Composition and distribution of indigenous trees and
shrubs as possible criteria for indicating adapted species in
semi-arid rangelands
Edward K. Mengich1*, Daniel K. Too2, Joseph M. Macharia3 and Ralph Mitloehner4
1
Rift Valley Eco-regional Research Programme, Kenya Forestry Research Institute (KEFRI), PO Box 382-20203, Londiani, Kenya, 2Department
of Natural Resources, Egerton University, PO Box 536-20115, Njoro, Kenya, 3Department of Botany, Egerton University, PO Box 536-20115,
Njoro, Kenya and 4Institute of Silviculture, Georg-August University, Busgenweg 1, D-37077, Goettingen, Germany
Abstract
This study assessed the composition and natural distribution of indigenous trees and shrubs as possible criteria for
selecting suitable species for rehabilitation of degraded sites
in semi-arid rangelands. Study sites were identified at
Nthangu, Kathonzweni and Kibwezi forests of Makueni
County, Kenya using existing vegetation, agro-climatic
maps and Landsat imageries. The sites had mean annual
rainfalls of 974 mm, 700 mm and 616 mm, respectively,
and moisture indices of 49%, 35% and 32%. Data were
collected by establishing sample plots and assessing species
counts and diameters at breast height (DBH). Basal area,
relative dominance, relative abundance, relative frequency
and important value indices (IVIs) were computed for
individual families and species at each site. The number of
families, genera and species declined from Nthangu (33,
60, 77) through Kibwezi (30, 48, 70) to Kathonzweni (28,
42, 69). Corresponding mean basal areas were
16.7 m2 ha1, 76.8 m2 ha1 and 19.3 m2 ha1. The
families Combretaceae, Burseraceae and Mimosaceae were
the most important and widely distributed. Based on
ecological importance values, candidate species for rehabilitation of degraded sites at Nthangu, Kathonzweni and
Kibwezi were Combretum molle and Acacia hockii; Combretum collinum, Commiphora campestris and Acacia tortilis;
and Commiphora africana and A. tortilis, respectively.
possibles pour la selection d’especes appropriees pour
rehabiliter des sites degrades dans des espaces semi arides.
Des sites d’etudes furent identifies dans les for^ets de
Nthangu, Kathonzweni et Kibwezi, dans le Makueni
County, au Kenya, en se basant sur la vegetation existante,
sur des cartes agro-climatiques et l’imagerie Landsat. Ces
sites ont une pluviosite annuelle moyenne de 974 mm,
700 mm et 616 mm respectivement et des indices d’humidite de 49%, 35% et 32%. Les donnees furent collectees
en etablissant des parcelles echantillons et en relevant le
nombre d’especes et leur DBH. La surface basale, la
dominance relative, l’abondance relative, la frequence
relative et l’indice d’importance des especes (IVI) furent
calcules pour chaque famille et pour chaque espece sur
chaque site. Le nombre de familles, de genres et d’especes
Kibwezi
allait en decroissant de Nthangu (33, 60, 77) a
(30, 48, 70) et enfin Kathonzweni (28, 42, 69). Les
surfaces basales moyennes correspondantes etaient de
16.7 m2 ha1, 76.8 m2 ha1 et 19.3 m2 ha1. Les
familles des Combretaceae, des Burseraceae et des Mimosaceae etaient les plus importantes et etaient largement
distribuees. En se basant sur les indices d’importance
ecologiques, les especes candidates pour la rehabilitation
Nthangu, Kathonzweni et Kibwezi
de sites degrades a
etaient: Combretum molle et Acacia hockii; Combretum
collinum, Commiphora campestris et Acacia tortilis; et Commiphora africana et A. tortilis, respectivement.
Key words: important value index, indigenous, Kenya,
rangelands, semi-arid, trees
Resume
Introduction
Cette etude a evalue la composition et la distribution
naturelle d’arbres et d’arbustes indigenes comme criteres
Woody vegetation cover in the semi-arid rangelands of
Kenya has declined over time because of over-exploitation
due to pressure from increasing human populations and
accompanying increases in demand for various tree
*Correspondence: E-mail: emengich3@hotmail.com
© 2013 John Wiley & Sons Ltd, Afr. J. Ecol., 53, 3–15
3
4 Edward K. Mengich et al.
products and services. In addition, the number of trees and
shrubs that can tolerate drought stress has diminished
because of other factors such as frequent outbreaks of
bushfires, and climate change, which have resulted in
harsher and drier conditions in rangelands (Ensor & Berger,
2009; Omambia, Shemsanga & Li, 2009). These factors
have resulted in moderate to severe land degradation and
desertification that are manifested in forms of impoverishment, depletion of vegetation cover, and deterioration of
physical, chemical and biological soil properties. There
have been concerted efforts to reverse the impacts of
degradation through tree planting in the semi-arid rangelands of Kenya (Kigomo, 2003). However, rehabilitation of
these sites has severely been constrained by a narrow
species selection base, lack of information to guide selection
of suitable species and use of exotic species that are not
adapted to local conditions (Reubens et al., 2011).
Planting of appropriate tree species in respective localities is key to successful rehabilitation of degraded sites
(Derbel & Chaieb, 2013). Provenance trials have been used
to identify suitable species to plant in the semi-arid
rangelands of Kenya. However, the trials have largely
tested suitability of exotic species (Milimo, Dick & Munro,
1994). Due to the long period that provenance trials take
to generate information and therefore, guide on appropriate species to plant, use of faster-growing exotic species
that are not adapted to local conditions is common. The
use of indigenous species for rehabilitation has not been
widely practised in Kenya, but the potential for their use is
widely acknowledged. Indigenous species have a wide
range of advantages over exotic species as they are adapted
to local conditions. When properly matched with planting
sites, they are easier to establish, more likely to survive and
environmentally friendly. There are also minimal risks of
indigenous tree species becoming invasive (Greening,
Landscape & Tree Management Section Development
Bureau, 2010).
Nevertheless, rehabilitation using indigenous species in
Kenya faces challenges as the country lacks guidelines on
species–site matching. Published guidelines (KEFRI, 1990;
Braun, Albrecht & Kamondo, 1993) are based on results of
years of range-wide reconnaissance surveys that recorded
occurrence of species in given localities. Braun, Albrecht &
Kamondo (1993) superimposed the results of the reconnaissance surveys on the forest seed zones of Kenya as a
further improvement to reduce risk of moving tree seed far
beyond its right ecological conditions. The publications
provide general guidelines at a national scope due to the
nature of data used in their development. However,
rehabilitation programmes for specific sites are still faced
with the challenge of correct species choices for prevailing
physiognomic characteristics. In this study, we assessed
the composition and natural distribution of indigenous
trees and shrubs and derived basal areas (BAs) and
important value indices (IVIs) as possible dynamic criteria
for selecting suitable tree and shrub species for the
rehabilitation of degraded sites for specific locations in
semi-arid rangelands.
Materials and methods
Study area
The study was conducted in Makueni County, Kenya
(Fig. 1). The county lies between 1°35′S and 2°35′S, and
37°10′E and 38°30′E and covers an area of 7,965.8 km2
(GoK, 1997). A large proportion of the county falls within
the semi-humid to arid regions (agro-climatic zones IV–VI)
with moisture indices of less than 50% and a mean annual
rainfall of much less than 1100 mm (Sombroek, Braun &
Van Der Pour, 1982). Agro-climatic zones IV–VII are
referred to as rangelands. Agro-climatic zones are delineated according to mean annual rainfall and temperatures
(Jaetzold and Schmidt, 1983). Makueni County is generally
low-lying with altitude ranging between 600 m above sea
level (a.s.l) at Tsavo and 1900 m a.s.l on Kilungu hills. It is
characterized by arid and semi-arid climates (Pratt,
Greenway & Gwynne, 1966; Maundu & Tengnas, 2005;
Van den Abeele, Ngatia & Macharia, 2005) with low and
unreliable annual rainfall ranging from 200 mm to
900 mm in the low-lying areas and from 800 mm to
1200 mm in the hills (GoK, 1997). The rainfall is bimodally
distributed and occurs in March/April and November/
December. The November/December rains are usually more
reliable both in amount and distribution and accounts for
58% of the annual total precipitation (Musembi, 1986).
Study sites
Three study sites were randomly selected using agroclimatic maps and Landsat imageries and on the basis of
existing vegetation (Lambrecht, 1989; Shiver & Borders,
1996; Fasona & Omojola, 2009; Fig. 1). The maps and
satellite pictures were obtained from the Survey of Kenya
and the Department of Resource Surveys and Remote
Sensing (DRSRS; GoK, 2006). Selection of sites was
complemented by a tour of the county and a survey of
the vegetation and local weather facilities.
© 2013 John Wiley & Sons Ltd, Afr. J. Ecol., 53, 3–15
Indigenous trees and shrubs as adaptation criteria
5
Fig 1 Location and agro-ecological zones
of Makueni County, Kenya
Selected sites were minimally disturbed natural forests,
woodlands and bushlands located in Nthangu (1575 m
a.s.l.; site I), Kathonzweni (1250 m a.s.l.; site II) and
Kibwezi (900 m a.s.l.; site III; Fig. 1). Mean annual rainfall
and moisture indices for Nthangu, Kathonzweni and
Kibwezi were 974 mm and 49%, 700 mm and 35%, and
616 mm and 32%, respectively. As there were no climatic
data recording stations within the selected sites, the
climatic data were those of nearby weather stations within
the same zone, that is, Katende forest station, KARI Kampi
ya Mawe and Makindu meteorological station, for the
three sites, respectively.
Data collection
Indigenous and shrubs were identified, and data were
collected by establishing sample plots at each study site.
© 2013 John Wiley & Sons Ltd, Afr. J. Ecol., 53, 3–15
Establishment of sample plots. Sample plots were established according to Lambrecht (1989) and Shiver &
Borders (1996). At Nthangu, the sample plots were
established along a 15-km earth road running from the
forester’s office in the north to Mitubu hill overlooking
Wote township in the south. The plots were established at
Mbenza, Kyamboo, Kangaea and Mitubu points of the
forest. At Kathonzweni, sample plots were established
along a 30-km earth road connecting Kambi ya Mawe
market along Kathonzweni–Wote Road and Miangeni
market near Athi River. The plots were established at
Kampi ya mawe, Mathemba A, Mathemba B and Miangeni. At Kibwezi, plots were established along the earth
road connecting Kisayani market on the Kibwezi–Athi
River road, and Kyanginywa market on the Kibwezi River.
The plots were established within the forest owned by the
University of Nairobi’s Institute of Drylands Research.
6 Edward K. Mengich et al.
Along each road, four benchmarks were established at
4–5-km intervals. One to 3 km away from each benchmark in alternate directions (to the right or left), a 50-m by
50-m (2500 m2) plot was established (Lambrecht, 1989).
In sloping areas, sample plots were aligned along the slope.
In flat areas, the alignment was random.
Identification and inventory of trees and shrubs. With
assistance from a forester and a parataxonomist, all trees
and shrubs in each plot were identified by their local
names, botanical names and families, using available
literature (Dale & Greenway, 1961; Beentje, 1994; Maundu & Tengnas, 2005). Trees and shrubs were counted and
recorded by species. For ease of assessment and to avoid
repetition, each sample plot was partitioned into five equal
units, and resultant data were aggregated for analysis. A
tree or shrub would be counted as occurring whether it
was a seedling, sapling or mature individual.
Assessment of basal areas (BAs). Basal areas were only
assessed for trees above 1.30 m in height. Diameters at
breast height (DBH; 1.30 m above ground) were measured
using diameter tapes. Where trees were below this height,
DBH was not taken. Basal areas were derived as follows:
BA ¼ p ðDBH=2Þ2
where BA = basal area; p = 3.14; DBH = diameter at
beast height. The BAs were reported on a per-hectare
basis.
Statistical analysis
Data were compiled and entered into MS-Excel, versions
2003 and 2007, where data management techniques such
as data checking for errors, exploration and general
pattern of the data were performed. Basal area (BA),
abundance (A), frequency (F %), relative dominance (RD
%), relative frequency (RF %), relative abundance (RA) and
IVIs of individual species and families were computed for
each study site.
In the case of BA, data were analysed using GenStat v10
(VSI International, Hemel Hempstead, UK), a general
statistical software. Analysis of variance (ANOVA) was
performed to compare the basal areas of members of the
same species growing in at least two study sites. Data were
log-transformed to satisfy the normality assumption of
ANOVA. Whenever the normal distribution of the data was
not observed, nonparametric tests (Mann–Whitney U and
Kruskal–Wallis) were used. Mann–Whitney U-test was
used for comparing the BA measurements of the species that
occurred in only two study sites. For these nonparametric
tests, mean ranks of the response variate for comparison of
independent variables were used, and statistical differences
were declared using Z-score and chi-square tests for
Mann–Whitney U and Kruskal–Wallis, respectively. All
statistically significant differences were declared at
P < 0.05.
Results
Species characterizing various sites
Determination of important value indices (IVI). As BA is a
factor in IVI determination, IVIs were only determined for
tree species whose BA could be calculated, that is, trees
with a minimum height of 1.30 m above ground. Count
and BA measurements were used to calculate IVIs for both
tree species and families. The IVIs were derived following
Misra (1968):
IVI ¼ relative abundance (RA) þ relative dominance (RD)
þ relative frequency (RF)
where: abundance (A) = number of individuals per hectare (N ha1); RA = abundance of a given species/total
abundance of all species 9 100; dominance (D) = basal
area (m2 ha1); RD = basal area of a given species/total
basal area of all species 9 100; frequency (F) = percentage (%) of plots in which the species is represented;
RF = frequency of a given species/total frequency of all
species 9 100.
Vegetation at Nthangu forest consisted of 77 different tree
and shrub species that may be categorized into 33 families
and 60 genera (Table 1).
The most common genera in order of frequency were
Combretum (5), Acacia (5), Grewia (2), Cassia (2), Searsia (2)
and Terminalia (2). The most common families were
Combretaceae (6), Mimosaceae (6), Papilionaceae (5),
Euphorbiaceae (5), Anacadiaceae (4) and Caesalpiniaceae (3).
Vegetation at Kathonzweni consists of 69 different tree
and shrub species that may be categorized into 28 families
and 42 genera (Table 2).
The most common genera in order of frequency were
Commiphora (8), Acacia (6), Combretum (4), Boscia (3),
Grewia (2) and Terminalia (2). The most common families
were Mimosaceae (9), Burseraceae (7), Combretaceae (6),
Capparaceae (4) and Papilionaceae (3).
© 2013 John Wiley & Sons Ltd, Afr. J. Ecol., 53, 3–15
Indigenous trees and shrubs as adaptation criteria
7
Table 1 The tree and shrub species of Nthangu forest, Makueni County
No.
Species local name
Botanical name
Family name
Type
IVI
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
Kaluma
Mukinyai
Kiama
Mutheu
Mukongoo
Munyua
Mukaati
Mukukuma
Muuku
Mutotoo
Mwaalika
Mukomoa
Mutoo muka
Musemei
Muuwa nzuki
Kiba
Munoa mathoka
Mukengeka
Mpingo
Mwaanzia
Utithi
Mung’uthe
Mulawa muka
Itithyo
Muketa
Kitolousuu
Mwaa
Mulawa isamba
Kivuti
Muvau
Mutula
Mukokola
Kithongoi
Muvuavoi
Muthia
Kithauna
Muthulu
Mulului
Musensili
Mutuva
Mwinthongoi
Musuusuu
Mutote
Mutote
Mutongu
Muvuu
Kithunzi
Kiaa
Lunguyu
Pithosporum viridiflorum
Euclea divinorum
Combretum molle
Searsia spp.
Diospyrus mespiliformis
Acacia hockii
Faurea saligna
Uvaria scheffleri
Terminalia brownii
Pachystigma schumannianum
Heeria reticulata
Vangueria infausta
Azanza garckeana
Acacia nilotica
Combretum apiculatum
Pappea capensis
Dicrostachys cinerea
Cassia sengueana
Dalbergia melanoxylon
Bridelia taitensis
Combretum collinum
Lonchocarpus eriocalyx
Grewia bicolour
Combretum zeyheri
Myrsine africana
Ziziphus abyssinica
Acacia tortilis
Grewia spp.
Erythrina abyssinica
Dombeya kirkii
Ximenia americana
Combretum exalatum
Dodonea angustifolia
Steganotaenia araliaceae
Acacia mellifera
Lannea schimperi
Croton megalocarpus
Balanites aegyptiaca
Gnidia latifolia
Grewia tembensis
Pavetha gardeniifolia
Crotalaria spp.
Carissa edulis
Carissa edulis
Solanum incanum
Grewia villosa
Maytenus heterophylla
Euphorbia candelabrum
Indigofera spp.
Pithosporaceae
Ebenaceae
Combretaceae
Anacardiaceae
Ebenaceae
Mimosaceae
Proteaceae
Annonaceae
Combretaceae
Rubiaceae
Anacardiaceae
Rubiaceae
Malvaceae
Mimosaceae
Combretaceae
Sapindacea
Mimosaceae
Caesalpiniaceae
Papilionoideae
Euphorbiaceae
Combretaceae
Papilionaceae
Tiliaceae
Combretaceae
Myrsinaceae
Rhamnaceae
Mimosoidiae
Tiliaceae
Papilionaceae
Sterculiaceae
Olacaceae
Combretaceae
Sapindaceae
Umbelliferae
Mimosaceae
Anacardiaceae
Euphorbiacea
Simaraubaceae
Thymelaeaceae
Tiliaceae
Rubiaceae
Papilionaceae
Apocynaceae
Apocynaceae
Solanaceae
Tiliaceae
Celastraceae
Euphobiacea
Papilionaceae
T, S
T
T
S,T
T
S, T
S, T
T,
T
S,T
T
S, T
T
T
T
S, T
T, S
T
S,T
S, T
S,T
S, T
S, T
S, T
S, T
T, S
T
T
T
S,T
T,S
T
S, T
T
S, T
T
T
T
S
S
S
S
S
S
S
S
S, T
T
S
21.6
21.3
20.2
19.8
18.6
16.9
11.7
11.4
10.4
8.9
8.7
7.6
6.6
6.3
6.1
5.8
4.7
4.6
4.4
4.0
3.9
3.9
3.5
3.3
3.1
3.1
2.8
2.0
1.9
1.9
1.8
1.8
1.6
1.6
1.1
1.1
0.9
0.8
–
–
–
–
–
–
–
–
–
–
–
© 2013 John Wiley & Sons Ltd, Afr. J. Ecol., 53, 3–15
8 Edward K. Mengich et al.
Table 1 (continued)
No.
Species local name
Botanical name
Family name
Type
IVI
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
Mwindenguwe
Mukakaa
Mukala
Mukandu
Mukayau
Mukenea
Mukiliuli
Mukuluu
Mukulwa
Mukubu
Mungendia nthenge
Mongolli
Munyunga-mai
Musomolo
Musovi
Mutandi
Muthika
Muthingii
Muthumula
Muti
Mutoo
Mutumbuu
Mutungate
Mutungu
Mutunguu
Muua
Muvatha
Mwalula
Triumfetta flavescens
Premna resinosa
Antidesma venosum
Ocimum suave
Salvadora spp.
Zanthoxylum chalybeum
Harrisonia abyssinica
Securinega virosa
Acalypha fruticosa
Craibia brownii
Kleinia squarrosa
Acacia senegal
Cassia didymobotrya
Lantana camara
Hoslundia opposita
Ochna inermis
Indigofera spp
Ormocarpus kirkii
Tamarindus indica
Aspilia mossambisensis
Terminalia prunoides
Scutia myrtina
Commiphora habessinica
Commiphora spp.
Thylachium thomasii
Sclerocarya birrea
Vernonia lesiopus
Croton dichogamus
Tiliaceae
Verbenacea
Euphorbiacea
Labiatae
Salvadoraceae
Rutaceae
Simaraubaceae
Euphorbiaceae
Euphorbiacea
Papilionoidiae
Compositae
Mimosaceae
Caesalpiniaceae
Verbenaceae
Labiatae
Ochnaceae
Papilionaceae
Papilionaceae
Caesalpiniaceae
Compositae
Combretaceae
Rhamnaceae
Burseraceae
Burseraceae
Capparaceae
Anacardiaceae
Compositae
Euphorbiacea
S
S
T
S
T, S
S, T
S, T
S
S
T
S
S, T
S, T
S
S
T
S
T
T
S
T
S, T
S, T
T
S, T
T
H,S
T
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
T, tree; S, shrub; B, bush; C, climber; H, herb; L, liana; IVI, important value index.
NB: Species indicated as ‘tree’ (T) but not having a value for IVI was <1.30 m in height.
Vegetation at Kibwezi consisted of 70 different tree and
shrub species that may be categorized into 30 families and
48 genera (Table 3).
The most common genera in order of frequency were
Acacia (8), Commiphora (7), Grewia (3), Boscia (3),
Combretum (2) and Lannea (2). The most common
families were Mimosaceae (11), Burseraceae (7), Capparaceae (4), Tiliaceae (3), Euphorbiaceae (3), Combretaceae
(3) and Anacardiaceae (3).
Basal areas of tree species
Overall mean BAs of trees at Nthangu, Kathonzweni and
Kibwezi were 16.7 m2 ha1, 19.3 m2 ha1 and
76.8 m2 ha1, respectively. The nine top tree species at
each site accounted for 70.7%, 86.5% and 78.3% of the
overall mean BA, respectively. Among three species, each
occurring in at least two of the study sites, mean BAs
differed between sites (Table 4). For instance, the BA of
Terminalia brownii was higher at Nthangu (0.17 m2 ha1)
than at Kathonzweni (0.07 m2 ha1), although the
differences
were
not
significant
(t1,19 = 1.26,
P > 0.05), while those of Acacia tortilis (t1,252 = 3.83,
P < 0.05) and Acacia nilotica (t1,71 = 1.96, P < 0.05)
were both significantly higher at Kathonzweni
(0.09 m2 ha1, 0.04 m2 ha1, respectively) than at Kibwezi (0.04 m2 ha1, 0.02 m2 ha1, respectively). At
Kathonzweni,
A. tortilis
had
the
highest
BA
(0.09 m2 ha1), followed by T. brownii (0.07 m2 ha1)
and A. nilotica (0.04 m2 ha1) in that order. The former
and the latter were significantly different (t1,252 = 3.83,
P < 0.05). At Kibwezi, A. tortilis had a BA (0.04 m2 ha1)
© 2013 John Wiley & Sons Ltd, Afr. J. Ecol., 53, 3–15
Indigenous trees and shrubs as adaptation criteria
9
Table 2 The tree and shrub species of Kathonzweni forest, Makueni County
No.
Local name
Botanical name
Family name
Type
IVI
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
Utithi
Muuwa nzuki
Mwaa
Musemei
Muthaalwa
Muuku
Iulu
Mulawa isamba
Ikuu
Itula
Kiusia
Muthia
Mukuswi
Kiluli
Mwaanzia
Kyoa kikaa
Mutandi
Ithityo
Mwai
Kyundua
Munyua
Muvuavoi
Mutoo
Mwalanthate
Iliva
Kyoa isamba
Musovi
Mukakaa
Musensili
Lunguyu
Muthika
Muvuluvulu
Isivu
Itumbukyamuu
Mupopotwe
Kiongwa
Kithea
Kithunzi
Kiva
Kyenzenze
Kiongwa
Kyuasi
Mutungu
Mutunguu
Mwindenguwe
Mukayau
Mukigeka
Mukiliuli
Mukokola
Mukuluu
Combretum collinum
Combretum apiculatum
Acacia tortilis
Acacia nilotica
Lannea triphylla
Terminalia brownii
Commiphora campestris
Grewia spp.
Commiphora africana
Commiphora baluensis
Sterculia africana
Acacia mellifera
Acacia brevispica
Boscia angustifolia
Bridelia taitensis
Commiphora ovalifolia
Ochna inermis
Combretum zeyheri
Platycelyphium voense
Albizia amara
Acacia hockii
Steganotaenia araliacea
Terminalia prunoides
Cassia abbreviata
Commiphora rostrata
Albizia anthelmintica
Hoslundia opposita
Premna resinosa
Gnidia latifolia
Indigofera spp.
Indigofera spp.
Opilia celtidifolia
Boscia coriacea
Faurea saligna
Maerua kirkii
Combretum paniculatum
Cordia monoica
Maytenus heterophylla
Pappea capensis
Boscia spp.
Combretum paniculatum
Lannea stuhlmanii
Solanum incanum
Thylachium thomasii
Triumfetta macrophyla
Salvadora persica
Cassia sengueana
Harrisonia abysinica
Combretum exalatum
Securinega virosa
Combretaceae
Combretaceae
Mimosoideae
Mimosaceae
Anacardiaceae
Combretaceae
Burseraceae
Tiliaceae
Burseraceae
Burseraceae
Sterculiaceae
Mimosoideae
Mimosaceae
Capparaceae
Euphorbiaceae
Burseraceae
Ochnaceae
Combretaceae
Papilionaceae
Mimosoideae
Mimosaceae
Umbelliferae
Combretaceae
Caesalpinioideae
Burseraceae
Mimosaceae
Labiatae
Verbenaceae
Thymealaeaceae
Papilionaceae
Papilionaceae
Opiliaceae
Capparaceae
Proteaceae
Capparidaceae
Combretaceae
Boraginaceae
Celastraceae
Sapindaceae
Capparaceae
Combretaceae
Anacardiaceae
Solanaceae
Capparaceae
Tiliaceae
Salvadoraceae
Caesalpiniaceae
Simaraubaceae
Combretaceae
Euphorbiaceae
S,T
T
T
T
S,T
T
T
T
T
T
T
S,T
S,T
S,T
S,T
S,T
T
T
T
T
S,T
T
S,T
T
S,T
T, B
S
S
S
S
S,
L
S, T
S, T
S, T
S, L
S, T
S, T
T
S, T
S, L
S, T
S
S, T
S
T, S
T
S, T
T
S
44.4
29.5
27.6
16.8
15.9
12.6
12.4
11.4
11.0
9.4
7.6
7.4
6.2
6.2
5.7
4.6
4.4
3.3
3.2
2.7
2.5
2.4
2.4
2.2
1.8
1.1
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
© 2013 John Wiley & Sons Ltd, Afr. J. Ecol., 53, 3–15
10
Edward K. Mengich et al.
Table 2 (continued)
No.
Local name
Botanical name
Family name
Type
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
66.
67.
68.
69.
Mukulwa
Mukume
Mulawa muka
Mongolli
Munoa mathoka
Musomolo
Mutaavesi
Muthuigi
Muthito
Mutongu
Mutoo muka
Mutotoo
Mutungate
Mututi
Mutuba
Mpingo
Muvuu
Mwaitha
Yongwa
Acalypha fruticosa
Haplocoelum foliolosum
Grewia bicolour
Acacia senegal
Dicrostachys cinerea
Lantana camara
Lantana camara
Ormocarpus kirkii
Cadaba farinosa
Solanum incanum
Azanza garckeana
P. schumannianum
Commiphora habessinica
Thunbergia holstii
Grewia tembensis
Dalbergia melanoxylon
Grewia villosa
Entada leptostachya
Commiphora riparia
Euphorbiaceae
Sapindaceae
Tiliaceae
Mimosaceae
Mimosaceae
Verbenaceae
Verbenaceae
Papilionaceae
Capparidaceae
Solanaceae
Malvaceae
Rubiaceae
Burseraceae
Acanthaceae
Tiliaceae
Papilionaceae
Tiliaceae
Mimosaceae
Burseraceae
S
S,
S,
S,
S,
S
S
T
S,
S
T
S
T
S
S
S,
S
C
T
T
T
T
T
T
T
IVI
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
T, tree; S, shrub; B, bush; C, climber; L, liana; IVI, important value index.
NB: Species indicated as tree (T) but not having a value for IVI was <1.30 m in height.
twice higher than that of A. nilotica (0.02 m2 ha1).
However, the differences were not statistically significant
(t1,71 = 1.96, P > 0.05).
Ecologically important families and trees
The most important families according to descending order
of their IVIs at Nthangu were Combretaceae (45.7),
Ebenaceae (39.9), Mimosaceae (31.8), Anacardiaceae
(29.6) and Pittosporaceae (21.6). At Kathonzweni, they
were Combretaceae (92.2), Mimosaceae (64.3), Burseraceae (39.2), Anacardiaceae (15.9) and Tiliaceae (11.4). At
Kibwezi, the most important families were Mimosaceae
(77.3), Burseraceae (40.6), Bombacaceae (38.3), Caesalpiniaceae (30.4) and Capparaceae (17.5). The other
families had overall IVIs of 41.6, 31.7 and 46.8 for the
three sites, respectively.
At Nthangu, ecologically important tree species according to descending order of their IVIs could be determined
for 38 species (Table 1). The most important species were
Pithosporum viridiflorum (21.6), Euclea divinorum (21.3),
Combretum molle (20.2), Searsia spp. (19.8) and D. mispiliformis (18.6). At Kathonzweni, ecological importance of
trees by their IVIs could be determined for 26 of the species
in descending order (Table 2). The most important species
were Combretum collinum (44.4), Combretum apiculatum
(29.5), A. tortilis (27.6), A. nilotica (16.8) and Lannea
triphylla (15.9). Ecologically important tree species at
Kibwezi are listed in Table 3 according to descending order
of IVIs. Only 42 tree species could have their IVIs
determined. The most important species were Adansonia
digitata (38.3), D. elata (25.7), A. tortilis (20.8), Commiphora africana (17.4) and Acacia mellifera (14.3).
Commonly occurring tree species
Predominant species that occurred commonly in all the
three sites and exhibited the highest IVIs were A. nilotica
(6.3) and A. tortilis at Kathonzweni (27.6) and Kibwezi
(20.8; Table 5).
Discussion
The number of species identified in this study (139)
represents less than a tenth of the trees and shrubs that
may be encountered somewhere in the ASALs of Kenya
(Ogolla & Mugabe, 1997; Maundu & Tengnas, 2005). In a
similar study covering an area comprising four counties of
© 2013 John Wiley & Sons Ltd, Afr. J. Ecol., 53, 3–15
Indigenous trees and shrubs as adaptation criteria
11
Table 3 The tree and shrub species of Kibwezi forest, Makueni County
No.
Species local name
Botanical name
Family name
Species type
IVI
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
Mwambo
Mwangi
Mwaa
Ikuu
Muthia
Munina
Kyoa isamba
Mulawa muka
Itula
Mutunguu
Musemei
Mulawa isamba
Mwalula
Kiluli
Kisaya
Kyenzenze
Kyoa kikaa
Mwalanthate
Mutoo
Mupopotwe
Kiaa
Mongolli
Munoa mathoka
Iliva
Iulu
Muvau
Mulela
Muthaalwa
Muvuavoi
Mukangakanywa
Kyaakyusi
Kiusia
Mutungate
Yongoa
Kikaiki
Muthuigi
Kyuasi
Mukaiao
Yumbu
Ikindu
Mutiligo
Mukami
Mutongu
Mugea
Kiongwa
Muvuu
Muthika
Musomolo
Isivu
Adansonia digitata
Delonix elata
Acacia tortilis
Commiphora africana
Acacia mellifera
Acacia elatior
Albizia anthelmintica
Grewia bicolour
Commiphora baluensis
Thylachium africanum
Acacia nilotica
Grewia spp.
Croton dichocamus
Boscia angustifolia
Bechermia discolour
Boscia spp.
Commiphora ovalifolia
Cassia abbreviata
Terminalia prunoides
Maerua kirkii
Euphorbia spp.
Acacia senegal
Dicrostachys cinerea
Commiphora rostrata
C. campestris
Dombeya kirkii
Acacia xanthophloea
Lannea triphylla
Steganotaenia eraliacea
Garcinia livingstonii
Combretum schumannii
Sterculia africana
Commiphora habessinica
Commiphora hildbraedii
Acacia thomasii
Ormocarpus kirkii
Lannea schumanii
Salvadora persica
Ficus spp.
Phoenix reclinata
Lawsonia inermis
Neutonia hildbrandii
Solanum incanum
Anisotes ukambensis
C. paniculatum
Grewia villosa
Indigofera spp.
Lantana camara
Boscia coriacea
Bombacaceae
Caesalpinioideae
Mimosoidiae
Burseraceae
Mimosoideae
Mimosoideae
Mimosaceae
Tiliaceae
Burseraceae
Capparaceae
Mimosoideae
Tiliaceae
Euphorbiaceae
Capparaceae
Rhamnaceae
Capparaceae
Burseraceae
Caesalpinioidiae
Combretaceae
Capparidaceae
Euphorbiaceae
Mimosoideae
Mimosoideae
Burseraceae
Burseraceae
Sterculiaceae
Mimosoideae
Anacardiaceae
Umbelliferae
Gittiferae
Combretaceae
Sterculiaceae
Burseraceae
Burseraceae
Mimosaceae
Papilionaceae
Anacardiaceae
Salvadoraceae
Moraceae
Palmae
Lythraceae
Mimosaceae
Solanaceae
Acanthaceae
Combretaceae
Tiliaceae
Papilionaceae
Verbenaceae
Capparaceae
T
T
T
T
S,T
T
T
S,T
T
S,T
T
T
S,T
T
T,S
S,T
S,T
T
S,T
S,T
T
S,T
T
S,T
T
S,T
T
S,T
T
S,T
T
T
T
T
T
T
S, T
T,S
T
T
S,T
T
S
S
S,L
S
S,H
S
S,T
38.3
25.7
20.8
17.4
14.3
14.1
9.1
7.4
6.8
6.4
5.8
5.8
5.7
5.7
5.4
5.4
5.1
4.7
4.5
4.4
3.8
3.7
3.5
3.4
3.3
3.2
3.2
2.9
2.7
2.6
2.5
2.4
2.4
2.2
2.0
1.5
1.2
1.2
1.0
1.0
0.8
0.8
–
–
–
–
–
–
–
© 2013 John Wiley & Sons Ltd, Afr. J. Ecol., 53, 3–15
12
Edward K. Mengich et al.
Table 3 (continued)
No.
Species local name
Botanical name
Family name
Species type
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
66.
67.
68.
69.
Kinatha
Kitanda mboo
Mwaanzia
Mwindenguwe
Mukakaa
Mukokola
Mukulwa
Mukuswi
Mulalambila
Mukulwa
Mung’uthe
Musilingu
Musovi
Mutandi
Mutungu
Mututi
Mutuba
Muvatha
Muvuluvulu
Mwaitha
Maerua decumbens
Capparis tomentosa
Bridelia taitensis
Triumfetta macrophylla
Premna resinosa
Combretum exalatum
Acalypha fruticosa
Acacia brevispica
Hibiscus spp.
Acalypha fruticosa
Lonchocarpus eriocalyx
Grewia fallax
Hoslundia opposita
Ochna inermis
Lannea alata
Thunbergia holstii
Grewia tembensis
Vernonia spp.
Opilia celtidifolia
Entada leptostachya
Capparaceae
Capparaceae
Euphorbiaceae
Tiliaceae
Verbenaceae
Combretaceae
Euphorbiaceae
Mimosoideae
Malvaceae
Euphorbiaceae
Papilionaceae
Tiliaceae
Labiatae
Ochnaceae
Anacardiaceae
Acanthaceae
Tiliaceae
Compositae
Opiliaceae
Mimosaceae
S
S,C
S,T
S
S
T
S
S,T
S
S
S,T
S,T
S
T
S,T
S
S
S
L
C
IVI
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
T, tree; S, shrub; B, bush; C, climber; L, liana; IVI, important value index.
NB: Species indicated as tree (T) but not having a value for IVI was <1.30 m in height.
Table 4 Mean basal areas (BAs) of the most important tree species at Nthangu, Kathonzweni and Kibwezi forests of Makueni County
Study site
Species
Mean BA
(m2 ha1)
Max
Min
Range
S.E
n
Nthangu
Pithosporum viridiflorum
Euclea divinorum
Combretum molle
Searsia spp.
Diospyrus mespiliformis
Acacia hockii
Faurea saligna
Terminalia brownii
Combretum collinum
Combretum apiculatum
Acacia tortilis
Acacia nilotica
Lannea triphylla
T. brownii
Commiphora campestris
Adansonia digitata
Delonix elata
A. tortilis
Commiphora africana
Acacia mellifera
Acacia elatior
0.13
0.07
0.13
0.02
0.04
0.04
0.13
0.17
0.02
0.004
0.09
0.04
0.02
0.07
0.04
8.47
0.86
0.04
0.05
0.02
0.09
0.363
0.349
0.608
0.102
0.385
0.212
0.385
0.407
0.332
0.132
0.369
0.138
0.053
0.266
0.224
20.79
2.684
0.342
0.363
0.302
0.622
0.006
0.008
0.001
0.001
0.004
0.005
0.014
0.014
0.001
0.002
0.004
0.004
0.003
0.006
0.004
2.290
0.001
0.001
0.001
0.001
0.001
0.358
0.341
0.607
0.101
0.381
0.207
0.371
0.393
0.332
0.130
0.365
0.135
0.050
0.256
0.220
18.50
2.683
0.341
0.362
0.301
0.622
0.004
0.005
0.014
0.000
0.004
0.001
0.008
0.007
0.000
0.000
0.024
0.000
0.000
0.002
0.001
27.09
0.300
0.001
0.001
0.001
0.016
15
15
21
67
32
24
7
5
257
65
47
33
47
14
27
4
8
205
75
123
24
Kathonzweni
Kibwezi
© 2013 John Wiley & Sons Ltd, Afr. J. Ecol., 53, 3–15
Indigenous trees and shrubs as adaptation criteria
Table 5 Important value indices (IVIs) of trees commonly occurring in Nthangu, Kathonzweni and Kibwezi forests of Makueni
County
Important value index (IVI)
Species
Nthangu
Kathonzweni
Kibwezi
Acacia tortilis
Acacia nilotica
Acacia mellifera
Grewia spp.
Steganotaenia erialiacea
Total
2.8
6.3
1.1
2.0
1.6
13.8
27.6
16.8
7.4
11.4
2.4
65.6
20.8
5.8
14.3
5.8
2.7
49.4
Kenya’s Eastern Province – Makueni, Machakos, Kitui and
Mwingi, DRSRS identified a total of 300 tree and shrub
species which were used to describe the local vegetation
(Ojiambo et al., 2001). In the nearby semi-arid Miombo
woodland area of eastern Tanzania, a total of 86 tree
species were identified (Back’eus et al., 2006). The low
species numbers observed in our study indicate that at
some point in time, there may have been a phenomenon,
which resulted in a drastic reduction in tree species
diversity. Studies elsewhere have attributed similar reductions in tree species diversity to (i) intensified human
activities that may have led to elimination of some of the
tree and shrub species through wood harvesting, fires,
livestock browsing/grazing and agricultural activities
(Back’eus et al., 2006; Suleiman, Buchroithner & Elhag,
2012; Van Der Hoek et al., 2013); (ii) natural phenomena
such as frequent droughts and wildlife damage (Birkett &
Stevens-Wood, 2005) that may have led to loss of some of
the more susceptible tree and shrub species; and
(iii) salinity of soils, which can only support a limited
number of hardy and adapted species (Macharia, 1981).
This study did not, however, gather information to support
or reject these propositions.
Nthangu had the largest number of species, families and
genera (77, 33, 60) followed by Kibwezi (70, 30, 48) and
Kathonzweni (69, 28, 42). Similar gradients have been
documented within the study area and its immediate
surroundings by Ego, Kiptot & Ochieng (2001) and Van
den Abeele, Ngatia & Macharia (2005). It is suggested that
the gradients observed in our study could be mainly a
function of the long-term effects of higher rainfall at
Nthangu compared with the other two sites. Climate
provides one of the strongest controls on the species
composition and characteristics of vegetation, and Kenya
is thought to owe its high biological diversity to the
enormous variation in climate and topography, which
© 2013 John Wiley & Sons Ltd, Afr. J. Ecol., 53, 3–15
13
result in a great range of habitats (Coughenour & Ellis,
1993; Back’eus et al., 2006; Yimer, Ledin & Abdelkadir,
2006). Our findings further agree with those obtained in a
study conducted in Turkana County of Kenya. In the
Turkana study, the regional herbaceous vegetation biomass reflected a gradient in which the highest levels of
biomass occurred where rainfall was >600 mm, and the
lowest levels occurred where rainfall was estimated at
150 mm–200 mm (Coughenour & Ellis, 1993). Similar
gradients have been observed at the Nairobi National Park
in southern Kenya (Helsa, Tieszen & Boutton, 1985) and
in a geologically homogeneous area in Australia (Myers &
Neales, 1984).
According to the results of our study and the physiognomic classifications described in the literature (Pratt &
Gwynne, 1977; Chidumayo & Gumbo, 2010), vegetation
at Nthangu, Kathonzweni and Kibwezi forests may be
categorized as Euclea-Combretum-Acacia thicket, Combretum-Terminalia-Acacia woodland, and Acacia-Commmiphora
woodland, respectively. In a vegetation survey conducted
at sites located at various points within Makueni County,
DRSRS identified different vegetation associations dominated by different plant species and genera as shown in this
study (Ojiambo et al., 2001). In a separate study, it was
difficult to delineate the dominant species in Kikumbulyu
location of Kibwezi division due to severe clearing and high
population density (Ngoda & Obwoyere, 2001).
Based on our study, the families Combretaceae, Burseraceae and Mimosaceae were the most important and
widely distributed in the area. Combretaceae was widely
represented at Nthangu and Kathonzweni, where it was
ecologically the most important at both the sites. Combretum was the most common genus in this family.
Burseraceae was the third most important at Kathonzweni
and the second most important at Kibwezi. Commiphora
was the most common genus in this family. Mimosaceae
was ecologically the most important at Kibwezi, second
most important at Kathonzweni and third most important
at Nthangu forest. Acacia was the most common genus in
this family. The observation on Acacia is consistent with
results obtained in arid northern Kenya, where the genus
Acacia was common and dominant in 29 of 30 sites
sampled for a vegetation survey (Coughenour & Ellis,
1993).
The ecological importance of these families and genera
indicate that they could provide candidate tree species for
rehabilitation of respective sites. This is because these species
and vegetation categories grew naturally and dominated the
respective sites, indicating that conditions prevailing at the
sites were favourable for the survival and growth of these
14
Edward K. Mengich et al.
species. Based on IVIs in the genus combretum, C. molle
would be a priority candidate for rehabilitation at Nthangu
followed by T. brownii, while C. collinum would be the right
choice at Kathonzweni followed by C. apiculatum. In the
genus Burseraceae, C. africana would be preferred at
Kibwezi, followed by C. baluensis, while at Kathonzweni,
Commiphora campestris would be preferred over C. africana.
In the genus Acacia, A. tortilis would be preferred at Kibwezi
and Kathonzweni, followed by A. mellifera and A. nilotica,
respectively. At Nthangu, Acacia hockii would be the most
suitable. Results of the basal area, which showed that these
species contributed significantly to the total BA in respective
sites, render credence to their use for rehabilitation purposes. Although the genera Pithosporum and Adansonia had
high IVIs at Nthangu and Kibwezi, respectively, they would
not be suitable candidates for rehabilitation due to their
limited spread.
Acknowledgements
Financial support to this study was received from the
German Academic Exchange Service (DAAD), the Kenya
Forestry Research Institute (KEFRI) and the Belgian
Technical Cooperation (BTC) through the KEFRI/BTC
Agroforestry for Integrated Development in Semi-arid areas
of Kenya (ARIDSAK) Project. Daniel Musya and James
Kivilu helped in the identification of indigenous trees by
their local names. Vincent Oeba assisted in the statistical
analysis of data. John Ngugi provided GIS support. Bernard
Kamondo helped in editing the manuscript. These institutions and individuals are gratefully acknowledged.
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