Global patterns of regional (gamma) plant diversity are relatively well known, but whether these patterns hold for local communities, and the dependence on spatial grain, remain controversial. Using data on 170,272 georeferenced local plant assemblages, we created global maps of alpha diversity (local species richness) for vascular plants at three different spatial grains, for forests and non-forests. We show that alpha diversity is consistently high across grains in some regions (for example, Andean-Amazonian foothills), but regional ‘scaling anomalies’ (deviations from the positive correlation) exist elsewhere, particularly in Eurasian temperate forests with disproportionally higher fine-grained richness and many African tropical forests with disproportionally higher coarse-grained richness. The influence of different climatic, topographic and biogeographical variables on alpha diversity also varies across grains. Our multi-grain maps return a nuanced understanding of vascular plant biodiversity patterns that complements classic maps of biodiversity hotspots and will improve predictions of global change effects on biodiversity.

In many parts of Africa, spatially-explicit information on plant α-diversity, i.e., the number of species in a given area, is missing as baseline information for spatial planning. We present an approach on how to combine vegetation-plot databases and remotely-sensed land surface phenology (LSP) metrics to predict plant α-diversity on a regional scale. We gathered data on plant α-diversity, measured as species density, from 999 vegetation plots sized 20 m × 50 m covering all major vegetation units of the Okavango basin in the countries of Angola, Namibia and Botswana. As predictor variables, we used MODIS LSP metrics averaged over 12 years (250-m spatial resolution) and three topographic attributes calculated from the SRTM digital elevation model. Furthermore, we tested whether additional climatic data could improve predictions. We tested three predictor subsets: (1) remote sensing variables; (2) climatic variables; and (3) all variables combined. We used two statistical modeling approaches, random forests and boosted regression trees, to predict vascular plant α-diversity. The resulting maps showed that the Miombo woodlands of the Angolan Central Plateau featured the highest diversity, and the lowest values were predicted for the thornbush savanna in the Okavango Delta area. Models built on the entire dataset exhibited the best performance followed by climate-only models and remote sensing-only models. However, models including climate data showed artifacts. In spite of lower model performance, models based only on LSP metrics produced the most realistic maps. Furthermore, they revealed local differences in plant diversity of the landscape mosaic that were blurred by homogenous belts as predicted by climate-based models. This study pinpoints the high potential of LSP metrics used in conjunction with biodiversity data derived from vegetation-plot databases to produce spatial information on a regional scale that is urgently needed for basic natural resource management applications.

Background: The Great Escarpment of southern Africa takes the form of an extended mountainous highland in central-western Namibia, commonly referred to as the ‘Khomas Hochland’. It is regarded as an area of high botanical diversity. Yet only few localised studies on the vegetation composition are available. The Khomas Hochland is formed on the southern part of the Damara Orogen and dominated by metamorphosed sediments. Climatically it forms a transition between the hot desert of the Namib and the slightly cooler hot steppe in the inland.Objectives: To classify and provide syntaxonomical descriptions of the vegetation of the Khomas Hochland.Methods: A dataset comprising 1151 relevés and 914 species was compiled from various surveys, mostly collected under, and to the standards of, the umbrella project ‘Vegetation Survey of Namibia’. For first classifications, the data set was reduced to a synusial set consisting of trees, shrubs, dwarf shrubs and grasses only.Results: The classification resulted in four major landscape units, being the Pre-Namib and Escarpment zone, the Khomas Hochland proper, riverine habitats as well as surrounding lowlands. The classification was further refined using Cocktail procedures to produce 30 associations, one with four sub-associations. These are described in this paper.Conclusion: A classification of synoptic data grouped the associations into five orders and one undefined cluster of associations on specialised desert habitats. Four of these orders correspond to the habitat types identified in the first classification. The fifth order, the Senegalio hereroensis–Tarchonanthoetalia camphorathi, represents high mountains of the central Khomas Hochland, which link biogeographically to the grassland biome in South Africa.

Termite mounds contribute to the heterogeneity and productivity of many semi‐arid ecosystems worldwide. Regular spatial patterns of termite mounds are well documented but the underlying pattern‐building mechanisms remain to be clarified. This study analysed a comprehensive data set of Macrotermes michaelseni mound metrics, spatial patterns and dynamics, recorded 12 years apart within 1 km ² of Namibian thornbush savanna. We used both unmarked and marked point pattern analyses to assess spatial distributions of termite mounds. We recorded 485 termite mounds in 2007 and 2019. Out of the investigated 142 active termite mounds in 2007, 81 were active 12 years later. Only 2% (n = 3/129) of remnant mound sites in 2007 were newly colonised in 2019. All mounds were distributed at regular distances. Mounds higher than 2 m and long‐term active mounds (active in 2007 and 2019) showed high regularity. Mounds up to 0.7 m height and new mounds occurred in clusters and showed a spatial repulsion to large and long‐term active colonies. The spatial distribution of Boscia albitrunca trees generally clustered around termite mounds, but did not cluster around young colonies. The results contradict the hypothesis of preferred colony settlement on previously inhabited mound sites. Remnant mounds are relatively unlikely to be recolonised when recently abandoned. The observed spatial patterns show that intraspecific competition between large colonies is the decisive factor for regular spatial patterns of termite colonies, whereas small and young colonies are limited to unoccupied patches. Surrounding trees are a pattern‐building factor of only secondary importance. The spatio‐temporal scale in our study reveals general trends of termite mound life histories and spatial pattern‐building mechanisms. Availability of not previously inhabited sites form the patterns of founding new colonies. Well‐established mounds are highly regulated by intraspecific competition. This highlights that termites contribute to ecosystem heterogeneity through self‐organised spatial regularity.

Objectives: Climate change is expected to have major impacts on plant species distribution worldwide. These changes can affect plant species in three ways: the timing of seasonal activities (phenology), physiology and distribution. This study aims to predict the effect of shifting climatic conditions on the major vegetation units along an aridity gradient through Namibia. Study area: Namibia’s vegetation is characterised by open woodland in the northeast to low open shrubland in the southern part of the country. These differences are a result of increasing aridity from north to south with a rainfall gradient from 100 mm to 600 mm. Namibia is projected to have an increase in annual mean temperature of 2°C by the end of the 21^st century. Methods: A vegetation classification was done for 1,986 relevés using cluster analysis, a Multi-Response Permutation Procedure and indicator species analysis. The current distribution of the vegetation classes was modelled with Random Forest. Future projections for the most important climate variables were used to model the potential distribution of the vegetation units in 2080. This modelling approach used two scenarios of Representative Concentration Pathways (4.5 and 8.5) from two Global Climate Models – the IPSL–CM5A–LR and HAdGEM2–ES. Results: The predicted distribution shows a high expansion potential of Eragrostis rigidior­­-Peltophorum africanum mesic thornbush savannas, Combretum africanum-Terminalia sericea broad-leafed savannas and Senegalia mellifera-Dichrostachys cinerea degraded thornbush savannas towards the south under both scenarios. Conclusions: The model indicated the ability to classify and predict vegetation units to future climatic conditions. Half of the vegetation units are expected to undergo significant contraction. Overall, RCP8.5 conditions favour the proliferation of certain vegetation types, particularly Combretum collinum-Terminalia sericea broad-leafed savannas and Senegalia mellifera-Dichrostachys cinerea degraded thornbush savannas, potentially displacing other vegetation types. Taxonomic reference: Klaassen and Kwembeya (2013) for vascular plants, except Kyalangalilwa et al. (2013) for the genera Senegalia and Vachellia s.l. (Fabaceae). Abbreviations: CDM = Community Distribution Model; CMIP5 = Coupled Model Inter-comparison Project Phase 5; EVI = Enhanced Vegetation Index; GCM = General Circulation Model; IV = Indicator Value; ISA = Indicator Species Analysis; MAP = mean annual precipitation; MAT = mean annual temperature; MRPP = Multi-Response Permutation Procedure; NMS = Non-Metric Multidimensional Scaling; RF = Random Forest; RCPs = Representative Concentration Pathways; SDM = species distribution model.

Aims: The Karstveld in Namibia has been recognized as an area of high plant diversity. However, this area is also recognised as a hotspot of various forms of degradation including bush encroachment. Minimal baseline data on the composition and diversity of vegetation in this area is available, therefore this paper is a first attempt to rectify this data deficiency. Study area: The Karstveld in Namibia is formed around the Otavi Mountain Range in northern Central Namibia, consisting of strongly karstified carbonate bedrock, rising up to 2000 m a.s.l. The Karstveld includes the Ovambo Basin plains with shallow calcrete soils north of the range, up to the Omuramba Ovambo. Because of orographic effects, the area receives some of the highest rainfall in Namibia, with up to 600 mm per year. Methods: A set of 889 relevés with 868 species was selected from the GVID ID AF-NA-001 database. A partial data set, using trees, shrubs, dwarf shrubs and grasses only, was used for the classification with modified TWINSPAN. The initial result yielded four main groups, according to which the data was split and further classified. Several vegetation types observed during field surveys were not reflected in the classification results; these were refined using Cocktail with known characteristic species. Results: The four main units represented wetlands and grasslands with six associations, a Thornbush savanna – Karstveld transition zone with four associations, Kalahari vegetation with four associations and the Karstveld proper with eight associations. The latter are grouped together as the Terminalietea prunioides, with two orders and three alliances recognised under them. We describe 16 associations according to the ICPN. Conclusions: Although the associations presented in this paper are clearly defined, there exists a high degree of diversity within these. The Karstveld is also extraordinary species rich within the context of the arid to semi-arid Namibian environment. Taxonomic reference: Klaassen and Kwembeya (2013) for vascular plants, with the exception of the genus Acacia s.l. (Fabaceae), for which Kyalangalilwa et al. (2013) was followed. Abbreviations: ga = annual grass; gp = perennial grass; GPS = Global Positioning System, referring to a hand-held ground receiver; hl = herb layer, containing all hemicryptophytes, therophytes and geophytes, but excluding grasses (Poaceae); ICPN = International Code of Phytosociological Nomenclature (Theurillat et al. 2021); MAP = mean annual precipitation; NMS = nonmetric multidimensional scaling (Kruskal 1964); RDL = Red Data List (IUCN Species Survival Commission 2001); s1 = tall shrubs, i.e. multi-stemmed phanerophytes between 1 and 5 m; s2 = short shrubs, i.e. chamaephytes or ‘dwarf shrubs’ below 1 m; SOTER = Global and National Soils and Terrain Digital Database (FAO 1993); t1 = tall trees, > 10 m; t2 = short trees, between 5 and 10 m; t3 = low trees, i.e. single-stemmed phanerophytes between 2 and 5 m; TWINSPAN = Two Way Indicator Species Analysis (Roleček et al. 2009); WGS84 = World Geodetic System, 1984 ensemble.