Defining units for savanna management in Sudano-sahelian areas

Forest Ecology and Management 236 (2006) 403–411 www.elsevier.com/locate/foreco

Defining units for savanna management in Sudano-sahelian areas Nicolas Picard a,*, Saidou Ouattara b, Dalla Diarisso b, Moussa Ballo b, Denis Gautier c a

Cirad, Forest Department, Campus International de Baillarguet, TA 10/D, 34398 Montpellier Cedex 5, France b Institut d’E´conomie Rurale, Programme Ressources Forestie`res, CRRA de Sotuba, BP 258, Bamako, Mali c Cirad, Forest Department, BP 1813, Bamako, Mali Received 8 September 2005; received in revised form 6 July 2006; accepted 21 September 2006

Abstract Management-oriented identification of vegetation types is needed in Mali for production savannas devoted to fuel-wood and charcoal production. This study seeks to determine which factor can be used to identify management units in relation to vegetation characteristics. Three production savannas, covering 1404 ha, were inventoried in the Zan Coulibaly district near Bamako. Structural variables on each inventoried plot, e.g. basal area or tree density, were measured to characterize the vegetation. Site variables such as soil depth and toposequence phase (plain, hillside or plateau) were determined to characterize the environment and anthropic pressure. Multivariate analyses were used to define vegetation units from structural variables, and to relate these to site variables. Five vegetation units were identified. A significant relationship was detected between these units and site variables. Significant differences were found in terms of structural variables between toposequence phases and between the three savannas. The results were consistent with a gradient from plain to plateau with decreasing logging intensity and decreasing soil depth. The vegetation in each toposequence phase was made up of a mosaic of vegetation units. In conclusion, toposequence phases can be used as the basis for stratifying production savannas in management units. # 2006 Elsevier B.V. All rights reserved. Keywords: Fuel-wood; Savanna; Toposequence; Vegetation type; West Africa

1. Introduction Fuel-wood and charcoal cover more than 90% of domestic energy needs in Mali, West Africa (Nouvellet et al., 2000), in the same manner as other African countries (Namaalwa et al., 2005). Tree formations (mainly savannas) are thus subject to intense exploitation, particularly around large cities. Nowadays, traders may procure supplies of wood and charcoal up to 200 km from Bamako. To better manage tree resources, the government in Mali has since 1998 been encouraging the creation of so-called ‘rural wood markets’ (Foley et al., 2002). These markets need a management plan for savanna exploitation at the village level in exchange for reduced taxes levied on marketed charcoal. The fact that a village has committed itself to the creation of a rural wood market is a good indicator of the importance of wood and charcoal production in the village economy and in the household (Hautdidier and Gautier, 2005). * Corresponding author. Tel.: +33 467 59 39 39; fax: +33 467 59 37 33. E-mail addresses: [email protected] (N. Picard), [email protected] (S. Ouattara), [email protected] (M. Ballo), [email protected] (D. Gautier). 0378-1127/$ – see front matter # 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.foreco.2006.09.033

As the monetary value of forest products is low, and as forest operators are rarely well trained, the management plan must above all be simple. The basis for this plan is the identification of vegetation units. This provides the ecological basis for predicting savanna potential in terms of wood production (Bellefontaine et al., 1997). The question we address in this paper is how to assess simply the vegetation units in the savannas of Mali (Diatta et al., 1998), and thus stratify the savanna. This question is partially addressed by different vegetation classifications such as the Yangambi classification, the FAO classification and the UNESCO classification (CSA, 1956; Aubre´ville, 1957; White, 1983; FAO, 1984; Bellefontaine et al., 1997). Putting aside their inherent limitations (Descoings, 1973; Nasi and Sabatier, 1988), these classifications are useful for naming vegetation types on a broad scale. Likewise, classifications obtained by conventional phyto-sociological techniques (e.g. Nasi, 1994) make sense on a broad scale. At a more local level corresponding to savanna areas a few hundred hectares in size and managed by villages, the vegetation is a mosaic of small units, each unit being possibly identified as one of the types in the classification (Nasi, 1994).

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On this intermediary scale, vegetation units may be more advantageously defined on the basis of field data processed by multivariate analysis (Hall, 1991; Mizrahi et al., 1997; Huang et al., 2003; Sagar et al., 2003; Kalacska et al., 2004; Zugliani Toniato and de Oliveira-Filho, 2004). The analysis may be based on structural characteristics (diameter distribution, density, etc.) and floristic composition (abundance of species), with the two being linked. However, characterizing savanna structure is insufficient. From a management perspective, this structure may be due to factors that affect the vegetation dynamics. The next step is to understand why the savanna is structured in a particular way, linking this to external factors such as fire (Sampaio and Salcedo, 1993; Louppe et al., 1995; Sawadogo et al., 2002), logging for fuel-wood (Renes, 1991; Nyg?ard et al., 2004), livestock grazing (Sawadogo et al., 2002), competition with grasses, or soil (Isichei and Muoghalu, 1992). The aim of this paper is to define management-oriented vegetation units in three Mali savannas that are logged for fuelwood, and attempt to relate observed savanna structure to external factors. The central question is: Which simple(s) factor(s) can be used to identify management units, and on what scale? Simplicity is required for the method to ultimately be adapted for use in defining management plans for production savannas. This study follows on from previous studies in the same area focused on a single toposequence phase on two different scales (0.5 ha, Picard et al., 2004; and 65 ha, Picard et al., 2005). These studies showed that vegetation could be described by small heterogeneous units (  100 m2 in area) that make up a spatially homogeneous mosaic in the studied toposequence phase. They also showed that the most important soil characteristic explaining vegetation distribution is depth. However, as they were confined to a single toposequence phase, they could not address the issue of management. The present study will enlarge the study area to more than 1000 ha covering several toposequence phases. Vegetation will be described by quantitative structural variables such as basal area and density, whereas, the environment will be described by site variables including soil depth which describes soil conditions.

Fig. 1. Map of the study site with the outlines of the three production savannas (shaded areas) at Sokouna, Korokoro and Fiena in the Commune of Zan Coulibaly, Mali. Grey areas are the main plateaus. The star in the inset shows the geographical situation of the study area in Mali.

Three villages in the Zan Coulibaly district were selected for the study, based on their fuel logging operations: Sokouna, Korokoro and Fiena (Fig. 1). Fuel logging in this area is mainly devoted to charcoal production, supplying the city of Bamako. The charcoal trade revolves around the tarmac road between Bamako and Se´gou that passes through Markakoungo and Korokoro. Sokouna and Korokoro have developed a significant charcoal business and both have set up rural wood markets. Sokouna was the first rural wood market created in Mali by the Strategy for Domestic Energy. However its activity is hampered by the distance between the road and the production savannas (about 15 km of tracks that are impassable during the rainy season). Korokoro benefits from direct access to the road and developed lively charcoal production, at least in the first years following the agreement between the Forestry Service and the rural wood market. Unlike Sokouna and Korokoro, Fiena has not yet developed a significant charcoal business. Its rural wood market was set up only in 2002.

2. Methods 2.2. Field survey and variables 2.1. Study area The study area (7 180 – 7 270 W, 12 450 – 12 540 N) is located within the Commune of Zan Coulibaly whose administrative centre is the village of Markakoungo, 90 km North-East of Bamako, on the road from Bamako to Segou (Fig. 1). The area is within the Southern-Sudan bioclimatic range (Nasi and Sabatier, 1988), and enjoys average rainfall of 790 mm year1 (average from 1977 to 1987). The relief consists mainly of plains except in the North where plains alternate with flat plateaus about 55 m high. The slope between the plains and the plateaus is 16.7 on average with a of standard deviation of 6.4 . The savannas are concentrated near the plateaus, the best agricultural soils on the plains being primarily devoted to crops.

The production savannas used by the three villages were delineated on foot by GPS under the supervision of a village leader (Fig. 1). It is noteworthy that Korokoro savanna was once located further to the South (Hautdidier et al., 2004). It is moving North as wood resources are depleted, creating conflict with neighbouring villages (in particular Fiena). The three delineated savannas cover a total of 1404 ha (Table 2). A systematic inventory was made along a regular square grid with sides 153 m in length. Each inventory plot corresponded to a disk with a 5 m radius. This size was chosen based on a study into the variance of the density estimator (Picard et al., 2004). The number of inventory plots was thus 613. All stems in each plot with a diameter at ground level (Sawadogo et al., 2002) of more than 3.2 cm (10 cm in girth)

N. Picard et al. / Forest Ecology and Management 236 (2006) 403–411

were measured for diameter. Many stems were resprouts caused by fire injuries, logging or herding, and in consequence the number of individuals was less than the number of stems. Each stem was attached to an individual, and the species of each individual was recorded. In all 6086 stems were measured, corresponding to 3541 individuals and 75 different species (Table 1). Stumps were also counted, soil depth was measured by digging a 30 cm  1 m hole down to the parent rock or to a depth of 1 m (if the parent rock was deeper than 1 m), and the position of each plot in the toposequence was classified by visual inspection as plain, hillside or plateau. Five structural variables and four site variables were derived from field measurements to characterize each inventory plot. Variables describing vegetation structure consist of number N of stems (with diameter  3.2 cm), basal area B, mean diameter D, species diversity d, and wood density w. Mean diameter was computed from the number of stems and basal area as: D ¼ ½ð4=pÞB=N0:5 . Since D (an average quantity) depends on the ratio of two cumulative quantities (B and N), this equation does not imply any a priori correlation between any two of these variables. Species diversity was computed as Simpson’s index of diversity. The wood density of a plot was defined as the weighted mean of the wood density of species found on this plot, with weights being the basal areas of the species on the plot. This was taken as an indicator of plot suitability for logging, with the denser species being the most sought after for charcoal production (Abbot P P and Lowore, 1999). This was computed as: w ¼ ð s Bs rs Þ=ð s Bs Þ, where Bs is the basal area of species s and rs its wood density, the sum covering all the species found in the inventory plot. Species wood densities rs were drawn from the literature (Reyes et al., 1992; Thiel et al., 1993; Nyga˚rd and Elfving, 2000 and references therein). However, no data was found for 23 species out of 75 (see Table 1), corresponding to 487 trees out of 3541 and only 13 plots out of 613. It should be noted that mean diameter D, species diversity d and wood density w were not defined for empty plots (when N ¼ 0). The four site variables consisted of soil depth, position in the toposequence (categorized as plain, hillside or plateau), number of stumps taken as an indicator of logging intensity, and distance to the nearest access path. The first two variables describe the physical environment and the two others anthropic pressure. 2.3. Statistical analyses The basic unit for statistical analyses was the inventory plot. All computations were made using R software (R Development Core Team, 2005) with package ‘ade4’ for multivariate analysis (Chessel et al., 2004) and package ‘pscl’ for ZIP models. 2.3.1. Mutivariate analysis of structural variables Inventory plots were classified into different vegetation units on the basis of structural variables using principal component analysis (PCA) and hierarchical cluster analysis. These multivariate analyses used a table with 613 lines and 5 columns giving each structural variable for each plot. Empty

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plots (46 plots) were excluded prior to the analyses. Missing data for wood density (13 plots) were replaced by its mean in the remaining 554 plots. In the cluster analysis the dissimilarity between two plots was defined as their Euclidean distance in the R5 space generated by the centred and scaled variables, and Ward’s method (Ward, 1963) was used for pairwise agglomeration of clusters. The cluster dendrogram was cut by visual inspection at a height where long branches occurred, forming groups of plots. The groups of inventory plots identified by the cluster analysis were considered as vegetation units. The PCA and cluster analysis were first performed on all plots, and were then repeated separately for each village (Sokouna, Korokoro or Fiena) and for each toposequence phase (plain, hillside or plateau). The construction of vegetation units from structural variables was complemented by the identification of characteristic species. For each species, a zero-inflated Poisson (ZIP) model was used to test whether the abundance of the species (in terms of number of stems) was different between the vegetation units. The ZIP model (Welsh et al., 1996; Ridout et al., 1998; Hall, 2000) describes the response variable as a mixture of a Bernoulli distribution and a Poisson distribution. It is convenient for modelling over-dispersed count data with extra zeros resulting from the absence of the counted elements (here stems) from many observational units. A species was considered to be characteristic of a vegetation unit if its abundance was significantly greater (with a 5% confidence level) in this vegetation unit than in the others. 2.3.2. Two-table analysis A two-table analysis was used to identify the link between structural variables and site variables. Different techniques were tested including co-inertia analysis (Dole´dec and Chessel, 1994) and juxtaposition (Dagne´lie, 1965). All the results were consistent, and we finally retained the simplest technique consisting of a multiple correspondence analysis (MCA) of the table combining site variables and the nominal variable giving the vegetation units. Each numerical site variable (soil depth, number of stumps, distance to the nearest access path) was categorized into three intervals (corresponding to the quantiles 0–33%, 33–67% and 67–100%) prior to the analysis. The RV coefficient (Escoufier, 1973), that ranges between 0 and 1 and measures the degree of coupling between two tables, was also computed between the table of site variables and the table of structural variables (that used for the PCA). 2.3.3. Comparing villages and toposequence phases We compared the three villages (Sokouna, Korokoro, Fiena) by comparing the means of the structural variables computed for each. Mean values for basal area (B), mean diameter (D) and species diversity (d) relative to each village were compared by the Kruskal–Wallis test. Mean values for the number of trees (N) and number of stumps (S) relative to each village were compared by the ZIP model. Differences between levels were considered to be significant if the p-value of the test (either Kruskal–Wallis or ZIP) was less than 5%. Whittaker’s (1972) index of b-diversity was also computed for each pair of

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Table 1 List of woody species (basal diameter  3:2 cm) identified in the three savannas surveyed in the Commune of Zan Coulibaly, Mali Family and species Anacardiaceae Lannea acida A. Rich. Lannea microcarpa Engl. & K. Krause Lannea velutina A. Rich. Ozoroa insignis Del. Sclerocarya birrea (A. Rich.) Hochst. Annonaceae Annona senegalensis Pers. Hexalobus monopetalus (A. Rich.) Engl. & Diels Apocynaceae Landolphia heudelotii A. DC. Bignoniaceae Stereospermum kunthianum Cham. Bombacaceae Bombax costatum Pellegr. & Vuillet Capparaceae Boscia salicifolia Oliv. Maerua angolensis DC. Celastraceae Maytenus senegalensis (Lam.) Exell Cesalpinaceae Afzelia africana Smith ex Pers. Burkea africana Hook. f. Cassia sieberiana DC. Cordyla pinnata (Lepr. ex A. Rich.) Milne-Redhead Daniellia oliveri (Rolfe) Hutch. & Dalz. Detarium microcarpum Guill. & Perr. Isoberlinia doka Craib & Stapf Piliostigma reticulatum (DC.) Hochst. Piliostigma thonningii (Schumach.) Milne-Redh. Tamarindus indica L. Chrysobalanaceae Parinari curatellifolia Planch. ex Benth. Combretaceae Anogeissus leiocarpus (DC.) Guill. & Perr. Combretum fragrans F. Hoffm. Combretum glutinosum Perr. ex DC. Combretum lecardii Engl. & Diels. Combretum micranthum G. Don Combretum molle R. Br. ex G. Don Combretum nigricans Lepr. ex Guill. & Perr. Combretum nioroense Aubre´v. ex Keay Combretum paniculatum Vent. Guiera senegalensis J.F. Gmel. Pteleopsis suberosa Engl. & Diels. Terminalia avicennioides Guill. & Perr. Terminalia laxiflora Engl. Terminalia macroptera Guill. & Perr. Ebenaceae Diospyros mespiliformis Hochst. ex A. Rich. Euphorbiaceae Euphorbia sudanica A. Chev. Flueggea virosa (Roxb. ex Willd.) Voigt Fabaceae Lonchocarpus laxiflorus Guill. & Perr. Pericopsis laxiflora (Benth.) van Meeuwen Pterocarpus erinaceus Poir. Pterocarpus lucens Guill. & Perr. Xeroderris stuhlmannii (Taub.) Mendonca & E.P. Sousa Hymenocardiaceae Hymenocardia acida Tul. Loganiaceae Strychnos innocua Del. Strychnos spinosa Lam.

Number of trees

Number of stems

Wood density (kg m3)

146 11 10 8 23

150 12 12 10 29

464 464 ? ? 509

10 73

22 128

? ?

2

2

?

2

2

637

40

40

306

6 2

7 2

? ?

3

4

?

2 8 14 39 9 68 5 18 1 7

2 8 21 44 9 87 5 26 3 9

683 ? 719 ? 488 580 653 647 623 760

2

3

618

106 3 402 7 143 18 255 106 3 219 132 1 1 178

128 4 784 20 534 20 396 426 4 359 244 1 1 228

754 635 694 ? 760 ? 752 ? ? 658 ? 638 659 677

10

23

642

12 6

12 14

? 684

1 2 43 44 9

1 2 46 114 12

? 790 679 836 575

3

4

?

18 497

23 557

? 693

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Table 1 (Continued ) Family and species Mimosaceae Acacia ataxacantha DC. Acacia erythrocalyx Brenan Acacia macrostachya Reichenb. ex DC. Acacia seyal Del. Albizia chevalieri Harms Dichrostachys cinerea (L.) Wight & Arn. Entada africana Guill. & Perr. Parkia biglobosa R. Br. ex G. Don Prosopis africana (Guill. & Perr.) Taub. Olacaceae Ximenia americana L. Polygalaceae Securidaca longepedunculata Fres. Rhamnaceae Ziziphus mauritiana Lam. Ziziphus mucronata Willd. Rubiaceae Crossopteryx febrifuga (Afzel. ex G. Don) Benth. Feretia apodanthera Del. Gardenia erubescens Stapf & Hutch. Gardenia sokotensis Hutch. Gardenia ternifolia Schumach. & Thonn. Sapotaceae Manilkara multinervis (Bak.) Dubard Vitellaria paradoxa Gaertn. f. Sterculiaceae Sterculia setigera Del. Tiliaceae Grewia bicolor Juss. Grewia flavescens Juss. Grewia venusta Fresen. Verbenaceae Vitex doniana Sweet Unknown

Number of trees

Number of stems

Wood density (kg m3)

33 21 290 1 3 10 33 1 19

71 45 480 1 3 15 46 1 21

694 741 740 730 642 861 548 494 788

29

67

643

14

15

624

1 6

1 19

517 645

27 47 18 2 24

33 101 22 5 34

618 676 ? ? 655

3 28

4 31

? 758

23

23

307

41 1 113

132 4 279

778 687 715

22 3

36 3

424 ?

Nomenclature follows Arbonnier (2004).

villages. The same analyses were repeated to compare the three toposequence phases (plain, hillside, plateau). 3. Results 3.1. Vegetation units The first two axes of the PCA performed on structural variables explained 71% of the total variance. Basal area and mean diameter were positively correlated, and together explained the first axis of the PCA. Species diversity and stem count were positively correlated, and together explained the second axis of the PCA. Wood density was mainly related to the third axis. The cluster dendrogram was used to define five vegetation units. A sixth unit grouped together the empty plots removed prior to the analysis. Table 2 gives the mean value computed for each variable in each vegetation unit, together with overall mean values. The vegetation units were then described as follows:  Unit 0: empty plots.  Unit 1: lowest tree density (less than 2 trees in 86% of cases), lowest basal area, and null species diversity. It contains the plots that were almost devoid of any tree vegetation.

 Unit 2: highest tree density and highest species diversity. Characteristic species: Acacia macrostachya, Combretum glutinosum, Combretum nigricans, Strychnos spinosa, Terminalia macroptera.  Unit 3: characteristics closest to the overall mean (according to the metric of the PCA). It contains most of the plots.  Unit 4: highest basal area and highest mean diameter. It contains the plots with the largest trees. Characteristic species: Combretum micranthum, Combretum nioroense.  Unit 5: similar to group 3 but with a higher tree density, a higher basal area, and a lower wood density. Characteristic species: Guiera senegalensis. If the number of groups is limited to four instead of five, units 2 and 5 are merged. As unit 2 has specific features, it was interesting to separate it from unit 5. If six groups are formed, unit 4 is split into two sub-groups along the gradient of basal area. The results of the PCA and cluster analysis were stable when the analyses were restricted to a village or to a toposequence phase, giving approximately the same correlation circle and the same classification into five vegetation units. The vegetation within each toposequence phase may thus be seen as a mosaic of the vegetation units previously defined.

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Table 2 Area of the production savanna, species richness, basal area (B), mean diameter (D), number of stems (N), number of stumps (S), and species diversity (d) for each vegetation unit, for each village, and for each toposequence phase in the Commune of Zan Coulibaly, Mali Factor levels

Area (ha)

Vegetation unit 0 105 1 115 2 261 3 502 4 236 5 186 Village Fiena 734 Korokoro 309 Sokouna 361 Toposequence phase Hillside 680 Plateau 502 Plain 222 Overall 1404

Richness

B (m2 ha1)

D (cm)

0 14 57 54 56 53

0 1.6 10.1 5.2 28.9 7.9

– 8.9 9.4 10.1 22.2 11.7

62 59 54

12.0 13.5a** 6.0 6.8b** 8.5 9.3c**

62 59 51 75

10.6 10.3 6.1 9.8





1.4 5.5 4.2 15.0 3.5

12.5a** 11.7a** 6.3b** 11.5



4.0 2.2 3.5 6.6 3.1

13.2 6.4a ** 10.3 4.9b** 12.2 6.5c ** 12.3 13.5 9.7 12.3



5.9a ** 7.0b** 4.5c ** 6.2

N (ha1)

d

0 224 1424 586 825 791

– 0.00 0.70 0.58 0.63 0.70



149 505 221 450 345

808 583a* 652 476b* 663 414b* 794 640 772 735



537a** 488b** 542b** 525



0.00 0.14 0.15 0.19 0.12

0.59 0.23ns 0.57 0.26ns 0.57 0.23ns 0.60 0.54 0.61 0.58



0.22a** 0.25b** 0.25a** 0.24

w (kg m3)

S (ha1)

– 704 694 703 597 561

28 112 74 113 30 157



39 44 40 158 60

656 101a* 653 102a* 680 81b* 656 678 645 661



102a** 91b** 89c** 97



76 204 149 181 67 227

73 154a** 141 211b** 84 150a** 81 77 154 91



164a** 146a** 218b** 170

Figures are mean standard deviation. Where tests indicated significant differences among levels ( p-value < 0:05), means followed by different letters indicate significant differences in multiple comparison tests. ns: not significant. * p-value < 0:05. ** p-value < 0:01.

3.2. Link with site variables The relationship between structural variables and site variables was significant (RV coefficient ¼ 0:39, pvalue ¼ 0:01). Fig. 2 shows the correlation between the first two axes of the MCA and site variables and vegetation units.

The first two axes explain only 26% of the total variance. On the first axis, plain is in opposition to plateau and hillside. On the second axis, plateau is in opposition to hillside. Plains which are closest to access paths, tended to be associated with deep soils, the presence of stumps, and vegetation unit 5. By contrast, plateau tended to be associated with shallow soils, medium distance from the access path, and vegetation unit 0. Hillside tended to be associated with moderately deep soils, substantial distance from access paths, vegetation units 2 and 4, and, to a lesser extent, to the absence of stumps. These are only tendencies as the relationships between the variables were weak (yet significant). 3.3. Differences between villages

Fig. 2. Scores of site variables and vegetation units on the first two axes of a multiple correspondence analysis. The table analyzed gives site variables and vegetation units for each of 613 plots (75 m2) surveyed in three savannas of the Commune of Zan Coulibaly in Mali. Site variables include distance from access path (three modalities: far, medium, close), toposequence phase (three modalities: hill, plain, plateau), number of stumps (three modalities: 0 stump, 1 stump, > 1 stump), and soil depth (three modalities: shallow, moderate, deep). Vegetation units are denoted U0–U5.

Plot basal area differed significantly between the three villages (Kruskal–Wallis test, X 2 ¼ 26:2, p-value < 0:001) as did mean diameter (Kruskal–Wallis test, X 2 ¼ 24:7, p-value < 0:001), the number of stems (ZIP model), and the number of stumps (ZIP model). However, there was no significant difference between the three villages in terms of species diversity (Kruskal–Wallis test, X 2 ¼ 2:2, p- value ¼ 0:33). Table 2 shows the mean values for these variables for each village. Whittaker’s index of b-diversity was 1.2 for all three village pairs, indicating that species richness was fairly similar in all three. Fiena had the greatest basal area, mean diameter and stem density, and the lowest stump density. The Fiena forest was thus the best preserved from logging, which is consistent with its late involvement in the rural market process. By contrast, Korokoro had the lowest basal area, mean diameter and stem density, and the highest stump density. Fuel logging in this village was the most intense, i.e. the most accessible and one of the oldest rural wood markets.

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few ha) corresponding to the toposequence phases, the vegetation appeared as a mosaic of all kinds of units but in different proportions. In conclusion, when scaling up from local (100 m2) to toposequence phases (up to a few dozens ha) and then to the entire savanna, heterogeneous vegetation units are first observed (local scale), followed by homogeneous mosaic (toposequence scale), and finally contrasted mosaic compositions (corresponding to different toposequence phases). This identification of the toposequence scale is actually not new as a close connection in the Sudanese climate between toposequence and pedogenesis has already been observed (Boulet et al., 1971; Boulet, 1978). The different toposequence phases therefore corresponds approximately to different soil conditions. Fig. 3. Proportion of each vegetation unit within each toposequence phase in the Commune of Zan Coulibaly in Mali. Vegetation units are labelled 0–5: 0 ¼ absence of any tree; 1 ¼ almost empty of trees; 2 ¼ high density and high species diversity; 3 ¼ characteristics that are the closest to the overall mean characteristics; 4 ¼ highest basal area and highest mean diameter; 5 ¼ Guiera senegalensis as a characteristic species.

3.4. Differences between toposequence phases All structural variables (basal area, mean diameter, stem density, species diversity, wood density and stump density) differed significantly between the three toposequence phases. Table 2 gives mean values for these variables for each toposequence phase together with the overall mean. Whittaker’s index of b-diversity was 1.2 for all three pairs of toposequence phases, indicating that species richness was fairly similar in hillside, plateau and plain. Plots on the plateau showed the greatest mean diameter, whereas, those on the plain had the lowest. Stump density was significantly lower on plateaus and hillside than on plains (ZIP model). Basal area was significantly higher on plateaus and hillsides than on plains (Kruskal–Wallis test, X 2 ¼ 9:2, pvalue ¼ 0:09). As the definition of vegetation units was stable when the analysis was restricted to a toposequence phase, these differences between toposequence phases correspond to variable proportions of the vegetation units within each toposequence phase. Fig. 3 shows the proportion of each vegetation unit within each toposequence phase. Plain was different by a higher proportion of unit 5 to the detriment of unit 4. Plateau was different by a higher proportion of unit 0 to the detriment of unit 2.

4.2. Confounding effects of site variables The role of soil depth in this Sudanese savanna is therefore confirmed (Picard et al., 2005), whereas, no relationship between soil properties and vegetation characteristics has been found in other savannas (Dezzeo et al., 2004). Large-scale differences in soil depth were observed in the Commune of Zan Coulibaly, with plains being associated with deep soils and plateau with shallow soils. Thus, soil depth and toposequence are not independent. Quite logically, the number of stumps was not independent of the distance to the nearest access path: the closer to the access path, the greater the number of stumps. This simply indicates that the most accessible areas are logged first. Finally, the distance to the nearest access path was not independent of the toposequence phase as villages are located on the plain where soil conditions for crops are more favourable and the access paths generally originate on the plain. Thus, plains are generally more accessible for villagers than plateaus and hillsides. As all site variables are correlated, it is difficult to distinguish between the effects of environmental variables (toposequence, soil depth) and those due to anthropic pressure (distance to nearest access path, number of stumps). This confounding effect of site variables is not an exception as village location in the most favourable areas (plains) with a circular geographical organization of land use is a general configuration (Gautier et al., 2000, 2003). Nevertheless, the significant differences between the three villages corresponding to different logging intensities, confirms that logging has a major impact on savanna characteristics.

4. Discussion 4.3. Implications for management 4.1. Mosaic homogeneity on the toposequence scale Differences were noted between the three toposequence phases, namely plain, plateau and hillside, in association with four vegetation units (labelled 0, 2, 4, and 5). As the plots are small (78.5 m2), the cluster analysis gave vegetation units that can be identified on a local scale (  100 m2). The definition of the vegetation units was stable when the analysis was restricted to a toposequence phase. In consequence, on a larger scale (a

Drawing up management plans for production savannas means stratifying savannas into units that make sense from a fuel-wood production perspective. The vegetation units described by multivariate analyses are not directly usable in this task as they reflect local conditions (  100 m2). The use of structural variables to characterize the vegetation gives a better idea of savanna structure than profiles of species abundance. They also offer a quantitative description of the savanna that is

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more precise than nominal classifications such as the Yangambi classification which is based on qualitative criteria and subjective assessment. The most important result was the link between vegetation units and toposequence phases, and may be considered as a summary of site conditions. Empty plots (unit 0) are characteristic of plateaus where the spatial distribution of trees showed clustering in patches (Picard et al., 2004). Highest wood volume are characteristics of hillside (either because soil conditions are more favourable, or because logging is less intense given that the steepness of the slope hampers hauling). Vegetation unit 5 characteristic of plains may be interpreted as regrowth after logging, as suggested by the high density of small stems and the abundance of the pioneer species Guiera senegalensis. This interpretation is supported by the fact that the higher proportion of unit 5 on plains is to the detriment of unit 4 containing the largest trees, thus indicating that the largest trees are logged first. In conclusion, toposequence phases seem to be the most suitable for defining management units. In the future, we intend to generalize these results by investigating another area in Southern Mali (Bougouni district). The subsequent step will be to connect vegetation units in a chronosequence (Nasi, 1994; Nansen et al., 2001) as a dynamic approach to savannas subject to fuel logging (Archer, 1995; Hoffmann, 1999; Namaalwa et al., 2005). Acknowledgements This study was supported by a grant from the French Ministry of Foreign Affairs (FSP ‘‘De´veloppement des ressources humaines du syste`me national de recherche agricole malien’’). The authors wish to thank the population of Zan Coulibaly for their welcome and cooperation, and IER technicians at Sotuba for their assistance with the inventory. The authors would like to thank Baptiste Hautdidier for Fig. 1, and two anonymous reviewers for helpful comments. References Abbot, P.G., Lowore, J.D., 1999. Characteristics and management potential of some indegenous firewood species in Malawi. For. Ecol. Manage. 119, 111– 121. Arbonnier, M., 2004. Trees, Shrubs and Lianas of West African Dry Zones. Cirad, Margraf Publishers GmbH, and Muse´um National d’Histoire Naturelle, Paris, France. Archer, S., 1995. Tree-grass dynamics in a Prosopis-thornscrub savanna parkland: reconstructing the past and predicting the future. Ecoscience 2, 83–99. Aubre´ville, A., 1957. Accord a` Yangambi sur la nomenclature des types africains de ve´ge´tation. Bois For. Trop. 51, 23–27. Bellefontaine, R., Gaston, A., Petrucci, Y., 1997. Ame´nagement des foreˆts naturelles des zones tropicales se`ches. Cahier FAO Conservation, vol. 32. FAO, Rome. Boulet, R., 1978. Topose´quences de sols tropicaux en Haute-Volta: e´quilibre et de´se´quilibre pe´dobioclimatique. Me´moires ORSTOM 85, ORSTOM, Paris. Boulet, R., Kaloga, B., Leprun, J.C., 1971. E´tude de la pe´dogene`se en re´gion a` longue saison se`che de l’Afrique occidentale: Haute-Volta. Notes Doc. Voltaı¨ques 4, 51–57. Chessel, D., Dufour, A.B., Thioulouse, J., 2004. The ade4 package - I: One-table methods. R News 4, 5–10.

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