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Journal of Arid Environments 74 (2010) 1516e1524

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Journal of Arid Environments

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Vegetation pattern divergence between dry and wet season in a semiaridsavanna e Spatio-temporal dynamics of plant diversity in northwest Namibia

S.K. Hassler a,*, J. Kreyling b, C. Beierkuhnlein b, J. Eisold c, C. Samimi d, H. Wagenseil e, A. Jentsch f

a Institute of Earth and Environmental Sciences, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, GermanybBiogeography, University of Bayreuth, 95440 Bayreuth, Germanyc Institute for Social-Ecological Research (ISOE), Hamburger Allee 45, 60486 Frankfurt, GermanydDepartment of Geography and Regional Research, University of Vienna, Althanstr. 14, 1090 Vienna, AustriaeNuremberg, GermanyfGeoecology/Physical Geography, Fortstrasse7, 76829 Landau, Germany

a r t i c l e i n f o

Article history:Received 2 July 2009Received in revised form26 April 2010Accepted 24 May 2010Available online 19 June 2010

Keywords:Beta diversityDistance decayPrecipitation gradientSpecies richnessSystematic samplingTree islands

* Corresponding author.E-mail addresses: sibylle.hassler@uni-potsdam.d

kreyling@uni-bayreuth.de (J. Kreyling), eisold@isoe.dunivie.ac.at (C. Samimi), Hendrik.Wagenseil@web.delandau.de (A. Jentsch).

0140-1963/$ e see front matter � 2010 Elsevier Ltd.doi:10.1016/j.jaridenv.2010.05.021

a b s t r a c t

African savannas are primarily used as pastures and are subject to changes in climate and managementstrategies. For sustainable management of these landscapes ecological knowledge on seasonal and long-term variability in plant community composition and the availability of green biomass is essential. In thisstudy, we assessed the effects of dry and wet season on species richness and beta diversity for three sitesalong a gradient of increasing vegetation cover and precipitation in northwest Namibia. A hexagonalsystematic sampling design was used to record floristic data. The Simple Matching, Soerensen, and multi-plot similarity coefficient and distance decay analyses were applied for examining beta diversity. Anal-yses were repeated while separating the plots according to the presence of woody vegetation. Speciesrichness nearly doubled from dry to wet season; compositional similarity increased from dry to wetseason and with increasing aridity of the study sites; distance decay was more pronounced in the dryseason without any link to the precipitation gradient. Woody elements in the landscape, which occuralong drainage lines or as tree islands, govern spatial and seasonal plant diversity fluctuations. Moni-toring them is important for conservation strategies and for establishing grazing rules that ensurea sustainable use of savanna ecosystems.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

Two fifths of Africa’s land area is covered by savannas (Scholesand Walker, 1993), characterised by low and highly variable precip-itation and by the coexistence of trees and grasses. The utilization ofgrass biomass in livestock farming provides the livelihood for a largepart of the local populations. However, several studies suggest thatsavannas under pastoral land use have become increasingly threat-ened by degradation and desertification due to a poor adaptation ofmanagement, non-adapted grazing strategies and over-utilization(e.g. Dean and Macdonald, 1994; Moorsom, 1995; Mistry, 2000;Quaas et al., 2007; Walker and Noy-Meir, 1982). In other cases,where local management strategies did not lead to a generaldegradation of the productivity of rangelands, a decrease in species

e (S.K. Hassler), juergen.e (J. Eisold), cyrus.samimi@(H. Wagenseil), jentsch@uni-

All rights reserved.

diversity could be observed (e.g. Bollig and Schulte, 1999; Mülleret al., 2007; Sullivan, 1999). Currently, in the light of climatechange and shifting management ideologies, it is essential to adoptmanagement techniques to changing environmental conditions asthe livelihoods of local inhabitants depend on the possibility tocontinually use the green biomass of semiarid environments.Particular challenges in adopting land use strategies to the naturaldynamics of semiarid ecosystems over larger scales include threemajor aspects; (1) intra-annual seasonality in precipitation patterns,(2) spatial gradients in savannavegetation, precipitation amount andvariability and (3) spatio-temporal patterns of plant species diversityand their implications for savanna stability.

First, the strong seasonality between the wet and the dry seasonrepeatedly leads to periodic restrictions in green biomass avail-ability over large spatial areas. Balance in the tree-grass coexis-tence, ecological site conditions, and spatial organisation ofvegetation are affected by these annual fluctuations in wateravailability (Guttal and Jayaprakash, 2007; Ludwig et al., 2001;Scanlon et al., 2005). Strong variability in biomass production isoccurring fromyear to year (Wagenseil and Samimi, 2006). In order

S.K. Hassler et al. / Journal of Arid Environments 74 (2010) 1516e1524 1517

to distinguish between long-term shifts and short-term fluctua-tions in plant diversity and the productivity of savanna ecosystems,a better understanding of the vegetation dynamics between the dryand the wet season is needed.

Secondly, semiarid savannas show spatial patterns in vegetationthat are both the cause and effect of variation in resource avail-ability, especially for water, and that are modified by disturbancefactors (Bucini and Hanan, 2007; Caylor et al., 2006; Ferreira et al.,2007; Jeltsch et al., 1999). An important pattern-forming mecha-nism in savannas is the distribution of woody vegetation, providingfavourable abiotic conditions and influencing biotic interactionssuch as competition and facilitation (Facelli and Brock, 2000; Okinet al., 2008; Scholes and Archer, 1997; Veblen, 2008; Vetaas, 1992).These patterns determine biomass availability on the one hand andinfluence the balanced tree-grass coexistence on the other hand(Sankaran et al., 2005). Thus, by considering the spatial structure ofsavannas and the distribution of their woody components, espe-cially along annual precipitation gradients, this could help toensure that they are sensibly and sustainably used.

Thirdly, there is a long lasting debate as to whether the speciesdiversity of plant communities is related to their stability againstenvironmental fluctuations such as drought (summary inBeierkuhnlein and Jentsch, 2005). During the last decade,hypotheses such as the resilience hypothesis (Walker et al., 1999)and the insurance hypothesis (Yachi and Loreau, 1999) weregenerated, suggesting that higher species diversity is associatedwith a higher probability of functional performance in the face ofa perturbation, e.g. a higher stability of biomass production in theface of drought.

Accordingly, McGradySteed et al. (1997) point at the higherpredictability of diverse communities and Naeem (1998) stressesthat in the face of sudden change, high diversity increases the“reliability” of communities. For savanna ecosystems, stability andpredictability are the main determinants of sustainable land useoptions (Quaas et al., 2007). Thus, changes in biodiversity need tobe monitored, in order to assess annual fluctuation, degradationand further management options or conservation plans.

In biodiversity monitoring, a further methodological challengearises. Biodiversity is most commonly assessed using species rich-ness or alpha diversity (Whittaker, 1972). Alpha diversity can givea first impression of landscape structure and hints at plantcommunity functional resilience (Walker et al., 1999). However,recent studies show that it is sensible to examine beta diversity aswell (Jurasinski and Kreyling, 2007; Moora et al., 2007; Vellendet al., 2007). Beta diversity describes species turnover (Whittaker,1972) and is necessary to detect changes in vegetation beyondlocal species immigration or extinction dynamics. Taken together,information on species richness and beta diversity can help toquantify spatio-temporal patterns of biodiversity at the landscapescale and thereby reveal vulnerable areas that are particularlyimportant for management and conservation (Buhk et al., 2007).Further, analyses of distance decay reveal spatial relationships ofcommunities at different scales (Nekola and White, 1999). In thisconcept a gradual decline of similarity according to a gradient in anecological parameter or varying dispersal mechanisms or nicherequirements for different species is expected.

Understanding the spatio-temporal distribution of plantspecies and resources such as green biomass in savannas is anessential prerequisite for the sustainable management of semiaridecosystems. Here, our underlying assumption is that speciesrichness in savannas is limited by water availability. For example,Hoffman et al. (1994) have shown that in the Karoo desert, speciesrichness increases along a moisture gradient. Other authors(Augustine, 2003; Dale, 1999; Prentice and Werger, 1985) foundthat vegetation patterns change from a regular to a clumped

distribution along a gradient of decreasing mean annual precipi-tation. Consequently, we assume that the seasonal change inspecies richness and vegetation cover from the wet to the dryseason to some extent resembles the spatial pattern of speciesrichness and vegetation cover along a gradient of decreasing meanannual precipitation.

In particular, we test the following hypotheses: (i) speciesrichness (alpha-diversity) and grass/herb cover increase along theseasonal change from the dry to the wet season as well as along thespatial gradient of increasing mean annual precipitation; (ii)vegetation similarity (beta-diversity) decreases along the seasonalchange from the dry to the wet season as well as along the spatialgradient of increasing mean annual precipitation; (iii) the presenceof woody species affects alpha-diversity, beta-diversity and grass/herb cover.

2. Methods

2.1. Study area

The Kaokoveld in North-Western Namibia is characterised bylarge plains and alternating north-south stretching mountainranges on a variety of bedrock, reaching from dolomites over schiststo Etendeka lavas (Mendelsohn et al., 2003). Mean annual precip-itation amounts to less than 50 mm a�1 at the coast and showsa rapid increase eastwards to over 300 mm (FAO,1995). Variation inannual precipitation ranges between 60% and 90% (Mendelsohnet al., 2003). A change in vegetation is related to this rainfallgradient. In the west, woody vegetation is only found along river-beds and to a certain extent on the slopes of the north-southstretching mountain ranges (Fig. 1). Woody and total vegetationcover increase from basically no cover in the west to between 40 %and 80 % in the east according to the rainfall gradient, with highercover values along the riverbeds (Wagenseil and Samimi, 2007).

Land use in the Kaokoveld is dominated by communal andsubsistence livestock farming. It is only around villages and whenwater is available that small gardens provide crops. The movementof livestock by traditional pastoralists used to follow the rainfallpatterns and the corresponding biomass production. Nowadayshowever livestock numbers are much higher (Bollig and Bubenzer,2008; Eisold, 2010) which together with other factors limits thedistance that is possible to walk the animals. The consequentgrazing pressure can lead to degrading pastures in many regions inthe Kaokoveld (Mendelsohn et al., 2003).

To cover the rainfall and vegetation cover gradient, three studysites were selected along a 130 km southeast-northwest transect(Figs. 1 and 2) on the basis of remote sensing data (furtherdescribed in the data analysis section). The sites were located inplain terrain on sandy soils with silt content ranging from 17.0 to32.5%. These plains are themain grazing areas. Themountain ridgesthat are characteristic of the Kaokoveld, which to a lesser extent arealso used for grazing, mainly by small ruminants, are excluded fromthe study. Within each of the three study sites, the examined soilparameters are fairly uniform. Thus, our sites represent threedifferent types of semiarid vegetation in northwestern Namibia,distinguished by vegetation characteristics, environmentalparameters (Tables 1 and 2, Fig. 1) and differences in mean annualrainfall. We classified the site “Purros” as semi-desert, the site“ZooDesert” as wooded grassland, and the site “Warmquelle” asopen woodland. Each of these sites exhibit a typical structure invegetation for the regions, mainly governed by the presence ofwoody vegetation located in tree islands and drainage lines or thelocation of grass tussocks for the study site Purros. Without woodyvegetation, this site extends the scope of this study beyond the dryborder of true savannas. However, the semi-desert site was

Fig. 1. Map of the study sites in the Kaokoveld, woody vegetation cover estimated from Landsat ETMþ (modified after Wagenseil and Samimi, 2007).

S.K. Hassler et al. / Journal of Arid Environments 74 (2010) 1516e15241518

included in our study, because small changes in precipitationpattern (e.g. due to climate change) or browsing pressure (e.g. dueto land use change) might drive savanna systems in this direction(Ringrose et al., 2002).

2.2. Sampling design

A systematic hexagonal sampling design was applied in eachstudy site consisting of 37 hexagonal plots arranged concentricallyin 6, 12 and 18 plots around a centre plot, with one diameter of thehexagonal design oriented in east-west direction. This designensures the equidistance of plot centres and thus excludes the effectof distance decay when comparing adjacent plots (Jurasinski andBeierkuhnlein, 2006). It inherently dictates the orientation andlocation of the plots, regardless of existing vegetation and landscapestructures. Plot hexagons were 11.2 m in diameter, covering an areaof 81.5 m2. The distance between neighbouring amounted to 100 m.

Fig. 2. Precipitation gradient along the transect (FAO, 1995).

Species lists from each plot were recorded in the dry season inSeptember 2004 and at the end of the following rainy season inMay 2005. Alpha diversity (species richness) was expressed ascumulative species numbers on each plot. Cover of various growthforms (tree, shrub, grass, herb) was estimated as the cover per plot.Species, which could not be identified in the field were taken to theNational Botanical Research Institute in Windhoek for identifica-tion. In case of missing identification, substitute names were usedfor subsequent analyses.

2.3. Data analysis

The vegetation cover gradient was quantified using remotesensing data from Landsat ETMþ. The cover of the woody vegeta-tion was estimated using a partial least squares regression (PLSR)with a R2 of 0.87 (Wagenseil and Samimi, 2007). The same methodwas used for the total vegetation cover with a lower R2 of 0.49.These regressions are based on ground samples at 102 locationsalong a transect from the Kaokoveld to Etosha National Park. Each

Table 1Vegetation characteristics during the wet season. Shown are cumulative speciesrichness for each study site and the distribution of species in the different growthform categories trees, shrubs, grasses and herbs.

Study site Species richness Growth form composition

Trees [%] Shrubs [%] Grasses [%] Herbs [%]

Warmquelle 27 10 7 50 33ZooDesert 30 3 23 10 65Purros 6 0 0 33 67

Table 2Study site locations and environmental parameters. Abbreviations: MAP¼mean annual precipitation, VMAP¼ variation in mean annual precipitation; for the soil parameters,25 samples were taken at each study site at a depth of 0e10 cm, pH was measured in H2O.

Study site Location Woody vegetation covera [%] Precipitation regime Soil parametersd

MAPb

[mm a�1]VMAPc [%] Silt [%] Clay [%] Rocks [%] Soil pH Soil EC

[mS cm�1]

Warmquelle 13�4704200 E 19�0905200 S 30e45 200 60e70 31.3 4.8 1.2 8.9 98.6ZooDesert 13�3201300 E 19�0302500 S 1e15 150 70 32.5 5.0 26.4 8.0 32.6Purros 13�0003300 E 18�4804800 S 0 100 80e90 17.0 2.5 10.0 9.1 28.2

a Data source: Wagenseil and Samimi (2007).b Taken from FAO (1995).c Data taken from Mendelsohn et al. (2003).d Based on own measurements.

S.K. Hassler et al. / Journal of Arid Environments 74 (2010) 1516e1524 1519

test site was approximately 100�100 m2 surrounded by the samevegetation type to exclude edge effects.

Beta diversity (species turnover) or dissimilarity between plotswithin each study site is commonlyassessedbyemploying similarityindices, scaled between 0 and 1 (Koleff et al., 2003). Many of theseindices, for example the Soerensen coefficient, undertake compari-sons between twoplots considering species that are shared betweentheplots and species that occur only ononeplot or the other. In casesof very low species numbers it is sensible to additionally considerspecies,whichdonotoccuron the comparedplots, but are partof thetotal encountered species pool of the study site. Otherwise, thecomparisons of plotswith no species shared, which are encounteredfrequently during the dry season in the Kaokoveld, always results inzero similarity even though there should be a difference betweenplots containing several different species and plots without anyspecies. Therefore, we applied the Simple Matching coefficient(Sokal and Michener, 1958), which also uses the total encounteredspecies pool (Equation (1)). Here, a comparison of two vegetation-free plots results in the maximum similarity value of 1.

bsm ¼ aþ daþ bþ cþ d

(1)

bsm: Simple Matching coefficient; a: species occurring on bothplots; b, c: species occurring on only one of the plots, d: species notoccurring in any of the two plots being compared, but are part ofthe total encountered species pool of the study site.

Symmetrical indices incorporating the total encounteredspecies pool are, however, discussed critically (Legendre andLegendre, 2006) because they expose a “double zero” problem asthey take species into account, which are absent from bothcompared units. Absence of species from a sampling site is usuallyconsidered less important in ecology than the presence of species.Furthermore, symmetrical similarity indices depend on totalspecies richness. We therefore additionally applied the Soerensencoefficient, as well as an index capable of calculating similaritybetween multiple plots irrespective of changes in species richness(Baselga et al., 2007). All results for these additional indices can befound in the electronic supplementary as no difference in overallpatterns was observed between them and the Simple Matchingcoefficient results presented here.

In order to notice changes in landscape structure, which mightaffect savanna stability, a spatial concept must be applied. Assimilarity measures are usually calculated between two plots, betadiversity considerations based on a regular grid must implementa method to project the similarity value of a plot compared to all ofits neighbours back onto the plot. Thus, beta diversity changes canbe linked to changes in environmental parameters on the plot itself.We employed a common method to combine similarity values ofa plot compared to many others, taking the mean value (Lennonet al., 2001; Williams, 1996).

Statistical analysis of similarity data requires special methodsbecause the data points of similarity matrices are not independent.Furthermore, our sample size within each study site is rather small.Therefore, the similarity of groups was compared using a permu-tation procedure, the function “diffmean” in the R package simba(Jurasinski, 2007). The difference in the mean between two groupsis calculated (delta), then the values of these two groups are putinto a combined set from which two random sets of the same sizeas the original sets are drawn. The difference in the mean betweenthese random sets is calculated and stored (permuted deltas).Repeating the last step 1000 times provides a potential significancelevel of p< 0.001 by testing the original delta against the distri-bution of the permuted deltas. Because of the small sample size andas we are testing against 1000 permutations, all differences inmean with p� 0.01 are understood to be not significant. Thispermutation technique was also applied to alpha diversity data asthe preconditions for parametric tests were not met.

First results of these analyses hinted at the importance of woodyvegetation in the dynamics of savanna systems. Thus, a pooledanalysis was undertaken. One pool was made up of comparisons ofplots exhibiting woody vegetation compared to all other plots withwoody vegetation on the study site. The other pool did the same forplots without woody vegetation. To exclude the obvious effect ofthe presence of woody vegetation, trees and shrubs were excludedfrom the analysis. The pool concept was applied both to speciesrichness and to dissimilarity data and was analysed statisticallyusing the permutation procedure described above.

For the distance decay analyses the Simple Matching andSoerensen similarity data were classified according to the distanceof the plot pairs. The relationship between similarity and distancewas tested for the dry and the wet season data, respectively. Alinear model was fitted to the data and significance in differencefrom 0 for the slope of the fitted line was tested with a Mantel-likepermutation procedure and a non-parametric correlation as rec-ommended by Legendre (1993) using the function “pcol” in the Rpackage simba (Jurasinski, 2007). The difference in slopes betweenthe dry and the wet season was calculated using the function“diffslope” in the R package simba, applying 1000 permutations(Jurasinski, 2007). This function can be used to calculate thedifference in slope between two data sets, each containing twovectors and follows the idea of Nekola and White (1999) forcalculating the statistical inference of the difference in slopebetween two regression lines. Differences in slopewith p� 0.01 areunderstood to be not significant.

Following the notion that woody species might be a key tosavanna structure, we further examined the relationship betweenSimple Matching (and Soerensen) similarity and differences in treecover. Significance was tested using the same procedures as for thedistance decay considerations.

All statistical analyses were performed using R (R DevelopmentCore Team, 2008).

Spec

ies

richn

ess

/ plo

t

Warmquelle Zoo Desert Zoo DesertPurros Warmquelle Purros

Fig. 4. The influence of the presence of woody species on the species richness patternin the three study sites separated by the dry and the wet season. Dark boxes: plots withwoody vegetation; white boxes: plots without woody vegetation. Statistical signifi-cance (p< 0.01) was tested using the permutation procedure and is marked byasterisks. The increases in species numbers for each pool and study site between thedry and the wet season are all significant.

S.K. Hassler et al. / Journal of Arid Environments 74 (2010) 1516e15241520

3. Results

3.1. Species richness and grass/herb cover

Species richness significantly increased at all study sites fromthe dry to the wet season. The absolute values of species richnesswere lowest for Purros, the semi-desert site located in the driestpart of the gradient, lacking woody vegetation.

A right-skewed distribution of species richness data, particu-larly during the wet season (Fig. 3) implies that a lot of plots did notgain many species from the dry to the wet season whereas someplots exhibit a markedly higher species number in the wet season.

Growth form distribution in the dry and the wet season showedthat the gain in species richness is mainly due to the occurrence ofannual herbs and grasses in the wet season.

Analysis of the data separated into pools of plots containingwoody vegetation and plots without woody vegetation resulted inthe following: Species richness was not significantly differentbetween wooded and non-wooded plots for the study site“Warmquelle” representing open woodland, neither in the dry norin the wet season (Fig. 4). For the site “ZooDesert” representingwooded grassland, plots which contained woody species hadsignificantly more species than plots without these. Species rich-ness roughly doubled from the dry to the wet season for wooded aswell as non-wooded plots. In general, this increase in speciesrichness was greater for plots containing woody vegetation than forplots without woody vegetation.

3.2. Vegetation similarity/beta diversity

Mean similarity of species composition significantly increasedfrom the dry to the wet season for two of the study sites, the openwoodland at “Warmquelle” and the semi-desert at “Purros” (Fig. 5,see also electronic supplementary for similar results for Soerensenor multi plot similarity indices). In general, vegetation similaritywas relatively high, ranging from 0.92 to 0.99 on a scale from 0 to 1.Similarity increased from the study site “Warmquelle” over “Zoo-Desert” to “Purros”, along the gradient of decreasing mean annualprecipitation.

Mean similarity of non-wooded plots for the sites “Warmquelle”and “ZooDesert” was significantly higher than the mean similarityof wooded plots. (Fig. 6, see also electronic supplementary for

plot

Warmquelle

Fig. 3. Species richness per plot in the three study sites differs between the dry and thewet season. Margins of the boxes indicate the 25th and 75th percentile of the data, theline within the box visualises the median, whiskers show the 10th and 90th percentile,dots stand for outliers (this also applies to Figs. 4e6). Statistical significance (p< 0.01)was tested using the permutation procedure and is marked by asterisks.

similar results for Soerensen or multi plot similarity indices). Fromthe dry to the wet season, both wooded and non-wooded plots onthe site “Warmquelle” increased in mean similarity, while this wasonly observed for non-wooded plots on the site ”ZooDesert”,mimicking the results for alpha diversity. However, the pool ofwooded plots on the site “ZooDesert” showed a decrease in simi-larity, with the median dropping from 0.94 to 0.90 between the dryand the wet season. Altogether, no general trend for the relativechanges between the wet and the dry season data was found alongthe precipitation gradient.

A significant distance decay in similarity was found for the studysites in the open woodland on the site “Warmquelle” and in thesemi-desert on the site “Purros”, where Simple Matching similaritydeclined with increasing distance (Fig. 7, see also electronicsupplementary for similar results for the Soerensen coefficient).The values for the slopes were generally small, resembling a ratherhomogeneous distribution of species in the study sites. The woodygrassland on the site “ZooDesert” exhibited no distance decay at all.The slopes of the distance decay for the other two study sites were

Sim

ple

Mat

chin

g si

mila

rity

Warmquelle

Fig. 5. Changes in mean beta diversity between the wet and the dry season. Shown isthe mean Simple Matching similarity between each plot and all other plots within eachstudy site. Statistical significance (p< 0.01) was tested using the permutation proce-dure and is marked by asterisks.

Sim

ple

Mat

chin

g si

mila

rity

WarmquelleWarmquelle

Fig. 6. The influence of the presence of woody species on the beta diversity in thethree study sites separated by the dry and the wet season. Shown is the mean SimpleMatching similarity between each plot and all other plots in each study site. Darkboxes: plots with woody vegetation; white boxes: plots without woody vegetation.Statistical significance (p< 0.01) was tested using the permutation procedure and ismarked by asterisks. Not indicated in the graph are comparisons between the dry andthe wet season, for the two pools in each study site. They showed significant increasesfor both pools in “Warmquelle” and the non-woody pool in “ZooDesert” and a signif-icant decrease in similarity for the woody pool of “ZooDesert”.

S.K. Hassler et al. / Journal of Arid Environments 74 (2010) 1516e1524 1521

steeper in the dry season than in the wet season (p¼ 0.038 for“Warmquelle” and p¼ 0.026 for “Purros”).

Differences in tree cover significantly explained turnover inspecies compositions (Fig. 8, see also electronic supplementary forsimilar results for the Soerensen coefficient). Both “Warmquelle”and “ZooDesert”, the two study sites with woody vegetation,showed a significant relationship between compositional similarityand difference in tree cover, for both the dry and the wet season.Thus, with increasing contrast in tree cover between the plots,similarity in non-woody species composition declines. The contrastbetween plots located in a drainage line, where most of the woodyvegetation occurs, with those in the interspace between drainage

Fig. 7. Distance decay for the three different study sites, separated between the dry and theplot pairs being compared.

lines is very high. Additionally, this relationship was stronger in thedry season compared to the wet season, and is visible in thesignificant difference of the regression slopes between the seasons(Fig. 8). This corresponds well with the observation that the vege-tation compositions between drainage lines and interspaces arevery unalike in the dry season, especially noticeable in the highnegative slope of �0.46 for “ZooDesert”, the study site with themost pronounced drainage line structure.

4. Discussion

4.1. Species richness

Hypothesis 1. Species richness (alpha-diversity) and grass/herbcover increase along the seasonal change from the dry to the wetseason as well as along the spatial gradient of increasing meanannual precipitation,

Alphadiversityor species richnessprovides afirst general pictureof vegetation distribution in a landscape and, in combination withgrowth form, can identify structuring processes and functionalresilience of vital ecosystem functions in savannas. Total speciesrichness of the area might be considerably higher, but we restrictedour study to the plains, which are the most important grazing areasfor livestock, especially inwet season (Eisold, 2010). The increase inspecies richness that almost doubled from the dry season to thewetseason is in accordance with our hypotheses. It is mainly caused byannual species emerging after rainfall events. The gain in speciesrichness from the dry to the wet season occurred in all study sites,irrespective of site characteristics such asmean annual precipitationor bedrock. Thus, seasonalfluctuations inmoisture availability seemto be prime determinants of species richness in savannas.

4.2. Vegetation similarity/beta diversity

Hypothesis 2. Vegetation similarity (beta-diversity) decreasesalong the seasonal change from the dry to the wet season as well asalong the spatial gradient of increasing mean annual precipitation.

wet season. Shown is the Simple Matching similarity in relation to the distance of the

Fig. 8. “Cover decay” for the two study sites exhibiting woody vegetation, separated between the dry and the wet season. Shown is the Simple Matching similarity in relation to thedifference in tree cover of the plot pairs being compared.

S.K. Hassler et al. / Journal of Arid Environments 74 (2010) 1516e15241522

Beta diversity exhibited a trend towards more uniform speciescompositions, i.e. higher community similarity between plots, inthe wet season compared to the dry season. During the wet season,the availability of the limited resource water promotes plantgrowth irrespective of small-scale differences in soil structure orgeomorphology. This partly balances out the pronounced influenceof structuring elements such as tree islands or drainage lines whichare areas of higher moisture availability during the dry season.

The patterns of beta diversity revealed by our analyses wereindependent from the chosen similarity index. Similar results fora symmetrical, an asymmetrical, and a multi plot similarity indeximply that the observed pattern was not driven by changes inspecies richness, but by species turnover (Baselga et al., 2007;Legendre and Legendre, 2006).

Distance decay was significant for open woodland on the site“Warmquelle” and semi-desert on the site “Purros” in the dry andthe wet season, but the slopes of the linear model fitted to the datawere very small. As there was no strong turnover in speciescomposition with increasing distance within any particular studysite, we conclude a quasi uniform vegetation pattern not driven bystrong ecological gradients or dispersal barriers at the site scale(compare Nekola and White, 1999). Within the open woodland onthe site “Warmquelle”, vegetation structure was characterised bymore or less regularly distributed tree islands. Within the semi-desert on the site “Purros”, there was only little variability in thecompositions of grass and herb species. Within the woodedgrassland on the site “ZooDesert”, there was no linear relationshipbetween compositional similarity and distance at all. This wasprobably due to the pronounced drainage lines, which cross thestudy site in a rather irregular manner. For the slopes of the linearfits, no significant difference could be found. Thus, the structure inthe landscape related to distance does not change between the dryand the wet season. The pattern of tree islands versus interspacesremains constant throughout the seasons due to the persistentgrowth form of trees and shrubs.

4.3. Woody species

Hypothesis 3. The presence of woody species affects alpha-diversity, beta-diversity and grass/herb cover.

Not all plots at the various study sites gain equally in speciesrichness from the dry to the wet season. A few plots showeda considerable increase in species richness whereas the majoritydid not change so markedly. This hints at structuring elements inthe landscape facilitating the growth of annuals. We identified

these structures to be tree islands or drainage lines, which amongother advantages, offer and sustain favourable moisture conditionsand support tree growth (D’Odorico et al., 2007; Vetaas, 1992). Wethus confirm our hypothesis and state that plots containing woodyvegetation exhibited higher species richness than those withouttrees or shrubs, contributing the main part to the species gain fromthe dry to the wet season.

Additionally, plots with woody vegetation were less similar toeach other than those without. Thus, those plots did not only holdhigher alpha diversity but also higher beta diversity, manifesting ina larger turnover of species from plot to plot. The trend towardshigher similarity in the wet season was still present except for thewoody pool of the study site “ZooDesert”. The very pronounceddrainage lines in this study site represent favoured locations forannuals. Such ephemeral biodiversity centres can explain high betadiversity or low compositional similarity in the wet season, rep-resenting distinct differences between drainage lines rich inannuals and interspaces with rather uniform species composition.

We found a strong relationship between woody vegetationcover and species composition. The influence of woody vegetationon species composition was generally stronger in the dry seasonthan in the wet season. In the dry season, similarity declined morerapidly with increasing difference in tree cover. This means that thecontrasts between woody and non-woody plots were morepronounced in the dry season. Furthermore, the study site “Zoo-Desert” displayed steeper slopes in the linear fit than the site“Warmquelle”, most likely caused by the distinct drainage lineswhich accentuate the difference between woody plots (in thedrainage lines) and non-woody plots (in the interspaces).

Vegetation patterns in these semiarid ecosystems thus dependlargely on structuring woody elements such as tree islands anddrainage lines. In other studies, these structures were identified as“islands of fertility”, because different types of facilitation takeplace. Abiotic components within these islands are more benign e

they offer favourable nutrient supply in the soil, a milder micro-climate and better ecohydrological conditions (Caylor et al., 2004;Facelli and Brock, 2000; Okin et al., 2008; Scholes and Archer,1997; Vetaas, 1992). Besides, there are interactions between thebiotic components, e.g. dispersal facilitation by attractiveness tobirds (Dean et al., 1999) and the interplay between competition andfacilitation of co-occurring species (Veblen, 2008). In semiaridsavannas however, the fluctuation in moisture availability duringthe shift from the dry to the wet season poses distinct changes insome of the abovementioned factors and subsequently affectscommunity structure and phytodiversity (Facelli and Brock, 2000;Veblen, 2008). This manifests in our findings of alpha and beta

S.K. Hassler et al. / Journal of Arid Environments 74 (2010) 1516e1524 1523

diversity being governed by the influence of woody species, whichoccur as tree islands and along drainage lines.

For degrading savannas, several implications arise: Sustainablegrazing rules as well as conservation considerations must take intoaccount the spatio-temporal structure of savannas. Here, it isimportant to distinguish between long-term shifts in plant diver-sity and short-term fluctuations in productivity between the dryand the wet season. Particular areas such as tree islands anddrainage lines exhibit much higher intra-annual fluctuations inspecies richness than the interspaces, as shown in this study.Temporary breaks in grazing during wet years would allow theregeneration of green biomass (Eisold, 2010; Müller et al., 2007).

5. Conclusions

In order to sustainably manage savanna ecosystems, it isimperative to consider vegetation structure in the sense of biodi-versity measures (D’Odorico et al., 2006). A comprehensiveassessment of their dynamics, however, can only be achieved byexamining both the dry and the wet season, as the structure ofsavanna communities markedly varies between seasons. This isvisible in increasing alpha diversity and decreasing beta diversity(increasing compositional similarity and steeper slopes in thedistance decay) from the dry to the wet season. However, the maindeterminant of seasonal and spatial fluctuations in these measuresis the presence of woody species located in tree islands anddrainage lines. They govern the magnitude and the spatial locationof alpha diversity gain and represent hotspots of beta diversity. It istherefore essential for monitoring programmes to consider thespatial patterns of woody structures and the seasonal rhythm ofthese patterns in order to provide a sound basis for the sustainableuse of savanna ecosystems.

Acknowledgements

The National Botanical Research Institute in Windhoek identi-fied many specimens collected in the field. Funding was providedby the Helmholtz Centre for Environmental Research (UFZ) and theGerman Research Foundation (DFG). We thank Meike Kuehlbreyand Ralf Schüpferling for their assistance in field work.

Appendix. Supplementary data

Supplementary data associated with this article can be found inthe online version, at doi:10.1016/j.jaridenv.2010.05.021.

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