DOI: 10.5061/dryad.rfj6q5786
title: An interpolated biogeographic framework for tropical Africa using plant species distributions and the physical environment

description: Aim: Existing phytogeographic frameworks for tropical Africa lack either
 spatial completeness, unit definitions smaller than the regional scale, or
 a quantitative approach. We investigate whether physical environmental
 variables can be used to interpolate floristically defined vegetation
 units, presenting an interpolated, hierarchical, quantitative
 phytogeographic framework for tropical Africa, which is compared to
 previously defined regions. Location: Tropical mainland Africa 24°N to
 24°S. Taxon: 31,046 vascular plant species and infraspecific taxa.
 Methods: We calculate a betasim dissimilarity matrix from a comprehensive
 whole-flora database of plant species distributions. We investigate
 environmental correlates of floristic turnover with local non-metric
 multidimensional scaling. We derive a hierarchical biogeographic framework
 by clustering the dissimilarity matrix. The framework is modelled using a
 classification decision tree method and 12 physical environmental
 variables to interpolate and downscale the framework across the study
 region. Results: Floristic turnover is related strongly to water
 availability and temperature, with smaller contributions from land cover,
 topographic ruggedness and lithology. Region can be predicted with 90%
 accuracy by the model. We define 19 regions and 99 districts. We find a
 novel arrangement of the arid regions. Regional subdivision within the
 savanna biome is supported with minor variation to borders. Within the
 forests of west and central Africa, our whole-flora gridded
 regionalisation supports the divisions identified by a previous analysis
 of trees only. Main conclusions: Physical environmental variables can be
 used to predict floristically defined vegetation units with very high
 accuracy, and the approach could be pursued for other inc ompletely
 sampled taxa and areas outside of tropical Africa. Geographic coherence is
 higher than in previous quantitative phytoregional definitions. For most
 tropical African vascular plant species, we provide predictions of which
 species will occur within each mapped district and region of tropical
 Africa. The framework should be useful for future studies in ecology,
 evolution and conservation.
description: Plant species records from
 tropical Africa were summarised uniquely at degree square resolution for
 tropical Africa to produce 533,383 records of 31,046 tropical African
 species and infraspecific taxa in 1,197 degree squares of tropical
 mainland Africa between 24°N and 24°S. Contributing datasets are cited in
 the dataset ReadMe and Appendix S1 of the manuscript. Larger datasets with
 DOI links have been included as cited works with the Dryad submission.
 Data cleaning, georeferencing and synonymy of the compiled data set are
 described in the dataset ReadMe and Appendix S1 of the associated
 manuscript. Environmental data were summarised at
 one degree square and half degree square. We summarised: Mean altitude
 from GMTED2010 at 30 arc second resolution (Danielson & Gesch,
 2011). Topographic ruggedness from GMTED2010 using the GDAL Terrain
 Ruggedness Index tool via QGIS. Climatic variables Bio1 to Bio35 at
 30-minute resolution for the years 1961-1990 from the CliMond database
 (Kriticos et al., 2012). Surficial lithology classification of Sayre et
 al. (2013).  Majority land cover class from GlobCover 2009 (Arino et al.,
 2012). We estimated completeness of taxon sampling for each degree square
 by comparing the number of species recorded as present with richness
 estimates of Barthlott, Mutke, Rafiqpoor, Kier, & Kreft,
 2005. A betasim
 dissimilarity matrix was created from these summarised data and a local
 NMDS performed. The same betasim dissimilarity matrix was clustered using
 Ward’s algorithm. The 19 cluster solution was defined as the regional
 level, and the 99 cluster solution as the district level. Random Forest
 classification models were built using the summarised environmental data
 as predictors, using the R package randomForest (Liaw & Wiener,
 2002): we trained one model on the 19 regions to predict the regional
 framework. We subsequently trained 19 models to predict the distribution
 of the 99 districts within each of the 19 regions, using the same
 selection of predictor variables. The interpolated regions, and districts,
 constitute the biogeographic framework presented here. The biogeographic framework was
 characterised by the number of taxa, number of endemic taxa, percent
 endemism, percent sampling completeness, number of indicator species and
 number of significant indicator species. Continuous environmental data
 used in the Random Forest model were summarised by their mean and standard
 deviation, minimum, median, maximum, interquartile range; lower and upper
 confidence intervals of the median are calculated using +/-1.58
 IQR/sqrt(n). Categorical data were summarised by their majority
 class.
description: Appendix S2: Input and
 output data for each one degree cell, including coordinates, sampling
 levels, classifications, ordination scores and environmental data
 (S2_onedegdata.csv). Appendix S3: Input and output
 data for each half degree cell, including coordinates, sampling levels,
 classifications, ordination scores and environmental data
 (S3_halfdegdata.csv). Appendix S4: Folder containing
 shape files for (i) the regional framework, (ii) the district framework
 (S4 _shapefiles.rar) in latlong and Albers Africa Equal Area
 projection. Appendix S5: Region attributes
 (S5_regionstats.csv) including region name, number of species and endemic
 species, summary of environmental data. Appendix S6: District attributes
 (S6_districtstats.csv) including region & district names, number
 of species and endemic species, summary of environmental data.
 Appendix S7: Species
 summary table (S7_speciesstats.csv) including species name and occurrence
 data by region and district, with indicator value for each region and
 district. Appendix S9: Folder of R scripts
 used in the analysis (S9_code.rar).