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An Assessment of Multiple Drivers Determining Woody Species Composition and Structure: A Case Study from the Kalahari, Botswana
An Assessment of Multiple Drivers Determining Woody Species Composition and Structure: A Case...
Meyer, Thoralf;Holloway, Paul;Christiansen, Thomas B.;Miller, Jennifer A.;D’Odorico, Paolo;Okin, Gregory S.
land Article An Assessment of Multiple Drivers Determining Woody Species Composition and Structure: A Case Study from the Kalahari, Botswana 1 2 , 3 , 1 1 Thoralf Meyer , Paul Holloway *, Thomas B. Christiansen , Jennifer A. Miller , 4 5 Paolo D’Odorico and Gregory S. Okin Department of Geography and then Environment, University of Texas at Austin, Austin, TX 78712, USA Department of Geography, University College Cork, Cork T12K8AF, Ireland Environmental Research Institute, University College Cork, Cork T23XE10, Ireland Department of Environmental Science, Policy, & Management, University of California Berkeley, Berkeley, CA 94720, USA Department of Geography, University of California Los Angeles, CA 90095, USA * Correspondence: firstname.lastname@example.org; Tel.: +353-490-2835 Received: 29 June 2019; Accepted: 1 August 2019; Published: 5 August 2019 Abstract: Savannas are extremely important socio-economic landscapes, with pastoralist societies relying on these ecosystems to sustain their livelihoods and economy. Globally, there is an increase of woody vegetation in these ecosystems, degrading the potential of these multi-functional landscapes to sustain societies and wildlife. Several mechanisms have been invoked to explain the processes responsible for woody vegetation composition; however, these are often investigated separately at scales not best suited to land-managers, thereby impeding the evaluation of their relative importance. We ran six transects at 15 sites along the Kalahari transect, collecting data on species identity, diversity, and abundance. We used Poisson and Tobit regression models to investigate the relationship among woody vegetation, precipitation, grazing, borehole density, and ﬁre. We identiﬁed 44 species across 78 transects, with the highest species richness and abundance occurring at Kuke (middle of the rainfall gradient). Precipitation was the most important environmental variable across all species and various morphological groups, while increased borehole density and livestock resulted in lower bipinnate species abundance, contradicting the consensus that these managed features increase the presence of such species. Rotating cattle between boreholes subsequently reduces the impact of trampling and grazing on the soil and maintains and/or reduces woody vegetation abundance. Keywords: conservation; ﬁre; grazing; savanna; woody vegetation 1. Introduction Unprecedented changes in climate, urbanization, and economic development are increasing the pressures that societies are enforcing on ecosystems . Developing sustainable ecosystem services is subsequently a priority for conservation management, with savanna ecosystems a landscape of primary concern. Savannas are mixed plant communities comprised of grasses and woody vegetation that cover approximately a quarter of the Earth’s land surface, including roughly half of the African continent . Savannas are an extremely important socio-economic landscape in Africa, with over 80% of savanna land used to raise livestock , underpinning the economic stability of many countries [4,5]. The dynamic nature of savannas means they are susceptible to changes, particularly shifts in plant community composition associated with an increase in woody vegetation [6,7]. A particularly concerning aspect of this increased density of woody vegetation is the reduction of grasses and herbs by encroaching woody Land 2019, 8, 122; doi:10.3390/land8080122 www.mdpi.com/journal/land Land 2019, 8, 122 2 of 14 species. These negative impacts are occurring at an increasingly frequent rate worldwide [8–10], which is a major threat to the ecosystem stewardship of these economically important landscapes. The transition of savanna ecosystems to open shrubland across Botswana, and in particular the western part of the Kalahari, presents a considerable threat to the conservation of the economically important ranching industry. In order to develop adaptive management strategies, the underlying environmental drivers of woody vegetation species need to be better understood. By understanding the environmental drivers responsible for the diversity and abundance of woody vegetation, we can develop predictive models to identify ‘high-risk’ areas, and provide managers, farmers, and governments with decision support across savanna landscapes. Previous research addressing the ecological processes responsible for the observed vegetation patterns have often found conﬂicting results regarding the importance and signiﬁcance of these environmental drivers [11–15], thus limiting the use of this knowledge as the basis for decision-making at a landscape scale. These dierences will be discussed below in the context of savanna ecosystems. 1.1. Precipitation Rainfall aects water availability, and this factor has been described as the most important determinant describing woody vegetation communities, particularly as it limits the amount of primary productivity within an area [16–20]. For example, in a continental study of African savannas, Sankaran et al.  identiﬁed that woody cover increased linearly with mean annual precipitation (MAP) above 150 mm until maximum woody cover was reached at 650 mm. Similarly, in a pot experiment studying the growth of Acacia (new Senegalia and Vachellia classiﬁcations) species, Kraaij and Ward  found that rainfall frequency was the most important factor aecting both germination and survival of seedlings. Joubert et al.  also found that at least two successive seasons of favorable rainfall was required for seed recruitment in Senegalia mellifera. While precipitation intensities , season lengths , and interactions with other factors (e.g., grazing ) all inﬂuence woody vegetation cover, the consensus is that MAP is the primary factor contributing to woody vegetation cover [10,16–20]. 1.2. Grazing The inﬂuence of grazing pressure as a driver for increased woody vegetation cover is a long established theory. Walter ’s  two-layered hypothesis proposes that in savannas, grasses dominate the top-most soil layers, while tree roots dominate lower layers. When grazing removes the grass cover, tree roots begin to dominate the upper layers and prevent the grasses from reestablishing. Studies have proven inconclusive for the two-layer hypothesis, ﬁnding evidence both in support [26–28] and in opposition [29–31]; however, while this theory is still accepted, the current consensus is that this hypothesis is too simplistic to represent the complex dynamic savanna processes . 1.3. Trampling Another explanation for the increased abundance of woody vegetation is the eect of trampling. Trampling from the high frequency and density of pastoral farming causes signiﬁcant declines in cyanobacterial soil crust [32,33]. Savannas are characterized by low soil nutrient content [34–36], although many areas have biological soil crusts that increase soil surface stability, thereby reducing nutrient loss by erosion and atmospheric nitrogen ﬁxation . Studies have found that the soil crust is greatly inﬂuenced by this pastoral trampling within 2 to 8 km of boreholes , and that Acacia (new Senegalia and Vachellia classiﬁcations) species are often found in higher abundances within areas closer to boreholes, due to their low palatability and the positive species-speciﬁc association between canopy and soil crust development . Boreholes are narrow shafts drilled into the ground in order to extract water and are the primary source of water for livestock farmers in southern Africa. Furthermore, cattle rarely stray more than 13–18 km from these water sources in Africa , meaning areas closer to boreholes may have increased woody vegetation cover. Land 2019, 8, 122 3 of 14 1.4. Fire Fire is a factor that restricts woody vegetation diversity and abundance, preventing the formation of canopies [40–42] as well as removing seedlings and subsequently preventing the establishment of new trees . Furthermore, for certain species fire can also kill the larger trees [44,45]. Seymour and Huyser  found that infrequent fires were enough to kill established Vachellia erioloba trees, which are an important keystone species in the region, meaning an increase in fire frequency could have implications on biodiversity. In unmanaged areas, the build-up of large quantities of grass biomass in the understory results in high-intensity fires that are capable of destroying juvenile trees . For example, Sankaran et al.  studied the effect of fire return intervals on the percentage of woody cover in African savannas and found that a shorter return interval reduced established woody cover, which kept the community in a juvenile state by ‘top-killing’ seedlings. In managed landscapes, fires are not as frequent or intense enough to have a discernible impact on mature trees , and a common feature of savannas is the reduction of fires due to mitigation strategies . However, Joubert et al.  note that fire is crucial to disrupt transition from grassy savanna to thicket, and that managers who prevent fires at this stage are likely to experience bush thickening in the future. 1.5. Research Gap and Questions Variation in species characteristics is fundamental to understanding biogeographic patterns . One reason for the possible lack of conclusive evidence explaining the main drivers of dierent woody vegetation patterns in previous research is the variation in how vegetation has been measured (e.g., single species, multiple species, richness, percent woody cover), as well as the dierences in spatial scales of the previous studies (ranging from garden experiments to coarse continental extents). Assessing diversity as total species richness does not always adequately characterize the way in which species dier from each other, and it is these dierences in traits, which often indicate that species respond in dierent ways to changes in the environment [50,51]. Alternatively, studying only one species in isolation could lead to species-speciﬁc results that are not generalizable to the larger system or to other species. Several mechanisms (outlined above) have been invoked to explain the processes responsible for woody vegetation composition; however, these are often investigated separately at scales not best suited to land-managers, thereby impeding the evaluation of their relative importance. Subsequently, this study focuses on the vegetation composition of the Botswana Kalahari, with the aim to investigate the relative inﬂuence of the environmental drivers of woody vegetation at a regional scale. By classifying species into morphological groups based on shared physiological traits, the drivers of woody vegetation richness and abundance can be interpreted more meaningfully at a regional scale that is more appropriate for landscape management decisions. This study will explore three main questions: (1) what is the woody vegetation composition of the Kalahari in western Botswana? (2) What are the environmental drivers of woody species richness? and (3) what are the environmental drivers of woody species abundance? 2. Materials and Methods 2.1. Study Area We conducted our research in western Botswana between 2009 and 2011 (Figure 1). We created a 950 km transect following the observed rainfall gradient along the western part of the Kalahari. This transect ran from Shakawe in the northwest of the country to Bokspits in the southwest of the country. Rainfall along the transect decreases from the north to south, ranging from a MAP of 550 mm to 350 mm . Along this transect, we identiﬁed 15 regions (Figure 1) where we conducted multiple vegetation surveys. We selected regions on their accessibility and a minimum distance of 75 km to the previous region. Land 2019, 8, 122 4 of 14 Land 2019, 8, 122 4 of 14 Figure 1. Location of the 15 regions along the Kalahari Transect where ﬁeldwork was undertaken. Figure 1. Location of the 15 regions along the Kalahari Transect where fieldwork was undertaken. 2.2. Data Collection 2.2. Data Collection Vegetation was surveyed using the line interception transect (LIT) method. Within each region, Vegetation was surveyed using the line interception transect (LIT) method. Within each region, we ﬁxed six 100 m transects radially from a center point. For the dry season, the direction of the ﬁrst we fixed six 100 m transects radially from a center point. For the dry season, the direction of the first transect was determined by a random number (between 0 and 360), and the further two transects were transect was determined by a random number (between 0 and 360), and the further two transects oset by 120 degrees. Transects of the wet season were spaced exactly between dry season transects, were offset by 120 degrees. Transects of the wet season were spaced exactly between dry season resulting in an oset of 60 degrees from the very ﬁrst transect laid. Transects were placed 200 m transects, resulting in an offset of 60 degrees from the very first transect laid. Transects were placed from the center point to avoid over-sampling a small area. See Krebs  for a further description 200 m from the center point to avoid over-sampling a small area. See Krebs  for a further of the LIT methodology. We recorded all woody vegetation that was taller than 25 cm following the description of the LIT methodology. We recorded all woody vegetation that was taller than 25 cm nomenclature provided by Palgrave , whereby average height, distance covered over the transect following the nomenclature provided by Palgrave , whereby average height, distance covered line, and distance and direction of the stem(s) were documented. Species richness and abundance over the transect line, and distance and direction of the stem(s) were documented. Species richness were recorded at all transects, and species identity were recorded at all sites, with the exception of and abundance were recorded at all transects, and species identity were recorded at all sites, with the the wet season transects at Sites 1, 3, 4, and 5 due to uncertain species identiﬁcation resulting from exception of the wet season transects at Sites 1, 3, 4, and 5 due to uncertain species identification missing leaves. The results of the vegetation survey meant we had data from 78 transects for use in the resulting from missing leaves. The results of the vegetation survey meant we had data from 78 statistical analysis. transects for use in the statistical analysis. Species were categorized into ﬁve morphological groups based on the classiﬁcation guidelines Species were categorized into five morphological groups based on the classification guidelines outlined by Meyer et al. . Morphological group I consisted of species characterized by bipinnate outlined by Meyer et al. . Morphological group I consisted of species characterized by bipinnate leaf structures and growth form ranging from multi-stemmed shrub like appearance to single-stemmed leaf structures and growth form ranging from multi-stemmed shrub like appearance to single- trees. Morphological group II included broad leaf species forming dense canopy structures where the stemmed trees. Morphological group II included broad leaf species forming dense canopy structures majority of the growth form is either multi-stemmed (generally less than ﬁve stems) or single-stemmed. where the majority of the growth form is either multi-stemmed (generally less than five stems) or Morphological group III contained multi-stemmed broad leaf shrubs with closed canopies, seldom single-stemmed. Morphological group III contained multi-stemmed broad leaf shrubs with closed exceeding 2 m in height. In contrast, morphological group IV contained shrub species characterized by canopies, seldom exceeding 2 m in height. In contrast, morphological group IV contained shrub open canopies. Morphological Group V included relatively short shrub species (<1.5 m) with small, species characterized by open canopies. Morphological Group V included relatively short shrub open canopies (<0.5 m in diameter). We also obtained data on precipitation, ﬁre frequency, cattle species (<1.5 m) with small, open canopies (<0.5 m in diameter). We also obtained data on density, and borehole locations that represent the possible drivers of diversity and abundance of woody precipitation, fire frequency, cattle density, and borehole locations that represent the possible drivers vegetation (Table 1). of diversity and abundance of woody vegetation (Table 1). Land 2019, 8, 122 5 of 14 Table 1. Description of the environmental drivers used to explore the diversity and abundance of woody vegetation in western Botswana. Variable Description Source We derived mean annual precipitation (MAP) from the isopleth Mean Annual Precipitation  vector data representing rainfall conditions across the Kalahari. We derived ﬁre frequency using the MODIS direct broadcast burned Fire Frequency area product (MCD64A1) as described in Giglio et al. . Fire  frequency product and generation outlined in Appendix A. We identiﬁed density of cattle using the latest available Department Grazing of Wildlife and National Parks aerial counts of wildlife. This survey  was conducted during the dry season of 2005. We counted the number of boreholes within an eight-kilometer Borehole Density  (based on Dougill et al. ) radius. 2.3. Data Analysis We performed regression analysis in order to explore the environmental drivers of woody species richness and species abundances. Environmental variables were checked for multicollinearity using variance inﬂation factor, then standardized using z-scores in order to compare their relative inﬂuence on the ecological indicators. We performed all regression analyses using R 3.3.0. . We selected regression analyses based on a preliminary evaluation of the data and their error distribution. Histogram exploration identiﬁed a mixture of Poisson and censored Gaussian distributions. We subsequently used a combination of generalized linear models with Poisson error distributions and Tobit regression models to analyze our data. For data that had a Poisson distribution, a Generalized Linear Model procedure with a Poisson error distribution and a log link function was used: log(y) = + X + ::: + X (1) 0 n n 1 1 where y is the abundances, X is the nth predictor, and is the Poisson regression coecient. n n A censored Gaussian distribution represents a dataset that has a normal error distribution, but has some limit, either from below or above. Ecological data is often collected with a large proportion of the observations just above zero, while data cannot extend below zero or above certain thresholds (e.g., percentage cover). Tobit regression overcomes this bias and has been shown to perform better than ordinary least squares (OLS) (e.g., ) and is widely used in criminology (e.g., ) and land use change research (e.g., ). Species richness of woody vegetation is censored at zero (i.e., there cannot be a species richness of 1), and so any parameter estimates obtained by conventional OLS would be biased. Developed by Tobin , the Tobit regression model ﬁts a set of parameters to where the dependent variable is left-censored at zero: y = x + " (2a) i i 0 if y 0 y = (2b) y if y > 0 i i where the subscript i = 1, 2, 3 ::: n, indicates the observation, y is an unobservable variable, x is a vector of explanatory variables, is a vector of unknown parameters, and " is the error term. To estimate the censored regression models, we used the censReg  and MaxLik  packages. Final models were selected based on Akaike Information Criterion (AIC) using both forwards and backwards stepwise selection of models. We investigated third and fourth order interactions, but these did not improve the ﬁnal models. Therefore, our ﬁnal models only include main eects and second order interactions. Land 2019, 8, 122 6 of 14 Land 2019, 8, 122 6 of 14 3. Results 3.1. Woo 3. Results dy Vegetation Surveys We identified 44 woody plant species across the 78 transects where taxonomic information was 3.1. Woody Vegetation Surveys recorded. We recorded the highest diversity at Kuke (site 7), with 21 species found in all six transects We identiﬁed 44 woody plant species across the 78 transects where taxonomic information was in the region (Figure 2a), and 13 species found along transect four during the wet season. In general, recorded. We recorded the highest diversity at Kuke (site 7), with 21 species found in all six transects in both richness and abundance decreased as data collection moved southwards which follows the the region (Figure 2a), and 13 species found along transect four during the wet season. In general, both precipitation gradient, although Kuke is the notable exception. We recorded the lowest total richness and abundance decreased as data collection moved southwards which follows the precipitation abundances for all six transects at NG5 (Site 6) and Bokspits (Site 15) and the highest abundances at gradient, although Kuke is the notable exception. We recorded the lowest total abundances for all six Quangwa (Site 4) and Kuke (Site 7) (Figure 2b). Supplementary Information provides the data which transects at NG5 (Site 6) and Bokspits (Site 15) and the highest abundances at Quangwa (Site 4) and includes geographic location (WGS 1984), a list of species recorded, and their morphological Kuke (Site 7) (Figure 2b). Supplementary Information provides the data which includes geographic classification. We recorded eight species in Morphological Group I (bipinnate leaf structure), fourteen location (WGS 1984), a list of species recorded, and their morphological classiﬁcation. We recorded species in Morphological Group II (tall dense canopies), fifteen species in Morphological Group III eight species in Morphological Group I (bipinnate leaf structure), fourteen species in Morphological (small dense canopy species), five species in Morphological Group IV (tall open canopies), and two Group II (tall dense canopies), ﬁfteen species in Morphological Group III (small dense canopy species), species in Morphological Group V (small open canopies). Due to the low number of species recorded ﬁve species in Morphological Group IV (tall open canopies), and two species in Morphological Group in Morphological Group V, these were withheld from the statistical analysis to prevent any V (small open canopies). Due to the low number of species recorded in Morphological Group V, these general were withheld ization or over- from the fitti statistical ng of the model analysis to s. prevent any generalization or over-ﬁtting of the models. Figure 2. Total (a) woody vegetation species richness and (b) woody vegetation species abundance Figure 2. Total (a) woody vegetation species richness and (b) woody vegetation species abundance summed across the six transects at each of the 15 zones across the Kalahari Transect, plotted against summed across the six transects at each of the 15 zones across the Kalahari Transect, plotted against annual precipitation (mm). Sites listed (1) Shakawe, (2) Tsodilo, (3) Gumare, (4) Quangwa, (5) Drotsky’s annual precipitation (mm). Sites listed (1) Shakawe, (2) Tsodilo, (3) Gumare, (4) Quangwa, (5) Caves, (6) Ng 5, (7) Kuke, (8) Ghanzi, (9) Ghanzi South, (10) Bere, (11) Tshane, (12) Tshane South, Drotsky’s Caves, (6) Ng 5, (7) Kuke, (8) Ghanzi, (9) Ghanzi South, (10) Bere, (11) Tshane, (12) Tshane (13) Mabuasehube, (14) Tsabong, and (15) Bokspits. South, (13) Mabuasehube, (14) Tsabong, and (15) Bokspits. Land 2019, 8, 122 7 of 14 3.2. Regression Analysis Our results indicated a number of important drivers of woody vegetation species richness and abundance (Table 2). Precipitation was the most important environmental variable when we considered all species together for both richness and abundance with borehole density and ﬁre included in the ﬁnal models. When species were deconstructed into morphological groups, we observed a variety of signiﬁcant environmental drivers and interactions between these variables (** signiﬁcant at < 0.01, * signiﬁcant at < 0.05). The relative importance of each environmental driver often changed when we compared the regression models for richness and abundance of the same morphological group, indicating that the processes that determine diversity are dierent from those determining abundance. Boreholes were the most important driver for morphological groups II abundance and III richness, while livestock was the most important driver for morphological group IV abundance. Table 2. Regression output for species richness (SR) and abundance (AB) for the four Morphological Groups (MG). ** signiﬁcant at < 0.01, * signiﬁcant at < 0.05. Tobit (T) and Poisson (P) regression analysis undertaken based on distribution of data. MG I MG I MG II MG II MG III MG III MG IV MG IV Total SR Total AB SR AB SR AB SR AB SR AB Regression T T T P T P T P P P AIC 319.72 597.98 218.54 398.95 223.60 588.33 226.76 450.45 126.61 488.37 Intercept 5.66 ** 20.05 ** 0.92 ** 1.81 1.18 ** 0.05 1.00 0.34 40.69 4.67 PPT 1.32 ** 11.07 ** 0.40 ** 1.71 ** 3.51 ** 2.36 ** 0.92 ** 0.01 27.03 4.32 Boreholes 0.57 ** 1.85 ** 0.31 4.06 ** 1.65 ** 3.24 ** 2.78 ** 2.51 ** 51.60 0.04 Livestock 12.97 1.14 ** 2.04 ** 1.62 ** 1.87 ** 132.0 10.02 Fire 3.16 ** 0.20 9.96 0.60 ** 0.61 ** 0.39 14.52 0.13 PPT * Boreholes 0.56 ** 5.80 ** 2.83 ** 56.83 PPT * Livestock 2.59 ** 4.17 ** 2.37 ** 0.79 1.68 ** 109.8 8.30 PPT * Fire 0.46 ** 0.28 0.29 ** 2.44 Boreholes * Livestock 5.05 ** 1.86 ** 1.31 2.82 ** Boreholes * Fire 0.76 1.19 27.79 Livestock * Fire 18.08 logSigma 0.57 ** 2.28 ** 0.01 0.27 ** 0.07 Precipitation and borehole density were included in all ﬁnal models for every morphological group, while livestock was not important when all species were considered together, but included for all morphological groups (both richness and abundance) with the exception of morphological group I richness. Similarly, ﬁre frequency was included for most morphological groups, with the exception of morphological group III and total species richness. Several two-way interactions were returned across the dierent models, and these were often signiﬁcant. Morphological groups I and III abundance had the most interactions among all variables, suggesting these species have a complex and dynamic relationship with the environment. Precipitation was a signiﬁcant variable in all ﬁnal regression models for species richness and abundance for all but three morphological groups, and it was the most important variable for total species richness and abundance, and morphological group I richness (Table 2). Precipitation had a positive relationship with species richness for morphological groups II and III, and abundance for morphological group II. This relationship was expected since these groups are characterized by dense canopy broad leaf species resulting in higher Leaf Area Index (LAI) and hence higher water requirements . A negative relationship for morphological group I (bipinnate species) richness and rainfall was identiﬁed. This could be due to the fact that in xeric environments such species outcompete the majority of broad-leaved vegetation due to their general morphological characteristics and ecological traits (such as long root traps ), meaning the diversity of these species increases in arid areas where other water-dependent species simply cannot survive. Land 2019, 8, 122 8 of 14 Livestock density was not included in the ﬁnal model as selected by AIC when all species were considered together, but it had a positive relationship with morphological groups II and III (Table 2). Small dense canopy species such as Grewia spp, Rhus tenuivirus and Ziziphus mucronata notably have relatively low palatability . Thus, if these species were already established when grazing increased in the area, they would not be aected by livestock. It was also the most important variable in determining abundance of morphological group IV (tall open canopy), reporting a negative relationship. Borehole density also had the most inﬂuence in determining both abundance of morphological group II and richness of morphological group III, forming a negative relationship with both ecological indicators. Boreholes also had a negative relationship with all response variables with the exception of morphological group I abundance. However, the interaction between boreholes and livestock density was signiﬁcant for morphological group I abundance, indicating a negative relationship. This interaction was also signiﬁcant for morphological groups II and III (although positive). These ﬁndings contradict previous research, and indicate that broad leaf species thrive in locations where there are more cattle and boreholes, while bipinnate species decrease. Fire had a negative inﬂuence on both richness and abundance at a regional scale (Table 2). Fire was generally negatively correlated to the overall abundance of woody species, but had a positive relationship with abundance of morphological groups III and IV, albeit not signiﬁcant. 4. Discussion Following the global trend in the conversion of savanna landscapes to woodier landscapes [7,27], the aim of this research was to investigate the variables responsible for woody vegetation composition in the western Kalahari, in particular those that cause high diversity and abundance of these species. We identiﬁed a variety of environmental drivers that are responsible for high diversity and abundance of woody vegetation, most notably precipitation, borehole density, grazing, and ﬁre. Our results generally agree with the observation that the rainfall gradient of the Kalahari is associated with an increase in woody vegetation [16–20]. Interestingly, the highest species richness was recorded at Kuke (Figure 1—Site 7), where the annual precipitation is 450 mm (in the middle of the rainfall gradient). The substantially higher species richness at Kuke can be explained by the site being located in an area buering the Ghanzi farm-block to the south and the wildlife areas to the north. Both livestock and wildlife numbers are low here, and furthermore, ﬁres have not occurred in this area due to both ﬁre prevention strategies and the existence of the veterinary cordon fences acting as ﬁre breaks. Therefore, our results indicate that while rainfall has a strong inﬂuence on woody vegetation, other factors also contribute signiﬁcantly. Our ﬁndings corroborate the positive association of bipinnate abundance (morphological group I) in areas close to boreholes , as well as an overall reduction in woody vegetation cover . The negative relationship with small dense species is intuitive, as trampling loosens the soil and prevents these species from rooting. However, when grazing is high, the signiﬁcant negative interaction between borehole density and grazing with bipinnate abundance contradicts the existing theories behind woody vegetation patterns. This relationship is a result of the fact that a higher number of boreholes and cattle represent more managed commercial ranches where cattle are routinely rotated between ﬁelds, and the regular use of multiple boreholes by the livestock negates the impact of trampling on the soil. This subsequently reduces the rate of bush encroachment by the unpalatable and thorny bipinnate species, and a positive relationship with other morphological groups is observed. The negative relationship between ﬁre frequency and woody vegetation corroborates observations from other dryland ecosystems [9,41] and supports a mechanistic understanding of the eect of ﬁres in mixed tree-grass plant communities [40,72–74]. These ﬁndings support the observations at Kuke, that absence of ﬁre does increase vegetation diversity and abundance (particularly for smaller species), and that the removal of ﬁre from a landscape could increase bush thickening . However, when ﬁre was included in the models, it was seldom the most important variable (Table 2), with the exception of a positive interaction between livestock and ﬁre when modelling morphological group I abundance Land 2019, 8, 122 9 of 14 (albeit not signiﬁcant). While diversity and abundance did decrease, the lesser impact compared to the other environmental variables suggests that frequent ﬁres may not have such severe implications on the ecosystem’s biodiversity as proposed . However, the MODIS MCD64A1 product used in this study (; Appendix A) does not account for ﬁre intensity which could still negatively impact the landscape. The deconstruction of species into morphological groups that are internally homogenous provided an opportunity for an improved understanding of the processes that underlie the patterns . Despite this, in savanna ecosystems, research has focused on individual species (e.g., [21,24,45]) where ﬁndings are generally not always scalable to the wider ecosystem as species do exhibit idiosyncratic responses to the environment . Subsequently, we feel that our analysis has related the importance of environmental drivers on the structure and physiological properties of the species, while it is not so speciﬁc that we cannot generalize processes to a scale that is useful for land managers. It should also be noted that other factors may inﬂuence woody vegetation patterns. Topographic heterogeneity , atmospheric carbon , and harvesting  have all been found to inﬂuence woody vegetation communities. These factors were excluded due to the topographically homogenous landscape under study, and the fact that regional data on carbon and harvesting are dicult to obtain; however, future research should continue to explore the impact of these factors. We also investigated time since last ﬁre as a variable in the regression analysis; however, ﬁre frequency was found to have more inﬂuence on woody vegetation patterns and was subsequently the only ﬁre variable retained in the ﬁnal models to prevent any issues of multicollinearity. Similarly, we measured grazing as density of cattle recorded from aerial surveys, although grazing could be represented using intensity (e.g., quantiﬁcation of herbaceous tissue removal or an assessment of high, medium, or low). However, available data on such features was not available to this study. Recently, the statistical eects of spatial autocorrelation have been noted  and methods to incorporate and explore this into regression models have become more common [79–81]. However, we made the decision not to incorporate spatial autocorrelation in our analysis so that discussion could focus speciﬁcally on the environmental factors across the transect. We used a combination of generalized linear models with Poisson error distributions and Tobit regression models to analyze our data. Biodiversity indicators such as species richness and abundance often exhibit distributions that are unsuitable for a number of statistical techniques. The literature surrounding the use of statistical analyses that do not account for lower limits to explore ecological questions is perhaps part of the reason we still have ambiguity surrounding the drivers of woody vegetation in savanna ecosystems. While our results corroborate the existence of well-established biodiversity-environment relationships (e.g., positive relationship with MAP), we also identiﬁed several novel biodiversity-environment relationships from the Tobit models (e.g., positive relationships with livestock). Subsequently, research should continue to explore more suitable statistical methodologies with which to analyze ecological data so that any management strategies implemented from ﬁndings are better informed. 5. Conclusions Savannas are an extremely important socio-economic landscape in Africa. These landscapes are inherently multi-functional, balancing the needs of pastoral societies with conservation of these dynamic ecosystems. Global trends of savanna to shrubland conversion [6,7] will have important ecological and economic consequences. Here we investigated the impact of regional scale environmental drivers (a scale that is more relevant to governments and land managers across Africa and beyond) on woody vegetation diversity and abundance. Data on over 44 species was collected over a two-year period at ﬁfteen sites along the Kalahari transect. At each site, six 100 m transects recorded diversity ranging from one species to thirteen species, and abundance ranging from two individuals to 62 individuals. A mixture of Poisson and Tobit regression models identiﬁed that rainfall was the most important environmental variable when all species were considered equally, corroborating previous research Land 2019, 8, 122 10 of 14 Interestingly, bipinnate species abundance decreased with increasing boreholes and livestock. These results contradict the consensus that borehole density and grazing increase the presence of such species, and suggest that by rotating cattle between boreholes, the impact of trampling and grazing on the soil is reduced and savanna landscapes are maintained. The deconstruction of species into different morphological groups provided better insights into the differences in the ways woody vegetation responds to environmental factors, and this deconstruction could aid in reconciling the divergent hypotheses surrounding woody vegetation patterns in savanna ecosystems, as all variables Land 2019, 8, 122 10 of 14 had a significant relationship with richness and abundance across all morphological groups. The results of this research should support land managers, governments and researchers working in conducted at continental [11,16] and garden [17,19] scales. Interestingly, bipinnate species abundance transitional savanna landscapes worldwide. decreased with increasing boreholes and livestock. These results contradict the consensus that borehole density and grazing increase the presence of such species, and suggest that by rotating cattle between Supplementary Materials: The following are available online at www.mdpi.com/xxx/s1, Data boreholes, the impact of trampling and grazing on the soil is reduced and savanna landscapes are maintained. The deconstruction of species into dierent morphological groups provided better Author Contributions: Conceptualization, T.M., P.H., J.A.M., T.B.C., P.D.O., G.S.O.; Data Collection, T.M., insights into the dierences in the ways woody vegetation responds to environmental factors, and P.D.O., G.S.O.; Data Analysis, T.M., P.H., J.A.M., T.B.C.; writing—original draft preparation, T.M., P.H., J.A.M., this deconstruction could aid in reconciling the divergent hypotheses surrounding woody vegetation T.B.C., P.D.O., G.S.O.; writing—review and editing, T.M., P.H., J.A.M. patterns in savanna ecosystems, as all variables had a signiﬁcant relationship with richness and abundance across all morphological groups. The results of this research should support land managers, Funding: This research was funded by National Science Foundation, grant number DEB-0717448 supported governments and researchers working in transitional savanna landscapes worldwide. T.M., P.D.O., and G.S.O., and grant number 0962198 supported P.H., and J.A.M. Supplementary Materials: The following are available online at http://www.mdpi.com/2073-445X/8/8/122/s1, Data. Acknowledgments: We would like to thank the numerous volunteers and field assistants who spent months Author Contributions: Conceptualization, T.M., P.H., J.A.M., T.B.C., P.D.O., G.S.O.; Data Collection, T.M., P.D.O., measuring vegetation under difficult field conditions. We would like to offer a special thank you for support in G.S.O.; Data Analysis, T.M., P.H., J.A.M., T.B.C.; writing—original draft preparation, T.M., P.H., J.A.M., T.B.C., the field to Kelley A Crews-Meyer and our field assistant Gully. Finally, we would like to thank editors and the P.D.O., G.S.O.; writing—review and editing, T.M., P.H., J.A.M. anonymous reviewers for their comments and suggestions. Funding: This research was funded by National Science Foundation, grant number DEB-0717448 supported T.M., P.D.O., and G.S.O., and grant number 0962198 supported P.H., and J.A.M. Conflicts of Interest: The authors declare no conflict of interest. Acknowledgments: We would like to thank the numerous volunteers and ﬁeld assistants who spent months measuring vegetation under dicult ﬁeld conditions. We would like to oer a special thank you for support in Appendix A. Der the ﬁeld to Kelley ived ABu Crews-Meyer rned Area and Pro ourduct ﬁeld assistant Gully. Finally, we would like to thank editors and the anonymous reviewers for their comments and suggestions. The MODIS MCD64A1 (LP DAAC 2010b) burned area product as described by Giglio et al. Conﬂicts of Interest: The authors declare no conﬂict of interest. (2009) was used to determine the fire locations and fire frequency across Botswana from year 2001 to Appendix A Derived Burned Area Product 2011. Fire frequency was calculated on a per pixel basis based on MODIS derived fire events, with The MODIS MCD64A1 (LP DAAC 2010b) burned area product as described by Giglio et al. (2009) frequency ranging from no fire to a maximum of eleven fires recorded for the northern part of the was used to determine the ﬁre locations and ﬁre frequency across Botswana from year 2001 to 2011. country along the northern border with Namibia. Due to fire frequency having more influence on Fire frequency was calculated on a per pixel basis based on MODIS derived ﬁre events, with frequency vegetation than time since last fire, the estimated uncertainty in date of burn was not incorporated in ranging from no ﬁre to a maximum of eleven ﬁres recorded for the northern part of the country along the fire frequency product as this does not impact the output. Figure A1 shows that most areas that the northern border with Namibia. Due to ﬁre frequency having more inﬂuence on vegetation than burned h time ave since a fire lastfre ﬁre, quen the estimated cy between o uncertainty ne fire in date and ofth burn ree fires for was not incorporated the observed t in the ﬁr ime fr e frequency ame. Most of product as this does not impact the output. Figure A1 shows that most areas that burned have a ﬁre these areas are located in the northern and central parts of the region. Across all field sites, fire frequency between one ﬁre and three ﬁres for the observed time frame. Most of these areas are located frequency ranged from 0–6, with a total of eight sites having been affected at least once during the in the northern and central parts of the region. Across all ﬁeld sites, ﬁre frequency ranged from 0–6, time period. Return intervals for fire occurrences seem to be higher at the northern site locations (sites with a total of eight sites having been aected at least once during the time period. Return intervals for 1–6) while all central to southern sites (sites 7–15) with the exception the Bere and Tshane South (site ﬁre occurrences seem to be higher at the northern site locations (sites 1–6) while all central to southern 10 and 12 sites ) we (sites re un 7–15) affect with ed the by exception fire. the Bere and Tshane South (site 10 and 12) were unaected by ﬁre. Figure A1. Fire frequency and burned area across Botswana from 2001 to 2011. Figure A1. Fire frequency and burned area across Botswana from 2001 to 2011. Land 2019, 8, 122 11 of 14 References 1. Chapin, S., III; Koﬁnas, G.P.; Folk, C. Principles of Ecosystem Stewardship: Resilience-Based Natural Resource Management in a Changing World; Springer Science and Business: New York, NY, USA, 2009. 2. Scholes, R.; Archer, S. Tree-grass interaction in savannas 1. Annu. Rev. Ecol. Syst. 1997, 28, 517–544. [CrossRef] 3. Grossman, D.; Gandar, M. Land transformation in South African savanna regions. S. Afr. Geogr. 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Multidisciplinary Digital Publishing Institute
An Assessment of Multiple Drivers Determining Woody Species Composition and Structure: A Case Study from the Kalahari, Botswana
Christiansen, Thomas B.
Miller, Jennifer A.
Okin, Gregory S.
, Volume 8 (8) –
Aug 5, 2019
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