Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-26T12:51:37.891Z Has data issue: false hasContentIssue false

Suffrutex grasslands in south-central Angola: belowground biomass, root structure, soil characteristics and vegetation dynamics of the ‘underground forests of Africa’

Published online by Cambridge University Press:  09 July 2021

Amândio L. Gomes*
Affiliation:
Faculty of Sciences, Agostinho Neto University, Av. 4 de Fevereiro 71 C.P. 815 Luanda, Angola Biodiversity, Evolution and Ecology of Plants, Institute of Plant Science and Microbiology, University of Hamburg, Ohnhorststr. 18, 22609 Hamburg, Germany
Rasmus Revermann
Affiliation:
Biodiversity, Evolution and Ecology of Plants, Institute of Plant Science and Microbiology, University of Hamburg, Ohnhorststr. 18, 22609 Hamburg, Germany Faculty of Natural Resources and Spatial Sciences, Namibia University of Science and Technology, Windhoek, Namibia
Francisco M. P. Gonçalves
Affiliation:
Biodiversity, Evolution and Ecology of Plants, Institute of Plant Science and Microbiology, University of Hamburg, Ohnhorststr. 18, 22609 Hamburg, Germany Herbarium of Lubango, ISCED Huíla, Department of Natural Sciences, Sarmento Rodrigues str., C.P. 230 Lubango, Angola
Fernanda Lages
Affiliation:
Herbarium of Lubango, ISCED Huíla, Department of Natural Sciences, Sarmento Rodrigues str., C.P. 230 Lubango, Angola
Marcos P. M. Aidar
Affiliation:
Plant Physiology and Biochemistry, Institute of Botany, CP 3005 CEP 01061-970, São Paulo, Brazil
Graciela A. Sanguino Mostajo
Affiliation:
Biodiversity, Evolution and Ecology of Plants, Institute of Plant Science and Microbiology, University of Hamburg, Ohnhorststr. 18, 22609 Hamburg, Germany
Manfred Finckh
Affiliation:
Biodiversity, Evolution and Ecology of Plants, Institute of Plant Science and Microbiology, University of Hamburg, Ohnhorststr. 18, 22609 Hamburg, Germany
*
Author for correspondence:*Amândio L. Gomes, Email: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Despite its importance for carbon stocks accounting, belowground biomass (BGB) has seldom been measured due to the methodological complexity involved. In this study, we assess woody BGB and related carbon stocks, soil properties and human impact on two common suffrutex grasslands (Brachystegia- and Parinari grasslands) on the Angolan Central Plateau. Data on BGB was measured by direct destructive sampling. Soil samples were analysed for select key parameters. To investigate vegetation dynamics and human impact, we used Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) and fire data retrieved via Google Earth Engine. Mean belowground woody biomass of sandy Parinari grasslands was 17 t/ha and 44 t/ha in ferralitic Brachystegia grasslands of which 50% correspond to carbon stocks. As such, the BGB of Brachystegia grasslands almost equals the amount of aboveground biomass (AGB) of neighbouring miombo woodlands. Almost the entire woody BGB is located in the top 30 cm of the soil. Soils were extremely acid, showing a low nutrient availability. Both grassland types differed strongly in EVI and fire seasonality. The Parinari grasslands burnt almost twice as frequent as Brachystegia grasslands in a 10-year period. Our study emphasizes the high relevance of BGB in suffrutex grasslands for carbon stock accounting.

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Introduction

Patterns of aboveground biomass (AGB) distribution in terrestrial ecosystems are reasonably well understood, whereas interest in belowground biomass (BGB) and its distribution has risen only in recent years (IPCC 2006, Ravindranath and Ostwald Reference Ravindranath and Ostwald2008, Rosillo-Calle et al. Reference Rosillo-Calle, de Groot, Hemstock and Woods2007). However, BGB contributes strongly to the total plant biomass for many plant communities (Cairns et al. Reference Cairns, Brown, Helmer and Baumgardner1997, Chidumayo Reference Chidumayo2013, de Castro and Kauffman Reference de Castro and Kauffman1998, Grace et al. Reference Grace, San José, Meir, Miranda and Montes2006, IPCC 2006, Ryan et al. Reference Ryan, Williams and Grace2010). Probably due to the difficulties in harvesting and measuring belowground organs, less attention has been given to BGB and methods of analysis have not been standardized (IPCC 2006, Lichacz et al. Reference Lichacz, Hardiman and Buckney2009, Sanford and Cuevas Reference Sanford, Cuevas, Mulkey, Chazdon and Smith1996, Wetzel and Howe Reference Wetzel and Howe1999).

Yet, AGB and BGB both are important components of terrestrial ecosystem carbon stocks (Mokany et al. Reference Mokany, Raison and Prokushkin2006). AGB, the most visible of all carbon pools, includes all biomass in living vegetation, both woody and herbaceous, above the soil including stems, stumps, branches, bark, seeds and foliage, whereas BGB is the entire biomass of all living roots, tubers, bulbs and rhizomes, excluding fine roots less than 2 mm in diameter because empirically, these cannot be easily distinguished from other components (Ravindranath and Ostwald Reference Ravindranath and Ostwald2008).

BGB is an important carbon pool for many vegetation types, ecosystems and land-use systems. Globally, BGB has a high share of total biomass in most grassland ecosystems (Coupland Reference Coupland and Coupland1992). In addition, many tropical grasslands are co-dominated by geoxylic suffrutices (du Rietz Reference du Rietz1931; White Reference White1976) or geoxyles (Lindman Reference Lindman1914; Simon et al. Reference Simon, Grether, de Queiroz, Skema, Pennington and Hughes2009), e.g. in the Brazilian cerrado or in the miombo woodland landscapes of south-central Africa. Geoxyles are small woody plants with annual or short-lived woody shoots sprouting from massive or extensive perennial woody underground axes (White Reference White1976), comprising xylopodia (Simon et al. Reference Simon, Grether, de Queiroz, Skema, Pennington and Hughes2009), lignotubers (Kolbek and Alves Reference Kolbek and Alves2008) or woody rhizomes (Pausas et al. Reference Pausas, Lamont, Paula, Appezzato-da-Glória and Fidelis2018). Most geoxylic biomass is located below ground (Robertson, Reference Robertson2005) in a complex network of rhizomes, roots, or tubers, and thus was referred to as ‘underground forests’ by White (Reference White1976) in his pioneering paper on geoxylic suffrutices.

The Zambezian centre of endemism is a hotspot of geoxyle diversity (White Reference White1976), but the reasons for this surprising diversity are still not well understood (Zigelski et al. Reference Zigelski, Gomes, Finckh, Huntley, Russo, Lages and Ferrand2019). On the Angolan Central Plateau, suffrutex grasslands cover a substantial part of the land surface (Stellmes et al. Reference Stellmes, Frantz, Finckh and Revermann2013a). While miombo woodlands grow on the hills and upper slopes, the lower slopes of most valleys are covered by open vegetation types dominated by grasses and geoxyles. Thus, to correctly quantify carbon allocation and storage of these particular ecosystems, BGB has to be taken into account.

Obtaining accurate estimates of BGB is recognized as essential for determining its contribution to carbon storage (Chamberlain et al. Reference Chamberlain, Ness, Small, Bonner and Hiebert2013), and thus required for reporting to the United Nations Framework Convention on Climate Change and REDD+. So far, most inventories have used an average root-to-shoot ratio and allometric equations to estimate BGB for several purposes such as carbon accounting (Chidumayo Reference Chidumayo2013, Nieto-Quintano et al. Reference Nieto-Quintano, Edward, Rolando, Marcele, Tim and Casey2018, Ryan et al. Reference Ryan, Williams and Grace2010). However, none of these methods can be applied to suffrutex grasslands due to the great difference between above and belowground organs (Robertson Reference Robertson2005). Thus, direct, destructive sampling is the only method to obtain accurate estimates of BGB of suffrutex grasslands. To our knowledge, BGB of suffrutex grasslands has so far not being quantified by direct sampling in the African tropics. Therefore, in this paper, we aim (1) to shortly describe and compare the ecology, structure and phenology of the two main types of suffrutex grasslands in central Angola; (2) to describe habitat preferences and morphology of the dominant geoxyle species of these two habitat types; (3) to assess BGB and carbon stocks of the suffrutex grasslands; (4) to discuss the relationship between BGB, soil properties and geoxyle morphology; and (5) to briefly assess current human impacts on suffrutex grasslands. The results will allow us to better understand the ecological importance of suffrutex grasslands and their role in the functioning of African savannas, which are still strongly data deficient (Ryan et al. Reference Ryan, Williams and Grace2010).

Study site

The study was conducted in the Cusseque area of the Chitembo Municipality in Bié Province, Angola (Figure 1a–d). The elevation of the study area varies between 1397 m and 1562 m. The landscape is dominated by miombo woodlands (main tree species belong to the Fabaceae genera Brachystegia, Cryptosepalum, Julbernardia and Isoberlinia). The vegetation distribution follows the topography of the landscape. While the hill tracts are dominated by closed-canopy woodlands, the valleys are dominated by geoxylic grasslands. In this area, two types of geoxylic grassland can be distinguished: Brachystegia russelliae-dominated ‘Anharas de Ongote’, hereafter called Brachystegia grasslands on ferralitic soils of the east–west running tributary rivers and Parinari capensis-dominated ‘Chanas de borracha’, hereafter called Parinari grasslands on sandy deposits of the main north–south stretching Cusseque valley (Revermann et al. Reference Revermann, Gonçalves, Gomes and Finckh2013, Reference Reverman, Gonçalves, Gomes and Finckh2017, Reference Revermann, Oldeland, Gonçalves, Luther-Mosebach, Gomes, Jürgens and Finckh2018). Brachystegia grasslands cover 23.3% of the study site and Parinari grasslands 8.5% (Schneibel et al. Reference Schneibel, Stellmes, Frantz, Finckh and Revermann2013). The diversity of geoxyles in both grassland types is high. Zigelski et al. (Reference Zigelski, Gomes, Finckh, Huntley, Russo, Lages and Ferrand2019) report more than 121 species of geoxyles for Angola of which more than 70 species occur in the study area (Revermann et al. Reference Reverman, Gonçalves, Gomes and Finckh2017; own unpublished data).

Figure 1. (a) The research site Cusseque of The Future Okavango (TFO) project (www.future-okavango.org) in the upper Cubango basin (red rectangle, 100 km2). The occurrence of open suffrutex grasslands on ferralitic soils (light green colour, e.g. in the Sovi River valley) and sandy deposits (white, eastern bank of the Cusseque River) is a characteristic feature in the valleys, contrasting with the miombo woodlands on the hills (dark green); (b) location in south-central Africa (the Okavango Basin marked with grey square); (c) the Cubango/Okavango Basin in the three countries Angola, Namibia and Botswana, black square indicates the research site Cusseque; (d) mean monthly rainfall in the study area (Fick and Hijmans Reference Fick and Hijmans2017).

The study area has a subhumid summer rainfall climate with a pronounced wet season lasting from October to April with a mean annual precipitation of 987 mm. Mean annual temperature is 20.4°C (Weber Reference Weber2013). Night frosts occur frequently during the winter (June and July) especially in the valleys (Revermann & Finckh Reference Revermann and Finckh2013, Finckh et al. Reference Finckh, Revermann and Aidar2016).

Material and Methods

Seasonal vegetation dynamics of suffrutex grasslands in central Angola

We used data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to compare the seasonal land cover dynamics of Brachystegia- and Parinari grasslands. For 20 sites systematically distributed over the suffrutex grasslands of the study area (10 in each vegetation unit), we used the Google Earth Engine to retrieve the Enhanced Vegetation Index (EVI) based on the combined MYD13Q1 and MOD13Q1 data products covering the observation period 2010–2019. The systematic sampling design was chosen to cover all main grassland tracts separated by fire breaks (forests, wetlands, roads) and to minimize the probability that several sampling points were affected by single fire events. To analyse the phenological development and to obtain a proxy for the (AGB) throughout the season, we calculated the mean annual course of the EVI for both vegetation types over the 10-year period.

Structure and morphology of woody belowground plant organs

The area covered by suffrutex grasslands exhibited two characteristic soil types, ferralitic and sandy soils. For each of the two present soil types, we chose the three most dominant geoxyle species for structural and morphological analyses. In ferralitic soils, these were Brachystegia russelliae I. M. Johnston, Cryptosepalum exfoliatum subsp. suffruticans (P. A. Duvign.) P. A. Duvign. and Brenan and Syzygium guineense subsp. huillensis (Hiern.) F. White; in the sandy soil, we chose Parinari capensis Harv., Pygmaeothamnus zeyheri (Sond.) Robyns and Ochna arenaria De Wild. and T. Durand. We excavated five individuals of each species. During excavation, we carefully removed the soil around the individuals with a shovel, knife and by hand, striving for the extraction of intact belowground organs (roots, shoots, tubers and buds). We observed and described in detail their complex woody belowground structures and morphology. For classification of the belowground bud bank (BBB) type, we followed Pausas et al. (Reference Pausas, Lamont, Paula, Appezzato-da-Glória and Fidelis2018). The taxon which we call in this paper Cryptosepalum exfoliatum subsp. suffruticans (P. A. Duvign.) P. A. Duvign. and Brenan does not fully match the description in Flora Zambesiaca. Specimens have been deposited at the herbaria LUBA and HBG under the collection numbers 132481, 132685, 132754, 132825, 133059, 134697 and 143366. D. Goyder and R. Polhill (both at Royal Botanic Gardens, Kew) consider it as a putative new Cryptosepalum species (pers. comm.). However, in order to maintain consistency with previous publications (e.g. Gomes et al. Reference Gomes, Revermann, Gonçalves, Lages, Aidar, Finckh and Jürgens2019, Revermann et al. Reference Revermann, Gonçalves, Gomes and Finckh2013, Reference Reverman, Gonçalves, Gomes and Finckh2017, Reference Revermann, Oldeland, Gonçalves, Luther-Mosebach, Gomes, Jürgens and Finckh2018, Zigelski et al. Reference Zigelski, Gomes, Finckh, Huntley, Russo, Lages and Ferrand2019), for the time being we continue to use the name.

Belowground biomass and carbon stocks

BGB per unit area was assessed based on field measurements of samples collected in 138 square pits dug within the study area (99 in Brachystegia grasslands and 39 in Parinari grasslands), harvesting all woody biomass. Pits were distributed in the following order: 60 pits (all in Brachystegia grasslands) were located in 2 1,000 m2 (20 m × 50 m) plots divided into 10 subplots of 10 m × 10 m each; for each subplot, we dug 3 pits of 0.5 m × 0.5 m × 0.5 m (length, width and depth) diagonally, with 2 pits in the opposite corners and 1 in the centre (Dengler Reference Dengler2009) as shown in Figure S1.

Furthermore, 48 pits (36 in Brachystegia grasslands and 12 in Parinari grasslands) of the same size were located in 16 plots of 10 m × 10 m spread in the geoxylic grasslands (12 in Brachystegia grasslands and 4 in Parinari grasslands); 20 pits (3 in Brachystegia grasslands and 17 in Parinari grasslands) were dug randomly in surrounding grasslands. Despite the highest woody biomass concentration being in many vegetation types in a depth of 0.3 m (de Castro and Kauffman Reference de Castro and Kauffman1998, Jackson et al. Reference Jackson, Canadell, Ehlinger, Mooney, Sala and Schulze1996, Ravindranath and Ostwald Reference Ravindranath and Ostwald2008), we harvested down to 0.5 m to include almost all BGB. Before digging, each pit was cleared of all AGB with pruning shears. During excavation, all woody roots and/or branches > 2 mm in diameter were carefully separated from the soil material. All harvested biomass was dried to constant weight. Dry mass was obtained using a digital scale, after eliminating the last remnants of soil material. For sake of comparability with data from the literature, all weights are given as t/ha.

Carbon stocks were estimated assuming that 50% of the biomass corresponds to carbon (Ciais et al. Reference Ciais, Bombelli, Williams, Piao, Chave, Ryan, Henry, Brender and Valentini2011, IPCC 2006, Nabuurs et al. Reference Nabuurs, Ravindranath, Paustian, Freibauer, Hohenstein, Makundi, Penman, Gytarsky, Hiraishi, Krug, Kruger, Pipatti, Buendia, Miwa, Ngara, Tanabe and Wagner2003, Schlesinger Reference Schlesinger1997). We used the measured dry mass to calculate the corresponding carbon stock in suffrutex grasslands.

Soil characteristics

Soil samples were taken in the centre of 46 plots (18 in ferralitic soils and 28 in sandy soils). Soil samples were taken at three depths: (1) 0 cm–10 cm; (2) 10 cm–30 cm; (3) 30 cm–50 cm. Soil analyses were made in the soil laboratory of the Instituto de Investigação Agronómica da Chianga, in Huambo, Angola. For each layer, we analysed texture (particle size class distribution by sedimentation test), pH (by potentiometer in water, KCl and CaCl2), exchangeable bases (EB) (by ammonium acetate method), exchangeable acidity (Al+H) (by KCl extraction), cation exchange capacity (CEC) by calculation (CEC=K+Ca+Mg+(H+Al)), extractable phosphorous (by Truog method), aluminium saturation (m) by calculation (m=100*Al3+/CEC), total exchangeable bases (TEB) by calculation (TEB=K++Ca2++Mg2++Na+) and base saturation (V) by calculation (V=100*TEB/CEC). Subsequently, we calculated the average of each parameter per plot and finally, the mean for the study area was calculated.

Human impact

Since 2011, we conducted field research on the southern slopes of the Bié Plateau, visiting the wider study area at least twice a year. Since then, we continuously surveyed the study area for human activities affecting the woodlands (including suffrutex grasslands) and observed changes in land-use practices. Of particular importance are human-made fires, which are often used in the study area during the dry season to facilitate hunting of small game. To quantify the fire frequency in the suffrutex grasslands and to compare fire dynamics between the vegetation units, we again used data from the MODIS. For the same 20 sites as used for the EVI, we retrieved the MCD64A1 Version 6 Burned Area data product to assess the seasonal fire of Brachystegia- and Parinari grasslands. Based on the 10-year period from 2010 to 2019, we calculated for both vegetation types the seasonal mean monthly fire incidence per plot.

Data analyses

One-way ANOVA was used to test for significant differences in mean values of BGB and soil properties between the two different grassland types. All statistical analyses were carried out using BioEstat (Version 2.0) and PAST (Version 2.16).

Results

Ecology, structure and seasonal vegetation dynamics of the two main types of suffrutex grasslands in central Angola

The suffrutex grasslands of the study area are subject to strong seasonal changes. Field observations indicate that geoxyles dominate from mid-August to December while grasses shape the appearance from January to June. Senescence of the leaves of geoxyles and grasses occurs in the cold dry season from mid-May to mid-August. The EVI curves of both vegetation types clearly depict this general seasonal pattern with the lowest values observed in July, corresponding to the peak of the dry season, a marked increase in September already prior to the onset of the rainy season, and the peak in December and January (Figure 2a and b).

Figure 2. Annual phenology of Brachystegia- (a) and Parinari- (b) grasslands in the Cusseque study site on the Angolan central plateau. The graph shows monthly mean EVI values of a 10-year observation period (2010–2019) for 20 systematically selected sample plots. Data were derived from the combined MYD13Q1 and MOD13Q1 data products via Google Earth Engine.

However, throughout the year, the EVI of Brachystegia grasslands is considerably higher than the EVI of Parinari grasslands. This higher EVI reflects the dense (micro-) canopy cover and high leaf biomass of the dominant Brachystegia russelliae, which has no equivalent in the Parinari grasslands (Figure 2).

Structure and morphology of complex belowground organs

The six geoxyle species displayed a wide spectrum of belowground organs in terms of their morphology, spatial distribution and area occupied, despite being encountered under similar environmental conditions (Table 1). However, all species showed BBB (Pausas et al. Reference Pausas, Lamont, Paula, Appezzato-da-Glória and Fidelis2018) in the thickened underground woody organs from which new aerial shoots regenerate after die-off of aboveground shoots through local disturbance such as fire, frost or herbivory.

Table 1. Habitat preferences and morphological description of selected geoxyle species

* According to the classification by Pausas et al. Reference Pausas, Lamont, Paula, Appezzato-da-Glória and Fidelis2018.

Table 1 describes the habitat preferences and the morphology of the dominant geoxyle species based on in situ measurements. The first three species dominate the ferralitic Brachystegia grasslands in the east–west stretching tributary valleys, the following three are characteristic of the Parinari grasslands in the north–south stretching main valley of the study area (Figure 1). Examples of the underground organs of the studied species are shown in Figure 3a–f.

Figure 3. The six geoxyle species are discussed in the text, in which four of them with topsoil are removed to show the complex woody rhizome belowground. (a) Brachystegia russelliae; (b) Cryptosepalum exfoliatum subsp. suffruticans; (c) Syzygium guineense subsp. huillense; (d) Parinari capensis; (e) Pygmaeothamnus zeyheri; (f) Ochna arenaria.

Belowground biomass and carbon stocks

BGB varied considerably within and between the two types of geoxylic grassland. In Parinari grasslands, BGB varied from 0.56 to 45.60 t/ha with an average and standard error of 16.61±3.05 t/ha, while in Brachystegia grasslands on ferralitic soil, BGB varied from 4.56 to 95.20 t/ha with an average and standard error of 44.25±3.99 t/ha (Table S1 and Figure 4). One-way ANOVA showed that BGB in Brachystegia grasslands was significantly higher than in Parinari grasslands (F=25; p<0.001). Assuming that 50% of the biomass is carbon (Ciais et al. Reference Ciais, Bombelli, Williams, Piao, Chave, Ryan, Henry, Brender and Valentini2011, Nabuurs et al. Reference Nabuurs, Ravindranath, Paustian, Freibauer, Hohenstein, Makundi, Penman, Gytarsky, Hiraishi, Krug, Kruger, Pipatti, Buendia, Miwa, Ngara, Tanabe and Wagner2003, Schlesinger, Reference Schlesinger1997), we can estimate that BGB-associated carbon stocks in Parinari- and Brachystegia grasslands are equivalent to 8.30 t/ha and 22.12 t/ha, respectively.

Figure 4. Box–Whisker plots showing the range of BGB in Brachystegia russelliae- and Parinari capensis grasslands.

Soil properties

Our analyses revealed that the soils in the study sites are extremely acidic, with low clay and high sand content, low contents of the main EB (K, Mg and Ca) and base saturation (V), very low CEC, very low exchangeable phosphorus and consequently very low soil fertility. Aluminium was the cation dominating CEC and sodium was completely absent in all soil samples from suffrutex grasslands (Table S2).

Comparing the two grassland types (Table 2), one-way ANOVA revealed significant differences in soil properties: Sand content and pHKCl were higher in Parinari- than in Brachystegia grasslands, while pHCaCl2, K, Ca, P, Al+H, CEC and EB were higher in Brachystegia- than in Parinari grassland soils. In general, sand content in Parinari grassland soils (92.3%) was higher than in Brachystegia grassland soils (84.6%). Clay content was very low (2.9%) in Parinari grassland soils and but slightly higher in Brachystegia grassland soils (5.6%). However, clay content did not show a constant vertical distribution pattern in the soil profiles (Tables S1 and S2).

Table 2. One-way ANOVA comparing mean soil properties in two types of grassland. Significant differences are marked in bold

Human impact on geoxylic grassland

To date, the main human impacts in both types of suffrutex grasslands are anthropogenic dry season fires. The analysis of fire frequency based on MODIS time series data showed that over a 10-year period, Parinari grassland sites experienced a much higher mean annual fire incidence (0.70±0.058 fires per year) than Brachystegia grassland sites (0.37±0.086 fires per year), meaning that Parinari grasslands burn in 2 out of 3 years while Brachystegia grasslands burn only (a bit more than) once in 3 years. These data for fire incidence are minimum values as small patchy fires might go undetected in the MODIS Burned Area data product with a resolution of 500 m. Besides that, the fire season in Parinari grasslands starts early in April peaking in May, while in the Brachystegia grasslands, the fire season starts slowly in May followed by a pronounced peak much later in July (Figure 5).

Figure 5. Monthly number of fires per plot (mean +/− SE) over the 10-year period from 2010 to 2019 for Parinari capensis- and Brachystegia russelliae grasslands in the study area (10 sample sites per vegetation type, based on the MODIS MCD64A1 Burned Area data product).

Some geoxyle species (e.g. Syzygium guineense, Landolphia gossweileri [Stapf] Pichon, Anisophyllea fruticulosa Engl. and Gilg. and Parinari capensis) are sources of edible fruits and medicines. During the rainy season, fruits are harvested in suffrutex grasslands and eaten by the local population or sold along the main roads (e.g. Chitembro-Mumbué). Moreover, many leaves, roots and rhizomes of grassland plants are used as medicine (Firmino Reference Firmino2016).

At present, only very small parts of the suffrutex grasslands are used for subsistence agriculture; where this is the case, the Parinari grasslands are preferred for cultivation as working the soils of the Brachystegia grasslands without machinery is almost impossible.

Discussion

Seasonal vegetation dynamics

Our results show strong seasonal changes of EVI in geoxylic grassland ecosystems with minima in the dry season and maxima during the peak of the rainy season. In so far, the EVI seems to follow seasonal patterns of aridity. However, a closer analysis of the data reveals that the EVI is rising already from mid-August onwards, more than 6 weeks before the onset of the rainy season in the first half of October. The period of 6–8 weeks of pre-rain green-up of suffrutex grasslands is in line with the phenological strategy of 53 +/− 18 days reported by Ryan et al. (Reference Ryan, Williams, Grace, Woollen and Lehmann2017) for wet miombo. Thus, in terms of phenology suffrutex, grasslands closely resemble the neighbouring woodland and forest ecosystems.

The parallel EVI curves of Parinari- and Brachystegia grasslands indicate constant differences in AGB and land cover between the two geoxylic grassland types throughout the year, with Brachystegia grasslands having significantly higher values than Parinari grasslands. Thus, the differences in EVI between the two grassland types are in line with the differences in BGB.

Structure and morphology of complex belowground organs

Our results show that geoxyles have a highly complex system of underground organs, their functional origins (stem, root or tubers) being difficult to determine based on morphological observations. Anatomical analyses are needed to describe precisely the complex morpho-anatomical system of the geoxyles, as shown by Vilhalva and Appezato da Glória (Reference Vilhalva and Appezzato-da-Gloria2006) who described geoxyle species occurring in the Brazilian cerrado biome.

Basal and or BBB (Clarke et al. Reference Clarke, Lawes, Midgley, Lamont, Ojeda, Burrows, Enright and Knox2013, Pausas et al. Reference Pausas, Lamont, Paula, Appezzato-da-Glória and Fidelis2018) are found in all studied geoxyle species. Buds positioned below ground level are protected by the soil against short-lasting temperature extremes due to the low thermal conductivity of soils (Clarke et al. Reference Clarke, Lawes, Midgley, Lamont, Ojeda, Burrows, Enright and Knox2013). Thus, they allow for rapid resprouting of aerial shoots after fire and frost, the two main local disturbances (Finckh et al. Reference Finckh, Revermann and Aidar2016, Revermann and Finckh Reference Revermann and Finckh2013) and thus for the persistence of these species in the ecosystem (Pausas et al. Reference Pausas, Lamont, Paula, Appezzato-da-Glória and Fidelis2018). Woody rhizomes are a characteristic feature amongst the dominant geoxyles of both grassland types, indicating their strong ability for vegetative, horizontal growth and lead to a competitive advantage.

Belowground biomass and carbon stock

This study is amongst the first to quantify BGB of geoxyle-dominated ecosystems in Africa. Our results show that an enormous amount of biomass is stored underground in these treeless vegetation types: Parinari grasslands showed an average of 16.61 t/ha BGB; in Brachystegia grasslands, BGB with 44.25 t/ha was significantly higher. Thus, structurally similar but floristically different suffrutex grasslands differ widely in their BGB.

BGB decreased quickly with depth and was mostly concentrated in the upper soil horizons (0–30 cm). These results concur with other results from tropical savannas, where more than 70% of BGB are reported to occur in the upper 30 cm of the soil (Jackson et al. Reference Jackson, Canadell, Ehlinger, Mooney, Sala and Schulze1996; de Castro & Kauffman Reference de Castro and Kauffman1998). Differences in BGB between Parinari- and Brachystegia grasslands can be attributed to the differences in the morphology of the woody underground organs, depending again on species-specific traits of the dominant geoxyle species.

Empirical studies around the globe of different vegetation types show BGB values to range from 1.1 t/ha (minimum in miombo woodland) to 206.3 t/ha (maximum in Douglas fir forests) (Table 3). The BGB recorded for the suffrutex grasslands in central Angola amount to similar values recorded in other grassland and tropical savanna ecosystems (Table 3). As such, they are also in the range reported by De Castro & Kauffmann (Reference de Castro and Kauffman1998) for the Brazilian cerrado, another geoxyle-rich ecosystem.

Table 3. Global compilation of data on BGB for different vegetation types

*Data are included as dead BGB; +Modelled data.

Comparing the obtained BGB values for the two types of suffrutex grasslands with regional studies on woodland vegetation shows that BGB in Parinari grasslands is at least as high and that Brachystegia grasslands partly even exceed these values: Ryan et al. (2011) recorded 17.2 t/ha in miombo woodlands in Mozambique and Chidumayo (Reference Chidumayo2013) estimated 18 t/ha and 44 t/ha (for regrowth and old-growth, respectively) in Zambian miombo woodlands. The relevance of the BGB of suffrutex grasslands is further illustrated by a comparison of our measured BGB data with AGB estimates of the surrounding miombo woodlands. Sichone et al. (Reference Sichone, De Cauwer, Chissingui, Gonçalves, Finckh, Revermann, Revermann, Krewenka, Schmiedel, Olwoch, Helmschrot and Jürgens2018) report, depending on the allometric equation used, a median of 48.8 or 60.4 t/ha AGB for the miombo woodlands on the Angolan Central Plateau. Accordingly, the BGB of the geoxylic Brachystegia grasslands almost equals the amount of AGB of the neighbouring woodlands.

According to the land cover classification of Schneibel et al. (Reference Schneibel, Stellmes, Frantz, Finckh and Revermann2013), Brachystegia grasslands cover about 23.3% of the study site and Parinari grassland a further 8.5% ,and thus cover a substantial share of the land surface on the Angolan Central Plateau (and further parts of the miombo region).

Although the AGB component of suffrutex grasslands is negligible (branches of most geoxyle species barely reach a few decimetres in height), these figures highlight the relevance of taking suffrutex grasslands into account for carbon stock assessments in the miombo region and also for African savannas if geoxyles form an important part of the vegetation. Especially for remote sensing-based studies, it should be highlighted that BGB of structurally similar vegetation types, in this case, Brachystegia and Parinari grasslands, can differ fundamentally in their BGB allocation.

Soil properties and physiological reasons for high BGB allocation

Many factors are thought to influence BGB allocation. Soil characteristics such as nutrient availability (Cavelier Reference Cavelier1992, Gower Reference Gower1987, Pérez-Harguindeguy et al. Reference Pérez-Harguindeguy, Diaz, Garnier, Lavorel, Poorter, Jaureguiberry, Bret-Harte, Cornwell, Craine, Gurvich, Urcelay, Veneklaas, Reich, Poorter, Wright, Ray, Enrico, Pausas, Vos, Buchmann, Funes, Quétier, Hodgson, Thompson, Morgan, Steege, Heijden, van der Sack, Blonder, Poschlod, Vaieretti, Conti, Staver, Aquino and Cornelissen2013) and texture (Keyes and Grier Reference Keyes and Grier1981, Vitousek and Sanford Jr. Reference Vitousek and Sanford1986, Vogt et al. Reference Vogt, Vogt, Brown, Tilley, Edmonds, Silver, Siccama, Lal, Kimble, Levine and Stewart1995, Waring and Schlesinger Reference Waring and Schlesinger1985) were reported to have a significant influence on root biomass allocation.

As shown by our analyses, the soils of the study region are dystrophic or nutrient-poor soils common in tropical regions (Ronquim Reference Ronquim2010) and characteristic of the miombo belt (Frost Reference Frost and Campbell1996). Soil properties did not vary considerably in our study area. However, some of the significant differences in soil properties between the two geoxylic grassland types (sand content, pH [in KCl and CaCl2], K, Ca, P, Al+H, CEC, SB, V% and m%) reflect differences due to parent material, landform and topographic position in the landscape, which also affect water permeability, soil moisture and transport of ions within soils and could explain the differences in species composition, BGB and carbon stocks between the two types of grasslands.

The absence of sodium in almost all analysed soil samples can be explained by its great solubility. Under conditions of high rainfall and coarse sandy texture in inclined landscapes, sodium is rapidly leached from the soil profile (Duchaufour Reference Duchaufour1982). The climate of our study area is sufficiently humid and the drainage of the sandy soils is good enough to rapidly remove soluble cations like sodium from the soil profile.

The results of the soil analyses are in line with Gröngröft et al. (Reference Gröngröft, Luther-Mosebach, Landschreiber and Eschenbach2013). Extreme soil conditions, associated with an intense local disturbance regime (fire and frost) appear to be contributing to high BGB allocation. As well as the main miombo species, geoxyles have developed adaptations to survive in nutrient-poor habitats, withdrawing nutrients before leaf shedding at the onset of the cold dry season and storing them in belowground organs for later use (Aerts and van der Peijl Reference Aerts and van der Peijl1993). This seems to be one of the main strategies used to cope with low soil nutrient availability. Leaf analysis of the main species from suffrutex grasslands at the Cusseque area revealed normal nutrient contents, not reflecting the low nutrient availability in soils (Gomes et al. Reference Gomes, Revermann, Gonçalves, Lages, Aidar, Finckh and Jürgens2019). Differences in biomass allocation (BGB vs. AGB) between woodlands and suffrutex grasslands in miombo suggest that geoxyles invest more in belowground structures as an adaptation to cope with the high disturbance regime aboveground (e.g. frost and fire) (Finckh et al. Reference Finckh, Revermann and Aidar2016, Maurin et al. Reference Maurin, Davies, Burrows, Daru, Yessoufou, Muasya, van der Bank and Bond2014).

Human impacts on suffrutex grasslands

So far, the low interest in agricultural use of the suffrutex grasslands has maintained these ecosystems and their stunning species diversity. With few exceptions, suffrutex grasslands in the study area were little impacted by human activities.

The only notable exception is man-made fire. Natural ignition is virtually absent during the grassland fire season, and thus almost all fires can be attributed to human activities (Stellmes et al. Reference Stellmes, Frantz, Finckh and Revermann2013b). It is important to highlight the difference in fire seasonality between the two structurally similar geoxylic grassland types as they have important management implications for handling and for the prevention of fires. Due to the denser vegetation and higher share of geoxyles in the vegetation cover, the Brachystegia grasslands retain higher humidity in the dry season and thus early dry season burning is reduced (M. Finckh, unpublished experimental data). However, in terms of late dry season fires (which imply a much higher risk to affect the adjacent dense miombo woodlands), the two grassland types do not differ significantly.

In both vegetation types, fires remove dry AGB of grasses and geoxyles and leave the landscape widely bare for a short period of time. However, geoxyles and also the associated grass and forb species are well adapted to this disturbance regime. The removal of AGB by fire may be an important factor leading to vegetative and reproductive renewal (Bond et al. Reference Bond, Woodward and Midgley2005). We noted that local people also use fire to manage or enhance the production of some of the wild edible fruits that grow in these grasslands.

The currently still widely natural state of the geoxyle-dominated ecosystems on the Angolan Central Plateau may, however, be strongly affected by the increasing availability of agricultural machinery and turn the Brachystegia grasslands into targets for agro-industrial transformation. This would lead to great losses of BGB and the corresponding release of the current carbon stocks into the atmosphere. Misdirected afforestation attempts may cause similar destructive consequences to these fascinating ecosystems and their associated flora and fauna (Veldman et al. Reference Veldman, Aleman, Alvarado, Anderson, Archibald, Bond, Boutton, Buchmann, Buisson, Canadell, Dechoum, Diaz-Toribio, Durigan, Ewel, Fernandes, Fidelis, Fleischman, Good, Griffith, Hermann, Hoffmann, Le Stradic, Lehmann, Mahy, Nerlekar, Nippert, Noss, Osborne, Overbeck, Parr, Pausas, Pennington, Perring, Putz, Ratnam, Sankaran, Schmidt, Schmitt, Fernando, Silveira, Staver, Stevens, Still, Strömberg, Temperton, Varner and Zaloumis2019).

Conclusion

The study revealed for the first time, based on empirical data, the high relevance of BGB stored in the ‘Underground Forests of Africa’, grasslands dominated by geoxyles that occur throughout south-central Africa. We reported data from the Angolan Central Plateau that show that belowground carbon stocks in these ecosystems are much higher than in neighbouring miombo woodlands and, in the case of Brachystegia grasslands, are almost as high as values for aboveground carbon stocks in the surrounding woodlands. Thus, any study on regional and global carbon stock assessments need to take these findings into account. Furthermore, we provided insights into the morphology, structure and environmental drivers leading to the success of the geoxylic life form. Currently, suffrutex grasslands are, due to their low soil fertility and the extended root network, largely excluded from agriculture cultivation. The advent of agro-industrial machinery may, however, change this situation rapidly.

Acknowledgements

The authors thank all people in the Cusseque valley who contributed during fieldwork, especially the traditional authorities in Cahololo, Calomba and Cusseque. We equally thank Dr Santos Quizembe, previous Director of the Laboratory of Soil and Plants of Instituto de Investigação Agronnómica da Chianga, Huambo for performing the soil analyses. Thanks to David Goyder for language editing and also to Paulina Meller for your contribution.

Financial support

This research was funded by the German Federal Ministry of Education and Research (BMBF) in the context of The Future Okavango project (TFO) Grant No. 01LL0912.

Conflict of interest

None.

Ethical statement

None.

Supplementary material

To view supplementary material for this article, please visit https://doi.org/10.1017/S0266467421000298.

References

Aerts, R and van der Peijl, MJ (1993) A simple model to explain the dominance of low-productive perennials in nutrient-poor habitats. Oikos 66, 144147. https://doi.org/10.2307/3545208.CrossRefGoogle Scholar
Bond, WJ, Woodward, FI and Midgley, GF (2005) The global distribution of ecosystems in a worldwithout fire. New Phytologist 165, 525538. https://doi.org/10.1111/j.1469-8137.2004.01252.x CrossRefGoogle Scholar
Cairns, MA, Brown, S, Helmer, EH and Baumgardner, GA (1997) Root Biomass Allocation in the World’s Upland Forests. Oecologia 111, 111. https://doi.org/10.1007/s004420050201.CrossRefGoogle ScholarPubMed
Cavelier, J (1992) Fine-root biomass and soil properties in a semideciduous and a lower montane rain forest in Panama. Plant and Soil 142, 187201. https://doi.org/10.1007/BF00010965.CrossRefGoogle Scholar
Chamberlain, JL, Ness, G, Small, CJ, Bonner, SJ and Hiebert, EB (2013) Modelling belowground biomass to improve sustainable management of Actaea racemosa, a globally important medicinal forest product. Forest Ecology and Management 293, 18. https://doi.org/10.1016/j.foreco.2012.12.042.CrossRefGoogle Scholar
Chidumayo, EN (2013) Estimating tree biomass and changes in root biomass following clear-cutting of Brachystegia-Julbernardia (miombo) woodland in central Zambia. Environmental Conservation 41, 5463. https://doi.org/10.1017/S0376892913000210.CrossRefGoogle Scholar
Ciais, P, Bombelli, A, Williams, M, Piao, SL, Chave, J, Ryan, CM, Henry, M, Brender, P and Valentini, R (2011) The carbon balance of Africa: synthesis of recent research studies. Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences 369, 20382057. https://doi.org/ 10.1098/rsta.2010.0328.CrossRefGoogle ScholarPubMed
Clarke, PJ, Lawes, MJ, Midgley, JJ, Lamont, BB, Ojeda, F, Burrows, GE, Enright, NJ and Knox, KJE (2013) Resprouting as a key functional trait: how buds, protection and resources drive persistence after fire. New Phytologist 197, 1935. https://doi.org/ 10.1111/nph.12001.CrossRefGoogle ScholarPubMed
Coupland, RT (1992) Mixed prairie. In Coupland, RT (Ed), Grasslands of the World. Cambridge: Cambridge University Press, pp. 151182.Google Scholar
de Castro, EA and Kauffman, JB (1998) Ecosystem structure in the Brazilian Cerrado : A vegetation gradient of aboveground biomass, root mass and consumption by fire. Journal of Tropical Ecology 14, 263283. https://doi.org/10.1017/S0266467498000212.CrossRefGoogle Scholar
Dengler, J (2009) A flexible multi-scale approach for standardised recording of plant species richness patterns. Ecological Indicators 6, 11691178. https://doi.org/10.1016/j.ecolind.2009.02.002.CrossRefGoogle Scholar
du Rietz, GE (1931) Life-forms of terrestrial flowering plants. Acta Phytogeographica Suecica 3, 195.Google Scholar
Duchaufour, P (1982) Pedology, Pedogenesis and Classification, 1st ed. London: George Allen & Unwin (Publishers) Ltd.CrossRefGoogle Scholar
Ekoungoulou, R, Liu, X, Loumeto, JJ and Ifo, SA (2014) Tree Above-and Belowground Biomass Allometries for Carbon Stocks Estimation in Secondary Forest of Congo. Journal of Environmental Science, Toxicology and Food Technology 8, 920. https://doi.org/10.9790/2402-08420920 CrossRefGoogle Scholar
Fiala, K (2011) Belowground plant biomass of grassland ecosystem and its variation according to ecological factors. Ekológia 29, 206210. https://doi.org/10.4149/ekol.Google Scholar
Fick, S and Hijmans, R (2017) WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37. https://doi.org/10.1002/joc.5086.CrossRefGoogle Scholar
Fidelis, A, Lyra, MFS and Pivello, V (2013) Above- and belowground biomass and carbon dynamics in Brazilian Cerrado wet grasslands. Journal of Vegetation Science 24, 356364.CrossRefGoogle Scholar
Finckh, M, Revermann, R and Aidar, MPM (2016) Climate refugees going underground – a response to Maurin et al. (2014). New Phytologist 209, 904909. https://doi.org/ 10.1111/nph.13567.CrossRefGoogle Scholar
Firmino, RKVS (2016) Valorização da flora de Cusseque e Caiundo no centro e sul de Angola e avaliação da biomassa lenhosa utilizada para combustível e construção. Potugal: Universidade de Lisboa.Google Scholar
Frost, P (1996) The ecology of miombo woodlands. In Campbell, B (Ed.), The Miombo in Transition: Woodlands and Welfare in Africa. Bogor: Centre for International Forestry Research, pp. 1157.Google Scholar
Gomes, AL, Revermann, R, Gonçalves, FMP, Lages, F, Aidar, MPM, Finckh, M and Jürgens, N (2019) Tree or not tree: Differences in plant functional traits among geoxyles and related trees species. South African Journal of Botany 127, 176184. https://doi.org/10.1016/j.sajb.2019.08.044.CrossRefGoogle Scholar
Gower, ST (1987) Relations between mineral nutrient availability and fine root biomass in two Costa Rican tropical wet forests: a hypothesis. Biotropica 19, 171175. https://doi.org/10.2307/2388741.CrossRefGoogle Scholar
Grace, J, San José, J, Meir, P, Miranda, HS and Montes, RA (2006) Productivity and carbon fluxes of tropical savannas. Journal of Biogeography 33, 387400. https://doi.org/10.1111/j.1365-2699.2005.01448.x CrossRefGoogle Scholar
Gröngröft, A, Luther-Mosebach, J, Landschreiber, L and Eschenbach, A (2013) Cusseque - Soils. Biodiversity & Ecology 5, 5154. https://doi.org/10.7809/b-e.00245.51 CrossRefGoogle Scholar
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories, IPCC National Greenhouse Gas Inventories Programme. Institute for Global Environ-mental Strategies, Tokyo. Institute for Global Environmental Strategies, Kanagawa.Google Scholar
Jackson, RB, Canadell, J, Ehlinger, JR, Mooney, HA, Sala, OE and Schulze, ED (1996) A global analysis of root distribution for terrestrial biomes. Oecologia 3, 389411. https://doi.org/10.1007/BF00333714.CrossRefGoogle Scholar
Keyes, MR and Grier, CC (1981) Above- and belowground net production in 40-year-old Douglas-fir stands on low and high productivity sites. Canadian Journal of Forestry Research 11, 599605. https://doi.org/10.1139/x81-082 CrossRefGoogle Scholar
Kolbek, J and Alves, RJV (2008) Impacts of cattle, fire and wind in rocky savannas, southeastern Brazil. Acta Universitatis Carolinae – Environmentalica 22, 111130.Google Scholar
Lichacz, W, Hardiman, S and Buckney, RT (2009) Wetlands (Australia), 2009 [WWW Document]. URL ojs.library.unsw.edu.au.Google Scholar
Limbu, DK and Koirala, M (2011) Above-ground and belowground biomass situation of Milke-Jaljale Rangeland at different altitudinal gradient. Our Nature 9, 107111. https://doi.org/10.3126/on.v9i1.5740.CrossRefGoogle Scholar
Lindman, CAM (1914) Några bidrag till frågan: buske eller träd? Särtryck sidan 231-287 i K. Vetenskapsakademiens årsbok Årg. Uppsala.Google Scholar
Maurin, O, Davies, TJ, Burrows, JE, Daru, BH, Yessoufou, K, Muasya, AM, van der Bank, M and Bond, WJ (2014) Savanna fire and the origins of the ‘underground forests’ of Africa. New Phytologist 204, 201214. https://doi.org/10.1111/nph.12936.CrossRefGoogle Scholar
Miranda, SC, Bustamante, M, Palace, M, Hagen, S, Keller, M and Ferreira, LG (2014) Regional Variations in biomass distribution in Brazilian savanna woodland. Biotropica 46, 125138. https://doi.org/10.1111/btp.12095 CrossRefGoogle Scholar
Mokany, K Raison, RJ and Prokushkin, AS (2006) Critical analysis of root:shoot ratios in terrestrial biomes. Global Change Biology 1, 8496. https://doi.org/10.1111/j.1365-2486.2005.001043.x CrossRefGoogle Scholar
Nabuurs, GJ, Ravindranath, NH, Paustian, K, Freibauer, A, Hohenstein, W and Makundi, W (2003) Forest land. In Penman, J, Gytarsky, M, Hiraishi, T, Krug, T, Kruger, D, Pipatti, R, Buendia, L, Miwa, K, Ngara, T, Tanabe, K and Wagner, F (eds), Good Practice Guidance for Land Use, Land Use Change and Forestry. Kanagawa: Institute for Global Environmental Strategies. https://doi.org/citeulike-article-id:1260638.Google Scholar
Nieto-Quintano, P, Edward, TAM, Rolando, O, Marcele, ABM, Tim, R and Casey, MR (2018) The mesic savannas of the Bateke Plateau: carbon stocks and floristic composition. Biotropica, 113. https://doi.org/10.1111/btp.12606.Google Scholar
Pausas, JG, Lamont, BB, Paula, S, Appezzato-da-Glória, B and Fidelis, A (2018) Unearthing belowground bud banks in fire-prone ecosystems. New Phytologist 217, 14351448. https://doi.org/10.1111/nph.14982.CrossRefGoogle ScholarPubMed
Pérez-Harguindeguy, N, Diaz, S, Garnier, E, Lavorel, S, Poorter, H, Jaureguiberry, P, Bret-Harte, MS, Cornwell, WK, Craine, JM, Gurvich, DE, Urcelay, C, Veneklaas, EJ, Reich, PB, Poorter, L, Wright, IJ, Ray, P, Enrico, L, Pausas, JG, Vos, AC, Buchmann, N, Funes, G, Quétier, F, Hodgson, JG, Thompson, K, Morgan, HD, Steege, H, Heijden, MGA, van der Sack, L, Blonder, B, Poschlod, P, Vaieretti, MV, Conti, G, Staver, AC, Aquino, S and Cornelissen, JHC (2013) New Handbook for standardized measurment of plant functional traits worldwide. Australiam Journal of Botany 61, 167234. https://doi.org//10.1071/BT12225.CrossRefGoogle Scholar
Ravindranath, NH and Ostwald, M (2008) Carbon Inventory Methods: Handbook for Greenhouse Gas Inventory, Carbon Mitigation and Roundwood Production Projects. Springer. https://doi.org/10.1007/978-1-4020-6547-7.CrossRefGoogle Scholar
Reverman, R, Gonçalves, FMP, Gomes, AL and Finckh, M (2017) Woody species of the miombo woodlands and geoxylic grasslands of the Cusseque area, south-central Angola. Checklist 13, 110.CrossRefGoogle Scholar
Revermann, R and Finckh, M (2013) Cusseque - Microclimate. Biodiversity & Ecology 5, 4750. https://doi.org/10.7809/b-e.00244.47.CrossRefGoogle Scholar
Revermann, R, Gonçalves, FM, Gomes, AL and Finckh, M (2013) Cusseque - Vegetation. Biodiversity & Ecology 5, 5963. https://doi.org/10.7809/b-e.00247.59.CrossRefGoogle Scholar
Revermann, R, Oldeland, J, Gonçalves, FM, Luther-Mosebach, J, Gomes, AL, Jürgens, N and Finckh, M (2018) Dry tropical forests and woodlands of the Cubango Basin in southern Africa - first classification and assessment of their woody species diversity. Phytocoenologia 48, 2350. https://doi.org/10.1127/phyto/2017/0154.CrossRefGoogle Scholar
Robertson, F (2005) Ecological processes within the four corners area. Occasional Publications in Biodiversity, Bulawayo. www.biodiversityfoundation.org.Google Scholar
Ronquim, CC (2010) Conceitos de fertilidade do solo e manejo adequado para as regiões tropicais. Boletim de Pesquisa e Desenvolvimento 8, 26.Google Scholar
Rosillo-Calle, F, de Groot, P, Hemstock, S, Woods, J (2007) The biomass accessment handbook, bioenergy for a sustainable environment. London: EARTHSCAN.Google Scholar
Ryan, CM, Williams, M and Grace, J (2010) Above- and belowground carbon stocks in a Miombo wooodland landscape of Mozambique. Biotropica 43, 423432. https://doi.org/10.1111.j.1744-7429.2010.00713.x.CrossRefGoogle Scholar
Ryan, CM, Williams, M, Grace, J, Woollen, E and Lehmann, CER (2017) Pre-rain green-up is ubiquitous across southern tropical Africa: implications for temporal niche separation and model representation. New Phytologist 213, 625633. https://doi.org/ 10.1111/nph.14262 CrossRefGoogle ScholarPubMed
Sanford, RLJ and Cuevas, E (1996) Root growth and rhizosphere interactions in tropical forests. In Mulkey, SS, Chazdon, RL and Smith, AP (eds), Tropical Forest Plant Ecophysiology. New York: Chapman and Hall, pp. 268300.CrossRefGoogle Scholar
Schlesinger, WH (1997) Biogeochemistry, an analysis of global change. New York: Academic Press.Google Scholar
Schneibel, A, Stellmes, M, Frantz, D, Finckh, M and Revermann, R (2013) Cusseque – Earth Observation. In Oldeland J, Erb C, Finckh M and Jürgens N (eds), Environmental assessments in the Okavango Region. Biodiversity & Ecology 5, 55–57.CrossRefGoogle Scholar
Sichone, P, De Cauwer, V, Chissingui, AV, Gonçalves, FMP, Finckh, M, Revermann, R (2018) Patterns of above-ground biomass and its environmental drivers: an analysis based on plot based surveys in the dry tropical forests and woodlands of southern Africa. In Revermann, R, Krewenka, KM, Schmiedel, U, Olwoch, JM, Helmschrot, J & Jürgens, N (eds),  Climate change and adaptive land management in southern Africa – assessments, changes, challenges, and solutions,  Biodiversity & Ecology. 6, Göttingen & Windhoek: KlausHess Publishers, pp. 309316. https://doi.org/10.7809/b-e.00338.Google Scholar
Simon, MF, Grether, R, de Queiroz, LP, Skema, C, Pennington, RT and Hughes, CE (2009) Recent assembly of the Cerrado, a neotropical plant diversity hotspot, by in situ evolution of adaptations to fire. PNAS 106, 2035923064. https://doi.org/10.1073/pnas.0903410106.CrossRefGoogle Scholar
Stellmes, M, Frantz, D, Finckh, M and Revermann, R (2013a) Fire frequency, fire seasonality and fire intensity within the Okavango region derived from MODIS fire products. Biodiversity & Ecology 5, 351362. doi: 10.7809/b-e.00288.CrossRefGoogle Scholar
Stellmes, M, Frantz, D, Finckh, M and Revermann, R (2013b) Okavango Basin-Earth Observation. Biodiversity & Ecology 5, 2328. https://doi.org/10.7809/b-e.00239.CrossRefGoogle Scholar
Veldman, JW, Aleman, JC, Alvarado, ST, Anderson, TM, Archibald, S, Bond, WJ, Boutton, TW, Buchmann, N, Buisson, E, Canadell, JG, Dechoum, MS, Diaz-Toribio, MH, Durigan, G, Ewel, JJ, Fernandes, GW, Fidelis, A, Fleischman, F, Good, SP, Griffith, DM, Hermann, J-M, Hoffmann, WA, Le Stradic, S, Lehmann, CER, Mahy, G, Nerlekar, AN, Nippert, JB, Noss, RF, Osborne, CP, Overbeck, GE, Parr, CL, Pausas, JG, Pennington, RT, Perring, MP, Putz, FE, Ratnam, J, Sankaran, M, Schmidt, IB, Schmitt, CB, Fernando, AO, Silveira, FAO, Staver, AC, Stevens, N, Still, CJ, Strömberg, CAE, Temperton, VM, Varner, JM and Zaloumis, NP (2019) Comment on “The global tree restoration potential”. Science https://doi.org/10.1126/science.aay7976 CrossRefGoogle Scholar
Vilhalva, DAA and Appezzato-da-Gloria, B (2006) Belowground morpho-anatomy system of Calea verticillata (Klatt) Pruski and Isostigma megapotamicum (Spreng.) Sherff – Asteraceae. Brazilian Journal of Botany 26, 3947. https://doi.org/10.1590/S0100-84042006000100005.Google Scholar
Vitousek, PM and Sanford, RL Jr (1986) Nutrient Cycling in Moist Tropical Forest. Annual Review of Ecology, Evolution and Systematics 17, 137167. https://doi.org/10.1146/annurev.ecolsys.17.1.137 CrossRefGoogle Scholar
Vogt, KA, Vogt, DJ, Brown, S, Tilley, JP, Edmonds, RL, Silver, WL and Siccama, TG (1995) Dynamics of forest floor and soil organic matter accumulation in boreal, temperate, and tropical forests. In Lal, R, Kimble, J, Levine, E and Stewart, BA (eds), Soil Management and Greenhouse Effect.  Florida USA: CRC Lewis Publishers, pp. 159178.Google Scholar
Waring, RH and Schlesinger, WH (1985) Forest ecosystems: concepts and management. Orlando: Academic Press.Google Scholar
Weber, T (2013) Cusseque climate. Biodiversity & Ecology 5, 4546. https://doi.org/10.7809/b-e.00243.CrossRefGoogle Scholar
Wetzel, RG and Howe, MJ (1999) High production in a herbaceous perennial plant achieved by continuous growth and synchronized population dynamics. Aquatic Botany 64, 111129. https://doi.org/10.1016/S0304-3770(99)00013-3.CrossRefGoogle Scholar
White, F (1976) The underground forests of Africa : a preliminary review. Gardens’s Bulletin Singapore 29, 5771. https://biostor.org/reference/134550.Google Scholar
Zigelski, P, Gomes, A and Finckh, M (2019). Suffrutex Dominated Ecosystems. In Huntley, BJ, Russo, V, Lages, F and Ferrand, N (eds), Biodiversity of Angola; Science and Conservation: A Modern Synthesis. Cham, Switzerland: Springer, pp. 109119.CrossRefGoogle Scholar
Figure 0

Figure 1. (a) The research site Cusseque of The Future Okavango (TFO) project (www.future-okavango.org) in the upper Cubango basin (red rectangle, 100 km2). The occurrence of open suffrutex grasslands on ferralitic soils (light green colour, e.g. in the Sovi River valley) and sandy deposits (white, eastern bank of the Cusseque River) is a characteristic feature in the valleys, contrasting with the miombo woodlands on the hills (dark green); (b) location in south-central Africa (the Okavango Basin marked with grey square); (c) the Cubango/Okavango Basin in the three countries Angola, Namibia and Botswana, black square indicates the research site Cusseque; (d) mean monthly rainfall in the study area (Fick and Hijmans 2017).

Figure 1

Figure 2. Annual phenology of Brachystegia- (a) and Parinari- (b) grasslands in the Cusseque study site on the Angolan central plateau. The graph shows monthly mean EVI values of a 10-year observation period (2010–2019) for 20 systematically selected sample plots. Data were derived from the combined MYD13Q1 and MOD13Q1 data products via Google Earth Engine.

Figure 2

Table 1. Habitat preferences and morphological description of selected geoxyle species

Figure 3

Figure 3. The six geoxyle species are discussed in the text, in which four of them with topsoil are removed to show the complex woody rhizome belowground. (a) Brachystegia russelliae; (b) Cryptosepalum exfoliatum subsp. suffruticans; (c) Syzygium guineense subsp. huillense; (d) Parinari capensis; (e) Pygmaeothamnus zeyheri; (f) Ochna arenaria.

Figure 4

Figure 4. Box–Whisker plots showing the range of BGB in Brachystegia russelliae- and Parinari capensis grasslands.

Figure 5

Table 2. One-way ANOVA comparing mean soil properties in two types of grassland. Significant differences are marked in bold

Figure 6

Figure 5. Monthly number of fires per plot (mean +/− SE) over the 10-year period from 2010 to 2019 for Parinari capensis- and Brachystegia russelliae grasslands in the study area (10 sample sites per vegetation type, based on the MODIS MCD64A1 Burned Area data product).

Figure 7

Table 3. Global compilation of data on BGB for different vegetation types

Supplementary material: File

Gomes et al. supplementary material

Gomes et al. supplementary material 1

Download Gomes et al. supplementary material(File)
File 41.4 KB
Supplementary material: File

Gomes et al. supplementary material

Gomes et al. supplementary material 2

Download Gomes et al. supplementary material(File)
File 32.2 KB
Supplementary material: File

Gomes et al. supplementary material

Gomes et al. supplementary material 3

Download Gomes et al. supplementary material(File)
File 28.8 KB
Supplementary material: File

Gomes et al. supplementary material

Gomes et al. supplementary material 4

Download Gomes et al. supplementary material(File)
File 38.9 KB