Research Article Diversity, Population Structure, and...

Research ArticleDiversity Population Structure and Above GroundBiomass in Woody Species on Ngomakurira MountainDomboshawa Zimbabwe

Clemence Zimudzi1 and Christopher Chapano2

1Department of Biological Sciences University of Zimbabwe PO Box MP 167 Mount Pleasant Harare Zimbabwe2National Herbarium and Botanic Garden PO Box A889 Avondale Harare Zimbabwe

Correspondence should be addressed to Clemence Zimudzi czimudziscienceuzaczw

Received 14 April 2016 Revised 8 June 2016 Accepted 23 June 2016

Academic Editor Alexandre Sebbenn

Copyright copy 2016 C Zimudzi and C ChapanoThis is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

The diversity structure species composition and above ground biomass of woody plants on Ngomakurira mountain in Zimbabwewere studied A systematic random sampling approach was adopted to establish 52 sampling plots measuring 10 times 10m across 3study strata in the 1266 ha study area Woody species occurring in each plot were identified and the circumferences of trees withdiameters gt80 cm at 13m height were measured A total of 91 species belonging to 74 genera and 39 families were identified inthe sample plots A Shannon-Wiener index mean value of 312 was obtained indicating high species diversity on the mountainTheDBH size class distribution showed inverse J distribution patterns across the three study strata but with only 3 individual plantswith DBH gt 30 cmMean basal area was 1521m2 haminus1 withU kirkiana and J globiflora contributing approximately 30 of the basalarea The estimated above ground biomass ranged from 345 to 651 t haminus1 Kruskal-Wallis-H test showed no significant differencesin species richness stem density basal area above ground biomass and evenness across the study strata (119901 lt 005) Ngomakurirawoodland has potential to regenerate due to the presence of many stems in the small diameter size classes

1 Introduction

Themiombo ecoregion covers approximately 27million km2of southern central and eastern Africa [1] and is one of theworldrsquos priority areas for conservation [2] The woodlandsprovide ecosystem socioeconomic and cultural services thatsustain local livelihoods [3] Miombo woodlands contributeconsiderably to household livelihoods by providing fuelconstruction materials grazing medicine food materials ofcultural importance and potentially supplementing incomesespecially for poor communal and urban dwellers [4] InZimbabwe the woodlands cover approximately 42 of thecountry [5] but the biodiversity composition and structureare mostly poorly documented

Miombo woodland dynamics is strongly influenced byanthropogenic factors [6ndash8] and most ecosystems are beingthreatened by the increased demand for services by theburgeoning human population [9] Woodland ecosystemsand their valuable biodiversity resources are being lost at

an accelerated rate [10ndash12] thereby altering ecosystem func-tion and consequently threatening the provision of servicesand goods especially to poor rural communities [13]

Knowledge of the biomass of a woodland is importantin estimating the carbon stocks of an area Such informationis invaluable in management for coming up with strategiesfor climate change mitigation [14] Carbon stocks data canalso be used to mobilise financial resources through C-based Payments for Ecosystem Services (PES) [15] There islimited quantitative information of carbon stocks in southernAfrican forests [15] despite the huge importance of suchinformation Data on carbon stocks species compositionand vegetation structure is invaluable to forest managementin designing strategies for sustainable management underdifferent disturbance regimes [14]

Ngomakurira means ldquodrums soundingrdquo in the localShona language and is a mountain located near the Dom-boshawa Caves just outside of Harare in Zimbabwe It formspart of a granitic topography with dome-shaped inselbergs

Hindawi Publishing CorporationInternational Journal of BiodiversityVolume 2016 Article ID 4909158 11 pageshttpdxdoiorg10115520164909158

2 International Journal of Biodiversity

near the village of Domboshawa in the headwaters of theMazowe river The mountain derives its name from thetraditional African drums that were played during religiousfestivals The mountain is considered sacred by the localcommunity and is used for various religious rites Old Sanrock art paintings are also found in caves and on the verticalrock face of the main mountain The mountain is protectedand managed by the National Museums and Monumentsof Zimbabwe to conserve its cultural and archaeologicalvalue biodiversity and catchment value Ngomakurira Trustformed by the Goromonzi Rural District Council in partner-ship with National Museums and Monuments of Zimbabweas well as the Environmental Management Agency (EMA)initiated the ecotourism project at Ngomakurira mountainby converting hills used communally for grazing fuel woodcollection and harvesting of wild fruits into a properlymanaged tourist facility

Ngomakurira mountain is surrounded by communalareas and such areas are vulnerable to anthropogenic activ-ities and edge effects [19] as local communities continue todepend on the woodlands for sustenance A participatoryforest management approach (PFM) which involves locals inmanagement decisions has recently been implemented for anecotourism project at Ngomakurira mountainThe success ofthe project will depend on an understanding of the woodlanddynamics and especially the role of disturbance factors inshaping the woodland structure

This study seeks to determine the following (a) thespecies composition structure and diversity and biomassaccumulation of woody plants on Ngomakurira mountain(b) the variability of these diversity indices across the sampleplots and (c) the relationships between species diversityrichness basal area and above ground biomass

2 Materials and Methods

21 Study Area Ngomakurira is a large granite mountainlocated in the Goromonzi district in Chinamora communallands approximately 30 km NE of Harare in Zimbabwe It islocated between 17∘321015840464610158401015840Sndash17∘351015840349210158401015840S latitudes and31∘141015840216110158401015840Endash31∘151015840518910158401015840E longitudes Altitude varies from1307 to 1638 metres above sea level The total area covered bythe mountain is 1266 ha

Ngomakurira mountain is located in agroecological zoneIIa [20] with rainfall fairly reliable falling from Novemberto MarchApril and mean annual rainfall ranging from750 to 1 000mmyear Mean annual temperature range is15ndash20∘C The soils are shallow and generally classified asparaferralitic soils [21] Deep soils are found at the base ofthe hill getting shallower and rocky to absent at the higherlevels Ngomakurira lies in a communal area with poor ruralhouseholds mainly involved in subsistence farming thoughsome are involved in horticulture to supply urban markets inHarare

The study area falls under the deciduousmiombo savannawoodland [22] a vegetation type dominated by Brachystegiaspp and Julbernardia globiflora Reference [23] described thistype of vegetation as dry miombo The vegetation on themountain is concentrated in the lower and middle zone of

the mountain with most of the top area consisting largely ofbare rock

22 Sampling Design and Data Collection Field work wasconducted in February 2016 An initial study of satelliteimages of the study area followed by a field reconnaissancesurvey [24] assisted inmaking decisions on sampling strategyto use From the surveys the vegetation of the mountainappears mixed with no obvious vegetation communitiesWe however divided the mountain into three sections thevegetation at the base middle and top of the mountain Ineach section quadrats measuring 10 times 10m were demarcatedfollowing the methods by [25] Random numbers were usedto locate the position of plots in each stratum [24] Wesampled 15 quadrats at the base 25 in themiddle and 12 at thetop Sample plots were distributed according to the relativesizes of the strata

Global positioning system (GPS) measurements weretaken for each sample plot using a Garmin 12XL GlobalPositioning System In each plot all woody species wereidentified in the field and a few were collected and verified atthe National Herbarium in Harare Tree height was visuallyestimated and stem circumference at 13m was measuredusing a tape measure for plants with a circumference ge8 cm [26] Formultistemmed plants the circumferences weremeasured separately

23 Data Analysis

231 Floristic Indices and Biodiversity The data consistingof plot numbers species and circumference was enteredinto an Excel spreadsheet and the following parameters werecalculated

(1) DBH = circumference120587(2) Basal area = 120587 lowast DBH2(3) Relative dominance = (total basal area for a

speciestotal basal area of all species) times 100(4) Relative density = (number of individuals of a

speciestotal number of individuals) times 100(5) Relative frequency = (frequency of a speciessum of

all frequencies) times 100(6) Relative diversity = (number of a species in a fam-

ilytotal number of species) times 100(7) The importance value index (IVI) = relative domi-

nance + relative density + relative frequency(8) The family importance value (FIV) = relative domi-

nance + relative density + relative diversity(9) Shannon-Wienerrsquos index of diversity 119867 = minusΣpi ln pi

where pi = 119899119894119873 119899119894 is the number of individual treespresent for species 119894 and 119873 is the total number ofindividuals [27]

(10) Evenness =1198671015840 ln 119878 where1198671015840 is the Shannon-Wienerindex and 119878 is the total number of species

(11) The Kruskal-Wallis 119867 test was done using SPSSstatistical package version 160 (2007)

International Journal of Biodiversity 3

Table 1 Allometric equations used to estimate above ground biomass

Equation(s) Source country Notes Reference119861 = 301119863 minus 748 Zambia Trees lt 10 cm DBH [16]119861 = 2002119863 minus 20337 Zambia Trees gt 10 cm DBH [16]119861 = exp(2516 ln(dbh) minus 2462 Tanzania For trees ⩾ 5 cm DBH [17]119861 = 00625 times 119863

2553 Tanzania For trees ⩾ 5 cm DBH [18]119863 = DBH 119861 = biomass (Kg)

Table 2 The five species with the highest importance value index (IVI) in each stratum at Ngomakurira mountain

Mountain strata Species Relativefrequency ()

Relativedensity ()

Relativedominance

()Species important value index300

Bottom

J globiflora 8064 25478 22830 56372L discolor 6452 6879 5333 18663

E matabelensis 2688 9427 6268 18383U kirkiana 2151 5733 10460 18343

P maprouneifolia 3763 3057 8465 15286

Middle

U kirkiana 3833 9269 20665 33767J globiflora 6620 11765 11848 30233

B glaucescens 6272 9002 12161 27435E matabelensis 2091 9804 9896 21790L discolor 5575 4724 6048 16347

Top

B glaucescens 6154 18878 19234 44265J globiflora 7692 11905 12374 31971U kirkiana 2308 8674 12473 23454

E matabelensis 3078 10034 6299 19410S guineense 1539 6633 9140 17312

(12) The Spearman rank correlations between diversityvariables were calculated in Excel 2007

232 Classification of Sample Plots Classification of the sam-ple plots was done using Agglomerative Hierarchical ClusterAnalysis in SPSS statistical package version 160 (2007)Classification was done using weighted species compositiondata with weighting achieved using the number of stems ofeach species measured in each plot Euclidean distance wasused to compute distances between plots

233 Above Ground Biomass Three allometric equations(Table 1) were used to estimate tree biomass The equa-tions show relationships between biomass and DBH Theseequations are general for all species as previous studieshave already shown that species specific equations are notnecessary in estimating biomass [14 28] The equationswere generated from neighbouring countries with similarecosystems climate and soils References [12 14 29] used asimilar approach in estimating biomass Biomass estimationswere made only for trees with DBH ge 5 as the equations weredeveloped to estimate biomass within these DBH ranges

3 Results

31 Tree Species Composition A total of 91 species belongingto 74 genera and 39 families were recorded (see Appendix)

In terms of the number of species the Rubiaceae is thelargest family with 11 species followed by the Caesalpiniaceae(8 species) Phyllanthaceae (6 species) Papilionaceae andCombretaceae (5 species each) Lamiaceae Mimosaceae andAnacardiaceae (4 species each) Burseraceae MyrtaceaeProteaceae and Sapindaceae (3 species each) ClusiaceaeDipterocarpaceae Ebenaceae Moraceae Ochnaceae andStrychnaceae (2 species each) and the remainder have onespecies each The number of species were 59 at the bottom62 at the middle and 48 at the top of the mountain

Table 2 shows the five dominant species at each stratum indecreasing importance value index (IVI) Julbernardia globi-flora dominates at the bottomUapaca kirkiana at themiddleandBrachystegia glaucescens at the top of themountain In thethree strata the common dominant species were J globifloraB glaucescens U kirkiana and Euphorbia matabelensis withmean IVI values of 395 2843 252 and 199 respectively

Some species increased in dominance with increasingaltitude whereas others decreased Combretum molle Ele-phantorrhiza goetzei Monotes engleri Parinari curatellifo-lia Strychnos spinosa Zanha africana and B glaucescensincreased up the mountain whereas C mossambicensisE matabelensis Margaritaria discoidea Pseudolachnostylismaprouneifolia Tapiphyllum velutinum and J globiflorashowed a decrease

There were some species confined to only one stratumof the mountain Acacia goetzei Afrocanthium lactescens


Table 3 Kruskal-Wallis H test results of the study variables at the bottom middle and top of Ngomakurira mountain (values in bracketsshow least values)

Variable Median (range)Bottom Middle Top 119901 value

Basal area (m2ha) 1423 (428) 1240 (338) 1899 (601) 0161Shannon-Wiener index 307 332 296 0299Above ground biomass (tha) 3449 (748) 4664 (566) 6517 (936) 0165Species richness 59 62 48 0479Evenness 080 (063) 086 (056) 078 (056) 0115Stem densityha 4900 (2700) 4400 (1000) 4850 (2100) 0646

Commiphora mollis Diospyros natalensis and Psydrax lividawere only recorded at the top of the mountain Brachy-laena discolor Combretum apiculatum Combretum collinumEmpogona kirkii Erythrina abyssinica Ficus natalensis Psid-ium guajava Rhus leptodictya and Vitex payos were foundat the middle and Cussonia arborea Dombeya rotundifoliaFaurea rochetiana Ficus sur Gardenia ternifolia Maesalanceolata Monotes glaber Mussaenda arcuata Pericopsisangolensis Protea welwitschii Strychnos madagascariensisand Ximenia caffra were found at the bottomThe number ofspecies confined to a particular stratum decreased from thebottom to the top

A number of fire tolerant species like P maprouneifoliaPterocarpus angolensis Combretum sp Burkea AfricanaTerminalia stenostachya and Diplorhynchus condylocarponwere also recorded on the mountain

32 Shannon-Wiener Diversity and Evenness Shannon-Wiener values for the bottom middle and top of themountain were 307 332 and 296 respectively with amean of 312 (Table 3) Kruskal-Wallis test results of theShannon-Wiener values show no significant differences(119901 gt 005) across the study strata The following speciescontributed most to the high diversity at the mountainJ globiflora (085) E matabelensis (072) B glaucescens(067) U kirkiana (059) and Lannea discolor (046) Speciesevenness is highest at the middle and lowest at the top ofthe mountain but no significant differences (119901 gt 005) wereobserved across the study strata (Table 3)

33 Stem Density and DBH A total of 2495 stems weremeasured in the sample plots About a quarter of the trees(257) were multistemmed and accounted for slightly overhalf of the stems (1770 stems) Mean stem density for stemswith DBH ge 5 cm was 2456 plusmn 1250 and 2342 plusmn 1286 stemhaminus1 for DBH lt 5 cm For stems with a DBH ge 25 cm stemdensity was highest at the bottom and least in the middle ofthe mountain The mean diameter was 55 plusmn 36 cm with themajority of trees (91) with DBH lt 10 cm Figure 1 showsthe diameter class distribution of the trees at Ngomakurirashowing an inverse J distribution Only three individual treeshad DBH gt 30 cm and these were species of S guineenseB boehmii and B glaucescens U kirkiana had more mem-bers in the higher size classes between 20 and 29 cmDBH

BottomMiddle

Top

050

100150200250300350400450500

Num

ber o

f ste

ms

4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 362Diameter class distribution (cm)

Figure 1 Diameter class distribution of the woody plants at thebottom middle and top of Ngomakurira mountain

34 Basal Area The basal area across the plots rangedfrom 338 to 41m2 haminus1 with a mean of 1521m2 haminus1 Thefollowing species contributed most to the basal area Ukirkiana (156) J globiflora (149) B glaucescens (124)E matabelensis (80) L discolor (55) S guineense (53)M engleri (41) P maprouneifolia (39) and B boehmii(38)

35 Above Ground Biomass Above ground biomassincreased from the bottom to the top of the mountain butshowed no significant difference between the study strataMean AGB among plots ranged from 57 to 1344 tonneshaminus1 Mean AGB ranged from 345 to 651 tonnes haminus1 amongthe study strata (Table 3) The top ten trees that contributedmost to the above ground biomass were U kirkiana (195)J globiflora (133) B glaucescens (129) E matabelensis(66) S guineense (65) L discolor (54) M engleri(53) P maprouneifolia (43) B boehmii (40) and Pcuratellifolia (35)

The AGB was highest in the 5ndash10 cm DBH class and leastin the 20ndash25 cm class (Figure 2) The 5ndash10 cm DBH classcontributed approximately 35 of the total AGB

36 Relationship among the Variables Table 4 shows Spear-manrsquos rank correlation coefficients for the different variablesassessed at Ngomakurira Basal area was positively correlated


Table 4 Spearmanrsquos rank correlation for diversity parameters (lowast indicates significance (119901 lt 005 df = 50))

Basal area Species richness Shannon AGB Stem density EvennessBasal area 1Species richness 0131 1Shannon 0037 0815lowast 1AGB 07858lowast minus0035 minus0085 1Stem density 0471lowast 0228 0049 0053 1Evenness 0061 0219 06889lowast minus0052 minus0132 1

9737

6508

4304

2733

4368

10 15 20 25 30DBH (cm)

0

2000

4000

6000

8000

10000

12000

Biom

ass (

tha)

Figure 2 Distribution of above ground biomass of species indifferent DBH classes

to all the diversity parameters Stem density was positivelycorrelated to basal area species richness diversity and AGBSignificant correlations were found between basal area andAGB (079) stem density and basal area (047) speciesrichness and Shannon-Wiener index (082) and evennessand Shannon-Wiener index (069) Weak correlations werefound between basal area and species richness (013) speciesrichness and stem density (023) and evenness and speciesrichness (022) Negative correlations were found betweenAGB and species richness (minus033) Shannon-Wiener (minus009)and evenness (minus005) and also stem density and evenness(minus013)

37 Vegetation Communities The dendrogram in Figure 3shows sample plots clustering into three vegetation commu-nities

The vegetation communities do not correspond to thealtitude strata used to sample the species as there is a mixtureof plots from the three altitude ranges in each vegetationsubtype The Caesalpinoid species B glaucescens and Jglobiflora and their associated species L discolor V infaustaand Combretum molle occur in all the three vegetationscommunities In community A these common species occurmixed mainly with E matabelensis T velutinum V payosC mossambicensis and M discoidea Community B includesthe common elements plus B boehmii P maprouneifoliaMengleri R longipes O pulchra and D viscosa whilst commu-nity C includes U kirkiana B spiciformis P curatellifolia RlongipesM engleri and P febrifugum

4 Discussion

41 Tree Species Composition Species diversity assessmentsare away of auditing an ecosystem to understand its dynamicsand quality and how disturbance factors are impacting onit [30] The number of species obtained here compares wellwith those obtained in similar dry miombo woodlands inZimbabwe [26] and elsewhere [18 31] Other studies havehowever recorded lower numbers for example [32 33]recorded 17 species and 59 at Mapembe Nature reserve andSave Runde respectively in Zimbabwe and [34] recorded 69species in Mozambique Reference [35] recorded numbers ashigh as 229 from Tanzania Differences in species richnessin miombo woodlands is related to climatic edaphic anddisturbance factors [31]

The dominance of miombo vegetation by legume families(Caesalpiniaceae Papilionaceae and Mimosaceae) is con-firmed in this study as similarly observed in other miomboregions [12 31] However observations of high numbers ofwoody species of the Rubiaceae are unusual as members ofthis family often dominate among the shrubs [31 36] Thisshows that differences in dominance patterns are possible ona local scale in miombo woodlands

Across the study strata IVI indices show altitudinalchanges in dominance patterns of a number of speciesObser-vations of B glaucescens being the dominant species at higheraltitudes and J globiflora at lower altitudes have also beenmade by [37] The middle altitude peak in species richnessobserved in this study is a commonly observed phenomenonin studies on species distribution along altitudinal gradients[38] Several hypotheses have been proposed to explain thisbut all are based on the fact that species ranges vary acrosselevational gradients becoming narrower at the top andhence less richness is observed at those altitudes At middlealtitudes the species ranges overlap thereby maximisingthe number of species [39] Many studies have shown thataltitude is the major determinant of species composition onmountains as such related variables like temperature andedaphic factors vary with altitude [40]

The prevalence of fire tolerant species on the mountainindicates that fire is one of the drivers of species compositionon the mountain References [41 42] made similar observa-tions in studies of fire and elephant affected miombo regionsin Zimbabwe and Mozambique respectively

42 Vegetation Communities The 3 communities shown inthe dendrogram in Figure 2 are typical miombo woodlands


31 40 44 51 30 49 47 50 20 42 39 35 48 16 19 17 45 46 52 29

ABCPlot numbers and woodland subtypes

11 12 22 18 43 25 28 4 13 24 37 1 10 3 8 6 7 5 9 38 41 26 2 21 33 36 32 15 27 14 23 34

0

5

10

15

20

25

Link

age d

istan

ce

Figure 3 Hierarchical cluster analysis dendrogram showing classification of vegetation plots (colour of plot numbersmdashred at the bottomblack middle and blue the top of the mountain)

dominated by the Caesalpinoid species B glaucescens andJ globiflora and their associated species References [31 43]noted that elevation was the most important factor shapingspecies communities inmiombowoodlandsThe results fromthis study support these observations as the spatial distribu-tion of species like B glaucescens J globiflora andU kirkianais influenced by altitude Presence of species confined todifferent strata also supports the influence of altitude inshaping communities Altitude influences water availabilitytemperature and soil characteristics which directly affectplant growth performance

43 Shannon-Wiener Diversity and Evenness The woodlandatNgomakuriramountain has high species diversity as shownby the Shannon-Wiener index values (Table 3) Reference [44]reported that Shannon-Wiener index values fall between 15and 35 for tropical woodlands so the value obtained here iswithin the expected range Ecosystemswith Shannon-Wienervalues greater than 2 are regarded as medium to highlydiverse in terms of species [7] Reference [45] attributed thehigh species diversity in miombo woodlands to the widediversity of habitats present These results are comparable tothose found in Zimbabwe by [26] (Shannon 342) and [30](Shannon 328) and in Tanzania by [31] (Shannon 344) Ourresults are relatively larger than those found by [46] (Shannon28) in Zambia [29] (Shannon 105) in Tanzania and [34](Shannon 125) inMozambique Higher Shannon values havealso been recorded fromTanzanian study [7] (Shannon 427)Differences in richness and diversity possibly result fromdifferences in rainfall regimes [12] anddisturbance history [1]

The similarity in evenness across the study strata showsthat the woodland is dominated by a few species [47]

44 Stem Density and DBH The multistemmed tree archi-tecture observed at Ngomakurira mountain is indicativeof a history of disturbance particularly herbivory [48]Herbivory is high at Ngomakurira mountain as local com-munities have free access to graze their animals Themean stem density obtained in this study (2456 plusmn 1251stems haminus1) was much higher when compared to densi-ties reported from other miombo woodlands (348ndash1495stems haminus1) [7 14 33 34 47] Ngomakurira mountainis therefore well stocked Our results however comparewith those obtained by [31 46] in Tanzania and Zambiarespectively

The DBH distribution (Figure 1) shows a common trendobtained in natural forests where stem densities decreasewith increasing diameter Forests with this inverse ldquoJrdquo shapedcurve DBH distribution pattern are generally said to showactive regeneration and recruitment [12] Recent studieshave questioned reliance on inverse J distributions in forestmanagement as they are based on a biologically unrealisticassumption of equal mortality among size classes [49]Declining populations may also show the classic ldquoinverse Jrdquoshape and some stable populations may not show this shapedue to differences in growth rates among size classes [50]Most of the common miombo woody species reach muchlarger DBH sizes inmature forests [26]The lack of larger sizeclasses may be due to harvesting of such trees for sale in theurban areas as evidenced by numerous stumps in the studyarea Exploitation for local consumption must be minimal asstudies elsewhere show preference for small size classes forfirewood poles and house construction [44] Fire may alsobe a factor as few seedlings may survive to reach mature sizeclasses [31]


45 Basal Area The estimated basal area obtained in thisstudy (1521m2 haminus1) compares with that of [31] (1355 plusmn552) and [49] (15ndash156) in Tanzania and [14] (1434 plusmn052) in Zambia References [25 51] reported lower basalareas Differences with our results may be due to speciescomposition and site differences

46 Above Ground Biomass The AGB of woody trees isthe largest carbon pool of a forest [29] and can be usedto calculate the carbon stock density as 50 of AGB isassumed to be carbon [34] The carbon stock estimate of awoodland is of importance to management for considerationof strategies that promote carbon storage Knowledge ofcarbon stocks is also important in the development of apayments system to reduce emissions from deforestation anddegradation [15] The estimates for above ground biomassfor Ngomakurira mountain (345ndash651 t haminus1) are comparableto those reported by [23] from Zambia and Zimbabwe(52 t haminus1) [29] from Tanzania (464 t haminus1) and [15] fromMozambique (422 t haminus1) Any differences may be due todifferences in growth conditions [42] species compositionallometric equations and plot sizes [29] Reference [12] ina study of sites of different utilisation levels in Tanzaniareported carbon stocks of 146 t haminus1 in high utilisation sites331 t haminus1 in sites of medium utilisation and 285 t haminus1 inareas of low utilisation Using these values as a benchmark wecan conclude that the woodland utilisation at Ngomakurira islow despite the many signs of human disturbance like woodcutting and grazing Formanagement thismeans thatmoder-ate utilisation should be promoted to enhance carbon stocksAGB of Ngomakurira mountain however must be higherthan estimates presented here as the allometric equationsused only included trees with DBH ge 5 cm thereby excludingall trees with DBH lt 5 cm

47 Relationship among the Diversity Variables The positivecorrelation between basal area species richness diversitystem density AGB and evenness means that to get the bestbasal area in a woodland each of the diversity parametersmust be at its maximum This of course in not achievabledue to disturbance factors competition among species andvariability of environmental factors Reference [15]made sim-ilar observations in Mozambique A significant correlationbetween basal area and biomass observed in this study hasalso been observed in similar studies elsewhere [52] Basalarea integrates both the number and size of trees and is agood predictor of biomass [52] The correlation coefficientof 07858 is however slightly lower than expected sincea positive linear relationship was expected This variationmay be a result of the fact that the allometric equationsused were not generated locally or differences in speciescomposition between our study area and the areas wheresuch equations were generated High correlations betweenShannon and species richness and evenness were expectedas these measures of diversity in a way represent the samephenomenon [53]

The observations of a negative correlation between AGBand species richness and diversity and a positive correlation

between AGB and stem density corroborate finding by[54]

5 Conclusions

The vegetation at Ngomakurira is typical miombo dominatedby the species Brachystegia glaucescens Julbernardia globi-flora and Uapaca kirkiana The woodland has high speciesrichness and diversity showing that it is in good conditionThis is further supported by the DBH distribution whichshows an inverse J shape typical of stable populations This isgood sign of the sustainability of the woodland stock whichensures sustainable supply of goods and services to localcommunities Effects of anthropogenic effects were evidentandwould requiremanagement but detailed studies on levelsof resource extraction required for sustainable managementof the woodland need to be carried out This is importantin conservation because humans residing next to protectedareas need resources from such areas and conflicts often arisewhen they are prevented from accessing such resources

Appendix

Woody Trees Recorded at Ngomakurira

Taxa

Anacardiaceae

Lannea discolor (Sond) EnglOzoroa reticulata (Baker f) R amp A FernRhus leptodictya DielsRhus longipes Engl

Annonaceae

Hexalobus monopetalus (A Rich) Engl amp Diels

Apocynaceae

Diplorhynchus condylocarpon (Mull Arg) Pi-chon

Araliaceae

Cussonia arborea A Rich

Asteraceae

Brachylaena discolor DC

Burseraceae

Commiphora marlothii EnglCommiphora mollis (Oliv) EnglCommiphora mossambicensis (Oliv) Engl

Celastraceae

Gymnosporia senegalensis (Lam) Loes


Chrysobalanaceae

Parinari curatellifolia Benth

Clusiaceae

Garcinia buchananii BakerPsorospermum febrifugum Spach

Combretaceae

Combretum apiculatum SondCombretum collinum FresenCombretum molle G DonTerminalia brachystemmaHiernTerminalia stenostachya Engl amp Diels

Dipterocarpaceae

Monotes engleri GilgMonotes glaber Sprague

Ebenaceae

Diospyros natalensis (Harv) BrenanEuclea natalensis A DC

Euphorbiaceae

Euphorbia matabelensis Pax

Fabaceae-Caesalpinioideae

Brachystegia boehmii TaubBrachystegia glaucescens Burtt Davy amp HutchBrachystegia spiciformis BenthBrachystegia utilis Burtt Davy amp HutchBurkea africanaHookJulbernardia globiflora (Benth) TroupinPeltophorum africanum SondSenna singueana (Delile) Lock

Fabaceae-Mimosoideae

Acacia goetzei Harms subsp microphylla Bre-nanAlbizia antunesianaHarmsDichrostachys cinerea (L) Wight amp ArnElephantorrhiza goetzei (Harms) Harms

Fabaceae-Papilionoideae

Bobgunnia madagascariensis (Desv) J HKirkbr amp WiersemaDalbergia nitidula BakerErythrina abyssinica DCPericopsis angolensis (Baker) MeeuwenPterocarpus angolensis DC

Flacourtiaceae

Flacourtia indica (Burm f) Merr

Heteropyxidaceae

Heteropyxis dehniae Suess

Lamiaceae

Rotheca myricoides (Hochst) D A Steane ampMabbTetradenia riparia (Hochst) CoddVitex mombassae VatkeVitex payos (Lour) Merr

Maesaceae

Maesa lanceolata Forssk

Meliaceae

Turraea nilotica Kotschy amp Peyr

Moraceae

Ficus natalensisHochst subsp natalensisFicus sur Forssk

Myrtaceae

Psidium guajava LSyzygium cordatumHochst ex C KraussSyzygium guineense (Willd) DC subspafromontanum F White

Ochnaceae

Ochna inermis (Forssk) PenzOchna pulchraHook subsp pulchra

Olacaceae

Ximenia caffra Sond

Phyllanthaceae

Bridelia cathartica G Bertol subsp melanthe-soides (Baill) J LeonardFlueggea virosa (Willd) Voigt subsp virosaMargaritaria discoidea (Baill) G LWebster varnitida (Pax) Radcl-SmPseudolachnostylis maprouneifolia PaxUapaca kirkianaMull ArgUapaca nitidaMull Arg var nitida

Pittosporaceae

Pittosporum viridiflorum Sims var


Proteaceae

Faurea rochetiana (A Rich) Pic SermFaurea salignaHarvProtea welwitschii Engl

Rhamnaceae

Ziziphus mucronataWilld

Rubiaceae

Afrocanthium lactescens (Hiern) LantzCatunaregam taylorii (S Moore) BridsonCoptosperma neurophyllum (S Moore) DegreefEmpogona kirkiiHook f subsp kirkiiGardenia ternifolia Schumach ampThonn subspjovis-tonantis (Welw) VerdcHymenodictyon floribundum (Hochst amp Steud)B L RobMussaenda arcuata PoirPavetta schumanniana K SchumPsydrax livida (Hiern) BridsonTapiphyllum velutinum (Hiern) RobynsVangueria infausta Burch subsp infausta

Rutaceae

Casimiroa edulis La Llave

Sapindaceae

Allophylus africanus P BeauvDodonaea viscosa JacqZanha africana (Radlk) Exell

Sapotaceae

Englerophytum magalismontanum (Sond) T DPenn

Sterculiaceae

Dombeya rotundifolia (Hochst) Planch

Strychnaceae

Strychnos madagascariensis PoirStrychnos spinosa Lam

Umbelliferae-Apiaceae

Steganotaenia araliaceaHochst

Urticaceae

Pouzolzia mixta Solms

Verbenaceae

Lantana camara L

Competing Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The authors would like to thank the Director of the NationalMuseums and Monuments Mr Bvocho for permission tocarry out the study atNgomakuriraMountainThey are grate-ful for support in the field byMuteyo TsitsiMadondoKudak-washe Haurovi Vimbayi Mazambani Simbarashe MapfumoEustina Mazivisa Rumbidzai and Tembo Michelle staff ofthe National Herbarium and Botanic Garden Transport tothe study site was provided by the National Herbarium andBotanic Garden and the Biological Sciences DepartmentUniversity of Zimbabwe

References

[1] B M Campbell P Frost and N Byron ldquoMiombo Woodlandsand their useoverview and key issuesrdquo in The Miombo inTransitionWoodlands and Welfare in Africa B Campbell Edpp 1ndash10 Centre for International Forestry Research BogorIndonesia 2006

[2] R A Mittermeier C G Mittermeier T M Brooks et alldquoWilderness and biodiversity conservationrdquo Proceedings of theNational Academy of Sciences of the United States of Americavol 100 no 18 pp 10309ndash10313 2003

[3] C M Shackleton and M Mander ldquoThe value of resources usedinwoodlandsrdquo in South African ForestryHandbook D L OwenEd pp 635ndash641 SAIF Pretoria South Africa 2000

[4] C M Shackleton and S E Shackleton ldquoThe use of woodlandresources for direct household provisioningrdquo in IndigenousForests and Woodlands in South Africa Policy People andPractice M J Lawes H C Eeeley C M Shackleton and B SGeach Eds pp 195ndash226 University of KwaZulu-Natal PressPietermaritzburg South Africa 2004

[5] Forestry Commission State of Forest Genetic Resources in Zim-babwe 2002ndash2011 A Country Report Ministry of Environmentand Natural Resources Management Harare Zimbabwe 2011

[6] T G OrsquoConnor ldquoInfluence of land use on plant communitycomposition and diversity in Highland Sourveld grassland inthe southern Drakensberg South Africardquo Journal of AppliedEcology vol 42 no 5 pp 975ndash988 2005

[7] R A Giliba E K Boon C J Kayombo E B Musamba AM Kashindye and P F Shayo ldquoSpecies composition richnessand diversity in Miombo Woodland of Bereku Forest ReserveTanzaniardquo Journal of Biodiversity vol 2 no 1 pp 1ndash7 2011

[8] J Kimaro and L Lulandala ldquoHuman influences on tree diversityand composition of a coastal forest ecosystem the case of ngum-buruni forest reserve Rufiji Tanzaniardquo International Journal ofForestry Research vol 2013 Article ID 305874 7 pages 2013

[9] E J Luoga E T F Witkowski and K Balkwill ldquoRegenerationby coppicing (resprouting) of miombo (African savanna) treesin relation to land userdquo Forest Ecology andManagement vol 189no 1ndash3 pp 23ndash35 2004

[10] K Rennolls and Y Laumonier ldquoSpecies diversity structureanalysis at two sites in the tropical rain forest of SumatrardquoJournal of Tropical Ecology vol 16 no 2 pp 253ndash270 2000


[11] I Backeus B Pettersson L Stromquist and C RuffoldquoTree communities and structural dynamics in miombo(Brachystegia-Julbernardia) woodland Tanzaniardquo Forest Ecol-ogy and Management vol 230 no 1ndash3 pp 171ndash178 2006

[12] E K K Jew A J Dougill S M Sallu J OrsquoConnell and TG Benton ldquoMiombo woodland under threat consequencesfor tree diversity and carbon storagerdquo Forest Ecology andManagement vol 361 pp 144ndash153 2016

[13] P A Dewees B M Campbell Y Katerere et al ldquoManaging themiombo woodlands of southern Africa policies incentives andoptions for the rural poorrdquo Journal of Natural Resources PolicyResearch vol 2 no 1 pp 57ndash73 2010

[14] F K Kalaba C H Quinn and A J Dougill Contributionof forest provisioning ecosystem services to rural livelihoods inCopperbeltrsquosMiombowoodlands Zambia Sustainability ResearchInstitute (SRI) vol No 41 School of Earth and EnvironmentThe University of Leeds Leeds UK 2012

[15] C M Ryan M Williams and J Grace ldquoAbove- and below-ground carbon stocks in a miombo woodland landscape ofMozambiquerdquo Biotropica vol 43 no 4 pp 423ndash432 2011

[16] E N Chidumayo Miombo Ecology and Management AnIntroduction IT Publications inAssociationwith the StockholmEnvironment Institute London UK 1997

[17] R E Malimbwi B Solberg and E Luoga ldquoEstimation ofbiomass and volume in miombo woodland at KitulangaloForest Reserve Tanzaniardquo Journal of Tropical Forest Science vol7 no 2 pp 230ndash242 1994

[18] S A O Chamshama A G Mugasha and E Zahabu ldquoBiomassand volume estimation for miombo woodlands at KitulangaloMorogoro Tanzaniardquo Southern African Forestry Journal vol200 pp 49ndash60 2004

[19] C Murcia ldquoEdge effects in fragmented forests implications forconservationrdquo Trends in Ecology amp Evolution vol 10 no 2 pp58ndash62 1995

[20] S Moyo Zimbabwe Environmental Dilemma BalancingResource Inequities Zimbabwe Environmental ResearchOrganization Harare Zimbabwe 2000

[21] K Nyamapfene Soils of Zimbabwe Nehanda Harare Zim-babwe 1991

[22] H Wild and L A G Barbosa Vegetation Map of the FloraZambesiaca Flora Zambesiaca Supplement MO CollinsHarare Zimbabwe 1968

[23] P Frost ldquoThe ecology of Miombo Woodlandsrdquo inThe Miomboin Transition Woodlands and Welfare in Africa B CampbellEd pp 11ndash57 Centre for International Forestry Research(CIFOR) Bogor Indonesia 1996

[24] D Mueller-Dombois and H Ellenburg Aims and Methods ofVegetation Ecology John Wiley amp Sons New York NY USA1974

[25] B M Campbell R N Cunliffe and J Gambiza ldquoVegeta-tion structure and small-scale pattern in Miombo WoodlandMarondera Zimbabwerdquo Bothalia vol 25 no 1 pp 121ndash1261995

[26] C Zimudzi A Mapaura C Chapano and W Duri ldquoWoodyspecies composition structure and diversity ofMazowe Botani-cal Reserverdquo Zimbabwe Journal of Biodiversity and Environmen-tal Sciences vol 3 no 6 pp 17ndash29 2013

[27] A E Magurran Measuring Biological Diversity BlackwellOxford UK 2004

[28] H K Gibbs S Brown J O Niles and J A Foley ldquoMonitoringand estimating tropical forest carbon stocks Making REDD

a realityrdquo Environmental Research Letters vol 2 no 4 article13 2007

[29] D D Shirima P K T Munishi S L Lewis et al ldquoCarbonstorage structure and composition of miombo woodlands inTanzaniarsquos Eastern Arc Mountainsrdquo African Journal of Ecologyvol 49 no 3 pp 332ndash342 2011

[30] V N Kalema Diversity use and resilience of woody plants ina multiple land-use equatorial African Savanna Uganda [PhDthesis] University of the Witwatersrand Johannesburg SouthAfrica 2010

[31] E E Mwakalukwa H Meilby and T Treue ldquoFloristic com-position structure and species associations of dry Miombowoodland in Tanzaniardquo ISRN Biodiversity vol 2014 Article ID153278 15 pages 2014

[32] N Muboko M R Mushonga N Chibuwe C Mashapa andE Gandiwa ldquoWoody vegetation structure and compositionin Mapembe Nature Reserve eastern Zimbabwerdquo Journal ofApplied Sciences and Environmental Management vol 17 no 4pp 475ndash481 2013

[33] P Gandiwa E Chinoitezvi and E Gandiwa ldquoStructure andcomposition of woody vegetation in two important bird areasin southern Zimbabwerdquo Journal of Animal amp Plant Sciences vol23 no 3 pp 813ndash820 2013

[34] M Williams C M Ryan R M Rees E Sambane J Fernandoand J Grace ldquoCarbon sequestration and biodiversity of re-growing miombo woodlands in Mozambiquerdquo Forest Ecologyand Management vol 254 no 2 pp 145ndash155 2008

[35] T Banda NMwangulango BMeyer et al ldquoThewoodland veg-etation of the Katavi-Rukwa ecosystem in western TanzaniardquoForest Ecology andManagement vol 255 no 8-9 pp 3382ndash33952008

[36] N R Ndah E A Egbe and E Bechem ldquoSpecies compositiondiversity and distribution in a disturbed Takamanda RainforestSouth West Cameroonrdquo African Journal of Plant Science vol 7no 12 pp 577ndash585 2013

[37] T Mujawo Influence of physiographic edaphic and anthro-pogenic factors on the structure composition and species diversityof vegetation in the Mazowe Botanical reserve Christon bank[MS thesis] University of Zimbabwe 2005

[38] J A Grytnes and O R Vetaas ldquoSpecies richness and altitude acomparison between null models and interpolated plant speciesrichness along the himalayan altitudinal gradient NepalrdquoAmerican Naturalist vol 159 no 3 pp 294ndash304 2002

[39] K R Bhattarai and O R Vetaas ldquoCan Rapoportrsquos rule explaintree species richness along the Himalayan elevation gradientNepalrdquo Diversity and Distributions vol 12 no 4 pp 373ndash3782006

[40] J Gao and Y Zhang ldquoDistributional patterns of species diver-sity of main plant communities along altitudinal gradient insecondary forest region Guandi Mountain Chinardquo Journal ofForestry Research vol 17 no 2 pp 111ndash115 2006

[41] I Mapaure and S R Moe ldquoChanges in the structure andcomposition of miombo woodlands mediated by elephants(Loxodonta africana) and fire over a 26-year period in north-western Zimbabwerdquo African Journal of Ecology vol 47 no 2pp 175ndash183 2009

[42] N S Ribeiro C N Matos I R Moura R A Washington-Allen and A I Ribeiro ldquoMonitoring vegetation dynamics andcarbon stock density in miombo woodlandsrdquo Carbon Balanceand Management vol 8 article 11 2013

[43] P K T Munishi R A P C Temu and G Soka ldquoPlant com-munities and tree species associations in a miombo ecosystem


in the Lake Rukwa basin Southern Tanzania Implications forconservationrdquo Journal of Ecology and the Natural Environmentvol 3 pp 63ndash71 2011

[44] P Savadogo M Tigabu L Sawadogo and P C Oden ldquoWoodyspecies composition structure and diversity of vegetationpatches of a Sudanian savanna in Burkina Fasordquo Bois et Foretsdes Tropiques vol 294 no 4 pp 5ndash20 2007

[45] B M Campbell P Frost and N Byron ldquoMiombo woodlandsand their use overview and key issuesrdquo inTheMiombo in Tran-sition Woodlands and Welfare in Africa 1996 B Campbell Edpp 1ndash5 Center for International Forestry Research (CIFOR)Bogor Indonesia 1996

[46] F K Kalaba C H Quinn A J Dougill and R VinyaldquoFloristic composition species diversity and carbon storage incharcoal and agriculture fallows and management implicationsin Miombo woodlands of Zambiardquo Forest Ecology and Manage-ment vol 304 pp 99ndash109 2013

[47] S M S Maliondo W S Abeli R E L O Meiludie GA Mugunga and A A Kimaro ldquoTree species compositionand potential timber production of a communal miombowoodlandin Handeni district Tanzaniardquo Journal of TropicalForestry Science vol 17 no 1 pp 104ndash120 2005

[48] A J Tanentzap E P Mountford A S Cooke and D ACoomes ldquoThe more stems the merrier advantages of multi-stemmed architecture for the demography of understorey treesin a temperate broadleaf woodlandrdquo Journal of Ecology vol 100no 1 pp 171ndash183 2012

[49] J A Isango ldquoStand structure and tree species compositionof Tanzania miombo woodlands a case study from miombowoodlands of community-based forest management in IringaDistrictrdquo in Proceedings of the 1st MITMIOMBO Project Work-shop vol 50 of Working Papers of the Finnish Forest ResearchInstitute pp 43ndash56 Morogoro Tanzania February 2007

[50] C B Virillo F R Martins J Y Tamashiro and F A M dosSantos ldquoIs size structure a good measure of future trends ofplant populations An empirical approach using five woodyspecies from the cerrado (Brazilian savanna)rdquo Acta BotanicaBrasilica vol 25 no 3 pp 593ndash600 2011

[51] A A Sitoe ldquoStructure composition and dynamics of a decidu-ousmiombo afterlogging and its application to themanagementfor timber productionrdquo in Proceedings of the Workshop onIntegrated Analysis and Management of Renewable NaturalResources in Mozambique P V Desanker and L Santos EdsMaputo Mozambique June 1999

[52] A B Torres and J C Lovett ldquoUsing basal area to estimateaboveground carbon stocks in forests la Primavera BiospherersquosReserve Mexicordquo Forestry vol 86 no 2 pp 267ndash281 2013

[53] E K Morris T Caruso F Buscot et al ldquoChoosing and usingdiversity indices insights for ecological applications from theGerman Biodiversity Exploratoriesrdquo Ecology and Evolution vol4 no 18 pp 3514ndash3524 2014

[54] S C Sahu H S Suresh and N H Ravindranath ldquoForeststructure composition and above ground biomass of treecommunity in tropical dry forests of Eastern Ghats IndiardquoNotulae Scientia Biologicae vol 8 no 1 pp 125ndash133 2016

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of


Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology


Molecular Biology International

GenomicsInternational Journal of


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


BioinformaticsAdvances in

Marine BiologyJournal of



Signal TransductionJournal of


BioMed Research International

Evolutionary BiologyInternational Journal of



Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Genetics Research International


Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational



Enzyme Research



Microbiology


near the village of Domboshawa in the headwaters of theMazowe river The mountain derives its name from thetraditional African drums that were played during religiousfestivals The mountain is considered sacred by the localcommunity and is used for various religious rites Old Sanrock art paintings are also found in caves and on the verticalrock face of the main mountain The mountain is protectedand managed by the National Museums and Monumentsof Zimbabwe to conserve its cultural and archaeologicalvalue biodiversity and catchment value Ngomakurira Trustformed by the Goromonzi Rural District Council in partner-ship with National Museums and Monuments of Zimbabweas well as the Environmental Management Agency (EMA)initiated the ecotourism project at Ngomakurira mountainby converting hills used communally for grazing fuel woodcollection and harvesting of wild fruits into a properlymanaged tourist facility

Ngomakurira mountain is surrounded by communalareas and such areas are vulnerable to anthropogenic activ-ities and edge effects [19] as local communities continue todepend on the woodlands for sustenance A participatoryforest management approach (PFM) which involves locals inmanagement decisions has recently been implemented for anecotourism project at Ngomakurira mountainThe success ofthe project will depend on an understanding of the woodlanddynamics and especially the role of disturbance factors inshaping the woodland structure

This study seeks to determine the following (a) thespecies composition structure and diversity and biomassaccumulation of woody plants on Ngomakurira mountain(b) the variability of these diversity indices across the sampleplots and (c) the relationships between species diversityrichness basal area and above ground biomass

2 Materials and Methods

21 Study Area Ngomakurira is a large granite mountainlocated in the Goromonzi district in Chinamora communallands approximately 30 km NE of Harare in Zimbabwe It islocated between 17∘321015840464610158401015840Sndash17∘351015840349210158401015840S latitudes and31∘141015840216110158401015840Endash31∘151015840518910158401015840E longitudes Altitude varies from1307 to 1638 metres above sea level The total area covered bythe mountain is 1266 ha

Ngomakurira mountain is located in agroecological zoneIIa [20] with rainfall fairly reliable falling from Novemberto MarchApril and mean annual rainfall ranging from750 to 1 000mmyear Mean annual temperature range is15ndash20∘C The soils are shallow and generally classified asparaferralitic soils [21] Deep soils are found at the base ofthe hill getting shallower and rocky to absent at the higherlevels Ngomakurira lies in a communal area with poor ruralhouseholds mainly involved in subsistence farming thoughsome are involved in horticulture to supply urban markets inHarare

The study area falls under the deciduousmiombo savannawoodland [22] a vegetation type dominated by Brachystegiaspp and Julbernardia globiflora Reference [23] described thistype of vegetation as dry miombo The vegetation on themountain is concentrated in the lower and middle zone of

the mountain with most of the top area consisting largely ofbare rock

22 Sampling Design and Data Collection Field work wasconducted in February 2016 An initial study of satelliteimages of the study area followed by a field reconnaissancesurvey [24] assisted inmaking decisions on sampling strategyto use From the surveys the vegetation of the mountainappears mixed with no obvious vegetation communitiesWe however divided the mountain into three sections thevegetation at the base middle and top of the mountain Ineach section quadrats measuring 10 times 10m were demarcatedfollowing the methods by [25] Random numbers were usedto locate the position of plots in each stratum [24] Wesampled 15 quadrats at the base 25 in themiddle and 12 at thetop Sample plots were distributed according to the relativesizes of the strata

Global positioning system (GPS) measurements weretaken for each sample plot using a Garmin 12XL GlobalPositioning System In each plot all woody species wereidentified in the field and a few were collected and verified atthe National Herbarium in Harare Tree height was visuallyestimated and stem circumference at 13m was measuredusing a tape measure for plants with a circumference ge8 cm [26] Formultistemmed plants the circumferences weremeasured separately

23 Data Analysis

231 Floristic Indices and Biodiversity The data consistingof plot numbers species and circumference was enteredinto an Excel spreadsheet and the following parameters werecalculated

(1) DBH = circumference120587(2) Basal area = 120587 lowast DBH2(3) Relative dominance = (total basal area for a

speciestotal basal area of all species) times 100(4) Relative density = (number of individuals of a

speciestotal number of individuals) times 100(5) Relative frequency = (frequency of a speciessum of

all frequencies) times 100(6) Relative diversity = (number of a species in a fam-

ilytotal number of species) times 100(7) The importance value index (IVI) = relative domi-

nance + relative density + relative frequency(8) The family importance value (FIV) = relative domi-

nance + relative density + relative diversity(9) Shannon-Wienerrsquos index of diversity 119867 = minusΣpi ln pi

where pi = 119899119894119873 119899119894 is the number of individual treespresent for species 119894 and 119873 is the total number ofindividuals [27]

(10) Evenness =1198671015840 ln 119878 where1198671015840 is the Shannon-Wienerindex and 119878 is the total number of species

(11) The Kruskal-Wallis 119867 test was done using SPSSstatistical package version 160 (2007)







Relativedensity ()

Relativedominance


Bottom




Middle



Top






3 Results














BottomMiddle

Top

050

100150200250300350400450500

Num

ber o

f ste

ms










9737

6508

4304

2733

4368

10 15 20 25 30DBH (cm)

0

2000

4000

6000

8000

10000

12000

Biom

ass (

tha)





4 Discussion







31 40 44 51 30 49 47 50 20 42 39 35 48 16 19 17 45 46 52 29


11 12 22 18 43 25 28 4 13 24 37 1 10 3 8 6 7 5 9 38 41 26 2 21 33 36 32 15 27 14 23 34

0

5

10

15

20

25

Link

age d

istan

ce













5 Conclusions


Appendix


Taxa

Anacardiaceae


Annonaceae


Apocynaceae


Araliaceae


Asteraceae


Burseraceae


Celastraceae



Chrysobalanaceae


Clusiaceae


Combretaceae


Dipterocarpaceae


Ebenaceae


Euphorbiaceae








Flacourtiaceae


Heteropyxidaceae


Lamiaceae


Maesaceae


Meliaceae


Moraceae


Myrtaceae


Ochnaceae


Olacaceae

Ximenia caffra Sond

Phyllanthaceae


Pittosporaceae



Proteaceae


Rhamnaceae


Rubiaceae


Rutaceae


Sapindaceae


Sapotaceae


Sterculiaceae


Strychnaceae




Urticaceae


Verbenaceae

Lantana camara L

Competing Interests


Acknowledgments


References


































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology







Relativedensity ()

Relativedominance


Bottom




Middle



Top






3 Results














BottomMiddle

Top

050

100150200250300350400450500

Num

ber o

f ste

ms










9737

6508

4304

2733

4368

10 15 20 25 30DBH (cm)

0

2000

4000

6000

8000

10000

12000

Biom

ass (

tha)





4 Discussion







31 40 44 51 30 49 47 50 20 42 39 35 48 16 19 17 45 46 52 29


11 12 22 18 43 25 28 4 13 24 37 1 10 3 8 6 7 5 9 38 41 26 2 21 33 36 32 15 27 14 23 34

0

5

10

15

20

25

Link

age d

istan

ce













5 Conclusions


Appendix


Taxa

Anacardiaceae


Annonaceae


Apocynaceae


Araliaceae


Asteraceae


Burseraceae


Celastraceae



Chrysobalanaceae


Clusiaceae


Combretaceae


Dipterocarpaceae


Ebenaceae


Euphorbiaceae








Flacourtiaceae


Heteropyxidaceae


Lamiaceae


Maesaceae


Meliaceae


Moraceae


Myrtaceae


Ochnaceae


Olacaceae

Ximenia caffra Sond

Phyllanthaceae


Pittosporaceae



Proteaceae


Rhamnaceae


Rubiaceae


Rutaceae


Sapindaceae


Sapotaceae


Sterculiaceae


Strychnaceae




Urticaceae


Verbenaceae

Lantana camara L

Competing Interests


Acknowledgments


References


































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology









BottomMiddle

Top

050

100150200250300350400450500

Num

ber o

f ste

ms










9737

6508

4304

2733

4368

10 15 20 25 30DBH (cm)

0

2000

4000

6000

8000

10000

12000

Biom

ass (

tha)





4 Discussion







31 40 44 51 30 49 47 50 20 42 39 35 48 16 19 17 45 46 52 29


11 12 22 18 43 25 28 4 13 24 37 1 10 3 8 6 7 5 9 38 41 26 2 21 33 36 32 15 27 14 23 34

0

5

10

15

20

25

Link

age d

istan

ce













5 Conclusions


Appendix


Taxa

Anacardiaceae


Annonaceae


Apocynaceae


Araliaceae


Asteraceae


Burseraceae


Celastraceae



Chrysobalanaceae


Clusiaceae


Combretaceae


Dipterocarpaceae


Ebenaceae


Euphorbiaceae








Flacourtiaceae


Heteropyxidaceae


Lamiaceae


Maesaceae


Meliaceae


Moraceae


Myrtaceae


Ochnaceae


Olacaceae

Ximenia caffra Sond

Phyllanthaceae


Pittosporaceae



Proteaceae


Rhamnaceae


Rubiaceae


Rutaceae


Sapindaceae


Sapotaceae


Sterculiaceae


Strychnaceae




Urticaceae


Verbenaceae

Lantana camara L

Competing Interests


Acknowledgments


References


































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology




9737

6508

4304

2733

4368

10 15 20 25 30DBH (cm)

0

2000

4000

6000

8000

10000

12000

Biom

ass (

tha)





4 Discussion







31 40 44 51 30 49 47 50 20 42 39 35 48 16 19 17 45 46 52 29


11 12 22 18 43 25 28 4 13 24 37 1 10 3 8 6 7 5 9 38 41 26 2 21 33 36 32 15 27 14 23 34

0

5

10

15

20

25

Link

age d

istan

ce













5 Conclusions


Appendix


Taxa

Anacardiaceae


Annonaceae


Apocynaceae


Araliaceae


Asteraceae


Burseraceae


Celastraceae



Chrysobalanaceae


Clusiaceae


Combretaceae


Dipterocarpaceae


Ebenaceae


Euphorbiaceae








Flacourtiaceae


Heteropyxidaceae


Lamiaceae


Maesaceae


Meliaceae


Moraceae


Myrtaceae


Ochnaceae


Olacaceae

Ximenia caffra Sond

Phyllanthaceae


Pittosporaceae



Proteaceae


Rhamnaceae


Rubiaceae


Rutaceae


Sapindaceae


Sapotaceae


Sterculiaceae


Strychnaceae




Urticaceae


Verbenaceae

Lantana camara L

Competing Interests


Acknowledgments


References


































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


31 40 44 51 30 49 47 50 20 42 39 35 48 16 19 17 45 46 52 29


11 12 22 18 43 25 28 4 13 24 37 1 10 3 8 6 7 5 9 38 41 26 2 21 33 36 32 15 27 14 23 34

0

5

10

15

20

25

Link

age d

istan

ce













5 Conclusions


Appendix


Taxa

Anacardiaceae


Annonaceae


Apocynaceae


Araliaceae


Asteraceae


Burseraceae


Celastraceae



Chrysobalanaceae


Clusiaceae


Combretaceae


Dipterocarpaceae


Ebenaceae


Euphorbiaceae








Flacourtiaceae


Heteropyxidaceae


Lamiaceae


Maesaceae


Meliaceae


Moraceae


Myrtaceae


Ochnaceae


Olacaceae

Ximenia caffra Sond

Phyllanthaceae


Pittosporaceae



Proteaceae


Rhamnaceae


Rubiaceae


Rutaceae


Sapindaceae


Sapotaceae


Sterculiaceae


Strychnaceae




Urticaceae


Verbenaceae

Lantana camara L

Competing Interests


Acknowledgments


References


































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology







5 Conclusions


Appendix


Taxa

Anacardiaceae


Annonaceae


Apocynaceae


Araliaceae


Asteraceae


Burseraceae


Celastraceae



Chrysobalanaceae


Clusiaceae


Combretaceae


Dipterocarpaceae


Ebenaceae


Euphorbiaceae








Flacourtiaceae


Heteropyxidaceae


Lamiaceae


Maesaceae


Meliaceae


Moraceae


Myrtaceae


Ochnaceae


Olacaceae

Ximenia caffra Sond

Phyllanthaceae


Pittosporaceae



Proteaceae


Rhamnaceae


Rubiaceae


Rutaceae


Sapindaceae


Sapotaceae


Sterculiaceae


Strychnaceae




Urticaceae


Verbenaceae

Lantana camara L

Competing Interests


Acknowledgments


References


































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


Chrysobalanaceae


Clusiaceae


Combretaceae


Dipterocarpaceae


Ebenaceae


Euphorbiaceae








Flacourtiaceae


Heteropyxidaceae


Lamiaceae


Maesaceae


Meliaceae


Moraceae


Myrtaceae


Ochnaceae


Olacaceae

Ximenia caffra Sond

Phyllanthaceae


Pittosporaceae



Proteaceae


Rhamnaceae


Rubiaceae


Rutaceae


Sapindaceae


Sapotaceae


Sterculiaceae


Strychnaceae




Urticaceae


Verbenaceae

Lantana camara L

Competing Interests


Acknowledgments


References


































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


Proteaceae


Rhamnaceae


Rubiaceae


Rutaceae


Sapindaceae


Sapotaceae


Sterculiaceae


Strychnaceae




Urticaceae


Verbenaceae

Lantana camara L

Competing Interests


Acknowledgments


References


































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology
























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

Research Article Diversity, Population Structure, and...

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