1. Introduction
Unprecedented changes in climate, urbanization, and economic development are increasing the pressures that societies are enforcing on ecosystems [
1]. 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 [
2]. Savannas are an extremely important socio-economic landscape in Africa, with over 80% of savanna land used to raise livestock [
3], 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 species. These negative impacts are occurring at an increasingly frequent rate worldwide [
8,
9,
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,
12,
13,
14,
15], thus limiting the use of this knowledge as the basis for decision-making at a landscape scale. These differences will be discussed below in the context of savanna ecosystems.
1.1. Precipitation
Rainfall affects 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,
17,
18,
19,
20]. For example, in a continental study of African savannas, Sankaran et al. [
16] 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 [
19] found that rainfall frequency was the most important factor affecting both germination and survival of seedlings. Joubert et al. [
21] also found that at least two successive seasons of favorable rainfall was required for seed recruitment in
Senegalia mellifera. While precipitation intensities [
22], season lengths [
23], and interactions with other factors (e.g., grazing [
24]) all influence woody vegetation cover, the consensus is that MAP is the primary factor contributing to woody vegetation cover [
10,
16,
17,
18,
19,
20].
1.2. Grazing
The influence of grazing pressure as a driver for increased woody vegetation cover is a long established theory. Walter’s [
25] 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,
27,
28] and in opposition [
29,
30,
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 [
17].
1.3. Trampling
Another explanation for the increased abundance of woody vegetation is the effect 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,
35,
36], although many areas have biological soil crusts that increase soil surface stability, thereby reducing nutrient loss by erosion and atmospheric nitrogen fixation [
33]. Studies have found that the soil crust is greatly influenced by this pastoral trampling within 2 to 8 km of boreholes [
37], 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 [
38]. 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 [
39], meaning areas closer to boreholes may have increased woody vegetation cover.
1.4. Fire
Fire is a factor that restricts woody vegetation diversity and abundance, preventing the formation of canopies [
40,
41,
42] as well as removing seedlings and subsequently preventing the establishment of new trees [
43]. Furthermore, for certain species fire can also kill the larger trees [
44,
45]. Seymour and Huyser [
45] 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 [
46]. For example, Sankaran et al. [
11] 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 [
40], and a common feature of savannas is the reduction of fires due to mitigation strategies [
47]. However, Joubert et al. [
48] 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 [
49]. One reason for the possible lack of conclusive evidence explaining the main drivers of different 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 differences 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 differ from each other, and it is these differences in traits, which often indicate that species respond in different 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?
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,
17,
18,
19,
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 buffering 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 [
38], as well as an overall reduction in woody vegetation cover [
71]. 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 effect of fires in mixed tree-grass plant communities [
40,
72,
73,
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 [
49]. 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 (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 [
45]. However, the MODIS MCD64A1 product used in this study ([
57];
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 [
50]. 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 [
75]. 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 [
76], atmospheric carbon [
46], and harvesting [
77] 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 difficult 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 effects of spatial autocorrelation have been noted [
78] and methods to incorporate and explore this into regression models have become more common [
79,
80,
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.