, 378, 381, 383, 385, 401, 403

Sedimentology 116–119, 128, 149, 295

Stokes Law 263

Succulent Karoo 225, 231

Surface samples 35, 37, 38, 40, 41, 70, 197, 270, 276, 286, 288, 320, 321, 323, 325, 327–331, 333, 336, 337

Upper Pleistocene 149, 376–378

Van Zinderen Bakker, E.M., 1–3, 8, 240, 243, 275, 276, 286, 288, 294, 303

Vegetation 7, 14, 20, 28–31, 52, 66, 70, 86, 96, 99, 100, 119, 121, 127, 136, 137, 140, 150, 159, 165, 173, 174, 185, 196, 199, 200, 209, 212, 216, 219, 261–263, 269, 270, 278, 283, 284, 286, 314, 338, 353, 357, 361–363, 365, 377, 383, 384, 387–390

Vegetation history 161–170, 326

Vegetation survey 185, 186, 212, 262, 264, 268–270, 276, 279, 280, 288, 338

Volcanic tuffs 144

Wetland 89, 108, 109, 111, 116, 118–120, 228, 230, 260, 284, 286–288, 302, 381
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Taylor & Francis

Taylor & Francis Group

http://taylorandfrancis.com
Palaeoecology of Africa

International Yearbook of Landscape Evolution and Palaeoenvironments

ISSN: 2372-5907

Volume 1-12 *Out of Print*

13. Palaeoecology of Africa and the Surrounding Islands
Editors: J.A. Coetzee & E.M. van Zinderen Bakker
1981, ISBN: 978-90-6191-203-3

14. Palaeoecology of Africa
Editors: J.A. Coetzee & E.M. van Zinderen Bakker
1982, ISBN: 978-90-6191-204-0

15. Palaeoecology of Africa
Editors: J.A. Coetzee, E.M. van Zinderen Bakker, J.C. Vogel, E.A. Voigt & T.C. Partridge
1982, ISBN: 978-90-6191-257-6

16. Palaeoecology of Africa
Editors: J.A. Coetzee & E.M. van Zinderen Bakker
1984, ISBN: 978-90-6191-510-2

17. Palaeoecology of Africa
Editors: J.A. Coetzee & E.M. van Zinderen Bakker
1986, ISBN: 978-90-6191-625-3

18. Palaeoecology of Africa
Editor: K. Heine
1987, ISBN: 978-90-6191-689-5

19. Palaeoecology of Africa – Out of Print
Editors: K. Heine & J.A. Coetzee
1988, ISBN: 978-90-6191-834-9

20. Palaeoecology of Africa
Editor: K. Heine
1989, ISBN: 978-90-6191-880-6

21. Palaeoecology of Africa – Out of Print
Editors: K. Heine & R.R. Maud
1990, ISBN: 978-90-6191-997-1

22. Palaeoecology of Africa
Editors: K. Heine, A. Ballouche & J. Maley
1991, ISBN: 978-90-5410-110-9
23. Palaeoecology of Africa and the Surrounding Islands
Editor: K. Heine
1993, ISBN: 978-90-5410-154-3

24. Palaeoecology of Africa
Editor: K. Heine
1996, ISBN: 978-90-5410-662-3

25. Palaeoecology of Africa – Out of Print
Editors: K. Heine, H. Faure & A. Singhvi
1999, ISBN: 978-90-5410-451-3

26. Palaeoecology of Africa and the Surrounding Islands
Editors: K. Heine, L. Scott, A. Cadman & R. Verhoeven
1999, ISBN: 978-90-5410-476-6

27. Palaeoecology of Africa and the Surrounding Islands: Proceedings
of the 25th Inqua Conference, Durban, South Africa, 3-11 August 1999
Editors: K. Heine & J. Runge
2001, ISBN: 978-90-5809-350-9

28. Dynamics of Forest Ecosystems in Central Africa During the Holocene:
Past – Present – Future
Editor: J. Runge
2007, ISBN: 978-0-415-42617-6

29. Holocene Palaeoenvironmental History of the Central Sahara
Editors: R. Baumhauer & J. Runge
2009, ISBN: 978-0-415-48256-1

30. African Palaeoenvironments and Geomorphic Landscape Evolution
Editor: J. Runge
2010, ISBN: 978-0-415-58789-1

31. Landscape Evolution, Neotectonics and Quaternary Environmental Change in
Southern Cameroon
Editor: J. Runge
2012, ISBN: 978-0-415-67735-6

32. New Studies on Former and Recent Landscape Changes in Africa
Editor: Jürgen Runge
2014, ISBN: 978-1-138-00116-9

33. Changing Climates, Ecosystems and Environments within
Arid Southern Africa and Adjoining Regions
Editor: Jürgen Runge
2016, ISBN: 978-1-138-02704-6

34. The African Neogene – Climate, Environments and People
Editor: Jürgen Runge
2018, ISBN: 978-1-138-06212-2