modern pollen-based biome reconstructions in east africa expanded to southern tanzania

nology 140 (2006) 187–212www.elsevier.com/locate/revpalbo

Review of Palaeobotany and Paly

Modern pollen-based biome reconstructions in East Africaexpanded to southern Tanzania

Annie Vincens ⁎, Laurent Bremond, Simon Brewer,Guillaume Buchet, Philippe Dussouillez

CEREGE, UMR 6635, CNRS-Université Paul Cézanne, BP 80, 13545 Aix-en-Provence cedex 04, France

Received 20 January 2006; received in revised form 26 April 2006; accepted 28 April 2006Available online 13 June 2006

Abstract

New detailed biome reconstructions are proposed in East Africa from modern pollen data derived from 150 sites located innorthern Kenya (40 sites), north-western Uganda (51 sites) and southern Tanzania (59 new sites presented as pollen diagram),which are representative of the major vegetation associations occurring in seven phytogeographical regions, mosaics or centres ofendemism. We use the standard biomisation method previously published for the African continent, but we reconsider the taxaassignment to plant functional types. We include in this approach all identified taxa (408) except aquatics, ferns and exotic taxa.The method is validated by comparison with local vegetation data and we show that 124 (82.6%) sites are assigned to the correctbiome and that for all the biomes under investigation, the number of correct assignments always exceeds the number of incorrectones. When an incorrect biome reconstruction occurs, mainly toward drier biomes, this is generally linked to the local open/degraded structure of the original vegetation or to the occurrence of a mosaic of open/closed vegetation. In turn, most of thereconstructions of more humid/closed biomes than the corresponding local vegetation (8.6%) remain unexplained. A comparison ofour reconstructed biomes with the main East African vegetation types of White's map indicates that 121 (80.6%) sites are assignedto the correct biomes. However, the majority of sites are incorrectly reconstructed compared to Olson and IGBP maps from satellitedata, mainly due to incorrect allocation of the land cover classes compared to the potential vegetation. The application of thismethod to our pollen data set demonstrates that modern pollen assemblages can successfully reconstruct the main modern EastAfrican vegetation types.© 2006 Elsevier B.V. All rights reserved.

Keywords: biomisation; East Africa; modern pollen; plant functional type

1. Introduction

Biomes can be defined as broad biological associationsfound within relatively uniform bioclimatic space andcharacterized by the adaptation of organisms to that par-ticular environment (Prentice et al., 1992; Cramer, 2002).Models of global vegetation distribution through time are

⁎ Corresponding author.E-mail address: [email protected] (A. Vincens).

0034-6667/$ - see front matter © 2006 Elsevier B.V. All rights reserved.doi:10.1016/j.revpalbo.2006.04.003

based on the concept of plant functional types (PFTs)(Prentice et al., 1992; Haxeltine and Prentice, 1996). Theirvalidation requires the reconstruction of equivalentbiomes derived frompalaeoecological data, mainly pollendata (Prentice et al., 1996, 1998; Jolly et al., 1998a;Prentice andWebb, 1998;Williams et al., 1998;Marchantet al., 2002, 2004). PFTs scores and biome reconstructionsfrom pollen data have also been used to assess climatemodels (Prentice et al., 1992; Claussen and Esch, 1994;Haxeltine and Prentice, 1996) and to test the sensibility of

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http://dx.doi.org/10.1016/j.revpalbo.2006.04.003

Table 1Location of the modern pollen samples in East Africa, derived vegetation and reconstructed biomes

Areas No. Samples Long. Lat. Alt.(m)

Location Regions (White,1983)

Local vegetation Biomes

Southern TanzaniaTa1 Rgw-03 S2 33.68 −9.28 2563 Rungwe Afroalpine Upland Ericaceous thicket XEROTa2 Rgw-03 SC 33.76 −9.11 2600 Rungwe Afroalpine Upland Protea thicket XEROTa3 Rgw 27 33.68 −9.17 2966 Rungwe Afroalpine Upland Ericaeous thicket XEROTa4 Rgw 26 33.68 −9.20 2550 Rungwe Afroalpine Upland Ericaeous thicket XEROTa5 Rgw 25 33.69 −9.16 2650 Rungwe Afroalpine Upland Ericaeous thicket XEROTa6 Rgw 24 33.70 −9.12 2600 Rungwe Afroalpine Upland Ericaeous thicket XEROTa7 Rgw 15 33.73 −9.17 2150 Rungwe Afromontane Upland grassland XEROTa8 Rgw 14 33.73 −9.17 2150 Rungwe Afromontane Upland grassland XEROTa9 Rgw 13 33.73 −9.17 2150 Rungwe Afromontane Upland grassland XEROTa10 Rgw 12 33.73 −9.17 2150 Rungwe Afromontane Upland grassland XEROTa11 Rgw 11 33.73 −9.17 2150 Rungwe Afromontane Upland grassland XEROTa12 Rgw 10 33.73 −9.17 2150 Rungwe Afromontane Upland grassland XEROTa13 Rgw 9 33.73 −9.17 2150 Rungwe Afromontane Upland grassland XEROTa14 Rgw 8 33.73 −9.17 2150 Rungwe Afromontane Upland edaphic grassland XEROTa15 Rgw 7A 33.73 −9.17 2150 Rungwe Afromontane Upland edaphic grassland XEROTa16 Rgw 6 33.73 −9.17 2150 Rungwe Afromontane Upland edaphic grassland XEROTa17 Rgw 5 33.73 −9.17 2150 Rungwe Afromontane Upland edaphic grassland XEROTa18 Rgw 4 33.73 −9.17 2150 Rungwe Afromontane Upland edaphic grassland XEROTa19 Rgw-03 SA 33.78 −9.14 2400 Rungwe Afromontane Upland forest glade XEROTa20 Rgw-03 SE 33.76 −9.27 2575 Rungwe Afromontane Upland forest XEROTa21 Rgw 20 33.73 −9.17 2150 Rungwe Afromontane Upland forest WAMFTa22 Rgw 19 33.73 −9.17 2150 Rungwe Afromontane Upland forest XEROTa23 Rgw 18 33.73 −9.17 2150 Rungwe Afromontane Upland forest WAMFTa24 Rgw 17 33.73 −9.17 2150 Rungwe Afromontane Upland forest/grassland mosaic XEROTa25 Rgw 16 33.73 −9.17 2150 Rungwe Afromontane Upland forest/grassland mosaic XEROTa26 Rgw 2 33.71 −9.16 2113 Rungwe Afromontane Upland forest WAMFTa27 Rgw 1 33.70 −9.20 1880 Rungwe Afromontane Upland forest WAMFTa28 Ng-03 SV 33.57 −9.10 1990 Ngozi Afromontane Upland forest WAMFTa29 Ng-03 SIV 33.59 −9.12 2100 Ngozi Afromontane Upland forest SAVATa30 Ng-03 SIII 33.61 −9.14 2100 Ngozi Afromontane Upland forest WAMFTa31 Ng-03 SII 33.63 −9.16 2150 Ngozi Afromontane Upland forest WAMFTa32 Ng-03 SI 33.65 −9.17 2200 Ngozi Afromontane Upland forest WAMFTa33 Ng-03 S3 33.65 −9.14 2043 Ngozi Afromontane Upland forest WAMFTa34 Ng-03 S2 33.65 −9.14 2140 Ngozi Afromontane Upland forest WAMFTa35 Ng-03 S1 33.65 −9.16 2250 Ngozi Afromontane Upland forest WAMFTa36 Ng-03 V1 33.65 −9.14 2043 Lake Ngozi Afromontane Upland forest WAMFTa37 Mbeya R 33.55 −8.87 2034 Mbeya Range Afromontane Upland forest WAMFTa38 Mbz-03 S1 33.07 −9.18 1670 Mbozi Zambezian Mid-altitude woodland TDFOTa39 Mas-03 S7 33.87 −9.43 998 Lugombo Zambezian Open woodland SAVATa40 Mas-03 S4 33.82 −9.33 940 Lugombo Zambezian Woodland TDFOTa41 Mas 3V 33.80 −9.36 840 Lake Masoko Zambezian Brachystegia/Uapaca woodland SAVATa42 Mas 2V 33.80 −9.36 840 Lake Masoko Zambezian Brachystegia/Uapaca woodland TDFOTa43 Mas 1V 33.80 −9.36 840 Lake Masoko Zambezian Brachystegia/Uapaca woodland SAVATa44 Mas 14 33.80 −9.36 878 Masoko Zambezian Brachystegia/Uapaca woodland TDFOTa45 Mas 12 33.80 −9.36 924 Masoko Zambezian Open Brachystegia/

Uapaca woodlandSAVA

Ta46 Mas 9 33.80 −9.36 909 Masoko Zambezian Brachystegia/Uapaca woodland TDFOTa47 Mas 8 33.80 −9.36 881 Masoko Zambezian Brachystegia/Uapaca woodland TDFOTa48 Mas 7 33.80 −9.36 901 Masoko Zambezian Brachystegia/Uapaca woodland TDFOTa49 Mas 3 33.80 −9.36 885 Masoko Zambezian Brachystegia/Uapaca woodland TDFOTa50 Mas 2 33.80 −9.36 885 Masoko Zambezian Brachystegia/Uapaca woodland TDFOTa51 Mas 1 33.80 −9.36 898 Masoko Zambezian Brachystegia/Uapaca woodland TDFOTa52 Kam-03 V1 33.90 −9.48 663 Kambangunguru swamp Zambezian Woodland TDFOTa53 Miombo 1 33.16 −8.74 1000 Chunia Zambezian Open woodland SAVATa54 CH 2 33.25 −8.50 1520 Chunia Zambezian Open Acacia/Brachystegia

wooded grasslandSTEP

188 A. Vincens et al. / Review of Palaeobotany and Palynology 140 (2006) 187–212

Table 1 (continued )




Southern TanzaniaTa55 CH 1 33.46 −8.56 1475 Chunia Zambezian Acacia wooded grassland SAVATa56 Saza 33.12 −8.42 1185 Saza Zambezian Acacia wooded grassland SAVATa57 RK 32.98 −8.43 969 Saza Zambezian Acacia wooded grassland SAVATa58 Mag 2 33.07 −8.61 840 Magadi Lake Zambezian Acacia wooded grassland SAVATa59 Mag 1 33.07 −8.59 840 Magadi Lake Zambezian Open Acacia wooded grassland STEP

Northern KenyaTurkana

T1 1 36.62 1.28 1900 Maralal Afromontane Upland dry coniferous forest WAMFT2 2 36.93 2.10 1200 South Horr Somalia Masai Acacia bushland STEPT3 3 36.82 1.78 1200 Baragoi Somalia–Masai Acacia bushland STEPT4 4 37.88 2.22 1200 Marsabit Somalia–Masai Bushland/dry (Olea/Croton) forest

transitionSAVA

T5 5 37.97 2.38 1200 Marsabit–North Horr Somalia–Masai Acacia reficiens bushland STEPT6 6 37.95 2.53 760 Marsabit–North Horr Somalia–Masai Acacia reficiens bushland SAVAT7 7 37.80 2.63 550 Marsabit–North Horr Somalia–Masai Acacia reficiens bushland SAVAT8 8 37.68 2.95 610 Marsabit–North Horr Somalia–Masai Acacia reficiens bushland SAVAT9 9 37.60 3.13 700 Marsabit–North Horr Somalia–Masai Acacia reficiens bushland SAVAT10 10 37.23 3.27 500 Marsabit–North Horr Somalia–Masai Acacia reficiens bushland STEPT11 11 37.05 3.33 500 North Horr Somalia–Masai Hyphaene palm grove STEPT12 12 36.78 3.33 700 North Horr–East Turkana Somalia–Masai Acacia reficiens bushland STEPT13 13 36.35 3.63 550 North Horr–East Turkana Somalia–Masai Acacia reficiens bushland STEPT14 14 36.37 3.87 460 North Horr–East Turkana Somalia–Masai Acacia reficiens bushland STEPT15 15 36.40 4.18 450 East Turkana (Karari) Somalia–Masai Acacia/Commiphora bushland STEPT16 16 36.40 4.18 400 East Turkana (Ileret) Somalia–Masai Acacia/Commiphora bushland STEPT17 17 36.22 4.32 400 East Turkana (Ileret) Somalia–Masai Acacia/Commiphora bushland STEPT18 18 36.22 4.32 400 East Turkana

(Bura Hasuma)Somalia–Masai Acacia/Commiphora bushland STEP

T19 19 36.33 3.88 400 East Turkana(Bura Hasuma)

Somalia–Masai Acacia/Commiphora bushland SAVA

T20 20 36.33 3.88 400 East Turkana(Bura Hasuma)

Somalia–Masai Acacia/Commiphora bushland STEP

T21 21 36.33 3.88 400 East Turkana (Tulu Bor) Somalia–Masai Acacia/Commiphora bushland STEPT22 22 36.23 4.22 400 East Turkana (Tulu Bor) Somalia–Masai Acacia/Commiphora bushland STEPT23 23 36.23 4.23 400 East Turkana (Tulu Bor) Somalia–Masai Acacia/Commiphora bushland STEPT24 24 36.23 4.23 400 East Turkana (Tulu Bor) Somalia–Masai Acacia/Commiphora bushland STEPT25 25 36.27 3.78 380 East Turkana (Allia Bay) Somalia–Masai Sporobolus spicatus grassland STEPT26 26 36.27 3.78 380 East Turkana (Allia Bay) Somalia–Masai Sporobolus spicatus grassland STEPT27 27 36.27 3.78 380 East Turkana (Allia Bay) Somalia–Masai Sporobolus spicatus grassland SAVAT28 28 36.27 3.78 380 East Turkana (Allia Bay) Somalia–Masai Sporobolus spicatus grassland STEP

SugutaS1 Sug S1 36.28 1.65 400 Kamuje Somalia–Masai Sub-desertic bushland STEPS2 Sug S2 36.28 1.60 406 Namruy Somalia–Masai Sub-desertic bushland STEPS3 Sug S3 36.28 1.60 406 Namruy Somalia–Masai Sub-desertic bushland STEPS4 Sug S4 36.42 2.07 460 Suguta Somalia–Masai Sub-desertic bushland STEPS5 Sug S5 36.25 1.80 660 Suguta–Lokori Somalia–Masai Sub-desertic bushland STEPS6 Sug S6 36.03 1.95 660 Lokori Somalia–Masai Sub-desertic bushland STEPS7 Sug S7 36.03 1.95 660 Lokori Somalia–Masai Sub-desertic bushland STEPS8 Sug S8 36.15 1.87 600 Loriu Plateau Somalia–Masai Sub-desertic bushland STEP

BogoriaB1 Bog S1 36.02 0.08 1500 Mugurin Somalia–Masai Mid-altitude Tarchonanthus

woodlandTDFO

B2 Bog S11 36.17 0.27 1500 Waseges escarpment Somalia–Masai Mid-altitude Acacia woodedgrassland

SAVA

B3 Bog S2 36.13 0.20 1600 View Point escarpment Somalia–Masai Open mid-altitude Dodonaeawoodland

SAVA

(continued on next page)

189A. Vincens et al. / Review of Palaeobotany and Palynology 140 (2006) 187–212





BogoriaB4 Kab S1 35.73 0.48 2000 Kabarnet Afromontane Upland dry coniferous forest WAMF

North-western UgandaU1 1 30.97 1.50 650 East Lake Albert

(Nkondo)Sudanian Wooded grassland SAVA

U2 2 30.97 1.50 650 East Lake Albert(Nkondo)

Sudanian Wooded grassland SAVA



U4 4 30.97 1.50 650 East Lake Albert(Sebugoro)










U9 9 30.97 1.50 650 East Lake Albert(Hohwa)


U10 10 30.97 1.50 650 East Lake Albert(Kaiso)




U12 12 30.28 0.92 700 South Lake Albert(Nyakabingo)






U15 15 30.27 0.88 750 South Lake Albert(Nyaburogo)
















U23 23 30.27 0.83 850 South Lake Albert(Karugutu)

Sudanian Wooded grassland with Borassusaethiopum

SAVA



SAVA



SAVA



SAVA



SAVA






North-western UgandaU28 28 31.00 1.45 850 East Lake Albert

(Kaseeta)Sudanian Wooded grassland SAVA

U29 29 30.13 0.80 900 Semliki Valley (Ntandi) Guineo-Congolian Mid-altitude semi-deciduous forest TSFOU30 30 30.13 0.80 900 Semliki Valley (Ntandi) Guineo-Congolian Mid-altitude semi-deciduous forest TSFOU31 31 31.00 1.40 950 Kaseeta Victoria mosaic Combretum wooded grassland SAVAU32 32 30.17 0.83 1000 West Ruwenzori Victoria mosaic Combretum wooded grassland TSFOU33 33 30.17 0.83 1000 West Ruwenzori Victoria mosaic Combretum wooded grassland SAVAU34 34 31.00 1.40 1000 Kaseeta Victoria mosaic Combretum wooded grassland SAVAU35 35 31.02 1.30 1100 Bugoma forest Victoria mosaic Mid-altitude semi-deciduous forest TSFOU36 36 31.02 1.30 1100 Bugoma forest Victoria mosaic Mid-altitude semi-deciduous forest/

wooded grassland mosaicSAVA

U37 37 31.02 1.30 1100 Bugoma forest Victoria mosaic Mid-altitude semi-deciduous forest TSFOU38 38 31.02 1.30 1100 Bugoma forest Victoria mosaic Mid-altitude semi-deciduous forest SAVAU39 39 31.08 1.23 1200 Bugoma forest Victoria mosaic Mid-altitude semi-deciduous forest/

wooded grassland mosaicSAVA

U40 40 31.08 1.23 1200 Bugoma forest Victoria mosaic Mid-altitude semi-deciduous forest TSFOU41 41 31.20 1.33 1200 Bugoma forest Victoria mosaic Mid-altitude semi-deciduous forest TSFOU42 42 31.20 1.33 1200 Bugoma forest Victoria mosaic Mid-altitude semi-deciduous forest TSFOU43 43 30.35 0.52 1200 Kibale forest Victoria mosaic Mid-altitude semi-evergreen forest TSFOU44 44 30.35 0.52 1200 Kibale forest Victoria mosaic Mid-altitude semi-evergreen forest WAMFU45 45 30.35 0.52 1200 Kibale forest Victoria mosaic Mid-altitude semi-evergreen forest TSFOU46 46 30.35 0.52 1200 Kibale forest Victoria mosaic Mid-altitude semi-evergreen forest TSFOU47 47 30.35 0.52 1200 Kibale forest Victoria mosaic Mid-altitude semi-evergreen forest TSFOU48 48 30.35 0.52 1200 Kibale forest Victoria mosaic Mid-altitude semi-evergreen forest TSFOU49 49 30.35 0.52 1200 Kibale forest Victoria mosaic Mid-altitude semi-evergreen forest TSFOU50 50 30.35 0.52 1200 Kibale forest Victoria mosaic Mid-altitude semi-evergreen forest TSFOU51 51 30.35 0.52 1200 Kibale forest Victoria mosaic Mid-altitude semi-evergreen forest TSFO


these models to changes in environmental variables suchas temperature, precipitation, seasonality or atmosphericCO2 concentration (Foley et al., 1996; Haxeltine andPrentice, 1996; Hély et al., 2006).

In order to validate and then apply the biomisationmethod to past and future potential vegetation land coverdistribution, numerous biomes reconstructions have beenmade using modern pollen data for many parts of theworld: e.g. in Europe (Prentice et al., 1996), NorthAmerica (Williams et al., 1998; Thompson andAnderson,2001), South America (Marchant et al., 2001), the formerSoviet Union and Mongolia (Tarasov et al., 1998), China(Yu et al., 1998) and Japan (Takahara et al., 2000;Gotandaet al., 2002). In Africa, such reconstructions have beenmade by Jolly et al. (1998a,b) for the continent and byPeyron et al. (2000) on 32 East African sites. The resultshave shown the robustness of the biomisation methodwhich is able to predict the major modern African vegetation types, however, difficulties remain, particularly inreconstructions of desert and tropical dry forest.

This paper focuses on modern biome reconstructionsin three East African regions (northern Kenya, north-western Uganda and southern Tanzania), performed on apollen data set of 150 sites including new pollen assem-

blages from lowland Zambezian Miombo woodland(tropical dry forest biome) not previously represented inAfrican biome reconstructions (Jolly et al., 1998b; Peyronet al., 2000) nor in the work of Gajewski et al. (2002) onmodern climate–vegetation–pollen relations. The biomi-sation method followed here is slightly different to thatproposed by Jolly et al. (1998b) and Peyron et al. (2000),mainly in that all identified native and non-edaphic taxahave been considered and their Plant Functional Types(PFTs) assignments largely revised.

2. The modern pollen data set

The modern pollen data set includes 150 samplesfrom East Africa (Table 1) and about 500 identified taxa.Among the samples, 146 were taken from surface soil,the sampling method following that of Wright (1967),and four are modern lacustrine muds collected under 1 mwater-depth in two Tanzanian crater lakes (Masoko andNgozi). At each site, a description of the surroundingvegetation was carried out including its structure and themain components, but without detailed and quantitativebotanical inventories. All the chemical treatments of thesamples (Faegri and Iversen, 1975) and the analyses


were performed in the same laboratory (CEREGE, Aix-en-Provence), warranting the homogeneity of the pollencounts and determinations. For each sample between 250and 700 pollen grains and spores were counted and theidentification was achieved with the help of a referencecollection of some 7000 modern specimens of tropicalAfrica at CEREGE and published works on East Africanpollen morphology (e.g. Bonnefille, 1971a,b; A.P.L.F.,1974; Hamilton, 1976; Bonnefille and Riollet, 1980).

2.1. Sample location and related vegetation

The location of the pollen samples is given in Table 1and also in Fig. 1 where, bymeans ofMapInfo software (V7.5 Professional), they are superimposed on enlargementsof the vegetationmap of themain study areas (White, 1983)(http://www.grid.unep.ch/). These areas are: northernKenya (Lakes Turkana [T1 to T28 and S1 to S8] andBogoria [B1 to B4] areas) (Fig. 1a), north-western Uganda(Lake Albert area [U1 to U51]) (Fig. 1b) and southernTanzania (Ta1 to Ta59) (Fig. 1c). According to White'smap (1983), the present study includes the modern pollenrain from seven East African phytogeographical regions,mosaics or centres of endemism (Fig. 1). They are:

2.1.1. The Somalia–Masai regionPollen samples representative of this region are located

exclusively in northern Kenya. In the Turkana area, 28samples were collected in semi-desert grassland and shrub-land, and in Acacia-Commiphora deciduous bushland andthicket (T2 to T28 and S1 to S8). In the vicinity of LakeBogoria, 3 samples are representative of mid-altitude mo-saic of evergreen bushland (B1) and secondary Acaciawooded grassland (B2 and B3) (Fig. 1a).

2.1.2. The Sudanian regionThis region is only represented by pollen samples from

north-western Uganda, east and south of Lake Albert (U1to U28). These areas are occupied by wooded grassland(Fig. 1b).

2.1.3. The Zambezian regionPollen samples are located exclusively in southern Tan-

zania (Ta38 to Ta59) and represent natural Miombo wood-land (Lake Masoko [Ta39 to Ta51], the Kambangunguruswamp [Ta52] and the Mbozi [Ta38] areas) and more orless open/degraded Miombo woodland or local wooded

Fig. 1. Location of the modern pollen samples and corresponding reconstruction1b—north-westernUganda; 1c— southernTanzania. (White’smapphytochoria nSudanian region; IV — Somalia–Masai region; VIII — Afromontane and Afrtransition zone;XI—Guinea-Congolia/Sudania regional transition zone;XII—L

grassland (Chunia [Ta53 to Ta55], Saza [Ta56 and Ta57]and Lake Magadi [Ta58 and Ta 59] areas) (Fig. 1c).

2.1.4. The Guineo-Congolian regionOnly two Ugandan pollen samples are representative

of this region (U29 and U30). They were collected at theeastern limit of this region, in the Semliki Valley occu-pied by drier types of rain forest (Fig. 1b).

2.1.5. The Lake Victoria mosaicThe pollen samples were taken from north-western

Uganda, which, at mid-altitude, is occupied by a mosaic ofrain forest and secondary wooded grassland andwoodland.Three main types of vegetation are represented: Combre-tumwooded grassland (U31 to U34, eastern escarpment ofLake Albert and western escarpment of RuwenzoriMount), moist semi-deciduous forest (Bugoma forest, eas-tern escarpment of Lake Albert, U35 to U42) and moistsemi-evergreen forest (Kibale forest, South of Lake Albert,U43 to U51). The latter two were mapped byWhite (1983)as belonging to the Guineo-Congolian region on the basisof their floristic composition (Fig. 1b).

2.1.6. The Afromontane centre of endemismTwenty three pollen samples are representative of this

centre of endemism. Two of themwere taken fromnorthernKenya (Maralal, South of Lake Turkana [T1], and Kaba-rnet, western escarpment of Lake Bogoria [B4]) (Fig. 1a)representing upland dry coniferous forest. All the others areunpublished and come from southern Tanzania, from up-land forest (Ngozi [Ta28 to Ta36] and Rungwe [Ta19 toTa27] Mountains and the Mbeya Range [Ta37]) or fromderived open formations (Rungwe crater [Ta7 to Ta 18])(Fig. 1c).

2.1.7. The Afroalpine centre of endemismThis centre of endemism, located above the Afromon-

tane one, is represented by 6 new (unpublished) samplescollected in southern Tanzania (Rungwe Mountain [Ta1to Ta6]), in altimontane formations such as Ericaceousand Protea thicket (Fig. 1c).

2.2. Main characteristics of the modern pollenassemblages

As many modern pollen assemblages used in thiswork have been previously published (Bonnefille and

of biomes in East Africa using White's map (1983). 1a— northern Kenya;omenclature: I—Guineo-Congolian region; II—Zambezian region; III—oalpine centres of endemism; X — Guinea-Congolia/Zambezia regionalakeVictoriamosaic;XIII—Zanzibar–Inhambane regional transition zone).

http://www.grid.unep.ch/

Fig. 2. Simplified pollen diagram of modern pollen assemblages from southern Tanzania (the pollen sum includes all counted taxa, excluding aquatic, anthropogenic and damaged taxa).

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Table 2Allocation of the pollen taxa derived from all the sites listed in Table 1 (with their codes) to the plant functional types

Family Taxons Code TSS WTE TE TR1 TR2 TR3 SF DF G

Malvaceae Abutilon Abutilon x x xMimosaceae Acacia Groupe I Acacia x x x xMimosaceae Acacia Groupe III Acacia x x x xMimosaceae Acacia xanthophloea⁎ Acacia_xan x xEuphorbiaceae Acalypha Acalypha x x x x x xEuphorbiaceae Acalypha villicaulis⁎ Acaly_vil x x xAcanthaceae Acanthaceae undiff. Acanthacea x x xAcanthaceae Acanthus⁎ Acanthus x xAmaranthaceae Achyranthes⁎ aspera Achyran_as x x xApocynaceae Acokanthera⁎ schimperi Acokant_sc x x xMalpighiaceae Acridocarpus Acridocarp x x x xPassifloraceae Adenia venenata⁎ Adenia_ven x x xApocynaceae Adenium obesum⁎ Adenium_ob x x xAmaranthaceae Aerva⁎ javanica Aerva_java x x xAmaranthaceae Aerva⁎ lanata Aerva_lana x x xFabaceae Aeschynomene baumii⁎ Aeschyn_ba x xCornaceae Afrocrania volkensii Afro_volke xRubiaceae Aidia⁎ micrantha Aidia_micr x xAizoaceae Aizoaceae undiff. Aizoaceae x x xAlangiaceae Alangium chinense Alangiu_ch xMimosaceae Albizia⁎ Albizia x x x xRosaceae Alchemilla Alchemilla xEuphorbiaceae Alchornea Alchornea x x xSapindaceae Allophylus Allophylus x x x x xAloaceae Aloe⁎ Aloe x x xAloaceae Aloe⁎ turkanensis Aloe_turka x x xAmaranthaceae/Chenopodiaceae Amaranthaceae/Chenopodiaceae undiff. Amar_cheno x x x xAnacardiaceae Anacardiaceae undiff. Anacardiac x x x x x xPrimulaceae Anagallis angustiloba⁎ Anagall_an xCombretaceae Anogeissus⁎ leiocarpus Anogeis_le x x xLoganiaceae Anthocleista Anthocleis x x x xRubiaceae Anthospermum Anthosperm x xEuphorbiaceae Antidesma⁎ Antidesma x x x xSapindaceae Aphania⁎ senegalensis Aphani_sen x xFlacourtiaceae Aphloia theiformis Aphlo_th xApiaceae Apiaceae undiff. Apiaceae x x x xApocynaceae Apocynaceae undiff. Apocynacea x x x x x xIcacinaceae Apodytes dimidiata Apodyte_di xEuphorbiaceae Argomuellera⁎ macrophylla Argomue_ma x xAsteraceae Artemisia Artemisia xAsparagaceae Asparagus Asparagus x x xAsparagaceae Asparagus buchananii⁎ Aspara_buc x x xAsteraceae Asteraceae undiff. Asteraceae x x x xAcanthaceae Asystasia Asystasia x x x xAcanthaceae Asystasia gangetica⁎ Asyst_gang x x x xSalvadoraceae Azima tetracantha Azima_tet x xBalanitaceae Balanites Balanites x x x xBalanitaceae Balanites aegyptiaca⁎ Balani_aeg x x x xBalanitaceae Balanites rotundifolia⁎ Balani_rot x xCaesalpiniaceae Baphiopsis parviflora Baphiop_pa x xAcanthaceae Barleria Barleria x x xBegoniaceae Begonia Begonia x x x xMelianthaceae Bersama abyssinica Bersama_ab x x xAcanthaceae Blepharis⁎ Blepharis x x xSapindaceae Blighia unijugata⁎ Blighia_un x x xNyctaginaceae Boerhavia⁎ Boerhavia x xBombacaceae Bombax rhodognaphalon⁎ Bombax_rho x x xPalmae Borassus⁎ aethiopum Bora_aethi x




Capparidaceae Boscia⁎ Boscia x x x xCaesalpiniaceae Brachystegia Brachysteg x xAcanthaceae Brachystephanus Brachystep x xBrassicaceae Brassicaceae undiff. Brassicace x x x xEuphorbiaceae Bridelia micrantha⁎ Bridel_mic x xEuphorbiaceae Bridelia⁎ scleroneura Bridel_scl x x xSimaroubaceae Brucea antidysenterica⁎ Bruce_an xScrophulariaceae Buchnera⁎ cryptocephala Buchne_cry x x xLoganiaceae Buddleja Buddleja x x xBuxaceae Buxus madagascarica⁎ Buxus_mada x x x x xBuxaceae Buxus obtusifolius⁎ Buxus_obtu x xCaesalpiniaceae Caesalpinia⁎ Caesalpini x x x xCaesalpiniaceae Caesalpiniaceae undiff. Caesalpinc x x x x xFabaceae Calpurnia aurea Calpu_au xCampanulaceae Campanula⁎ Campanula xFabaceae Canavalia⁎ Canaval x xLamiaceae Capitanya⁎ otostegioides Capita_oto xCapparidaceae Capparidaceae undiff. Capparidac x x xCapparidaceae Capparis fascicularis⁎ Cappari_fa x xCapparidaceae Capparis tomentosa⁎ Cappari_to x x xSapindaceae Cardiospermum halicacabum⁎ Cardio_hal x x xAsteraceae Carduus⁎ Carduus x xApocynaceae Carissa edulis⁎ Carissa_ed x x xCaryophyllaceae Caryophyllaceae undiff. Caryophyl x x x x x xCaesalpiniaceae Cassia⁎ didymobotrya Cassia_did x x xCaesalpiniaceae Cassia⁎ italica Cassia_ita xCaesalpiniaceae Cassia⁎ mimosoides Cassia_mim x xRhizophoraceae Cassipourea celastroides⁎ Cassip_cel xRubiaceae Caturanegam⁎ Caturan x x xMalpighiaceae Caucanthus⁎ edulis Caucan_edu xBombacaceae Ceiba pentandra Ceiba_pent x xCelastraceae Celastraceae undiff. Celastrac x x x xCelastraceae/Hippocrateaceae Celastraceae/Hippocrateaceae undiff. Celast_hip x x x x x xAmaranthaceae Celosia argentea⁎ Celosia_ar x xAmaranthaceae Celosia⁎ trigyna Celosia_tr x x xUlmaceae Celtis Celtis x x xCaryophyllaceae Cerastium afromontanum⁎ Cerasti_af xCaryophyllaceae Cerastium⁎ octandrum Cerasti_oc xChenopodiaceae Chenopodiaceae undiff. Chenopodia x x xOleaceae Chionanthus mildbraedii⁎ Chion_mil xSapotaceae Chrysophyllum⁎ Chrysophyl x x x xAsteraceae Cichorieae undiff. Cichorie x x x xMenispermaceae Cissampelos⁎ mucronata Cissamp_mu x x xVitaceae Cissus Cissus x x x x xVitaceae Cissus quadrangularis⁎ Cissus_qua x x xRutaceae Clausena anisata Clausen_an x xEuphorbiaceae Cleistanthus⁎ polystachyus Cleist_pol x x xRanunculaceae Clematis⁎ Clematis x x x xCapparidaceae Cleome⁎ brachycarpa Cleome_bra x xCapparidaceae Cleome⁎ gynandra Cleome_gyn x x xVerbenaceae Clerodendrum Clerodendr x x x x x xRosaceae Cliffortia nitidula⁎ Cliffor_ni x xEuphorbiaceae Clutia Clutia x x xCucurbitaceae Coccinia Coccinia x x x x xMenispermaceae Cocculus hirsutus⁎ Coccu_hirs x xCombretaceae Combretaceae undiff. Combretace x x x x x xCombretaceae/Melastomataceae Combretaceae/Melastomataceae undiff. Comb_melas x x x x x xCombretaceae Combretum⁎ aculeatum Combret_ac x x





Combretaceae Combretum⁎ molle Combret_mo x x xCommelinaceae Commelina⁎ benghalensis Commel_ben x x xCommelinaceae Commelina⁎ forskalaei Commel_for x xCommelinaceae Commelinaceae undiff. Commelicea x x x xBurseraceae Commiphora Commiphora x x xBurseraceae Commiphora africana⁎ Commiph_af x x xBurseraceae Commiphora edulis⁎ Commiph_ed x xConvolvulaceae Convolvulaceae undiff. Convolvula x x xTiliaceae Corchorus⁎ fascicularis Corcho_fas x xBoraginaceae Cordia africana⁎ Cordia_afr x x x xAsteraceae Crassocephalum⁎ montuosum Crasso_mon xRubiaceae Craterispermum laurinum⁎ Crateri_la x xCapparidaceae Crateva adansonii Crateva_ad x x xRubiaceae Crossopteryx febrifuga Crosso_feb x xFabaceae Crotalaria Crotalaria x x x xEuphorbiaceae Croton⁎ Croton x x x x xCucurbitaceae Cucumis dipsaceus⁎ Cucumi_dip x x xCucurbitaceae Cucurbitaceae undiff. Cucurbit x x x xAraliaceae Cussonia Cussonia x x x xAmaranthaceae Cyathula⁎ orthacantha Cyathul_or x x xVitaceae Cyphostemma⁎ Cyphostemm x x x x xCaesalpiniaceae Delonix Delonix xMimosaceae Dichrostachys cinerea⁎ Dichro_cin x x xAmaranthaceae Digera⁎ muricata Digera_mur x xDioscoreaceae Dioscorea dumetorum⁎ Diosco_dum x x xEbenaceae Diospyros Diospyros x x x xDipsacaceae Dipsacus pinnatifidus⁎ Dipsac_pin xDipterocarpaceae Dipterocarpaceae undiff. Dipterocar x x xSapindaceae Dodonaea viscosa⁎ Dodona_vis xSterculiaceae Dombeya⁎ Dombeya x x x xMoraceae Dorstenia foetida⁎ Dorste_foe xMoraceae Dorstenia zanzibarica⁎ Dorste_zan x x x xDracaenaceae Dracaena schizantha⁎ Dracaen_sc x x x xDracaenaceae Dracaena steudneri⁎ Dracaen_st xCaryophyllaceae Drymaria cordata⁎ Drymar_cor xAcanthaceae Duosperma Duosperma x xAcanthaceae Dyschoriste⁎ Dyschorist x xEbenaceae Ebenaceae undiff. Ebenaceae x x x x x xAcanthaceae Ecbolium Ecbolium x xBoraginaceae Ehretia Ehretia x x x x x xMeliaceae Ekebergia⁎ capensis Ekeberg_ca x x x x xMimosaceae Entada⁎ Entada x x x xEricaceae Ericaceae undiff. Ericaceae xFabaceae Erythrina abyssinica⁎ Erythr_aby x xEuphorbiaceae Erythrococca⁎ Erythrococ x x x x x xEbenaceae Euclea Euclea x x x xEuphorbiaceae Euphorbia⁎ Euphorbia x x x xEuphorbiaceae Euphorbia⁎ tirucalli Euphor_tir x x x xEuphorbiaceae Euphorbiaceae undiff. Euphorbiac x x x x x xConvolvulaceae Evolvulus⁎ Evolvulus x x xFabaceae Fabaceae undiff. Fabaceae x x x xChenopodiaceae Fadenia zygophylloides Fadeni_zyg xMoraceae Ficus Ficus x x x x xMalpighiaceae Flabellaria⁎ paniculata Flabel_pan x x x xEuphorbiaceae Flueggea virosa Fluegg_vir x x xApocynaceae Funtumia⁎ africana Funtum_afr x xRubiaceae Galiniera saxifraga Galinie_sa xClusiaceae Garcinia Garcinia x x x x xClusiaceae Garcinia gnetoides⁎ Garcin_gne x




Clusiaceae Garcinia granulata⁎ Garcin_gra x x x x xClusiaceae Garcinia livingstonei⁎ Garcin_liv x xClusiaceae Garcinia volkensii⁎ Garcin_vol xRubiaceae Gardenia⁎ imperialis Garden_imp x xRubiaceae Gardenia⁎ ternifolia Garden_ter x x xGeraniaceae Geranium Geranium xThymelaeaceae Gnidia⁎ chrysantha Gnidia_chr x x x xTiliaceae Grewia⁎ Grewia x x xTiliaceae Grewia⁎ tenax Grewia_ten x x xHalorrhagaceae Gunnera perpensa Gunnera_pe x xRosaceae Hagenia abyssinica Hagenia_ab xRubiaceae Hallea⁎ rubrostipulata Hallea_rub xHypericaceae Harungana Harungana x x xHypericaceae Harungana madagascariensis⁎ Harung_mad x x xBoraginaceae Heliotropium steudneri⁎ Heliot_ste x x xSterculiaceae Hermannia⁎ Hermannia x xMalvaceae Hibiscus⁎ Hibiscus x x x xConvolvulaceae Hildebrandtia⁎ obcordata Hilde_obcor x xAsteraceae Hirpicium⁎ diffusum Hirpici_di x x xUlmaceae Holoptelea grandis Holopt_gra xHymenocardiaceae Hymenocardia acida⁎ Hycard_aci x xRubiaceae Hymenodictyon⁎ floribundum Hydict_flo x xHypericaceae Hypericum Hypericum x xPalmae Hyphaene⁎ Hyphaene xAcanthaceae Hypoestes⁎ Hypoestes x x x xLamiaceae Hyptis Hyptis x x xAquifoliaceae Ilex mitis Ilex_mitis xBalsaminaceae Impatiens Impatiens x x x xFabaceae Indigofera Indigofera x x x xFabaceae Indigofera schimperi⁎ Indigo_sch x x x xConvolvulaceae Ipomoea⁎ Ipomoea x x x x xIrvingiaceae Irvingiaceae undiff. Irvingiace x xCaesalpiniaceae Isoberlinia⁎ Isoberlini xAcanthaceae Isoglossa Isoglossa x x xOleaceae Jasminum Jasminum x x x x xOleaceae Jasminum abyssinicum⁎ Jasmin_aby xEuphorbiaceae Jatropha⁎ Jatropha x xCaesalpiniaceae Julbernardia⁎ paniculata Julber_pan xCaesalpiniaceae Julbernardia⁎ seretii Julber_ser x xCupressaceae Juniperus⁎ procera Juniper_pr xAcanthaceae Justicia anselliana⁎ Justi_anse x x xAcanthaceae Justicia striata⁎ Justi_stri x x x xAcanthaceae Justicia⁎ flava Justi_flav x x xAcanthaceae Justicia⁎ odora Justi_odor x xCrassulaceae Kalanchoe Kalanchoe x x x xCucurbitaceae Kedrostis foetidissima⁎ Kedro_foet x x xRubiaceae Keetia⁎ gueinzii Keetia_gue xBignoniaceae Kigelia africana Kigelia_af x x xRubiaceae Kohautia caespitosa⁎ Kohaut_cae x x xFabaceae Kotschya africana⁎ Kotschy_af x x xAsteraceae Lactuceae undiff. Lactuceae x x x xLamiaceae Lamiaceae undiff. Lamiaceae x x x xAnacardiaceae Lannea⁎ Lannea x x x x xVerbenaceae Lantana⁎ ukambensis Lantan_uka x xVerbenaceae Lantana⁎ viburnoides Lantan_vib x x xUrticaceae Laportea⁎ aestuens Lapport_ae x x xLeguminosae Leguminosae undiff. Leguminosa x x x x x x x xLamiaceae Leonotis⁎ Leonotis x x x





Acanthaceae Lepidagathis Lepidagath x x xLamiaceae Leucas⁎ Leucas x x xLinaceae Linaceae undiff. Linaceae x xLobeliaceae Lobelia holstii⁎ Lobeli_hol xLobeliaceae Lobelia rhynchopetalum⁎ Lobeli_rhy xLoganiaceae/Salvadoraceae Loganiaceae/Salvadoraceae undiff. Nuxia/Dobe x x xLoranthaceae Loranthaceae undiff. Loranthac x x x x xCucurbitaceae Luffa⁎ cylindrica Luffa_cyli x x xSolanaceae Lycium Lycium x xEuphorbiaceae Macaranga⁎ Macaranga x x x xFabaceae Macrotyloma axillare⁎ Macroty_ax x x xCapparidaceae Maerua⁎ Maerua x x x xMyrsinaceae Maesa lanceolata⁎ Maesa_lanc x x x xMalvaceae Malvaceae undiff. Malvaceae x x x xEuphorbiaceae Margaritaria discoidea Margar_dis x x xMelastomataceae Melastomataceae undiff. Melastomat x xMeliaceae Meliaceae undiff. Meliaceae x x x x xAcanthaceae Mellera⁎ Mellera x xEuphorbiaceae Micrococca⁎ mercurialis Microco_me x x xMoraceae Milicia⁎ excelsa Milicia_ex x xMimosaceae Mimosaceae undiff. Mimosaceae x x x x x xAcanthaceae Mimulopsis⁎ Mimulopsis xSapotaceae Mimusops⁎ kummel Mimuso_kum xRubiaceae Mitracarpus villosus Mitrac_vil x x xCucurbitaceae Momordica⁎ rostrata Momor_ros x x xEuphorbiaceae Monadenium⁎ Monadenium x x xMonocotyledoneae Monocotyledoneae undiff. Monocotyl x x x x x x x xMoraceae Moraceae undiff. Moraceae x x x x xMoraceae Myrianthus⁎ holstii Myriant_ho x xMyricaceae Myrica Myrica xMyrsinaceae Myrsine africana Myrsine_af x xMyrtaceae Myrtaceae undiff. Myrtaceae x x x xEuphorbiaceae Neoboutonia⁎ macrocalyx Neobout_ma xLoganiaceae/Salvadoraceae Nuxia/Dobera Nuxia/Dobe x x xLoganiaceae/Theaceae Nuxia/Ficalhoa Nuxia/Fica xNyctaginaceae Nyctaginaceae undiff. Nyctaginac x x xLauraceae Ocotea Ocotea xRubiaceae Oldenlandia⁎ Oldenlandi x x x xEuphorbiaceae Oldfieldia⁎ Oldfielda x xOleaceae Olea capensis⁎ Olea_capen x xOleaceae Olea europaea⁎ Olea_europ xAnacardiaceae Ozoroa⁎ insignis Ozoroa_ins x x xSapindaceae Pancovia⁎ bijuga Pancov_bij x xSapindaceae Pappea capensis Pappea_cap x x xChrysobalanaceae Parinari⁎ Parinari x x x x xCaryophyllaceae Paronychia Paronychia x xSapindaceae Paullinia pinnata Paulli_pin x xRubiaceae Pavetta abyssinica⁎ Pavet_abys xRubiaceae Pavetta elliottii⁎ Pavet_elli xRubiaceae Pavetta gardeniifolia⁎ Pavet_gard x x x xRubiaceae Pentanisia ouranogyne⁎ Penta_oura x x xAcanthaceae Phaulopsis⁎ imbricata Phaul_imbr x xPalmae Phoenix reclinata⁎ Phoeni_rec x xEuphorbiaceae Phyllanthus⁎ boehmii Phyll_boeh x xEuphorbiaceae Phyllanthus⁎ muellerianus Phyll_muel x xEuphorbiaceae Phyllanthus⁎ reticulatus Phyll_reti x x xEuphorbiaceae Phyllanthus⁎ rivae Phyll_riva xUrticaceae Pilea⁎ bambuseti Pilea_bamb xCaesalpiniaceae Piliostigma thonningii⁎ Pilios_tho x x




Plantaginaceae Plantago Plantago x x x x xPlantaginaceae Plantago africana⁎ Planta_afr xPlantaginaceae Plantago lanceolata⁎ Planta_lan x x xLoranthaceae Plicosepalus Plicosepal x x xPoaceae Poaceae undiff. Poaceae xPodocarpaceae Podocarpus Podocarpus xPolygalaceae Polygala⁎ Polygala x x xAraliaceae Polyscias fulva⁎ Polysc_ful x xPortulacaceae Portulacaceae undiff. Portulacea x x xProteaceae Protea⁎ Protea x xRosaceae Prunus africana⁎ Prunus_afr x xAmaranthaceae Psilotrichum⁎ elliottii Psilotr_el x x xRubiaceae Psychotria Psychotria x x x xRubiaceae Psychotria amboniana⁎ Psychot_am x x x xRubiaceae Psydrax⁎ schimperiana Psydrax_sc x xFabaceae Pterocarpus⁎ Pterocarpu x x xDipsacaceae Pterocephalus⁎ fructescens Pteroc_fru xCaesalpiniaceae Pterolobium stellatum Pterol_ste xAmaranthaceae Pupalia⁎ lappacea Pupalia_la x x xMyristicaceae Pycnanthus Pycnanthus x xMyristicaceae Pycnanthus angolensis⁎ Pycna_ango x xCombretaceae Quisqualis⁎ indica Quisqua_in x x xRanunculaceae Ranunculaceae undiff. Ranunaceae x x x xMyrsinaceae Rapanea melanophloeos⁎ Rapanea_me x xApocynaceae Rauvolfia Rauvolfia x x xResedaceae Resedaceae undiff. Resedaceae x x xRhamnaceae Rhamnaceae undiff. Rhamnaceae x x x xAnacardiaceae Rhus⁎ Rhus x x xAnacardiaceae Rhus⁎ vulgaris Rhus_vulga x x xFabaceae Rhynchosia⁎ Rhynchosia x x xEuphorbiaceae Ricinus communis Ricinus_co x x xRubiaceae Rubia⁎ Rubia x xRubiaceae Rubia⁎ cordifolia Rubia_cord x xRubiaceae Rubiaceae undiff. Rubiaceae x x x x x x xRosaceae Rubus pinnatus⁎ Rubus_pinn x xAcanthaceae Ruellia Ruellia x x xPolygonaceae Rumex Rumex x x x xRutaceae Rutaceae undiff. Rutaceae x x x x xSalicaceae Salix subserrata⁎ Salix_subs x xSalvadoraceae Salvadora persica⁎ Salvad_per x xSapindaceae Sapindaceae undiff. Sapindacea x x x x xSapotaceae Sapotaceae undiff. Sapotaceae x x x x xDipsacaceae Scabiosa⁎ Scabiosa xAraliaceae Schefflera abyssinica⁎ Schef_abys xAraliaceae Schefflera myriantha⁎ Schef_myri xGentianaceae Sebaea brachyphylla⁎ Sebaea_bra xAmaranthaceae Serichostachys⁎scandens Serich_sca x xPedaliaceae Sesamum Sesamum x x xFabaceae Sesbania sesban Sesban_ses x x xEuphorbiaceae Shirakia⁎ elliptica Shiraki_el x x x xCaryophyllaceae Silene⁎ burchellii Silen_burc xPoaceae Sinarundinaria alpina Sinarun_al xAsteraceae Solanecio⁎ mannii Solanec_ma xSolanaceae Solanum⁎ Solanum x x xAnacardiaceae Sorendeia⁎ madagascariensis Sorind_mad x xRubiaceae Spermacoce⁎ Spermacoce x x xApiaceae Steganotaenia⁎ Steganotae x x xMenispermaceae Stephania⁎ abyssinica Steph_abys x x x





Sterculiaceae Sterculia⁎ Sterculia x x x xAsteraceae Stoebe kilimandscharica⁎ Stoeb_kili xOlacaceae Strombosia scheffleri⁎ Stromb_sch x x xFabaceae Stylosanthes fructicosa⁎ Stylosa_fr x xChenopodiaceae Suaeda monoica⁎ Suaeda_mon x xGentianaceae Swertia usambarensis⁎ Swerti_kil xGentianaceae Swertia⁎ kilimandscharica Swerti_usa xClusiaceae Symphonia globulifera Sympho_glo x x xMyrtaceae Syzygium⁎ Syzygium x x x xAsclepiadaceae Tacazzea⁎ apiculata Tacaz_apic x xCaesalpiniaceae Tamarindus⁎ indica Tamarin_in x x xTamaricaceae Tamarix Tamarix x xLoranthaceae Tapinanthus⁎ Tapinanthu x x x x xAsteraceae Tarchonanthus⁎ camphoratus Tarcho_cam xRubiaceae Tarenna⁎ graveolens Tarenna_gr xRutaceae Teclea⁎ Teclea x xFabaceae Tephrosia uniflora⁎ Tephros_un xEuphorbiaceae Tetrorchidium Tetrorchid xRanunculaceae Thalictrum Thalictrum x xAcanthaceae Thunbergia Thunbergia x x x x x xThymelaeaceae Thymelaeaceae undiff. Thymelaeac x x x x xTiliaceae Tiliaceae undiff. Tiliaceae x x x xMenispermaceae Tiliacora⁎ Tiliacora x x xRutaceae Toddalia asiatica Toddali_as x x x xEuphorbiaceae Tragia⁎ hildebrandtii Tragia_hil x x xUlmaceae Trema⁎ orientalis Trema_orie x xAizoaceae Trianthema⁎ Trianthema x x xTribulaceae Tribulus Tribulus x xMeliaceae Trichilia⁎ Trichilia x x xHamamelidaceae Trichocladus ellipticus⁎ Trichoc_el xFabaceae Trifolium⁎ acaule Trifol_aca xMoraceae Trilepisium⁎ madagascariensis Trilepi_ma x x xEuphorbiaceae Uapaca Uapaca x x xEuphorbiaceae Uapaca kirkiana⁎ Uapaca_kir xEuphorbiaceae Uapaca nitida⁎ Uapaca_nit xCaryophyllaceae Uebeliana⁎ abyssinica Uebeli_aby xRubiaceae Uncaria⁎ africana Uncaria_af x x xUrticaceae Urticaceae undiff. Urticaceae x x x xRubiaceae Vangueria⁎ madagascariensis Vanguer_ma x x x xFabaceae Vatovaea⁎ pseudolablab Vatova_pse xRutaceae Vepris⁎ dainellii Vepris_dai xRutaceae Vepris⁎ nobilis Vepris_nob x xRutaceae Vepris⁎ trichocarpa Vepris_tri x xVerbenaceae Verbenaceae undiff. Verbenacea x x x xAsteraceae Vernonia perrottetii⁎ Vernon_per x x xAsteraceae Vernonia⁎ schimperi Vernon_sch xAsteraceae Vernonieae undiff. Vernonieae x x x x xFabaceae Vigna⁎ Vigna x x xFabaceae Vigna⁎ luteola Vigna_lut x x xVitaceae Vitaceae undiff. Vitaceae x x x x xAmaranthaceae Volkensinia prostrata Volken_pros xOlacaceae Ximenia Ximenia x x xMimosaceae Xylia⁎ schliebenii Xylia_schl xSapindaceae Zanha golungensis⁎ Zanha_golu x xRutaceae Zanthoxylum⁎ Zanthoxylu x x x x xRutaceae Zanthoxylum⁎ chalybeum Zantho_cha x x xRutaceae Zanthoxylum⁎ usambarense Zantho_usa xZygophyllaceae Zygophyllum Zygophyl x x


Table 3Plant functional types proposed for the East African areas underinvestigation compared to previous ones defined by Jolly et al. (1998b)and Peyron et al. (2000)

PFT Jolly et al. (1998b) Peyron et al. (2000) This paper

te Tropical evergreen Tropicalevergreen

Tropicalevergreen

te1 *Wet tropicalevergreen

_ _

te2 *Dry tropicalevergreen

_ _

tr1 Seasonal tropicalraingreen

Wet tropicalraingreen

Wet tropicalraingreen

tr2 Dry tropical raingreen Dry tropicalraingreen

Dry tropicalraingreen

tr3 Dryest woodlands andsavannas

_ Dryest tropicalraingreen

sf Steppe forb/shrub Steppe forb Steppe forb/shrub

df Desert forb/shrub Desert forb Desert forb/shrub

wte Warm-temperatebroad- and needle-leaved evergreen

Warm-temperatebroad-leavedevergreen

Warm-temperatebroad- andneedle-leavedevergreen

tss Temperate sclerophyll/succulent

Temperatesclerophyll/succulent

Temperatesclerophyll

g _ Grass Grassub _ Ubiquitous _

Table 4East African biomes and their characteristic plant functional types(abbreviations for PFTs as in Table 2), and main relatedphytogeographical affinities (White, 1983)

Biomes Plantfunctionaltypes

Main phytogeographicalaffinities (White, 1983)

TRFO : tropical rain forest te Guineo-Congolian regionTSFO : tropical

seasonal foresttr1 Guinea-Congolian/Zambezia

regional transition zone/LakeVictoria mosaic

TDFO : tropical dryforest

tr2 Zambezian region

SAVA : savanna tr3+g Sudanian region/Somalia–Masai region

STEP : steppe sf+g Somalia–Masai regionDESE : desert df+g Somalia–Masai regionWAMF : warm mixed

forestwte Afromontane centre

of endemismXERO : temperate

xerophytic woods/scrubtss+g Afroalpine centre

of endemism


Vincens, 1977; Vincens, 1982; Nakimera-Ssemmanda,1991; Vincens et al., 1997, in press) and are availablefrom the African Pollen Database web site (http://medias.obs-mip.fr/apd), only a brief outline of theirmicrofloristic composition is given here. However, thenumerous new pollen assemblages from southernTanzania are presented here in more detail as a pollendiagram, drawn using the PSIMPOLL program (Ben-nett, 2002) (Fig. 2). All percentage values given in thissection are calculated on a total of pollen grains andspores that excludes only aquatic, anthropogenic anddamaged taxa. Spectra are described following thephytogeographical areas they come from.

2.2.1. Modern pollen rain from the Somalia–Masai RegionThe pollen assemblages from the Lake Turkana area are

characterized by an abundance of Poaceae (mean values of45%). The other important herbaceous taxa are Chenopo-diaceae (Suaeda monoica-type, Fadenia zygophylloides),Amaranthaceae (Volkensinia prostrata), Acanthaceae (Jus-ticia-type and Blepharis-type), and local Indigofera, Eu-phorbia-type and Nyctaginaceae. The main arboreal taxa

are Acacia, Commiphora and Salvadora persica (Bonne-fille and Vincens, 1977; Vincens, 1982; Vincens et al., inpress).

In the pollen assemblages from the Lake Bogoriaarea, mean frequencies of the Poaceae are 30% and 50%in evergreen bushland and secondary Acacia woodedgrassland, respectively. Among the other herbaceoustaxa, Acanthaceae (Justicia-type) and Amaranthaceae(Achyranthes-type aspera, Cyathula-type orthacantha)are only abundant in secondary Acacia woodedgrassland. The main arboreal taxa are Tarchonanthus-type camphoratus (dominant in the evergreen bush-land), Dodonaea viscosa-type and the Combretaceae(unpublished data but available from the APD web site).

2.2.2. Modern pollen rain from the Sudanian RegionAll pollen assemblages are characterized by the regular

abundance of Poaceae (mean value of 70%) associated,among herbaceous taxa, with Aloe-type, Indigofera, Por-tulacaceae, Commelinaceae, Amaranthaceae/Chenopo-diaceae and Euphorbia-type. The main local arborealtaxa are Tiliaceae, Capparidaceae, Balanites, Tamarindus-type indica, Euclea, Borassus-type aethiopum, Piliostig-ma thonnengii-type and Acacia (Nakimera-Ssemmanda,1991; Vincens et al., 1997).

2.2.3. Modern pollen rain from the Zambezian RegionThis region is represented by several new pollen

assemblages from southern Tanzania (Fig. 2). In theMiombo woodland, Poaceae are locally abundant (4 to55%) but compared to the samples from the Sudanian

http://medias.obsip.fr/apd


Table 6Numerical comparison between pollen-derived biomes (‘p’) and themain East African vegetation types defined by White (1983) (fornomenclature see Fig. 1)

2 11a 25 29a 45 54b 42 19a

TRFO(p) 0 0 0 0 0 0 0 0TSFO(p) 0 16 0 0 0 0 0 0TDFO(p) 0 0 11 0 1 0 0 0SAVA(p) 0 4 9 30 0 6 1 3STEP(p) 0 0 2 0 1 24 3 0WAMF(p) 0 1 0 0 2 0 0 13XERO(p) 0 0 0 0 0 0 0 23


region, they are less regular. The only herbaceous orundifferentiated taxa found in significant percentagesare Asteraceae (mean value of 13.6%), and locally Mit-racarpus villosus (maximum 4%) and Acalypha (max-imum 5.8%). Fern spores are significantly presentincluding Adianthum-type (maximum 46%), Pteri-dium-type aquilinum (mean value 8%) and undifferen-tiated monolete (9.6%) and trilete (6.7%) types. Themain tree taxon identified is Uapaca (U. kirkiana-type[0.2 to 25.8%] and U. nitida-type [0.2 to 31%]), asso-ciated locally with Syzygium-type (maximum 13%) andRhus-type vulgaris (maximum 5%). Brachystegia andJulbernardia-type, important components of the Zam-bezian woodland, are scarce and rarely reach frequen-cies of more than 1% (Fig. 2). In contrast with Uapaca,these plants probably have low pollen dispersion qua-lities. No fundamental differences occur between pollenassemblages from soil samples and modern lacustrinesediments (Ta41 to Ta43) collected in the Masoko area.This is probably due to the small size of the LakeMasoko catchment that supports only one vegetationtype, viz., Miombo woodland. In such a geomorphologi-cal configuration, the pollen rain registered in the mo-dern lacustrine sediments such as in soil samplesrepresents mainly local vegetation with reduced inputof extra-local and regional Afromontane taxa from rivertransportation. This is in contrast to that registered inmedium or large lakes of the East African rift (Vincens,1984; Vincens, 1987; DeBusk, 1997).

In open/degraded Miombo woodland and local woo-ded grassland, Poaceae are regularly present at highfrequencies (50 to 70%) associated with some Aster-aceae (3 to 5%), and more locally Commelina-type(maximum 13.5%) and Indigofera (maximum 4.5%).The main arboreal taxa are Combretaceae (mean valueof 8.6%), locally associated with some Lannea-type(maximum 3%), Maerua-type (maximum 7%), Acacia(maximum 4.5%) and Brachystegia (maximum 2.5%).Contrary to the natural Miombo woodland, all fern typesare very scarce (Fig. 2).

Table 5Numerical comparison between pollen-derived (‘p’) and observed (‘o’) biom

TRFO(o) TSFO(o) TDFO(o) SAVA(o

TRFO(p) 0 0 0 0TSFO(p) 0 15 0 1TDFO(p) 0 0 12 0SAVA(p) 0 3 6 36STEP(p) 0 0 0 2WAMF(p) 0 1 0 0XERO(p) 0 0 0 0DESE(p) 0 0 0 0

2.2.4. Modern pollen rain from the Guineo-Congolianregion

The two pollen assemblages representative of thisregion are characterized by high frequencies of arborealtaxa (mean value of 70%) with dominant componentsLannea-type, Celtis and Phoenix reclinata-type, whichare associated with some Combretaceae and Moraceae,and very low frequencies of Poaceae (mean value of 6%)(Nakimera-Ssemmanda, 1991; Vincens et al., 1997).

2.2.5. Modern pollen rain from the Lake Victoria mosaicIn the Combretum wooded grassland, mean Poaceae

frequencies are about 40% and the dominant arborealtaxon is Combretum-type, locally associated with Aca-cia, Alchornea, Celtis and Phoenix reclinata-type.

The assemblages of the moist semi-deciduous forest(Bugoma forest) are characterized by moderate arborealpollen frequencies (mean value of 47%) dominated byCeltis, Combretaceae, Alchornea, Croton-type, Zanthox-ylum-type and Olea. Mean frequencies of Poaceae areabout 22% associated with Asteraceae and some Rubia-ceae. In the moist semi-evergreen forest (Kibale Forest),arboreal taxa are more diversified and their frequencies arehigher than in the previous forest (mean values of 57%)with high values of Celtis,Olea,Diospyros, Parinari-type,Moraceae, Funtumia-type africana, Chrysophyllum-type,Sterculia-type, Symphonia globulifera and Clerodendrum.Poaceae frequencies are very low (mean value of 6%) and

es at each sampled site

) STEP(o) WAMF(o) XERO(o) DESE(o)

0 0 0 00 0 0 00 0 0 07 1 0 0

28 0 0 00 15 0 00 5 18 00 0 0 0

Table 7Comparison between pollen-derived biomes (‘p’) and Olson (1994a, b) East African ecosystem classes

Tropicalrainforest

Seasonaltropicalforest

Woodysavanna

Savanna(woods)

Hot andmildgrasses/shrubs

Succulentand thornscrub

Semi-desertshrubs

Baredesert

Montanetropicalforests

Fieldsandwoodysavanna

Crops,grass,shrubs

Grasscrops

Cropsandtown

Forestandfield

TSFO(p) 11 18 26 0 3 0 0 0 0 6 0 1 35 0TDFO(p) 0 0 6 0 0 0 0 0 0 94 0 0 0 0SAVA(p) 0 4 5 4 5 1 8 3 0 37 0 29 4 0STEP(p) 0 0 1 4 3 3 35 47 0 6 0 0 2 0WAMF(p) 1 1 26 44 1 1 0 0 5 9 0 6 4 2XERO(p) 0 0 8 79 1 0 0 0 0 10 1 0 0 0


fern spores (monolete-type) are abundant (Nakimera-Ssemmanda, 1991; Vincens et al., 1997).

2.2.6. Modern pollen rain from the Afromontane centreof endemism

In the two pollen spectra fromnorthernKenya,Maralaland Kabernet forests, the total arboreal pollen frequenciesare relatively high at 55% and 62%, respectively. Thedominant components are Podocarpus, Juniperus-type,Olea and Dodonaea viscosa-type. The Poaceae frequen-cies are low at 10% and 27% (Vincens, 1982 and unpub-lished data).

In southern Tanzania, the pollen assemblages from theNgozi area display relatively low frequencies of arborealpollen (mean value of 26.5%). The main components areAphloia theiformis (0.4 to 16.5%), Nuxia/Ficalhoa (0 to9.3%), and more locally Allophylus (maximum 6%),Dombeya-type (maximum 2.4%), Polyscias fulva-type(maximum 7.8%), Ilex mitis (maximum 12.4%), Macar-anga-type (maximum 4.3%) and Hagenia abyssinica(maximum 9.7%). Poaceae are scarce (3 to 15.7%) andUrticaceae are locally present (maximum 18%). In con-trast, ferns (monolete-type) are very abundant (meanvalue of 43%) associated with some Cyathea-type man-niana (Fig. 2). As in theMasoko area, the pollen rain fromthe soil samples and a modern sediment from Lake Ngozi(Ta36) is not fundamentally different, except for lowervalues of ferns (monolete-type) in the latter.

Table 8Comparison between pollen-derived biomes (‘p’) and IGBP (Belward, 1996

Evergreenbroadleafforest

Woodysavannas

Savannas Closedshrublands

Opeshru

TSFO(p) 29 22 4 0 0TDFO(p) 0 6 0 0 0SAVA(p) 4 6 4 0 8STEP(p) 0 0 1 3 36WAMF(p) 6 25 45 1 0XERO(p) 1 8 75 0 0

On the RungweMountain, the pollen assemblages havea different microfloristic composition. In the forestedenvironment, arboreal taxa are locally abundant (total meanvalue of 30%) including Rubus pinnatus-type (maximum13.7%), Afrocrania volkensii (maximum 22.8%), Podo-carpus (maximum 22.8%), Macaranga-type (maximum58.3%), Neoboutonia-type macrocalyx (maximum 4%),Stoebe kilimandscharica-type (maximum 10.7%) andSchefflera (maximum 6.5%). Poaceae have relatively lowfrequencies (mean value of 11%) and ferns (monolete-type,Lycopodiaceae and Cyathea-type manniana) are locallywell represented (mean values of 22%, 21% and 2.9%,respectively). When, local conditions do not allow thegrowth and/or themaintenance of a forested formation, e.g.inside the Rungwe crater (samples Ta7 to Ta18), openformations occurred, characterized in the pollen assem-blages by very high frequencies of Poaceae (mean value of53.2%) associated with some Ericaceae (mean value of6.3%) and Anthospermum (mean value of 3%). Somearboreal pollen taxa are regularly present, for exampleMyrica (mean value of 3.4%), Dodonaea viscosa-type(mean value of 2.2%) and Macaranga-type (2%) (Fig. 2).

2.2.7. Modern pollen rain from the Afroalpine centre ofendemism

The pollen assemblages from the highest zone of theRungwe Mountain are dominated, among the arborealpollen taxa (totalmean value of 45.7%), byEricaceae (6 to

) East African land cover classes

nbland

Grassland Barren orsparselyvegetated

Cropland/naturalvegetationmosaic

Croplands

3 0 6 360 0 94 05 4 30 392 46 8 31 0 11 101 0 14 0


60%) with locally high frequencies of Protea-type (maxi-mum 47.8%). Among herbaceous taxa, Poaceae (5 to39%) and Asteraceae (9.5 to 36.4%) are well representedand are associated with Anthospermum (2 to 9.3%). Fernsare scarce (Fig. 2).

3. The biomisation method and application to themodern pollen data set

3.1. Method

The biomisation method classifies the plant taxarepresented in the pollen assemblages into a smallnumber of plant functional types (PFTs; Smith et al.,1997), which are broad classes of plants defined by sta-ture, leaf form, phenology and bioclimatic factors. Sincea biome is defined as a combination of PFTs potentiallypresent in it (Prentice et al., 1992), it is then possible toset up a taxon vs. biome matrix indicating which taxacan occur in which biome(s). This information is used tocalculate affinity values between each pollen assem-blage and each biome. The biome assigned is the onethat has the highest affinity value.

The biomisation method and its application to pollendata has been described in detail by Prentice et al. (1996)and Marchant et al. (2001). Several steps of calculationare involved. First, the original counts are collated into thetaxon vs. site matrix. Aquatic taxa, ferns or introducedplants, more indicative of edaphic conditions or of recentanthropogenic impact than of the natural surroundingvegetationwere excluded. In the present work, contrary toprevious reconstruction of biomes derived from pollendata in Africa (Jolly et al., 1998a; Peyron et al., 2000), allthe remaining 408 pollen identified taxa in our 150samples are considered individually and their nomencla-ture follows that of the African Pollen Database (http://medias.obs-mip.fr/apd). In this first matrix pollen taxa areidentified by a maximum ten-letter code (Table 2).

The second matrix (taxon vs. PFT matrix) involves theallocation of pollen taxa to plant functional types (Table 2).This was based on the known modern ecological range ofthe parent plants responsible for producing the pollen, withreference to several tropical African flora (e.g. Flore duCongo Belge et du Ruanda-Urundi, 1948–1963; Flora ofTropical East Africa, 1949–2002; Hutchinson and Dalziel,1954–1972; Flora Zambesiaca, 1960–2004; Flore duCongo, du Rwanda et du Burundi, 1967–1971; Flore d'Afrique Centrale (Zaire–Rwanda–Burundi), 1972–2004;Troupin, 1978–1983; White, 1983; Flora of Ethiopia andEritrea, 1989–2000; Lebrun and Stork, 2003 or morelocally Eggeling and Dale, 1951; Dale and Greenway,1961; Agnew, 1974). The nine PFTs proposed in this paper

are not fundamentally different to those previously definedby Jolly et al. (1998b) for tropical Africa, except theabsence of differentiation inside tropical evergreen PFT (te)of wet (te1) and dry types (te2) due to insufficient literature,and Poaceaewhichwere considered as an individual PFT inPeyron et al. (2000) (Table 3). Further, pollen taxa–PFTassignments have been largely improved. According to thelevel of identification of pollen taxa, one ormore PFTs havebeen assigned to each one (Table 2).

The final matrix involves the allocation of plantfunctional types to biomes. With the PFT definitions,biomes occurring in East African areas under investi-gation can be defined as a PFT or a simple combinationof two PFTs, as indicated in Table 4.

When all the matrices are constructed, the biomisationprocess is performed using PPPbase software (Guiot andGoeury, 1996) which calculates affinity scores of PFTs foreach pollen assemblage. This calculation is based on anumerical value calculated for each pollen taxon (sum ofthe square roots of pollen percentages calculated on thetotal sum of pollen grains excluding aquatics, ferns andexotic taxa) present in the pollen assemblages, with athreshold pollen percentage of 0.5 in order to reduce theinfluence of long distance transport of pollen grains. Thesum of the scores of the PFTs included in each biomeprovides a biome score. Lastly, a biome is assigned ac-cording to the highest affinity score obtained at each site.

3.2. Results and discussion

3.2.1. Comparison between reconstructed biomes andvegetation at each sampled site

Among the 150 pollen sites considered in this study, apotential biome was correctly reconstructed at 124 (82.6%)sites (Table 1; Fig. 1). Further, for all the biomes present inEast African areas under investigation, the number ofcorrect assignments always exceeds the number of incor-rect ones (Table 5), indicating that the major vegetationassociations can be successfully predicted. When incorrectassignments occur (26 [17.3%] sites), the method usuallyindicates a biome that is contiguous in bioclimatic space. Ifthe local vegetation and/or themicrofloristic composition isbetter taken into account, an explanation can be found for13 (8.7%) of the incorrectly assigned biomes.

Reconstructions of drier than expected biomes at ele-ven sites are due to the locally open/degraded status ofthe original vegetation or to the occurrence of a vegetationmosaic. For example, at three Tanzanian sites (Ta39,Ta45, Ta53) and one Kenyan site (B3) where a tropicaldry forest biome was expected, savanna is instead re-constructed due to the local occurrence of open (de-graded) woodland. The same problem was observed at




two Tanzanian sites (Ta54 and Ta59), reconstructed assteppe instead of savanna, due to the occurrence of openwooded savanna. When a mosaic of open and closedvegetation or a transition from glades into forest weresampled, it is always the most open type of formationwhich is reconstructed. For example, at twoUgandan sites(U36 and U39) where a savanna biome rather thantropical seasonal forest was reconstructed, as well as atthree uplandTanzanian sites (Ta19, Ta24 and Ta25)wherea xerophytic woods/scrub biome was reconstructedinstead of a warm mixed forest.

At one Ugandan site (U32), a more humid biome wasreconstructed, viz., tropical seasonal forest, than the ob-served one, savanna. A closer examination of the relatedpollen assemblage shows that it contains numerous extra-local well dispersed pollen taxa from the nearby Semlikitropical seasonal forest that significantly influence thebiome reconstruction.

At the Kenyan site T4 where the pollen sample wascollected in the transition between bushland (steppe biome)and dry montane forest (warm mixed forest biome), asavanna biome was reconstructed, corresponding to a ve-getation type intermediate between these two types. How-ever, this intermediate assignment cannot be considered asfully correct.

With the exception of the Ugandan site U32 at whichthe registered modern pollen rain is not representative ofthe local vegetation, the reconstructed vegetation at all ofthese sites can be considered as correct in terms of thelocal structure and floristic composition of the sampledvegetation. At the 13 (8,7%) remaining sites for which anincorrect biome is assigned, no explanation can beproposed, as their corresponding pollen assemblages donot show fundamental differences with the assemblagesfrom correctly reconstructed contiguous sites. For exam-ple, the Ugandan site U44 from the Kibale forest is re-constructed as warm mixed forest instead of the tropicalseasonal forest reconstructed at the contiguous sites U43and U45 to U51. Similarly, the Kenyan sites T6–T9, T19and T27, were reconstructed as savanna instead of steppe,and xerophytic woods/scrub and savanna biomes atTanzanian sites inside warm mixed forest (Ta20, Ta22and Ta29, respectively).

Finally, it can be noted that no site was incorrectlyreconstructed as tropical rain forest and desert, twobiomes that are not represented in our data base.

3.2.2. Comparison between reconstructed biomes andWhite's East African vegetation types

The results of the comparison (Table 6) show that at121 (80.6%) sites, pollen-derived biomes are in agree-ment with White's East African vegetation types (Fig. 1)

indicating, as in the previous comparison, a majority ofcorrect reconstructions. For the other 29 sites, someincorrect biome assignments are linked, as above, to open/degraded structure of the local vegetation, to local occur-rence of mosaics or of too small formations that cannot bemapped at the scale of White's map. They are, however,also linked to incorrect latitudinal and longitudinal coor-dinates (mainly the oldest sites whose coordinates werecalculated from local topographic maps rather than aGlobal Positioning System [GPS]). Incorrect assignmentsalso occur with sites located between two vegetation typesonWhite's map (for example, the Kenyan sites B2 andB3are included in Afromontane undifferentiated forest ins-tead of in contiguous lowland vegetations).

3.2.3. Comparison between reconstructed biomes andOlson's and IGBP East African land cover classes

The Global Land Cover Characteristics Database isbased on 1-km Advanced Very High Resolution Radi-ometer (AVHRR) data spanning April 1992 throughMarch 1993 (Loveland et al., 2000; http://edcdaac.usgs.gov/glcc/af_int.html) and each continental databasecontains unique elements based on their specific geo-graphic aspects. From this database, different derivedthematic maps have been produced through the aggrega-tion of seasonal land cover regions such as the GlobalEcosystems (Olson, 1994a,b) and the International Geo-sphere Biosphere Programme (IGBP) land cover maps(Belward, 1996). The modern pollen sites were projectedonto these two maps using MapInfo software. For eachsite, the land cover class represented in the grid cell(1×1 km2) where it was located was considered, as wellas the land cover classes from the contiguous cells(3×3 km2), in order to take into account the imprecisionof some site coordinates.

The results of this comparison between pollen recons-tructed biomes and Olson's and IGBP land cover classesare given in Tables 7 and 8, respectively, in which valuesare percentages of grid cells of each land cover class ineach pollen biome. The values in the two tables show that,with the exception of the most open and driest types ofvegetation (semi-desert shrubs [Table 7], open shrublandsand barren or sparsely vegetated [Table 8]) for which asteppe biome is correctly assigned, the majority of theother reconstructed biomes do not correspond with thelocal cover classes obtained from the Olson and IGBPclassifications. This is clearly demonstrated for warmmixed forest and temperate xerophytic woods/scrub bio-mes that were reconstructed at sites where savanna vege-tation classifications are assigned and in the case ofsavanna and tropical dry forest biomes at sites mainlyoccupied by croplands. In contrast to the comparison

http://edcdaac.usgs.gov/glcc/af_int.html

http://edcdaac.usgs.gov/glcc/af_int.html


made with White's vegetation types, these results showthat classifications such as those of Olson and IGBP,mainly established from satellite data, cannot be used at alocal or regional scale to validate modern pollen biomereconstructions without a more thorough control in thefield.

4. Conclusion

This work shows that the procedure of biomisationused here, particularly the revised assignments of PFTs totaxa and the consideration of all identified taxa, has beensuccessfully applied on our East African modern pollendatabase. For the first time in Africa, all reconstructedbiomes have been discussed with reference to the localvegetation (natural, open/degraded, mosaic or glades) ateach sample site, which has allowed an explanation of themajority of incorrect biome assignments. Further, a com-parison between our reconstructed biomes and con-tinental-scale vegetation class maps shows goodagreement with the main East African vegetation typesidentified by White (1983), but not with the Olson andIGBP land cover classes proposed for this region and thatmainly rely on an incorrect interpretation and classifica-tion of the local vegetation from satellite data. Mountainand intensively cultivated areas, for example, providedunreliable maps. These results confirm the difficulty invalidating vegetation models by comparison with satel-lite-derived vegetation data that represent an instanta-neous picture. However, better comparisons with pollendata have been obtained for the reconstruction of potentialnatural vegetation rather than actual vegetation altered byland use (Gachet et al., 2003; Hély et al., 2006).

From these results, we believe that the biomisationprocedure used here can now be used with confidencefor the reconstruction of past vegetation from small lakesediment sequences, where there is minimum inputof extra-local and regional pollen grains by rivers. The45,000 cal. yr B.P. pollen sequence obtained from the craterlakeMasoko (Garcin et al., 2006; Vincens et al., submitted)could be a good candidate for such an application.

Acknowledgements

Part of this work (the collecting of new modernpollen data from southern Tanzania) was financiallysupported by the ECLIPSE CLEHA program of theInstitut National des Sciences de l'Univers (CNRS) andthe ACI Ecologie Quantitative of the French Ministry ofResearch (project RESOLVE). Thanks to the Tanzanianauthorities for their help in field operations and to J.Guiot, R. Marchant and L. Scott for helpful comments

and language revision on the manuscript. After pub-lications, Tanzanian pollen data will be stored in theAfrican Pollen Database (APD) and in the CLEHAMultiproxy-Database of the INSU-Eclipse program.

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