PULA Journal of African Studies, vo.1 11 nO.2 (1997) The patterns of Acacia albida species from twenty provenances across Africa N. M. Maleele* & C. Marunda** • Department of Environmental Sciences, University of Botswana - Forestry Commission, Forest Research Centre, P. O. Box 595, Highlands, Harare, Zimbabwe. This study was carried out to determine genetic variation patterns among the Acacia albida species from different provenances in Africa. A wide genetic base would offer tree breeders a large amount of material from which to select and manipulate through breeding for genotypes that suit particular environmental conditions. Seeds of the species were collected from twenty provenances around Africa and seedlings raised in Canberra, Australia, and various traits measured over a six months period. Patterns were observed in the data set. Groups of countries with Acacia albida populations sharing the same genetic patterns (characteristics) were produced using the principal component analysis. Three groups of Acacia albida were obtained: the species that belong to Southern Africa, West Africa and Northeast Africa. The three groups obtained show some relationship with the phytogeogrgphy regions of Africa. However, the population within each group obtained are not compacted, which implies that they may still be some significant variation within each group. This paper therefore recommends that genetic patterns on Acacia albida at a local level should be further investigated. Introduction In recent years, Africa has been faced with dwindling natural forests. According to FAO (1981) quoted in Marunda (1993), nearly 0.44% of indigenous tree fonnations in west, east and southern Africa are cleared annually. Such a position is a result of the over dependence by Africa's rural populations on the natural forest resource for construction and home building timber, fuel wood and leaffodder for grazing livestock. At the turn of the century, the building of railways, mining of minerals and construction of settlement infrastructure has lead to an unprecedented use of timber which has caused further depletion of natural forest resource (Marunda, 1993). To address this situation, African countries invested in industrial plantation forests emphasising primarily on exotic fast growing pines and eucalyptus species, with hope that these would reduce the pressure on natural woodlots (Tietema, 1984). Most of the exotic species used in industrial plantations, however, require prime sites in tenns of climatic and edaphic conditions. This specific requirement resulted in most of the plantations to be located on high quality sites (Marunda, 1993). The sites endowed with less productive natural forests were neglected leaving regenerations to maintain the forest status. There is a need to know whether natural forests could be improved, especially in the fragile ecology of the savannas. Particular attention is given to improving the situation by planting Acacia species indigenous to Africa. Marunda (1993) and Walker (1992) noted that one of the species whose potential has been identified in many countries within Africa is Acacia albida. This potential lies in its versatility as a source of fodder, complementary in tree-crop agroforestry systems and ability to fix nitrogen. Fanners in semi-arid Africa and especially in Senegal, Sudan and Ethiopia intercrop or manage Acacia albida with cereal crops both for the benefit of the crop and especially to supply winter fodder or dry season fodder (Walker, 1992). Acacia albida is also important for construction timber, livestock kraals, firewood and fencing cultivated fields. Therefore, 166 to take full advantage of this species, there is a need to improve it genetically, Like many other tree species, the first step is to identifY the patterns of species variation throughout its natural range. Purpose of the Study The purpose of this study is to determine whether there is any phenotypic (genetic) variation patterns among the Acacia albida from different provenances in Africa. A wide genetic base would offer tree breeders a large amount of material from which to select and manipulate through breeding for genotypes that suit particular environmental conditions (Marunda, 1993). The study will also test the usefulness of the principal component analysis (PCA) in search for the patterns. The traits measured from seeds of different provenances in Africa will be used in the PCA to classifY the provenances into similar groups (if any). Table 1 The Provenances and Their Countries of Orif;!in, Accession No. Provenance Country 10000 Kuiseb Namibia 20000 Taupye Botswana 30000 Gonarezhou Zimbabwe 40000 Lukunguni Zimbabwe 50000 Gokwe Zimbabwe 60000 Palm Tree Zimbabwe 70000 Mana Pools Zimbabwe 80000 Kafue Flats Zimbabwe 90000 Chiyenda Zimbabwe 100000 Chilanga Zambia Chizombo Malawi 110000 Bwanje Malawi 120000 Lodwar Kenya 130000 Wenji Ethiopia 140000 Debrezeit Ethiopia 150000 Kokologho Burkina Faso 160000 Makary Cameroon 170000 Bignoma Senegal 180000 Tera Niger 190000 Oursi Burkina Faso 200000 Materials and Methods Field and Laboratory procedures sed in this analysis represents part of the data that was collected by Th e d a ta set U h A I' N' I U' ' . Chrispine Marunda of the Forestry Department at t e ustra Ian atlOna mverslty III 167 1991. Seeds of Acacia a/bida were collected from twenty provenances around Africa (Table 1 above), whose temperatures and latitudes were recorde~.. , . Arrangements were made to raise seedlings under q~a.r~ntme restnctlOns smce the species is prohibited in Australia (Marunda, 1993). FaCIlItIes at the ~anberra C~~RO Division of Plant Industry were used. The seeds were grown under umform condItIOns in a glasshouse and thirteen traits (Table 2) measured over a six months period, and the details about measurements are provided in Marunda (1993). Table 2 Traits Measured From Seedlinfls and Abbreviations Used in the Analvsis Trait Name Abbreviation Measurement unit Total root dry weight Totr g Biomass Bio g Latitude Lat Temperature Tep °c Seed weight Sdwt no.seedslkg Root collar diameter RCD mm Height after 5 months HT5 em Leaf numbers Lfno number Stipule length Stle em Branch numbers Brno number Stem dry weight Stwt g Leaf dry weight Lfwt g Total shoot dry weight Tots g Fibrous root dry weight Fbr g Hard (tap) root dry weight Hrd g Seed weight was determined by counting four sets of fifty good seeds and weighing them on a Mettler electronic (p600) balance for each provenance. The weight of one seed was thereafter calculated using simple proportion. Some of the seed samples that were used to determine seed weight were also used to raise seedlings in a glasshouse experiment. Three seeds from each provenance were sown per pot (15 X 8 cm diameter) filled with perlite and vermiculite in a SO/50mixture. Seeds of Acacia albida have hard seed coats. A nail cutter was used to scarify the seed coat by nicking on the micropylar end of the seed. A week after germination the weaker seedlings were culled leaving one, from which all the traits were measured. After harvesting (at six months) and washing off excess growth media from the roots, the stems were separated from the roots at the soil line. The two parts were put in separate drying bags and were oven dried for 72 hours at a temperature of 70°C. The leaves were removed from the stem and weighed using a Satorius electronic balance, the weight of the stem together with the branches was also determined. The roots were divided into two parts: fibrous portion consisting of all rootlets attached to the tap root and the hard portion referring to the tap root. Some provenances had several large roots joined just below the soil line instead of one large tap root. These roots were classified as being hard. The dry weight of the two root portions was determined. The total 168 biomass of the seedlings for each provenance was determined by summing up all the dry weights of each discussed component. Statistical Analysis Principal component analysis (peA) was considered appropriate for the aims of the study because it allows one to take the traits and find combinations of these traits to produce principal components or factors which are uncorrelated (Manly, 1986). Simple cluster analysis test could have been used to explore for the patterns but it was not going to be able to show the degree of correlation between factors and variables. For more detailed discussions on principal component analysis refer to, Hotelling(1933), Daultry (1976), Manly (1986) and Nash (1979). Results The data set was first tested for the degree of correlation, since one of the most important requirements for the test is a high degree of correlation between the traits. The result shows a very high correlation (both negative and positive) amongst the traits (Table 3). The high degree of correlation might be that bigger seeds produce seedlings that grow faster (higher biomass, leaf weight, branch numbers etc.) and vice versa for smaller seeds. There are, however, few traits which are weakly correlated with each other. For example stem weight to leaf weight (-0.02255), leaf weight to branch numbers (0.02970), leaf weight to temperature (0.02282) and others (Table 3 on next page) are all weakly correlated. There were fifteen traits in the data set, and fifteen principal components were produced. The first principal component (factor I) accounted for 61.28% (9.192) of the variance in the original data set, the second for 13.94% and the third for 7.94%. The three components together explained 83.14% of the variation in the original data. The three factors were retained by the 'mineigen' criterion, which was set at 1 (only factors that explained more than lout of 15 variation were retained). The other 16.86% . variation was explained by the other twelve principal components which have not been retained. Table 4 below shows that the principal component or factor 1 was highly correlated to biomass (0.97670), latitude (0.87069), seed weight (0.89543), root collar diameter (0.95098), height after 5 months (0.95115), stipule length (0.944229), total shoot dry weight (0.98458), fibrous root dry weight(0.92614) and total root dry weight (0.93279). 169 * *~ . 9- * 'E * on * '" on *N on '" '" <0- * <0- *00 <0- * or, <0- - on *<0- <0- '" <0- 0 r-- N 0 on 9 '" a- 9 - 0 :<: .~ 9 0 9 0 9 9 . S- o 9 9 . ~ *N a- 0 * * a- 0 -9 * * r-- 0 00 on * *<0- QQ 0 *r-- 00 0 * *N a- 0 * ~ *a-'" on 9 0 9 '" <0- N 0 <0- N 0 * '" a- 0 * *Cl - a- '"~ . . . .~ c- * * * * 00 * * * *~ ~ a- .. * . "" * * * on - N '"0'" ~ 0 on '" N 00 N '"0 9 '"0<0- *0 Ci .... a- 0 a- 0 00 0 9 00 0 a- 0 a- 0 9 '" N 0 - '"0 N - r-- N N 0 <0- 0 r-- 0 - on * ~ 00 - on '" 0 N 0 .- (, 0 <0- N -~ 0 ~ 0 9 0 0 9 0 0 0 0 9 - 0 0 ~ iii - N 0 '"0 N - 9-- - - QQ 0 on 0 0 N 0 t- 0 ~ - ~ N 0 - - '" S- * *0 N ~ 0'" 0 N <0- N 0 N ~ t- * N . * ~ a- *'" *on * * <0- * * on '" N 0 C5" <0- ~ ~ ~ 0'" ~ <0- '" ~ ~ <0- <0- 00 N 0 <0- ~ * - 0 - on 0 0 0 N '" ~ '"~ 0'" <0- <0- * * * on * * ** * * .- * * *<0- " ';:l CJJ * '" '"0 '"9 *t- t- 0 '" on 9 9 N QQ *00 co 0 *N '"0 ~ t- N * *0 - a- <0- 9 9 N - on 0 * * '"0'" a-'"0 ~ <0- . - ~ 9 - '"'"~ ~~ 9- .- ~ 9~- ~ 9 '"-9 * * *0 * * co N ~ a- * t- oo a- - 0 N on <0- N ::J" N 0 N QQ ~ 0 0 0 0 .~ * * * * *on **N * *N . ~ .~ * * * . * *on on :E a- 0 t- a- 0 t- 0 t- 9 QQ 0 on co 0 *0 - 9 N a- 0 <0- 9 - - t- 0 on 0 * <0- a- 0 *N '"0 ~ '(!) 110000 ODD 40000 Y Y Y 80000 ••• 120000 000 lJOOOO A A A 140000 888 I~OOOO C C C 160000 ODD 170000 E! E 180000 F F f 190000 G G 9 200000 Figure 1: A Two Dimensional Plot of the Seed Sourees on Factors 1 and 2. 172 Figure 2 1 19 Factor 3. o .8 1 -0 66 o 91 -1. 14 143 1 . 19 Factor 2 -1 Figure 2: A Three Dimensional Plot of Seed Sources on Factors 1, 2 and 3 173 The second cluster (B) has very low values of factor 1, low values of factor 2 and very high values for factor 3. These are the seeds from the countries listed in Table 6. Table 6 Countries That Belonf! to Group B Accession No. Country of Origin 180000 Senegal 190000 Niger 200000 Burkina Faso 160000 Burkina Faso 170000 Cameroon The third cluster (C) has high values of factor I, very high values of factor 2 and very high values of factor 3. These are the seeds from countries shown in Table 7. Table 7 Countries That Belonf! to Group C Accession No. Country of Origin 140000 Ethiopia 150000 Ethiopia 130000 Kenya 70000 Zimbabwe The groups depicted in this illustration are logical for reasons to be discussed in a later section of this paper. Factor 3 contributes very little in the separation of the three clusters and it accounts for only 7.92 % of the variance in the data set. Discussion The PCA produced groups of countries where Acacia albida populations are generally sharing the same phenotypic/genetic characteristics. However, it should be noted that there is a great variation among cluster individuals which is indicated by the lack of compaction within each group. This is attributed to the few variables which are uncorrelated in the original data set that led to the 16.86% variation unexplained by the three factors which were retained. The formation of the PCA groups is both logical and clear. Group A represents the countries of southern Africa, group B are those countries which belong to west Africa and group C are countries in north-east Africa (with the exception of one region in Kenya and one in Zimbabwe). This pattern in the groups can be explained, as each region has similar climatic conditions to those of the members of the group. de Vos (1975) and Werger (1978) conclude that the existing vegetation in Africa is dependent upon the amount and seasonal distribution of rainfall. Altitude can also play an important role in determining phytogeography. Groups A, Band C belong to the Zambezian, Sudanian and Oriental Domains respectively, which together fall under the Sudano-Zambezian phytogeography region (Werger, 1978). This region is characterised by a rich flora that extends into southern Africa. It comprises the vast stretches of woodland, savanna and grassland vegetation with occasional dry forests and thickets, and patches of edaphically controlled swampy vegetation (Werger and Coetze, 1978). The Zambezian Domain has a rich flora and ecological diversity due to a wide range of altitudes and related climatic conditions (de Vos, 1975). The seasonal character is 174 very clear in this domain. Rainfall and temperature ranges are very wide in this zone due to the diversity of heights. Precipitation decreases from about 1800mm in the North to 250mm in the South (de Vos, 1975; Werger and Coetze, 1978). There is also a decrease in precipitation from the east coast westward, and precipitation is normally received during the period November-May. A large proportion of the species have a very wide distribution area, hence centres of endeminism can be distinguished rather than sub- zones. The Sudanian Domain is an area of extensive semi-arid lands south of the Sahelian zone. Rainfall varies between 600-l250mm, and temperature differences between day and night are high and increase near the Sahelian zone (de Vos, 1975). During the dry season the Harmattan (a dry wind) blows from the north-east. Temperatures are high throughout the year. The major differences between the Zambezian Domain and the Sudanian Domain which probably results in different vegetation characteristics, and possibly different Acacia albida species traits, are in precipitation and relief. The Sudanian Domain is more uniform in relief and the coefficient of variation of annual precipitation is lower than in the Zambezian domain. Acacia albida in group C belongs to the Oriental Domain, which is constituted of highlands and is situated astride the equator (de Vos, 1975). Ecological systems vary from desert (characterised by low rainfall of maximum 150mm per annum and little or no vegetatition at all) to evergreen vegetation (2000mm annual rainfall). The vegetation in this region reflects the distribution pattern ofrainfall and can also be influenced by the broken topography of this zone (Werger, 1978; Werger and Coetze, 1978 and de Vos, 1975). This is the most diversified zone, ecologically. If the groups produced are a true representation, then each ofthe countries within that group have an Acacia albida of almost the same genetic pattern. Group A Acacia albida (the Zambezian Domain) can be differentiated from the other groups by its high positive correlation with total roots dry weight, biomass, latitude, seed weight, root collar diameter, height after 5 months, stipule length, total shoot weight and fibrous roots dry weight and it is negatively correlated to temperature, branch numbers and hard roots dry weight. The Zambezian Domain Acacia albida has more roots, probably longer vertical and horizontal roots to extract the limited moisture from a wider area. The Acacia albida from group B, the Sudanian Domain is characterised by more of stem weight and branch numbers than the other groups, with less leaves (see Table 4). Acacia albida of the Oriental Domain has much more leaf numbers and leaf weight. The Oriental Domain Acacia albida like the Zambezian Domain Acacia albida has very high loadings of total root dry weight, biomass, latitude, seed weight, root collar diameter, height after 5 months, stipule length, total shoot weight, fibrous roots dry weight and low temperature, branch numbers and hard root dry weight. However, the Oriental Domain Acacia albida can be differentiated from the Zambezian Domain species with its very high leaf numbers and leaf dry weight. The Oriental Domain probably has more and broader leaves than all the groups obtained (see Table 4 and Figure 2). Note should however be taken that all the three groups have relatively high values for factor 3, indicating that they are positively correlated to stem weight and branch numbers (see Table 4). Conclusion This analysis therefore concludes that there ~e some major phen~typic~genetic variations among the Acacia albida species from the d1ffer~ntpr?ve~ances m Africa. The groups of Acacia albida species obtained show some relatIOnshIpWIththe phytogeography groups of Africa. However, the populations within each group are not compacted, which implies that there may still be some significant. variation within ea.ch gr~up. It is therefore recommended that genetic or phenotYPIc patterns on AcaCia alblda at a local level 175 (Domain and individual countries) should be further investigated. Many more groups could be discovered if data is collected uniformly across Africa. References Daultry, S. (1976) Principal Component Analysis. Concepts and Techniques of Modern Geography 8 University of East Anglia de Vos, A. (1975) Africa, The Devastated Continent?: Man's Impact on the Ecology of Africa The Hague: Dr W. Junk b.v. Publisher Hotelling, H. (1933) 'Analysis of a Complex Statistical Variables into Principal Components" Journal of Education Psychology 24: 417-441 Manly, B. F. I. (1986) Multivariate Statistical Methods Chapman and Hall, New York Marunda, C. (1993) "Acacia albida from different Provenances in Africa" Unpublished draft of MSc thesis, Australian National University, Canberra Nash, J. C. (1979) Compact Numerical Methods for Computers Adam Hilger, Bristol Tietema, T. (1984) Seminar Proceedings;' Current and Planned Research in Relation to Reafforestation woodlots and firewood Proceedings of a Conference Held 6th October by NIR, University of Botswana Walker, P. K. (1992) "The Role and Potential of Tree Growing on Farms in the Barolong District-Botswana" Unpublished MSc thesis, University of Aberdeen, Aberdeen Werger, M. J. A. (1978) "Biogeographical Division of Southern Africa", in M. J. A. Werger (ed.) Biogeography and Ecology of Southern Africa. The Hague: Dr W. Junk b.v. Publishers Werger, M. J. A. & BJ. Coetze (1978) "The Sudano-Zambezian Region", in M. J. A. Werger(ed.) Biogeography and Ecology of Southern Africa The Hague: Dr W. Junk b.v. Publishers 176