Conservation of mountain ecosystems can benefit from knowledge of habitats and their distribution patterns. This benefit is particularly true for diverse ecosystems with high conservation values such as the “Afromontane” rainforests. We mapped the vegetation of one such forest: the rugged Bwindi Impenetrable Forest, Uganda—a World Heritage Site known for its many restricted-range plants and animal taxa including several iconic species. Given variation in elevation, terrain and human impacts across Bwindi, we hypothesised that these factors influence the composition and distribution of tree species. To test this, detailed surveys were carried out using stratified random sampling. We established 289 georeferenced sample sites (each with 15 trees ≥20 cm dbh) ranging from 1,320 to 2,467 m a.s.l. and measured 4,335 trees comprising 89 species that occurred in four or more sample sites. These data were analysed against twenty-one digitally mapped biophysical variables using various analytical techniques including non-metric multidimensional scaling (NMDS) and random forests. We identified six tree species assemblages with distinct compositions. Among the biophysical variables, elevation had the strongest correlation with the ordination (r²=0.5; p<0.001). The “out-of-bag” (OOB) estimate of the error rate for the best final model was 50.7% meaning that nearly half of the variation was accounted for using a limited set of variables. We demonstrate that it is possible to predict the spatial pattern of such a forest based on sampling across a highly complex landscape. Such methods offer accurate mapping of composition that can guide conservation.

Study design

To account for the different environmental conditions, we employed a stratified random sampling approach. The park was divided into five strata based on geological formations visible on the Digital Elevation Model (DEM) of Bwindi and the starting points on the boundary of the DEM in each stratum were selected randomly with the random point function within ArcGIS (version 10.5; ESRI, Redlands, CA, USA). Line transects were drawn on the DEM in each stratum from the random starting points to traverse the topographic positions of the ridges (Figure 1; Table 1). The number and length of the transects selected varied with area, accessibility and shape of the strata. We then superimposed the transect drawings on high-resolution (0.5m) true color, digital aerial photographs of Bwindi. The aerial photos were visually interpreted along the transects by drawing polygons around areas perceived to be of uniform tree community structure based on differences in tone and texture. This allowed the sample sites to be placed in what we perceived to be distinct tree communities.

Tree species sampling

We carried a printed copy of the digitized polygons, overlaid with a coordinate grid, and used a hand-held Global Positioning System (GPS) device to locate the digitized polygons in the field. A single random point within each digitized polygon was selected for tree species sampling. At each random sample point, we used the point-to-tree distance technique or plotless sampling method to sample trees (Hall, 1991; Sheil et al., 2003; Klein & Vilcko, 2006). This technique involved selecting the nearest 15 trees (≥20 cm dbh) around the random center-point. The selected trees were identified to species level and we measured the diameter at breast height (dbh) of each individual. We named the tree species following nomenclature used in Kalema and Hamilton (2020). The distance from the sample site center-point to the 15^th farthest tree was measured and regarded as the sample site radius. This procedure is suitable for rapid and robust assessments of vegetation where tree density varies, such as in patchy and disturbed tropical forest (Sheil et al., 2003; Klein & Vilcko, 2006). At each center-point, eight environmental attributes were recorded: aspect – as the compass direction facing down slope; and steepness of the slope using a clinometer. Untransformed aspect and slope are poor for quantitative analysis, so slope was transformed to a more suitable index by taking the sine of the slope in degrees; aspect was also transformed into a suitable index by taking the negative cosine of the angle in degrees minus 35 (McCune & Grace, 2002). Four physiographic positions of valley, hillside, ridge tops and gully were simply recorded as “1” if the sample site was in that physiographical class and “0” otherwise. The final recorded site characteristics consisted of spatial variables – the Universal Transverse Mercator (UTM) coordinates of easting and northing (datum WGS 84) using a hand-held GPS unit, standardized to zero mean and unit variance. All the data were collected at 289 sample sites spread across the forest (Figure 1).