DOI: 10.5061/dryad.rv15dv4fp
title: Predictive mapping of tree species assemblages in an African montane rainforest

description: 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&lt;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.
description: <strong>Study design</strong> 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.
 <strong>Tree species sampling</strong>
 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<sup>th</sup> 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).
description: # Predictive mapping of tree species assemblages in an African montane
 rainforest Predictive
 mapping of tree species assemblages in an African montane rainforest
 Babaasa, Dennis, Mbarara University,
 Finn, John T., University of
 Massachusetts Schweik, Charles M., University of Massachusetts Fuller,
 Todd K., University of Massachusetts Sheil, Douglas, Wageningen University
 Research facility: Institute of Tropical
 Forest Conservation Published January …., 2024 on Dryad.
 Cite this dataset:
 Babaasa, Dennis, Finn, John T., Schweik, Charles M., Fuller, Todd K.,
 Sheil, Douglas (2024). Predictive mapping of tree species assemblages in
 an African montane rainforest. [Dataset] Dryad.
 ### Abstract In this
 study, Bwindi Impenetrable National Park, Uganda, was divided into five
 strata based on geological formations visible on the Digital Elevation
 Model (DEM) of the study area. Random transects were established with the
 starting points on the boundary of the DEM in each stratum. 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. A single random point within each
 digitized polygon was selected for tree sampling. At each random sample
 point, we selected 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. A sample
 site-versus-species matrix (using basal values of each tree species
 relative to the area of the sample site [m2 ha-1] was created. 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. Slope was transformed 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 minus 35. 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 (Arc 60 35S)
 using a hand-held GPS unit. ### Data File Descriptions These datasets were
 collected within the boundaries of Bwindi Impenetrable National Park,
 Uganda (2010 – 2012). 1. BITR-23-152_R2_Tree_data.csv A sample
 site-versus-tree species matrix. The figures in the matrix are the basal
 area of the tree species in each plot (m2 ha-1) and the names of the tree
 species are coded 2. BITR-23-152_R2_Environmental_data.csv The
 environmental variables field measurements for each sample site – aspect,
 steepness of the slope, four physiographic positions of valley, hillside,
 ridge tops and gully and UTM coordinates of easting and northing 3.
 BITR-23-152_R2_Metadata.csv Name codes for the tree species, full
 taxonomic (Scientific) names for the tree species, new taxonomic
 (scientific) names (if the species has been renamed) for the tree species
 and family name ### Funding Institute of Tropical Forest, Mbarara
 University of Science and Technology Wildlife Conservation Society
 International Foundation for Science Mohammed bin Zayed Species
 Conservation Fund British Ecological Society University of Massachusetts,
 Amherst