Density and species diversity of trees in four - Makerere University

Diversity and Distributions, (Diversity Distrib.) (2004) 10, 303–312 

Blackwell Publishing, Ltd. 

BIODIVERSITY 

RESEARCH 

*Department of Forest Biology and Ecosystems 

Management, Makerere University, PO Box 

7062, Kampala, Uganda, E-mail: 

eilu@forest.mak.ac.ug; †Ministry of Water, 

Lands & Environment, PO Box 1752, Kampala, 

Uganda; and ‡Department of Botany, Makerere 

University, PO Box 7062, Kampala, Uganda 

*Correspondence: Gerald Eilu, Department of 

Forest Biology and Ecosystems Management, 

Makerere University, PO Box 7062, Kampala, 

Uganda. E-mail: eilu@forest.mak.ac.ug 

INTRODUCTION 

ABSTRACT 

The distribution of trees in the Albertine rift forests of western 

Uganda is relatively well documented by Polhill (1952) in the 

Flora of Tropical East Africa (FTEA). The forest department 

(Howard et al., 1996) inventoried woody plants in 60 Ugandan 

forest reserves including forests of the Albertine rift but the 

relationships between environmental factors and tree species 

distribution was not analysed. The major environmental factors 

influencing tree species diversity and distribution remained 

unclear. Hamilton (1982, 1989) postulated that the Bwindi 

Impenetrable forest was richer in tree species than the other 

forests of western Uganda because of proximity to the Pleistocene 

refuge. Other studies from elsewhere (e.g. Nekola, 1999) showed 

that colonization history influenced species diversity and composition. 

Studies conducted in other parts of the tropics have 

assessed the relation between tree species distribution, diversity 

and environmental factors. For example, Vázquez & Givnish 

(1998), Lovett et al. (2001) and Lieberman et al. (1996) noted 

that forest composition varied with altitude whereby species 

numbers generally declined with elevation. Tuomisto et al., 

1994), Clark et al. (1999), and Stevens & Carson (1999) recorded 

associations between plant species and soil type. Other studies 

(Hubbell et al., 1999; Brokaw & Busing, 2000; De Carvalho et al., 

2000) assessed the relationship between tree diversity and light- 

Density and species diversity of trees in 

four tropical forests of the Albertine rift, 

western Uganda 

Gerald Eilu*, David L. N. Hafashimana† and John M. Kasenene‡ 

We assessed tree species density and diversity in 12 1-ha plots in four forests of the 

Albertine rift, western Uganda. There were 5747 trees of diameter ≥ 10 cm in 53 

families, 159 genera, and 212 species. Density ranged between 344 and 557 trees ha −1 

(average 479 trees ha −1 ). Tree species diversity was highest in the Bwindi and 

Budongo forests. The Euphorbiaceae family was the most species rich (25 species) 

followed by Rubiaceae and Meliaceae with 16 species each. Canonical Correspondence 

Analysis (CCA) showed that major gradients in environmental variables 

influenced tree species distribution. Sample scores on ordination axes 1 and 2 were 

strongly correlated with pH and altitude, respectively. Correlated with rainfall and 

other soil factors, pH and altitude are presumed to be among the most important in 

influencing the distribution of tree species in the Albertine rift forests. Strategies that 

take account of variations in pH and elevation are required to conserve tree species 

in forests of the Albertine rift. 

Keywords 

Environmental factors, ordination, soils, species distribution, tree inventory. 

gap disturbances or canopy gaps. Tuomisto et al. (1995) studied 

the relationship between present day habitat heterogeneity and 

species distributions in tropical forests. Pitman et al. (1999, 

2001) analysed the distribution patterns of trees in the Amazon 

showing that the majority was geographically widespread and 

occurred at low densities. Mechanisms of species coexistence in 

the tropical forests have been reviewed by Wright (2002), but 

data from African forests were hardly included in the analyses. 

African forests of the Albertine rift have in the past, received little 

attention but the area is now becoming increasingly important 

for the conservation of fauna and flora (Plumptre et al., 2003). 

The Albertine rift is now recognized as one of the most speciesrich 

regions in Africa for flora and fauna, some of them endangered. 

The study sites are important for primate conservation 

and contain between them nine primate species (Struhsaker, 

1981). 

In this paper, we analyse tree species distribution in the Albertine 

rift forests in relation to soil characteristics (physical and 

chemical) and other environmental factors such as canopy 

density, slope, rainfall and altitude. The aim is to assess the relative 

importance of the regional variables (e.g. rainfall and altitude) 

and local variables (e.g. soil characteristics, slope and canopy 

cover). Studies such as Hamilton (1982, 1989) attributed the 

patterns of tree species diversity in forests of the Albertine rift 

mainly to high rainfall, and proximity to Pleistocene refugia. The 

© 2004 Blackwell Publishing Ltd www.blackwellpublishing.com/ddi 303

G. Eilu et al. 

findings will be used in formulating scientifically sound management 

and conservation strategies for the forest ecosystems in the 

Albertine rift. 

STUDY SITE 

The study site is the Albertine rift that stretches from the northern 

tip of Lake Albert to the southern tip of Lake Tanganyika. 

This encompasses the natural habitat within about 100 km of the 

border of the Democratic Republic of Congo (Plumptre et al., 

2003). The four study forests are located approximately along 

the same longitude, forming a discontinuous belt between 1°8′ 

South (Bwindi Impenetrable National Park, Bwindi) through 

Kasyoha-Kitomi Forest Reserve (Kasyoha) and Kibale National 

Park (Kibale) to 1°55′ North (Budongo Forest reserve, Budongo). 

The forests cover 331, 399, 760 and 793 km 2 , respectively. The 

study plots lie between 1000 and 1530 m above sea level in 

Budongo and Bwindi, respectively. The climate is tropical with 

two rainfall peaks from March to May and September to November. 

The mean temperature is between a minimum of 7 °C in 

Bwindi and a maximum of 29 °C in Budongo. The annual rainfall 

ranges between 1100 mm in Kasyoha, 1900 mm in Bwindi. 

The rainfall is bimodal with two distinct rainy seasons. The driest 

months in Bwindi are December–January and June–July while 

the wet seasons are February to May and August to November 

(Leggat & Osmaston, 1961). Dry seasons are shorter and wet seasons 

longer in Bwindi than in Kasyoha where the driest months 

are December to March and June to August, and the wet seasons 

are April–May and September to November. Further north in 

Kibale, the wet seasons occur in March to May and September to 

November, while the dry seasons occur in December to February 

and June to August (Struhsaker, 1997). In Budongo, most rain 

falls in September to November and March to May. January 

to March is the driest period in Budongo (Sheil, 1996). The 

vegetation at the sample sites in Bwindi and Kasyoha is Parinari 

mixed forest (Howard, 1991). Study sites in Kibale are located 

in Parinari-dominated forest in the north, Diospyros-Makharmia- 

Strombosia-Newtonia mixed forest in the central block and 

Pterygota or Cynometra forest in the south (Langdale-Brown 

et al., 1964). The sample sites in Budongo consist of Celtis- 

Khaya-Cynometra mixed forest (Eggeling, 1947; Howard, 1991). 

In Bwindi forest, selective timber harvesting covered most 

parts except the nature reserves. Kasyoha forest is one of the least 

disturbed of Uganda’s forest reserves where over 80% are considered 

intact (Howard, 1991). Selective timber harvesting in Kibale 

forest covered about 17% of the forest while 16% was degraded 

by agricultural encroachment. The status of 67% of the forested 

portion of Kibale is not well known, but most of the forest sampled 

is considered relatively intact. In Budongo, about 22% of the 

forest was affected by selective timber harvesting. The remaining 

88% was subjected to arboricide treatment to remove ‘undesirable’ 

tree species. Sample plots for the present study were established 

in the nature reserves that are least disturbed. Although 

the status of the forests could have changed over the past 

10 years, the present study focused on sites considered to be relatively 

undisturbed and generally away from the forest edges. 

METHODS 

Data collection 

Three plots of 20 × 500 m (1 ha) were established in subjectively 

selected sites of relatively intact forest in each study site. The plots 

are referred to, in subsequent text, as Bwindi (Bw) 1–3; Kasyoha 

(Kk) 1–3; Kibale (Ki) 1–3; and Budongo (Bu) 1–3. Assessments 

were carried out systematically in 20 × 50 m (0.1 ha) subplots. 

Data on Overstorey density, altitude, slope and annual precipitation 

were recorded. Soil samples were collected from 10-cm deep 

pits then analysed for pH, particle size, organic matter (Om) and 

Om loss on Ignition (I), N, total P, available P, and the exchangeable 

bases (Al, K, Na, Ca and Mg). 

Trees of diameter at breast height (d.b.h., 1.3 m) ≥ 10 cm were 

enumerated, measured for d.b.h., and identified based on the 

FTEA (Polhill, 1952). Individuals that could not be identified 

were included in the samples under one family named ‘unknown’. 

A representative voucher was collected from each species in 

each plot and from each site for identification and for deposition 

in herbaria at the Royal Botanic Gardens (Kew), Botanical 

Museum (University of Copenhagen) and Makerere University 

Herbarium. 

Data analysis 

The Margalef’s (D Mg), Fisher’s alpha and Shannon’s (H′) diversity 

indices (Magurran, 1988), and Evenness or equitability (E), 

Pielou (1969), were calculated to determine tree species diversity 

in the 1 ha plots. The first-order Jackknife estimator of species 

richness, Jack1 (Burnham & Overton, 1979; Heltshe & Forrester, 

1983; Palmer, 1991) was calculated based on 0.1-ha subunits of 

the 1-ha plots to provide estimates for the entire plots. Total 

species richness was also estimated using the Chao 1 (or S1*) 

estimate (Colwell & Coddington, 1994). 

Environmental variables were analysed based on 0.1-ha 

subunits of the plots. Variables measured on different scales were 

ranged to enable direct comparison. Ranging transforms variables 

onto a 0–1 scale without altering the relative positions of 

observations. The ranged variables were log transformed prior to 

statistical analyses (Økland, 1990). 

Canonical Correspondence Analysis (CCA) using the computer 

program canoco version 4.0 (Ter Braak & Smilauer, 1998) 

was used to explore patterns of variation in tree species distribution 

explained by the environmental variables recorded. CCA 

combines aspects of direct gradient analysis (regression) and 

indirect gradient analysis based on a unimodal response model 

(ordination). Global permutation tests were used to test significance 

of the relation between tree species and the environmental variables. 

Automatic forward selection of environmental variables was carried 

out by canoco to identify the most important variables. The 

variables were selected in order of the variance each explained 

without considering other environmental variables (marginal 

effects, λ 1), and in order of their inclusion in the model after 

successively selecting of the most important variables (conditional 

effects, λ A). This analysis shows the additional variance 

304 Diversity and Distributions, 10, 303–312, © 2004 Blackwell Publishing Ltd

Table 1 Tree species numbers and diversity in 12 1-ha (20 × 500 m) plots of forests of the Albertine rift 

each variable explained, significance of the variable, the P-value and 

the test statistic (F-value) at inclusion in the model. In the CCA 

analysis, environmental variables with large variance inflation 

factors (VIFs) of > 20 namely calcium (Ca), magnesium (Mg), sand 

and clay were omitted and the resulting VIFs dropped to < 13 

avoiding the multicollinearity problem ( Jongman et al., 1995). 

Omitted variables were perfectly correlated to other variables, 

had no unique contribution to the regression equation, and if 

retained, the canonical coefficient would not merit interpretation. 

Detrended Correspondence Analysis (DCA) was performed on 

the same data set. Detrending was by segments (Hill & Gauch, 

1980). The total number of active samples was 120 and total 

number of species was 212. 

RESULTS 

Tree species richness and diversity 

The highest density of trees in the 1-ha plots was recorded in 

Budongo 2 while the lowest was from Bwindi 3 (Table 1). Average 

stem density for the three plots in each forest ranged between 

424 and 522 trees −1 recorded in Bwindi and Budongo, respectively. 

Overall average stem density for 12 plots was 479 ha −1 . 

Basal area ranged between 14.05 and 45.19 m 2 ha −1 (average 

34.03 m 2 ). The number of species ranged between 30 and 67 ha −1 . 

A similar trend was obtained with the Jack 1 and Chao 1 estimates 

of species richness. Bwindi 2 was the most diverse plot 

while Budongo 3 (Cynometra forest) was the least. Evenness was 

highest in Kasyoha 3 followed by Bwindi 2. When plot data were 

pooled for each forest, Bwindi and Kibale had the highest 

numbers of families, genera, and species (Table 2). Budongo and 

Kibale had the highest numbers of individuals. 

In total, we recorded 5747 individual trees comprising 212 

species in 53 families and 159 genera from 12 ha. Of these, 22 

families were represented by one species each, and the remainder 

was represented by 2–25 species each (Table 3). The family 

Trees in Ugandan tropical forests 

Plot Density Basal area (m 2 ) Species Jack 1 Chao 1 H′ D Mg Fisher’s alpha E 

Bw1 480 39.32 56 73 69 3.289 8.91 16.4 0.817 

Bw2 449 40.49 66 89 82 3.555* 10.64* 21.3* 0.852* 

Bw3 344* 35.83 43 60 60 2.935 7.19 13.0 0.780 

Kk1 485 14.50* 53 69 65 3.084 8.41 15.2 0.781 

Kk2 448 31.30 47 63 64 3.145 7.54 13.2 0.817 

Kk3 446 31.48 51 65 59 3.184 8.20 14.8 0.853* 

Ki1 550 31.57 52 74 84 2.686 8.08 14.1 0.680 

Ki2 539 39.28 41 56 56 2.749 6.36 10.3 0.740 

Ki3 439 25.26 45 64 88 2.826 7.23 12.6 0.742 

Bu1 535 45.19* 67* 94* 109 2.811 10.51 20.2 0.673 

Bu2 557* 41.09 52 77 117* 2.552 8.07 14.0 0.649 

Bu3 475 33.03 30* 41* 43* 1.729* 4.71* 7.1* 0.508 

* Indicate the highest and lowest values; Jack 1 and Chao 1 are estimators of total species richness; H′, D Mg and Fisher’s alpha are the Shannon’s, Margalef’s 

and Fisher’s alpha diversity indices; E represents evenness. 

Table 2 Summary of taxa of trees in four forests of the Albertine 

rift. The numbers (ha −1 ) are averages computed from data of 3 ha 

sampled from each forest 

Forest Bwindi Kasyoha Kibale Budongo 

Number of trees 424 460 509 522 

Number of families 14 11 12 11 

Number of genera 28 22 28 27 

Number of species 33 28 32 32 

Euphorbiaceae had the highest number of species followed by 

Rubiaceae, Meliaceae, Fabaceae and Sapotaceae. 

Out of the 212 species, 16 species (7.5%) occurred in four forests, 

29 (13.7%) in three forests, 54 (25.5%) in two forests and 

the largest proportion of 113 (53.3%) in one of the forests. Of 

the species occurring in only one forest, 38 were recorded from 

Budongo, 31 from Kibale, 27 from Bwindi and 17 from Kasyoha. 

Seventeen of the 46 species on the IUCN Red list for Uganda 

were recorded. Three of the species (Lovoa swynnertonii, Brazzeia 

longipedicellata and Dialium excelsum) are endangered, seven 

are vulnerable while four are listed as lower risk, and two of them 

(Irvingia gabonensis and Milicia excelsa) are near threatened. 

Factors influencing tree species distribution 

The CCA-biplot of species and environmental variables (Fig. 1) 

shows the relationship between tree species distribution and 

environmental variables. The eigenvalues of CCA and DCA, and 

correlations between site scores of CCA with environmental 

variables (Tables 4 and 5) justify an ecological interpretation of 

results ( Jongman et al., 1995). The first DCA axis had a length of 

5.269 standard deviation (SD) units of species turnover implying 

that some species showed a clear unimodal response along the 

Diversity and Distributions, 10, 303–312, © 2004 Blackwell Publishing Ltd 305


Family 

No. of 

Genera 


Species Family 

gradient (Ter Braak & Smilauer, 1998). The first four axes of 

CCA and DCA, respectively, explained 16.2 and 19.1% of the 

cumulative variance in species data (Table 4). The third and 

fourth axes with eigenvalues < 0.250 were less important in ecological 

terms and are not considered further. The sample scores 

on the first CCA axis were most strongly correlated with pH and 

those of the second axis with altitude. Correlations between 

environmental variables and DCA ordination axes confirmed 


Genera 

Euphorbiaceae 16 25 Ochnaceae 2 2 

Rubiaceae 12 16 Olacaceae 2 2 

Meliaceae 8 16 Verbenaceae 2 2 

Fabaceae 12 13 Icacinaceae 1 2 

Sapotaceae 6 11 Agavaceae 1 1 

Sapindaceae 7 8 Alangiaceae 1 1 

Apocynaceae 5 8 Aquifoliaceae 1 1 

Moraceae 5 8 Araliaceae 1 1 

Ulmaceae 4 8 Arecaceae 1 1 

Flacourtiaceae 6 7 Asteraceae 1 1 

Sterculiaceae 5 7 Balanitaceae 1 1 

Annonaceae 4 6 Canellaceae 1 1 

Unknown 1 5 Chrysobalanaceae 1 1 

Anacardiaceae 4 4 Cyatheaceae 1 1 

Clusiaceae 4 4 Dichapetalaceae 1 1 

Simarubaceae 4 4 Ebenaceae 1 1 

Rutaceae 3 4 Hypericaceae 1 1 

Tiliaceae 3 4 Loganiaceae 1 1 

Rhizophoraceae 1 4 Melastomataceae 1 1 

Bignoniaceae 3 3 Melianthaceae 1 1 

Capparidaceae 3 3 Monimiaceae 1 1 

Oleaceae 3 3 Myristicaceae 1 1 

Rhamnaceae 3 3 Myrtaceae 1 1 

Boraginaceae 2 2 Rosaceae 1 1 

Cecropiaceae 2 2 Theaceae 1 1 

Celastraceae 2 2 Violaceae 1 1 

Lauraceae 2 2 — — — 


Species 

Table 3 Tree families, numbers of genera and 

species recorded from Bwindi, Kasyoha, Kibale 

and Budongo forests of the Albertine Rift 

Table 4 Eigenvalues of the first four axes of canonical correspondence analysis (CCA) and Detrended correspondence analysis (DCA) of all 

plots and the amount of variance explained of the species data and of the species-environment relation by the CCA axes. The length of gradient 

is shown for DCA 

Axes 1 2 3 4 Total inertia 

Eigenvalues (CCA) 0.480 0.259 0.228 0.128 6.757 

(DCA) 0.709 0.251 0.207 0.149 6.879 

Lengths of gradient (DCA) 5.259 2.772 2.700 2.191 — 

Species-environment correlations (CCA) 0.861 0.820 0.950 0.755 — 

Cumulative percentage variance of species data (CCA) 7.1 10.9 14.3 16.2 — 

(DCA) 10.3 14.0 17.0 19.1 — 

Cumulative percentage variance of the species-environment 

relation (CCA) 

30.1 46.4 60.7 68.7 — 

Sum of all unconstrained eigenvalues (CCA) — — — — 6.757 

(DCA) — — — — 6.757 

Sum of all canonical eigenvalues (CCA) — — — — 1.593 

results of CCA analysis with axis 1 most strongly correlated with 

pH (r = 0.565). 

Automatic forward selection of environmental variables by 

canoco (Table 6) showed that pH explained the most variance 

(0.39) while silt explained the least (0.08%). After selecting pH, 

altitude, rainfall, loss of organic matter on Ignition, potassium 

(K), available phosphorus and slope contributed significantly (at 

5% level) to the model of already included variables. 


Figure 1 Canonical Correspondence 

Analysis ordination diagrams of tree species 

and environmental variables. A: biplot of 

species and environmental variables jointly 

reflecting species’ distributions along 

gradients of environmental variables. 

Variables on axis 1 explained more variation 

than those on axis 2. The importance of 

variables is proportional to the length of the 

arrows. B: scatter of species only. The codes 

refer to species listed in Appendix 1. 




Table 5 Canonical coefficients and interset correlations between the site scores on the first four constrained axes and environmental variables 

generated by canonical correspondence analysis of 120 subplots. The strongest correlations are indicated with asterisks 

Environmental 

DISCUSSION 

Canonical coefficients variable Interset correlations variable 

axis 1 axis 2 axis 3 axis 4 axis 1 axis 2 axis 3 axis 4 

Altitude (m)* 0.296 −0.336 −0.170 0.214 0.009 −0.703 0.022 0.312 

Rainfall (mm)* 0.150 −0.152 0.504 −0.146 −0.041 −0.285 0.869 0.099 

pH* 0.622 −0.067 0.076 −0.046 0.675 0.472 0.025 −0.120 

Exc. Al −0.268 −0.147 0.082 −0.181 −0.673 −0.431 −0.110 0.024 

Av P (mg/kg) 0.100 −0.143 0.018 0.026 0.185 −0.288 −0.171 −0.018 

Organic C (%) 0.211 0.038 0.013 0.145 −0.073 −0.488 −0.457 −0.137 

Total N (%) 0.077 −0.003 −0.010 0.009 0.018 −0.459 −0.491 −0.168 

K (me/100 g) −0.005 −0.103 0.016 −0.277 0.448 −0.100 −0.263 −0.436 

Na (me/100 g) 0.029 −0.035 0.012 −0.061 0.181 0.154 −0.128 −0.239 

Slope (°) −0.030 −0.021 0.042 −0.022 −0.026 −0.039 0.525 −0.043 

Silt (%) 0.053 0.050 0.003 0.075 −0.122 0.037 −0.137 0.369 

Total P (%) −0.017 0.048 0.027 0.075 0.294 −0.136 −0.061 0.228 

Loss on ignition (%) 0.000 −0.178 −0.071 −0.260 −0.099 −0.477 −0.506 −0.204 

Canopy density (%) −0.061 0.013 0.008 −0.054 −0.064 0.000 0.360 −0.108 

Marginal effects Conditional effects 

Variable* λ 1 Variable λ A P F 

pH 0.39 pH 0.36 0.005 7.02 

Exc. Al (me/100 g) 0.37 Altitude 0.26 0.005* 4.99 

Rainfall (mm) 0.23 Rainfall 0.23 0.005* 4.37 

Altitude (m) 0.22 Loss on Ignition 0.13 0.005* 2.58 

K (me/100 g) 0.21 K 0.09 0.005* 1.82 

Loss on Ignition (%) 0.19 Av. P 0.08 0.020* 1.59 

Organic C (%) 0.18 Slope 0.08 0.025* 1.48 

Total N (%) 0.17 Canopy density 0.06 0.080 1.28 

Slope (Deg.) 0.11 Total N 0.06 0.180 1.12 

Av. P (mg/kg) 0.11 Organic C 0.05 0.380 1.03 

Total P% 0.11 Exc. Al 0.05 0.420 0.99 

Canopy density (%) 0.08 Silt 0.05 0.445 1.00 

Na (me/100 g) 0.08 Total P 0.04 0.585 0.91 

Silt (%) 0.08 Na 0.05 0.790 0.84 

*Variables are arranged in descending order of variance explained by each variable. λ 1 shows 

variance explained without considering other variables; λ A shows variance explained after 

successively selecting the most important variables. 

Compared with other studies from tropical forests, the Albertine 

rift forests are considered ‘low’ in species richness (Turner, 2001). 

The Neotropical wet forests such as those in Ecuador and 

Colombia are richer than the present study sites. 

The present results emphasise the importance of soil physicochemical 

properties in influencing tree species richness. There 

were slight differences in tree species composition from south to 

north with species in Bwindi being tolerant to lower pH through 

Kasyoha, to Kibale and Budongo with the least acid soils. This 

may explain the postulated south to north gradient in species 

Table 6 Variables explaining the tree 

species-environment relation selected by 

Canonical Correspondence Analysis using 

automatic forward selection 

composition in Uganda proposed by Hamilton (1974, 1982) 

but attributed to the existence of a Pleistocene refugium close 

to forests of south-western Uganda. Hall & Swaine (1976) noted 

that in closed canopy forests of Ghana, soil factors such as pH 

and richness in bases that showed notable variation with 

species distribution were dependent on rainfall. Importance of 

pH in forest soils is in terms of influence on the availability of 

phosphorus, calcium, magnesium, and trace elements. Tanner 

(1977) noted that the limiting factor in one of four montane 

rain forests of Jamaica (Mor Ridge forest) was the extremely 

low pH, a factor that may be considered limiting in our study 

sites. 


A number of studies (e.g. Wolf, 1994; Lieberman et al., 1996; 

Vázquez & Givnish, 1998; Lovett et al., 2001) showed the 

influence of altitudinal gradients on tree species distribution. 

Although the altitude gradient in the present study was short 

(530 m), it played a major role in distribution of tree species 

demonstrated by the P-value of 0.005 in the automatic forward 

selection by canoco. This is mainly through its relationship with 

rainfall and pH. Rainfall was highest in Bwindi (with the highest 

elevation) while pH conformed to the reverse trend over this 

altitudinal range. Hall & Swaine (1976) noted that altitude was 

important in influencing tree species distribution, but mainly in 

the wetter forests. Friis (1992) showed that critical altitudes 

existed for groups of taxa (based on trees) in north-eastern tropical 

Africa and Hamilton (1975) noted that the number of species 

and families of trees declined with altitude. Friis (1992) noted that 

interaction between rainfall and altitude made correlation with 

diversity complex. The correlation between altitude and rainfall 

of 0.394 in the present data set was not particularly strong. 

Dry season length at the study sites ranged between a total of 

4–7 months and may influence the results, but the magnitude 

of this influence could not be established because of the slight 

variation between sites. 

The southernmost sites in this study were the wettest progressing 

towards the drier sites north. There were many species, 

particularly in Bwindi, that were favoured by high rainfall, while 

in Budongo some species tolerated lower levels of rainfall. Hall & 

Swaine (1976) found that tree species were most related to the 

rainfall gradient. Decrease in precipitation northwards explains 

part of the floristic impoverishment in forests of western Uganda 

along a south–north gradient. 

Effects of regional variables such as annual rainfall and altitude 

on distribution have been proved in many studies and are 

not discussed further. When effects of pH, altitude and rainfall 

are accounted for (Table 6), loss of organic carbon on ignition, K, 

available P and slope contributed significantly to the model of 

already included variables. In individual forests geographical 

position (Plumptre, 1996) influenced tree species composition 

possibly as a reflection of major gradients in environmental 

factors. Walaga (1993) noted that forest compartments of Budongo 

had differences in tree species composition attributed to differences 

in exchangeable potassium, nitrogen, sodium, and sand. 

The differences were attributed to accumulation of organic 

matter, mineral decomposition, leaching losses and erosion of 

topsoil. In the present study, the Budongo plots and Kibale 3 had 

species (occurring in Cynometra forest) that tolerate relatively 

high levels of exchangeable potassium, high pH (less acid soils), 

gentle slopes, relatively low elevations and low rainfall. This cluster 

of plots included tree species requiring relatively high levels of 

potassium as suggested by Walaga (1993). 

According to Walaga (1993), 11 soil variables explained 8.9% 

of the variation in tree species distribution. The present study 

explained about twice this percentage, emphasising the relative 

importance of environmental factors recorded. The present 

study did not assess the influence of disturbance but considered 

only sites of intact forest where impacts of human disturbance 

were minimal. 


Environmental factors measured therefore only partly explained 

tree species richness and diversity in forests of the Albertine rift. 

For example, Hall (1977) noted that soil type was the basis for the 

main division of forest into groups, with discontinuous forest 

variation reflecting the presence of two contrasting major soil 

units in Nigerian forests. Tree species richness and diversity is 

therefore influenced by geographical position (Plumptre, 1996), 

soil depth and composition, length and frequency of rainfall (or 

dry season), successional stage, and management history. Soil 

depth may be limiting because of inadequate support provided 

by very shallow soil (Tanner, 1977). 

In conclusion, local environmental variables (e.g. soil factors) 

as well as regional environmental factors (e.g. rainfall and 

altitude) played important roles in influencing tree species distribution 

in the Albertine rift forests. Differences in tree species 

distribution observed over the soil nutrient gradients and over 

gradients of other environmental factors should be taken into 

account when designing management strategies. The most 

important variable in forests of the Albertine rift is therefore soil 

pH which is correlated with mechanical soil properties and gross 

climate. Major environmental gradients cut across forests and 

strategies for managing forests of the Albertine rift should be 

designed at the landscape level. 

ACKNOWLEDGEMENTS 

The study was funded by the Danish International Development 

Agency (DANIDA) under the ‘Biodiversity Research and Training 

in Tanzania and Uganda’ project through the Makerere– 

Copenhagen University Enhancement of Research Capacity 

(ENRECA) project. I. Friis, J.M. Kasenene and A.D. Poulsen supervised 

the work. M. Rejmánek and three anonymous referees 

provided comments that greatly improved the manuscript. 

Various staff at the Kew herbarium, particularly K. Vollesen, B. 

Verdcourt, M. Lock and D. Bridson helped with identification 

of specimens. D.N. Nkuutu, J. Kyomuhendo and S. Kimuli 

collected the specimens. 

REFERENCES 

Brokaw, N. & Busing, T. (2000) Niche versus chance and tree 

diversity in forest gaps. Trends in Ecology and Evolution, 15, 

183–188. 

Burnham, K.P. & Overton, W.S. (1979) Robust estimation of population 

size when capture probabilities vary among animals. 

Ecology, 60, 927–936. 

Clark, D.B., Palmer, M.W. & Clark, D.A. (1999) Edaphic factors 

and the landscape-scale distributions of tropical rain forest 

trees. Ecology, 80, 2662–2675. 

Colwell, R.K. & Coddington, J.A. (1994) Estimating terrestrial 

biodiversity through extrapolation. Philosophical Transactions 

of the Royal Society of London B, 345, 101–118. 

De Carvalho, L.M.T., Fontes, M.A.L. & De Oliviera-Filho, A.T. 

(2000) Tree species distribution in canopy gaps and mature 

forest in an area of cloud forest of the Ibitipoca range, southeastern 

Brazil. Plant Ecology, 149, 9–22. 



Eggeling, W.J. (1947) Observations on the ecology of the 

Budongo rain forest, Uganda. Journal of Ecology, 34, 20–87. 

Friis, I. (1992) Forests and forest trees of north-east tropical Africa. 

Kew bulletin additional series, XV. Her Majesty’s Stationery 

Office, London. 

Hall, J.B. (1977) Forest types in Nigeria: an analysis of preexploitation 

forest enumeration data. Journal of Ecology, 65, 

187–199. 

Hall, J.B. & Swaine, M.D. (1976) Classification and ecology of 

closed-canopy forest in Ghana. Journal of Ecology, 64, 913–951. 

Hamilton, A.C. (1974) Distribution patterns of forest trees in 

Uganda and their historical significance. Vegetatio, 29, 21–35. 

Hamilton, A.C. (1975) A quantitative analysis of altitudinal 

zonation in Uganda forests. Vegetatio, 30, 99–106. 

Hamilton, A.C. (1982) Environmental history of East Africa. 

Academic Press, New York, NY. 

Hamilton, A.C. (1989) Tropical rain forest ecosystems. Ecosystems 

of the world, 14B (ed. by H. Leith and M.J. Werger), pp. 155– 

182. Elsevier, Amsterdam. 

Heltshe, J.F. & Forrester, N.E. (1983) Estimating species richness 

using the Jackknife procedure. Biometrics, 39, 1 – 12. 

Hill, M.O. & Gauch, H.G. (1980) Detrended correspondence 

analysis: an improved ordination technique. Vegetatio, 42, 47–58. 

Howard, P.C. (1991) Nature conservation in Uganda’s tropical 

forest reserves. IUCN, Gland, Switzerland. 

Howard, P.C., Davenport, T. & Mathews, R. (1996) Biodiversity 

reports Nos 1–33. Forest Department, Kampala. 

Hubbell, S.P., Forster, R.B., O’Brien, S.T., Harms, K.E., Condit, R., 

Wechsler, B., Wright, J. & Loo de Lao, S. (1999) Light-gap 

disturbances, recruitment limitation, and tree diversity in a 

neotropical forest. Science, 283, 554–557. 

Jongman, R.H.G., Ter Braak, C.J.F. & van Tongeren, O.F.R. 

(1995) Data Analysis in community and landscape ecology. 

Cambridge University Press, Cambridge. 

Langdale-Brown, I., Osmaston, H.A. & Wilson, J.G. (1964) The 

vegetation of Uganda and its bearing on land use. Government 

of Uganda, Entebbe. 

Leggat, G.J. & Osmaston, H.A. (1961) Working Plan for the 

Impenetrable Central Forest Reserve, Kigezi District, western 

Uganda, 1961–71. Uganda Forest Department, Entebbe. 

Lieberman, D., Lieberman, M., Peralta, R. & Hartshorn, G.S. (1996) 

Tropical forest structure and composition on a large-scale 

altitudinal gradient in Costa Rica. Journal of Ecology, 84, 137–152. 

Lovett, J.C., Clarke, G.P., Moore, R. & Morrey, G.H. (2001) 

Elevational distribution of restricted range forest tree taxa in 

eastern Tanzania. Biodiversity and Conservation, 10, 541–550. 

Magurran, A.E. (1988) Ecological diversity and its measurement. 

Chapman & Hall, London. 

Nekola, J.C. (1999) Palaeorefugia and Neorefugia: the influence 

of colonization history on community pattern and process. 

Ecology, 80, 2459–2473. 

Økland, R.H. (1990) Vegetation ecology: theory, methods and 

applications with reference to Fennoscandia. Sommerfeltia 

Suppl., 1, 4–233. 

Palmer, M.W. (1991) Estimating species richness: the secondorder 

jackknife reconsidered. Ecology, 72, 1512–1513. 

Pielou, E.C. (1969) An introduction to mathematical ecology. 

Wiley, New York. 

Pitman, N.C.A., Terborgh, J., Silman, M.R. & Nunêz, P.V. (1999) 

Tree species distributions in an upper Amazonian Forest. 

Ecology, 80, 2651–2661. 

Pitman, N.C.A., Terborgh, J., Silman, M.R., Nunêz, P.V., Neil, D.A., 

Cerón, C.E., Palacios, W.A. & Aulestia, M. (2001) Dominance 

and distribution of tree species in upper Amazonian Terra 

Firme forests. Ecology, 82, 2101–2117. 

Plumptre, A.J. (1996) Changes following 60 years of selective 

timber harvesting in the Budongo Forest Reserve, Uganda. 

Forest Ecology and Management, 89, 101–113. 

Plumptre, A.J., Behangana, M., Davenport, T., Kahindo, C., 

Kityo, R., Ndomba, E., Ssegawa, P., Eilu, G., Nkuutu, D. & 

Owiunji, I. (2003) The Biodiversity of the Albertine Rift. 

Albertine rift technical reports no. 3. World Conservation Society, 

New York. 

Polhill, R.M., ed. (1952) Flora of tropical East Africa. A. A. 

Balkema, Rotterdam. 

Sheil, D. (1996) The ecology of long term change in a Ugandan 

rain forest. PhD Thesis, University of Oxford, Oxford. 

Stevens, M.H.T. & Carson, W.P. (1999) Plant density determines 

species richness along an experimental fertility gradient. 

Ecology, 80, 455–465. 

Struhsaker, T.T. (1981) Forest and primate conservation in East 

Africa. African Journal of Ecology, 19, 99–114. 

Struhsaker, T.T. (1997) Ecology of an African rain forest. Logging 

in Kibale and the conflict between conservation and exploitation. 

University Press of Florida, Gainesville. 

Tanner, E.V.J. (1977) Four montane rain forests of Jamaica: a 

quantitative characterisation of the floristics, the soils and the 

foliar mineral levels, and a discussion of the interrelations. 

Journal of Ecology, 65, 883–918. 

Ter Braak, C.J.F. & Smilauer, P. (1998) canoco reference manual 

and user’s guide to canoco for Windows: software for canonical 

community ordination Version 4. Microcomputer Power, Ithaca, 

NY, USA. 

Tuomisto, H. & Ruokolainen, K. (1994) Distribution of Pteridophyta 

and Melastomataceae along an edaphic gradient in an 

Amazonian rain forest. Journal of Vegetation Science, 5, 25–34. 

Tuomisto, H., Ruokolainen, K., Linna, A., Danjoy, W. & Rodriguez, 

Z. (1995) Dissecting Amazonian biodiversity. Science, 269, 63–66. 

Turner, I.M. (2001) Rainforest ecosystems plant diversity. Encyclopaedia 

of biodiversity, Vol. 5, pp. 13–23. Academic Press, San 

Diego. 

Vázquez, J.A. & Givnish, T.J. (1998) Altitudinal gradients in 

tropical forest composition, structure in Sierra de Manantlán. 

Journal of Ecology, 86, 999–1020. 

Walaga, C. (1993) Species distribution and the development of 

the climax vegetation of the Budongo Forest reserve. MSc Thesis, 

Makerere University, Kampala. 

Wolf, J.H.D. (1994) Factors controlling the distribution of vascular 

and non-vascular epiphytes in the northern Andes. Vegetatio, 

112, 15–28. 

Wright, S.J. (2002) Plant diversity in tropical forests: a review of 

mechanisms of species coexistence. Oecologia, 130, 1–14. 



Appendix 1 Tree species with at least five individuals recorded in four forests of the Albertine rift, western Uganda (species codes and number 

of individuals in 12 ha are shown) 

Code Tree taxon Family Budongo Bwindi Kasyoha Kibale 

Aidimicra Aidia micrantha (K. Schum.) F. White Rubiaceae — 22 83 — 

Alanchin Alangium chinense (Lour.) Harms Alangiaceae 3 3 — — 

Albigumm Albizia gummifera (J.F. Gmel.) C.A.Sm. Fabaceae — 5 1 2 

Alchlaxi Alchornea laxiflora (Benth.) Pax & K. Hoffm. Euphorbiaceae 5 — — — 

Allakimb Allanblackia kimbiliensis Spirlet Clusiaceae — 22 — — 

Allodumm Allophylus dummeri Bak. f. Sapindaceae 4 — — 1 

Alstboon Alstonia boonei De Wild. Apocynaceae 15 — — — 

Antrmicr Antrocaryon micraster A. Chev. & Guill. Anacardiaceae — 5 1 — 

Aphasene Aphania senegalensis (Juss. ex Poir.) Radlk. Sapindaceae — — — 8 

Baphparv Baphiopsis parviflora Bak. Fabaceae — 28 36 90 

Beilugan Beilschmiedia ugandensis Rendle Lauraceae — 34 26 1 

Belohypo Belonophora hypoglauca (Welw. ex Hiern) A. Chev Rubiaceae 22 — — — 

Bequobla Bequaertiodendron oblanceolatum S. Moore Sapotaceae 4 — — 8 

Bligunij Blighia unijugata Bak. Sapindaceae 1 2 5 1 

Caragran Carapa grandiflora Sprague Meliaceae — 78 142 1 

Casebatt Casearia battiscombei R. E. Fr. Flacourtiaceae — — 6 — 

Caseengl Casearia engleri Gilg Flacourtiaceae 1 8 1 1 

Casscong Cassipourea congoensis DC. Rhizophoraceae — 6 11 — 

Cassruwe Cassipourea ruwensorensis (Engl.) Alston Rhizophoraceae — 1 — 8 

Casssp. Cassipourea sp. Rhizophoraceae — 1 13 — 

Celtdura Celtis durandii Engl. Ulmaceae 11 4 4 183 

Celtmild Celtis mildbraedii Engl. Ulmaceae 213 — — 1 

Celtwigh Celtis wightii Planch. Ulmaceae 109 — — — 

Celtzenk Celtis zenkeri Engl. Ulmaceae 46 — — — 

Chaearis Chaetacme aristata Planch. Ulmaceae 3 — — 17 

Chryalbi Chrysophyllum albidum G. Don Sapotaceae 20 — — 117 

Chrygoru Chrysophyllum gorungosanum Engl. Sapotaceae — 6 — — 

Chryperp Chrysophyllum perpulchrum Hutch. & Dalz. Sapotaceae 11 — — — 

Colapier Cola pierlottii R. Germain Sterculiaceae — 12 — — 

Cordmill Cordia millenii Bak. Boraginaceae 3 — — 2 

Cyatmann Cyathea manniana Cyatheaceae — 7 19 — 

Cynoalex Cynometra alexandri C. H. Wright Fabaceae 89 — — 17 

Depldewe Desplatsia dewevrei (De Wild. & T. Dur.) Burret Tiliaceae 5 — — — 

Dictarbo Dictyandra arborescens Welw. ex Benth. & Hook. f. Rubiaceae 1 1 14 17 

Diosabys Diospyros abyssinica (Hiern) F. White Ebenaceae 1 1 6 146 

Dombkirk Dombeya kirkii Mast. Sterculiaceae 1 — — 21 

Dovysp. Dovyalis sp. Flacourtiaceae — — — 8 

Drypgerr Drypetes gerrardii Hutch var gerrardii Euphorbiaceae — 4 54 — 

Drypgerr Drypetes gerrardii Hutch var grandifolia Euphorbiaceae — 26 28 — 

Drypugan Drypetes ugandensis (Rendle) Hutch. Euphorbiaceae 10 33 109 — 

Entaango Entandrophragma angolense (Welw.) C.DC. Meliaceae 4 — — 1 

Entacyli Entandrophragma cylindricum (Sprague) Sprague Meliaceae 3 1 4 — 

Erytsuav Erythrophleum suaveolens (Guill. & Perr.) Brenan Fabaceae 5 — — — 

Funtafri Funtumia africana (Benth.) Stapf Apocynaceae 5 45 35 114 

Funtelas Funtumia elastica (Preuss) Stapf Apocynaceae 59 — — — 

Garcsp. Garcinia sp. nov. Clusiaceae — 5 — — 

Greesuav Greenwayodendron suaveolens (Engl. & Diels) Verd Annonaceae 15 — 92 — 

Grewmild Grewia mildbraedii Burret Tiliaceae — 110 — — 

Guarcedr Guarea cedrata (A. Chev.) Pellegr. Meliaceae 7 1 11 — 

Guarmayo Guarea mayombensis Pellegr. Meliaceae — 16 32 — 

Hannlong Hannoa longipes (Sprague) G. Gilbert Simaroubaceae — 28 2 — 

Harrabys Harrisonia abyssinica Oliv Simaroubaceae — — — 11 

Hologran Holoptelea grandis (Hutch.) Mildbr. Ulmaceae 15 — — — 

Khayanth Khaya anthotheca (Welw.) C.DC. Meliaceae 15 — — — 

Klaigabo Klainedoxa gabonensis Pierre ex Engl. Irvingiaceae 5 — 2 — 

Lasimild Lasiodiscus mildbraedii Engl. Rhamnaceae 313 — — — 

Leptdaph Leptaulus daphnoides Benth. Icacinaceae 2 34 19 — 

Leptmild Leptonychia mildbraedii Engl. Sterculiaceae — — — 64 

Lindmild Lindackeria mildbraedii Gilg Flacourtiaceae 1 1 — 6 



Appendix 1 Continued 

Code Tree taxon Family Budongo Bwindi Kasyoha Kibale 

Lovoswyn Lovoa swynnertonii Bak. f. Meliaceae — — 6 1 

Lychcero Lychnodiscus cerospermus Radlk. Sapindaceae 3 — — 3 

Macabart Macaranga barteri Muell. Arg. Euphorbiaceae — 32 61 — 

Macakili Macaranga kilimandscharica Pax Euphorbiaceae — — 16 — 

Maesemin Maesopsis eminii Engl. Rhamnaceae — 9 — — 

Maesflor Maesobotrya floribunda Verdcourt var hirtella Euphorbiaceae — 5 — — 

Margdisc Margaritaria discoidea (Baill.) Webster Euphorbiaceae 3 3 3 — 

Marklute Markhamia lutea K. Schum Bignoniaceae 1 2 — 48 

Maytsp. Maytenus sp. Celastraceae — — — 14 

Mearduch Maerua duchesnei (de Wild.) F. White Capparidaceae 6 — — — 

Milldura Milletia dura Dunn Fabaceae — 3 8 1 

Mimubags Mimusops bagshawei S. Moore Sapotaceae 4 1 — 15 

Musaleo- Musanga leo-errerae Hauman and J. Leonard Moraceae — 14 10 — 

Myrihols Myrianthus holstii Engl. Moraceae 7 12 3 1 

Newtbuch Newtonia buchananii (Baker) Gilb. & Bout. Fabaceae — 10 3 3 

Oleacape Olea capensis Linn. Oleaceae 2 — — 7 

Oxyaspec Oxyanthus speciosus Hook. f. Rubiaceae 1 7 12 8 

Pachbrev Pachystela brevipes(Baker) Engl. Sapotaceae — 9 — 1 

Pancturb Pancovia turbinata Radlk. Sapindaceae — 1 4 5 

Pariexce Parinari excelsa Sabine Rosaceae — 3 23 5 

Paurdewe Pauridiantha dewevrei Bremek. Rubiaceae — — 40 — 

Piptafri Piptadeniastrum africanum (Hook. f) Brenan Fabaceae 1 18 1 12 

Pleipycn Pleiocarpa pycnantha (K. Schum.) Stapf Apocynaceae — 1 4 3 

Polyfulv Polyscias fulva (Hiern) Harms Araliaceae — 10 — — 

Premango Premna angolensis Guerke Verbenaceae 2 1 3 7 

Pseumicr Pseudospondias microcarpa (A. Rich.) Engl. Anacardiaceae 3 1 1 6 

Ptermild Pterygota mildbraedii Engl. Sterculiaceae 2 — — 13 

Raphfari Raphia farinifera (Gaertn.) Hylander Palmae 7 — — — 

Rawsluci Rawsonia lucida Harv. & Sond. Flacourtiaceae 1 88 143 14 

Rinobeni Rinorea beniensis Engl. Violaceae 117 — 1 — 

Ritcalbe Ritchiea albersii Gilg Capparidaceae — 9 2 — 

Rothurce Rothmannia urcelliformis (Hiern) Bullock ex Robyns Rubiaceae — — 1 8 

Sapielli Sapium ellipticum (Hochst. ex Krauss) Pax Euphorbiaceae — 6 6 2 

Strosche Strombosia scheffleri Engl. Olacaceae — 136 98 43 

Strotetr Strombosiopsis tetrandra Engl. Olacaceae — 42 — — 

Strymiti Strychnos mitis S. Moore Loganiaceae 5 — — 3 

Sympglob Symphonia globulifera L. f. Clusiaceae — 7 4 5 

Syzyguin Syzygium guineense (Willd.) DC. Myrtaceae — 21 9 — 

Tabehols Tabernaemontana holstii K. Schum. Apocynaceae 7 6 22 55 

Tabeodor Tabernaemontana odoratissima Stapf Apocynaceae — 5 2 17 

Tapufisc Tapura fischeri (Engl.) Engl. Chailletiaceae 13 — — — 

Tetrdidy Tetrorchidium didymostemon (Baill.) Pax & K. Hoffm. Euphorbiaceae 1 26 — — 

Tremorie Trema orientalis (L.) Bl. Ulmaceae — 6 4 — 

Tricanom Tricalysia anomala var montana Rubiaceae — 6 — — 

Tricdreg Trichilia dregeana Sond. Meliaceae 1 23 19 — 

Tricmart Trichilia martineaui Aubrev. and Pellegr. Meliaceae — 10 5 — 

Tricrube Trichilia rubescens Oliv. Meliaceae 31 16 11 1 

Triculug Trichoscypha ulugurensis Mildbr. Anacardiaceae — 19 12 — 

Trilmada Trilepisium madagascariense DC. Moraceae 16 51 3 149 

Unkndead Unknown sp. Unknown 1 11 9 3 

Unknsp. Unknown sp. Unknown — 17 — — 

Unknsp. Unknown sp. Unknown 1 7 2 — 

Uvarcong Uvariopsis congensis Robyns and Ghequiere Annonaceae 252 — — 128 

Veprgran Vepris grandifolia (Engl.) W.Mziray Rutaceae 2 — — 5 

Veprnobi Vepris nobilis (Delile) W.Mziray Rutaceae 2 3 1 26 

Vernconf Vernonia conferta Benth. Compositae — 5 — — 

Vismsp. Vismia sp. nov. Hypericaceae — 6 — — 

Xylostau Xylopia staudtii Engl. Annonaceae — 3 7 — 

Xymamono Xymalos monospora (Harv.) Warb. Monimiaceae — 6 23 5 

Zantlepr Zanthoxyllum leprieurii Guill. & Perr. Rutaceae 2 4 8 —

Density and species diversity of trees in four - Makerere University

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?